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Electronic Theses, Treatises and Dissertations
The Graduate School
2007
Reverse Stock Splits: Motivations,
Effectiveness and Stock Price Reactions
Barry Marchman
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF BUSINESS
REVERSE STOCK SPLITS: MOTIVATIONS, EFFECTIVENESS AND
STOCK PRICE REACTIONS
By
BARRY MARCHMAN
A Dissertation submitted to the
Department of Finance
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Summer Semester 2007
The members of the Committee approve the dissertation of Barry
Marchman defended June 5, 2005
Gary Benesh
Professor Directing Dissertation
Rick Morton
Outside Committee Member
Stephen Celec
Committee Member
Yingmei Cheng
Committee Member
Approved:
Caryn L Beck-Dudley, Dean, College of Business
The Office of Graduate Studies has verified and approved the above named
committee members.
ii
For RC JAM and Evelyn
iii
ACKNOWLEDGEMENTS
A work of this nature is never done in a vacuum. I am grateful for all of the help
and encouragement from family, friends, and colleagues. My deepest debt of gratitude
and most sincere appreciation goes to Gary Benesh.
Thank you for seeing me
through.
I am grateful to my committee members, Rick Morton, Yingmei Cheng, and
Steve Celec for helping me finish. I would also like to thank Don Nast and Bill Anthony
for their constant encouragement and their belief in me. I thank Bill Christiansen,
James Ang, and David Peterson for all that they taught me. Pam Peterson got me
started on this study and I acknowledge her contribution. I thank Scheri Martin for
keeping me on track. I am grateful to Caroline Jernigan and Daiho Uhm for SAS help
and I am thankful to all my FSU officemates, classmates, and other colleagues for their
support and friendship.
I am grateful to my friends and colleagues at SBI, especially Amos Bradford and
Charles Evans, for giving me the means to provide for my family during this process. I
thank Kenneth Gray and Colin Benjamin for their constant encouragement.
I am grateful to my family. I thank my parents, Mac and Tillie Marchman, for all
of their love, support, and encouragement through this process. I owe a special debt of
gratitude to Jack and Dianne Steen for making this all possible.
I am especially
indebted to my biggest cheerleader and best friend without whom I could not have
finished, Evelyn.
I am ultimately thankful to God for all of these relationships and for His
blessings.
iv
TABLE OF CONTENTS
LIST OF TABLES .........................................................................................................viii
LIST OF FIGURES......................................................................................................... x
ABSTRACT ................................................................................................................... xi
CHAPTER 1: INTRODUCTION.....................................................................................1
1.1 Introduction and Motivation ...................................................................................1
1.2 What Is a Reverse Stock Split? .............................................................................3
1.2.2 A Brief Overview of the Evidence on Forward and Reverse Stock Splits ........4
1.2.2.1 Anecdotal ..................................................................................................4
1.2.2.2 Forward Split Empirical Findings ...............................................................6
1.2.2.3 Reverse Split Empirical Findings...............................................................7
1.3 Why Reverse Split?...............................................................................................7
1.3.1 Listing Requirements.......................................................................................8
1.3.2 Transaction Costs .........................................................................................10
1.3.3 Marginability ..................................................................................................10
1.3.4 Signaling........................................................................................................11
1.4 Hypotheses .........................................................................................................12
1.4.1 Stock Price Reactions ...................................................................................12
1.4.2 Financial Health.............................................................................................14
1.4.3 Listing Requirements.....................................................................................15
1.5 Outline of This Dissertation .................................................................................16
CHAPTER 2: LITERATURE REVIEW.........................................................................17
2.1 Why Does a Firm Split Its Stock? ........................................................................17
2.1.1 Asymmetric Information (Signaling)...............................................................19
2.1.2 Signaling Combined with Transaction Costs .................................................21
2.1.3 Trading Range...............................................................................................21
2.1.4 Institutional Reasons to Reverse Split...........................................................24
2.1.5 Margin Regulations .......................................................................................25
2.1.6 Reorganization ..............................................................................................26
2.1.7 Liquidity .........................................................................................................27
2.1.8 Risk ...............................................................................................................27
2.1.9 Investor Behavior in Relation to Stock Splits .................................................28
2.1.9.1 Framing ...................................................................................................29
2.1.9.2 Price Preference......................................................................................30
2.1.9.3 Herding....................................................................................................31
2.1.10 Optimal Tick Size (Market Microstructure)...................................................31
2.2 Conclusion ..........................................................................................................32
2.2.1 What We Know..............................................................................................32
2.2.2 What We Do Not Know .................................................................................33
2.2.2.1 Managers’ Self-interest............................................................................34
2.2.2.2 Shareholders’ Best Interest .....................................................................35
v
CHAPTER 3: METHODOLOGY AND RESULTS ........................................................36
3.1 Stock Price Reactions .........................................................................................36
3.1.2 Raw Returns Around the Announcement Day...............................................38
3.1.3 Market Adjusted Returns Around the Announcement Day ............................40
3.1.4 Market Model Adjusted Returns Around the Announcement Day .................41
3.1.5 Announcement Day Conclusions ..................................................................42
3.1.6 Ex-date Raw Returns ....................................................................................42
3.1.7 Ex-date Market Adjusted Returns..................................................................44
3.1.8 Ex-date Market Model Adjusted Returns.......................................................44
3.1.9 The relation between ex-date returns and announcement date returns ........45
3.1.10 Analysis of Subsamples Based on Firm Categories....................................46
3.1.10.1 Industry..................................................................................................46
3.1.10.2 Delisting threat and Motivations for Reverse Splits ...............................49
3.1.10.3 Price ......................................................................................................52
3.1.10.4 Size of the Firm ....................................................................................53
3.1.10.5 Consolidation.........................................................................................54
3.1.11 Long-run Returns Following Reverse Stock Split ........................................55
3.1.12 Long Run Buy and Hold Returns.................................................................55
3.1.13 Long-run Abnormal Returns ........................................................................59
3.1.14 Matched Firm Analysis ................................................................................62
3.1.15 Analysis of Subsamples Based on Firm Categories....................................64
3.1.15.1 Industry..................................................................................................64
3.1.15.2 Delisting Threat and Motivations for Reverse Stock Splits ....................65
3.1.15.3 Price Considerations .............................................................................66
3.1.15.4 Size of the Firm .....................................................................................66
3.1.15.5 Consolidation.........................................................................................67
3.1.15.6 Long-run Analysis Concluding Remarks................................................68
3.2 Market Movements and Reverse Stock Splits .....................................................68
3.2.1 Hypothesis.....................................................................................................68
3.2.2 Testing ..........................................................................................................69
3.2.3 Results ..........................................................................................................70
3.3 Financial Distress and Future Performance ........................................................70
3.3.1 Hypothesis.....................................................................................................70
3.3.2 Testing ..........................................................................................................71
3.3.3 Results ..........................................................................................................72
3.3.4 Concluding remarks ......................................................................................74
3.4: NASDAQ Regulatory Changes..........................................................................74
3.4.1 NASDAQ Listing Rules – Before The Pilot Program .....................................75
3.4.2 The Pilot Program .........................................................................................76
3.4.3 Hypotheses ...................................................................................................77
3.4.4 Reverse splits and the Moratorium................................................................78
3.4.5 Delistings and the Moratorium.......................................................................79
vi
CHAPTER 4: CONCLUSIONS .....................................................................................81
4.1 Overview .............................................................................................................81
4.2 Results and Suggestions for Further Research...................................................81
4.2.1 Stock Price Reactions – Announcement Date...............................................81
4.2.2 Stock Price Reactions – Ex-date ...................................................................82
4.2.3 Stock Price Reactions – Firm Characteristics................................................82
4.2.4 Stock Price Reactions – Long Run................................................................83
4.2.5 Market Movements........................................................................................83
4.2.6 Projecting Post Split Performance.................................................................84
4.2.7 NASDAQ Rule Change .................................................................................84
4.3 Contribution .........................................................................................................85
APPENDICES ..............................................................................................................87
BIBLIOGRAPHY ........................................................................................................169
BIOGRAPHICAL SKETCH.........................................................................................176
vii
LIST OF TABLES
Table 1 Forward and Reverse Stock Splits and Split Ratios 1962-2002 ................87
Table 2 Average Stock Price per Year and Yearly Stock Splits 1962-2002 ...........88
Table 3 Changes in Margin Requirements .............................................................90
Table 4 Winners Versus Losers After Reverse Stock Split.....................................90
Table 5 Summary of Stock Split Literature and Key Findings ................................91
Table 6 The Daily Raw Returns of Reverse Stock Splits Around the
Announcement Date ................................................................................96
Table 7 The Daily Market Adjusted Returns of Reverse Stock Splits Around
the Announcement Date ..........................................................................96
Table 8 The Daily Market Model Adjusted Returns of Reverse Stock Splits
Around the Announcement Date ..............................................................98
Table 9 The Daily Raw Returns of Reverse Stock Splits Around the Ex-date......100
Table 10 The Daily Abnormal Returns of Reverse Stock Splits Around the Exdate - Market Adjusted..........................................................................102
Table 11 The Daily Market Model Adjusted Returns of Reverse Stock Splits
Around the Ex-date ...............................................................................106
Table 12 Ex-date Returns vs. Announcement Returns ........................................110
Table 13 Number of Reverse Stock Splits by Industry 1962-2003 .......................111
Table 14 Ex-date Returns by Industry ..................................................................112
Table 15 Returns Based on Reason Given .........................................................114
Table 16 The Returns of Reverse Stock Splits by Price.......................................115
Table 17 The Returns of Reverse stock splits and the Size of the Firm ...............116
Table 18 The Returns of Reverse stock splits and Consolidation ........................117
Table 19 Reverse Stock Split Delistings Within 250 Days of Ex-date (19622003).....................................................................................................118
Table 20 Average Buy and Hold Returns of Reverse stock splits from 1962 to
2003......................................................................................................119
Table 21 Average Market Adjusted Buy and Hold Abnormal Returns of
Reverse stock splits from 1962 to 2003 ................................................121
Table 22 Matched Firm Statistics .........................................................................123
Table 23 Average Abnormal Returns of Reverse stock splits from 1962 to
2003 Based on Matched Firm...............................................................124
Table 24 Long Term Returns By Industry.............................................................125
Table 25 Long Term Returns by Reason Given and Low-Priced Versus High
Priced....................................................................................................130
Table 26 Long Term Returns By Size, Consolidation and Post Split Price..........131
Table 27 Monthly Stock Splits Related to Market Movements .............................132
Table 28 Long Run Returns Regressed on Lagged Market Returns....................133
Table 29 Regression Analysis of Financial Distress Variables – Definitions ........134
Table 30 Correlation Matrix: The Returns of Reverse Stock Splits and
Financial Distress Variables..................................................................135
Table 31 Regression Analysis of Financial Distress Variables – Single
Independent Variable............................................................................137
viii
Table 32 Multivariate Regression Reduced Model ...............................................138
Table 33 Nasdaq Requirements September 2001 ...............................................139
Table 34 Low-priced Nasdaq Stocks and Reverse Stock Splits 1998 - 2005.......140
Table 35 Delistings Around the NASDAQ 2001 Moratorium ................................142
ix
LIST OF FIGURES
Figure 1 Reverse Stock Splits 1962-2002 ............................................................144
Figure 2 Split Ratios of Stock Splits .....................................................................145
Figure 3 Distribution of 250-Day Buy and Hold Returns.......................................146
Figure 4 Buy and Hold Returns of Reverse Stock Splits .....................................147
Figure 5 Market Adjusted Returns of Reverse Stock Splits..................................148
Figure 6 Matched Firm Abnormal Returns ...........................................................149
x
ABSTRACT
Stock price reactions to reverse stock splits are examined in relation to various
firm characteristics and market movements. It is found that the market reacts stronger
on the ex-date than on the announcement date. The market reaction varies by industry
and by reason given for the reverse stock split. Firms that split for regulatory reasons
perform worse than firms that split for other reasons. In the long run, there is little
evidence of a negative abnormal buy and hold abnormal return (BHAR) after a reverse
stock split, but there is some evidence BHARs varying by industry.
An examination of financial ratios and variables reveals that firms with better
sales performance and higher operating-income-to-assets have better ex-date returns.
In the long run, firms with lower debt relative to their assets do better after the reverse
stock split.
Operating income expressed as a percent of assets is also positively
related to the 250-day BHARs.
The NASDAQ minimum bid price rule change of 2001 is explored to determine if
it had an impact on reverse stock splits or delistings. The evidence is mixed. The
number of firms delisted, expressed as a percentage of firms that would have been
delisted under the old rules, decreased. However, the percentage of reverse stock
splits, expressed as a percentage of low priced firms, did not change.
xi
CHAPTER 1: INTRODUCTION
1.1 Introduction and Motivation
The wealth effects of reverse splits have been studied previously with little
disagreement in the literature. The consensus of Vafeas (2001), Desai and Jain
(1997), Han (1995), Peterson and Peterson (1992), etc. is that a reverse stock
split is a negative informational event. Given the harmony of the literature, what is
the purpose of another study on reverse stock splits? There are three reasons that
further study is warranted.
First, more data are available. As documented in Table 1 and Figure 1,
reverse stock splits have been more frequent in the late 1990s and 2000s than in
previous periods.
Since 1992 the number of reverse stock splits has almost
doubled. This is analogous to the number of smokers doubling after the Surgeon
General has proven conclusively that smoking is bad for your health. Research
has shown that the market reacts negatively to reverse stock splits, yet the
number of firms using this strategy has dramatically increased.
The research
needs to be updated to determine if the reverse stock split is still followed by a
negative market reaction. Perhaps something has changed in the way the market
perceives the reverse stock split or perhaps there are valid reasons for a firm to
consolidate its shares.
Second, the type of firm that is reverse splitting its stock has changed over
the years. In previous reverse split studies, most of the sample consisted of
established firms that fell into financial difficulty.
In recent years, perhaps a
consequence of the Internet bubble burst, a new type of reverse splitting firm has
emerged.1 In the months preceding the bubble burst, the initial public offering
(IPO) market was at its peak. New technology-based and Internet-based firms
were in high demand. After the bubble burst, many of these technology and
1
This refers to the beginning of the bear market of 2000 and 2001, when the NASDAQ composite
fell dramatically. The beginning of this period of reversal is often referred to as the Internet Bubble
burst.
1
Internet start-up firms lost a large percentage of their market value and their stock
price fell below the minimum bid price required for continued listing. Some of these
start-up firms chose to reverse split their stock in order to maintain compliance.
Previous studies have examined reverse splits using primarily well established
companies. Recent IPOs make up a large segment of this study.2
Third, a number of reverse splits with extraordinary post-reverse split
performance have caught the attention of journalists who write for financial
publications such as Forbes magazine. Journalists report that some reverse split
stocks have experienced price increases of more than 100% in the months
following the reverse split.3 They have speculated that a reverse split may not be
the negative signal, or negative news event, that it was once thought to be. This
study applies the rigors of academic research to the data to determine if these
speculations are justified.
Given the market rule changes, the changing composition of the sample,
and perhaps a different dynamic in the market, results provided in this study may
be beneficial to both regulators and corporate managers. The sheer number of
reverse stock splits that have been announced during the past few years suggest
that reverse stock splits are a strategic corporate decision that many managers
now consider. Additionally, the 2001 change in the NASDAQ continued listing
requirements that allowed low-priced firms more time to bring their bid price back
into compliance (above $1.00) provides a unique opportunity to study the impact of
such an event.4
In this dissertation, reverse stock splits are investigated on three fronts.
First, the stock price reaction to the reverse stock split is examined. The reaction
is examined in relation to certain firm characteristics and the reasons that a firm
gives for reverse splitting. Daily abnormal returns around the announcement of
2
Based on a regression analysis that revealed that that the number of days since market listing is
inversely related to the date of the reverse stock spilt. Coefficient –0.42 and p-value<0.0001.
3
David Simmons, Forbes, January 25, 2002, for example.
4
This opportunity is not available with NYSE and AMSE listing standard changes because so few
stocks are affected.
2
the reverse stock split are explored. The ex-date returns and long-run abnormal
returns are also investigated. The methodology is similar to Vafeas (2001), Desai
and Jain (1997), Han (1995), and Peterson and Peterson (1992).
Second, accounting variables taken from the financial distress literature are
tested for their usefulness in forecasting post reverse split performance. There is
wide variation in firm performance after a reverse stock split and this analysis
seeks to provide insight as to what firm specific variables are most related to post
split performance.
Third, reverse stock splits are examined in light of the changes in
NASDAQ’s continued listing requirements that were introduced in September
2001. These changes granted additional time to low-priced firms to bring their bid
price back into compliance (above $1.00). Even more time was granted if the
company could maintain certain listing requirements besides minimum bid price.
As a result this change, low-priced firms had less incentive to reverse split their
stock. This study seeks to discover whether this rule change was effective.
1.2 What Is a Reverse Stock Split?
A forward stock split is the exchange of one share of stock for multiple
shares. For example, in a 2-for-1 split, each stockholder receives two shares for
each share held, and in a 3-for-1 split, each stockholder receives three shares for
each share. When a firm splits its stock, the immediate value of the firm should not
be affected. Theoretically, a holder of a $50 share that splits 2-for-1 is left with two
shares worth $25 each.
Companies may also reverse split stocks. Unlike a forward split, which
leaves the shareholder with more shares, the reverse split leaves the shareholder
with fewer shares. For example, a 1-for-2 reverse split requires the shareholder to
trade two shares for one share of the new stock, and a 10-for-1 reverse split
requires the shareholder to relinquish ten shares and receive one share in return.
A reduction in the number of shares outstanding increases earnings per share and
3
stock price, but, like the forward stock split, it is a cosmetic change in ownership.
Whether a firm forward splits or reverse splits its shares, the market value of the
total number of shares, which is the market capitalization, should remain the
same.
Firms split their stocks at varying ratios. The most common forward split
ratio is 2-for-1, and the most common reverse split ratio is 1-for-10, followed
closely by 1-for-5. Table 1 presents the split ratios of both forward and reverse
splits from 1962 to 2002.
It is not shown in Table 1, but when the sample is divided into pre-2001
splits and 2001-2002 splits, the 1-for-10 split increased from approximately 19% to
25% of the total.5 This increase in large reverse splits (consolidation) may be due
to the fact that the market as a whole, especially NASDAQ, was in sharp decline
and companies used the reverse split to stay in compliance with market listing
requirements. The forward split distribution has also changed in recent years. In
the pre-2001 sample, the 2-for-1 split is most frequent (45%), but in 2001 and
2002, the smaller 3-for-2 split has about the same frequency as the 2-for-1 split
(40%). The average share price per year for the entire market is given in Table 2.
It is interesting to note that the average remains in the same general range
throughout the years. The range does not seem to rise with inflation. Table 2 also
shows the percentage of firms that split and reverse split their stock each year.
The number shown is a percentage of firms listed in the CRSP database for that
year.
1.2.2 A Brief Overview of the Evidence on Forward and Reverse Stock Splits6
1.2.2.1 Anecdotal
In the aftermath of the Internet Bubble, over 200 firms reverse split their
stock. Regardless of the reason given by the company, the popular press usually
5
The frequency of 1-for-10 splits was 20% for the entire sample.
An extensive overview is presented in Chapter 2.
6
4
portrays the reverse split as a negative news event for the particular firm. This is
consistent with the findings of academic researchers who, in general, observe
negative abnormal returns following the reverse split. The reverse split is usually
understood in the press and in academic research as a signal that bad news is on
the horizon. There are some reports, however, that claim a reverse split may not
necessarily be a negative event for a company. For example, Forbes magazine
reports on “reverse split losers” and “reverse split winners,” with double- and tripledigit returns documented for the winners.7 A winner is defined as a firm that has
positive returns after the stock split (the ex-date) and a loser is defined as a firm
that has negative returns after the split. There are no market or risk adjustments in
these returns, and therefore they can be taken only as anecdotal. This anecdotal
evidence provided the catalyst for this study.
In 2002, Forbes Magazine reported that there were 113 reverse stock splits
in 2001 and 84 of these stocks were still trading in January 2002.8 Six companies
were acquired, four went out of business, and six split to facilitate transactions
such as spin-offs or special dividends. Contrary to the popular belief that reverse
split stocks wind up trading on the pink sheets, the over the counter (OTC) bulletin
board, or worse, about three quarters of 2001 reverse splits maintained a post-split
price above $1 and remained listed.9 The fact that the reverse split stocks
performed so well in the middle of a bear market is even more surprising.
According to the conclusions of previous researchers, most reverse splits should
have negative abnormal returns. This, coupled with the systematic decline in
market prices, makes it interesting that such a large percentage of these firms had
positive price increases. Of the 84 still trading on January 2002, the winners (39)
were up an average of 66% and the losers (45) were down an average of 84%.
7
Forbes, January 25, 2002 David Simmons
CRSP data revealed that there were actually 127 reverse stock splits in 2001 and 95 in 2000 with
102 still trading on January 31, 2001
9
Popular belief means the consensus of previous researchers who show that reverse split stocks in
general are followed by negative abnormal returns and then suffer delisting or bankruptcy. This is
expanded and fully documented in the literature review in Chapter 2. The popular press also takes
a dim view of the future of firms that reverse split. This, too, is expanded in Chapter 2.
8
5
Even among the losers, 22 (about half) maintained the $1 listing requirement.
Internet-based firms, or “dot.coms,” made up about one fourth of the 2001 reverse
splits. Of the 23 dot.com companies, the winners (10) were up an average of 79%
and the losers (13) were down an average of 63%.10 A more comprehensive
record of winners and losers is given in Table 4.11
Industry seems to have played a role in how the media covered some of
these firms. Does industry play a role in post split firm performance? Is there any
pre-split information like industry or financial ratios that is predictive of post-split
performance? These questions are explored in this dissertation.
1.2.2.2 Forward Split Empirical Findings
On average, firms that split their stock have been characterized by positive
abnormal returns following the stock split. This is curious because the stock split
event adds no value to the firm; it is merely a cosmetic adjustment. Even if the
stock split is viewed as a positive signaling event, the market should respond to
the good news on the stock split announcement day rather than on the ex-date. If
there were information leakage from the company about the upcoming split, then
there should be some upward price drift leading up to the announcement day.
Researchers find that forward stock splits are associated with a small preannouncement price drift, a positive reaction on announcement day, and shortterm and long-term positive abnormal returns. Researchers attribute the positive
abnormal returns to either a sluggish response to the positive information (underreaction) or asymmetric information. It may be that managers signal their belief in
strong performance in the future by splitting the stock, but the market is skeptical
and reacts slowly, waiting for confirmation. Other researchers contend that the
forward split is the result of a price preference by managers, investors, and
institutions.
10
11
Forbes, January 25, 2002, referencing Thomson Financial/First.
This table is referenced again in chapter 3.
6
1.2.2.3 Reverse Split Empirical Findings
Researchers have generally found that when a firm reverse splits its stock,
the stock will suffer negative abnormal returns in both the short and long terms. A
reverse split is often viewed as a last-ditch effort to artificially maintain the firm’s
market listing or to delay inevitable bankruptcy. The price of the stock is not the
cause of bankruptcy, but bankruptcy follows many reverse stock splits. This may
be due to the firm’s losing market value because of increased idiosyncratic risk.
The riskier the firm becomes, the lower its price becomes. Researchers provided
empirical evidence indicating that, in general, negative abnormal returns follow
reverse splits. As with forward stock splits, the market reaction to the reverse split
is only partial at the time of the announcement. Perhaps investors are hoping for a
turnaround, or perhaps the coverage by the press and stock analysts is so light on
these thinly traded companies that it takes a while for the bad news to be
disseminated into the market.12 Researchers have observed negative abnormal
returns following reverse stock splits, but the anecdotal evidence suggests that
this paradigm may need updating. Perhaps market reaction to the reverse split is
conditioned on other information that is available to investors.13 It is possible that
the market response has become more sophisticated in that corporate information
is more readily available and more widely disbursed that in the past. Readily
available information may lead to more firm specific market responses.
1.3 Why Reverse Split?
In theory, the total value of equity of the firm should be the same pre-split
and post-split.14 If there are no restrictions on companies with regard to stock price
and if the stock price does not affect investors’ transaction costs, the market
12
Daniel, Hirshleifer, and Subrahmanyam (1988) model how analysts might overweight their own
priors when valuing firms and thus underweight new information such as split announcements.
13
Stocks that reverse split are sometimes delisted shortly thereafter. For example, PlanetRx.com
did an 8-for-1 reverse split on December 14, 2000, and was delisted two weeks later. Eglobe did a
47-for-10 split on November 13, 2000, and was delisted nine days later. Why did these companies
incur the expense of the reverse split?
14
The market value of equity is shares outstanding times share price.
7
should be indifferent when a firm splits its stock, either forward or backward. The
market, however, does not seem to view the split as a neutral-wealth event. In
fact, researchers show that the market treats the reverse stock split as a negative
event with both short-term and long-term negative abnormal returns. If reverse
splits result in negative abnormal returns, why would a company choose to reverse
split its stock?
There are numerous reasons cited by companies to reverse split their
stock. These reasons include (1) complying with listing requirements, (2) reducing
the transaction costs of investors, (3) making the stock marginable, and (4) making
the stock appear more reputable to investors by raising the share price of the
stock.
1.3.1 Listing Requirements
The primary listing requirement of interest in this study is the minimum bid
price for continued listing. The continued listing requirements are less stringent
than the initial listing requirement. Each market has slightly different requirements
for initial and continued listing, yet some requirements are the same. For example,
the NYSE, the AMEX, and NASDAQ all have a $1 minimum bid price for continued
listing.15 If a firm’s bid price drops below $1 for an extended period, the firm will be
expelled from the market and no longer eligible for trade in that particular market.
For example, both NASDAQ and the NYSE begin the delisting process when a
firm trades for less than $1 for 30 consecutive days. However, the markets differ
on initial bid price, market capitalization, and other qualitative public interest
criteria. Firms not meeting the minimum bid price requirement may choose to
15
All current requirements for initial listing and continued listing are tabulated in Table 33.
8
reverse split their stock.16 An interesting event to study is the changing of the
rules. How do regulatory decisions affect the firms that they govern?
Prior to 1997, NASDAQ had no minimum bid price, but in 1997 it saw the
need to improve its image by divesting itself of low-priced stocks that were, as a
group, highly speculative and prone to fraud. In 1997, NASDAQ first instituted its
minimum bid price of $1 per share for continued listing. If a firm’s stock price fell
below the minimum, the company then had 90 days to improve the price of the
stock, either by reverse splitting or by taking actions to improve its market value.
NASDAQ had a recent rule change involving grace periods granted to
minimum bid price violators, but the NYSE and the AMEX had no such changes
related to bid price or to any other criteria of interest to low-priced stocks. In
addition, the vast majority of the stocks in the later sample are NASDAQ stocks.
For example, in 2001 and 2002, there were no AMEX reverse splits and only five
NYSE reverse splits per year. In contrast, there were more than 100 reverse splits
on NASDAQ in each of these two years. Given the limited sample size and the
stability of bid price requirements in the NYSE and the AMEX, the focus of this
dissertation regarding rule changes will be on NASDAQ stocks and the effects of
these rule changes on reverse splits and low-priced stocks in general.
On September 27, 2001, NASDAQ issued a moratorium on the
enforcement of the $1 minimum bid price rule in response to the depressed stock
prices in the overall market. NASDAQ offered longer grace periods—180 rather
than 90 days—for firms to bring their company into compliance. If criteria other
than bid price were in order but the bid price was too low, even longer grace
periods were granted. In January 2002, NASDAQ instituted a pilot program in
which the moratorium was extended for two years.
Companies in both the
National and Small Cap Markets now had extra time to address bid price
deficiencies.
The program was slated to expire in December 2003 but was
extended to December 2004. The pilot program was slightly different for the two
16
The AMSE section 970 says, “The Exchange may recommend to the management of a company,
whose common stock sells at a low price per share for a substantial period of time, that it submit to
its shareholders a proposal providing for a combination (’reverse split’) of such shares.”
9
NASDAQ markets. The National market issuers had 180 days (formerly 90 days)
to bring their stock’s bid price back into compliance. At the end of this time the
firm could move to the Small Caps market if it wanted to stay listed. Firms in the
Small Caps market were given 180 days to comply (formerly 90 days) and were
given an additional 180 days (formerly 0 days) if certain other market capitalization
criteria are met. The Small Caps were then given an additional 90 days to comply
if they met certain non-price criteria at the end of the second 180-day period.
These criteria (continued listing requirements) are shown in Table 33 and are
discussed in Chapter 3.
1.3.2 Transaction Costs
Researchers have shown that the higher price range after a reverse stock
split results in lower percentage bid-ask spreads, and thus the transaction costs
are lowered. The lower bid-ask spread is probably due to the increased liquidity of
the stock at its post reverse split price. However, a possible lost tax savings after
a reverse stock split can also be considered a “transaction cost.”
Volatility
decreases after a reverse stock split, and firms (and investors) are less able to
take advantage of tax laws that allow them to expense a paper loss.
1.3.3 Marginability
A stock is purchased on margin when the investor uses someone else’s
money to fund a portion of his investment. An investor may borrow up to 50% of
the funds for an initial investment. This requirement has fluctuated over the years
from 10% to 50%, as shown in Table 3, but has remained fixed since 1974.
Brokerage firms will monitor portfolios and “call” a margin if the equity that is less
than fully funded drops in value. The investor must either divest or “cover” the
10
margin by sending additional funds to the broker. A stock must have a bid price of
$5 or more to be considered marginable.17
Increasing the price of a stock through a reverse split allows more investors
to use margin accounts to purchase the security. The stock is more marketable
because there are more potential buyers. Institutional investors are also wary of
low-priced stocks and generally avoid those that are unmarginable. When firms
reverse split their shares, the resulting price is often greater than $5. Why would a
firm reverse split to a price that is not marginable (i.e., less than $5)? Because, a
low bid price is not the only criterion that a firm is struggling to meet in order to
remain listed on an exchange. There are also minimum market valuations and
minimum shares outstanding requirements. If a firm does not have enough shares
outstanding to reverse split to over $5, then marginability is not a likely motive for
the reverse split.
Closely associated with marginability is institutional interest. For example,
Judy Bruner, CFO of Palm, Inc., cites the loss of institutional investors as one
reason for its 1-for-20 reverse split in October 2002.18
1.3.4 Signaling
Empirical studies on reverse stock splits are in agreement that, on average,
reverse splits reduce the value of the splitting firm; abnormal negative returns tend
to follow the reverse split. This observation suggests that reverse splitting conveys
negative information about the firm. However, as mentioned earlier, some
companies have recently experienced positive share price increases in the months
after a reverse split. This observation suggests that the reverse split event is not
always negative and that the market reaction to a reverse split may instead be
driven by other factors.19 Perhaps the returns associated with a reverse split are
17
Margin requirement rules, Securities Exchange Act of 1934.
Quoted to Tim Reason, Reverse Psychology Today, CFO, Magazine for Senior Financial
Executives, December 2002.
19
Price increases are not necessarily indicative of positive abnormal returns, but this is interesting
because, historically, decreasing prices follow reverse splits. This issue is addressed formally and
18
11
due to the information conveyed by a reverse split, conditional upon information
about the motivation for the split or the financial health of the splitting firm. This
study adds to the reverse split literature by showing how accounting variables or
firm characteristics affect the signal of a reverse stock spilt.
1.4 Hypotheses
Hypotheses are developed and tested in this dissertation to explore the
stock price reactions to reverse stock splits. The stock price reactions are
examined in the context of the market environment, regulatory influences, the
Internet Bubble burst, and the health of the stock as related to reverse stock splits.
1.4.1 Stock Price Reactions
The first hypothesis tested relates to the stock price reactions associated
with reverse stock splits. Researchers have documented abnormal negative
returns around the announcement date and negative abnormal returns around the
ex-date. The increase in reverse splits during the late 1990s and early 2000s is
shown in Figure 1. This wealth of new data provides an enticing reason to revisit
this topic. Using the expanded data set (compared to previous studies), the
abnormal returns surrounding the ex-date and the announcement date are
examined.
The primary hypothesis of this study, stated in the null, is:
There is no stock price reaction associated with the event of a
reverse stock split.
The typical company performing a reverse stock split is generally smaller
and less well known and trading in its stock may be quite thin. It may be the case,
then, that the effect on shareholders’ wealth is not confined to the event day, but
documented thoroughly in Chapter 3, in which abnormal returns following the reverse split are
examined.
12
rather is spread out over a longer period as more information about the company
is revealed. Announcement day effects are examined in an 11-day window around
the announcement. Ex-date effects and long run effects are also examined. The
first wealth effect is examined specifically with the following two hypotheses:
There is no short-term stock price reaction associated with the
event of a reverse stock split.
There is no long-term stock price reaction associated with the
event of a reverse stock split.
One facet of this study deals with the regulatory environment and one of the
question of interest is whether the reverse splits were motivated by the threat of
delisting. If there is a difference in the returns for firms that have been forced to
reverse split their stock versus firms that have voluntarily reverse split, then it is
possible that the exchange regulations may be having effects in the market. The
hypothesis, stated in the null, is then:
There is no difference in the stock price reaction on the stock of
firms which reverse split their stock for voluntary versus
regulatory reasons.
Closely related to the wealth effects is the enormous loss in the value of
NASDAQ composite stock index during 2000 and 2001. This loss of equity value
in technology stocks has been referred to as the Internet Bubble burst. Laying
aside the arguments concerning whether or not there was an Internet Bubble, the
systematic loss of equity value during this period is given as the motivation for
exchange rule changes and other regulatory changes. Did the systematic loss of
equity value contribute to a firm’s decision to reverse split its stock? Firms that
may never have considered reverse splitting their stocks may have been caught in
the systematic downdraft of equity prices and therefore felt obligated to prop up
their otherwise healthy company’s stock price. They may have felt that the price
was too low due to exchange requirements, trading range goals, liquidity
concerns, or other market considerations.
13
The next hypothesis, stated in the null, is:
The frequency of reverse stock splits is not related to prior market
performance.
A casual observation of the yearly reverse stock splits (Table 1) reveals no
obvious pattern. When prices drop in times of market decline, it seems that more
firms would reverse split their stock. This idea is tested. Perhaps a relationship
exists that is not apparent from casual observation.
1.4.2 Financial Health
One of the motivations for the third hypothesis, is to determine if a
distinction can be made between good firms that may have been caught in the
downdraft of the 2000-2001 bull market versus firms that were destined for failure
regardless. Some firms had little chance of survival, while other firms were simply
caught in the wake of the NASDAQ plummet. The irrational exuberance (Alan
Greenspan’s assessment) of the time preceding the plummet may have fueled the
public’s hunger for IPOs of dot-com companies that were not quite strong enough
to go public.20 The public’s demand for more and more tech companies may have
led to an environment in which sound business valuation and common sense were
replaced by a gambler’s mentality. IPOs in the high-tech industries, especially
Internet-related firms, seemed extremely popular to investors. This section of the
dissertation borrows variables from the financial distress literature and seeks to
determine if the same variables that predict financial distress can be used to
forecast a firm’s performance after a reverse stock split.
If the distress variables
for a firm do not indicate distress, then the bid price deficiency may be the result of
getting caught up in a public panic to sell. For example, if sales were still strong
20
Federal Reserve Board Chairman Alan Greenspan made these remarks in his speech at the
Annual Dinner and Francis Boyer Lecture of The American Enterprise Institute for Public Policy
Research in Washington, D.C., on December 5, 1996. The catchphrase later became the title of a
2000 book about the overpriced market. Irrational Exuberance, by Robert J. Shiller, won the 2000
Commonfund Prize for Best Contribution to Endowment Management Research. Shiller is the
Stanley B. Resor Professor of Economics at Yale University.
14
and a business had a full pipeline of orders for the next several quarters, then
perhaps the market’s valuation of the equity was unjust. If a firm in this situation
reverse splits its stock, perhaps it sends a false signal to the market. Did the
market see through the false signal?
The next hypothesis, stated in the null, is:
The financial health of a stock does not help to predict firm’s
returns after a reverse stock split.
1.4.3 Listing Requirements
The stock markets are interested in preserving the integrity and reputation
of their exchanges. The markets have rules that specify the minimum bid price that
a firm must maintain to remain listed on the exchange. The NYSE, the AMSE, and
NASDAQ created these rules to enhance the reputation of the markets because
low-priced stocks have been associated with price manipulation and stock scams.
In 2000 and 2001, NASDAQ relaxed its minimum bid requirements and
granted automatic extensions for firms that perhaps were caught in the downdraft
of the bear market. The stated objective was to give the good firms adequate time
to recover. Did the rule relaxation increase the probability that a low-priced stock
would recover, or did the rule changes simply delay the inevitable bankruptcy or
delisting? Were firms less likely to reverse split their shares in the aftermath of the
rule changes? The next hypotheses, stated in the null, are:
The moratorium on the minimum bid price rules had no effect on
whether or not a NASDAQ firm reverse split its stock.
The moratorium on minimum bid price rules did not result in fewer
delistings for NASDAQ firms whose stock had a bid price of less
than $1.00
15
1.5 Outline of This Dissertation
The remainder of this dissertation consists of a literature review followed by
development and testing of the hypotheses. Chapter 2, the reverse split literature
review, is followed by Chapter 3, in which the hypotheses are motivated and
tested. Chapter 4 summarizes this research and its constraints while suggesting
extensions for further research.
16
CHAPTER 2: LITERATURE REVIEW
2.1 Why Does a Firm Split Its Stock?
Stock splits present an enigma to market observers. A stock split produces
no material change in a firm, yet through the years the stock split has remained a
popular fixture in the U.S. financial markets. For example, in 1930 almost 20% of
the firms listed on the NYSE had split their shares in the prior decade (Conroy &
Harris, 1999). In modern times, stock splits still endure, with 5-10% of NYSE firms
announcing a split each year. One of the results of the incessant stock split is that
average stock prices in U.S. financial markets have remained remarkably constant
over time. Over the last half-century, the average NYSE share price has remained
in the $30-$40 range regardless of rising inflation, consumer prices, or firm equity
value (Conroy & Harris, 1999).
A stock split is, at one level, only a cosmetic change; the same-size pie is
merely sliced into smaller pieces.21 The investor retains the same partial
ownership of the company’s equity and the same voting power as before the split.
Yet, in spite of this seemingly cosmetic change, empirical researchers continue to
demonstrate that stock splits have real effects in the financial markets. In some
ways, the effects of forward splits seem beneficial: equity value increases, trading
volume increases, and the number of shareholders increases. In other ways, the
effects of forward splits seem less desirable: shareholder risks increase, and some
transaction costs, such as bid-ask spreads, are higher after forward splits.22 The
reverse splits mirror the forward splits. Value decreases, volume decreases, the
number of shareholders decreases, shareholder risks decrease, and transaction
costs decrease.
21
"You'd better make it four; I don't think I can eat six pieces."—Yogi Berra, when asked if he wanted his
pizza cut into four or six pieces (http://www.brainyquote.com/quotes/quotes/y/yogiberra141844.html).
22
Even if the bid-ask spread is the same pre- and post-split, the lower price of the stock makes the
bid-ask spread a higher percentage of the stock price.
17
In an attempt to explain these varied benefits and consequences of stock
splitting, the literature presents several reasons that a firm may reverse split its
stock. These reasons include asymmetric information, moving the stock’s price to
a more desirable trading range, exchange regulations, margin regulations,
institutional limitations, reorganization, privatization, and liquidation. Key findings
of major studies are presented in Table 5. Even though the literature presents
several hypothetical reasons that a firm might split its stock, the truth is that no one
knows the true motivations except the managers who actually make the decision
to split. With this in mind, researchers conduct surveys. Unfortunately, the
managers who are surveyed are not always forthcoming in their explanations. In a
1977 study of reverse splits, nearly half of the firms that reverse split their stock
offered no public explanation at all for the split.23 Of the firms that made public
announcements, the most common reason given was image improvement. This
was followed by exchange requirements, then appealing to a different type of
investor, and finally, reduction in shareholder service costs (Gillespie & Seitz,
1977). In a similar survey on forward splits, 70% of managers who responded
stated that moving the stock to a preferred price range or improving liquidity was
the reason for the stock split. Signaling was the primary motivation of only 14% of
the responding managers (Baker & Powell, 1993).
Studies of reverse splits in the late 1970s and early 1980s find that there is
a small increase in volume and a decrease in the number of shareholders after the
split, but the institutional interest remains unchanged. The studies conclude that
reverse splits are not justified because they are, in general, detrimental to
shareholder wealth. Moreover, reverse stock splits are not justified by the reasons
cited by managerial surveys because there are no tangible benefits related to
those reasons. In addition, Radcliffe and Gillespie (1979) studied only firms that
survived their test period, so it is likely that they understated the negative effects of
the reverse splits on shareholder wealth (Woolridge & Chambers, 1983).
23
Gillespie and Seitz (1977) found that only 13 of 24 firms gave a public reason for the reverse split.
18
Another negative effect of the reverse split is the decrease of the tax option
value of stocks. The investor has more possible tax alternatives when stock is
more volatile. High volatility allows investors to take advantage of U.S. tax laws by
offsetting capital gains with capital losses. Reverse splits decrease volatility and
therefore decrease the tax option value. Forward splits, on the other hand,
increase volatility (Lemoureux & Poon, 1987).
Although researchers have shown that reverse splits are detrimental to
shareholder wealth, firms continue to engage in the practice of reverse splitting
their stocks. The stock split literature gives a variety of explanations for forward
and reverse stock splits, and these are discussed in the following sections. The
first explanation considered is asymmetric information, or signaling.
2.1.1 Asymmetric Information (Signaling)
The asymmetric information hypothesis is that managers will split the firm’s
stock to send a signal to investors that conveys private or inside information about
the firm. For some reason, the firm is unable to disclose information such as trade
secrets, a revolutionary breakthrough, or some other secret competitive
advantage. The managers cannot reveal their private information, but they feel
that if their secrets were widely known, the company’s market value would
improve. A stock split is a way to signal investors that the managers believe that
the company is undervalued and that there is a positive expectation about the
future. According to this theory, the main purpose of stock splits is to provide
private information to the market. The positive abnormal returns that follow stock
splits are often attributed to this signaling effect. A forward split is a sign of
strength and confidence that the share price will continue to increase. A reverse
split, on the other hand, is a sign of weakness and could be viewed as the only
remaining option to stay listed on an exchange or as a means of complying with
creditor demands. It is a negative signal to the market. Succinctly, the signaling
view is that management can influence market perceptions of their firm by
changing its share price.
19
Various researchers argue that forward stock splits are a favorable signal
from management about future firm performance. They argue that managers of
undervalued firms will split the firm’s shares to signal positive private information
about the company. Even if the managers have no information to convey, the
researchers suggest that managers are seeking to attract attention to the company
in hopes that investors and analysts will notice the company is undervalued. The
split is a costly signal to the market that a weaker competitor would not likely
imitate. It is a show of strength by the corporation and a disclosure by
management indicating they think the firm is more valuable than the current
market price dictates. Reverse splits, however, have been shown to result in
negative abnormal returns for the firm and are seen as a negative signal to the
market (e.g., Brennan & Copeland, 1988; Brennan and Hughes, 1991; Conroy &
Harris, 1999; Grinblatt, Masulis, & Titman, 1984; Ikenberry, Rankin, & Stice, 1996;
Leland & Pyle, 1977; Ross, 1977). Some studies have concluded that even if
managers are trying to signal the market, there is no evidence (or mixed evidence)
supporting a change in information asymmetry after the split announcement.
However, even if the split itself is not a signal, the evidence does seem to support
the notion that the size of the split factor signals information (Brennan & Copeland,
1988; Brennan & Hughes, 1991).
The studies discussed above conclude that forward stock splits convey
positive information about a firm to the market, but more recently, researchers
have extended this line of reasoning by examining whether the positive signal
conveyed by the stock split announcement has an effect on companies in the
same industry that did not have a stock split. They find that, on average, the firms
that did not split had significant positive abnormal returns following the split
announcement of their intra-industry counterpart (Tawatnuntachai & D’Mello,
2002). This finding seems to strengthen the signaling theory.
20
2.1.2 Signaling Combined with Transaction Costs
Theories that combine signaling with transaction costs offer further insight
into stock splits. The view is that for a signal to be credible, it must be costly. One
source of increased cost is the higher transaction costs of lower-priced shares
after a forward split. (Brennan & Copeland, 1988). Transaction costs are reduced
for reverse split stocks (Han, 1995). The primary transaction cost is the bid-ask
spread, which is proportionally higher per share after the forward split and
proportionally lower per share after the reverse split.
The empirical findings in the 1990s support the view that investors respond
to costly signals. Investors respond to signaling even more favorably when the firm
is small. Specifically, returns to forward split announcements are negatively
correlated with firm size, and the post-split price is positively correlated with the
size of the split factor. The costly signaling explanation is that managers will not
forward split unless they have exceptionally good information about the future of
the firm (Ikenberry et al., 1996; McNichols & Dravid, 1990; Pilotte & Manuel,
1996).
When surveyed, most managers deny the signaling aspect of the split and
will respond that their primary goal is to move the stock price to a more favorable
trading range, where the liquidity is improved and the price is more attractive to
investors (Baker & Gallagher, 1980; Baker & Powell, 1993). Trading range
manipulation and liquidity are discussed next.
2.1.3 Trading Range
What do executives say when asked why they split their stock? The answer
seems to be consistent over time. A survey reveals that from 1900 to 1930, 90% of
managers of stock-splitting firms cited a wider distribution in shares as their
primary motive (Dolley, 1933). Presumably, the lower price per share not only
increases the number of shares outstanding, it also facilitates trading. During the
1940s and 1950s, managers responding to a survey indicated that they wanted to
21
bring their share price down to the $15-$40 range (Dewing, 1953). Of the
managers responding to a survey in the late 1980s (1987-1990), more than 70%
cited a preferred price range as their primary motivation for splitting, and 14% cited
signaling (Baker & Powell, 1993). Managers responding to a similar survey in the
early 1990s reported that their main motivation for splitting is their belief that
investors prefer stock prices in the $20-$35 range (Baker, Phillips, & Powell,
1995). Managers want to increase individual investor ownership because they feel
that a broad base of ownership increases the value of the firm (Lakonishok & Lev,
1987).
Trading range motivation exists when the firm’s management believes that
there is a desirable price range in which their stock is more liquid. Firms will split
the stocks when the price gets too high and out of range for investors who must
purchase shares in round lots.24 Firms will reverse split stocks when the price gets
too low. Margin requirements and institutional perception make moving up to a
higher trading range look like an attractive option for low-priced stocks. In addition
to perceptions, it has also been shown that traders actually have lower transaction
costs after a reverse split (Peterson & Peterson, 1992; West & Brouilette, 1970).
Uninformed trades, proxied by non-institutional trades, increase after a forward
split, and these trades are more expensive to the trader (Easley, O’Hara, & Saar,
2001). Easley et al. interpreted their findings to be consistent with the trading
range hypothesis since the number of small holdings increases—a stated goal of
managers citing trading range as their motivation for splitting stock. An alternate
explanation of their findings is that forward splits move tick sizes relative to the
stock price to a desired level (Angel, 1997; Anshuman & Kalay, 2002). The idea is
that the proportionately larger tick size may provide market makers (brokers) with
an additional incentive to promote the stock to small investors.25 Brokerage firms
24
A round lot is 100 shares.
This implies that stocks can be marketed or promoted at higher price levels and that irrational
and naive investors can be taken advantage of by salespeople. This goes against economic theory
that says in aggregate investors are rational and all-knowing. If promoters of consumer goods
such as automobiles can benefit from promotional campaigns, why can’t promoters of equity
offerings? This is beyond the scope of this study but an interesting study for later.
25
22
may encourage the split to preserve commission income (Brennan & Hughes,
1991). Supporting research demonstrates that there are indeed more small orders
than large orders after a forward split, and the bulk of the orders are buys (Schultz,
2000). This is consistent with the trading range hypothesis and the view that
forward splits act to promote stock trading among small individual investors. It is
also consistent with studies that show increasing numbers of shareholders after
forward stock splits (Lamoureux & Poon, 1987; Maloney & Mulherin, 1992). In
addition, it has been shown that forward stock splits are followed by increased
trading activity by small investors (Angel, Brooks, & Mathew, 2004). This is a
desirable outcome because when there is incomplete information in a market, a
company may have lower capital costs as the number of unique shareholders
increases (Merton, 1987). The demand for the stock is higher; thus, the price is
higher and returns are lower.
The trading range motivation for forward stock splits is well established in
the literature, but trading range is a motive for reverse stock splits as well
(Peterson & Peterson, 1992). Forward and reverse splits are both used to place a
stock in a more attractive trading range. For reverse splits, this generally results in
lower transaction costs for the traders (Peterson & Peterson, 1992). Marketability
is another motive for reverse splits. Low-priced securities can be seen as
speculative or disreputable, but if a firm reverse splits its stock, the price is higher
and the view is that the stock is more appealing or more available to the mass
market. These marketing issues are important because companies desire to have
their stock purchased by institutional investors. Institutional investors may have
trouble justifying a portfolio with low-priced stocks. A stock is viewed as more
marketable if it is listed on a major market, and NASDAQ and the AMEX do not
allow low-priced stocks to persevere. In fact, a company is encouraged to reverse
split if the stock trades at a low price for an extended period. The NYSE does not
have a minimum share price for continued listing, but it can delist companies in the
case of unusually low share prices, typically less than $1 for more than 30 days.
23
Low-priced securities are often seen as speculative or disreputable and
cannot be bought with a margin account. Thus, companies have incentive to raise
the price via a reverse stock split so that the stock is available to a broader base of
individual investors at a more favorable trading price (Spudeck & Moyer, 1985).
Research on why companies choose certain stock split ratios shows that
the ratio is chosen to bring the equity price in line with the average market share
price and, to a lesser extent, the industry-wide average share price (Lakonoshok &
Lev, 1997). This supports the trading range theory. Lakonoshok and Lev have
also presented evidence that forward splits are confirmations of past activity rather
than signals of future prospects. Companies that forward split their stock are often
companies that have recent abnormal growth in earnings and dividends. The high
growth does not persist after the forward split. This is a strike against the signaling
theory. Some researchers have suggested that the signaling theory is valid for
stock splits because the empirical properties of stock dividends seem to support it.
If stock splits and stock dividends are the same corporate event, except on
different scales, this is a valid argument. Lakonoshok and Lev argued that
empirical properties of stock splits and the empirical properties of stock dividends
are fundamentally different, and therefore it is inappropriate to draw conclusions
about splits based on the properties of dividends.
2.1.4 Institutional Reasons to Reverse Split
Some firms desire to appease institutional investors because they want to
attract institutional capital. If a company has a low share price, institutional
investors may be more prone to purchase a stock after a reverse stock split. The
fact that institutional investors shy away from low-priced stocks gives firms a
motive to reverse split their stock. The institutions are bound by covenants with
their investors and often these covenants specify a minimum bid price.26 In
26
Vincent Sbarra, a senior partner with HBC Capital, says, “A lot of investment funds have
covenants that don't let them buy stocks under certain prices—usually $3 or $5.” Ulrico Font, senior
analyst for Ned Davis Research, adds, “Everybody feels most comfortable with stocks priced
24
addition, institutions have to justify their investments and investors are more likely
to second-guess the fund managers when a fund includes low-priced stocks that
underperform (Lakonishok, Shleifer, & Vishny, 1992).
2.1.5 Margin Regulations
“Buying on margin” occurs when an investor borrows money from the
broker to buy a security. The Securities Exchange Act of 1934 regulates margin
accounts, but each brokerage firm has the power to place additional constraints on
their customers. Margin requirements dictate the minimum share price and
percentage of investment that can be borrowed from the brokerage firm. Currently,
a customer can borrow up to 50% of the price of a new investment for securities
that trade for $5 or more. Some brokerage firms keep a list of “unmarginable”
securities. The brokerage firm has decided, for whatever reason, that the firms on
this list are too risky to be bought on margin. Margin regulations can make a stock
difficult to obtain for individual investors; thus, a company with low-priced shares
may choose to increase the price through a reverse split. This allows more access
to the security through margin accounts, resulting in a wider base of potential
investors. However, bid price is not the only consideration that the brokers or the
Securities and Exchange Commission (SEC) use to disqualify a stock from being
“marginable”; therefore, a reverse split may not always be productive in this
regard. Factors such as trading volume, public interest (percentage of shares
outstanding), volatility, and perceived risk (e.g., IPOs) may weigh into a broker’s
decision to designate a stock unmarginable.
Margin regulations have changed over the years. In 1958, margin
requirements were changed three times. They were reduced from 70% to 50% in
January, raised back to 70% in August, and raised again to 90% in October. From
1960 to 1974, the margin requirements changed eight more times, fluctuating
between $5 and $50.” AT&T and Palm give similar reasons for reverse splitting (Tim Reason,
CFO, Magazine for Senior Financial Executives, December 2002).
25
between 50% and 80%. The last change, in 1974, set the margin requirement at
50%, and this rule has not been changed since then.27
Generally, to be marginable, a stock cannot be a penny stock. In 1990, the
SEC attempted to curb penny stock fraud and abuse by issuing the 1990
Securities Enforcement Remedies and Penny Stock Reform Act (PSRA). The
centerpiece of this legislation is the severe restrictions placed on IPOs that are
priced below $5 and not traded on major exchanges or a national automated
quotation system. The reasoning is that low-priced IPOs are low-quality IPOs. The
regulation succeeded in reducing the number of IPOs priced below $5, but it did
not succeed in improving the overall quality of IPOs. Studies find that delisting risk,
a proxy for IPO quality, did not decline significantly after the passage of the PSRA.
As evidenced by the decline in abnormal returns earned by a portfolio of postPSRA IPOs, the regulation had the unintended consequence of drawing
speculative (i.e., more risky) IPOs in the higher price range (Beatty & Kadiyala,
2004).
2.1.6 Reorganization
In addition to the “marketing” considerations discussed previously, there
could be a few other reasons that a firm chooses to reverse split its stock. When a
company declares bankruptcy, creditors could dictate the split as a part of the
reorganization. Reverse stock splits could also be used to reduce the number of
shareholders so that the corporation can be privatized and avoid full disclosure
requirements. Reverse splits can squeeze out minority shareholders if the
managers desire to take a public company private. Some states will not allow
investors to own fractional shares. Therefore, the reverse split forces minority
shareholders to sell. In addition, reducing the number of shareholders reduces the
cost of servicing those shareholders.
27
This is from “Chronology of Significant Events,” California Department of Finance.
26
2.1.7 Liquidity
Han (1995) showed that the liquidity of equity is improved after a reverse
stock split. He proxied liquidity with the bid-ask spread, trading volume, and the
number of non-trading days. He compared split firms with control firms and
concluded that for the reverse split stocks, the spread is reduced, the volume is
increased, and there are fewer days during which the stock is not traded. In
addition, he showed that bid-ask spread decreases and trading volume increases
after the reverse split.
Han (1995) says that reverse split improves the liquidity of a stock because
the increased share price allows investors to buy the stock on margin. Brokers do
not allow their customers to buy low-priced shares on margin. Therefore, a reverse
split improves liquidity by allowing margin investors access to the stock.
Another reason Han (1995) gave is that the increase in share price
improves the market’s perception of the stock. As discussed above, institutional
investors are more likely to purchase shares that they can justify to their clients.
Low-priced stocks are not easily justified.
The evidence for liquidity in forward splits is mixed, but one interesting
study involving American Depository Rights (ADRs) found that when there is an
ADR split announcement, both the underlying stock and the ADR will experience
positive price gains (Muscarella & Vetsuypens, 1996). The stock price increases
even when there is no stock split announcement in the firm’s home market. The
ADR split is accompanied by increased trading activity in the underlying stock.
This increased volume is cited as evidence of increased liquidity. Muscarella and
Vetsuypens did not test reverse split ADRs.
2.1.8 Risk
Peterson and Peterson (1992) found that the total risk of returns of reverse
split stocks declined after the reverse split. Brennan and Copeland (1998a)
documented decreasing risk shifts for forward splits. However, Peterson and
Peterson showed that systematic risk does not change after reverse splits. In
27
contrast, Brennan and Copeland found that systematic risk decreased around
forward splits.
Peterson and Peterson (1992) found positive wealth effects for companies
that were forced to reverse split. Companies that were not forced to reverse split
did not exhibit the same wealth effect. Their research showed that firms were less
risky after a reverse split because of decreased nonsystematic risk.
2.1.9 Investor Behavior in Relation to Stock Splits
The idea of behavioral effects has been around since at least 1953, when
A. S. Dewing noted that even though arguments for forward and reverse splits are
equally sound, managers are reluctant to reverse split. He believed that a reverse
split is an admission that the stock has been previously overpriced, and such an
admission goes against human inclination since it may imply that the managers
are either unethical or incompetent (Dewing, 1953).
Thirty-five years later, behaviorists offer a model that shows how analysts,
or the market as a whole, may underreact to new information such as a stock split
announcement. People tend to overweight their own beliefs and perceptions.
While new information may update or change these beliefs and perceptions,
people tend to ascribe more weight to their prior beliefs than they do to the new
information. For example, people tend to be slow to change their minds about
religious and political views that they “know” to be true—even in the face of
overwhelming evidence to the contrary (Barberis, Schleifer, & Vishny, 1988;
Daniel, Hirshleifer, & Subrahmanyam, 1988).
Are managers sensitive to underpricing or overpricing of their equity?
Ikenberry, Lakonishok, and Vermaelen (1995, 2000) reported long-horizon return
evidence in the United States and, more recently, in Canada. These authors found
that, at least for repurchases, managers seem just as sensitive to underpricing, as
their counterparts seem sensitive to overpricing. Ritter (1991) reported that
managers appeared to be timing the market at a relative peak when initially
issuing equity, since subsequent long-horizon abnormal returns were negative.
28
Seasoned equity offerings follow the same pattern as the IPOs (Loughran & Ritter,
1995; Speiss & Affleck-Garaves, 1995). Adding to the evidence of managers being
able to determine whether their stock is over- or underpriced are Baker and
Wurgler (2000), who reported that the aggregate flows of equity offerings appear
to have predictive power for overall market returns. Lakonishok and Vermaelen
(1990) examine equity repurchases and find that managers are often sensitive to
underpricing. If managers know when their stock is underpriced, this supports the
signaling hypothesis of stock splits. These managers split to signal their private
information.
The self-selection hypothesis is a synthesis of two explanations of the stock
split phenomenon. This hypothesis combines the trading range hypothesis and the
signaling hypothesis. Prices after a split are at or below the stock price of similarsized firms. The five-day announcement return confirms prior research that the
splits convey favorable information. The market reaction is greater for small firms,
low book-to-market firms, and firms splitting to low-share prices.
Investors underreact to a split announcement. The inverse relationship
between pre-split run-up and post-split returns suggests that momentum is not a
factor. The evidence is consistent with the self-selection hypothesis. Market
underreacting to corporate events is not unique to stock splits. The market
underreacts to many corporate events.
2.1.9.1 Framing
Framing occurs when an investor’s perception of gains and losses are
linked to a reference point. For example, an investor may consider $10 to be a fair
price for a security. If the investor buys at $8 (a 20% discount) and sells at $9.75,
the investor may still feel a sense of loss because the security was “framed” in the
mind of the investor to be worth $10. The investor feels that he lost $0.50 even
though he in fact gained $1.75. Even if an investor frames the price correctly,
losses and gains are not viewed in the same way. The utility (positive feelings) of
29
gains is not symmetric to the negative utility of a loss. Bad feelings about a loss
must be offset by a greater gain. For example, when the investor loses $5, it may
take a gain of $10-$15 to compensate for the emotional trauma of the loss. Other
factors influencing the framing include a person’s habits, what he or she considers
normal, and his or her view of the decision maker’s expectations (Kahneman &
Tversky, 1979; Shefrin & Statman, 1984). Lewin (1996) discussed, in a broader
sense, the links between economics and psychology and argued that investors
may use a company’s split history to decipher the information content of the
current stock split, even if investors have no particular price preference.
2.1.9.2 Price Preference
Do investors have a price preference? Theoretically, they should not, but
public statements by fund managers underline the fund managers’ belief that
investors prefer lower-priced stocks (Conroy & Harris, 1999). Designers of
financial products recognize the behavioral aspects of investors as they market
their products. For example, new securities are often designed to be priced within
a certain trading range (Shefrin & Statman, 1993).
Perhaps investors do not have a specific price level preference, but
evidence suggests that regardless, the firm’s previous post-split performance is
useful for interpreting the meaning of the current split. If managers typically split
the stock to a certain price level, then post-split prices below that level are seen as
an especially positive signal, and the market reacts accordingly (Pilotte & Manuel,
1996). The idea that managers have a preferred price level is supported without
dispute in many studies (e.g., Baker & Powell, 1993). Firm-specific price
preferences may be the result of a combination of behavioral and microstructure
considerations. Market microstructure is discussed in the next section. Evidence
further supports the price level preference in that analysts’ earnings forecasts tend
to change consistent with the signal sent by a forward or reverse stock split
(Conroy & Harris, 1999).
30
2.1.9.3 Herding
Does herding exist in financial markets? One model suggests that investors
with little information will abandon their own information and follow other investors
who are perceived to have better information, even if the “leader” has, in fact, very
little information. The modelers call this conforming behavior. This conforming
behavior leads individuals to join the herd or, as the researchers phrase it, “to
converge on one course of action” (Bikhchandani, Hirshleifer, & Welch, 1992).
Herding also occurs in mutual fund clients. Mutual fund clients often depend on the
advice of a single broker for their information. Reliance on this “undiversified
source” makes investors especially vulnerable to misinformation (Brennan, 1995).
Further herding behavior may be caused by misleading performance information
on commodity funds, as reported by the relatively few news outlets that cover
them. Researchers have found that investors who are both misinformed and
“unsophisticated” exhibit “puzzling behavior,” such as herding, in the marketplace
(Elton, Gruber, & Rentzler, 1989).
2.1.10 Optimal Tick Size (Market Microstructure)
As noted in the previous section, evidence suggests that share price
changes caused by stock splits are followed by market reactions. One explanation
for this market reaction is that the historical prices may capture firm-specific
market microstructure factors that have stabilized over time. One of these
microstructure factors is tick size. A forward split results in a larger tick size
relative to the share price. The bid-ask spread is a larger percentage of the share
price. The regrettable higher transaction cost is offset by desirable results such as
greater protection for limit orders, lower-cost trader negotiation, and fewer trading
errors. The optimal relative tick size depends on various firm characteristics, but if
there is an optimal tick size, then a stock split that results in a price below an
optimal (liquidity-based) price can be viewed as a signal that conveys positive
information to the market. The managers expect the price to rise after the split,
31
back to the optimally liquid price (Angel, 1997). In essence, Angel’s reasoning is
an extension of the signaling hypothesis discussed previously. Angel also argued
that the idea of an optimal tick size is a contributing factor to the consistency of the
average NYSE stock price over the years. Further research based on the
examination of intraday trades and quotes provides little support for the tick size
hypothesis (Schultz, 1997).
2.2 Conclusion
2.2.1 What We Know
We know that firms continue to split their stock. In general, there are
positive abnormal returns after a forward split and negative abnormal returns after
a reverse split. Ikenberry et al. (1996) have shown that the market sometimes
reacts to corporate events and that firms with low share price or negative pre-split
returns have positive announcement returns, but negative returns in the year
following the split. This means good news, followed by disappointment.
The trading range hypothesis, blended with signaling, seems to be the
prevailing theory as to why managers would take the costly action of splitting the
firm’s stock. Companies that execute forward splits do so to target a specific price
range. When a company splits, the split factor adds strength to the signal. Bigger
factors amplify the signal for a split. A large factor in a forward split brings the price
down below historic averages and the market expects the stock to increase
further. Bigger factors in a reverse split mean that the reverse split is less severe
and less negative of a signal. For example a split factor of 0.9 (10 shares to 9) is
less severe than a split factor of 0.1 (10 shares to 1).
We know that firms may reverse split their stock because of continued
listing requirements, and there is evidence that the firms that split due to listing
requirements outperform firms that reverse split for other reasons.
32
We also know that institutional preferences factor into the firm’s stocksplitting decision. Institutions shy away from low-priced firms and they seem to
have a preferred trading range; thus, firms will forward or reverse split their stock
to cater to these preferences.
2.2.2 What We Do Not Know
The most recent literature on stock splits concedes that stock splits are still
one of the least understood phenomena in equity markets (Easley et al., 2001).
Over the years, many attempts have been made to explain why a rational firm
would choose to split its stock. The drawbacks are numerous. Stock splits are not
costless to implement. For what seems to be a non-economic event, the company
will pay underwriting costs, accounting costs, printing costs, postage, and other
expenses associated with performing what appears to be a purely cosmetic
operation. In addition, since exchange fees are based on the number of shares
outstanding, a company must pay higher exchange fees when it issues a stock
split (lower fees for a reverse split). There seems to be no financial reason for a
firm to bear these costs. After all, there is no upward bound for stock prices.
Berkshire Hathaway, for instance, is a firm that has chosen to forego stock splits.
The “A” shares cost well over $10,000 per share and the CEO explains that the
company is looking for a “certain type of investor,” implying that investors have
stock price preferences and high per-share prices will drive away unsophisticated
investors.
Besides costs to the firm, empirical research has documented other
negative effects of forward splits, such as greater volatility, larger proportional bidask spreads, and higher transaction costs. Reverse splits result in decreased
volatility, increased liquidity, smaller proportional spreads, lower exchange fees,
and lower transaction costs, so it may make more economic sense to reverse split
than to forward split. However, reverse splits, even with all of the positive
residuals, are historically followed by negative abnormal returns, or worse—
delisting or bankruptcy.
33
Although there are many drawbacks, and although many studies have
shown that forward splits are inefficient and reverse splits are detrimental to
corporate wealth, firms continue to announce stock splits.
One of the common explanations for a stock split is that it sends positive
information to the market. The short-interest studies seem to confirm this for
forward splits, but what happens to short interest following reverse splits? Is the
reverse split a negative signal? Most firms that reverse split have prices too low to
be shorted and low-priced securities in general have lower-option interest, so it is
difficult to understand the impact of a secondary signal such as short interest or
put interest.
We still do not have a unifying theory as to why corporations split their
stock. When the management of a firm chooses to split its stock, there can be only
two logical reasons for the split: either the split is in the manager’s best interest or
it is in the shareholder’s best interest.
2.2.2.1 Managers’ Self-interest
Perhaps the managers are under pressure from irrational shareholders to
split the stock. Something about stock splits is exciting to shareholders. They have
a sense of gain when they have more shares. Perhaps the managers think that an
increased ownership base after a forward split will confound a hostile takeover and
they can keep their job. Perhaps managers believe that the company and their
jobs are more secure if the company is merged or privatized after the reverse split,
even though there is no evidence to support this belief. Maybe managers think that
a reverse split will improve the company’s image. Institutions will buy their stock if
it is priced higher and maybe managers’ gain some satisfaction or prestige as they
are courted by Wall Street.
34
2.2.2.2 Shareholders’ Best Interest
Perhaps, with the forward split, the managers really are signaling to the
shareholders that the company is strong and its prospects for the future are good
and perhaps managers really feel that a reverse split will buck the odds and result
in positive recognition and increased value for the shareholder. Maybe the
managers are reverse splitting in an effort to improve liquidity or reduce
transaction costs. The empirical evidence seems to indicate that such attempts are
futile, so why do managers reverse split?
35
CHAPTER 3: METHODOLOGY AND RESULTS
This chapter is divided into three sections. In section one, the stock price
reaction to a reverse stock split is examined using event study methodology and
long-run buy-and-hold return analysis. Section one also contains an analysis of
post split firm returns based on characteristics such as: industry, firm size,
consolidation, motivation for the reverse split, and the stock price at the time of the
reverse split. In the second section, the information content of a reverse stock
split is investigated further by regressing returns on financial distress variables.
The final section contains an analysis of NASDAQ’s change in continued listing
requirements in 2001 and its effects on reverse stock splits and delistings.
3.1 Stock Price Reactions
This section deals with the stock price reaction to a reverse stock split.
Event study methodology is employed around the announcement date and the exdate of the reverse stock split and long-run returns are examined using buy-andhold methodology. This section begins with the analysis around the event itself
and concludes with an analysis of the long-run returns.
As discussed in Chapter 2, researchers have documented negative
abnormal returns around both the announcement date and the ex-date of reverse
stock splits. The reverse split seems to have gained in popularity in recent years
and thus should be re-explored in light of all of the new data that is available.
Previous studies were based on much smaller data sets than used in this study.
For example, Woolridge and Chambers (1983) examined 57 reverse splits,
Peterson and Peterson (1992) examined 196 reverse splits, and Vafeas (2001)
examine 229 reverse splits. There are 2,075 reverse splits in my raw sample of
which 1,956 are for ordinary common shares. Of these 1,956, announcement
dates are hand collected for 1,494 firms. However, some of these companies
made
other
announcements
concurrent
with
the
reverse
stock
split
announcement. These firms are deleted with the result being a “pure” sample of
36
1,398 firms. Some companies in the sample announced the re-election of board
members concurrent with the reverse stock split announcement, but this is
considered to be a benign event and thus those companies are included in the
“pure” sample.28 With 1,956 ex-dates and 1,398 pure announcements, it is clear
that the reverse split has gained popularity in the time since the previous studies.
Previous research indicates that the reverse stock split is a negative event for a
company. If that is true, then why are so many firms utilizing the reverse stock
split in recent years? Is the reverse stock split still a negative event?
The primary hypothesis of this section, stated in the null, is as follows:
There is no stock price reaction associated with the event of a
reverse stock split.
Measuring the stock price reaction around an event date has some
complications - especially for smaller more obscure companies.
Under ideal
circumstances and with perfect and instantaneous information, the market should
respond the same day, if not instantaneously, to company news. Unfortunately,
this study is not rampant with ideal circumstances.
For example, the typical
company performing a reverse stock split is a smaller and less well-known firm
characterized by thinly traded stock.
When trading is thin, there may be an
information shortage because each trade, theoretically, conveys information to the
market. In addition to light trading, there may be few, if any, analysts covering
these firms.
In these less than ideal circumstances, it may take longer for
information to be disseminated.
It may be the case, then, that the effect on
shareholders’ wealth is not confined to the event day, but rather spread out over a
longer period. Event studies typically use a multi-day announcement window to
allow for the fact that press releases may be published after the close of trading. If
an announcement was made before or during trading hours, then the market
should react that same day. However, if the press release is published after hours
28
An announcement of the status quo remaining unchanged is more of a formality than an
announcement. The re-election of board members announcement is deemed to have had no
information content. It is equivalent to an announcement such as “corporate headquarters is at the
same address.”
37
then the market reaction should occur the next day. Using a two-day
announcement period (days t=0 and t+1) captures both possibilities and is the
method employed in this study. The days t-5 to t-1 are also examined to see if any
information leakage or rumor trading is present.
Following the announcement
period, the days t+2 to t+5 are examined to see if any patterns of information
dissemination lag might be present. This results in an 11-day window over which
the returns around the announcement of a reverse stock split are examined. The
hypothesis of interest here is:
There is no short-term stock price reaction associated with the
event of a reverse stock split.
Results are presented for daily raw returns, daily market adjusted returns,
and daily market model adjusted returns. The CRSP equally weighted index is
used as a proxy for market because most of the sample firms are relatively small.
Since small firm returns have a greater impact on the equally weighted index than
they do on the value weighted index, the equally weighted index is more
characteristic of the sample. However, for robustness, calculations involving a
market index are repeated using the CRSP value weighted index as an alternate
proxy for the market. If using the value-weighted index impacts the test results, it
is noted in the discussion of that test.
3.1.2 Raw Returns Around the Announcement Day
Of the 1,398 firms in the pure sample, 44 are dropped because there are no
usable estimation period returns in the CRSP database.29 Of the remaining 1,354
firms, an additional 91 are dropped because of missing return data for at least one
of the days in the 11-day test period. An analysis using the remaining 1,263 firms
is presented below. The larger sample of 1,354 firms was also examined, missing
daily returns were ignored, and results are virtually the same as those presented
here.
29
The estimation period of t-160 to t-40 is used to estimate market model coefficients.
38
The mean raw return for each day (-5 to +5) surrounding the announcement
day is presented in Table 6. The mean cumulative return for three different periods
is also presented. Of these three periods, the “announcement date” (day 0 to +1)
is of primary interest. The cumulative periods referred to throughout this section
are:
PRE-AD Days -5 to -1
AD
POST-AD
Days 0 to +1
Days +2 to +5
Using all 1,263 firms, the mean AD cumulative return (CR) is -5.01% (0.001
significance) as shown in the bottom section of Panel A (Days 0,+1).30
The
average PRE-AD CR (Days -5,-1) is 1.93% (0.01 significance) and the average
POST-AD CR (Days +2 +5) is 0.27% but not significantly different than zero.
Some firms have the same announcement date and ex-date.31 To detach
the announcement day effects from the ex-date effects, the firms with a unique
announcement date (Panel B) are tested separately. In this sample of 721 firms,
there is no other corporate event on the announcement day and the
announcement day effect is not confounded by the ex-date. As such, this sample
should provide the truest test of the stock price reaction to the announcement of a
reverse stock split.
The sample of firms with identical announcement and ex-dates (Panel C) is
also tested to determine if there is a different stock price reaction than for the pure
AD sample. The two groups of firms are hereinafter referred to as:
30
The Patell Z (Patell 1976) is used to determine whether a return is significantly different than
zero. This test is published in many event studies (for example: Linn and McConnel, 1983;
Schipper and Smith, 1986). The actual Patell’s Z is given in the tables, but for ease of reading and
interpretation, the significance levels are reported in the text as 0.001, 0.01, 0.05, 0.1 or not
significant. The phrase “5.01% (0.001 significance)” means that the value of 5.01% is significantly
different than zero at the 0.001 level according to the Patell’s Z score.
31
All firms in the sample have an ex-date in the CRSP database. This is the day of the actual
reverse-split. The announcement date is found by searching for the first available news article in
Lexis Nexis. A Wall Street Journal article or a company press release is the preferred source.
39
UAD
firms with Unique Announcement Day
IAE
firms with Identical Announcement day and Ex day
Comparing the average AD returns of the two samples, it is found that the
stock price reaction is strongest for the IAEs. The average AD cumulative return is
-7.31% (0.001 significance) for the IAEs (Panel C) and -3.29% (0.001 significance)
for the UADs (Panel B). Given the IAEs’ stronger stock price reaction and higher
Z score, it appears that the market reacts stronger on the ex-date than it does on
the announcement date.
This could also be evidence that the market reacts
stronger to an announcement when the event is immediately forthcoming.
3.1.3 Market Adjusted Returns Around the Announcement Day
Daily market adjusted returns are calculated as:
ARit = Rit - Rmt
where ARit is the abnormal return of stock i at time t, Rit is the return of stock i at
time t, and Rmt is the market return at time t that is proxied by the CRSP equally
weighted or value weighted index.
The average daily market adjusted returns are shown in Table 7.
The
average AD cumulative abnormal return (CAR) is -5.19% (0.001 significance) for
the full sample as shown in Panel A. The average AD CAR is -3.43% (0.001
significance) for the UADs (Panel B) and -7.54% (0.001 significance) for the IAEs
(Panel C). The comparison between Panels B and C reveals that the AD stock
price reaction is strongest when the announcement date and the ex-date are the
same. This is further evidence that the market reaction to reverse stock splits is
stronger on the ex-date than on the announcement date.
These results incorporate the CRSP equally weighted index as the proxy for
the market.
For robustness, the CRSP value weighted index is used as an
alternate market proxy and the results are shown on the second page of Table 7.
The results are nearly identical. The only difference is that the average PRE-AD
40
CAR and the average POST-AD CAR are slightly more significant for the IAEs
(Panel C).
3.1.4 Market Model Adjusted Returns Around the Announcement Day
The analysis of raw returns around the announcement date reveals a
significant negative stock price reaction to the reverse stock split (-5.01% CR from
Table 6). This negative reaction remains about the same for the market-adjusted
returns (-5.19% CAR from Table 7). The investigation of abnormal returns (CARs)
around the announcement date proceeds by applying the market model.
The daily abnormal returns are calculated using the market model
estimates as:
∧
∧
AR it = R it − α i − β i (R mt )
where ARit is the abnormal return of stock i at time t, Rit is the return of stock i at
∧
∧
time t, α i is the intercept estimate from the market model, β i is the slope estimate
from the market model and Rmt is the market return at time t that is proxied by the
CRSP equally weighted or value weighted index. The coefficients are estimated
using OLS regression for t equals -160 to -40 relative to the announcement date.
This represents about six months of trading days in a period prior to the event, but
it excludes a possible volatile trading period in the 40 trading days (about two
months) prior to the announcement date. The estimation model is:
Rit = αi + βi (Rmt) + εit
where Rit, αi, βi, and Rmt are defined previously and εit is the error term in the
regression.
The average market model adjusted returns around the announcement date
are presented in Table 8. As with the raw returns and the market-adjusted returns,
the strongest stock price reaction is seen for the IAEs (Panel C). The full sample
of announcement dates, shown in Panel A, has an average AD CAR of -5.30%
(0.001 significance). The PRE-AD CAR of 1.29% is also significant at the 0.01
level.
41
The UAEs (Panel B) have an average AD CAR of -3.51% (0.001
significance) and the IAEs (Panel C) have an average AD CAR of -7.68% (0.001
significance). Again, the stronger reaction and larger Z-score for the IAEs provides
evidence that there is a stronger stock price reaction to a reverse stock split on the
ex-date than on the announcement day.
As with the market-adjusted returns, the CRSP value weighted index is
used an alternate proxy for the market. The results are provided on the second
page of Table 8. The average CARs and the significance levels are about the
same regardless of the market proxy used.
3.1.5 Announcement Day Conclusions
The analysis of raw and abnormal returns around the announcement date is
in harmony with the existing literature in that the mean abnormal returns are
negative on the announcement date.
However, the IAE analysis reveals an
unexpected result. There are positive CARS in both the PRE-AD and POST-AD
periods.
This is not true for the full sample or for the UAEs. The PRE-AD
cumulative returns are influenced most heavily by the positive and significant
returns on day -1. Since only the IAE firms exhibit this phenomenon, it is most
likely an ex-date effect rather than an announcement day effect.
While explanations for the positive and significant PRE-AD CARs are rather
hard to come by, the positive and significant POST-AD CRs and CARs for the
IAEs may suggest an initial AD over-reaction to bad news (the surprise reverse
stock split) followed by a correction.
3.1.6 Ex-date Raw Returns
Since the analysis of the average AD return provides evidence of an exdate effect, the ex-date returns are also analyzed. Unlike the announcement date,
which may be a little vague in terms of when the market assimilates the
information, the ex-date is a precise point in time. Therefore no multi-day returns
42
are used in the analysis of ex-date returns. However, for comparison purposes,
cumulative returns are presented for days -5 to -1 and +1 to +5.
As with the announcement day returns, the sample is culled because of
missing return data in the CRSP database. Of the 1,956 firms with ex-dates, 51
firms are dropped because no useable returns are available in the estimation
period and 94 firms are dropped because at least one daily return is missing
during the 11-day test period. The remaining 1,811 firms have an average ex-date
raw return of -6.08% (0.001 significance) as shown Panel D of Table 9.32
For some firms, there was no announcement date found in the Wall Street
Journal or in the Lexis-Nexis database. The average ex-date raw return for this
subsample is -5.86% (0.001 significance) as shown in Panel E of Table 9. There
are two assumptions that can be made about the firms with missing
announcement dates. Either an announcement exists that was not reported in the
two sources mentioned above, or there was no announcement.
If the market knew about the reverse stock split prior to the ex-date, then
there was an “announcement” and the firm should be included in the sample of
firms in which the ex-date is not the announcement date. The average ex-date
raw return for firms in which the announcement date is not the ex-date (and firms
with missing announcement dates are included) is -5.78% (0.001 significance).
Average cumulative returns and the average daily returns for this subsample are
shown in Table 9 Panel F.
If the market did not know about a firm’s reverse stock split prior to the exdate, then there was no announcement and the firm should not be included in the
sample of firms in which the ex-date is not the announcement date. The average
ex-date raw return for firms in which the announcement date is not the ex-date
(and firms with missing announcement dates are not included) is -5.74% (0.001
significance). Average cumulative returns and the average daily returns for this
subsample are shown in Table 9 Panel G. This subsample is the purest sample of
32
As with the announcement day results, the results presented are virtually the same as the results
using the full sample of 1,956 firms. Even with the full sample, 51 firms were dropped due to
missing estimation period data.
43
ex-date returns. The ex-date is different from the announcement date and firms
with missing announcement dates are not included.
Based on the average raw returns presented in Table 9 there seems to be a
stronger stock price reaction to reverse stock splits on the ex-date than on the
announcement date. Comparing Panel G (purest ex-date returns) with Table 6
Panel B (purest AD returns), the average ex-date return is -5.74% with a Z-score
of -20.669 while the average AD cumulative return is -3.29% with a Z-score of
-8.156. The average stock price reaction on the ex-date is stronger and the Zscore is greater in magnitude. This suggests an ex-date effect, but this effect may
not persist if the abnormal returns are considered. The following analysis of the
ex-date abnormal returns mirrors the procedure used to examine announcement
day returns.
3.1.7 Ex-date Market Adjusted Returns
Market adjusted returns are calculated as before and are presented in
Table 10.
The significant average ex-date returns persist. The average ex-date
return for all firms is -6.18% (0.001 significance). For firms with no announcement
date, the average ex-date return is -5.96% (0.001 significance). The average exdate return for firms in which the announcement date is not the ex-date is -5.85%
(0.001 significance) when firms with missing announcement dates are included
(Panel F) and -5.81% (0.001 significance) when firms with missing announcement
dates are not included (Panel G). The CRSP equally weighted index is used as a
proxy for the market in calculating these results. For robustness, the CRSP value
weighted index is used as an alternate proxy, but the results (last two pages of
Table 10) change by only a couple of hundredths of a percent and the significance
remains the same.
3.1.8 Ex-date Market Model Adjusted Returns
Market model adjusted returns are calculated as before and are presented
in Table 11.
The significant average ex-date return continues to persist. The
44
average ex-date return for all firms is -6.21% (0.001 significance). For firms with
no announcement date, the average ex-date return is -5.97% (0.001 significance).
The average ex-date return for firms in which the announcement date is not the
ex-date is -5.86% (0.001 significance) when firms with missing announcement
dates are included (Panel F) and -5.81% (0.001 significance) when firms with
missing announcement dates are not included (Panel G). The average market
model adjusted returns are nearly identical to the average market adjusted returns.
In addition, the results are robust to the market proxy used. Using the CRSP
equally weighted index versus the CRSP value weighted index has a negligible
effect on the results (as shown on the last two pages of Table 11).
The analysis of announcement day and ex-date returns shows that there is
an ex-date effect that is stronger and more significant than the announcement day
effect. The effect is present in raw, market adjusted, and market model adjusted
returns.
3.1.9 The relation between ex-date returns and announcement date returns
In an efficient market the market’s full response to the reverse stock split
should occur on or around the announcement day. In general, there is no
information content on the ex-date.
This is just a confirmation of a previous
announcement. However, a strong significant ex-date effect is observed for our
samples. Even though there is no obvious reason for a correlation to exist, it may
be possible that the ex-date effect is somehow related to the announcement day
effect, and we assess whether this is the case.
Ex-date returns are regressed on announcement date returns for firms
where the announcement date is different than the ex-date.
Using the AD
cumulative return as the independent variable and the ex-date return as the
dependent variable, the OLS regression reveals a significant, positive relationship.
When market adjusted or market model adjusted returns are used in the
regression, the relationships remain positive and significant.
45
The regression
results are presented in Table 12. As shown in the table, the results are robust to
the market index used in the calculations.
3.1.10 Analysis of Subsamples Based on Firm Categories
In this part of the analysis, subsamples of firms are formed based on five
firm specific criteria: the industry of the firm, the firm’s motivation for the reverse
stock split, the firm’s stock price after the reverse stock split, the size of the firm,
and the degree of consolidation.33
3.1.10.1 Industry
The first subsample analyzed is based on industry classification. Some
industries seem to get more press than others leaving a causal observer with the
impression that market downturns and reverse stock splits are concentrated in
certain industries. For example, the market down turn of 2000-2001 has been
called the Internet bubble burst even though firms from many industries suffered
stock price reductions during that period.
It may be true that firms in certain
industries have a stronger stock price reaction to reverse stock splits and in this
section we assess whether this is the case.
The hypothesis of interest here,
stated in the null, is:
The stock price reaction to a reverse stock split is not related to
the industry classification of a firm.
Table 13 shows the actual concentration of reverse stock splits by industry.
The industry classification assigned to a firm is based on the Fama and French
(1997) industry definitions listed in Appendix C. According to Fama and French
the firms within these classifications should have similar risk characteristics.
There are seven ways to partition the market into industry classifications. The first
33
Consolidation is the number of shares that were turned into one share. A 10 to 1 reverse stock
split was a larger consolidation than a 2 to 1 reverse stock split. This is the same idea as a split
factor.
46
way is to partition the market into five broad industry groups. The remaining ways
divide the market into 10, 12, 17, 30, 38, or 48 industry classifications. This type
of grouping results in more firms per group than the ordinary method of grouping
industries by the first two digits of the SIC. Having more firms in an industry group
is helpful in the statistical analysis because larger samples have higher degrees of
freedom and, all things being equal, that results in a greater statistical power
(higher t-values).
In addition, using this industry classification makes sense
because firms with dissimilar SIC codes may be part of similar industries.
With firms classified as described above, the average ex-date return is
calculated for each industry classification. These results are presented in Table
14 and it appears that there are some differences across classifications. The
differences are determined as follows.
For each set of industry classifications some of the individual classifications
do not have an average ex-date return that is statistically different from zero at the
5% level (a t-value greater than 1.96).34 For instance, there are five classifications
in the first set, but only four of the classifications have an average ex-date return
that is significantly different than zero.
The largest and smallest significant
average returns are compared within the category grouping. For example, in the
Five Industries category, the returns of “Utils” (-4.77%) exceed the returns of
“Shops” (-8.06%) by almost two to one, but the returns of “Utils” is not significant at
the 5% level (t-value = -1.38) so it is ignored.35 The classification with the best
significant average ex-date return is “Money.” Even though “Money” has a 2.91%
better mean return than “Shops,” these two means may not be statistically
different. Therefore a two-sample t-test for equal means is used to determine if
the two population-means, u1 and u2, are equal (Snedecor and Cochran, 1989).
H0:
u1 = u2
34
If a number is not statistically different than zero, it does not necessarily mean that the number is
zero; it means that there is not enough data to confidently say what the number is. This is an
important distinction because, in some groups, an average return of zero would be the best return
for the group.
35
For the reminder of this section the phrase “returns of “Shops” (-8.06%)” is shorthand for “the
average ex-date return for firms in the industry classification named “Shops” is -8.06%.”
47
u1 ≠ u2
Ha:
The test statistic is
T=
Y1 −Y2
s / N1 +s22 / N2
2
1
where Y is the sample mean, s is the sample standard deviation and N is the
sample size.
When “Money” and “Shops” (best and worst) are compared, the test of
equal means indicates that there is no difference in means at the 5% level
(T=1.78). There is therefore no insight in the analysis of the five broad industry
classifications.
However, when the industry classifications are narrowed, a pattern
emerges. For instance, in the Ten Industries category the returns of “Oil” (-4.46%)
exceed the returns of “Manuf” (-8.23%) almost two to one. In this category T is
2.89 indicating that the means are statistically different at the 5% level. Both
returns are negative and significant so higher returns do not necessarily mean that
one industry does well. It simply means that one industry does better in a relative
sense than the other.
In the Twelve Industries category the returns of “Energy” (-4.46%) exceed
the returns of “BusEq” (-8.29%) almost two to one. A T of 2.82 indicates that the
means are statistically different at the 5% level.
In the Seventeen Industries category the returns of “Oil” (-4.38%) exceed
the returns of “Clths” (-12.8%) almost three to one but a T of 1.79 indicates that
the means are statistically different at only the 10% level.
The next worst
classification is “Retail” and the difference in the returns of “Oil” and “Retail” is
significant at the 5% level (T=2.22)
The Thirty Industries category is even more dramatic. The returns of “Oil” (4.37%) exceed the returns of “Clths” (-17.40%) almost six to one with a T 2.43.
This seems to be the same result as in the Seventeen Industry definitions, but the
“Oil” and “Clths” classifications are slightly different in the two categories.
48
Appendix C identifies the SICs that are associated with each classification in each
group.
In the Thirty-eight Industries category, the returns of “Ptrlm” (+4.51%)
exceed the returns of “Apprl” (-20.39%) with a T of 4.64.
And finally in the Forty-eight Industries category, the returns of “BldMt” (4.16%) exceed the returns of “Clths” (-17.40%) more than four to one (T=2.37).
The returns of “Oil” (-4.37%) are the second best in the group and the mean
returns of “Oil” and “Clths” are significantly different at the 5% level (T=2.43) but
this is not shown on the table.
Industry Conclusions
It seems that the industries that are non-discretionary, like food, energy and
oil, do better than the discretionary industries like retail, clothes, apparel, and
business equipment. “Oil,” “Petroleum,” or “Energy” are the best performers in
most categories. Interestingly, one of the classifications with the worst returns, the
“business equipment” category from “Twelve Industries,” contains many of the
famed “Internet companies.” When people talk about an Internet bubble burst,
they are more often than not referring to firms in this industry category.
These results may be interpreted to mean that companies that provide
essential non-discretionary items or services may fare better than other companies
in periods of financial distress.
However, the top performer in “Forty-eight
Industries” is “building materials,” so we will not be dogmatic in our interpretations
other than to say that we have shown a difference in stock price reactions to
reverse stock splits across industries.
3.1.10.2 Delisting threat and Motivations for Reverse Splits
One facet of this study deals with the regulatory environment. Specifically,
firms that reverse split their stock due to the threat of delisting are analyzed
separately from those that reverse split for other reasons. If there is a difference in
the results for these different groups of firms, then it is possible that the exchange
49
regulations may be having an impact on the market’s reaction to reverse stock
splits. Signaling theory, as documented in the literature, asserts that managers are
sending information to the market when they split or reverse split their stock. The
best signals are those that are easily recognizable by the market, relatively
inexpensive to use, and hard to copy by under-performing competitors. Stock
repurchases and dividend increases are positive signals to the market that under
performing companies would find hard to duplicate. The literature reveals that
forward splits are generally followed by positive abnormal returns and reverse
splits are generally followed by negative abnormal returns. The conclusion is that
this is evidence of signaling. Forward stock splits are a positive signal, and reverse
stock splits are a negative signal. Though reverse stock splits are generally
considered a negative signal, the statements by management at the time of the
reverse stock are rarely negative in tone. Rather managers tend to put a positive
face on the reverse stock split announcement. They are “improving liquidity,”
“appealing to institutional investors,” “making the price more attractive,” or giving
some other positive reason. If the reverse stock split is a negative signal, then
managers seem to be trying to counter the negative signal with their press
releases.
Many companies are in poor financial condition when they resort to a
reverse stock split to prop up their share price. They may be admitting that they
have no other means of addressing the problems they currently face. However, it
may also be true that some managers do not desire to reverse split their stock but
are forced to do so for regulatory reasons. They may believe that the company’s
long-run prospects are good and may be very reluctant to send a negative signal
to the market, except that an even worse fate, market delisting, is in their shortterm future if they do not act. In order to stay listed on a market exchange, a
minimum price must be maintained in the short term. The current low price may be
due to market conditions or industry conditions (systematic) rather than to
specifics of the particular company (idiosyncratic). Reverse stock splits undertaken
because of regulatory pressures rather than financial pressures might produce a
50
different wealth effect, on average, and this is the issue that is addressed below.
The next wealth effect hypothesis, stated in the null, is then:
There is no difference in the wealth effects on the stock of firms
which reverse split their stock for voluntary versus regulatory
reasons.
Peterson and Peterson (1992), in a very small sample, find positive wealth
effects for companies that reverse split for regulatory reasons.
Two-day
announcement period abnormal returns were significantly positive for “pure” firms
that reverse split for regulatory reasons. Pure firms are those firms that had no
announcements other than the reverse stock split. Abnormal returns were defined
as mean adjusted returns where the firm’s return was adjusted by its own average
post announcement return.36
The above hypothesis is tested by grouping firms into subsamples based
on public statements concurrent with the reverse split announcement and then
comparing the average daily returns of companies that split because of minimum
bid price requirements with those of companies that split for other reasons. The
reasons identified were given in press releases found in the Wall Street Journal or
in the Lexis-Nexis database. Many firms gave no reason at all and these firms are
not included in any of the samples for these tests.
In our sample the most common reasons include, threat of delisting (384),
reorganization (159), attracting attention (99), merging with another firm (97),
moving to a desired trading range (74), increasing liquidity (35), or maintaining
NMS status (36). The returns are shown by reason in Table 15. The “N” shown is
the number of firms that have valid returns for days -2 to +2. For example, there
are 384 firms citing “delisting threat” as the reason for the reverse stock split, but
only 373 of these firms have non-missing returns in the CRSP database for all five
days.
36
In their study, the daily return was averaged from day t+2 to day t+101 with t=0 being the
announcement day.
51
The average ex-date return for firms that cite “delisting threat” is -6.31%
(0.001 significance). This is the worst average return for any motivation. Other
significant average ex-date returns were found for firms that reverse split their
stock due to: a reorganization (-5.74%, 0.001 significance), their desire to remain
on the NMS (-5.51%, 0.001 significance), a merger or acquisition (-3.51%, 0.05
significance), a trading range adjustment (-2.46%, 0.01 significance), or to attract
attention to the firm (-3.35%, 0.001 significance).
Ex-date returns are used
throughout this study, but the announcement date (AD) returns yielded similar
results.
Earlier studies (cited above) found that firms that reverse split for
regulatory reasons outperformed firms that reverse split for other reasons. The
results of this test, taken from a larger sample, do not support these earlier
findings.
3.1.10.3 Price
After the individual reasons given are analyzed, a second test is conducted.
Motivations are tested based on pre-split price rather than the reason given by
management. The assumption is that any firm with a stock price of less than one
dollar is reverse split for regulatory reasons regardless of the managers’ given
reason. The rationale is that compliance is at least a partial motivation when the
price is under one dollar. This type of motivation test is unique in the literature.
Table 16 shows the results of this testing. Stocks with pre-split prices below
$1.00 have an average ex-date return of -8.75% to -8.58% and stocks with presplit prices above $1.00 have an average ex-date return of -2.65% to -2.49%. The
means are significantly different as shown by the large t-values (two sample t-test
of equal means). Stocks in danger of delisting due to low price have worse returns
than stocks that reverse split for other reasons.
Related to price is the post split price that the firm is trying to achieve. The
rationale is that firms that aim for a higher post split price have the lowest
expectations regarding future performance. Consolidation to the higher price will
52
buy the company more time to turn around before it is threatened by delisting
again.
On the other hand, a post split price of at least $5 may be desirable
because many institutions avoid stocks that are priced below $5. In addition, a
common margin requirement for traders is that the stock bought on margin be
priced at least $5 per share.
The average returns according to target post split price are also shown in
Table 16. Target post split price is defined as the average price for the twenty
days before the announcement date times the number of shares consolidated. The
table shows the difference in returns for stocks with post splits prices over and
under $5. Firms that attempt to reverse split to the higher price levels have, on
average, better event day returns than those firms that attempt to split to a lower
price level. Higher priced firms, $10 and above are also examined. The results
indicate that stocks aiming for a higher trading range have better (or less bad)
returns on the event day.
3.1.10.4 Size of the Firm
Large firms may have more resources at their disposal and thus may be
more resilient than small firms. Ericsson and Priceline.Com are two large firms
that received a lot of press coverage around the time of their reverse stock split.
The fact that they have recovered well (they have positive post split abnormal
returns) may leave the general public with the impression that large companies are
less susceptible to the negative stock price reaction that normally follows a reverse
stock split. Tests are conducted to assess whether this is the case.
Size is
calculated as the number of shares multiplied by the price per share and the test
results are given in Table 17. The Ex-day returns are regressed on firm size.
There is no relationship between size and ex-date returns. The coefficient of size
is not significantly different than zero for any model. Results are similar for AD
returns. The conclusion from this test is that the market reaction to reverse stock
splits is not a function of firm size.
53
3.1.10.5 Consolidation
A reverse stock split consolidates the firm's shares into fewer shares.
Companies with the worst prospects for the future generally need to consolidate
the most. For example the company splitting 10 for 1 may have a dimmer outlook
than the company splitting 2 for 1.
The larger consolidation may also be a
negative consideration if it results in the company being near continued listing
limits on shares outstanding or number of shareholders. If the reverse stock split
sends a signal, a larger share consolidation may send a stronger signal and the
market may react less favorably to the larger consolidation. To test this idea, exdate returns and AD cumulative returns are regressed on the number of shares
consolidated such that:
R0 = α + β(number of shares consolidated).
Both AD returns and ex-date returns are discussed here because the AD results
differ from the ex-date results. The results are presented in Table 18.
If the AD cumulative return is the dependent variable, the regression
coefficient is -0.0007 with a t-value of -1.81.
The coefficient is marginally
significant so there is a slight relation between the AD CRs and the number of
shares consolidated. Results are similar for the market adjusted AD CAR, but
when the market model adjusted AD CAR is the dependent variable, the
regression coefficient is -0.0008 and significant (t-value = -2.19). Even though
there is a significant relation between market model adjusted AD CARs and
consolidation, it may be argued that there is not a strong “economic” relation. A 10
to 1 consolidation is only 0.4% worse than a 5 to 1 consolidation (10X0.0008 5X0.0008). However, considering this is a daily return and 0.4% compounded
daily for 250 trading days (about one year) is 171%, it is economically significant.
A “normal” average daily return for the market would be around one tenth of this
difference. For example, a 0.04% return compounded daily for a 250-days would
yield 10.5% annually.
54
The AD CR is marginally related to consolidation, but if the ex-date return is
the dependent variable, the regression coefficient is -0.0013 and the t-value is
-6.09. This is evidence of a significant relation between the number of shares
consolidated and the ex-date return. The negative coefficient means that larger
consolidations will generally result in more pronounced negative reactions on the
ex-date. As with the AD returns, the coefficient seems small, but as demonstrated
above the relationship is economically and statistically significant. Results are
similar when the market adjusted ex-date return or the market model adjusted exdate return is regressed on consolidation.
These results show that consolidation
has a stronger effect on the ex-date returns than on the announcement date
returns.
This concludes the analysis of the market reaction to reverse stock splits on
the announcement day and on the ex-day. The next section begins the analysis of
long-run returns after the reverse stock split.
3.1.11 Long-run Returns Following Reverse Stock Split
The analysis of long-run returns after the reverse stock split follows a
similar sequence as before.
The raw return analysis is followed by an
investigation of market adjusted abnormal returns and then by an investigation
based on a matched firm method.
Long-run returns are further examined by
analyzing subsamples of firms based on five firm specific criteria: the industry of
the firm, the firm’s motivation for the reverse stock split, the firm’s stock price after
the reverse stock split, the size of the firm, and the degree of consolidation.
3.1.12 Long Run Buy and Hold Returns
Raw returns are examined first. In this section, each stock’s long-run “buyand-hold” return (BHR) is calculated for holding periods of one trading day to 250
trading days after the ex-date. The 250-day holding period corresponds to
approximately one calendar year. Over the course of the year, some firms have
missing returns.
Some firms are missing many returns in a row over several
55
different periods. The return data starts and stops and starts again. The firms
with sporadic or missing returns are removed from the sample leaving a sample
size of 1,245 firms. Of these firms, 27 are dropped because there is no return
data after the reverse split.37 There are 1,218 remaining firms.
Though firms with missing returns are removed from the sample, the firms
delisted before the end of the 250-day period are not removed as long as returns
are consecutive up to the delisting day.
Of the 1,218 firms remaining in the
sample, 239 are delisted by the end of the 250-day period.
These firms are
delisted at a rate of about 20 per month. Since these firms stay in the sample, and
must be dealt with in the calculations, it is useful to examine the nature of the
delistings.
Table 19 provides a breakdown of the reasons for delisting as recorded in
the CRSP database. Of the 239 firms that disappear from the sample, 24 firms
(10%) merged with other firms. These 24 firms have an average daily return of
positive 1.2% on the delisting day. Most of the firms, 214, were delisted for noncompliance with exchange rules.38
The average daily return on delisting day is
-15.4% for these firms. The most common non-compliance issue is bid price (57),
followed by insufficient float or assets (32), insufficient capital (29), non-payment of
exchange fees (22), and insufficient equity (16). Interestingly, only 11 of the 239
firms gave bankruptcy as a reason for delisting.
Delisting does not result in a total loss to the investor. In fact, only one
company in the sample declared bankruptcy in the year following delisting.
Furthermore, the average delisting return taken from the CRSP database is
-13.5% on the day that a firm is delisted. If delisting is not a total loss, then what
return should be used when a firm is delisted? Two methods are employed here.
First, it is assumed that a delisted firm is sold and the portfolio rebalanced. The
second method employed here substitutes the return of a broad-based market
portfolio for the return of the delisted security for the remaining days in the test
37
Some firms were merged with other firms; others were taken private via the reverse split.
The remaining firm (of the 239) was liquidated. This firm is missing return data, but it has a
positive liquidation price of $0.375 per share and is therefore not a total loss.
38
56
period. The latter assumption neutralizes the effect of the delistings when the
market adjusted model or market model is used to analyze returns.
In this section raw returns are examined first. The buy-and-hold return
(BHR) for a firm is calculated as
T
BHR iT = ∏ (1 + rit ) − 1
t =1
where BHRiT is the raw return for stock i over the holding period T and rit is the
return on the security i (including dividends) on day t after the ex-date. The mean
return for each holding period is calculated as
N
∑ BHR
iT
i =1
BHRT =
N
where N is the number of firms in the sample.
As discussed above, missing
returns are handled two different ways. One way to handle missing returns is to
replace the missing return with the average portfolio return for that trading day.
When a firm’s return is not reported by CRSP, the return for that day is replaced
by the average return of all firms in the sample for that trading day such that:
NT
∑r
r it =
jt
j =1
NT
where NT is the number of firms in the sample on day T and t is the number of
trading days after the ex-date. The mean calculated in this manner is the return
an investor would realize by selling the delisted firms and using the proceeds to
rebalance the equal weighted portfolio. The 250-day mean BHR calculated in this
way is 8.80% (t-value=2.25) as shown on Table 20.
The second method of dealing with missing returns is to replace them with
zero. The assumption here is that once the firm is delisted, it neither adds to nor
reduces portfolio value. The 250-day mean BHR calculated in this way is 5.60%
57
(t-value=1.44).39 Since the average daily return is higher than zero in the latter
days, replacing returns with zero lowers the average BHR.
These positive mean returns seem to be in conflict with the idea of a
reverse stock split being a negative event. However, upon closer examination of
the distribution of the BHRs (where missing returns are replaced with the average
return for that trading day), the skewness is 6.0 which indicates a positive bias
from normal and the kurtosis is 53.1 which indicates very thin tails compared to the
normal distribution. These two indicators reveal that there are a few very large
values that are driving the average up.
In this case, the median may be a better indicator of the average than the
mean. The 250-day median return (50% quartile) is -0.24%. Other quantiles of
interest are the 75th (BHR = 24.6%) and the 25th (BHR = -58.3%). Even though
the median may be a better indicator of the average, the question of whether an
investor would be better off or worse off for buying and holding reverse stock splits
is more closely related to the mean return. For the 250-day holding period, history
shows that an investor would have lost on about 3 out of 4 firms, but that 1 out of 4
firms would have returned better than 24.6%.
This high return on a few firms
would have offset the many losses and resulted in an 8.80% raw gain.
The
distribution of these returns is shown in Figure 3.
Table 20 shows the BHR for reverse split stocks for 10-day intervals
through 250 days. The data confirm previous studies that show that returns to
reverse stock splits are generally negative for a period following the ex-date. The
average BHRs are negative for the first 180 days, which represents about 8.6
months of calendar time. However, after this period, the mean BHR turns positive
and by day 250 the BHR is 8.80%. These BHRs are plotted in Figure 4.
Alternatively, the missing returns for the delisted stocks can be replaced
with the market return for that day. The assumption here is that for the remaining
days in the test period, a delisted firm will, on average, earn the same return as
39
Replacing the returns with zero implies that the delisted firm is sold for cash and the cash is held
until the end of the 250-day period. There are no further losses or gains on that firm. If it is cost
prohibitive to rebalance the portfolio, this may mimic investor behavior.
58
the market. In this model, the CRSP equally weighted index return is used as a
proxy for the market. The model then becomes:
N
⎛
T
∑ ⎜⎜ ∏ (1 + r
BHRT =
i =1
⎝
it
t =1
⎞
) − 1⎟⎟
⎠
N
where
T
∏ (1 + r
it
) − 1 = BHR iT
t =1
is the buy-and-hold return for firm i for T days as before except that rit is replaced
with the market return when returns are missing. N is the number of firms in the
sample and T is the holding period. When returns are calculated in this manner
the mean BHR is 7.59% with a t-value of 1.95. Table 20 (second page) shows the
BHR for reverse split stocks for 10-day intervals through 250 days. If the CRSP
value weighted index is substituted for missing returns, then the 250-day BHR is
6.27% with a t-value of 1.61.
3.1.13 Long-run Abnormal Returns
The analysis now turns to abnormal returns. The BHAR provides insight
into how an investor would have fared against a benchmark. The BHRs may be
positive and significant over longer periods, but the same may not be true for the
abnormal returns. The buy-and-hold abnormal return, or BHAR, for security i is
calculated for each holding period T as
T
T
BHARiT = ∏ [1 + rit ] − ∏ [1 + E (rit )]
t =1
t =1
where E(rit) is the return on a reference portfolio.
The alternative is to use
cumulative abnormal returns (CARs) calculated as
CARiT = ∑t =1[ Rit − E ( Rit )]
T
where E(Rit) is defined as the expected return of a reference portfolio such as a
market index. However, Barber and Lyon (1997) argue that for long-run returns,
BHARs are more meaningful than CARS. Barber and Lyon document that CARs
59
are a positively biased predictor of BHARs, which may lead to improper
inferences; that is, there may be significant CARs when in fact, no significant
BHARs are present.
Furthermore, CARs assume a daily rebalancing of the
portfolio and investors do not manage their portfolio that way. Barber and Lyon
(1997) also document problems with the mean BHAR; it could be biased due to
new listings, rebalancing the benchmark portfolio may cause bias, and the multiyear returns of individual firms are skewed positive.
compounded, may cause bias.
The skewness, when
The bias can be controlled by having a large
sample size or by careful construction of the benchmark portfolio.
Fama (1998) argues that the BHAR methodology can yield significant
abnormal returns when none are present due to the “bad model problem.” The
benchmark is an imperfect projection of expected returns. Fama (1999) argues for
the calendar-time portfolio method because: this approach accounts for cross
correlation in firm returns, the distribution of the calendar time abnormal returns
are approximately normal (ease of statistical interpretation), and the bad model
problem is not as pronounced when monthly returns are used.
Lyon, Barber, and Tsai (1999) and Loughran and Ritter (1999) counter
Fama (1998) by arguing that the calendar time abnormal returns have low power
to detect abnormal returns and they do not reflect the investor’s experience.
Furthermore, they offer two bootstrap procedures that can be used for statistical
interpretation of the mean BHAR when the sample is not sufficiently large.
Mitchell and Stafford (2000) offer a rebuttal to Lyon et. al. arguing that the BHAR
methodology is flawed because it assumes independence of multi-year abnormal
returns and this assumption produces test statistics that are too large.
In this study there is a large sample size, the benchmark portfolio is the
market index (or a carefully chosen matched firm portfolio), and the long-run
returns are only 250 days (one year of trading days versus a multi-year approach).
For these reasons, BHARs are used in this analysis without reservation.
The mean BHAR is calculated for each holding period T as
60
N
⎧
T
∑ ⎨∏ [1 + r
BHART =
i =1
⎩ t =1
it
T
⎫
] − ∏ [1 + E (rit )]⎬
t =1
⎭
N
where E(rit) is the daily return of the CRSP equally weighted index. If rit is missing,
it is replaced by the average return of the remaining firms in the sample (as
before).
The mean 250-day BHAR is -23.19% with a t-value of –6.11. If E(rit) is the daily
return of the CRSP value weighted index, then the 250-day BHAR is not
significantly different than zero (t-value = -0.93). Values for the mean BHAR for
holding periods from 10 days to 250 days, in 10-day increments, are provided in
Table 21 and plotted in Figure 5.
As with the raw returns, the analysis is also conducted by substituting the
CRSP equally weighted index daily return for missing returns in the sample. In
this case, the mean BHAR is calculated as
T
⎧T
⎫
+
−
r
[
1
]
[1 + E (rit )]⎬
⎨∏
∑
∏
it
i =1 ⎩ t =1
t =1
⎭
BHART =
N
N
where the daily return, rit, is replaced with the daily CRSP equally weighted return
when rit is missing. The 250-day BHAR calculated in this way is -24.4% with a tvalue of -6.46. These numbers are shown on the second page of Table 21. If an
alternative market proxy, the CRSP value weighted index, is used then the 250day BHAR is -6.10% with a t-value of -1.59. This is not significantly different than
zero at normal acceptance levels.
To summarize, the average 250-day BHR following a reverse stock split is
positive and significant, but the BHAR results are mixed. If the CRSP equally
weighted index is used in the abnormal return calculations, then the average 250day BHAR is negative and significant. However, if the CRSP value weighted index
is used in the abnormal return calculations, then the 250-day BHAR is not
significantly different than zero. To further investigate this issue, another method of
calculating abnormal returns is employed.
61
3.1.14 Matched Firm Analysis
When calculating market adjusted or market-model BHARs, a proxy for the
market is used in calculating an expected return for a firm. The matched firm
method uses a companion firm, rather than the market, as a basis for determining
expected returns. Each sample firm is matched to an out of sample firm based on
several firm specific characteristics and all things being equal, the sample firm
should have the same returns as the matched firm. If the firms are matched well,
then the only distinguishing characteristic between the firms is a corporate event
(in this case the reverse stock split). Any difference in the returns of the two firms
is considered to be the “abnormal” return that is associated with the corporate
event.
The matched firm method of calculating abnormal returns may be
preferable here due to the unique characteristics of firms that reverse split their
stock. Typically, these firms are much smaller than the average size of firms in the
CRSP indices, so adjustment based on market movements may lead to
questionable conclusions.
Pairing the sample firms with firms of similar
characteristics and then testing for differences may then be the preferred
alternative, and at the very least, it provides a robustness check.
Firms in the sample are matched with out-of-sample firms based on size,
industry, and price.
Size is proxied by market value of equity and industries are
matched according the 2-digit SIC codes. The matched firms are priced within a
dollar of the original firm and they are the same size plus or minus 15 percent. In
the case where several matches are found, the best price match is chosen.
Sample firms are not allowed to match other sample firms, even in different time
periods. However, a matched firm is allowed to match more than one firm from
the sample as long as the matches occur in different months. The average sample
firm price is $3.73 (standard deviation 4.957) and the average matched firm price
is $3.71 (standard deviation 4.961). The average market value of equity of the
sample firm is $47,168,470 (standard deviation $202,323,470) and the average
market value of equity of the matched firm is $46,648,060 (standard deviation
62
$192,322,130). These statistics are tabulated in Table 22. These matching criteria
are too constraining for some firms and no match is found. The resulting sample
size is 691 firms.
In the matched firm method, returns are “abnormal” if the average return of
the sample of reverse stock splits is significantly different from the average return
for the sample of matched firms. The buy-and-hold abnormal return (BHAR) is
calculated as
T
T
BHARiT = ∏ [1 + rit ] − ∏ [1 + E (rit )]
t =1
t =1
where BHARiT is the abnormal return for stock i for the holding period T, and E(rit)
is the return of a firm matched to the sample firm based on size, price, and
industry.
Each firm’s long-run BHAR is calculated for holding periods of one
trading day to 250 trading days and the average matched firm BHARs are
calculated as
T
⎫
⎧T
r
[
1
]
[1 + E (rit )]⎬
+
−
⎨
∑
∏
∏
it
i =1 ⎩ t =1
t =1
⎭
BHART =
N
N
where N is the number of firm in the sample for holding period T.
Table 23 presents the matched firm adjusted BHARs.
The 250-day
matched firm adjusted returns are -8.26% with an unpaired sample t-value of
-1.01. The sample average raw return is 23.27% and the matched firm average
raw return is 31.53%. The unpaired sample t-value is calculated as
t=
BHRST − BHRMT
var( BHRST ) var( BHRMT )
+
N
N
where BHRST is the buy-and-hold return of the sample for holding period T,
BHRMT is the buy-and-hold return of the matched firm for holding period T, and
“var” is variance of the sample for holding period T. Figure 6 shows the BHARs
plotted from day 0 to day 250 from the ex-date. Table 23 reveals that the BHARs
are significantly negative in the short run, but after about 8 months, the matched
63
firm returns and the sample returns are not significantly different from each other.
Similar results (long-run BHAR = 0) were found above when the BHARs were
calculated using the CRSP value weighted index as a market proxy.
To summarize, the average 250-day BHR after a reverse stock split is
positive and significant, but the average 250-day BHAR is either not statistically
different than zero or negative and significant. Two tests show no significant 250day returns, but one test (the market adjusted BHAR using the CRSP equal
weighted index as the market adjustment) shows negative and significant BHARs.
3.1.15 Analysis of Subsamples Based on Firm Categories
The analysis of long-run returns continues by examining subsamples of
firms based on firm characteristics. As in the investigation of announcement day
and ex-day returns, subsamples are created based on industry, motivation, price,
size, and consolidation.
3.1.15.1 Industry
Firms are grouped into industries based on the Fama and French (1997)
industry classifications and the average 250-day BHR by industry is presented in
Table 24.
As before, there are seven sets of classifications in which firms are
grouped into 5, 10, 12, 17, 30, 38, or 48 industry classifications.
When industries are divided into five classifications, there is a significant
difference between the best and worst industry BHR. In determining the best and
worst BHR, the BHRs are ignored that are not statistically different from zero at the
10% level.40
In the Five Industries category the returns of “Shops” (27.22%)
exceed the returns of “Utils” (-28.22%) by 55.44%.41 A t-value of 19.78 indicates
that the means are statistically different.42
40
With the ex-date returns, 5% significance was used. A significance level of 10% is used here
because there are so few 250-day BHRs that are significant at the 5% level.
41
As before, the phrase “returns of “Shops” (27.22%)” is shorthand for “the average 250-day buyand-hold return of firms in the industry classification "shops” is 27.22%.”
42
As with the ex-date industry returns in the earlier section, a two-sample t-test is conducted here.
64
In the Ten Industries category, “Shops” (27.64%) leads “Money” (21.98%)
and these means are significantly different as evidenced by the two-sample t-test
of 2.14.
In the Twelve Industries category the returns of “Chems” (51.29%) exceed
the returns of “Shops” (-25.91%) more than two to one. Again, the two-sample ttest of 3.07 indicates the means are statistically different at the 5% level.
In the Seventeen Industries category the returns of “Other” (28.97%)
exceed the returns of “Durable” (-21.65%) and the two-sample t-value is 32.28.
The Thirty Industries have few significant returns, but “ElcEq” (60.49%)
exceeds “Servs” (33.52%) by nearly two to one. The two-tailed t-test of 2.43
indicates that these are statistically different.
In the Thirty-eight Industries category the returns of “Elctr” (41.51%) exceed
the returns of “Garbg” (-38.00%) and “Food” (-20.04%) with two-sample t-statistics
of 14.42 and 10.64 respectively.
And finally in the Forty-eight Industries category the returns of “ElcEq”
(60.49%) exceed the returns of “Books” (-15.25%) with a two-sample t-value of
7.35.
The returns of “Food” were also on the low end (–17.03%), but not
statistically different than zero at the 10% level (t-value = -1.23).
In conclusion, there are significant differences in the 250-day BHRs across
industry classifications, but there is no clear pattern as there was for the average
ex-date returns.
Most classifications do not have significant BHRs and most
classifications that do have significant BHRs are only significant at the 10% level.
Therefore any conclusions drawn concerning industry would have little evidentiary
support.
3.1.15.2 Delisting Threat and Motivations for Reverse Stock Splits
The average 250-day BHRs for different motivations are provided in Table
25. The BHRs are highest for the subsample of firms threatened with delisting
(51.92% with a t-value of 3.72). The only other significant mean 250-day BHR
65
was for the “No Reason Given” (16.96% with a t-value of 2.88).
For market
adjusted returns (BHARs), using the CRSP equally weighted index as the market,
there are no motivational groups with a mean BHAR that is significantly different
than zero.
3.1.15.3 Price Considerations
To continue the motivation analysis we assume that, regardless of their
public statements, low priced firms reverse split their stock for regulatory reasons.
We assess whether there is a long-run performance difference for firms that have
compliance problems and those who do not.
Table 25 shows the results of the testing. Stocks with pre-split prices below
$1.00 have an average 250-day BHR of 27.07% (t-value=4.82) and an average
BHAR of 0.13% (t-value=0.56). Stocks with pre-split prices above $1.00 have an
average 250-day BHR of 2.74% (t-value=0.41) and average 250-day BHAR of
-0.11% (t-value=-0.25). A two-sample t-test of equal means is calculated as
T=
Y1 −Y2
s / N1 +s22 / N2 .
2
1
T=32.11 for the BHRs and T=94.39 for the BHARs indicating that the means are
not equal in either case. However, finding a significant difference in means for the
two average BHARs that are indistinguishable from zero seems questionable.
Since the average BHARs of firms with prices above $1.00 and below $1.00 are
both insignificant, there is no evidence that firm that split for regulatory reasons
perform any differently than firms that split for other reasons.
3.1.15.4 Size of the Firm
Larger firms have more resources and they may be able to recover from the
reverse stock split over time. The 250-day buy-and-hold raw returns (BHRs) and
the 250-day market adjusted returns (BHARs) are regressed on the size of the
firm. The CRSP equally weighted index is used as a proxy for the market and the
66
number of shares multiplied by the price per share is used as a proxy for company
size. Neither the BHRs, nor the BHARs, exhibit any significant relationship with
size.
The regression coefficient is zero to four decimal places for both
regressions. The conclusion from these tests is that firm size has no effect on 250day BHRs or BHARs. The regression results are presented in Table 26.
3.1.15.5 Consolidation
Finally, the long-run returns are regressed on consolidation.
As stated
before, a firm reverse splitting 10 for 1 has a dimmer outlook on the future than a
company reverse splitting 2 for 1. In addition, if larger consolidation is indicative of
a lower quality firm, then the long-run returns should be lower for firms with larger
consolidation. This idea is tested by regressing 250-day returns on number of
shares consolidated:
R0 = α + β(number of shares consolidated).
Both BHRs and BHARs are tested. The results are presented in Table 26.
When the 250-day BHR is the dependent variable, the regression coefficient is
-0.0001 with a t-value of -1.21. When the 250-day market adjusted BHAR is the
dependent variable, the regression coefficient is -0.0013 with a t-value of -1.34.
Both regression coefficients are outside of normal acceptance levels but a weak
argument can be made that the negative coefficient indicates that larger
consolidations result in a lower 250-day return.
Related to consolidation is the target price of the consolidation. A larger
price target may indicate that the firm has a dim outlook on the future. Both BHRs
and BHARs are regressed on the post split price and no significant relation is
found. The long-run returns are not related to the post split price. The regression
results are presented in Table 26.
67
3.1.15.6 Long-run Analysis Concluding Remarks
This concludes the analysis of the long-run returns after a reverse stock
split. In summary, the average BHR and BHAR increase as the year progresses.
However, the 8.8% 250-day average raw return is offset by the average CRSP
equally weighted market index return of 32%. The average CRSP value weighted
return of 12.3% is also higher than the average BHR, but when the BHAR is
calculated using the value weighted index, the 250-day BHAR is not significantly
different from zero. The 250-day average BHAR calculated using the matched
firm method is also insignificant.
Different industry groupings have significantly different 250-day returns, as
do different motivational groupings. However, no relation to long-run returns was
found for size, consolidation, or post split price.
3.2 Market Movements and Reverse Stock Splits
3.2.1 Hypothesis
Reverse stock splits, in general, are preceded by a period of negative
returns for the firm.
As the price of a firm declines, it sometimes becomes
necessary to reverse split the stock in order to maintain compliance with market
listing requirements.
Since a reverse stock split is generally associated with
declining prices, it seems likely that there would be more reverse stock splits in
periods immediately following substantial market declines. A cursory review of
Table 1, Reverse Stock Splits by Year, reveals no obvious relation between
market performance and the frequency of reverse stock splits, but further analysis
is warranted.
And even if no relation is found between the frequency of reverse
stock splits and prior market performance, it is possible that the post reverse stock
split performance of firms may vary with the market environment that preceded the
reverse stock split. This issue is examined below as well.
68
First, the relation between the frequency of reverse stock splits and lagged
market returns is examined. The null hypothesis here is:
The frequency of reverse stock splits is not related to prior market
performance.
3.2.2 Testing
To test this hypothesis, the frequency of reverse stock splits in a given month is
regressed on a lagged market return. The frequency of reverse stock splits in a
given month is calculated as the percentage of total firms in the market that
reverse split in that month. The independent variable is either the 12 month or the
24 month lagged buy and hold return on the CRSP equally weighted index. The
equally weighted index is used because it gives greater weight to small firms, and
these are the firms that are most likely to be candidates for reverse stock splits.
The regression equations is:
FRSt = α + βRm,lagged + εt
where FRSt is the percent of total firms that reverse split in month t and Rm,lagged is
either the 12 or 24 month lagged buy and hold return on the Equally Weighted
CRSP Index.
Results are presented in Table 27. The relationship is marginally significant
(t=-1.87) when the market return is lagged 12 months, and more significant when
the market return is lagged 24 months (t-value –3.67).
This suggests that the
frequency of reverse stock splits is more pronounced after market downturns.
To assess whether post reverse split stock returns are related to market
conditions prior to the reverse stock split, the 250-day BHRs are regressed on
lagged market BHRs.
BHRi,250 = α + βRm,lagged + εt
where BHRi,250 is the 250-day BHR of firm i after the reverse stock split, and
Rm,lagged is either the 12 or 24 month lagged buy and hold return on the CRSP
equally weighted index. The conjecture is that firms that are forced to reverse
stock split partly because of price declines attributable to general market declines
69
may perform better on average after the split than firms that reverse split due to
idiosyncratic reasons only.
3.2.3 Results
The regression results are provided in Table 28.
As conjectured, there is
an inverse relationship between 250-day BHRs and lagged market returns. In
both cases (12 month and 24 month lagged market returns), the relationship
between the BHRs and the lagged market return is negative and significant,
indicating an inverse relationship between long run BHRs on stocks that reverse
split and lagged market returns.
Continuing along this path, the 250-day market adjusted BHARs obtained
using the equally weighted CRSP Index are regressed on lagged market returns.
These results are also provided in Table 28.
Here too, a significant inverse
relationship is found between the 250-day BHARs and the lagged market returns,
for both the 12-month and the 24-month lag.
In summary, evidence is provided that the frequency of reverse stock splits
is higher subsequent to market declines. Also, it appears that stocks that reverse
split their stock after an extended market decline tend to do better than firms that
reverse split after a period where the market has performed well.
3.3 Financial Distress and Future Performance
3.3.1 Hypothesis
The performance after a reverse stock split varies widely by firm and the
purpose of this section is to investigate pre-split information that may be useful for
forecasting post split performance.
Reverse stock splits are thought to be
negative signals to the market because, on average, reverse splitting firms have
been shown to have abnormal negative returns around the announcement date
and the ex-date. In addition, the average long run BHAR following a reverse stock
split has been shown to be negative on average. However many firms in the
70
sample do very well after the reverse stock split. Depending on how “do very well”
is defined, 1/4 to 1/3 of the sample firms do well after the reverse stock split.
Table 4 shows “winners” versus “losers” with various holding periods and
various return hurdles for being classified as a winner or loser. The lowest hurdle
is 20% raw returns and the highest hurdle is 200%. The holding periods range
from 30 days to 250 days (about one year of trading days). Since some firms in
the sample do well after the reverse stock split, this section of the analysis
explores the possibility that there may be some public information that is useful for
forecasting which firms will do better (or worse) after the reverse stock split. For
example, firms that are generally healthy might be expected to outperform firms
that are generally unhealthy.
The financial distress literature provides some financial ratios that can be
used to infer the health of a particular stock. If these ratios are predictive of firm
performance after a reverse stock split, then investors may be able to exploit this
information. The hypothesis to be tested, stated in the null, is:
The financial health of a stock does not help to predict firm’s
returns after a reverse stock split.
3.3.2 Testing
To test this hypothesis, the ex-date returns and the long run returns are
regressed on the financial distress variables posited by Altman (1968),
Theodossiou, Kahya, Saidi, and Philippatos (1996), and Kahya and Theodossiou
(1999). In these articles, the authors modeled financial distress using debt,
liquidity, and operational performance ratios in a discriminant function framework.
The researcher collects many variables and seeks to determine which are useful
in forecasting bankruptcy. Compustat variables include: debt to assets, operating
income to assets, inventory to sales, sales, assets, net working capital to assets,
current assets to current liabilities, quick assets to current liabilities, EBIT to
assets, retained earnings to assets, long-term debt to total assets, market value of
equity to long-term debt, and fixed assets to assets. Regression analysis is used
71
to determine which variables are most useful in identifying winners and losers. The
coefficients (βs) are estimated using maximum likelihood and they show whether
an independent variable is positively or negatively related to the dependent
variable. The model is
Ri = β0 + β1dta + β2opincta + β3invtsls + β4lgsales + β5lgassets + β6nwcta + β7cacl
+ β8qacl + β9ebita + β10reta + β11ltdta + β12mveltd + β13fata.
The model is used in several regressions where Ri is either the ex-date raw return
or the long run BHR as calculated before. The independent variables remain the
same for each regression and dta is debt to assets, opincta is operating income to
assets, invtsls is inventory to sales, lgsales is the log of sales, lgassets is the log of
assets, nwcta is net working capital to assets, cacl is current assets to current
liabilities, qacl is quick assets to current liabilities, ebita is EBIT to assets, reta is
retained earnings to assets, ltdta is long-term debt to total assets, mveltd is market
value of equity to long-term debt, and fata is fixed assets to assets.
3.3.3 Results
A correlation matrix of these variables is presented in Table 30 and the
regression results are presented in Table 31. The correlation matrix shows that
the ex-date returns are related to the following firm specific variables: assets,
sales, the EBIT to assets ratio, and the operating income to assets ratio. The 250day BHRs are positively related to the EBIT-to-assets ratio, the operating-incometo-assets ratio, and the net-working-capital-to-assets ratio.
The 250-day BHRs
are inversely related to the retained-earnings-to-assets ratio, the debt-to-asset
ratio and the long-term-debt-to-assets ratio.43
Given the coefficients in the correlation matrix, it seems that firms with
larger sales, more assets, larger operating-income-to-assets, and more earningsto-assets outperform on the ex-date, and companies with higher income, higher
earnings, lower net working capital and lower debt do better in the long run.
43
For the long run return results presented in the correlation matrix and the dependent variable in
the regressions is the BHR. Using BHARs (market model adjusted or market-adjusted) yields the
same results with only slight changes in the p-values.
72
The results of the full model with all independent variables included are
presented in Table 31. The regression coefficients are shown for BHRs of 30, 60,
90, 125, and 250 days. Regressing the returns of firms that reverse stock split on
financial distress variables is unique in the literature but a related study, Kiang, et
al (2005), uses Altman’s Z and a type of neural network to forecast which firms will
eventually go bankrupt.
In this model, lgsales has the only significant coefficient but even then it is
only significant for the ex-date returns. It is not significantly correlated to any of the
long run returns.
Since the correlation matrix reveals that there is multicollinearity in many of
the variables, a reduced model is proposed based on the work of Theodossiou,
Kahya, Saidi, and Philippatos (1996). These authors use discriminate analysis to
find that the variables DA, OIA, ITS, and S are significant predictors of financial
distress.44 Using only these variables the reduced model becomes:
Ri = β0 + β1dta + β2opincta + β3invtsls + β4lgsales.
The results of this regression are given in Table 32. For the ex-date returns
(RI is the ex-date return) there is a positive relation to opincta (operating income to
assets) at the 1% significance level (t-value = 2.54) and there is positive relation to
lgsales (sales) at the 10% significance level (t-value = 1.65).
There is no
significant relation to dta or invtsls.
The regression results presented use the ex-date return as the dependent
variable, but the results are similar if the dependent variable is market adjusted exdate return or market model adjusted ex-date return. The market adjusted ex-date
returns are significantly related to opincta (t-value 2.54) and lgsales (t-value 1.59)
and the market model adjusted ex-date returns are significantly related to opincta
(t-value 2.40) and lgsales (t-value 1.83).
When the dependent variable is the 250-day (12 month) BHR, then the only
marginally significant relations are with opincta (t-value = 1.65), and dta (t-value =
44
Discriminate analysis is used to eliminate variables form a model. Theodossiou et al also find
change in employment to be a significant predictor of financial distress.
73
-1.59). When the 250-day BHAR is used as the dependent variable, the only
significant (inverse) relationship is with dta. Both the market adjusted BHARs and
the market model adjusted BHARS are significantly related to dta with t-values of
-2.14 and -2.11 respectively.
3.3.4 Concluding remarks
Even though the correlation matrix shows a significant relation between
returns and some of the accounting variables the conjecture that reverse stock
splits are a negative signal to the market is not strongly challenged by these
results. Many of the accounting variables are significantly correlated with each
other and isolating the variables that have the most effect on returns is difficult. In
the full model, only one variable was significantly related to ex-date returns and
there were no variables with a significant relationship to the long run returns.
In a reduced model, variables were used that have been shown to be
significantly related to financial distress. Significant relationships were found for
both ex-date and long run returns. Ex-date BHRs and BHARs are found to be
related to operating-income-to-assets and sales. Long run BHARs are related to
debt-to-assets and long run BHRs are related to operating-income-to-assets.
These results suggest that in the short-run, firms with better sales and
better operating income (in relation to assets) will fare better on the days around
the reverse stock split. In the long run, a company with less debt will probably not
be hurt by the reverse stock split as much as companies with more debt.
3.4: NASDAQ Regulatory Changes
As discussed earlier, many of the firms in this sample reverse split their
stock in order to comply with NASDAQ’s minimum bid price requirement. If the
minimum bid price requirement is relaxed, it is likely that fewer firms would reverse
74
split their stock and fewer firms would be delisted. The NASDAQ rule change of
2001 provides an opportunity to test this conjecture.
The rule change was instituted after a culmination of extraordinary market
conditions.
The NASDAQ, home of many Internet start-up firms, suffered a
substantial loss in the value during 2000 and 2001. 45 Some viewed this severe
market decline as a mandate for regulatory changes. As revisions in minimum bid
price rules were being discussed, the impetus for change was accelerated by the
September 11, 2001 attacks on the World Trade Center and the Pentagon. The
market was in disarray and in an effort help firms make it through the difficult
times, NASDAQ announced on September 27, 2001 a suspension of minimum bid
price rules and public float requirements. A pilot program soon followed in which
the minimum bid price and continued listing requirements were temporarily
relaxed.
The following sections describe the rules before the pilot program and the
rules during the pilot program. Two tests are conducted to test the effectiveness
of the rule change.
3.4.1 NASDAQ Listing Rules – Before The Pilot Program
Each market demands that its members meet certain minimum standards
that its policy makers set forth. NASDAQ has two sets of standards: a National
Market standard for larger companies and a Small Cap standard for smaller
companies. A firm must meet the more stringent “initial listing requirements” to be
accepted into the market and maintain the somewhat less stringent “continued
listing requirements” to remain in the market. For example, to be listed on the
NASDAQ Small Cap Market, a firm must have a minimum bid price of $4.00 and 1
million shares on the market. To stay listed the firm must maintain a minimum bid
price of $1.00 and 500,000 shares on the market. There are additional listing
45
NASDAQ, as proxied by the NASDAQ 100 Trust (QQQQ) lost 75.48% of its value from March 27,
2000 to September 21, 2001. This percentage was calculated as (ending price minus beginning
price) divided by (beginning price) and the prices are adjusted for splits and dividends. Source:
Yahoo.com.
75
requirements concerning assets, capitalization, net income, market value, number
of market makers, number of shareholders, etc. These requirements are given in
Table 33.
Prior to the rule change, a firm that failed to meet the continued listing
standards for 30 consecutive days would receive a citation from NASDAQ. The
firm then had a 90-day “grace period” to bring their stock back into compliance. If
a firm’s infraction was low price, then it could use this 90-day period to consolidate
shares and thereby increase the share price of the stock.
During the 90-day
period a company that did not meet continued listing requirements for at least 10
consecutive days was subject to delisting.
During the suspension period beginning September 27, 2001, companies
were not cited for failing to meet minimum bid price or public float requirements,
and therefore not subject to delisting.
When the suspension period ended in
January 2002, NASDAQ immediately implemented a pilot program that extended
the grace period to 180 days so that companies had more time to bring their bid
price and public float into compliance.
3.4.2 The Pilot Program
Under the pilot program, “Small Cap” firms that failed to meet minimum bid
price requirements or public float requirements for 30 consecutive days were given
180 days to bring their stock back into compliance. If the firm’s minimum bid price
were still out of compliance at the end of this 180-day grace period, the company
was given another 180 days to comply if it complied with other listing criteria.
It
had to have either
•
$5 million in equity,
•
$50 million market value, or
•
$750,000 in net income from continued operations in the most recent
fiscal year or two of the last three fiscal years.
At the end of the second 180-day grace period, firms with non-compliant
price or float were given an additional 90 days to comply if the equity, market
76
value, or net income criteria (above) were met. The firm was finally subject to
delisting at the end of this 450-day period.
The larger companies listed on NASDAQ’s “National Market System”
(NMS) were also given consideration under the pilot program. Their grace period
was increased from 90 days to 180 days. A NMS company still only had one
grace period in which to meet continued listing requirements (for at least 10
consecutive days).
But, if the NMS firm could not comply with the NMS
requirements, it had the option of participating in the Pilot Program (as described
above) if it voluntarily lowered its status to “Small Cap.”
As shown in Table 33, the requirements for the NMS issuers are more
complex than the requirements for the Small Cap market issuers. There are three
ways for a NMS company to gain initial acceptance and there are two ways to
qualify for continued listing.
3.4.3 Hypotheses
Since the majority of firms that execute reverse splits cite regulatory
reasons as their motivation, the action is probably not voluntary.
The
consolidation is a costly and inconvenient endeavor – like adding pollution
controls. It is not something the company chooses to do to enhance shareholder
wealth. If the regulations were relaxed, the firm would probably not choose to bear
the cost and inconvenience of the reverse stock split. Therefore if the rule change
and Pilot Program were effective, the number of reverse stock splits should
decline from what it would have been under the old rules.
In addition, if the
companies were given more time to bring the firm into compliance and if the
overall market condition (rather than firm performance) were responsible for
temporary compliance issues, then one result of the Pilot Program should have
been fewer delistings.
The hypotheses, stated in the null, are:
The moratorium on the minimum bid price rules had no effect on
whether or not a NASDAQ firm reverse split its stock.
77
The moratorium on minimum bid price rules did not result in fewer
delistings for NASDAQ firms whose stock had a bid price of less
than $1.00
3.4.4 Reverse splits and the Moratorium
The number of reverse stock splits before and after the moratorium is
calculated as a percentage of low-priced firms. The time “before” the moratorium
is defined as month t-30 to month t-7 where month t=0 is September 2001. This
filters out any noise (rumor activity) that may be present in the data in the 6
months prior to the rule change. The time “after” the moratorium is defined as t=1
to t=24. A “low-priced” firm is defined as one whose bid price has been below
$1.00 for 30 consecutive trading days. This was the pre-moratorium delisting flag
for NASDAQ and in the “after” period it represents the firms that would have been
out of compliance before the rule change.
The monthly number of low-priced firms, number of reverse stock splits,
and ratios of reverse-splits-per-low-priced-firms are shown in Table 34. This table
also shows rolling averages. The mean percentage of reverse stock splits per
low-priced firm is calculated for months t-30 to t-6 and compared to the mean
percentage for months 1 to 24. The Wilcoxon non-parametric test of equal means
is calculated to be 1644. This corresponds to a two-tailed p-value of 0.30. This
number means that when the median difference of reverse stock splits is zero,
then there is a 30% chance that the difference found would be as much or greater
than calculated. Lower p-values provide stronger evidence of unequal means. A
p-value of 0.30 is not enough evidence to say that the percentages of reverse
stock splits before and after the rule change are different.
As measured by the number of reverse stock splits, the intent of the rule
change, to help firms make it through a systemic decline was not realized. If the
rule change were effective, then the post-moratorium percentage of reverse stock
splits would be lower than the pre-moratorium percentage of revere stock splits.
78
However, after the rule change, firms were still engaging in this costly and often
involuntary activity at about the same rate.
3.4.5 Delistings and the Moratorium
If the rule change was successful then there should be fewer companies
delisted in the months following the change. To ascertain whether this is the case,
a comparison is made between the number of firms that were delisted before the
rule change and the number of firms that were delisted after the rule change. This
number is calculated as a percentage of firms that would have been delisted under
the old rule. The number of firms that would have been delisted in a period is the
number of firms that have had a price of less than $1.00 for 120 consecutive days.
A firm was out of compliance for 30 days, warned, and then (should have been)
delisted 90 days later. If the price was greater than $1.00 for 10 consecutive days
during this time, the firm was not counted because it was considered to be back in
compliance.
If the rule change were effective, then there should be fewer delistings after
the rule change. In the 24 months prior to the rule change (t-30 to t-7) there were
187 NASDAQ firms that were delisted due to low share price (out of 240 firms
eligible for delisting).
In the 24 months after the rule change there were 133
NASDAQ firms that were delisted because of share price (out of 700 meeting old
delisting criteria). Before the rule change 78% of the firms that met the low price
delisting criteria were delisted. After the rule change only 19% of the firms that
met the old low price delisting criteria were delisted. It seems that the rule change
had an immediate impact on delistings.
The delistings before and after the rule change are shown on Table 35.
Also shown is the delistings in six-month increments. After the rule change the
delisting ratio drops from an average of 32% (for the six-month increments) to
single digits.
The two-sample t-test of equal means yields a t-value of 967
indicating that the means are not equal at the 0.0001 level.
The number of
delistings shown for t-30 to t-7 is the sum of the delistings in the six-month periods.
79
The number of “firms that should have been delisted” for t-30 to t-7 is not the sum
of the values in the six-month increments because firms could be eligible for
delisting in more than one period.
In conclusion, the 2001 NASDAQ moratorium and subsequent pilot
program did not seem to have an impact on the percentage of firms that reverse
split their stock. It did, however impact the number of delistings. The pilot program
allowed firms up to 450 days to bring their price back into compliance. If the rule
change were not effective, it would merely delay delistings and a wave of
delistings would occur at the end of the first 450-day grace period after the
change. This is not the case. In the 15+ months (450 days) after the rule change
there was no new wave of delistings.
If the goal of the regulatory change was to help companies weather the
market downturn, then according to these delisting measurements, it was a
success.
On the other hand, there was no reduction in reverse stock splits after
the rule change, and as such, the two tests conducted here do not provide
collaborating evidence.
80
CHAPTER 4: CONCLUSIONS
4.1 Overview
Despite numerous studies that have shown negative market reactions to
reverse stock splits, firms continue to consolidate their shares. In fact, the total
number of reverse stock splits has nearly doubled since 1992. The increase in
available data, some unusual market conditions in 2001, and a NASDAQ rule
change provide an impetus to re-examine the reverse stock split.
Stock price reactions to reverse stock splits are examined in relation to
various firm characteristics and market movements.
This analysis is followed by
an examination of financial ratios and variables that may be predictive of post split
performance.
Finally, the NASDAQ minimum bid price rule change of 2001 is
explored to determine if it had an impact on reverse stock splits or delistings.
4.2 Results and Suggestions for Further Research
4.2.1 Stock Price Reactions – Announcement Date
The announcement day results of this study confirm previous research. On
average, there is a negative cumulative abnormal return for the announcement
day periods examined. Announcement dates are classified two ways. Firms that
issue a press release before the ex-date constitute one sample, and firms that
issue no press release until the ex-date are placed in the second group. When the
first news is on the ex-date the negative and significant reaction on that
“announcement-day” is followed by positive and significant post announcement
day returns. In the case where there is no previous announcement and the exdate is the announcement day, this evidence may suggest that an initial AD
overreaction to bad news is followed by a correction.
81
4.2.2 Stock Price Reactions – Ex-date
One unique contribution of this study is the finding that the market appears
to react stronger to the reverse stock split on the ex-date than on the
announcement day. The ex-date returns are greater in absolute magnitude than
the return on the announcement day for firms that announce prior to the ex-date.
This finding is somewhat peculiar given that the stock becomes more liquid and
more tradable after the price is adjusted to levels above $1.00. Many institutions,
as a matter of policy, shy away from low-priced stocks.
In addition, many
brokerage firms do not allow their customers to trade low-priced shares on margin.
Why the wider availability of the stock would lead to the large negative ex-date
reaction is puzzling. More research is required to determine the exact cause of the
reaction. One possible explanation is increased short activity on the stock, as
shorting would generally have been impossible at pre-consolidation price levels.
4.2.3 Stock Price Reactions – Firm Characteristics
Further insight into the stock price reaction is gained by considering firm
specific characteristics of firms in the sample. For example, significant differences
in stock price reactions across some industries are detected. In addition, the firm’s
motivation for reverse splitting is a factor in how the firm performs on the ex-date.
In an apparent conflict with the earlier literature, the results of this study show
firms reverse splitting for regulatory reasons underperform (rather than
outperform) firms that split for other reasons. Specifically, this study reveals that
firms that reverse split for regulatory reasons perform worse than do firms that split
for any other reason. This finding is confirmed by comparing firms with pre-split
prices below $1.00, which are assumed to reverse split for regulatory reasons, to
firms with pre-split prices above $1.00.
The relation between a firm’s ex-date performance and its post-split target
price is also examined.
Findings indicate firms with higher post split prices
(greater than $5.00) tend to perform better on the ex-date.
82
The ex-date return is not related to the size of the firm, but there is a mild
relationship with consolidation. The more shares a firms consolidates, the worse
the firm performs on the ex-date.
4.2.4 Stock Price Reactions – Long Run
The mean 250-day BHR (250 days from the ex-date) is positive, but the
median 250-day BHR is negative. The mean is skewed due to a small number of
reverse stock split firms having large positive returns for the 250-day period. Two
tests of long-run 250-day BHARs reveal no significant results, but one test (the
market adjusted BHAR using the CRSP equal weighted index as the market
adjustment) shows negative and significant BHARs.
The BHRs are examined as related to various firm characteristics and it is
found that the 250-day BHRs vary some by industry.
However, there is no
significant size, price, motivation, or consolidation effect for the 250-day BHRs.
4.2.5 Market Movements
Evidence is presented that the frequency of reverse stock splits (as a
percentage of total firms) increases after periods of market decline. In addition, it
is found that firms that reverse split their stock after a period of market decline
have higher 250-day BHRs than firms that reverse split their stock after a period
where the market has performed well. In other words, a market effect of sorts is
detected. Specifically, the 250-day BHARs are greater for firms that reverse split
following periods of substantial market declines.
This may suggest an
overreaction followed by a correction, but further research is needed to understand
why the BHARs vary as they do in different market environments.
Further
research could also be conducted as to a possible trading strategy to take
advantage of this difference in these BHARs.
83
4.2.6 Projecting Post Split Performance
Variables taken from the financial distress literature are related to ex-date
returns and BHARs. This is unique in the literature. Some of these variables are
significantly related to post split returns. The results suggest that firms with better
sales performance and higher operating-income-to-assets have better ex-date
returns. In the long run, companies with lower debt-to-assets do better after the
reverse stock split. Operating income expressed as a percent of assets is also
positively related to the 250-day BHRs.
These results provide evidence that the post reverse stock split
performance is dependent on the health of the company at the time of the reverse
stock split. A generally healthy company that reverse splits its stock should not be
automatically considered to be a bad investment. There is a correlation between
financial distress and reverse splits, but it is unclear if there is causation. More
research is required to determine if there is a way for investors to exploit this
relationship.
4.2.7 NASDAQ Rule Change
The NASDAQ moratorium on minimum bid price requirements and the
extended compliance time did not impact the number of reverse stock splits. This
finding suggests that the rule change may not have had the intended effect. Firms
were still assuming the cost and inconvenience of the reverse stock split at a
relatively high rate. On the other hand, the number of delistings (as a percentage
of firms that would have been delisted under the old rules) dropped significantly
after the rule change. Using this metric, the rule change was successful because
more firms were able to remain listed on the market.
Further research could be
conducted into other rule changes in other markets at various times to see if
NASDAQ or another market has more effective policies.
84
4.3 Contribution
This dissertation is useful to investors because the reverse stock split is
shown to be a conditional event. It is true that on average, the reverse stock split
is followed by negative BHARs, but if a firm is in a non-discretionary industry, has
a high target price, little debt and a strong operating-income-to-assets ratio, it may
do better in the long run.
This dissertation is useful to regulators because it provides a performance
measure to assess whether the rule change was effective.
If the goal of the
regulatory change was to retain firms in the market, then the rule change was
successful.
85
APPENDIX A: TABLES
86
Table 1 Forward and Reverse Stock Splits and Split Ratios 1962-2002
This data, from the Center for Research on Security Prices is also represented in Appendix B Figure 1 and Figure 2.
Reverse Stock Splits by Year
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
2
5
11
5
4
1
3
3
0
5
6
12
5
7
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
11
6
9
6
5
15
25
36
36
48
30
71
56
70
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
106
99
157
114
92
101
96
106
182
114
73
124
85
Split Ratios of Stock Splits 1962 to 2002
Reverse Stock Splits
Split ratio
1 for x
1 for 2 or less
1 for 2.01-3.49
1 for 3.5-4.99
1 for 5
1 for 5.01-9.99
1 for 10
1 for 10.0-24.99
1 for 25-34.99
1 for 35-49.99
1 for 50 or more
Forward Stock Splits
Split ratio
x for 1
Number of splits
<1.25 for 1
1.25 for 1 (5 for 4)
1.251-1.5 for 1
1.5 for 1 (3 for 2)
1.51-1.99 for 1
2 for 1
2.01-2.99 for 1
3 for 1
3.01-4.99 for 1
5 or more for 1
199
255
281
364
188
398
161
27
44
65
87
Number of splits
359
1,339
543
4,848
74
6,673
117
615
134
126
Table 2 Average Stock Price per Year and Yearly Stock Splits 1962-2002
This table provides the yearly number of firms in the market, the number of forward stock splits and the number of reverse
stock splits. Percentages are a percent of total.
Year
Total Firms In
Percent
Forward Stock
Reverse Stock Percent Reverse Average Price
The Market
Forward Stock
Splits
Splits
Stock Splits
of All Firms
Splits
1962
1606
71
4.42
4
0.25
1963
2071
61
2.95
5
0.24
23.31
25.18
1964
2117
116
5.48
11
0.52
26.27
1965
2164
146
6.75
5
0.23
27.16
1966
2191
174
7.94
4
0.18
26.30
1967
2206
119
5.39
1
0.05
30.24
1968
2218
241
10.87
3
0.14
33.41
1969
2297
220
9.58
3
0.13
28.83
1970
2413
50
2.07
0
0.00
19.96
1971
2520
110
4.36
5
0.20
23.14
1972
2929
159
5.43
6
0.20
23.15
1973
5672
210
3.70
12
0.21
14.65
1974
5393
101
1.87
5
0.09
10.54
1975
5255
124
2.36
7
0.13
11.15
1976
5249
264
5.03
10
0.19
12.85
1977
5215
296
5.68
6
0.12
13.22
1978
5093
410
8.05
9
0.18
14.39
1979
5010
368
7.35
6
0.12
15.24
1980
5053
548
10.85
5
0.10
16.34
1981
5389
586
10.88
15
0.28
15.77
1982
5544
310
5.59
25
0.45
13.28
1983
5917
883
14.92
36
0.61
17.19
1984
6424
414
6.44
36
0.56
14.10
1985
6424
587
9.14
49
0.76
15.29
1986
6601
833
12.62
29
0.44
16.99
1987
7125
682
9.57
70
0.98
15.77
1988
7170
267
3.72
55
0.77
13.85
1989
6976
402
5.76
70
1.00
15.36
1990
6871
262
3.81
106
1.54
13.24
1991
6804
304
4.47
98
1.44
14.74
1992
6977
490
7.02
157
2.25
15.96
1993
7419
554
7.47
115
1.55
17.87
1994
8151
422
5.18
92
1.13
17.37
19.11
1995
8373
512
6.11
101
1.21
1996
8840
642
7.26
98
1.11
21.12
1997
9152
745
8.14
104
1.14
23.36
1998
9035
737
8.16
182
2.01
25.63
1999
8580
488
5.69
114
1.33
25.84
2000
8398
512
6.10
73
0.87
25.93
2001
7818
230
2.94
124
1.59
25.22
2002
7289
222
3.05
124
1.70
26.00
2003
6883
244
3.54
90
1.31
28.41
2004
6769
329
4.86
43
0.64
34.97
2005
6757
349
5.17
57
0.84
36.10
Source: Center for Research in Security Prices
88
Table 3 Margin Requirements
Margin requirements refer to how much money an investor may borrow to purchase securities. A margin requirement of 80%
means that the investor must use 80% of his own money when purchasing securities. Margin requirements may be different
for different brokers. This table shows how SEC minimum margin requirements have changed over the years.
Date
Margin Requirement
Before August 5, 1958*
50%
August 5, 1958
70%
October 16, 1958**
90%
July 28, 1960
70%
July 10, 1962
50%
November 5, 1963
70%
June 8, 1968
80%
May 6, 1970
65%
December 6, 1971
55%
January 3, 1974
50%
Present
50%
*Prior date unknown.
**Highest level in 11 years.
Source: California Department of Finance.
89
Table 4 Winners Versus Losers After Reverse Stock Split
Various returns are shown below. A “winner” has a buy and hold raw return (BHR) greater than the
return in the left column and a “loser” has a BHR less than that rate. The BHRs are computed for
various trading days as shown. The numbers of winners and losers varies because firms with the
exact return are not counted. As a reference point, the average return for the CRSP equal-weighted
index in this study is about 32%.
Trading Days
Return
20%
30%
50%
100%
150%
200%
30
60
90
125
250
winners
620
560
539
533
456
losers
1282
1342
1363
1369
1446
% winners
33%
29%
28%
28%
24%
winners
587
517
509
488
403
losers
1315
1385
1393
1414
1499
% winners
31%
27%
27%
26%
21%
winners
534
455
436
398
318
losers
1368
1447
1466
1504
1584
% winners
28%
24%
23%
21%
17%
winners
424
341
297
264
181
losers
1478
1561
1605
1638
1721
% winners
22%
18%
16%
14%
10%
winners
347
266
204
183
122
losers
1555
1636
1698
1719
1780
% winners
18%
14%
11%
10%
6%
winners
293
196
142
124
82
losers
1609
1706
1760
1778
1820
% winners
15%
10%
7%
7%
4%
90
Table 5 Summary of Stock Split Literature and Key Findings
Summary of studies of reverse stock splits
Authors
Year Type of Firms
Period Key Findings
Studied
Gleason, Rosenthal, and Lee
2004
1,072 NYSE, AMEX, and 1992-2001 Negative signal to the market. Reverse splits are a confirmation of
Nasdaq reverse splits
poor performance, especially when done for the purpose of staying
listed.
Kim, Jain, Jiang, Mcinish, and
Wood
2003
1,363 NYSE, AMEX, and 1983-2002 Presplit prices of $5 have positive abnormal returns. Postsplit prices
Nasdaq reverse stock
greater than $5 have increased institutional ownership
splits
Jing
2002
116 reverse splits on the
Hong Kong exchange
1991-2001 Negative abnormal CARs around announcement date (-4%) and the
ex-date (-10%). 12 month performance of reverse splits is inferior to
matched firms (-10.6% vs. 1.21%). Liquidity improvements evidenced
by greater adjusted volume post reverse split. Transactions costs (bid
ask spread) reduction after reverse split.
Vafeas
2001
229 reverse splits
1985-1993 Reverse split firms have inferior earnings to matched firms in pre split
years. Investors expect inferior earnings to continue.
Desai and Jain
1997
5596 forward stock splits 1976-1991 Forward splits, on average, have one-year BHAR of 7.05% and threeand 76 reverse stock split
year BHAR of 11.87% after the announcement month. Reverse splits,
announcements
on average, have one-year BHAR of -10.76% and three-year BHAR of
-33.90%. Underreaction to split event is cited.
Han
1995
136 NYSE, AMEX,
Nasdaq reverse splits
1963-1990 Liquidity is improved after reverse stock split
Maloney and Mulherin
1992
446 Nasdaq 1.25 for 1 or
greater splits
1984-1996 Reverse splits are followed by negative abnormal returns. Forward
splits do not increase volume. Forward splits result in enlarged
ownership base, increased number of small trades, small buy orders by
individuals after split.
Peterson and Peterson
1992
1057 NYSE, AMEX,
Nasdaq
1973-1989 Risk of returns of reverse split stocks decline after reverse split.
Lakonishok and Lev
1987
1015 stock splits and
1257 stock dividends
1963-1982 Pre split price (and dividend) run up precedes forward splits.
Managers want to increase individual investor ownership. Managers
have a target price - market wide average and industry averages.
Percentage price appreciation during the year before split
Lemoureux and Poon
1987
213 forward splits and 49 1962-1985 Reverse stock splits decrease the tax option value of stocks by
reverse splits. NYSE
decreasing volatility. Forward splits result in enlarged ownership base,
and AMEX.
increased number of small trades, small buy orders by individuals after
split. Forward stock split volatility increases.
Spudeck and Mayer (1985)
1985
21 NYSE and AMEX reverse splits
Grinblatt, Masulis, and Titman
1984
1,762 splits and
dividends
1967-1976 Reverse splits are followed by negative abnormal returns and forward
splits are followed by positive abnormal returns. Stock dividends can
also be considered stock splits.
Woolridge and Chambers
1983
57 NYSE and AMEX
reverse splits
1973-1983 Reverse splits are not justified because they are followed by negative
BHARs around announcement date and ex-date
Radcliffe and Gillespie
1979
NYSE, AMEX
1960-1976 Reverse splits, in general, are detrimental to shareholder wealth
Gillespie and Seitz
1977
Most common reasons for reverse split - image improvement,
exchange requirements, appealing to different type of investor,
reduction in shareholder service costs
West and Brouileter
1970
Traders have lower transactions costs after reverse split
91
Follow-up to Woolridge and Chambers indicates that there are no
negative BHARs around announcement date or ex-date.
Table 5 (Continued)
Summary of studies of forward stock splits
Authors
Year Type of Firms
Period Key Findings
Studied
Admati and Pfleiderer (1988)
1988
Splits not only attract informed traders, but also noise traders
because of lower post-split share prices.
Angel
1994
Firms desire to control the relative tick size at which their shares
trade.
Angel
1997
Firm specific market microstructure factors are stable over time
and a firms optimal price can be understood in terms of
maintaining an optimal relative tick size, which itself, depends on
firm specific characteristics. A larger relative tick size "means
greater protection for limit orders, fewer trading errors adn lower
costs of negotiation between traders" (Schultz, 1997). What does
tick size mean now that there is no tick (decimalization)?
Angel, Brooks, and Matthew
2004
Return Volatility increases after splits - does not support trading
range hypothesis. Trading activity by small investors increases.
Asquith, Healy, and Palepu
1989
Baker and Gallager
1980 CFOs of 100 NYSE
firms
Existence of positive excess returns consistent with signaling
hypothesis. Firms that forward split have superior earnings to
matched firms in pre split years.
Managers use splits to increase ownership by individual investors
(conflict with Szewczyk and Tsetsekos)
Baker and Phillips
1994
Baker and Powell
1993 136 Managers
1987-1990 70% of managers surveyed cited a preferred price range or a
stock's liquidity as the reason to forward split. Only 14% pointed
to signaling
Baker, Phillips, and Powell
1995 Survey Artilce on
forward splits
1978-1993 Review article on splits. Managers want price in $20 to $35 range
Boehme and Sorescu
2002 NYSE, AMEX, Nasdaq 1927-1998 Equally weighted calendar time portfolios yield positive abnormal
with dividend
returns but value weighting the same portfolios yields no
resumptions
significant abnormal return. Changes in risk loadings help explain
long term returns
Boehme, Danielsen, and
Sorescu
2003 5,550 stock splits and
556 large stock
dividends NYSE,
AMEX and Nasdaq
Brennan and Copeland
1988 1,034 forward splits
The evidence on the reduction and the extent of information
asymmetry is mixed. Decreasing risk shifts for forward splits and
systematic risk decreases after forward splits. Splits are costly
since the fixed cost element of commissions increases the per
share trading costs of low priced stocks. The size of the split
factor provides signal. Signals must be costly to be credible
(harder for other firms to copy). One cost may be the increased
transaction costs of lower priced shares
Brennan and Copeland
1988 1,034 forward splits
Beta increases on both announcement date and ex-date. Beta
increase becomes permanent after ex-date. Firm specific event
increases systematic risk.
Brennan and Hughes
1991
Brokerage firms want to preserver commission income. The size
of the split factor provides signal. Wider spreads give brokerage
firms the incentive to cover (provide information about) firm and
bring in new investors
Byun and Rozeff
2002 12,747 forward stock
splits
1978
Managers frequently justify splits on the basis that they improve
liquidity and marketability
1949-2000 No positive 12 month CTARS. Abnormal returns from
announcement to delivery date. Post announcement drift
attributable to trading frictions rather than behavioral biases.
Seeks to resolve dispute between Byun and Rozeff versus
Inkenberry and Ramnath.
1927-1996 Market efficiency is not challenged by stock split anomaly. There
are few if any 12-month CTARs or abnormal returns compared to
match firms. Find abnormal returns in 2-1 splits. Abnormal
returns calculated after ex-date.
92
Table 5 (Continued)
Chen, Roll and Ross
1995
Conroy and Harris
1999 Stock splits and stock
distributions (>25%)
NYSE
1925-1996 A firm's history of stock splits plays a crucial role in both the
design and effect of current splits. Post split prices below
previous post split price is especially good information.
Conroy, Harris, and Benet
1990 133 NYSE forward
splits of 1.2 for 1 or
greater
1981-1983 No improved liquidity post split based on daily inside bid/ask
quotes from specialists. Liquidity is worse after split.
Conroy, Harris, and Benet
1990 133 NYSE forward
splits of 1.2 for 1 or
greater
1981-1983 Forward splits do not increase trading volume, but bid-ask spread
increases
Corwin and Lipson
2000 469 intraday trading
1995-1996 Forward splits result in enlarged ownership base, increased
halts in NYSE common
number of small trades, small buy orders by individuals after split.
stocks
Volatility increases.
Desai, Nimalendran, and
Venkataraman
1998
Dennis and Strickland
2002 Quarterly ownership
composition of 1,392
NYSE, AMEX, and
Nasdaq forward splits
1990-1993 Abnormal stock split announcement returns are greatest for firms
with low pre split institutional ownership. Liquidity changes most
for firms with low pre split institutional ownership. Largest post
split institutional ownership increases for firms with low pre split
institutional ownership.
Desai and Jain
1997 5596 forward stock
splits and 76 reverse
stock split
announcements
Dolley
1933 88 companies
1976-1991 Forward splits, on average, have one-year BHAR of 7.05% and
three-year BHAR of 11.87% after the announcement month.
Reverse splits, on average, have one-year BHAR of -10.76% and
three-year BHAR of -33.90%. Underreaction to split event is
cited.
90% of managers want wider distribution of shares and thus split
their stock.
Dravid
1987
Return Volatility increases after splits - does not support trading
range hypothesis
Dubofsky and French
1986
Return Volatility increases after splits - does not support trading
range hypothesis
Dubofsky
1991
Return Volatility increases after splits - does not support trading
range hypothesis
Easly, O'hara, and Sarr
2001 75 NYSE forward splits
Fernando, Krishnamurthy,
and Spindt
1999 194 Mutual fund splits
1978-1993 Splitting funds experience significant increases (relative to nonsplitting matched funds) in net assets and shareholders.
Evidence of marketability increase. Survey of mutual fund
mangers - lower net asset value per share attracts small investors
Fernando, Krishnamurthy,
and Spindt
1999 194 Mutual fund splits
1978-1993 Firms going public appear to use offering price to influence
investor interest in the issue
Grinblatt, Masulis, and
Titman
Ikenberry and Ramnath
1984
2002 3,028 NYSE, AMEX,
and Nasdaq forward
stock splits of five-forfour or greater.
Ikenberry, Rankine, and Stice 1996 1,275 NYSE & AMEX
firms with two-for-one
stock splits
Evidence of momentum in stock returns over a six-month to 1
year time period. Since stock splits are preceded by a run up, the
post split abnormal returns may be attributed to momentum rather
than the split.
Forward splits result in enlarged ownership base, increased
number of small trades, small buys orders by individuals after
split. Volatility increases.
1995
Microstructure study indicates that forward splits are positive
signals. Larger ownership base protects management from
takeovers. Trading range helps attract more investors
Managers forward split stock when they are optimistic
1988-1997 One year BHAR of 9% for forward stock splits attributed to market
underreaction. Financial analysts tend to underestimate firms
earnings (forecast bias)
1975-1990 Post forward split positive abnormal returns of 7.93% for 12
months, 12.15% for 36 months — market underreacts.
Announcement excess return of 3.38% Market reacts to other
corporate events. Managers split stock when they are optimistic
(self select by conditioning on expected future performance).
93
Table 5 (Continued)
Kadiyala and Vetsuypens
2002 NYSE splits with short
interest data
1990-1994 Short interest should be used to measure signaling strength.
Short interest declines in light of positive information. Short
interest does not decline around stock splits, but it does decline
for the subsample characterized by favorable industry-adjusted
pre-split performance. It increases significantly for firms that
have post-split liquidity improvements.
Kryzanowski and Zhang
1996
Lakonishok and Lev
1987 1015 stock splits and
1257 stock dividends
1963-1982 Pre split price (and dividend) run up proceeds forward splits.
Managers want to increase individual investor ownership.
Managers have a target price - market wide average and industry
averages. Percentage price appreciation during the year before
split had no explanatory power for split ratios
Maloney and Mulherin
1992 446 Nasdaq 1.25 for 1
or greater splits
1984-1996 Forward splits do not increase volume, but they result in enlarged
ownership base, more small trades.
McNichols and David
1990 all stock dividends and 1976-1983 Existence of positive excess returns consistent with signaling
splits
hypothesis. Managers split when they are optimistic
Merton
1987 Theoretical
Wider distribution of stock may lead to lower capital costs in a
market with incomplete information
Muscarella and Vetsuybens
1996 ADRs
Price of ADR and underlying increase on the announcement of
ADR split even when underlying does not split. Forward splits
result in enlarged ownership base, increased number of small
trades, small buy orders by individuals after split.
Nayar and Rozeff
2001 forward splits
Record date not really important
Nayek and Prabhala
2001 forward splits
Disentangling Dividend Information in Splits: A decomposition
using conditional event study methods. 54% of split
announcement effects attributed to dividend information. Splits
and dividends are partial substitutes.
Ohlson and Penman
1985 1,257 forward splits of
2-1 or greater. NYSE
only.
Pilotte and Manuel
1996 776 firms that forward 1970-1988 Investor learning. Investors use a firms previous post split
split their stock at least
earnings performance to interpret a newly announced split.
twice
Rozeff
1988 167 Mutual fund splits
Schultz
2000 2 for 1 splits or greater
on CRSP, NASDAQ,
NYSE, AMEX
Sheikh
1989 CBOE optionable
1976-1983 Return Volatility increases after splits - does not support trading
stocks with split factors
range hypothesis
greater than 0.25 with
no mergers
Szewczyk and Tsetsekos
1993 175 NYSE and AMEX
stock splits with dist
ratio of 1.25 or greater
Tawatnuntachai and D'Mello
2002 327 NYSE, AMEX, and 1986-1995 Positive signal of splitting firm is contagious to firms in the same
Nasdaq split factor
industry. Non-splitting industry firms have positive abnormal
<=0.5
returns upon announcement of split. Abnormal 1 year CAR of
3.82%+E36
Forward splits result in enlarged ownership base, increased
number of small trades, small buy orders by individuals after split.
1962-1981 Forward splits are followed by increased return volatility.
Lamoureux and Poon (1987) extend with reverse splits.
Behavioral concerns in the context of mutual fund splits. A
particular price level may frame how investors classify losses or
gains. No evidence o f increased inflows vs. non split.
1993-1994 Forward splits result in enlarged ownership base, increased
number of small trades, small buy orders by individuals after split.
Questions tick size hypothesis based on his examination of
intraday trades and quotes.
1972-1986 Firms that split have lower managerial ownership than their
matches. Inverse relationship between managerial ownership
and post split abnormal returns.
94
Table 6 The Daily Raw Returns of Reverse Stock Splits Around the Announcement Date
These panels show the average raw returns around the announcement date of reverse stock splits from 1962 to 2003. The number of positive versus negative returns is given followed by the
standardized abnormal return test, Patell's Z (Patell, 1976). This test is used to determine whether the mean is significantly different than zero. The symbols $, *, **, and *** denote statistical
significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a 1-tail test. The symbols (, <, <<, <<<, or ), >, >>, >> show the direction of the generalized sign test (not shown) and
denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a 1-tail test. Cumulative returns are given in the bottom panel for the three periods shown.
Panel A
Panel B
Panel C
All Firms with
Announcements of Reverse Stock Splits
Firms with Unique Annoncement Day (UAD)
(Announcement Date is not the Ex-Date)
Firms with Identical
Announcement Date and Ex Date (IAE)
Day
N
Mean
Abnormal
Return
N
Mean
Abnormal
Return
-5
1263
0.17%
392:444
0.542
721
0.42%
228:237
-4
1263
0.32%
-3
1263
0.13%
383:434
1.692 $
721
0.29%
385:438
0.485
721
0.18%
-2
1263
0.60%
388:427
1.550
721
-1
1263
0.72%
412:415>
3.145 **
721
Positive:
Negative
Patell
Z
N
Mean
Abnormal
Return
0.981
542
-0.17%
164:207
213:237
0.676
542
0.36%
170:197
1.803 $
211:247
0.614
542
0.06%
174:191
0.031
0.38%
209:243
0.618
542
0.88%
179:184
1.653 $
0.24%
212:247
0.529
542
1.35%
200:168>>>
4.190 ***
Positive:
Negative
Patell
Z
Positive:
Negative
0
1263
-3.38%
365:646
-12.572 ***
721
-0.72%
195:286
-2.293 *
542
-6.92%
170:360
+1
1263
-1.63%
382:585
-7.307 ***
721
-2.57%
195:327
-9.241 ***
542
-0.39%
187:258>
+2
1263
0.68%
1.665 $
721
-0.18%
243:277>>
-1.114
542
1.81%
199:208>>
+3
1263
-0.22%
409:515>
-0.263
721
-0.17%
229:277
-0.092
542
-0.28%
+4
1263
0.11%
412:476>
0.681
721
-0.47%
208:262
-0.967
542
0.89%
+5
1263
-0.30%
377:521
-2.411 *
721
-0.41%
208:289
-2.513 *
542
-0.15%
Days
Mean
Cumulative
N
Return
442:485>>>
Positive:
Negative
(-5,-1)
1263
1.93%
660:527>>>
(0,+1)
1263
-5.01%
425:738>>
(+2,+5)
1263
0.27%
601:606>>>
Patell
Z
Mean
Cumulative
N
Return
Positive:
Negative
3.316 ***
721
1.52%
542
2.49%
721
-3.29%
239:388>
-8.156 ***
542
-7.31%
721
-1.22%
333:344>>>
-2.343 *
542
2.27%
95
1.529
Mean
Cumulative
N
Return
-14.057 ***
-0.164
356:314>>>
Patell
Z
180:238
204:214>>>
169:232
Positive:
Negative
304:213>>>
186:350>
268:262>>>
Patell
Z
-0.304
-16.545 ***
-0.497
3.826 ***
-0.295
2.154 *
-0.783
Patell
Z
3.298 ***
-12.051 ***
2.451 *
Table 7 The Daily Market Adjusted Returns of Reverse Stock Splits Around the Announcement Date
These panels show the average daily market model adjusted returns around the announcement date of reverse stock splits from 1962 to 2003. Abnormal returns are the daily return minus the
daily market index. The market adjusted returns presented on this page were obtained using the CRSP equal-weighted index as a proxy for the market. The results on the following page were
obtained using the CRSP value-weighted index as a proxy for the market. The number of positive versus negative returns is given followed by the standardized abnormal return test, Patell's Z
(Patell, 1976). This test is used to determine whether the mean is significantly different than zero. The symbols $,*,**, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001
levels, respectively, using a 1-tail test. The symbols (, <, <<, <<<, or ), >, >>, >> show the direction of the generalized sign test (not shown) and denote statistical significance at the 0.10, 0.05,
0.01 and 0.001 levels, respectively, using a 1-tail test. Cumulative abnormal returns are given in the bottom panel for the three periods shown.
Panel A
Panel B
Panel C
All Firms with
Announcements of Reverse Stock Splits
Firms with Unique Annoncement Day (UAD)
(Announcement Date is not the Ex-Date)
Firms with Identical
Announcement Date and Ex Date (IAE)
Day
N
Mean
Abnormal
Return
-5
1263
0.09%
558:705
0.193
721
0.38%
333:388)
-4
1263
0.25%
570:693>
1.294
721
0.21%
319:402
-3
1263
0.06%
538:725
-0.044
721
0.11%
307:414
-2
1263
0.49%
576:687>
0.933
721
0.31%
568:695)
Positive:
Negative
Patell
Z
N
Mean
Abnormal
Return
N
Mean
Abnormal
Return
0.966
542
-0.30%
225:317
0.356
542
0.31%
251:291>
1.565
0.225
542
-0.01%
231:311
-0.326
331:390)
0.222
542
0.73%
245:297
1.168
256:286>
Positive:
Negative
Patell
Z
-1
1263
0.58%
0
1263
-3.48%
+1
1263
-1.71%
501:762(
+2
1263
0.57%
556:707
1.026
+3
1263
-0.29%
524:739
-0.679
721
-0.24%
303:418
+4
1263
0.01%
551:712
0.080
721
-0.56%
304:417
+5
1263
-0.36%
524:739
721
-0.48%
297:424
-2.920 **
Days
Mean
Cumulative
Abnormal
N
Return
437:826<<<
Positive:
Negative
Positive:
Negative
2.342 *
721
0.11%
312:409
-0.044
542
1.19%
-13.277 ***
721
-0.79%
270:451<<
-2.718 **
542
-7.06%
167:375<<<
-7.783 ***
721
-2.64%
274:447<
-9.647 ***
542
-0.48%
227:315
721
-0.26%
311:410
-1.652 $
542
1.68%
245:297
-0.416
542
-0.35%
221:321
-1.443
542
0.77%
247:295)
542
-0.20%
227:315
-2.777 **
Patell
Z
Mean
Cumulative
Abnormal
N
Return
Positive:
Negative
Patell
Z
Positive:
Negative
-0.819
3.626 ***
-17.131 ***
-0.755
3.471 ***
-0.556
1.786 $
-0.871
Patell
Z
(-5,-1)
1263
1.47%
629:634>>>
2.110 *
721
1.13%
344:377>>
542
1.93%
285:257>>>
2.332 *
(0,+1)
1263
-5.19%
439:824<<<
-14.892 ***
721
-3.43%
255:466<<<
-8.743 ***
542
-7.54%
184:358<<<
-12.648 ***
(+2,+5)
1263
-0.06%
563:700
721
-1.54%
314:407
-3.216 **
542
1.90%
-1.175
The results on this page are based on using the CRSP equally weighted index as a market proxy.
96
0.771
Mean
Cumulative
Abnormal
N
Return
Patell
Z
249:293)
1.915 $
Table 7 (Continued)
Panel A
Panel B
Panel C
All Firms with
Announcements of Reverse Stock Splits
Firms with Unique Annoncement Day (UAD)
(Announcement Date is not the Ex-Date)
Firms with Identical
Announcement Date and Ex Date (IAE)
Day
N
Mean
Abnormal
Return
-5
1263
0.11%
595:668
0.307
721
0.42%
353:368)
-4
1263
0.34%
624:639>
1.727 $
721
0.27%
-3
1263
0.12%
583:680
0.257
721
0.17%
-2
1263
0.58%
609:654)
1.397
721
-1
1263
0.62%
608:655
2.580 **
721
Positive:
Negative
Patell
Z
N
Mean
Abnormal
Return
Positive:
Negative
N
Mean
Abnormal
Return
1.110
542
-0.30%
242:300
351:370
0.594
542
0.43%
273:269>
330:391
0.446
542
0.07%
253:289
-0.122
0.40%
343:378
0.545
542
0.82%
266:276
1.504
0.14%
334:387
0.033
542
1.27%
274:268>
3.900 ***
Patell
Z
Positive:
Negative
0
1263
-3.44%
473:790<<<
-12.925 ***
721
-0.73%
296:425<<
-2.485 *
542
-7.04%
177:365<<<
+1
1263
-1.68%
518:745<<<
-7.504 ***
721
-2.62%
283:438<<<
-9.481 ***
542
-0.43%
235:307
+2
1263
0.64%
585:678
1.386
721
-0.24%
325:396
-1.553
542
1.81%
260:282
+3
1263
-0.21%
554:709
-0.297
721
-0.20%
315:406
-0.248
542
-0.23%
239:303
+4
1263
0.04%
564:699
0.343
721
-0.53%
312:409
-1.268
542
0.80%
252:290
+5
1263
-0.29%
556:707
-2.377 *
721
-0.42%
311:410
-2.613 **
542
-0.13%
245:297
Days
(-5,-1)
Mean
Cumulative
Abnormal
N
Return
1263
1.77%
(0,+1)
1263
-5.11%
(+2,+5)
1263
0.17%
Positive:
Negative
Patell
Z
Mean
Cumulative
Abnormal
N
Return
Positive:
Negative
641:622>>>
2.803 **
721
1.39%
446:817<<<
-14.445 ***
721
-3.35%
261:460<<<
721
-1.39%
315:406
579:684
-0.473
The results on this page are based on using the CRSP value weighted index as a market proxy.
97
349:372
Patell
Z
1.220
Mean
Cumulative
Abnormal
N
Return
Positive:
Negative
Patell
Z
-0.811
1.951 $
-16.862 ***
-0.520
3.906 ***
-0.167
1.985 *
-0.615
Patell
Z
542
2.29%
292:250>>>
2.872 **
-8.461 ***
542
-7.47%
185:357<<<
-12.291 ***
-2.841 **
542
2.26%
264:278
2.555 *
Table 8 The Daily Market Model Adjusted Returns of Reverse Stock Splits Around the Announcement Date
These panels show the average market model adjusted returns around the announcement date of reverse stock splits from 1962 to 2003. The market model adjusted returns presented on this
page are obtained using the CRSP equal-weighted index as a proxy for the market. The results on the following page are obtained using the CRSP value weighted index as a proxy for the
market. The estimation period for the market model parameters is t-160 to t-40 where t is the announcement date. The number of positive versus negative returns is given followed by the
standardized abnormal return test, Patell's Z (Patell, 1976). This test is used to determine whether the mean is significantly different than zero. The symbols $,*,**, and *** denote statistical
significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a 1-tail test. The symbols (, <, <<, <<<, or ), >, >>, >> show the direction of the generalized sign test (not shown) and
denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a 1-tail test. Cumulative abnormal returns are given in the bottom panel for the three periods shown.
Panel A
Panel B
Panel C
All Firms with
Announcements of Reverse Stock Splits
Firms with Unique Annoncement Day (UAD)
(Announcement Date is not the Ex-Date)
Firms with Identical
Announcement Date and Ex Date (IAE)
Day
N
Mean
Abnormal
Return
-5
1263
0.06%
575:688
0.468
721
0.38%
342:379)
-4
1263
0.24%
579:684
1.644
721
0.25%
-3
1263
0.07%
574:689
0.597
721
0.18%
Positive:
Negative
Patell
Z
N
Mean
Abnormal
Return
Positive:
Negative
N
Mean
Abnormal
Return
1.116
542
-0.36%
233:309
331:390
0.719
542
0.23%
248:294
336:385
0.979
542
-0.07%
238:304
Patell
Z
Positive:
Negative
Patell
Z
-0.573
1.680 $
-0.218
-2
1263
0.42%
576:687
1.033
721
0.29%
330:391
0.430
542
0.58%
246:296
1.080
-1
1263
0.50%
586:677)
2.458 *
721
0.06%
313:408
0.055
542
1.09%
273:269>>
3.689 ***
0
1263
-3.53%
454:809<<<
-12.915 ***
721
-0.82%
286:435<
-2.396 *
542
-7.13%
168:374<<<
+1
1263
-1.77%
498:765<<<
-7.748 ***
721
-2.69%
275:446<<<
-9.612 ***
542
-0.55%
223:319
+2
1263
0.46%
570:693
1.061
721
-0.33%
322:399
-1.538
542
1.51%
248:294
+3
1263
-0.32%
529:734
-0.251
721
-0.23%
306:415
-0.018
542
-0.43%
223:319
+4
1263
-0.10%
546:717
0.083
721
-0.64%
301:420
-1.342
542
0.61%
245:297
+5
1263
-0.44%
555:708
-2.573 *
721
-0.52%
321:400
-2.575 *
542
-0.34%
234:308
Days
(-5,-1)
Mean
Cumulative
Abnormal
N
Return
1263
1.29%
Positive:
Negative
Patell
Z
630:633>>>
2.773 **
-14.611 ***
(0,+1)
1263
-5.30%
438:825<<<
(+2,+5)
1263
-0.40%
546:717
-0.840
Mean
Cumulative
Abnormal
N
Return
721
1.17%
Positive:
Negative
346:375>
Patell
Z
1.475
Mean
Cumulative
Abnormal
N
Return
542
1.46%
721
-3.51%
259:462<<<
-8.491 ***
542
-7.68%
721
-1.71%
310:411
-2.737 **
542
1.35%
The results on this page are based on using the CRSP equally weighted index as a market proxy.
98
Positive:
Negative
-16.950 ***
-0.742
3.394 ***
-0.363
1.674 $
-0.957
Patell
Z
284:258>>>
2.531 *
179:363<<<
-12.510 ***
236:306
1.874 $
Table 8 (Continued)
Panel A
Panel B
Panel C
All Firms with
Announcements of Reverse Stock Splits
Firms with Unique Annoncement Day (UAD)
(Announcement Date is not the Ex-Date)
Firms with Identical
Announcement Date and Ex Date (IAE)
Day
N
Mean
Abnormal
Return
-5
1263
-0.02%
583:680
0.070
721
0.29%
349:372>
-4
1263
0.19%
569:694
1.502
721
0.19%
325:396
-3
1263
0.05%
567:696
0.445
721
0.17%
-2
1263
0.44%
572:691
1.141
721
0.26%
-1
1263
0.52%
577:686
2.543 *
721
0
1263
-3.57%
458:805<<<
-13.030 ***
+1
1263
-1.81%
491:772<<<
-7.838 ***
+2
1263
0.49%
567:696
1.120
+3
1263
-0.34%
531:732
+4
1263
-0.10%
555:708
+5
1263
-0.44%
551:712
Days
Mean
Cumulative
Abnormal
N
Return
Positive:
Negative
Positive:
Negative
N
Mean
Abnormal
Return
0.765
542
-0.43%
234:308
-0.776
0.588
542
0.19%
244:298
1.613
329:392
0.831
542
-0.10%
238:304
-0.279
327:394
0.377
542
0.67%
245:297
1.307
0.05%
308:413
-0.012
542
1.15%
269:273>>
3.895 ***
721
-0.87%
288:433<
-2.586 **
542
-7.17%
170:372<<<
721
-2.70%
280:441<<
-9.621 ***
542
-0.64%
211:331<
721
-0.36%
322:399
-1.646 $
542
1.61%
245:297
-0.431
721
-0.29%
303:418
-0.217
542
-0.39%
228:314
0.046
721
-0.66%
302:419
-1.424
542
0.65%
253:289
721
-0.52%
314:407
-2.535 *
542
-0.32%
237:305
Patell
Z
-2.613 **
Patell
Z
N
Mean
Abnormal
Return
Positive:
Negative
Mean
Cumulative
Abnormal
N
Return
Positive:
Negative
Patell
Z
Positive:
Negative
Patell
Z
-16.908 ***
-0.868
3.609 ***
-0.407
1.712 $
-1.066
Patell
Z
1263
1.18%
621:642>>>
2.549 *
721
0.95%
542
1.48%
286:256>>>
2.577 **
(0,+1)
1263
-5.39%
431:832<<<
-14.756 ***
721
-3.57%
252:469<<<
-8.632 ***
542
-7.80%
179:363<<<
-12.569 ***
(+2,+5)
1263
-0.38%
538:725
721
-1.83%
296:425(
-2.911 **
542
1.55%
The results on this page are based on using the CRSP value weighted index as a market proxy.
99
1.140
Mean
Cumulative
Abnormal
N
Return
Positive:
Negative
(-5,-1)
-0.939
335:386
Patell
Z
242:300
1.924 $
Table 9 The Daily Raw Returns of Reverse Stock Splits Around the Ex-date
These panels show the average raw returns around the ex-date of reverse stock splits from 1962 to 2003. The number of positive versus
the number of negative returns is shown and the value of the standardized abnormal return test, Patell's Z (Patell, 1976) is given. These
tests are used to determine whether the mean is significantly different than zero. The symbols $,*,**, and *** denote statistical
significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a 1-tail test. The symbols (,< or ),> etc. correspond to $,* and show
the significance and direction of the generalized sign test (not shown).
Day
N
Panel D
Panel E
All
Ex Dates
Ex Date with
No Announcement Date Found
Mean
Return
Positive:
Negative
Patell
Z
N
Mean
Return
Positive:
Negative
Patell
Z
-5
1811
0.43%
499:617
1.535
403
1.35%
91:116
0.722
-4
1811
0.20%
500:593
0.443
403
0.11%
92:120
-0.503
-3
1811
-0.17%
500:602
-0.722
403
-0.15%
97:113
0.723
-2
1811
0.77%
510:572
403
1.98%
115:100>>
6.100 ***
-1
1811
-0.15%
510:623
403
-0.72%
91:134
0
1811
-6.08%
545:1209>>
403
-5.86%
121:273>>>
+1
1811
0.10%
620:773>>>
0.497
403
0.23%
140:149>>>
+2
1811
0.24%
594:721>>>
0.566
403
-0.41%
118:152>>
+3
1811
-0.62%
403
-0.60%
101:149
-2.165 *
+4
1811
0.23%
403
-0.15%
129:149>>>
-0.926
+5
1811
-0.33%
403
-0.68%
105:147
-2.008 *
Days
Mean
Cumulative
N
Return
532:773>
593:721>>>
536:730>
Positive:
Negative
4.737 ***
-0.427
-32.171 ***
-3.403 ***
0.078
-2.647 **
Mean
Cumulative
N
Return
Patell
Z
Positive:
Negative
-0.731
-19.791 ***
0.6
0.376
Patell
Z
(-5,-1)
1811
1.09%
857:782>>>
2.489 *
403
2.58%
169:166>>>
2.823 **
(0,0)
1811
-6.08%
545:1209>>
-32.171 ***
403
-5.86%
121:273>>>
-19.791 ***
(+1,+5)
1811
-0.38%
779:964>>>
-2.196 *
403
-1.61%
161:214>>>
-1.843 $
100
Table 9 (Continued)
Day
Panel F
Panel G
Ex Date is Not Announcement Date
Missing Announcements Included
Ex Date is Not Announcement Date
Missing Announcements Not Included
N
Mean
Return
Positive:
Negative
Patell
Z
-5
1287
0.64%
338:418
-4
1287
0.11%
335:403
-0.639
-3
1287
-0.25%
334:417
-0.835
-2
1287
0.75%
340:393
-1
1287
-0.75%
320:458
-2.942 **
375:866>
-28.210 ***
N
1.861 $
4.872 ***
Mean
Return
Positive:
Negative
Patell
Z
884
0.32%
247:302
884
0.11%
243:283
-0.432
1.758 $
-1.498
884
-0.30%
237:304
884
0.19%
225:293
1.754 $
884
-0.76%
229:324
-3.058 **
254:593
-20.669 ***
0
1287
-5.78%
884
-5.74%
+1
1287
0.31%
436:524>>>
0.984
884
0.34%
296:375>>>
+2
1287
-0.45%
398:525>>>
-2.093 *
884
-0.47%
280:373>>
0.782
-2.782 **
+3
1287
-0.76%
358:543
-3.934 ***
884
-0.83%
257:394
-3.286 **
+4
1287
-0.02%
398:516>>>
-1.154
884
0.03%
269:367)
-0.767
+5
1287
-0.38%
374:505>
-2.686 **
884
-0.25%
269:358)
-1.885 $
Days
Mean
Cumulative
N
Return
Positive:
Negative
(-5,-1)
1287
0.51%
(0,0)
1287
-5.78%
375:866>
567:573>>>
(+1,+5)
1287
-1.31%
524:702>>>
Mean
Cumulative
N
Return
Patell
Z
1.036
Positive:
Negative
884
-0.44%
398:407>>>
-28.210 ***
884
-5.74%
254:593
-3.973 ***
884
-1.17%
363:488>>>
101
Patell
Z
-0.66
-20.669 ***
-3.550 ***
Table 10 The Daily Market Adjusted Abnormal Returns of Reverse Stock Splits Around
the Ex-date
These panels show the average daily market adjusted returns around the ex date of reverse stock splits from 1962 to 2003. Abnormal
returns are the daily return minus the daily market index. The market adjusted returns presented on this page use the CRSP equalweighted index as a proxy for the market. The results on the following page use the CRSP value-weighted index as a proxy for the market.
The positive versus negative returns is given followed by the standardized abnormal return test, Patell's Z (Patell, 1976). These tests are
used to determine whether the mean is significantly different than zero. The symbols $,*,**, and *** denote statistical significance at the
0.10, 0.05, 0.01 and 0.001 levels, respectively, using a 1-tail test. The symbols (,< or ),> etc. correspond to $,* and show the significance
and direction of the generalized sign test (not shown).
Panel D
Panel E
All
Ex Dates
Ex Date with
No Announcement Date Found
Mean
Return
Positive:
Negative
Patell
Z
Positive:
Negative
Patell
Z
N
-5
1811
0.37%
794:1017
0.995
403
1.34%
181:222
0.756
-4
1811
0.15%
830:981>>
0.169
403
0.07%
177:226
-0.623
-3
1811
-0.26%
-1.499
403
-0.25%
171:232
0.306
772:1039
-2
1811
0.61%
773:1038
-1
1811
-0.27%
740:1071
N
Mean
Return
Day
3.581 ***
-1.356
403
1.82%
176:227
403
-0.81%
151:252(
124:279<<<
5.569 ***
-1.067
0
1811
-6.18%
556:1255<<< -32.726 ***
403
-5.96%
+1
1811
0.02%
766:1045
-0.029
403
0.14%
+2
1811
0.13%
769:1042
-0.286
403
-0.58%
165:238
-0.516
+3
1811
-0.69%
707:1104<<
-4.010 ***
403
-0.66%
155:248
-2.358 *
180:223
-19.640 ***
0.295
+4
1811
0.13%
780:1031
-0.551
403
-0.27%
170:233
-1.22
+5
1811
-0.39%
776:1035
-2.886 **
403
-0.76%
173:230
-2.064 *
Days
(-5,-1)
(0,0)
(+1,+5)
Mean
Cumulative
Abnormal
N
Return
1811
1811
1811
0.61%
-6.18%
-0.80%
Positive:
Negative
Mean
Cumulative
Abnormal
N
Return
Patell
Z
849:962>>>
0.845
556:1255<<< -32.726 ***
749:1062
-3.471 ***
403
403
403
2.17%
-5.96%
-2.13%
The results on this page are based on using the CRSP equally weighted index as a market proxy.
102
Positive:
Negative
Patell
Z
179:224
124:279<<<
158:245
2.210 *
-19.640 ***
-2.622 **
Table 10 (Continued)
Day
Panel F
Panel G
Ex Date is Not Announcement Date
Missing Announcements Included
Ex Date is Not Announcement Date
Missing Announcements Not Included
N
Mean
Return
Positive:
Negative
Patell
Z
N
Mean
Return
Positive:
Negative
Patell
Z
-5
1287
0.60%
574:713
1.526
884
0.26%
393:491
-4
1287
0.07%
587:700>
-0.85
884
0.07%
410:474>
-0.605
-3
1287
-0.34%
552:735
-1.513
884
-0.38%
381:503
-2.034 *
-2
1287
0.59%
537:750
3.812 ***
884
0.02%
361:523
-1
1287
-0.86%
493:794<<
-3.682 ***
884
-0.88%
342:542<
0
1287
-5.85%
390:897<<<
-28.482 ***
884
-5.81%
266:618<<<
+1
1287
0.23%
545:742
+2
1287
-0.55%
526:761
0.513
1.33
0.834
-3.724 ***
-21.100 ***
884
0.27%
365:519
0.42
-2.918 **
884
-0.54%
361:523
-3.175 **
+3
1287
-0.82%
495:792<<
-4.455 ***
884
-0.90%
340:544<
-3.783 ***
+4
1287
-0.11%
543:744
-1.665 $
884
-0.04%
373:511
-1.185
+5
1287
-0.44%
557:730
-2.897 **
884
-0.30%
384:500
-2.100 *
Days
Mean
Cumulative
N
Return
Positive:
Negative
(-5,-1)
1287
0.05%
(0,0)
1287
-5.85%
575:712)
390:897<<<
(+1,+5)
1287
-1.70%
507:780<
Mean
Cumulative
N
Return
Patell
Z
-0.316
Positive:
Negative
884
-0.91%
396:488
-28.482 ***
884
-5.81%
266:618<<<
-5.108 ***
884
-1.50%
349:535(
The results on this page are based on using the CRSP equally weighted index as a market proxy.
103
Patell
Z
-1.877 $
-21.100 ***
-4.393 ***
Table 10 (Continued)
Day
N
Panel D
Panel E
All
Ex Dates
Ex Date with
No Announcement Date Found
Mean
Return
Positive:
Negative
Patell
Z
N
Mean
Return
Positive:
Negative
Patell
Z
-5
1811
0.39%
830:981
1.182
403
1.38%
183:220
0.936
-4
1811
0.21%
856:955
0.515
403
0.07%
180:223
-0.675
-3
1811
-0.23%
819:992
-1.442
403
-0.25%
187:216
0.177
-2
1811
0.66%
832:979
-1
1811
-0.20%
807:1004
3.900 ***
-0.826
403
1.87%
195:208
403
-0.73%
171:232
0
1811
-6.14%
577:1234<<< -31.885 ***
403
-5.94%
+1
1811
0.06%
798:1013
403
0.17%
+2
1811
0.20%
810:1001
403
-0.52%
174:229
-0.524
+3
1811
-0.62%
766:1045<<
-3.627 ***
403
-0.61%
169:234
-2.060 *
+4
+5
1811
1811
0.18%
-0.34%
815:996
798:1013
-0.145
-2.495 *
403
403
-0.22%
-0.68%
188:215
177:226
-0.927
-1.692 $
Days
(-5,-1)
(0,0)
(+1,+5)
Mean
Cumulative
Abnormal
N
Return
1811
1811
1811
0.82%
-6.14%
-0.52%
Positive:
Negative
0.187
0.013
Mean
Cumulative
Abnormal
N
Return
Patell
Z
865:946)
1.488
577:1234<<< -31.885 ***
776:1035<
-2.713 **
403
403
403
2.33%
-5.94%
-1.86%
The results on this page are based on using the CRSP value weighted index as a market proxy.
104
128:275<<<
5.781 ***
-0.759
188:215
-18.821 ***
0.267
Positive:
Negative
Patell
Z
184:219
128:275<<<
162:241<
2.442 *
-18.821 ***
-2.207 *
Table 10 (Continued)
Day
Panel F
Panel G
Ex Date is Not Announcement Date
Missing Announcements Included
Ex Date is Not Announcement Date
Missing Announcements Not Included
N
Mean
Return
Positive:
Negative
Patell
Z
N
1.736 $
Mean
Return
Positive:
Negative
Patell
Z
-5
1287
0.63%
593:694
884
0.28%
410:474
1.462
-4
1287
0.10%
594:693
-0.668
884
0.11%
414:470
-0.351
-3
1287
-0.33%
578:709
-1.566
884
-0.37%
391:493
-2.011 *
-2
1287
0.61%
575:712
-1
1287
-0.79%
545:742<
0
1287
-5.81%
401:886<<<
+1
1287
0.27%
568:719
+2
1287
-0.50%
553:734(
884
0.03%
380:504
-3.192 **
3.993 ***
884
-0.82%
374:510<
-27.650 ***
884
-5.76%
273:611<<<
884
0.32%
380:504
-2.785 **
0.648
884
-0.49%
379:505
0.909
-3.341 ***
-20.650 ***
0.601
-3.008 **
+3
1287
-0.78%
537:750<<
-4.212 ***
884
-0.85%
368:516<
-3.692 ***
+4
1287
-0.06%
573:714
-1.299
884
0.01%
385:499
-0.941
+5
1287
-0.41%
562:725
-2.611 **
884
-0.28%
385:499
-2.008 *
Days
(-5,-1)
Mean
Cumulative
Abnormal
N
Return
1287
0.21%
Positive:
Negative
585:702
Mean
Cumulative
Abnormal
N
Return
Patell
Z
0.135
884
-0.76%
Positive:
Negative
401:483
Patell
Z
-1.49
(0,0)
1287
-5.81%
401:886<<<
-27.650 ***
884
-5.76%
273:611<<<
-20.650 ***
(+1,+5)
1287
-1.47%
517:770<<<
-4.589 ***
884
-1.29%
355:529<<
-4.047 ***
The results on this page are based on using the CRSP value weighted index as a market proxy.
105
Table 11 The Daily Market Model Adjusted Returns of Reverse Stock Splits Around the
Ex-date
These panels show the average market model adjusted returns around the ex date of reverse stock splits from 1962 to 2003. The market
model adjusted returns presented on this page use the CRSP equal-weighted index as a proxy for the market. The results on the following
page use the CRSP value-weighted index as a proxy for the market. The estimation period for the market model parameters is t-160 to t-40
where t is the ex date. The positive versus negative returns is given followed by the standardized abnormal return test, Patell's Z (Patell,
1976). These tests are used to determine whether the mean is significantly different than zero. The symbols $,*,**, and *** denote
statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a 1-tail test. The symbols (,< or ),> etc. correspond to $,*
and show the significance and direction of the generalized sign test (not shown).
Day
N
Panel D
Panel E
All
Ex Dates
Ex Date with
No Announcement Date Found
Mean
Return
Positive:
Negative
Patell
Z
N
Mean
Return
Positive:
Negative
Patell
Z
-5
1811
0.28%
805:1006
0.989
403
1.17%
182:221
0.555
-4
1811
0.09%
852:959>
0.385
403
0.05%
192:211
-0.338
-3
1811
-0.29%
827:984
-1.093
-2
1811
0.52%
816:995
3.711 ***
-0.33%
193:210
1.68%
195:208
403
-0.77%
177:226
403
-5.97%
121:282<<<
0.59
5.627 ***
-1
1811
-0.31%
818:993
0
1811
-6.21%
553:1258<<< -32.724 ***
+1
1811
0.01%
783:1028
0.57
403
0.18%
186:217
0.778
+2
1811
0.09%
797:1014
0.326
403
-0.59%
176:227
0.053
+3
1811
-0.68%
749:1062<<
-3.430 ***
403
-0.60%
181:222
-2.084 *
+4
1811
0.06%
794:1017
-0.391
403
-0.35%
177:226
-1.159
+5
1811
-0.44%
795:1016
-2.856 **
403
-0.74%
182:221
-2.106 *
Days
(-5,-1)
(0,0)
(+1,+5)
Mean
Cumulative
Abnormal
N
Return
1811
1811
1811
0.29%
-6.21%
-0.95%
Positive:
Negative
-0.921
403
403
Mean
Cumulative
Abnormal
N
Return
Patell
Z
857:954>
1.374
553:1258<<< -32.724 ***
755:1056<
-2.585 **
403
403
403
1.81%
-5.97%
-2.10%
The results on this page are based on using the CRSP equally weighted index as a market proxy.
106
-0.722
-20.267 ***
Positive:
Negative
Patell
Z
183:220
121:282<<<
168:235(
2.554 *
-20.267 ***
-2.020 *
Table 11 (Continued)
Day
Panel F
Panel G
Ex Date is Not Announcement Date
Missing Announcements Included
Ex Date is Not Announcement Date
Missing Announcements Not Included
N
Mean
Return
Positive:
Negative
Patell
Z
N
Mean
Return
Positive:
Negative
Patell
Z
-5
1287
0.50%
579:708
1.392
884
0.20%
397:487
1.305
-4
1287
0.01%
611:676)
-0.655
884
0.00%
419:465)
-0.562
-3
1287
-0.35%
599:688
-1.074
884
-0.36%
406:478
-1.696 $
-2
1287
0.52%
578:709
884
-0.02%
383:501
-1
1287
-0.85%
557:730
884
-0.89%
380:504
0
1287
-5.86%
+1
1287
0.25%
564:723
1.155
+2
1287
-0.53%
550:737(
+3
1287
-0.79%
532:755<<
+4
1287
-0.14%
+5
1287
-0.45%
Days
(-5,-1)
(0,0)
(+1,+5)
Mean
Cumulative
Abnormal
N
Return
1287
1287
1287
-0.17%
-5.86%
-1.66%
385:902<<<
4.046 ***
-3.168 **
-28.576 ***
884
-5.81%
884
0.28%
378:506
-2.167 *
884
-0.51%
374:510
-3.905 ***
884
-0.88%
351:533<<
-3.305 ***
560:727
-1.368
884
-0.04%
383:501
-0.868
571:716
-2.818 **
884
-0.31%
389:495
-1.977 *
Positive:
Negative
Patell
Z
585:702
385:902<<<
520:767<<<
0.242
-28.576 ***
-4.071 ***
Mean
Cumulative
Abnormal
N
Return
884
884
884
-1.07%
-5.81%
-1.46%
The results on this page are based on using the CRSP equally weighted index as a market proxy.
107
264:620<<<
1.077
-3.337 ***
-20.789 ***
0.868
-2.652 **
Positive:
Negative
Patell
Z
402:482
264:620<<<
352:532<<
-1.437
-20.789 ***
-3.548 ***
Table 11 (Continued)
Day
N
Panel D
Panel E
All
Ex Dates
Ex Date with
No Announcement Date Found
Mean
Positive:
Patell
Return
Negative
Z
N
Mean
Positive:
Patell
Return
Negative
Z
-5
1811
0.23%
787:1024
0.777
403
1.16%
181:222
0.533
-4
1811
0.06%
839:972
0.103
403
0.02%
201:202
-0.48
-3
1811
-0.31%
799:1012
-1.237
403
-0.34%
184:219
0.481
-2
1811
0.59%
815:996
4.031 ***
403
1.83%
206:197)
-1
1811
-0.28%
811:1000
-0.72
403
-0.74%
185:218
0
1811
-6.23%
562:1249<<< -32.608 ***
403
-5.99%
121:282<<<
+1
1811
-0.03%
783:1028
0.345
403
0.17%
196:207
+2
1811
0.09%
791:1020
0.276
403
-0.59%
176:227
0.105
+3
1811
-0.71%
403
-0.63%
178:225
-2.057 *
748:1063<<
-3.582 ***
5.951 ***
-0.598
-20.157 ***
0.689
+4
1811
0.03%
795:1016
-0.572
403
-0.39%
185:218
-1.194
+5
1811
-0.46%
789:1022
-3.008 **
403
-0.75%
173:230
-2.037 *
Days
(-5,-1)
Mean
Cumulative
Abnormal
N
Return
1811
0.30%
Positive:
Negative
855:956)
Mean
Cumulative
Abnormal
N
Return
Patell
Z
1.321
403
1.93%
Positive:
Negative
183:220
(0,0)
1811
-6.23%
562:1249<<< -32.608 ***
403
-5.99%
121:282<<<
(+1,+5)
1811
-1.08%
751:1060<<
403
-2.19%
173:230
-2.926 **
The results on this page are based on using the CRSP value weighted index as a market proxy.
108
Patell
Z
2.633
-20.157
-2.010
Table 11 (Continued)
Panel F
Panel G
Ex Date is Not Announcement Date
Missing Announcements Included
Ex Date is Not Announcement Date
Missing Announcements Not Included
Mean
Return
Positive:
Negative
Patell
Z
Positive:
Negative
Patell
Z
N
-5
1287
0.47%
561:726
1.251
884
0.15%
380:504
1.15
-4
1287
-0.02%
604:683
-0.929
884
-0.03%
403:481
-0.797
-3
1287
-0.36%
571:716
-1.195
884
-0.37%
387:497
-1.768 $
-2
1287
0.59%
580:707
884
0.02%
374:510
-1
1287
-0.83%
554:733(
884
-0.87%
369:515(
392:895<<<
N
Mean
Return
Day
4.300 ***
-3.049 **
0
1287
-5.88%
884
-5.83%
+1
1287
0.23%
576:711
-28.477 ***
1.028
884
0.26%
380:504
271:613<<<
+2
1287
-0.57%
548:739<
-2.310 *
884
-0.57%
372:512
1.165
-3.277 **
-20.743 ***
0.774
-2.860 **
+3
1287
-0.85%
528:759<<
-4.054 ***
884
-0.94%
350:534<<
-3.503 ***
+4
1287
-0.20%
553:734(
-1.618
884
-0.12%
368:516(
-1.145
+5
1287
-0.49%
561:726
-2.950 **
884
-0.37%
388:496
-2.183 *
Days
(-5,-1)
Mean
Cumulative
Abnormal
N
Return
1287
-0.16%
Positive:
Negative
583:704
Mean
Cumulative
Abnormal
N
Return
Patell
Z
0.169
884
-1.11%
Positive:
Negative
400:484
Patell
Z
-1.577
(0,0)
1287
-5.88%
392:895<<<
-28.477 ***
884
-5.83%
271:613<<<
-20.743 ***
(+1,+5)
1287
-1.88%
517:770<<<
-4.429 ***
884
-1.74%
344:540<<<
-3.988 ***
The results on this page are based on using the CRSP value weighted index as a market proxy.
109
Table 12 Ex-date Returns vs. Announcement Returns
This table shows the results of regressing ex-date returns on the announcement day returns. The announcement day is defined
as the day of the announcement and the day after the announcement. Raw returns and abnormal returns are used in the
regressions as indicated in the first column. The first column defines the ex-date and AD returns used in the regression. The
numbers in bold Italics are t-values. Higher (absolute) t-values indicate that the coefficients are not equal to zero. CR is
cumulative returns (raw) and CAR is cumulative abnormal return.
Ex Date
Returns
Raw
Ex
=
Returns
Market Adjusted Returns
Ex
=
Equally Weighted Index
Market Adjusted Returns
Ex
=
Value Weighted Index
Market Model Adjusted Returns
Ex
=
Equally Weighted Index
Market Model Adjusted Returns
Value Weighted Index
Ex
=
Intercept
Coefficient
-0.0600
0.2114
-12.86
6.85
-0.0613
0.1367
-12.93
4.51
-0.0611
0.1314
-12.90
4.35
-0.0621
0.1191
-13.12
3.95
-0.0622
0.1198
-13.10
3.97
110
AD Returns
x
AD CR
x
AD CAR
x
AD CAR
x
AD CAR
x
AD CAR
Table 13 Number of Reverse Stock Splits by Industry 1962-2003
Reverse splits are categorized according to the Fama and French industry classifications that are defined in Appendix C. These industry
categories were obtained from Kenneth French's web site.
Five Industries
1 Manuf
643
2 Utils
28
3 Shops
213
4 Money
216
5 Other
918
Ten Industries
1 NoDur
87
2 Durbl
58
3 Oil
194
4 Chems
104
5 Manuf
402
6 Telcm
77
7 Utils
12
8 Shops
555
8 Shops
555
9 Money
215
10 Other
308
Twelve Industries
1 NoDur
87
2 Durbl
41
3 Manuf
153
4 Enrgy
194
5 Chems
29
6 BusEq
369
7 Telcm
77
8 Utils
12
9 Shops
218
10 Hlth
216
11 Money
215
12 Other
401
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Food
Beer
Smoke
Games
Books
Hshld
Clths
Hlth
Chems
Txtls
Cnstr
Steel
FabPr
ElcEq
Autos
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Agric
Mines
Oil
Stone
Cnstr
Food
Smoke
Txtls
Apprl
Wood
Chair
Paper
Print
Chems
Ptrlm
Rubbr
Lethr
Glass
Metal
Thirty Industries
34
16
5
17
3
18
76
19
20
20
24
21
16
22
216
23
22
24
2
25
58
26
13
27
46
28
85
29
18
30
Carry
Mines
Coal
Oil
Util
Telcm
Servs
BusEq
Paper
Trans
Whlsl
Rtail
Meals
Fin
Other
6
100
1
191
11
77
328
139
12
25
100
67
57
211
42
Thirty-eight Industries
12
20 MtlPr
97
21 Machn
191
22 Elctr
5
23 Cars
23
24 Instr
31
25 Manuf
3
26 Trans
2
27 Phone
16
28 TV
4
29 Utils
2
30 Garbg
11
31 Steam
20
32 Water
103
33 Whlsl
2
34 Rtail
14
35 Money
3
36 Srvc
6
37 Govt
13
38 Other
27
94
151
21
97
13
25
51
26
12
15
1
0
99
109
213
485
1
1
111
1
2
3
4
5
6
7
8
9
Food
Mines
Oil
Clths
Durbl
Chems
Cnsum
Cnstr
Steel
Seventeen Industries
46
10 FabPr
102
11 Machn
194
12 Cars
25
13 Trans
35
14 Utils
22
15 Rtail
87
16 Finan
60
17 Other
13
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Agric
Food
Soda
Beer
Smoke
Toys
Fun
Books
Hshld
Clths
Hlth
MedEq
Drugs
Chems
Rubbr
Txtls
BldMt
Cnstr
Steel
FabPr
Mach
ElcEq
Autos
Aero
Forty-eight Industries
8
25 Ships
25
26 Guns
1
27 Gold
5
28 Mines
3
29 Coal
11
30 Oil
64
31 Util
13
32 Telcm
24
33 PerSv
16
34 BusSv
72
35 Comps
69
36 Chips
75
37 LabEq
22
38 Paper
12
39 Boxes
2
40 Trans
35
41 Whlsl
23
42 Rtail
13
43 Meals
4
44 Banks
41
45 Insur
84
46 RlEst
17
47 Fin
5
48 Other
8
194
23
34
11
104
207
844
1
0
73
27
1
190
11
77
30
305
57
61
21
8
4
25
99
67
57
47
24
32
103
31
Table 14 Ex-date Returns by Industry
The columns represent the number of firms, the average ex-date return, and the standard deviation. As a point of reference, the average ex-date return for
the full sample is -0.06. The t-values shown represent the value of a two-sample test of equal means. The values tested are the significant high and low
values that are highlighted. Reverse splits are categorized according to the Fama and French industry classifications that are defined in Appendix C.
These industry categories were obtained from Kenneth French's web site.
No
2
4
5
1
3
Name
Utils
Money
Other
Manuf
Shops
Five Industries
N
Mean
Std
t-value
27
-4.77% 0.1802
-1.38
212
-5.15% 0.1328
-5.65 *
898
-6.36% 0.1676 -11.37
624
-7.58% 0.1536 -12.33
208
-8.06% 0.1951
-5.96 *
2 Sample Test for Equal Means T =
No
7
3
1
9
2
10
6
8
4
5
Name
Utils
Oil
NoDur
Money
Durbl
Other
Telcm
Shops
Chems
Manuf
Ten Industries
N
Mean
Std
t-value
11
-4.32% 0.1128
-1.27
189
-4.46% 0.1426
-4.30 *
87
-5.04% 0.1508
-3.12
212
-5.15% 0.1328
-5.65
58
-5.71% 0.1278
-3.40
304
-6.85% 0.1924
-6.21
75
-7.11% 0.1828
-3.37
544
-7.38% 0.1721 -10.00
103
-7.62% 0.1631
-4.74
386
-8.23% 0.1543 -10.47 *
2 Sample Test for Equal Means T =
No
8
4
1
11
3
5
2
10
7
12
9
6
Name
Utils
Enrgy
NoDur
Money
Manuf
Chems
Durbl
Hlth
Telcm
Other
Shops
BusEq
1.78
No
10
9
6
14
1
3
16
5
12
17
11
7
2
13
8
15
4
Name
FabPr
Steel
Chems
Utils
Food
Oil
Finan
Durbl
Cars
Other
Machn
Cnsum
Mines
Trans
Cnstr
Rtail
Clths
Seventeen Industries
N
Mean
Std
t-value
7
2.23% 0.0886
0.66
13
-1.97% 0.1410
-0.50
22
-3.00% 0.1175
-1.20
10
-3.24% 0.1127
-0.91
46
-3.25% 0.1332
-1.66
189
-4.38% 0.1419
-4.25 *
206
-4.91% 0.1314
-5.37
34
-5.19% 0.1096
-2.76
24
-6.24% 0.1371
-2.23
825
-7.22% 0.1670 -12.42
189
-7.35% 0.1473
-6.86
86
-7.48% 0.1760
-3.94
97
-8.25% 0.2115
-3.84
34
-8.32% 0.1201
-4.04
60
-9.24% 0.1891
-3.78
102
-9.52% 0.2087
-4.61
25 -12.80% 0.2291
-2.79 *
2 Sample Test for Equal Means T =
2.89
Twelve Industries
N
Mean
Std
t-value
11
-4.32% 0.1128
-1.27
189
-4.46% 0.1426
-4.30 *
87
-5.04% 0.1508
-3.12
212
-5.15% 0.1328
-5.65
149
-6.25% 0.1439
-5.31
29
-6.41% 0.1462
-2.36
41
-6.85% 0.1371
-3.20
211
-6.87% 0.1559
-6.40
75
-7.11% 0.1828
-3.37
392
-7.34% 0.1808
-8.04
214
-7.87% 0.1931
-5.96
359
-8.29% 0.1658
-9.47 *
2 Sample Test for Equal Means T =
1.79
No
3
10
1
12
9
20
5
30
19
24
15
29
2
4
25
26
8
21
22
28
11
23
17
13
14
27
6
16
7
18
Name
Smoke
Txtls
Food
Steel
Chems
Util
Books
Other
Oil
Paper
Autos
Fin
Beer
Games
Trans
Whlsl
Hlth
Telcm
Servs
Meals
Cnstr
BusEq
Mines
FabPr
ElcEq
Rtail
Hshld
Carry
Clths
Coal
Thirty Industries
N
Mean
Std
t-value
3
1.56% 0.0327
0.83
2
1.12% 0.1021
0.15
34 -1.15% 0.1030
-0.65
13 -1.97% 0.1410
-0.50
22 -3.18% 0.1173
-1.27
10 -3.24% 0.1127
-0.91
20 -3.51% 0.1634
-0.96
43 -3.91% 0.1489
-1.72
188 -4.37% 0.1423
-4.21
12 -4.46% 0.1402
-1.10
20 -4.79% 0.1322
-1.62
209 -5.22% 0.1337
-5.64
5 -5.57% 0.2149
-0.58
74 -6.15% 0.1599
-3.31
24 -6.59% 0.1010
-3.20
99 -6.71% 0.1839
-3.63
211 -6.87% 0.1559
-6.40
75 -7.11% 0.1828
-3.37
321 -7.14% 0.1621
-7.89
56 -7.48% 0.1946
-2.88
57 -7.90% 0.1940
-3.08
134 -8.08% 0.1467
-6.38
96 -8.10% 0.2121
-3.74
43 -8.78% 0.1526
-3.77
85 -9.64% 0.1923
-4.62
67 -10.26% 0.1997
-4.21
23 -13.15% 0.1746
-3.61
6 -14.10% 0.0913
-3.78
16 -17.40% 0.2107
-3.30
1 -22.22% 0.0000
2 Sample Test for Equal Means T =
2.82
112
2.43
Table 14 (Continued)
Thirty-eight Industries
No Name
N
Mean
Std
Forty-eight Industries
t-value
38 Other
1
16.67% 0.0000
15 Ptrlm
2
4.51% 0.0233
7 Smoke
3
1.56% 0.0327
0.83
8 Txtls
2
1.12% 0.1021
0.15
2.73 *
No Name
N
Mean
Std
t-value
1 Agric
8
4.02% 0.0898
1.27
5 Smoke
3
1.56% 0.0327
0.83
16 Txtls
2
1.12% 0.1021
0.15
3 Soda
1
0.00% 0.0000
1 Agric
12
0.90% 0.0994
0.31
20 FabPr
4
-0.51% 0.0582
-0.18
25 Manuf
13
-1.84% 0.0897
-0.74
8 Books
13
-0.67% 0.1203
-0.20
17 Lethr
3
-1.89% 0.0974
-0.34
15 Rubbr
12
-0.72% 0.0930
-0.27
16 Rubbr
14
-1.97% 0.0957
-0.77
45 Insur
25
-1.79% 0.0717
-1.25
19 Metal
13
-1.97% 0.1410
-0.50
19 Steel
13
-1.97% 0.1410
-0.50
6 Food
31
-3.20% 0.1230
-1.45
2 Food
25
-2.85% 0.1051
-1.36
28 TV
25
-3.36% 0.1670
-1.01
14 Chems
22
-3.18% 0.1173
-1.27
13 Print
20
-3.51% 0.1634
-0.96
31 Util
10
-3.24% 0.1127
-0.91
20 MtlPr
26
-3.54% 0.1189
-1.52
11 Hlth
75
-3.49% 0.1561
-1.94
29 Utils
11
-4.32% 0.1128
-1.27
38 Paper
9
-4.07% 0.1532
-0.80
3 Oil
186
-4.46% 0.1428
-4.26
17 BldMt
35
-4.16% 0.1117
-2.20 *
30 Garbg
15
-4.47% 0.2249
-0.77
30 Oil
188
-4.37% 0.1423
-4.21 **
35 Money
212
-5.15% 0.1328
-5.65
47 Fin
103
-4.48% 0.1116
-4.07
-1.62
12 Paper
11
-6.18% 0.1307
-1.57
23 Autos
20
-4.79% 0.1322
36 Srvc
481
-6.40% 0.1611
-8.71
48 Other
31
-5.15% 0.1652
-1.74
26 Trans
24
-6.59% 0.1010
-3.20
7 Fun
63
-5.56% 0.1606
-2.75
10 Wood
4
-6.63% 0.1075
-1.23
4 Beer
5
-5.57% 0.2149
-0.58
33 Whlsl
99
-6.71% 0.1839
-3.63
39 Boxes
3
-5.60% 0.1185
-0.82
23 Cars
24
-6.92% 0.1501
-2.26
46 RlEst
33
-6.23% 0.1510
-2.37
14 Chems
103
-7.62% 0.1631
-4.74
40 Trans
24
-6.59% 0.1010
-3.20
2 Mines
92
-7.79% 0.2132
-3.50
41 Whlsl
99
-6.71% 0.1839
-3.63
21 Machn
92
-8.15% 0.1496
-5.22
35 Comps
57
-6.94% 0.1594
-3.29
27 Phone
50
-8.99% 0.1890
-3.36
34 BusSv
299
-7.01% 0.1642
-7.39
18 Glass
8
-9.27% 0.1943
-1.35
32 Telcm
75
-7.11% 0.1828
-3.37
34 Rtail
109
-9.29% 0.2049
-4.74
27 Gold
72
-7.45% 0.1594
-3.97
22 Elctr
150
-9.30% 0.1658
-6.87
43 Meals
56
-7.48% 0.1946
-2.88
24 Instr
86
-9.36% 0.1395
-6.22
44 Banks
48
-7.90% 0.1798
-3.04
11 Chair
2 -12.37% 0.1632
-1.07
13 Drugs
74
-8.09% 0.1699
-4.10
-2.39
5 Cnstr
22 -13.86% 0.2721
31 Steam
1 -14.29% 0.0000
37 Govt
1 -15.00% 0.0000
4 Stone
5 -16.77% 0.1716
9 Apprl
16 -20.39% 0.2043
-2.19
-3.99 *
32 Water
2 Sample Test for Equal Means T =
4.64
33 PerSv
29
-8.82% 0.1547
-3.07
36 Chips
57
-8.87% 0.1230
-5.44
37 LabEq
20
-9.11% 0.1749
-2.33
12 MedEq
62
-9.49% 0.1315
-5.68
6 Toys
11
-9.53% 0.1588
-1.99
21 Mach
39
-9.63% 0.1571
-3.83
22 ElcEq
85
-9.64% 0.1923
-4.62
25 Ships
1
28 Mines
24
42 Rtail
-10.00%
-10.07% 0.0000
67
-10.26% 0.1997
-4.21
9 Hshld
23
-13.15% 0.1746
-3.61
18 Cnstr
22
-13.86% 0.2721
-2.39
24 Aero
5
-14.93% 0.0996
-3.35
10 Clths
16
-17.40% 0.2107
-3.30 *
29 Coal
1
-22.22% 0.0000
26 Guns
2 Sample Test for Equal Means T =
113
2.37
Table 15 Returns Based on Reason Given
Managers of reverse split firms relate their motivations for reverse splitting the firm’s stock. This table shows the mean
ex-date return for firms grouped according to reasons given for the reverse stock split. The symbols $, *, **, ***
represent significance at the 10% level, 5% level, 1% level or 0.5% level respectively.
Day
Delisting
-2
-1
0
+1
+2
N
Mean
Patell Z
Day
Attention
-2
-1
0
+1
+2
373
373
373
373
373
1.17%
-1.52%
-6.31%
0.80%
1.59%
2.27
-3.50
-11.28
0.69
2.57
96
96
96
96
96
1.65%
-0.19%
-3.35%
2.15%
-0.16%
2.31 *
-0.22
-5.65 ***
2.64 **
-1.06
Reorgization
-2
147
-1
147
0
147
+1
147
+2
147
2.93%
-0.62%
-5.74%
-0.38%
-0.71%
2.84 **
-0.24
-8.20 ***
-1.43
-0.13
Liquidity
-2
-1
0
+1
+2
34
34
34
34
34
0.02%
0.14%
-2.96%
0.46%
-0.53%
0.17
0.30
-1.18
0.57
-1.14
Stay on the NMS (Nasdaq)
-2
34
0.67%
-1
34
-1.27%
0
34
-5.51%
+1
34
2.14%
+2
34
-2.48%
1.00
-0.51
-4.26 ***
0.65
-2.47 *
Margin Requirements
-2
5
-1
5
0
5
+1
5
+2
5
0.01%
-2.14%
-1.86%
-0.51%
0.01%
-0.07
-0.81
-0.41
-0.48
0.03
Merger or Acquisition
-2
85
0.96%
-1
85
1.92%
0
85
-3.51%
+1
85
-0.95%
+2
85
-1.64%
1.08
-0.05
-2.22 *
-0.15
-1.95 $
Number of Shares
-2
14
-1
14
0
14
+1
14
+2
14
0.78%
1.99%
-0.77%
-0.52%
-1.31%
0.57
0.32
-0.97
-0.69
-1.04
Trading Range
-2
69
-1
69
0
69
+1
69
+2
69
-2.20 *
0.82
-2.84 **
0.32
-0.86
Privitization
-2
3
-1
3
0
3
+1
3
+2
3
2.22%
3.79%
10.19%
-5.15%
0.35%
0.37
1.01
1.07
-0.74
0.10
-1.60%
0.24%
-2.46%
0.54%
-0.13%
*
***
***
*
114
N
Mean
Patell Z
Table 16 The Returns of Reverse Stock Splits by Price
This table shows the ex-date returns of reverse stock splits whose pre-spilt price was less than $1.00 versus firms whose stock
price was more than $1.00 before the split. Also shown are the ex-date returns of firms that had a post split target price of less
than $5.00 versus firms with a target price of more than $5.00. Target price was calculated based on pre-split price and the split
factor. For example a firm with a pre-split price of $0.60 that reverse split 10 to 1 would have a post split target price of $6.00.
Ex date
Pre split price less than $1.00
Raw Return
Market Adjusted Return (equal weighted)
Market Adjusted Return (value weighted)
Market Model Adjusted Return (equal weighted)
Market Model Adjusted Return (value weighted)
N
1386
1386
1386
1374
1374
Mean
-8.58%
-8.67%
-8.63%
-8.77%
-8.75%
Std
18.10%
18.07%
18.10%
18.12%
18.11%
min
-80.0%
-80.8%
-80.6%
-80.1%
-80.1%
max
60.0%
60.1%
60.8%
60.9%
60.9%
N
615
615
615
605
605
mean
-2.49%
-2.59%
-2.59%
-2.65%
-2.56%
Std
10.10%
10.09%
10.14%
10.28%
10.65%
min
-92.3%
-92.4%
-92.8%
-95.6%
-94.5%
max
42.2%
42.7%
42.9%
42.6%
71.8%
Pre split price greater than $1.00
Raw Return
Market Adjusted Return (equal weighted)
Market Adjusted Return (value weighted)
Market Model Adjusted Return (equal weighted)
Market Model Adjusted Return (value weighted)
t-value to test difference in means
Raw Return
Market Adjusted Return (equal weighted)
Market Adjusted Return (value weighted)
Market Model Adjusted Return (equal weighted)
Market Model Adjusted Return (value weighted)
-1515.1
-1515.7
-1495.1
-1479.0
-1452.6
Post split target less than $5.00
Raw Return
Market Adjusted Return (equal weighted)
Market Adjusted Return (value weighted)
Market Model Adjusted Return (equal weighted)
Market Model Adjusted Return (value weighted)
N
1235
1235
1235
1222
1222
mean
-8.30%
-8.41%
-8.37%
-8.47%
-8.47%
Std
18.32%
18.29%
18.30%
18.34%
18.32%
min
-92.3%
-92.4%
-92.8%
-92.6%
-92.2%
max
60.0%
60.1%
60.8%
60.9%
60.9%
N
781
781
781
771
771
mean
-4.32%
-4.38%
-4.37%
-4.55%
-4.45%
Std
12.17%
12.16%
12.21%
12.37%
12.63%
min
-91.5%
-91.5%
-91.6%
-95.6%
-94.5%
max
50.0%
49.5%
49.5%
49.4%
71.8%
Std
11.31%
11.26%
11.32%
11.57%
12.03%
min
-91.5%
-91.5%
-91.6%
-95.6%
-94.5%
max
40.6%
41.0%
40.0%
40.2%
71.8%
Post spilt target greater than $5.00
Raw Return
Market Adjusted Return (equal weighted)
Market Adjusted Return (value weighted)
Market Model Adjusted Return (equal weighted)
Market Model Adjusted Return (value weighted)
t-value to test difference in means
Raw Return
Market Adjusted Return (equal weighted)
Market Adjusted Return (value weighted)
Market Model Adjusted Return (equal weighted)
Market Model Adjusted Return (value weighted)
-862.8
-876.1
-864.0
-826.6
-836.4
Post split taraget greater than $10.00
Raw Return
Market Adjusted Return (equal weighted)
Market Adjusted Return (value weighted)
Market Model Adjusted Return (equal weighted)
Market Model Adjusted Return (value weighted)
N
472
472
472
464
464
115
mean
-3.6%
-3.7%
-3.6%
-3.8%
-3.6%
Table 17 The Returns of Reverse stock splits and the Size of the Firm
The dependent variable is regressed on the size of the firm proxied by number of shares times price. Announcement
day returns are the two day cumulative return on the announcement day and the day after the announcement.
Raw returns, market model adjusted returns and market adjusted returns are used as the dependent varaible
Ex day returns regressed on firm size
Dependant
intercept
estimate
Ex Date Raw Return
0.00499
0.43
0.00000
-0.42
0.00395
0.36
0.00000
-0.51
0.00303
0.28
0.00000
-0.36
0.00277
0.27
0.00000
-0.46
0.00186
0.18
0.00000
-0.34
Ex Date Market Adjusted Return
t value
market value (mv)
estimate
t value
(equally weighted index)
Ex Date Market Adjusted Return
(value weighted index)
Ex Date Market Model Adjusted Return
(equally weighted index)
Ex Date Market Model Adjusted Return
(value weighted index)
116
Table 18 The Returns of Reverse Stock Splits and Consolidation
The dependent variable is regressed on consolidation. Announcement day returns are the two day cumulative return from the
day of the announcement to the day after the announcement. Raw returns, market model adjusted returns and market adjusted
returns are used as the dependent varaible
Announcement Day Returns Regressed on Consolidation
intercept
Dependant
shares consolidated
estimate
t value
estimate
t value
AD Raw Return
-0.0339
-4.73
-0.0007
-1.81
AD Market Adjusted Return
-0.0357
-4.99
-0.0007
-1.85
-0.0351
-4.91
-0.0007
-1.81
-0.0362
-5.09
-0.0008
-2.17
-0.0365
-5.11
-0.0008
-2.19
(equally weighted index)
AD Market Adjusted Return
(value weighted index)
AD Market Model Adjusted Return
(equally weighted index)
AD Market Model Adjusted Return
(value weighted index)
Ex-Date Returns Regressed on Consolidation
shares consolidated
intercept
Dependant
estimate
t value
estimate
t value
Ex Date Raw Return
-0.0554
-12.83
-0.0013
-6.09
Ex Date Market Adjusted Return
-0.0563
-13.05
-0.0013
-6.06
-0.0561
-12.97
-0.0013
-6.01
-0.0559
-12.90
-0.0014
-6.41
-0.0565
-13.10
-0.0014
-6.37
(equally weighted index)
Ex Date Market Adjusted Return
(value weighted index)
Ex Date Market Model Adjusted Return
(equally weighted index)
Ex Date Market Model Adjusted Return
(value weighted index)
117
Table 19 Reverse Stock Split Delistings Within 250 Days of Ex-date (1962-2003)
The delisting codes and delisting returns are taken from the Center for Research in Security Prices (CRSP). For firms that
reverse split their stock, then number of firms delisted within 250 trading days (about 1 year) is reported. The number of each
type of delisting is shown with its percentage of the total delistings. The average delisting return for each category is reported.
Delisting Code Code Explaination
Number
Percent of
Total
Average Delisting
Return
231
Merged and shareholders received common stock
14
6%
1.3%
233
Issue exchanged primarily for cash
11
4%
1.2%
261
Issue exchanged for combination of cash and other securities
1
0%
0.0%
331
Issue exchanged for another class of stock
1
0%
-42.3%
460
Issue liquidated - no further information is available
1
0%
0.0%
500
Issue stopped trading on exchange - reason unavailable
5
2%
8.1%
550
Delisted insufficient number of market makers
5
2%
-34.4%
551
Delisted insufficient number of shareholders
1
0%
-12.5%
552
Delisting - price fell below acceptable level
57
23%
-12.8%
560
Delisted - insufficient capital, surplus, and/or equity
32
13%
-10.9%
561
Delisted - insufficient (or non-compliance with rules of) float or assets
32
13%
-13.2%
570
Delisted - company request (no reason given)
5
2%
-24.3%
573
Delisted - company request, liquidation
2
1%
-0.8%
574
Delisted - company request, bankruptcy, declared insolvent
11
4%
-30.1%
580
Delisted - delinquent in filing or non-payment of fees
23
9%
-14.2%
582
Delisted - failure to meet exception or equity requirements
16
6%
-17.7%
584
Delisted - does not meet exchange's financial guidelines for continued listing
15
6%
-25.5%
585
Delisted - protection of investors and the public interest
12
5%
-16.3%
587
Delisted - corporate governance violation
2
1%
-14.1%
591
Delisted - delisting required by Securities Exchange Commission
1
0%
0.0%
247
100%
Total
118
-13.5%
Table 20 Average Buy and Hold Returns of Reverse stock splits from 1962
to 2003
Average BHRs are presented for holding periods of up to 250 days (about 1 calendar year). Returns are missing in the data and
these missing returns are handled three ways. A missing return is replaced with the the daily average portfolio return, zero, or the
market return.
Missing returns
replaced with daily average
Day
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
n
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
mean
-2.80%
-7.31%
-9.28%
-9.87%
-9.51%
-10.25%
-9.76%
-9.45%
-8.83%
-8.66%
-8.70%
-6.87%
-6.99%
-6.77%
-6.09%
-4.56%
-3.93%
-2.61%
0.03%
1.06%
1.81%
3.36%
6.74%
7.67%
8.70%
8.80%
Missing returns
replaced with zero
t
-5.88
-9.25
-9.56
-9.04
-7.67
-7.86
-6.62
-6.18
-5.32
-4.79
-4.52
-3.25
-3.38
-3.25
-2.79
-1.89
-1.60
-0.98
0.01
0.30
0.46
0.88
1.29
1.50
1.92
2.25
Day
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
119
n
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
mean
-2.80%
-7.31%
-9.27%
-9.88%
-9.54%
-10.30%
-9.85%
-9.63%
-9.07%
-8.94%
-9.05%
-7.35%
-7.51%
-7.39%
-6.83%
-5.46%
-5.03%
-3.88%
-1.45%
-0.59%
0.05%
1.38%
4.48%
5.15%
5.87%
5.60%
t
-5.88
-9.25
-9.55
-9.04
-7.70
-7.90
-6.68
-6.31
-5.47
-4.95
-4.71
-3.48
-3.64
-3.56
-3.14
-2.27
-2.06
-1.46
-0.42
-0.17
0.01
0.36
0.86
1.01
1.30
1.44
Table 20 (Continued)
Missing returns
replaced with equal weighted index
Day
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
n
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
mean
-2.80%
-7.31%
-9.26%
-9.85%
-9.46%
-10.16%
-9.67%
-9.42%
-8.83%
-8.62%
-8.66%
-6.93%
-7.03%
-6.82%
-6.16%
-4.68%
-4.13%
-2.81%
-0.25%
0.74%
1.47%
2.94%
6.20%
6.99%
7.80%
7.59%
Missing returns
replaced with value weighted index
t
-5.88
-9.25
-9.56
-9.03
-7.62
-7.77
-6.53
-6.14
-5.30
-4.75
-4.47
-3.27
-3.39
-3.27
-2.81
-1.94
-1.68
-1.05
-0.07
0.21
0.37
0.77
1.19
1.37
1.73
1.95
Day
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
120
n
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
1218
mean
-2.80%
-7.31%
-9.27%
-9.85%
-9.49%
-10.22%
-9.77%
-9.54%
-8.97%
-8.82%
-8.88%
-7.18%
-7.31%
-7.13%
-6.52%
-5.12%
-4.63%
-3.40%
-0.94%
-0.03%
0.62%
2.01%
5.17%
5.84%
6.53%
6.27%
t
-5.88
-9.25
-9.56
-9.02
-7.65
-7.82
-6.61
-6.24
-5.40
-4.88
-4.61
-3.39
-3.53
-3.43
-2.99
-2.13
-1.89
-1.27
-0.27
-0.01
0.16
0.53
0.99
1.15
1.45
1.61
Table 21 Average Market Adjusted Buy and Hold Abnormal Returns of Reverse stock splits from 1962 to 2003
The BHARs are calculated by subtracting a market index from the average BHR. Missing returns in the data are either replaced with the average return for that day (this page) or
replaced with the daily market index return (next page).
Market Adjusted BHARs using the CRSP Equal Weighted Index
Missing returns replaced with daily average
Days
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
BHAR
N
mean
1218
-2.92%
1218
-8.34%
1218 -11.42%
1218 -12.99%
1218 -13.65%
1218 -15.28%
1218 -15.86%
1218 -16.61%
1218 -16.97%
1218 -17.98%
1218 -19.30%
1218 -18.65%
1218 -20.24%
1218 -21.45%
1218 -22.31%
1218 -22.11%
1218 -22.98%
1218 -23.18%
1218 -21.95%
1218 -22.33%
1218 -22.96%
1218 -22.76%
1218 -20.79%
1218 -21.48%
1218 -21.97%
1218 -23.19%
=
t
-6.16
-10.78
-12.12
-12.29
-11.38
-12.12
-11.15
-11.31
-10.65
-10.41
-10.48
-9.21
-10.25
-10.81
-10.70
-9.57
-9.72
-8.97
-6.45
-6.40
-5.93
-6.10
-4.06
-4.29
-4.96
-6.11
BHR
mean
-2.80%
-7.31%
-9.28%
-9.87%
-9.51%
-10.25%
-9.76%
-9.45%
-8.83%
-8.66%
-8.70%
-6.87%
-6.99%
-6.77%
-6.09%
-4.56%
-3.93%
-2.61%
0.03%
1.06%
1.81%
3.36%
6.74%
7.67%
8.70%
8.80%
t
-5.88
-9.25
-9.56
-9.04
-7.67
-7.86
-6.62
-6.18
-5.32
-4.79
-4.52
-3.25
-3.38
-3.25
-2.79
-1.89
-1.60
-0.98
0.01
0.30
0.46
0.88
1.29
1.50
1.92
2.25
Market Adjusted BHARs using the CRSP Value Weighted Index
Missing returns replaced with daily average
ewBHR
mean
t
0.13%
5.54
1.04%
9.69
2.14% 13.10
3.12% 15.27
4.14% 17.08
5.04% 18.03
6.10% 19.58
7.16% 21.06
8.15% 22.12
9.31% 23.81
10.60% 26.21
11.77% 27.80
13.25% 30.59
14.69% 32.61
16.22% 34.24
17.55% 35.23
19.05% 36.21
20.57% 37.69
21.98% 38.59
23.39% 39.33
24.78% 40.42
26.12% 41.26
27.54% 42.01
29.15% 43.07
30.66% 44.16
31.98% 45.08
Days
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
121
BHAR
N
mean
t
1218
-2.87% -6.05
1218
-7.76% -9.93
1218 -10.22% -10.73
1218 -11.22% -10.53
1218 -11.22% -9.24
1218 -12.27% -9.65
1218 -12.29% -8.53
1218 -12.41% -8.33
1218 -12.12% -7.50
1218 -12.46% -7.09
1218 -13.08% -6.97
1218 -11.70% -5.69
1218 -12.56% -6.24
1218 -12.94% -6.38
1218 -12.88% -6.03
1218 -11.72% -4.96
1218 -11.66% -4.83
1218 -10.97% -4.17
1218
-8.72% -2.53
1218
-8.20% -2.32
1218
-7.98% -2.03
1218
-6.94% -1.83
1218
-4.06% -0.78
1218
-3.73% -0.74
1218
-3.30% -0.73
1218
-3.58% -0.93
=
BHR
mean
-2.80%
-7.31%
-9.28%
-9.87%
-9.51%
-10.25%
-9.76%
-9.45%
-8.83%
-8.66%
-8.70%
-6.87%
-6.99%
-6.77%
-6.09%
-4.56%
-3.93%
-2.61%
0.03%
1.06%
1.81%
3.36%
6.74%
7.67%
8.70%
8.80%
t
-5.88
-9.25
-9.56
-9.04
-7.67
-7.86
-6.62
-6.18
-5.32
-4.79
-4.52
-3.25
-3.38
-3.25
-2.79
-1.89
-1.60
-0.98
0.01
0.30
0.46
0.88
1.29
1.50
1.92
2.25
vwBHR
mean
t
0.08%
2.54
0.45%
4.56
0.94%
6.93
1.35%
8.56
1.71%
9.14
2.03%
9.72
2.53% 11.20
2.96% 12.08
3.29% 12.48
3.80% 13.58
4.38% 15.23
4.83% 16.20
5.58% 18.29
6.18% 19.62
6.79% 20.78
7.16% 20.97
7.73% 21.80
8.36% 22.80
8.75% 23.21
9.26% 23.64
9.80% 24.24
10.30% 24.90
10.80% 25.11
11.40% 25.79
11.99% 26.43
12.37% 26.56
Table 21 (Continued)
Market Adjusted BHARs using the CRSP Equal Weighted Index
Missing returns replaced with CRSP equally weighted index
Days
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
BHAR
N
mean
1218
-2.92%
1218
-8.34%
1218 -11.40%
1218 -12.97%
1218 -13.60%
1218 -15.20%
1218 -15.76%
1218 -16.58%
1218 -16.98%
1218 -17.94%
1218 -19.26%
1218 -18.71%
1218 -20.29%
1218 -21.51%
1218 -22.37%
1218 -22.24%
1218 -23.18%
1218 -23.38%
1218 -22.22%
1218 -22.65%
1218 -23.30%
1218 -23.18%
1218 -21.34%
1218 -22.16%
1218 -22.86%
1218 -24.40%
=
t
-6.16
-10.78
-12.12
-12.29
-11.33
-12.03
-11.05
-11.25
-10.62
-10.35
-10.41
-9.21
-10.25
-10.83
-10.72
-9.63
-9.81
-9.05
-6.53
-6.50
-6.02
-6.22
-4.16
-4.42
-5.18
-6.46
BHR
mean
-2.80%
-7.31%
-9.26%
-9.85%
-9.46%
-10.16%
-9.67%
-9.42%
-8.83%
-8.62%
-8.66%
-6.93%
-7.03%
-6.82%
-6.16%
-4.68%
-4.13%
-2.81%
-0.25%
0.74%
1.47%
2.94%
6.20%
6.99%
7.80%
7.59%
t
-5.88
-9.25
-9.56
-9.03
-7.62
-7.77
-6.53
-6.14
-5.30
-4.75
-4.47
-3.27
-3.39
-3.27
-2.81
-1.94
-1.68
-1.05
-0.07
0.21
0.37
0.77
1.19
1.37
1.73
1.95
Market Adjusted BHARs using the CRSP Value Weighted Index
Missing returns replaced with CRSP value weighted index
ewBHR
mean
t
0.13%
5.54
1.04%
9.69
2.14% 13.10
3.12% 15.27
4.14% 17.08
5.04% 18.03
6.10% 19.58
7.16% 21.06
8.15% 22.12
9.31% 23.81
10.60% 26.21
11.77% 27.80
13.25% 30.59
14.69% 32.61
16.22% 34.24
17.55% 35.23
19.05% 36.21
20.57% 37.69
21.98% 38.59
23.39% 39.33
24.78% 40.42
26.12% 41.26
27.54% 42.01
29.15% 43.07
30.66% 44.16
31.98% 45.08
Days
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
122
BHAR
N
mean
t
1218
-2.92% -6.16
1218
-7.76% -9.93
1218 -10.21% -10.74
1218 -11.20% -10.51
1218 -11.20% -9.22
1218 -12.25% -9.61
1218 -12.29% -8.53
1218 -12.51% -8.39
1218 -12.27% -7.58
1218 -12.62% -7.18
1218 -13.26% -7.07
1218 -12.00% -5.84
1218 -12.88% -6.40
1218 -13.31% -6.57
1218 -13.31% -6.25
1218 -12.28% -5.21
1218 -12.37% -5.14
1218 -11.76% -4.48
1218
-9.69% -2.81
1218
-9.29% -2.63
1218
-9.18% -2.34
1218
-8.30% -2.20
1218
-5.63% -1.09
1218
-5.56% -1.10
1218
-5.46% -1.22
1218
-6.10% -1.59
=
BHR
mean
-2.80%
-7.31%
-9.27%
-9.85%
-9.49%
-10.22%
-9.77%
-9.54%
-8.97%
-8.82%
-8.88%
-7.18%
-7.31%
-7.13%
-6.52%
-5.12%
-4.63%
-3.40%
-0.94%
-0.03%
0.62%
2.01%
5.17%
5.84%
6.53%
6.27%
t
-5.88
-9.25
-9.56
-9.02
-7.65
-7.82
-6.61
-6.24
-5.40
-4.88
-4.61
-3.39
-3.53
-3.43
-2.99
-2.13
-1.89
-1.27
-0.27
-0.01
0.16
0.53
0.99
1.15
1.45
1.61
vwBHR
mean
t
0.08%
2.54
0.45%
4.56
0.94%
6.93
1.35%
8.56
1.71%
9.14
2.03%
9.72
2.53% 11.20
2.96% 12.08
3.29% 12.48
3.80% 13.58
4.38% 15.23
4.83% 16.20
5.58% 18.29
6.18% 19.62
6.79% 20.78
7.16% 20.97
7.73% 21.80
8.36% 22.80
8.75% 23.21
9.26% 23.64
9.80% 24.24
10.30% 24.90
10.80% 25.11
11.40% 25.79
11.99% 26.43
12.37% 26.56
Table 22 Matched Firm Statistics
Firms are matched on SIC, price, and size. Firm size is proxied by the number of shares times price per
share. This table shows how close the sample was matched. Standard deviation is shown in parenthesis.
Matches Found - 697
Average Price – Sample Firms
$3.73
(4.957)
Average Price – Matched Firms
$3.71
(4.961)
Difference
0.02
% Difference
0.54%
Average Size – Sample Firms
47,618,470
(202,323,470)
Average Size – Matched Firms
46,648,060
(192,322,130)
Difference
970,410
% Difference
2.04%
123
Table 23 Average Abnormal Returns of Reverse stock splits from 1962 to 2003
Based on Matched Firm
This table shows the abnormal buy-and-hold returns for reverse stock splits. The buy-and-hold abnormal returns shown are the
average returns for holding a reverse split stock for a certain number of days minus the average return of a portfolio of firms
matched on industry, size, and price. The two tailed t-stat tests mean not equal to zero.
Matched
0.15%
Average Abnormal
Holding Period Return
-3.96%
Holding Period
10
Sample
-3.81%
unpaired t
-3.12
20
-6.88%
0.43%
-7.31%
-4.63
30
-8.28%
-0.19%
-8.09%
-4.26
40
-7.95%
0.87%
-8.82%
-4.18
50
-8.34%
2.67%
-11.01%
-4.47
60
-7.57%
2.59%
-10.16%
-3.68
70
-6.00%
3.97%
-9.97%
-3.37
80
-5.55%
5.66%
-11.21%
-3.49
90
-3.76%
8.20%
-11.97%
-3.09
100
-3.39%
9.44%
-12.83%
-3.12
110
-1.32%
11.08%
-12.40%
-2.54
120
-2.12%
14.77%
-16.88%
-3.16
130
-1.66%
15.65%
-17.31%
-2.9
140
0.93%
18.10%
-17.17%
-2.68
150
4.46%
17.92%
-13.46%
-2.18
160
5.53%
18.52%
-12.99%
-2.22
170
6.57%
20.43%
-13.87%
-2.28
180
10.31%
20.95%
-10.64%
-1.64
190
12.67%
21.57%
-8.91%
-1.37
200
13.96%
24.85%
-10.89%
-1.57
210
17.14%
26.53%
-9.40%
-1.28
220
22.42%
28.65%
-6.23%
-0.7
230
21.14%
31.23%
-10.08%
-1.18
240
22.86%
31.62%
-8.76%
-1.04
250
23.27%
31.53%
-8.26%
-1.01
124
Table 24 Long Term Returns By Industry
This table shows the 250-day raw returns by industry using the Fama French Industry Classifications. A 2-sample t-test
for difference in means is performed on the highest and lowest average return. In addition, a 2-sample t-test is
performed on the highest significant average return and the lowest significant average return. The asterisk denotes
highest and lowest significant average return.
5 Industries
N
Mean
t
2
Utils
14
-0.2822
-2.87
1
Manuf
337
0.1724
2.49
5
Other
471
0.2121
3.13
4
Money
116
0.2198
1.68
3
Shops
124
0.2722
2.01
N
Mean
t
Utils
5
-0.1062
-0.56
2
Durbl
38
-0.0939
-0.91
1
NoDur
46
-0.0385
-0.33
10 Industries
7
2-sample T
*
High vs. Low
2-sample T
Significant high
vs significant low
*
19.78
19.78
3
Oil
107
0.0882
1.00
10
Other
153
0.0974
1.25
9
Money
116
0.2198
1.68
5
Manuf
201
0.2319
2.49
8
Shops
302
0.2764
2.85
4
Chems
56
0.3333
1.51
6
Telcm
38
0.5410
1.42
3.60
2.14
N
Mean
t
2-sample T
2-sample T
High vs. Low
12 Industries
8
Utils
5
-0.1062
-0.56
2
Durbl
25
-0.0873
-0.61
1
NoDur
46
-0.0385
-0.33
3
Manuf
84
0.0559
0.75
12
Other
199
0.0592
0.67
4
Enrgy
107
0.0882
1.00
10
Hlth
114
0.0969
0.79
11
Money
116
0.2198
1.68
9
Shops
128
0.2591
1.97
6
BusEq
186
0.5074
3.71
5
Chems
14
0.5129
2.01
7
Telcm
38
0.5410
1.42
125
*
*
Significant high
vs significant low
*
*
3.60
3.07
Table 24 (Continued)
17 Industries
N
Mean
t
5
Durbl
21
-0.2165
-2.29
1
Food
25
-0.1505
-1.15
13
Trans
16
-0.1311
-0.80
2
Mines
32
-0.1216
-1.17
12
Cars
16
-0.0791
-0.48
14
Utils
4
-0.0045
-0.02
10
FabPr
5
0.0572
0.26
6
Chems
11
0.0575
0.37
3
Oil
109
0.0905
1.05
16
Finan
114
0.1088
1.66
9
Steel
8
0.2236
1.11
11
Machn
108
0.2243
1.94
4
Clths
14
0.2619
0.81
17
Other
439
0.2897
3.52
8
Cnstr
28
0.3019
1.31
15
Rtail
62
0.3405
1.51
7
Cnsum
50
0.3844
1.56
126
2-sample T
*
High vs. Low
2-sample T
Significant high
vs significant low
*
8.62
32.28
Table 24 (Continued)
30 Industries
N
Mean
t
2-sample T
-1.70
High vs. Low
2
Beer
3
-0.3413
3
Smoke
1
-0.2650
25
Trans
12
-0.2537
-1.31
1
Food
18
-0.1463
-1.21
2-sample T
Significant high
vs significant low
30
Other
28
-0.1288
-1.03
17
Mines
32
-0.1216
-1.17
15
Autos
14
-0.0906
-0.55
10
Txtls
2
-0.0241
-0.03
20
Util
4
-0.0045
-0.02
13
FabPr
25
-0.0025
-0.03
4
Games
42
0.0654
0.20
19
Oil
107
0.0882
1.00
8
Hlth
114
0.0969
0.79
9
Chems
10
0.1227
0.78
1.27
26
Whlsl
58
0.2027
29
Fin
115
0.2234
1.70
12
Steel
8
0.2236
1.11
6
Hshld
14
0.2248
0.74
28
Meals
33
0.2255
1.07
5
Books
9
0.2419
0.59
11
Cnstr
29
0.2512
1.16
23
BusEq
74
0.2992
1.85
24
Paper
7
0.3267
1.00
27
Rtail
41
0.3283
1.08
22
Servs
173
0.3352
2.82
16
Carry
2
0.3651
0.73
7
Clths
7
0.4004
0.73
21
Telcm
38
0.5410
1.42
14
Autos
42
0.6049
1.94
127
*
*
6.89
2.43
Table 24 (Continued)
38 Industries
N
Mean
17
Lethr
1
-0.6471
38
Other
1
-0.6143
t
2-sample T
High vs. Low
2-sample T
Significant high
vs significant low
30
Garbg
9
-0.3800
7
Smoke
1
-0.2650
-3.58
26
Trans
12
-0.2537
6
Food
17
-0.2004
-1.69
16
Rubbr
12
-0.1488
-1.12
*
-1.31
2
Mines
30
-0.1315
-1.19
29
Utils
5
-0.1062
-0.56
15
Ptrlm
1
-0.0989
20
MtlPr
14
-0.0977
25
Manuf
8
-0.0369
-0.11
24
Instr
44
-0.0341
-0.21
8
Txtls
2
-0.0241
-0.03
0.19
*
-0.89
4
Stone
2
0.0266
10
Wood
2
0.0388
0.11
23
Cars
15
0.0470
0.29
11
Chair
2
0.0667
0.29
3
Oil
106
0.0899
1.01
21
Machn
52
0.1178
1.12
33
Whlsl
58
0.2027
1.27
35
Money
116
0.2198
1.68
19
Metal
8
0.2236
1.11
13
Print
9
0.2419
0.59
36
Srvc
265
0.2577
2.66
27
Phone
25
0.3065
1.61
14
Chems
56
0.3333
1.51
34
Rtail
66
0.3334
1.56
1
Agric
5
0.3500
0.75
0.83
9
Apprl
9
0.3696
22
Elctr
75
0.4151
1.98
12
Paper
6
0.4836
1.42
5
Cnstr
13
0.5137
1.17
18
Glass
2
0.5420
0.87
28
TV
13
0.9921
0.93
128
*
10.64
1.20
14.42
Table 24 (Continued)
48 Industries
4
Beer
N
Mean
t
2-sample T
2-sample T
3
-0.3413
-1.70
High vs. Low
Significant high
5
Smoke
1
-0.2650
40
Trans
12
-0.2537
-1.31
vs significant low
6
Toys
5
-0.1952
-0.92
2
Food
14
-0.1703
-1.23
8
Books
7
-0.1525
-1.95
48
Other
18
-0.1443
-0.81
27
Gold
21
-0.1345
-0.87
12
MedEq
29
-0.1179
-0.95
15
Rubbr
10
-0.1011
-0.69
28
Mines
11
-0.0969
-1.25
23
Autos
14
-0.0906
-0.55
46
RlEst
18
-0.0776
-0.77
1
Agric
4
-0.0625
-0.23
16
Txtls
2
-0.0241
-0.03
31
Util
4
-0.0045
-0.02
21
Mach
23
-0.0037
-0.04
20
FabPr
2
0.0111
0.14
45
Insur
14
0.0253
0.14
17
BldMt
16
0.0380
0.23
11
Hlth
43
0.0694
0.46
30
Oil
107
0.0882
1.00
7
Fun
37
0.1006
0.27
14
Chems
10
0.1227
0.78
47
Fin
54
0.1906
2.05
38
Paper
4
0.1936
0.52
36
Chips
31
0.1947
0.70
41
Whlsl
58
0.2027
1.27
19
Steel
8
0.2236
1.11
9
Hshld
14
0.2248
0.74
43
Meals
33
0.2255
1.07
13
Drugs
42
0.2734
0.97
34
BusSv
157
0.3241
2.54
42
Rtail
41
0.3283
1.08
35
Comps
32
0.3632
1.97
24
Aero
2
0.3651
0.73
10
Clths
7
0.4004
0.73
37
LabEq
11
0.4076
0.74
39
Boxes
3
0.5042
0.75
18
Cnstr
13
0.5137
1.17
32
Telcm
38
0.5410
1.42
44
Banks
29
0.5669
1.18
33
PerSv
18
0.5751
1.88
22
ElcEq
42
0.6049
1.94
129
*
*
6.89
7.35
Table 25 Long Term Returns by Reason Given and
Low-Priced Versus High Priced
Managers give various reasons in their press releases as to why they are reverse splitting the firm’s stock. This
table shows the mean 250-day return grouped by reason given. Buy and hold returns are denoted “BHR” and the
buy and hold market adjusted returns using the equal-weighted CRSP index are denoted “BHAR.” It can be
argued that low-priced firms are reverse split to avoid delisting – regardless of reason given, so the returns of lowpriced versus high-priced firms are given. A low priced firm is priced below $1.00 and a high-priced firm is priced
above $1.00.
Reason Given
Return
N
Mean
Attract Attention
BHR
68
5.01%
t-value
Delisting Threat
BHR
195
51.92%
3.72
Increase Liquidity
BHR
22
-7.09%
-0.56
Margin Requirements
BHR
3
-11.39%
-0.43
Maintian NMS Requirements
BHR
20
21.98%
0.68
No Reason Given
BHR
537
16.96%
2.88
Taking the Company Private
BHR
1
113.55%
Reorginazation
BHR
98
19.96%
1.38
Increase Number of Share
BHR
9
-12.63%
-1.56
Trading Range
BHR
55
12.00%
0.97
Attract Attention
BHAR
68
-0.42%
-0.72
Delisting Threat
BHAR
195
0.53%
1.05
Increase Liquidity
BHAR
22
-0.91%
-0.64
Margin Requirements
BHAR
3
-2.52%
-1.37
Maintian NMS Requirements
BHAR
20
1.85%
1.79
No Reason Given
BHAR
537
0.04%
0.13
Taking the Company Private
BHAR
1
0.57%
Reorginazation
BHAR
98
0.18%
0.45
0.43
Increase Number of Share
BHAR
9
-0.73%
-1.02
Trading Range
BHAR
55
0.21%
0.33
Low Price
BHR
771
27.07%
4.82
High Price
BHR
71
2.74%
0.41
2 sample test of equal means t-value
32.11
Low Price
BHAR
771
0.13%
0.56
High Price
BHAR
71
-0.11%
-0.25
2 sample test of equal means t-value
94.39
130
Table 26 Long Term Returns By Size, Consolidation,
and Post Split Price
The 250-day ex-date BHRs are regressed on the size of the firm proxied by the number of shares times
price on the ex-date. The returns are also regressed on consolidation and the post split share price.
Consolidation is the number of shares that the shareholder has to convert to one share at the reverse
stock split. The regressions are repeated using the 250-day market adjusted BHAR as the dependent
variable. The t-values are in italics under the regression coefficients.
Size Vs. 250-Day Returns
Intercept
BHR
=
t-value
0.0015
+
0.92
BHAR
=
0.0002
Regression
Dependent
Coefficient
Variable
t-value
0.0000
X
Shares Outstanding X Price
X
Shares Outstanding X Price
0.01
+
0.12
0.0000
-0.01
Consolidation Versus 250-Day Returns
Intercept
BHR
=
t-value
0.0027
+
1.41
BHAR
=
0.0015
Regression
Dependent
Coefficient
Variable
t-value
-0.0001
X
Shares Consolidated
X
Shares Consolidated
-1.21
+
0.80
-0.0013
-1.34
Post Split Price Versus 250-Day Returns
Intercept
BHR
=
t-value
0.00133
+
0.77
BHAR
=
0.00003
0.02
Regression
Dependent
Coefficient
Variable
t-value
0.00006
X
Post Split Price
X
Post Split Price
0.36
+
0.00006
0.35
131
Table 27 Monthly Stock Splits Related to Market Movements
The number of stock splits per month (as a percentage of total firms in the market) is regressed on
recent market returns. The market index is the CRSP equally weighted index. The independent variable
is the buy and hold return of the market index for the previous 12 months or the the previous 24 months.
Intercept
Estimate
t-value
Dependent
Variable
Parameter
Estimate
t-value
Independent
Variable
% RSS per Month
=
0.00088
23.01
+
-0.00026
-1.87
X
12 Month EW Index
% RSS per Month
=
0.00096
21.38
+
-0.00037
-3.67
X
24 Month EW Index
132
*
Table 28 Long Run Returns Regressed on Lagged Market Returns
The dependent variable is either the the buy-and-hold return or the market adjusted buyand-hold market adjusted return. The lagged market is the buy-and-hold market return of
the CRSP equal weighted index from 12 or 24 months prior to the ex-date.
Dependent
Variable
(long run returns)
Intercept
Estimate
t-value
Parameter
Estimate
t-value
Independent
Variable
(lagged mkt)
250 Day BHRs
=
0.155
3.04
+
-0.922
-4.80
X
EW Index
12 month
*
250 Day BHRs
=
0.168
2.74
+
-0.520
-3.53
X
EW Index
24 month
*
250 Day BHARs
EW Index Adjusted
=
-0.202
-4.47
+
-0.458
-2.69
X
EW Index
12 month
*
250 Day BHARs
EW Index Adjusted
=
-0.185
-3.46
+
-0.290
-2.25
X
EW Index
24 month
*
250 Day BHARs
VW Index Adjusted
=
-0.012
-0.25
+
-0.666
-3.60
X
EW Index
12 month
*
250 Day BHARs
VW Index Adjusted
=
0.004
0.07
+
-0.399
-2.84
X
EW Index
24 month
*
133
Table 29 Regression Analysis of Financial Distress Variables – Definitions
Researchers, such as Theodossiou, Kahya, Saidi, and Philippatos (1996), have identified variables that may be
predictive of financial distress. Regression analysis of the full sample has shown that reverse stock splits in general
have negative returns. Other research has shown an unfavorable survival rate for firms that reverse split. The purpose
of this table is to show correlation between reverse stock splits and some of the accounting values that are used in
analyzing financial distress.
Variable
Description
COMPUSTAT VALUES
dta
debt to assets
data54/data44
opincta
operating income to assets
data21/data44
invtsls
inventories to sales
data38/data2
lgsales
log of sales
log(data2)
lgassets
log of assets
log(data44)
nwcta
net working capital to assets
(data40-data49)/data44
cacl
current assets to current liabilibies
data40/data49
qacl
quick assets to current liabilibies
(data40-data38)/data49
ebita
EBIT to assets
(data23+data22)/data44
reta
retained earnings to assets
data58/data44
ltdta
long term debt to assets
data51/data40
mveltd
market value of equity to long term debt
(data61*abs(pn1))/data51
fata
fixed assets to total assets
data42/data44
COMPUSTAT
data2
sales
data21
operating income before depreciation
data22
interest expense
data23
pretax income
data38
inventories
data40
current assets
data42
property, plant, and equipment
data44
assets total
data49
current liabilities
data51
long term debt
data54
liabilities total
data58
retained earnings
data61
common shares outstanding
pn1
price one day prior to split
Returns
r0
ex-date raw return
ma0e
market adjusted ex-date return using CRSP equal-weighted index
ma0v
market adjusted ex-date return using CRSP value-weighted index
mm0e
market model adjusted ex-date return using CRSP equal-weighted index
mm0v
market model adjusted ex-date return using CRSP value-weighted index
r30
30 day holding period return
r60
60 day holding period return
r90
90 day holding period return
r125
125 day holding period return
r250
250 day holding period return
134
Table 30 Correlation Matrix: The Returns of Reverse Stock Splits and Financial Distress Variables
The table presents the correlations between ex-date returns, 250-day BHRs and various financial distress variables.
Summary Information
Variable
N
Mean Std Dev
r0
885 -0.06909 0.16193
r250
891 -0.09464 1.35869
dta
860
0.1942 0.25952
opincta
739 -0.05694 0.41406
invtsls
789 1.00226 6.31133
lgsales
826 1.83512 2.20676
lgassets
869 3.61029 1.86927
nwcta
788 0.08858 0.97682
cacl
788 2.66551 6.91255
qacl
771 2.17809 5.54878
ebita
645 -0.10826 0.61856
reta
772 -35.6595 615.6494
ltdta
781 1.00218 2.79656
mveltd
648 32.95378 207.4896
fata
850 0.27389 0.26425
Number of Observations
r0
r250
dta
opincta
invtsls
lgsales
lgassets
nwcta
cacl
qacl
ebita
reta
ltdta
mveltd
fata
r0
885
883
848
728
778
815
857
777
777
760
637
762
770
637
838
r250
883
891
853
732
783
820
862
782
782
765
640
767
776
642
844
dta
848
853
860
733
781
805
860
781
781
764
642
766
781
648
843
opincta
728
732
733
739
678
700
739
679
679
663
570
668
673
560
733
invtsls
778
783
781
678
789
789
789
720
720
720
593
708
715
607
774
lgsales
815
820
805
700
789
826
814
736
736
720
612
722
731
621
795
lgassets
857
862
860
739
789
814
869
788
788
771
645
772
781
648
850
nwcta
777
782
781
679
720
736
788
788
788
771
590
770
779
589
784
135
cacl
777
782
781
679
720
736
788
788
788
771
590
770
779
589
784
qacl
760
765
764
663
720
720
771
771
771
771
575
755
762
580
768
ebita
637
640
642
570
593
612
645
590
590
575
645
580
587
499
628
reta
762
767
766
668
708
722
772
770
770
755
580
772
766
580
769
ltdta
770
776
781
673
715
731
781
779
779
762
587
766
781
590
777
mveltd
637
642
648
560
607
621
648
589
589
580
499
580
590
648
633
fata
838
844
843
733
774
795
850
784
784
768
628
769
777
633
850
Table 30 (Continued)
Correlation Matrix
r0
p-value
r250
p-value
dta
p-value
opincta
p-value
invtsls
p-value
lgsales
p-value
lgassets
p-value
nwcta
p-value
r0
1.0000
r250
0.0111
0.7415
dta
0.0293
0.3938
opincta
0.0616
0.0970
invtsls
-0.0174
0.6284
lgsales
0.1233
0.0004
lgassets
0.1358
<.0001
nwcta
0.0388
0.2799
cacl
-0.0307
0.3930
qacl
-0.0279
0.4417
ebita
0.1021
0.0100
reta
0.0014
0.9690
ltdta
0.0586
0.1040
mveltd
0.0641
0.1062
fata
0.0101
0.7694
1.0000
-0.0780
0.0227
0.0704
0.0570
-0.0075
0.8334
0.0189
0.5892
0.0197
0.5636
0.0661
0.0647
0.0504
0.1592
0.0430
0.2354
0.0796
0.0440
-0.1038
0.0040
-0.0632
0.0785
-0.0284
0.4726
-0.0244
0.4789
1.0000
0.0048
0.8965
-0.0244
0.4961
0.2781
<.0001
0.3026
<.0001
-0.0691
0.0537
-0.0823
0.0215
-0.0992
0.0060
-0.0261
0.5089
0.0291
0.4217
0.4001
<.0001
-0.1418
0.0003
0.2095
<.0001
1.0000
-0.0734
0.0560
0.3177
<.0001
0.2880
<.0001
0.8691
<.0001
0.0218
0.5709
0.0183
0.6384
0.9508
<.0001
0.0149
0.7002
0.0341
0.3767
0.0060
0.8882
-0.0881
0.0171
1.0000
-0.1810
<.0001
-0.0482
0.1765
0.0831
0.0258
0.0347
0.3527
0.0367
0.3249
-0.0132
0.7487
0.0062
0.8701
-0.0324
0.3870
-0.0189
0.6421
-0.0675
0.0605
1.0000
0.8035
<.0001
-0.0466
0.2069
-0.0994
0.0069
-0.1504
<.0001
0.1865
<.0001
0.1459
<.0001
0.0051
0.8905
-0.0471
0.2408
0.0277
0.4349
1.0000
0.1645
<.0001
-0.0673
0.0591
-0.0655
0.0693
0.2565
<.0001
0.0443
0.2185
0.0966
0.0069
-0.0859
0.0288
0.0603
0.0789
1.0000
0.1541
<.0001
0.1619
<.0001
0.6932
<.0001
0.0473
0.1901
-0.2046
<.0001
0.0764
0.0640
-0.2412
<.0001
1.0000
0.9461
<.0001
0.0370
0.3699
0.0198
0.5839
-0.0868
0.0154
0.0120
0.7722
-0.1597
<.0001
1.0000
0.0286
0.4936
0.0201
0.5817
-0.0877
0.0155
0.0291
0.4843
-0.1696
<.0001
1.0000
0.1350
0.0011
0.0280
0.4980
0.0050
0.9113
-0.0348
0.3840
1.0000
-0.0275
0.4478
0.0129
0.7570
-0.1021
0.0046
1.0000
-0.0659
0.1097
0.3640
<.0001
1.0000
-0.0386
0.3328
cacl
p-value
qacl
p-value
ebita
p-value
The numbers shown here represent the Pearson's Correlation Coefficient which is
usually signified by r (rho). It can take on the values from -1.0 to 1.0. Where -1.0 is a
perfect negative (inverse) correlation, 0.0 is no correlation, and 1.0 is a perfect
positive correlation.
The value r0 represents the raw ex-date return and the value r250 is raw buy and hold
return 250 trading days after the ex-date. The other values are defined in Table 29.
reta
p-value
ltdta
p-value
A low p-value for this test (less than 0.10 for example) means that there is a statistically significant
relationship between the two variables. The p-value is in italics below the correlation coefficient.
mveltd
p-value
fata
1.0000
136
Table 31 Regression Analysis of Financial Distress Variables – Single Independent Variable
This table shows the results of regressing raw ex-date returns on various accounting variables. Each column represents a regression with the dependent variable
being the column heading and the accounting variables in the far left column being the independent variables. The regression coefficient of each independent
variable is followed by its p-value in italics.
Multiple Variable Regression Results - Full Model Fitted
Long Run Buy and Hold Returns
Ex-Date Returns
Independent
Variables
r0
ma0e
ma0v
mm0e
mm0v
r30
r60
r90
r125
r250
p-value
p-value
p-value
p-value
p-value
p-value
p-value
p-value
p-value
p-value
dta
0.0148
0.0197
0.0197
0.0073
0.0163
0.7254
0.6396
0.6388
0.8663
0.6985
opincta
0.0172
0.0230
0.0203
0.0517
0.0229
0.8593
0.8121
0.8335
0.6035
0.8125
invtsls
0.0002
0.0002
0.0001
0.0003
0.9059
0.9423
0.9473
lgsales
0.0226
0.0210
0.0206
0.0192
0.0291
(0.0158)
0.1478
lgassets
nwcta
cacl
qacl
ebita
reta
ltdta
mveltd
fata
(0.1680)
(0.0938)
(0.1366)
(0.1414)
(0.1269)
0.3972
0.5563
0.4849
0.5147
0.6713
(0.1583)
0.0632
0.1945
0.1810
0.6566
0.7287
0.8632
0.6656
0.7170
0.3401
0.0003
(0.0002)
(0.0105)
(0.0131)
(0.0097)
(0.0056)
0.8814
0.8830
0.9838
0.1898
0.1810
0.3694
0.7096
0.0213
0.0216
0.0460
(0.0342)
(0.0474)
0.0076
0.0292
0.0325
0.0325
0.0249
0.3090
0.3470
0.2878
0.8772
0.6681
(0.0145)
(0.0139)
(0.0136)
(0.0143)
(0.0552)
0.0499
0.0529
(0.0008)
(0.0408)
0.1833
0.2033
0.2296
0.1922
0.2826
0.2274
0.2967
0.9887
0.5980
0.0490
0.0473
0.0470
(0.0067)
0.0405
(0.8785)
0.1732
0.4139
0.4623
0.3988
0.2911
0.3071
0.3099
0.8884
0.3807
0.0001
0.3242
0.0553
0.0538
0.2263
(0.0112)
(0.0108)
(0.0104)
(0.0028)
(0.0087)
0.0605
(0.1013)
(0.1421)
(0.1669)
(0.1373)
0.5228
0.5395
0.5512
0.8752
0.6197
0.4651
0.1282
0.0822
0.0659
0.2714
0.0208
0.0201
0.0197
0.0168
0.0189
0.0264
0.0900
0.0964
0.1273
0.0896
0.2351
0.2514
0.2601
0.3516
0.2798
0.7488
0.1755
0.2366
0.1594
0.4715
0.0130
0.0116
0.0124
0.0091
0.0073
0.4780
0.0151
(0.1563)
(0.1575)
(0.4219)
0.8350
0.8527
0.8425
0.8866
0.9061
0.1045
0.9490
0.5896
0.6244
0.3411
(0.0002)
(0.0002)
(0.0002)
(0.0001)
(0.0001)
0.0033
0.0038
0.0036
0.0030
0.0032
0.7984
0.7691
0.7945
0.8396
0.8586
0.2911
0.1360
0.2384
0.3823
0.4910
(0.0090)
0.0040
0.0037
0.0037
0.0031
0.0040
(0.0098)
(0.0062)
(0.0117)
(0.0081)
0.3252
0.3666
0.3654
0.4658
0.3288
0.6105
0.6872
0.5394
0.7014
0.7567
0.0000
0.0000
0.0000
0.0000
0.0000
(0.0002)
(0.0002)
(0.0002)
(0.0003)
(0.0002)
0.7223
0.6951
0.6871
0.7256
0.7036
0.2487
0.2456
0.2582
0.2428
0.4302
0.0450
0.0411
0.0416
0.0347
0.0427
0.0489
0.1784
0.3141
0.2975
0.3580
0.2415
0.2839
0.2783
0.3802
0.2655
0.7867
0.2200
0.0786
0.1332
0.1894
137
Table 32 Multivariate Regression Reduced Model
This table shows the results of regressing ex-date returns on various accounting variables. It also shows the results of regressing long run buy and hold
returns on various accounting variables. Each column represents a regression with the dependent variable being the column heading and the accounting
variables in the far left column being the independent variables. Column heading definitions are given in Table 28. The variable bhar205ma is the 250-day
buy and hold market adjusted return and bhar250mm is the 250-day buy and hold abnormal return using the market model. Each regression coefficient
estimate has its p-value below in italics.
Multiple Variable Regression Results - Reduced Model
Long Run Buy and Hold Returns
Ex-Date Returns
Independent
Variables
r0
ma0e
ma0v
mm0e
mm0v
r125
r250
bhar250ma
bhar250mm
p-value
p-value
p-value
p-value
p-value
p-value
p-value
p-value
p-value
dta
0.00981
0.01258
0.01223
0.00721
0.01365
-0.11072
0.7268
0.6524
0.6613
0.8013
0.6286
0.4615
0.1127
0.0325
0.035
opincta
0.08169
0.08146
0.08002
0.07818
0.07589
0.29247
0.45576
0.36658
0.36330
0.0114
0.0112
0.0127
0.0169
0.0186
0.0914
0.0993
0.2013
0.2067
invtsls
0.00533
0.00512
0.00515
0.00601
0.00555
0.01078
0.00373
0.01671
0.01678
0.1003
0.1123
0.1102
0.0673
0.0861
0.5341
0.8928
0.5704
0.5699
lgsales
0.00007
0.00010
0.00012
0.00012
0.00011
-0.00194
-0.00057
-0.00199
-0.00194
0.9401
0.918
0.8985
0.898
0.9039
0.7035
0.9445
0.8122
0.817
138
-0.38094
-0.54777
-0.54563
Table 33 NASDAQ Requirements September 2001
The listing requirements are different for small firms and larger firms. The National Market is for larger firms. The
charts show initial and continued listing requirements. To be listed on a market a firm must meet the initial listing
requirements. To stay listed on a market, the firm must meet the continued listing requirements.
NASDAQ Small Cap Market Requirements
Requirements
Net Tangible Assets
Market Capitalization
Net Income (in latest fiscal year or 2 of last 3 fiscal years)
Initial Listing
Continued Listing
$4 million
$2 million
Or
or
$50 million
$35 million
Or
or
$750,000
$500,000
Public Float (shares)
1 million
500,000
Market Value of Public Float
$5 million
$1 million
Minimum Bid Price
$4
$1
Market Makers
3
2
300
300
1 year
N/A
Shareholders (round lot holders)
Operating History
or
Market Capitalization
$50 million
Corporate Governance
Yes
Yes
NASDAQ National Market Requirements
Requirements
1
2
3
Continued
Listing
1&2
Net Tangible Assets
$6 million
$18 million
N/A
$4 million
N/A
Market Capitalization
N/A
N/A
$75 million
N/A
$50 million
Plan
Initial Listing
Initial Listing
Initial Listing
Continued
Listing
3
or
or
Total Assets
$75 million
$50 million
and
and
Total Revenue
$75 million
$50 million
Pretax Income (in latest fiscal
year or 2 of last 3 fiscal years)
Public Float (shares)
Operating History
Market Value of
$1 million
N/A
N/A
N/A
N/A
1.1 million
1.1 million
1.1 million
750,000
1.1 million
N/A
2 years
N/A
N/A
N/A
$8 million
$18 million
$20 million
$5 million
$15 million
Public Float
Minimum Bid Price
$5
$5
$5
$1
$5
Shareholders (round lot
holders)
Market Makers
400
400
400
400
400
3
3
4
2
4
Corporate Governance
Yes
Yes
Yes
Yes
Yes
139
Table 34 Low-priced NASDAQ Stocks and Reverse Stock Splits 1998 - 2005
This table shows how many stocks are trading for less than $1.00 each month from 1998 to 2005. The monthly figure
shown is the average daily low-priced firms. The number of low priced firms per day is totaled for the month and divided
by 30. The number of reverse stock splits per month and the reverse stock splits as a percentage of low-priced firms is
shown.
Rolling Averages
Year/Month
Low Priced
Stocks
199801
199802
199803
199804
199805
199806
199807
199808
199809
199810
199811
199812
199901
199902
199903
199904
199905
199906
199907
199908
199909
199910
199911
199912
200001
200002
200003
200004
200005
200006
200007
200008
200009
200010
200011
200012
200101
200102
200103
200104
200105
200106
200107
200108
200109
200110
184
166
176
165
144
174
186
226
300
341
243
255
166
140
153
126
92
92
79
93
88
95
90
82
44
16
7
32
63
69
76
114
94
166
194
308
243
187
286
298
238
215
231
273
232
369
Reverse
Percent Reverse
Stock Splits
Stock Splits
13
19
14
10
33
21
10
5
11
12
16
18
16
13
14
8
8
11
9
12
4
5
6
8
7
3
3
4
12
5
7
4
3
6
8
11
4
6
8
10
15
20
17
15
10
11
7.1%
11.4%
7.9%
6.1%
23.0%
12.1%
5.4%
2.2%
3.7%
3.5%
6.6%
7.1%
9.6%
9.3%
9.2%
6.4%
8.7%
11.9%
11.5%
12.9%
4.5%
5.2%
6.7%
9.8%
15.8%
18.8%
42.5%
12.6%
19.0%
7.3%
9.2%
3.5%
3.2%
3.6%
4.1%
3.6%
1.6%
3.2%
2.8%
3.4%
6.3%
9.3%
7.4%
5.5%
4.3%
3.0%
140
6 month
rolling
average
12 month
rolling
average
18 month
rolling
average
24 month
rolling
average
11.3%
11.0%
9.4%
8.7%
8.3%
5.6%
4.7%
5.4%
6.6%
7.5%
8.0%
8.4%
9.2%
9.5%
10.1%
9.3%
9.1%
8.8%
8.4%
9.2%
10.1%
16.5%
17.7%
19.7%
19.3%
18.2%
15.7%
9.1%
7.6%
5.1%
4.5%
3.3%
3.2%
3.2%
3.1%
3.5%
4.4%
5.4%
5.8%
6.0%
6.0%
8.0%
8.2%
8.0%
8.1%
8.2%
7.0%
7.0%
7.5%
8.4%
8.4%
8.6%
8.6%
8.8%
9.3%
10.1%
12.9%
13.4%
14.3%
13.9%
13.7%
12.9%
12.8%
12.7%
12.4%
11.9%
10.7%
9.4%
6.1%
5.4%
4.3%
4.5%
4.3%
4.5%
4.6%
4.5%
8.4%
8.6%
8.7%
8.5%
8.5%
7.6%
7.4%
8.0%
8.9%
11.1%
11.6%
12.3%
12.3%
12.3%
12.0%
11.6%
11.5%
11.2%
10.8%
10.2%
9.7%
9.6%
9.5%
9.5%
9.4%
9.0%
8.2%
6.1%
5.6%
8.4%
8.8%
9.1%
10.5%
10.8%
10.6%
10.4%
10.6%
10.6%
10.6%
10.6%
10.5%
10.4%
10.0%
9.8%
9.5%
9.4%
9.3%
9.2%
9.0%
8.7%
8.7%
8.6%
Table 34 (Continued)
Year/Month
Low Priced
Stocks
200111
200112
200201
200202
200203
200204
200205
200206
200207
200208
200209
200210
200211
200212
200301
200302
200303
200304
200305
200306
200307
200308
200309
200310
200311
200312
200401
200402
200403
200404
200405
200406
200407
200408
200409
200410
200411
200412
200501
200502
200503
200504
200505
200506
200507
200508
200509
200510
200511
200512
299
261
240
230
238
265
279
286
375
381
351
435
311
312
286
263
288
255
184
121
101
82
58
54
36
43
25
21
26
25
38
43
54
71
62
68
61
44
40
42
59
72
80
76
66
71
61
62
62
57
Reverse
Percent Reverse
Stock Splits
Stock Splits
1
7
4
4
6
5
12
8
21
11
13
19
13
9
36
16
8
18
18
36
5
20
8
7
4
7
6
3
4
2
5
2
4
0
4
5
1
7
2
4
4
2
8
4
2
5
4
7
6
9
0.3%
2.7%
1.7%
1.7%
2.5%
1.9%
4.3%
2.8%
5.6%
2.9%
3.7%
4.4%
4.2%
2.9%
12.6%
6.1%
2.8%
7.1%
9.8%
29.8%
4.9%
24.4%
13.8%
13.1%
11.2%
16.3%
23.7%
14.6%
15.4%
7.9%
13.1%
4.6%
7.4%
0.0%
6.5%
7.4%
1.6%
16.0%
4.9%
9.5%
6.8%
2.8%
10.0%
5.2%
3.0%
7.1%
6.6%
11.2%
9.7%
15.9%
141
6 month
rolling
average
12 month
rolling
average
18 month
rolling
average
24 month
rolling average
5.0%
3.9%
2.9%
2.3%
2.0%
1.8%
2.5%
2.5%
3.1%
3.3%
3.5%
3.9%
3.9%
3.9%
5.1%
5.6%
5.5%
5.9%
6.9%
11.3%
10.1%
13.1%
14.9%
15.9%
16.2%
13.9%
17.1%
15.4%
15.7%
14.8%
15.2%
13.2%
10.5%
8.0%
6.6%
6.5%
4.6%
6.5%
6.1%
7.7%
7.7%
6.9%
8.4%
6.6%
6.2%
5.8%
5.8%
7.2%
7.2%
8.9%
4.2%
4.2%
4.2%
4.0%
4.0%
3.9%
3.7%
3.2%
3.0%
2.8%
2.8%
2.9%
3.2%
3.2%
4.1%
4.5%
4.5%
4.9%
5.4%
7.6%
7.6%
9.4%
10.2%
10.9%
11.5%
12.6%
13.6%
14.3%
15.3%
15.4%
15.7%
13.6%
13.8%
11.7%
11.1%
10.7%
9.9%
9.8%
8.3%
7.9%
7.1%
6.7%
6.5%
6.5%
6.2%
6.7%
6.8%
7.1%
7.8%
7.7%
4.5%
4.3%
3.9%
3.8%
3.7%
3.6%
3.6%
3.6%
3.8%
3.8%
3.8%
3.9%
3.8%
3.4%
3.7%
3.8%
3.7%
3.9%
4.4%
5.9%
6.1%
7.4%
8.0%
8.6%
9.0%
9.7%
10.7%
11.4%
12.0%
12.2%
12.7%
12.8%
12.5%
12.2%
12.4%
12.4%
12.0%
11.2%
11.2%
10.4%
10.0%
9.4%
9.4%
8.7%
7.6%
7.2%
6.7%
6.9%
6.7%
7.3%
8.3%
8.0%
7.4%
6.7%
5.1%
4.6%
4.0%
3.8%
3.7%
3.7%
3.7%
3.7%
3.7%
3.7%
4.1%
4.3%
4.3%
4.4%
4.6%
5.4%
5.3%
6.1%
6.5%
6.9%
7.4%
7.9%
8.8%
9.4%
9.9%
10.2%
10.5%
10.6%
10.7%
10.6%
10.7%
10.8%
10.7%
11.2%
10.9%
11.1%
11.2%
11.1%
11.1%
10.0%
10.0%
9.2%
8.9%
8.9%
8.8%
8.8%
Table 35 Delistings Around the NASDAQ 2001 Moratorium
Delistings for various periods around the NASDAQ moratorium on the minimum bid price September 2001.
Column A is the actual number of delistings due to low price for the period. Column B is the firms that should
have been delisted under rules prior to September 2001. Under the old rules, a firm was issued a warning after
30 days of non-compliance and then delisted 90 days later if it did not meet continued listing standards. Month
t=0 is September 2001.
(A)
Period
Months
begin
end
Delistings
(C)
(B)
Firms that
Should Have
Been
Percentage
Delisted
(A)/(B)
36 months prior to rule change*
t-42 to t-7
199803 200102
283
352
80%
36 months after rule change
t+1 to t+36
200110 200409
133
709
19%
Six month increments
t-42 to t-37
199803 199808
20
98
20%
t-36 to t-31
199809 199902
76
215
35%
t-30 to t-25
199903 199908
61
182
34%
t-24 to t-19
199909 200002
39
127
31%
t-18 to t-13
200003 200008
11
102
11%
t-12 to t-7
200009 200102
76
119
64%
t-6 to t-1
200103 200108
123
259
47%
t+1 to t+6
200110 200203
3
371
1%
t+7 to t+12
200204 200209
60
416
14%
t+13 to t+18
200210 200303
48
513
9%
t+19 to t+24
200304 200309
22
423
5%
t+25 to t+30
200310 200403
0
333
0%
t+31 to t+36
200404 200409
0
301
0%
t+37 to t+42
200410 200503
2
299
1%
pre-moratorium and post-mortorium average delistings compared
two sample t-test of equal means T=
2035
(means not equal)
142
APPENDIX B: FIGURES
143
200
180
160
140
120
Number
100
of Splits
80
60
40
20
0
1963
1966
1969
1972
1975
1978
1981
1984
Year
Figure 1 Reverse Stock Splits 1962-2002
Source: Data from the Center for Research in Security Prices
144
1987
1990
1993
1996
1999
2002
450
400
350
300
Number of 250
reverse splits 200
150
100
50
0
1 for 50 or more
1 for 35 to 49.99
5 or more for 1
3.01 to 4.99 for 1
Figure 2 Split Ratios of Stock Splits
3 for 1
2.01 to 2.99 for 1
Split ratio
1 for 25 to 34.99
1 for 10.0 to 24.99
2 for 1
1.51 to 1.99 for 1
1.5 for 1 B22
1.251 to 1.5 for 1
1.25 for 1
Less than 1.25 for 1
145
1 for 10
1 for 5.01 to 9.99
1 for 5
1 for 3.5 to 4.99
1 for 2.01 to3.49
1 for 2 or less
Split ratio
8000
7000
6000
5000
Number of
4000
forward splits
3000
2000
1000
0
17. 5
15. 0
12. 5
P
e
r
c
e
n
t
10. 0
7. 5
5. 0
2. 5
0
-1
0
1
2
3
4
5
6
7
8
bhr
Figure 3 Distribution of 250-Day Buy and Hold Returns
146
9
10
11
12
13
14
15
16
Buy and Hold Returns
15.00%
10.00%
5.00%
0.00%
BHR
0
20
40
60
80
100
120
140
160
180
200
220
240
-5.00%
-10.00%
-15.00%
-20.00%
-25.00%
-30.00%
Days after Ex-date
Legend: top to
bottom on day 250
BHR missing = daily average
BHR missing = 0
Figure 4 The Average Buy and Hold Return of a Reverse Stock Split After the Ex-date
The average buy and hold return for days after the ex-date is plotted. Missing returns are replaced with the average daily return or with
zero.
147
Buy and Hold Abnormal Returns
15.00%
10.00%
5.00%
0.00%
BHAR
0
20
40
60
80
100
120
140
160
180
200
220
240
-5.00%
-10.00%
-15.00%
-20.00%
-25.00%
-30.00%
Days after Ex-date
Legend: top to
bottom on day 250
1. VW BHAR missing = daily average
2. VW BHAR missing = VW index
3. EW BHAR missing = daily average
4. EW BHAR missing = EW index
Figure 5 Market Adjusted Returns of Reverse Stock Splits
The market adjusted average buy and hold abnormal return for days after the ex-date is plotted. Market is either the CRSP value
weighted index (VW) or the CRSP equal weighted index (EW). Missing returns are replaced with the average daily return, with zero,
or with a market index.
148
Matched Firm Buy and Hold Abnormal Returns
15.00%
10.00%
5.00%
0.00%
BHAR
10
30
50
70
90
110
130
150
170
190
210
230
-5.00%
-10.00%
-15.00%
-20.00%
-25.00%
-30.00%
Days After Ex-date
Matched Firm BHARs
Figure 6 The Average Matched Firm BHAR of a Reverse Stock Split After the Ex-date
The matched firm adjusted average buy and hold abnormal return for days after the ex-date is plotted. The abnormal return is
calculated by subtracting the return of an out of sample firm with similar size, price and industry.
149
250
APPENDIX C: FAMA AND FRENCH INDUSTRY CLASSIFICATIONS
This Appendix contains the Standard Industry Classification (SIC) codes that are
used in determining the industry classification of a firm. These industry classifications
were found on Kenneth French’s website. Some researchers use the first two digits of
the SIC code to determine which industries have similar characteristics. This type of
classification is an attempt to group like industries in a more thoughtful way. In the first
panel, all industries are divided into five broad categories and in the last panel all
industries are divided into 48 more narrow categories.
5 INDUSTRIES
4 Money Finance
6000-6999
5 Other Agric, Mines, Oil, Construct, Transport, Telecom
Health and Legal Services
1 Manuf Manufacturing
2000-3999
2 Utils Utilities
4900-4999
3 Shops Wholesale, Retail, and Some Services
5000-5999
7000-7999
10 INDUSTRIES
1 NoDur Consumer NonDurables
0100-0999
2000-2399
2700-2749
2770-2799
3100-3199
3940-3989
5 Manuf Manufacturing
2440-2499
2520-2589
2600-2699
2750-2769
3200-3629
3660-3709
3712-3713
3715-3715
3717-3749
3752-3791
3793-3909
2 Durbl Consumer Durables
2400-2439
2500-2519
2590-2599
3000-3099
3630-3659
3710-3711
3714-3714
3716-3716
3750-3751
3792-3792
3910-3939
3990-3999
3 Oil
6 Telcm Telephones and Television
4800-4899
7 Utils Utilities
4900-4949
8 Shops Wholesale, Retail, and Some Services
5000-5999
7000-7999
Oil, Gas, and Coal Extraction and Products
1200-1399
2900-2999
9 Money Finance
6000-6999
4 Chems Chemicals and Allied Products
2800-2899
10 Other Everything Else
150
12 INDUSTRIES
1 NoDur Consumer NonDurables -- Food, Tobacco,
Textiles, Apparel, Leather, Toys
5 Chems Chemicals and Allied Products
2800-2829
2840-2899
0100-0999
2000-2399
2700-2749
2770-2799
3100-3199
3940-3989
6 BusEq Business Equipment -- Computers, Software,
and Electronic Equipment
3570-3579
3660-3692
3694-3699
3810-3829
7370-7379
2 Durbl Consumer Durables -- Cars, TV's,
Furniture, Household Appliances
2500-2519
2590-2599
3630-3659
3710-3711
3714-3714
3716-3716
3750-3751
3792-3792
3900-3939
3990-3999
7 Telcm Telephone and Television Transmission
4800-4899
8 Utils Utilities
4900-4949
3 Manuf Manufacturing -- Machinery, Trucks,
Planes, Off Furn, Paper, Com Printing
9 Shops Wholesale, Retail, and Some Services
(Laundries, Repair Shops)
2520-2589
2600-2699
2750-2769
3000-3099
3200-3569
3580-3629
3700-3709
3712-3713
3715-3715
3717-3749
3752-3791
3793-3799
3830-3839
3860-3899
5000-5999
7200-7299
7600-7699
10 Hlth Healthcare, Medical Equipment, and Drugs
2830-2839
3693-3693
3840-3859
8000-8099
11 Money Finance
6000-6999
4 Enrgy Oil, Gas, and Coal Extraction and Products
12 Other Everything Else -- Mines, Constr, BldMt, Trans,
Hotels, Bus Serv, Entertainment
1200-1399
2900-2999
151
17 INDUSTRIES
1 Food Food
0100-0199 Agric production - crops
0200-0299 Agric production - livestock
0700-0799 Agricultural services
0900-0999 Fishing, hunting & trapping
2000-2009 Food and kindred products
2010-2019 Meat products
2020-2029 Dairy products
2030-2039 Canned-preserved fruits-vegs
2040-2046 Flour and other grain mill products
2047-2047 Dog and cat food
2048-2048 Prepared feeds for animals
2050-2059 Bakery products
2060-2063 Sugar and confectionery products
2064-2068 Candy and other confectionery
2070-2079 Fats and oils
2080-2080 Beverages
2082-2082 Malt beverages
2083-2083 Malt
2084-2084 Wine
2085-2085 Distilled and blended liquors
2086-2086 Bottled-canned soft drinks
2087-2087 Flavoring syrup
2090-2092 Misc food preps
2095-2095 Roasted coffee
2096-2096 Potato chips
2097-2097 Manufactured ice
2098-2099 Misc food preparations
5140-5149 Wholesale - groceries & related prods
5150-5159 Wholesale - farm products
5180-5182 Wholesale - beer, wine
5191-5191 Wholesale - farm supplies
2 Mines Mining and Minerals
1000-1009 Metal mining
1010-1019 Iron ores
1020-1029 Copper ores
1030-1039 Lead and zinc ores
1040-1049 Gold & silver ores
1060-1069 Ferroalloy ores
1080-1089 Mining services
1090-1099 Misc metal ores
1200-1299 Bituminous coal
1400-1499 Mining and quarrying non-metallic minerals
5050-5052 Wholesale - metals and minerals
3 Oil
Oil and Petroleum Products
1300-1300 Oil and gas extraction
1310-1319 Crude petroleum & natural gas
1320-1329 Natural gas liquids
1380-1380 Oil and gas field services
1381-1381 Drilling oil & gas wells
1382-1382 Oil-gas field exploration
1389-1389 Oil and gas field services
2900-2912 Petroleum refining
5170-5172 Wholesale - petroleum and petro prods
4 Clths Textiles, Apparel & Footwear
2200-2269 Textile mill products
2270-2279 Floor covering mills
2280-2284 Yarn and thread mills
2290-2295 Misc textile goods
2296-2296 Tire cord and fabric
2297-2297 Nonwoven fabrics
2298-2298 Cordage and twine
2299-2299 Misc textile products
2300-2390 Apparel and other finished products
2391-2392 Curtains, home furnishings
2393-2395 Textile bags, canvas products
2396-2396 Auto trim
2397-2399 Misc textile products
3020-3021 Rubber and plastics footwear
3100-3111 Leather tanning and finishing
3130-3131 Boot, shoe cut stock, findings
3140-3149 Footwear except rubber
3150-3151 Leather gloves and mittens
3963-3965 Fasteners, buttons, needles, pins
5130-5139 Wholesale - apparel
5 Durbl Consumer Durables
2510-2519 Household furniture
2590-2599 Misc furniture and fixtures
3060-3069 Fabricated rubber products
3070-3079 Misc rubber products
3080-3089 Misc plastic products
3090-3099 Misc rubber and plastic products
3630-3639 Household appliances
3650-3651 Household audio visual equip
3652-3652 Phonographic records
3860-3861 Photographic equip (Kodak etc, but also
Xerox)
3870-3873 Watches clocks and parts
3910-3911 Jewelry-precious metals
3914-3914 Silverware
3915-3915 Jewelers' findings, materials
3930-3931 Musical instruments
3940-3949 Toys
3960-3962 Costume jewelry and notions
5020-5023 Wholesale - furniture and home furnishings
5064-5064 Wholesale - electrical appliance TV and radio
5094-5094 Wholesale - jewelry and watches
5099-5099 Wholesale - durable goods
6 Chems Chemicals
2800-2809 Chemicals and allied products
2810-2819 Industrial inorganical chems
2820-2829 Plastic material & synthetic resin
2860-2869 Industrial organic chems
2870-2879 Agriculture chemicals
2890-2899 Misc chemical products
5160-5169 Wholesale - chemicals & allied prods
7 Cnsum Drugs, Soap, Prfums, Tobacco
2100-2199 Tobacco products
2830-2830 Drugs
2831-2831 Biological products
2833-2833 Medicinal chemicals
2834-2834 Pharmaceutical preparations
2840-2843 Soap & other detergents
2844-2844 Perfumes cosmetics
5120-5122 Wholesale - drugs & propietary
5194-5194 Wholesale - tobacco and tobacco products
8 Cnstr Construction and Construction Materials
0800-0899 Forestry
1500-1511 Build construction - general contractors
1520-1529 Gen building contractors - residential
1530-1539 Operative builders
1540-1549 Gen building contractors - non-residential
152
17 INDUSTRIES (continued)
3580-3580 Refrig & service ind machines
3581-3581 Automatic vending machines
3582-3582 Commercial laundry and dry cleaning
machines
3585-3585 Air conditioning, heating, refrid eq
3586-3586 Measuring and dispensing pumps
3589-3589 Service industry machinery
3590-3599 Misc industrial and commercial equipment and
mach
3600-3600 Elec mach eq & supply
3610-3613 Elec transmission
3620-3621 Electrical industrial appar
3622-3622 Industrial controls
3623-3629 Electrical industrial appar
3670-3679 Electronic components
3680-3680 Computers
3681-3681 Computers - mini
3682-3682 Computers - mainframe
3683-3683 Computers - terminals
3684-3684 Computers - disk & tape drives
3685-3685 Computers - optical scanners
3686-3686 Computers - graphics
3687-3687 Computers - office automation systems
3688-3688 Computers - peripherals
3689-3689 Computers - equipment
3690-3690 Miscellaneous electrical machinery and equip
3691-3692 Storage batteries
3693-3693 X-ray, electromedical app
3694-3694 Elec eq, internal combustion engines
3695-3695 Magnetic and optical recording media
3699-3699 Electrical machinery and equip
3810-3810 Search, detection, navigation, guidance
3811-3811 Engr lab and research equipment
3812-3812 Search, detection, navigation, guidance
3820-3820 Measuring and controlling equipment
3821-3821 Lab apparatus and furniture
3822-3822 Automatic controls - Envir and applic
3823-3823 Industrial measurement instru
3824-3824 Totalizing fluid meters
3825-3825 Elec meas & test instr
3826-3826 Lab analytical instruments
3827-3827 Optical instr and lenses
3829-3829 Meas and control devices
3830-3839 Optical instr and lenses
3950-3955 Pens pencils and office supplies
5060-5060 Wholesale - electrical goods
5063-5063 Wholesale - electrical apparatus and
equipment
5065-5065 Wholesale - electronic parts
5080-5080 Wholesale - machinery and equipment
5081-5081 Wholesale - machinery and equipment (?)
1600-1699 Heavy Construction - not building contractors
1700-1799 Construction - special contractors
2400-2439 Lumber and wood products
2440-2449 Wood containers
2450-2459 Wood buildings-mobile homes
2490-2499 Misc wood products
2850-2859 Paints
2950-2952 Paving & roofing materials
3200-3200 Stone, clay, glass, concrete etc
3210-3211 Flat glass
3240-3241 Cement hydraulic
3250-3259 Structural clay prods
3261-3261 Vitreous china plumbing fixtures
3264-3264 Porcelain electrical supply
3270-3275 Concrete gypsum & plaster
3280-3281 Cut stone and stone products
3290-3293 Abrasive and asbestos products
3420-3429 Hand tools and hardware
3430-3433 Heating equip & plumbing fix
3440-3441 Fabricated struct metal products
3442-3442 Metal doors, frames
3446-3446 Architectural or ornamental metal work
3448-3448 Pre-fab metal buildings
3449-3449 Misc structural metal work
3450-3451 Screw machine products
3452-3452 Bolts, nuts screws
5030-5039 Wholesale - lumber and construction materials
5070-5078 Wholesale - hardware, plumbing, heating equip
5198-5198 Wholesale - Paints, varnishes, and supplies
5210-5211 Retail - lumber & other building mat
5230-5231 Retail - paint, glass, wallpaper
5250-5251 Retail - hardward stores
9 Steel Steel Works Etc
3300-3300 Primary metal industries
3310-3317 Blast furnaces & steel works
3320-3325 Iron & steel foundries
3330-3339 Prim smelt-refin nonfer metals
3340-3341 Secondary smelt-refin nonfer metals
3350-3357 Rolling & drawing nonferous metals
3360-3369 Non-ferrous foundries and casting
3390-3399 Misc primary metal products
10 FabPr Fabricated Products
3410-3412 Metal cans and shipping containers
3443-3443 Fabricated plate work
3444-3444 Sheet metal work
3460-3469 Metal forgings and stampings
3470-3479 Coating and engraving
3480-3489 Ordnance & accessories
3490-3499 Misc fabricated metal products
11 Machn Machinery and Business Equipment
3510-3519 Engines & turbines
3520-3529 Farm and garden machinery
3530-3530 Constr, mining material handling machinery
3531-3531 Construction machinery
3532-3532 Mining machinery, except oil field
3533-3533 Oil field machinery
3534-3534 Elevators
3535-3535 Conveyors
3536-3536 Cranes, hoists
3540-3549 Metalworking machinery
3550-3559 Special industry machinery
3560-3569 General industrial machinery
3570-3579 Office computers
12 Cars Automobiles
3710-3710 Motor vehicles and motor vehicle equip
3711-3711 Motor vehicles & car bodies
3714-3714 Motor vehicle parts
3716-3716 Motor homes
3750-3751 Motorcycles, bicycles and parts (Harley &
Huffy)
3792-3792 Travel trailers and campers
5010-5015 Wholesale - autos and parts
5510-5521 Retail - auto dealers
5530-5531 Retail - auto and home supply stores
5560-5561 Retail - recreational vehicle dealers
153
17 INDUSTRIES (continued)
5430-5431 Retail - fruite and vegetable markets
5440-5441 Retail - candy, nut, confectionary stores
5450-5451 Retail - dairy product stores
5460-5461 Retail - bakeries
5490-5499 Retail - miscellaneous food stores
5540-5541 Retail - gasoline service stations
5550-5551 Retail - boat dealers
5600-5699 Retail - apparel & acces
5700-5700 Retail - home furniture and equipment stores
5710-5719 Retail - home furnishings stores
5720-5722 Retail - household appliance stores
5730-5733 Retail - radio, TV and consumer electronic stores
5734-5734 Retail - computer and computer software stores
5735-5735 Retail - record and tape stores
5736-5736 Retail - musical instrument stores
5750-5750 Retail - (?)
5800-5813 Retail - eating places
5890-5890 Eating and drinking places
5900-5900 Retail - misc
5910-5912 Retail - drug & proprietary stores
5920-5921 Retail - liquor stores
5930-5932 Retail - used merchandise stores
5940-5940 Retail - misc
5941-5941 Retail - sporting goods stores, bike shops
5942-5942 Retail - book stores
5943-5943 Retail - stationery stores
5944-5944 Retail - jewelry stores
5945-5945 Retail - hobby, toy and game shops
5946-5946 Retail - camera and photo shop
5947-5947 Retail - gift, novelty
5948-5948 Retail - luggage
5949-5949 Retail - sewing & needlework stores
5960-5963 Retail - non-store retailers (catalogs, etc)
5980-5989 Retail - fuel & ice stores (Penn Central Co)
5990-5990 Retail - retail stores
5992-5992 Retail - florists
5993-5993 Retail - tobacco stores
5994-5994 Retail - news dealers
5995-5995 Retail - computer stores
5999-5999 Retail stores
5570-5571 Retail - motorcycle dealers
5590-5599 Retail - automotive dealers
13 Trans Transportation
3713-3713 Truck & bus bodies
3715-3715 Truck trailers
3720-3720 Aircraft & parts
3721-3721 Aircraft
3724-3724 Aircraft engines, engine parts
3725-3725 Aircraft parts
3728-3728 Aircraft parts
3730-3731 Ship building and repair
3732-3732 Boat building and repair
3740-3743 Railroad Equipment
3760-3769 Guided missiles and space vehicles
3790-3790 Misc trans equip
3795-3795 Tanks and tank components
3799-3799 Misc trans equip
4000-4013 Railroads-line haul
4100-4100 Transit and passenger trans
4110-4119 Local passenger trans
4120-4121 Taxicabs
4130-4131 Intercity bus trans (Greyhound)
4140-4142 Bus charter
4150-4151 School buses
4170-4173 Motor vehicle terminals, service facilities
4190-4199 Misc transit and passenger transportation
4200-4200 Motor freight trans, warehousing
4210-4219 Trucking
4220-4229 Warehousing and storage
4230-4231 Terminal facilities - motor freight
4400-4499 Water transport
4500-4599 Air transportation
4600-4699 Pipelines, except natural gas
4700-4700 Transportation services
4710-4712 Freight forwarding
4720-4729 Travel agencies, etc
4730-4739 Arrange trans - freight and cargo
4740-4742 Rental of railroad cars
4780-4780 Misc services incidental to trans
4783-4783 Packing and crating
4785-4785 Motor vehicle inspection
4789-4789 Transportation services
14 Utils Utilities
4900-4900 Electric, gas, sanitary services
4910-4911 Electric services
4920-4922 Natural gas transmission
4923-4923 Natural gas transmission-distr
4924-4925 Natural gas distribution
4930-4931 Electric and other services combined
4932-4932 Gas and other services combined
4939-4939 Combination utilities
4940-4942 Water supply
15 Rtail Retail Stores
5260-5261 Retail - nurseries, lawn, garden stores
5270-5271 Retail - mobile home dealers
5300-5300 Retail - general merchandise stores
5310-5311 Retail - department stores
5320-5320 Retail - general merchandise stores (?)
5330-5331 Retail - variety stores
5334-5334 Retail - catalog showroom
5390-5399 Retail - Misc general merchandise stores
5400-5400 Retail - food stores
5410-5411 Retail - grocery stores
5412-5412 Retail - convenience stores
5420-5421 Retail - meat, fish mkt
16 Finan Banks, Insurance Companies, and Other Financials
6010-6019 Federal reserve banks
6020-6020 Commercial banks
6021-6021 National commercial banks
6022-6022 State banks - Fed Res System
6023-6023 State banks - not Fed Res System
6025-6025 National banks - Fed Res System
6026-6026 National banks - not Fed Res System
6028-6029 Banks
6030-6036 Savings institutions
6040-6049 Trust companies, nondeposit
6050-6059 Functions closely related to banking
6060-6062 Credit unions
6080-6082 Foreign banks
6090-6099 Functions related to deposit banking
6100-6100 Nondepository credit institutions
6110-6111 Federal credit agencies
6112-6112 FNMA
6120-6129 S&Ls
6140-6149 Personal credit institutions (Beneficial)
6150-6159 Business credit institutions
6160-6163 Mortgage bankers
154
17 INDUSTRIES (continued)
3640-3644 Electric lighting, wiring
3645-3645 Residential lighting fixtures
3646-3646 Commercial lighting
3647-3647 Vehicular lighting
3648-3649 Lighting equipment
3660-3660 Communication equip
3661-3661 Telephone and telegraph apparatus
3662-3662 Communications equipment
3663-3663 Radio TV comm equip & apparatus
3664-3664 Search, navigation, guidance systems
3665-3665 Training equipment & simulators
3666-3666 Alarm & signaling products
3669-3669 Communication equipment
3840-3849 Surg & med instru
3850-3851 Ophthalmic goods
3991-3991 Brooms and brushes
3993-3993 Signs, advertising specialty
3995-3995 Burial caskets
3996-3996 Hard surface floor cover
4810-4813 Telephone communications
4820-4822 Telegraph and other message communication
4830-4839 Radio-TV Broadcasters
4840-4841 Cable and other pay TV services
4890-4890 Communication services (Comsat)
4891-4891 Cable TV operators
4892-4892 Telephone interconnect
4899-4899 Communication services
4950-4959 Sanitary services
4960-4961 Steam, air conditioning supplies
4970-4971 Irrigation systems
4991-4991 Cogeneration - SM power producer
5040-5042 Wholesale - professional and commercial
equipment and supplies
5043-5043 Wholesale - photographic equipment
5044-5044 Wholesale - office equipment
5045-5045 Wholesale - computers
5046-5046 Wholesale - commercial equip
5047-5047 Wholesale - medical, dental equip
5048-5048 Wholesale - ophthalmic goods
5049-5049 Wholesale - professional equip and supplies
5082-5082 Wholesale - construction and mining equipment
5083-5083 Wholesale - farm and garden machinery
5084-5084 Wholesale - industrial machinery and equipment
5085-5085 Wholesale - industrial supplies
5086-5087 Wholesale - machinery and equipment (?)
5088-5088 Wholesale - trans eq except motor vehicles
5090-5090 Wholesale - misc durable goods
5091-5092 Wholesale - sporting goods, toys
5093-5093 Wholesale - scrap and waste materials
5100-5100 Wholesale - nondurable goods
5110-5113 Wholesale - paper and paper products
5199-5199 Wholesale - non-durable goods
7000-7000 Hotels, other lodging places
7010-7011 Hotels motels
7020-7021 Rooming and boarding houses
7030-7033 Camps and recreational vehicle parks
7040-7041 Membership hotels and lodging
6172-6172 Finance lessors
6199-6199 Financial services
6200-6299 Security and commodity brokers
6300-6300 Insurance
6310-6312 Life insurance
6320-6324 Accident and health insurance
6330-6331 Fire, marine, property-casualty ins
6350-6351 Surety insurance
6360-6361 Title insurance
6370-6371 Pension, health, welfare funds
6390-6399 Insurance carriers
6400-6411 Insurance agents
6500-6500 Real estate
6510-6510 Real estate operators
6512-6512 Operators - non-resident buildings
6513-6513 Operators - apartment buildings
6514-6514 Operators - other than apartment
6515-6515 Operators - residential mobile home
6517-6519 Lessors of real property
6530-6531 Real estate agents and managers
6532-6532 Real estate dealers
6540-6541 Title abstract offices
6550-6553 Real estate developers
6611-6611 Combined real estate, insurance, etc
6700-6700 Holding, other investment offices
6710-6719 Holding offices
6720-6722 Investment offices
6723-6723 Management investment, closed-end
6730-6733 Trusts
6790-6790 Miscellaneous investing
6792-6792 Oil royalty traders
6794-6794 Patent owners & lessors
6795-6795 Mineral royalty traders
6798-6798 REIT
6799-6799 Investors, NEC
17 Other Everything Else
2520-2549 Office furniture and fixtures
2600-2639 Paper and allied products
2640-2659 Paperboard containers, boxes, drums, tubs
2661-2661 Building paper and board mills
2670-2699 Paper and allied products
2700-2709 Printing publishing and allied
2710-2719 Newspapers: publishing-printing
2720-2729 Periodicals: publishing-printing
2730-2739 Books: publishing-printing
2740-2749 Misc publishing
2750-2759 Commercial printing
2760-2761 Manifold business forms
2770-2771 Greeting card publishing
2780-2789 Book binding
2790-2799 Service industries for print trade
2835-2835 In vitro, in vivo diagnostics
2836-2836 Biological products, except diagnostics
2990-2999 Misc petroleum products
3000-3000 Rubber & misc plastic products
3010-3011 Tires and inner tubes
3041-3041 Rubber & plastics hose and belting
3050-3053 Gaskets, hoses, etc
3160-3161 Luggage
3170-3171 Handbags and purses
3172-3172 Personal leather goods, except handbags
3190-3199 Leather goods
3220-3221 Glass containers
3229-3229 Pressed and blown glass
3230-3231 Glass products
3260-3260 Pottery and related products
3262-3263 China and earthenware table articles
3269-3269 Pottery products
3295-3299 Non-metalic mineral products
3537-3537 Trucks, tractors, trailers
155
17 INDUSTRIES (continued
7397-7397 Services - commercial testing labs
7399-7399 Services - business services
7500-7500 Services - auto repair, services
7510-7519 Services - truck, auto, trailer rental and leasing
7520-7523 Services - automobile parking
7530-7539 Services - auto repair shops
7540-7549 Services - auto services, except repair (car
washes)
7600-7600 Services - Misc repair services
7620-7620 Services - Electrical repair shops
7622-7622 Services - Radio and TV repair shops
7623-7623 Services - Refridg and air conditioner repair
7629-7629 Services - Electrical repair shops
7630-7631 Services - Watch, clock and jewelry repair
7640-7641 Services - Reupholster, furniture repair
7690-7699 Services - Misc repair shops
7800-7829 Services - motion picture production and
distribution
7830-7833 Services - motion picture theatres
7840-7841 Services - video rental
7900-7900 Services - amusement and recreation
7910-7911 Services - dance studios
7920-7929 Services - bands, entertainers
7930-7933 Services - bowling centers
7940-7949 Services - professional sports
7980-7980 Amusement and recreation services
7990-7999 Services - misc entertainment
8000-8099 Services - health
8100-8199 Services - legal
8200-8299 Services - educational
8300-8399 Services - social services
8400-8499 Services - museums, galleries, botanic gardens
8600-8699 Services - membership organizations
8700-8700 Services - engineering, accounting, research,
management
8710-8713 Services - engineering, accounting, surveying
8720-8721 Services - accounting, auditing, bookkeeping
8730-8734 Services - research, development, testing labs
8740-8748 Services - management, public relations,
consulting
8800-8899 Services - private households
8900-8910 Services - misc
8911-8911 Services - engineering & architect
8920-8999 Services - misc
7200-7200 Services - personal
7210-7212 Services - laundry, cleaners
7213-7213 Services - linen
7215-7216 Services - coin-op cleaners, dry cleaners
7217-7217 Services - carpet, upholstery cleaning
7218-7218 Services - industrial launderers
7219-7219 Services - laundry, cleaners
7220-7221 Services - photo studios, portrait
7230-7231 Services - beauty shops
7240-7241 Services - barber shops
7250-7251 Services - shoe repair
7260-7269 Services - funeral
7290-7290 Services - misc
7291-7291 Services - tax return
7299-7299 Services - misc
7300-7300 Services - business services
7310-7319 Services - advertising
7320-7323 Services - credit reporting agencies, collection
services
7330-7338 Services - mailing, reproduction, commercial art
7340-7342 Services - services to dwellings, other buildings
7349-7349 Services - cleaning and builging maint
7350-7351 Services - misc equip rental and leasing
7352-7352 Services - medical equip rental
7353-7353 Services - heavy construction equip rental
7359-7359 Services - equip rental and leasing
7360-7369 Services - personnel supply services
7370-7372 Services - computer programming and data
processing
7373-7373 Computer integrated systems design
7374-7374 Services - computer processing, data prep
7375-7375 Services - information retrieval services
7376-7376 Services - computer facilities management service
7377-7377 Services - computer rental and leasing
7378-7378 Services - computer maintenance and repair
7379-7379 Services - computer related services
7380-7380 Services - misc business services
7381-7382 Services - security
7383-7383 Services - news syndicates
7384-7384 Services - photofinishing labs
7385-7385 Services - telephone interconnections
7389-7390 Services - misc business services
7391-7391 Services - R&D labs
7392-7392 Services - management consulting & P.R.
7393-7393 Services - detective and protective (ADT)
7394-7394 Services - equipment rental & leasing
7395-7395 Services - photofinishing labs (School pictures)
156
30 INDUSTRIES
1 Food Food Products
0100-0199 Agric production - crops
0200-0299 Agric production - livestock
0700-0799 Agricultural services
0910-0919 Commercial fishing
2000-2009 Food and kindred products
2010-2019 Meat products
2020-2029 Dairy products
2030-2039 Canned-preserved fruits-vegs
2040-2046 Flour and other grain mill products
2048-2048 Prepared feeds for animals
2050-2059 Bakery products
2060-2063 Sugar and confectionery products
2064-2068 Candy and other confectionery
2070-2079 Fats and oils
2086-2086 Bottled-canned soft drinks
2087-2087 Flavoring syrup
2090-2092 Misc food preps
2095-2095 Roasted coffee
2096-2096 Potato chips
2097-2097 Manufactured ice
2098-2099 Misc food preparations
2 Beer Beer & Liquor
2080-2080 Beverages
2082-2082 Malt beverages
2083-2083 Malt
2084-2084 Wine
2085-2085 Distilled and blended liquors
3 Smoke Tobacco Products
2100-2199 Tobacco products
4 Games Recreation
0920-0999 Fishing, hunting & trapping
3650-3651 Household audio visual equip
3652-3652 Phonographic records
3732-3732 Boat building and repair
3930-3931 Musical instruments
3940-3949 Toys
7800-7829 Services - motion picture production and distribution
7830-7833 Services - motion picture theatres
7840-7841 Services - video rental
7900-7900 Services - amusement and recreation
7910-7911 Services - dance studios
7920-7929 Services - bands, entertainers
7930-7933 Services - bowling centers
7940-7949 Services - professional sports
7980-7980 Amusement and recreation services (?)
7990-7999 Services - misc entertainment
5 Books Printing and Publishing
2700-2709 Printing publishing and allied
2710-2719 Newspapers: publishing-printing
2720-2729 Periodicals: publishing-printing
2730-2739 Books: publishing-printing
2740-2749 Misc publishing
2750-2759 Commercial printing
2770-2771 Greeting card publishing
2780-2789 Book binding
2790-2799 Service industries for print trade
3993-3993 Signs, advertising specialty
2840-2843 Soap & other detergents
2844-2844 Perfumes cosmetics
3160-3161 Luggage
3170-3171 Handbags and purses
3172-3172 Personal leather goods, except handbags
3190-3199 Leather goods
3229-3229 Pressed and blown glass
3260-3260 Pottery and related products
3262-3263 China and earthenware table articles
3269-3269 Pottery products
3230-3231 Glass products
3630-3639 Household appliances
3750-3751 Motorcycles, bicycles and parts (Harley & Huffy)
3800-3800 Misc inst, photo goods, watches
3860-3861 Photographic equip (Kodak etc, but also Xerox)
3870-3873 Watches clocks and parts
3910-3911 Jewelry-precious metals
3914-3914 Silverware
3915-3915 Jewelers' findings, materials
3960-3962 Costume jewelry and notions
3991-3991 Brooms and brushes
3995-3995 Burial caskets
7 Clths Apparel
2300-2390 Apparel and other finished products
3020-3021 Rubber and plastics footwear
3100-3111 Leather tanning and finishing
3130-3131 Boot, shoe cut stock, findings
3140-3149 Footwear except rubber
3150-3151 Leather gloves and mittens
3963-3965 Fasteners, buttons, needles, pins
8 Hlth Healthcare, Medical Equipment, Pharmaceutical Products
2830-2830 Drugs
2831-2831 Biological products
2833-2833 Medicinal chemicals
2834-2834 Pharmaceutical preparations
2835-2835 In vitro, in vivo diagnostics
2836-2836 Biological products, except diagnostics
3693-3693 X-ray, electromedical app
3840-3849 Surg & med instru
3850-3851 Ophthalmic goods
8000-8099 Services - health
9 Chems Chemicals
2800-2809 Chemicals and allied products
2810-2819 Industrial inorganical chems
2820-2829 Plastic material & synthetic resin
2850-2859 Paints
2860-2869 Industrial organic chems
2870-2879 Agriculture chemicals
2890-2899 Misc chemical products
10 Txtls Textiles
2200-2269 Textile mill products
2270-2279 Floor covering mills
2280-2284 Yarn and thread mills
2290-2295 Misc textile goods
2297-2297 Nonwoven fabrics
2298-2298 Cordage and twine
2299-2299 Misc textile products
6 Hshld Consumer Goods
2047-2047 Dog and cat food
2391-2392 Curtains, home furnishings
2510-2519 Household furniture
2590-2599 Misc furniture and fixtures
157
3580-3580 Refrig & service ind machines
3581-3581 Automatic vending machines
3582-3582 Commercial laundry and dry cleaning machines
3585-3585 Air conditioning, heating, refrid eq
3586-3586 Measuring and dispensing pumps
3589-3589 Service industry machinery
3590-3599 Misc industrial and commercial equipment and mach
30 INDUSTRIES (continued)
2393-2395 Textile bags, canvas products
2397-2399 Misc textile products
11 Cnstr Construction and Construction Materials
0800-0899 Forestry
1500-1511 Build construction - general contractors
1520-1529 Gen building contractors - residential
1530-1539 Operative builders
1540-1549 Gen building contractors - non-residential
1600-1699 Heavy Construction - not building contractors
1700-1799 Construction - special contractors
2400-2439 Lumber and wood products
2450-2459 Wood buildings-mobile homes
2490-2499 Misc wood products
2660-2661 Building paper and board mills
2950-2952 Paving & roofing materials
3200-3200 Stone, clay, glass, concrete etc
3210-3211 Flat glass
3240-3241 Cement hydraulic
3250-3259 Structural clay prods
3261-3261 Vitreous china plumbing fixtures
3264-3264 Porcelain electrical supply
3270-3275 Concrete gypsum & plaster
3280-3281 Cut stone and stone products
3290-3293 Abrasive and asbestos products
3295-3299 Non-metallic mineral products
3420-3429 Hand tools and hardware
3430-3433 Heating equip & plumbing fix
3440-3441 Fabricated struct metal products
3442-3442 Metal doors, frames
3446-3446 Architectural or ornamental metal work
3448-3448 Pre-fab metal buildings
3449-3449 Misc structural metal work
3450-3451 Screw machine products
3452-3452 Bolts, nuts screws
3490-3499 Misc fabricated metal products
3996-3996 Hard surface floor cover
14 ElcEq Electrical Equipment
3600-3600 Elec mach eq & supply
3610-3613 Elec transmission
3620-3621 Electrical industrial appar
3623-3629 Electrical industrial appar
3640-3644 Electric lighting, wiring
3645-3645 Residential lighting fixtures
3646-3646 Commercial lighting
3648-3649 Lighting equipment
3660-3660 Communication equip
3690-3690 Miscellaneous electrical machinery and equip
3691-3692 Storage batteries
3699-3699 Electrical machinery and equip
15 Autos Automobiles and Trucks
2296-2296 Tire cord and fabric
2396-2396 Auto trim
3010-3011 Tires and inner tubes
3537-3537 Trucks, tractors, trailers
3647-3647 Vehicular lighting
3694-3694 Elec eq, internal combustion engines
3700-3700 Transportation equipment
3710-3710 Motor vehicles and motor vehicle equip
3711-3711 Motor vehicles & car bodies
3713-3713 Truck & bus bodies
3714-3714 Motor vehicle parts
3715-3715 Truck trailers
3716-3716 Motor homes
3792-3792 Travel trailers and campers
3790-3791 Misc trans equip
3799-3799 Misc trans equip
16 Carry Aircraft, ships, and railroad equipment
3720-3720 Aircraft & parts
3721-3721 Aircraft
3723-3724 Aircraft engines, engine parts
3725-3725 Aircraft parts
3728-3729 Aircraft parts
3730-3731 Ship building and repair
3740-3743 Railroad Equipment
12 Steel Steel Works Etc
3300-3300 Primary metal industries
3310-3317 Blast furnaces & steel works
3320-3325 Iron & steel foundries
3330-3339 Prim smelt-refin nonfer metals
3340-3341 Secondary smelt-refin nonfer metals
3350-3357 Rolling & drawing nonferous metals
3360-3369 Non-ferrous foundries and casting
3370-3379 Steel works etc
3390-3399 Misc primary metal products
13 FabPr Fabricated Products and Machinery
3400-3400 Fabricated metal, except machinery and trans eq
3443-3443 Fabricated plate work
3444-3444 Sheet metal work
3460-3469 Metal forgings and stampings
3470-3479 Coating and engraving
3510-3519 Engines & turbines
3520-3529 Farm and garden machinery
3530-3530 Constr, mining material handling machinery
3531-3531 Construction machinery
3532-3532 Mining machinery, except oil field
3533-3533 Oil field machinery
3534-3534 Elevators
3535-3535 Conveyors
3536-3536 Cranes, hoists
3538-3538 Machinery
3540-3549 Metalworking machinery
3550-3559 Special industry machinery
3560-3569 General industrial machinery
158
17 Mines Precious Metals, Non-Metallic, and Industrial Metal Mining
1000-1009 Metal mining
1010-1019 Iron ores
1020-1029 Copper ores
1030-1039 Lead and zinc ores
1040-1049 Gold & silver ores
1050-1059 Bauxite and other aluminum ores
1060-1069 Ferroalloy ores
1070-1079 Mining
1080-1089 Mining services
1090-1099 Misc metal ores
1100-1119 Anthracite mining
7353-7353 Services - heavy construction equip rental
7359-7359 Services - equip rental and leasing
7360-7369 Services - personnel supply services
7370-7372 Services - computer programming and data processing
7374-7374 Services - computer processing, data prep
7375-7375 Services - information retrieval services
7376-7376 Services - computer facilities management service
7377-7377 Services - computer rental and leasing
7378-7378 Services - computer maintenance and repair
7379-7379 Services - computer related services
7380-7380 Services - misc business services
7381-7382 Services - security
7383-7383 Services - news syndicates
7384-7384 Services - photofinishing labs
7385-7385 Services - telephone interconnections
7389-7390 Services - misc business services
7391-7391 Services - R&D labs
7392-7392 Services - management consulting & P.R.
7393-7393 Services - detective and protective (ADT)
7394-7394 Services - equipment rental & leasing
7395-7395 Services - photofinishing labs (School pictures)
7396-7396 Services - trading stamp services
7397-7397 Services - commercial testing labs
7399-7399 Services - business services
7500-7500 Services - auto repair, services
7510-7519 Services - truck, auto, trailer rental and leasing
7520-7529 Services - automobile parking
7530-7539 Services - auto repair shops
7540-7549 Services - auto services, except repair (car washes)
7600-7600 Services - Misc repair services
7620-7620 Services - Electrical repair shops
7622-7622 Services - Radio and TV repair shops
7623-7623 Services - Refridg and air conditioner repair
7629-7629 Services - Electrical repair shops
7630-7631 Services - Watch, clock and jewelry repair
7640-7641 Services - Reupholster, furniture repair
7690-7699 Services - Misc repair shops
8100-8199 Services - legal
8200-8299 Services - educational
8300-8399 Services - social services
8400-8499 Services - museums, galleries, botanic gardens
8600-8699 Services - membership organizations
8700-8700 Services - engineering, accounting, research,
management
8710-8713 Services - engineering, accounting, surveying
8720-8721 Services - accounting, auditing, bookkeeping
8730-8734 Services - research, development, testing labs
30 INDUSTRIES (continued)
1400-1499 Mining and quarrying non-metallic minerals
18 Coal Coal
1200-1299 Bituminous coal
19 Oil
Petroleum and Natural Gas
1300-1300 Oil and gas extraction
1310-1319 Crude petroleum & natural gas
1320-1329 Natural gas liquids
1330-1339 Petroleum and natural gas
1370-1379 Petroleum and natural gas
1380-1380 Oil and gas field services
1381-1381 Drilling oil & gas wells
1382-1382 Oil-gas field exploration
1389-1389 Oil and gas field services
2900-2912 Petroleum refining
2990-2999 Misc petroleum products
20 Util Utilities
4900-4900 Electric, gas, sanitary services
4910-4911 Electric services
4920-4922 Natural gas transmission
4923-4923 Natural gas transmission-distr
4924-4925 Natural gas distribution
4930-4931 Electric and other services combined
4932-4932 Gas and other services combined
4939-4939 Combination utilities
4940-4942 Water supply
21 Telcm Communication
4800-4800 Communications
4810-4813 Telephone communications
4820-4822 Telegraph and other message communication
4830-4839 Radio-TV Broadcasters
4840-4841 Cable and other pay TV services
4880-4889 Communications
4890-4890 Communication services (Comsat)
4891-4891 Cable TV operators
4892-4892 Telephone interconnect
4899-4899 Communication services
22 Servs Personal and Business Services
7020-7021 Rooming and boarding houses
7030-7033 Camps and recreational vehicle parks
7200-7200 Services - personal
7210-7212 Services - laundry, cleaners
7214-7214 Services - diaper service
7215-7216 Services - coin-op cleaners, dry cleaners
7217-7217 Services - carpet, upholstery cleaning
7218-7218 Services - industrial launderers
7219-7219 Services - laundry, cleaners
7220-7221 Services - photo studios, portrait
7230-7231 Services - beauty shops
7240-7241 Services - barber shops
7250-7251 Services - shoe repair
7260-7269 Services - funeral
7270-7290 Services - misc
7291-7291 Services - tax return
7292-7299 Services - misc
7300-7300 Services - business services
7310-7319 Services - advertising
7320-7329 Services - credit reporting agencies, collection
services
7330-7339 Services - mailing, reproduction, commercial art
7340-7342 Services - services to dwellings, other buildings
7349-7349 Services - cleaning and building maint
7350-7351 Services - misc equip rental and leasing
7352-7352 Services - medical equip rental
159
4220-4229 Warehousing and storage
4230-4231 Terminal facilities - motor freight
4240-4249 Transportation
4400-4499 Water transport
4500-4599 Air transportation
4600-4699 Pipelines, except natural gas
4700-4700 Transportation services
4710-4712 Freight forwarding
4720-4729 Travel agencies, etc
4730-4739 Arrange trans - freight and cargo
4740-4749 Rental of railroad cars
4780-4780 Misc services incidental to trans
4782-4782 Inspection and weighing services
4783-4783 Packing and crating
4784-4784 Fixed facilities for vehicles, not elsewhere classified
4785-4785 Motor vehicle inspection
4789-4789 Transportation services
30 INDUSTRIES (continued)
8740-8748 Services - management, public relations, consulting
8800-8899 Services - private households
8900-8910 Services - misc
8911-8911 Services - engineering & architect
8920-8999 Services - misc
23 BusEq Business Equipment
3570-3579 Office computers
3622-3622 Industrial controls
3661-3661 Telephone and telegraph apparatus
3662-3662 Communications equipment
3663-3663 Radio TV comm equip & apparatus
3664-3664 Search, navigation, guidance systems
3665-3665 Training equipment & simulators
3666-3666 Alarm & signaling products
3669-3669 Communication equipment
3670-3679 Electronic components
3680-3680 Computers
3681-3681 Computers - mini
3682-3682 Computers - mainframe
3683-3683 Computers - terminals
3684-3684 Computers - disk & tape drives
3685-3685 Computers - optical scanners
3686-3686 Computers - graphics
3687-3687 Computers - office automation systems
3688-3688 Computers - peripherals
3689-3689 Computers - equipment
3695-3695 Magnetic and optical recording media
3810-3810 Search, detection, navigation, guidance
3811-3811 Engr lab and research equipment
3812-3812 Search, detection, navigation, guidance
3820-3820 Measuring and controlling equipment
3821-3821 Lab apparatus and furniture
3822-3822 Automatic controls - Envir and applic
3823-3823 Industrial measurement instru
3824-3824 Totalizing fluid meters
3825-3825 Elec meas & test instr
3826-3826 Lab analytical instruments
3827-3827 Optical instr and lenses
3829-3829 Meas and control devices
3830-3839 Optical instr and lenses
7373-7373 Computer integrated systems design
24 Paper Business Supplies and Shipping Containers
2440-2449 Wood containers
2520-2549 Office furniture and fixtures
2600-2639 Paper and allied products
2640-2659 Paperboard containers, boxes, drums, tubs
2670-2699 Paper and allied products
2760-2761 Manifold business forms
3220-3221 Glass containers
3410-3412 Metal cans and shipping containers
3950-3955 Pens pencils and office supplies
26 Whlsl Wholesale
5000-5000 Wholesale - durable goods
5010-5015 Wholesale - autos and parts
5020-5023 Wholesale - furniture and home furnishings
5030-5039 Wholesale - lumber and construction materials
5040-5042 Wholesale - professional and commercial equipment and
supplies
5043-5043 Wholesale - photographic equipment
5044-5044 Wholesale - office equipment
5045-5045 Wholesale - computers
5046-5046 Wholesale - commercial equip
5047-5047 Wholesale - medical, dental equip
5048-5048 Wholesale - ophthalmic goods
5049-5049 Wholesale - professional equip and supplies
5050-5059 Wholesale - metals and minerals
5060-5060 Wholesale - electrical goods
5063-5063 Wholesale - electrical apparatus and equipment
5064-5064 Wholesale - electrical appliance TV and radio
5065-5065 Wholesale - electronic parts
5070-5078 Wholesale - hardware, plumbing, heating equip
5080-5080 Wholesale - machinery and equipment
5081-5081 Wholesale - machinery and equipment (?)
5082-5082 Wholesale - construction and mining equipment
5083-5083 Wholesale - farm and garden machinery
5084-5084 Wholesale - industrial machinery and equipment
5085-5085 Wholesale - industrial supplies
5086-5087 Wholesale - machinery and equipment (?)
5088-5088 Wholesale - trans eq except motor vehicles
5090-5090 Wholesale - misc durable goods
5091-5092 Wholesale - sporting goods, toys
5093-5093 Wholesale - scrap and waste materials
5094-5094 Wholesale - jewelry and watches
5099-5099 Wholesale - durable goods
5100-5100 Wholesale - nondurable goods
5110-5113 Wholesale - paper and paper products
5120-5122 Wholesale - drugs & proprietary
25 Trans Transportation
4000-4013 Railroads-line haul
4040-4049 Railway express service
4100-4100 Transit and passenger trans
4110-4119 Local passenger trans
4120-4121 Taxicabs
4130-4131 Intercity bus trans (Greyhound)
4140-4142 Bus charter
4150-4151 School buses
4170-4173 Motor vehicle terminals, service facilities
4190-4199 Misc transit and passenger transportation
4200-4200 Motor freight trans, warehousing
4210-4219 Trucking
160
5950-5959 Retail
5960-5969 Retail - non-store retailers (catalogs, etc)
5970-5979 Retail
5980-5989 Retail - fuel & ice stores (Penn Central Co)
5990-5990 Retail - retail stores
5992-5992 Retail - florists
5993-5993 Retail - tobacco stores
5994-5994 Retail - news dealers
5995-5995 Retail - computer stores
5999-5999 Retail stores
30 INDUSTRIES (continued)
5130-5139 Wholesale - apparel
5140-5149 Wholesale - groceries & related prods
5150-5159 Wholesale - farm products
5160-5169 Wholesale - chemicals & allied prods
5170-5172 Wholesale - petroleum and petro prods
5180-5182 Wholesale - beer, wine
5190-5199 Wholesale - non-durable goods
27 Rtail Retail
5200-5200 Retail - bldg material, hardware, garden
5210-5219 Retail - lumber & other building mat
5220-5229 Retail
5230-5231 Retail - paint, glass, wallpaper
5250-5251 Retail - hardware stores
5260-5261 Retail - nurseries, lawn, garden stores
5270-5271 Retail - mobile home dealers
5300-5300 Retail - general merchandise stores
5310-5311 Retail - department stores
5320-5320 Retail - general merchandise stores (?)
5330-5331 Retail - variety stores
5334-5334 Retail - catalog showroom
5340-5349 Retail
5390-5399 Retail - Misc general merchandise stores
5400-5400 Retail - food stores
5410-5411 Retail - grocery stores
5412-5412 Retail - convenience stores
5420-5429 Retail - meat, fish mkt
5430-5439 Retail - fruit and vegetable markets
5440-5449 Retail - candy, nut, confectionary stores
5450-5459 Retail - dairy product stores
5460-5469 Retail - bakeries
5490-5499 Retail - miscellaneous food stores
5500-5500 Retail - auto dealers and gas stations
5510-5529 Retail - auto dealers
5530-5539 Retail - auto and home supply stores
5540-5549 Retail - gasoline service stations
5550-5559 Retail - boat dealers
5560-5569 Retail - recreational vehicle dealers
5570-5579 Retail - motorcycle dealers
5590-5599 Retail - automotive dealers
5600-5699 Retail - apparel & acces
5700-5700 Retail - home furniture and equipment stores
5710-5719 Retail - home furnishings stores
5720-5722 Retail - household appliance stores
5730-5733 Retail - radio, TV and consumer electronic stores
5734-5734 Retail - computer and computer software stores
5735-5735 Retail - record and tape stores
5736-5736 Retail - musical instrument stores
5750-5799 Retail
5900-5900 Retail - misc
5910-5912 Retail - drug & proprietary stores
5920-5929 Retail - liquor stores
5930-5932 Retail - used merchandise stores
5940-5940 Retail - misc
5941-5941 Retail - sporting goods stores, bike shops
5942-5942 Retail - book stores
5943-5943 Retail - stationery stores
5944-5944 Retail - jewelry stores
5945-5945 Retail - hobby, toy and game shops
5946-5946 Retail - camera and photo shop
5947-5947 Retail - gift, novelty
5948-5948 Retail - luggage
5949-5949 Retail - sewing & needlework stores
28 Meals Restaurants, Hotels, Motels
5800-5819 Retail - eating places
5820-5829 Restaurants, hotels, motels
5890-5899 Eating and drinking places
7000-7000 Hotels, other lodging places
7010-7019 Hotels motels
7040-7049 Membership hotels and lodging
7213-7213 Services - linen
29 Fin Banking, Insurance, Real Estate, Trading
6000-6000 Depository institutions
6010-6019 Federal reserve banks
6020-6020 Commercial banks
6021-6021 National commercial banks
6022-6022 State banks - Fed Res System
6023-6024 State banks - not Fed Res System
6025-6025 National banks - Fed Res System
6026-6026 National banks - not Fed Res System
6027-6027 National banks, not FDIC
6028-6029 Banks
6030-6036 Savings institutions
6040-6059 Banks (?)
6060-6062 Credit unions
6080-6082 Foreign banks
6090-6099 Functions related to deposit banking
6100-6100 Nondepository credit institutions
6110-6111 Federal credit agencies
6112-6113 FNMA
6120-6129 S&Ls
6130-6139 Agricultural credit institutions
6140-6149 Personal credit institutions (Beneficial)
6150-6159 Business credit institutions
6160-6169 Mortgage bankers
6170-6179 Finance lessors
6190-6199 Financial services
6200-6299 Security and commodity brokers
6300-6300 Insurance
6310-6319 Life insurance
6320-6329 Accident and health insurance
6330-6331 Fire, marine, property-casualty ins
6350-6351 Surety insurance
6360-6361 Title insurance
6370-6379 Pension, health, welfare funds
6390-6399 Insurance carriers
6400-6411 Insurance agents
6500-6500 Real estate
6510-6510 Real estate operators
6512-6512 Operators - non-resident buildings
6513-6513 Operators - apartment buildings
6514-6514 Operators - other than apartment
6515-6515 Operators - residential mobile home
6517-6519 Lessors of real property
6520-6529 Real estate
6530-6531 Real estate agents and managers
161
6790-6791 Miscellaneous investing
6792-6792 Oil royalty traders
6793-6793 Commodity traders
6794-6794 Patent owners & lessors
6795-6795 Mineral royalty traders
6798-6798 REIT
6799-6799 Investors, NEC
30 INDUSTRIES (continued)
6532-6532 Real estate dealers
6540-6541 Title abstract offices
6550-6553 Real estate developers
6590-6599 Real estate
6610-6611 Combined real estate, insurance, etc
6700-6700 Holding, other investment offices
6710-6719 Holding offices
6720-6722 Investment offices
6723-6723 Management investment, closed-end
6724-6724 Unit investment trusts
6725-6725 Face-amount certificate offices
6730-6733 Trusts
6740-6779 Investment offices
30 Other Everything Else
4950-4959 Sanitary services
4960-4961 Steam, air conditioning supplies
4970-4971 Irrigation systems
4990-4991 Cogeneration - SM power producer
38 INDUSTRIES
1 Agric Agriculture, forestry, and fishing
0100-0999
2 Mines Mining
1000-1299
3 Oil Oil and Gas Extraction
1300-1399
4 Stone Nonmetallic Minerals Except Fuels
1400-1499
5 Cnstr Construction
1500-1799
6 Food Food and Kindred Products
2000-2099
7 Smoke Tobacco Products
2100-2199
8 Txtls Textile Mill Products
2200-2299
9 Apprl Apparel and other Textile Products
2300-2399
10 Wood Lumber and Wood Products
2400-2499
11 Chair Furniture and Fixtures
2500-2599
12 Paper Paper and Allied Products
2600-2661
13 Print Printing and Publishing
2700-2799
14 Chems Chemicals and Allied Products
2800-2899
15 Ptrlm Petroleum and Coal Products
2900-2999
16 Rubbr Rubber and Miscellaneous Plastics Products
3000-3099
17 Lethr Leather and Leather Products
3100-3199
18 Glass Stone, Clay and Glass Products
3200-3299
19 Metal Primary Metal Industries
3300-3399
20 MtlPr Fabricated Metal Products
3400-3499
21 Machn Machinery, Except Electrical
3500-3599
22 Elctr Electrical and Electronic Equipment
3600-3699
23 Cars Transportation Equipment
3700-3799
24 Instr Instruments and Related Products
3800-3879
25 Manuf Miscellaneous Manufacturing Industries
3900-3999
26 Trans Transportation
4000-4799
27 Phone Telephone and Telegraph Communication
4800-4829
28 TV Radio and Television Broadcasting
4830-4899
29 Utils Electric, Gas, and Water Supply
4900-4949
30 Garbg Sanitary Services
4950-4959
31 Steam Steam Supply
4960-4969
32 Water Irrigation Systems
4970-4979
33 Whlsl Wholesale
5000-5199
34 Rtail Retail Stores
5200-5999
35 Money Finance, Insurance, and Real Estate
6000-6999
36 Srvc Services
7000-8999
37 Govt Public Administration
9000-9999
38 Other Everything Else
162
48 INDUSTRIES
2391-2392 Curtains, home furnishings
2510-2519 Household furniture
2590-2599 Misc furniture and fixtures
2840-2843 Soap & other detergents
2844-2844 Perfumes cosmetics
3160-3161 Luggage
3170-3171 Handbags and purses
3172-3172 Personal leather goods, except handbags
3190-3199 Leather goods
3229-3229 Pressed and blown glass
3260-3260 Pottery and related products
3262-3263 China and earthenware table articles
3269-3269 Pottery products
3230-3231 Glass products
3630-3639 Household appliances
3750-3751 Motorcycles, bicycles and parts (Harley & Huffy)
3800-3800 Misc inst, photo goods, watches
3860-3861 Photographic equip (Kodak etc, but also Xerox)
3870-3873 Watches clocks and parts
3910-3911 Jewelry-precious metals
3914-3914 Silverware
3915-3915 Jewelers' findings, materials
3960-3962 Costume jewelry and notions
3991-3991 Brooms and brushes
3995-3995 Burial caskets
1 Agric Agriculture
0100-0199 Agric production - crops
0200-0299 Agric production - livestock
0700-0799 Agricultural services
0910-0919 Commercial fishing
2048-2048 Prepared feeds for animals
2 Food Food Products
2000-2009 Food and kindred products
2010-2019 Meat products
2020-2029 Dairy products
2030-2039 Canned-preserved fruits-vegs
2040-2046 Flour and other grain mill products
2050-2059 Bakery products
2060-2063 Sugar and confectionery products
2070-2079 Fats and oils
2090-2092 Misc food preps
2095-2095 Roasted coffee
2098-2099 Misc food preparations
3 Soda Candy & Soda
2064-2068 Candy and other confectionery
2086-2086 Bottled-canned soft drinks
2087-2087 Flavoring syrup
2096-2096 Potato chips
2097-2097 Manufactured ice
4 Beer Beer & Liquor
2080-2080 Beverages
2082-2082 Malt beverages
2083-2083 Malt
2084-2084 Wine
2085-2085 Distilled and blended liquors
5 Smoke Tobacco Products
2100-2199 Tobacco products
6 Toys Recreation
0920-0999 Fishing, hunting & trapping
3650-3651 Household audio visual equip
3652-3652 Phonographic records
3732-3732 Boat building and repair
3930-3931 Musical instruments
3940-3949 Toys
7 Fun Entertainment
7800-7829 Services - motion picture production and
distribution
7830-7833 Services - motion picture theatres
7840-7841 Services - video rental
7900-7900 Services - amusement and recreation
7910-7911 Services - dance studios
7920-7929 Services - bands, entertainers
7930-7933 Services - bowling centers
7940-7949 Services - professional sports
7980-7980 Amusement and recreation services (?)
7990-7999 Services - misc entertainment
8 Books Printing and Publishing
2700-2709 Printing publishing and allied
2710-2719 Newspapers: publishing-printing
2720-2729 Periodicals: publishing-printing
2730-2739 Books: publishing-printing
2740-2749 Misc publishing
2770-2771 Greeting card publishing
2780-2789 Book binding
2790-2799 Service industries for print trade
10 Clths Apparel
2300-2390 Apparel and other finished products
3020-3021 Rubber and plastics footwear
3100-3111 Leather tanning and finishing
3130-3131 Boot, shoe cut stock, findings
3140-3149 Footware except rubber
3150-3151 Leather gloves and mittens
3963-3965 Fasteners, buttons, needles, pins
11 Hlth Healthcare
8000-8099 Services - health
12 MedEq Medical Equipment
3693-3693 X-ray, electromedical app
3840-3849 Surg & med instru
3850-3851 Ophthalmic goods
13 Drugs Pharmaceutical Products
2830-2830 Drugs
2831-2831 Biological products
2833-2833 Medicinal chemicals
2834-2834 Pharmaceutical preparations
2835-2835 In vitro, in vivo diagnostics
2836-2836 Biological products, except diagnostics
14 Chems Chemicals
2800-2809 Chemicals and allied products
2810-2819 Industrial inorganical chems
2820-2829 Plastic material & synthetic resin
2850-2859 Paints
2860-2869 Industrial organic chems
2870-2879 Agriculture chemicals
2890-2899 Misc chemical products
9 Hshld Consumer Goods
2047-2047 Dog and cat food
163
3444-3444 Sheet metal work
3460-3469 Metal forgings and stampings
3470-3479 Coating and engraving
48 INDUSTRIES (continued)
15 Rubbr Rubber and Plastic Products
3031-3031 Reclaimed rubber
3041-3041 Rubber & plastic hose and belting
3050-3053 Gaskets, hoses, etc
3060-3069 Fabricated rubber products
3070-3079 Misc rubber products (?)
3080-3089 Misc plastic products
3090-3099 Misc rubber and plastic products (?)
16 Txtls Textiles
2200-2269 Textile mill products
2270-2279 Floor covering mills
2280-2284 Yarn and thread mills
2290-2295 Misc textile goods
2297-2297 Nonwoven fabrics
2298-2298 Cordage and twine
2299-2299 Misc textile products
2393-2395 Textile bags, canvas products
2397-2399 Misc textile products
17 BldMt Construction Materials
0800-0899 Forestry
2400-2439 Lumber and wood products
2450-2459 Wood buildings-mobile homes
2490-2499 Misc wood products
2660-2661 Building paper and board mills
2950-2952 Paving & roofing materials
3200-3200 Stone, clay, glass, concrete etc
3210-3211 Flat glass
3240-3241 Cement hydraulic
3250-3259 Structural clay prods
3261-3261 Vitreous china plumbing fixtures
3264-3264 Porcelain electrical supply
3270-3275 Concrete gypsum & plaster
3280-3281 Cut stone and stone products
3290-3293 Abrasive and asbestos products
3295-3299 Non-metallic mineral products
3420-3429 Hand tools and hardware
3430-3433 Heating equip & plumbing fix
3440-3441 Fabricated struct metal products
3442-3442 Metal doors, frames
3446-3446 Architectural or ornamental metal work
3448-3448 Pre-fab metal buildings
3449-3449 Misc structural metal work
3450-3451 Screw machine products
3452-3452 Bolts, nuts screws
3490-3499 Misc fabricated metal products
3996-3996 Hard surface floor cover
18 Cnstr Construction
1500-1511 Build construction - general contractors
1520-1529 Gen building contractors - residential
1530-1539 Operative builders
1540-1549 Gen building contractors - non-residential
1600-1699 Heavy Construction - not building
contractors
1700-1799 Construction - special contractors
21 Mach Machinery
3510-3519 Engines & turbines
3520-3529 Farm and garden machinery
3530-3530 Constr, mining material handling machinery
3531-3531 Construction machinery
3532-3532 Mining machinery, except oil field
3533-3533 Oil field machinery
3534-3534 Elevators
3535-3535 Conveyors
3536-3536 Cranes, hoists
3538-3538 Machinery
3540-3549 Metalworking machinery
3550-3559 Special industry machinery
3560-3569 General industrial machinery
3580-3580 Refrig & service ind machines
3581-3581 Automatic vending machines
3582-3582 Commercial laundry and dry cleaning machines
3585-3585 Air conditioning, heating, refrid eq
3586-3586 Measuring and dispensing pumps
3589-3589 Service industry machinery
3590-3599 Misc industrial and commercial equipment and mach
22 ElcEq Electrical Equipment
3600-3600 Elec mach eq & supply
3610-3613 Elec transmission
3620-3621 Electrical industrial appar
3623-3629 Electrical industrial appar
3640-3644 Electric lighting, wiring
3645-3645 Residential lighting fixtures
3646-3646 Commercial lighting
3648-3649 Lighting equipment
3660-3660 Communication equip
3690-3690 Miscellaneous electrical machinery and equip
3691-3692 Storage batteries
3699-3699 Electrical machinery and equip
23 Autos Automobiles and Trucks
2296-2296 Tire cord and fabric
2396-2396 Auto trim
3010-3011 Tires and inner tubes
3537-3537 Trucks, tractors, trailers
3647-3647 Vehicular lighting
3694-3694 Elec eq, internal combustion engines
3700-3700 Transportation equipment
3710-3710 Motor vehicles and motor vehicle equip
3711-3711 Motor vehicles & car bodies
3713-3713 Truck & bus bodies
3714-3714 Motor vehicle parts
3715-3715 Truck trailers
3716-3716 Motor homes
3792-3792 Travel trailers and campers
3790-3791 Misc trans equip
3799-3799 Misc trans equip
19 Steel Steel Works Etc
3300-3300 Primary metal industries
3310-3317 Blast furnaces & steel works
3320-3325 Iron & steel foundries
3330-3339 Prim smelt-refin nonfer metals
3340-3341 Secondary smelt-refin nonfer metals
3350-3357 Rolling & drawing nonferous metals
3360-3369 Non-ferrous foundries and casting
3370-3379 Steel works etc
3390-3399 Misc primary metal products
20 FabPr Fabricated Products
3400-3400 Fabricated metal, except machinery and trans eq
3443-3443 Fabricated plate work
164
48 INDUSTRIES (continued)
24 Aero Aircraft
3720-3720 Aircraft & parts
3721-3721 Aircraft
3723-3724 Aircraft engines, engine parts
3725-3725 Aircraft parts
3728-3729 Aircraft parts
25 Ships Shipbuilding, Railroad Equipment
3730-3731 Ship building and repair
3740-3743 Railroad Equipment
26 Guns Defense
3760-3769 Guided missiles and space vehicles
3795-3795 Tanks and tank components
3480-3489 Ordnance & accessories
27 Gold Precious Metals
1040-1049 Gold & silver ores
28 Mines Non-Metallic and Industrial Metal Mining
1000-1009 Metal mining
1010-1019 Iron ores
1020-1029 Copper ores
1030-1039 Lead and zinc ores
1050-1059 Bauxite and other aluminum ores
1060-1069 Ferroalloy ores
1070-1079 Mining
1080-1089 Mining services
1090-1099 Misc metal ores
1100-1119 Anthracite mining
1400-1499 Mining and quarrying non-metallic minerals
29 Coal Coal
1200-1299 Bituminous coal
30 Oil
Petroleum and Natural Gas
1300-1300 Oil and gas extraction
1310-1319 Crude petroleum & natural gas
1320-1329 Natural gas liquids
1330-1339 Petroleum and natural gas
1370-1379 Petroleum and natural gas
1380-1380 Oil and gas field services
1381-1381 Drilling oil & gas wells
1382-1382 Oil-gas field exploration
1389-1389 Oil and gas field services
2900-2912 Petroleum refining
2990-2999 Misc petroleum products
31 Util Utilities
4900-4900 Electric, gas, sanitary services
4910-4911 Electric services
4920-4922 Natural gas transmission
4923-4923 Natural gas transmission-distr
4924-4925 Natural gas distribution
4930-4931 Electric and other services combined
4932-4932 Gas and other services combined
4939-4939 Combination utilities
4940-4942 Water supply
33 PerSv Personal Services
7020-7021 Rooming and boarding houses
7030-7033 Camps and recreational vehicle parks
7200-7200 Services - personal
7210-7212 Services - laundry, cleaners
7214-7214 Services - diaper service
7215-7216 Services - coin-op cleaners, dry cleaners
7217-7217 Services - carpet, upholstery cleaning
7219-7219 Services - laundry, cleaners
7220-7221 Services - photo studios, portrait
7230-7231 Services - beauty shops
7240-7241 Services - barber shops
7250-7251 Services - shoe repair
7260-7269 Services - funeral
7270-7290 Services - misc
7291-7291 Services - tax return
7292-7299 Services - misc
7395-7395 Services - photofinishing labs (School pictures)
7500-7500 Services - auto repair, services
7520-7529 Services - automobile parking
7530-7539 Services - auto repair shops
7540-7549 Services - auto services, except repair (car washes)
7600-7600 Services - Misc repair services
7620-7620 Services - Electrical repair shops
7622-7622 Services - Radio and TV repair shops
7623-7623 Services - Refridg and air conditioner repair
7629-7629 Services - Electrical repair shops
7630-7631 Services - Watch, clock and jewelry repair
7640-7641 Services - Reupholster, furniture repair
7690-7699 Services - Misc repair shops
8100-8199 Services - legal
8200-8299 Services - educational
8300-8399 Services - social services
8400-8499 Services - museums, galleries, botanic gardens
8600-8699 Services - membership organizations
8800-8899 Services - private households
34 BusSv Business Services
2750-2759 Commercial printing
3993-3993 Signs, advertising specialty
7218-7218 Services - industrial launderers
7300-7300 Services - business services
7310-7319 Services - advertising
7320-7329 Services - credit reporting agencies, collection
services
7330-7339 Services - mailing, reproduction, commercial art
7340-7342 Services - services to dwellings, other buildings
7349-7349 Services - cleaning and building maint
7350-7351 Services - misc equip rental and leasing
7352-7352 Services - medical equip rental
7353-7353 Services - heavy construction equip rental
7359-7359 Services - equip rental and leasing
7360-7369 Services - personnel supply services
7370-7372 Services - computer programming and data
processing
32 Telcm Communication
4800-4800 Communications
4810-4813 Telephone communications
4820-4822 Telegraph and other message
communication
4830-4839 Radio-TV Broadcasters
4840-4841 Cable and other pay TV services
4880-4889 Communications
4890-4890 Communication services (Comsat)
4891-4891 Cable TV operators
4892-4892 Telephone interconnect
4899-4899 Communication services
165
3826-3826 Lab analytical instruments
3827-3827 Optical instr and lenses
3829-3829 Meas and control devices
3830-3839 Optical instr and lenses
48 INDUSTRIES (continued)
7374-7374 Services - computer processing, data prep
7375-7375 Services - information retrieval services
7376-7376 Services - computer facilities management
service
7377-7377 Services - computer rental and leasing
7378-7378 Services - computer maintenance and repair
7379-7379 Services - computer related services
7380-7380 Services - misc business services
7381-7382 Services - security
7383-7383 Services - news syndicates
7384-7384 Services - photofinishing labs
7385-7385 Services - telephone interconnections
7389-7390 Services - misc business services
7391-7391 Services - R&D labs
7392-7392 Services - management consulting & P.R.
7393-7393 Services - detective and protective (ADT)
7394-7394 Services - equipment rental & leasing
7396-7396 Services - trading stamp services
7397-7397 Services - commercial testing labs
7399-7399 Services - business services
7510-7519 Services - truck, auto, trailer rental and
leasing
8700-8700 Services - engineering, accounting,
research, management
8710-8713 Services - engineering, accounting,
surveying
8720-8721 Services - accounting, auditing,
bookkeeping
8730-8734 Services - research, development, testing
labs
8740-8748 Services - management, public relations,
consulting
8900-8910 Services - misc
8911-8911 Services - engineering & architect
8920-8999 Services - misc
35 Comps Computers
3570-3579 Office computers
3680-3680 Computers
3681-3681 Computers - mini
3682-3682 Computers - mainframe
3683-3683 Computers - terminals
3684-3684 Computers - disk & tape drives
3685-3685 Computers - optical scanners
3686-3686 Computers - graphics
3687-3687 Computers - office automation systems
3688-3688 Computers - peripherals
3689-3689 Computers - equipment
3695-3695 Magnetic and optical recording media
7373-7373 Computer integrated systems design
36 Chips Electronic Equipment
3622-3622 Industrial controls
3661-3661 Telephone and telegraph apparatus
3662-3662 Communications equipment
3663-3663 Radio TV comm equip & apparatus
3664-3664 Search, navigation, guidance systems
3665-3665 Training equipment & simulators
3666-3666 Alarm & signaling products
3669-3669 Communication equipment
3670-3679 Electronic components
3810-3810 Search, detection, navigation, guidance
3812-3812 Search, detection, navigation, guidance
38 Paper Business Supplies
2520-2549 Office furniture and fixtures
2600-2639 Paper and allied products
2670-2699 Paper and allied products
2760-2761 Manifold business forms
3950-3955 Pens pencils and office supplies
39 Boxes Shipping Containers
2440-2449 Wood containers
2640-2659 Paperboard containers, boxes, drums, tubs
3220-3221 Glass containers
3410-3412 Metal cans and shipping containers
40 Trans Transportation
4000-4013 Railroads-line haul
4040-4049 Railway express service
4100-4100 Transit and passenger trans
4110-4119 Local passenger trans
4120-4121 Taxicabs
4130-4131 Intercity bus trans (Greyhound)
4140-4142 Bus charter
4150-4151 School buses
4170-4173 Motor vehicle terminals, service facilities
4190-4199 Misc transit and passenger transportation
4200-4200 Motor freight trans, warehousing
4210-4219 Trucking
4220-4229 Warehousing and storage
4230-4231 Terminal facilities - motor freight
4240-4249 Transportation
4400-4499 Water transport
4500-4599 Air transportation
4600-4699 Pipelines, except natural gas
4700-4700 Transportation services
4710-4712 Freight forwarding
4720-4729 Travel agencies, etc
4730-4739 Arrange trans - freight and cargo
4740-4749 Rental of railroad cars
4780-4780 Misc services incidental to trans
4782-4782 Inspection and weighing services
4783-4783 Packing and crating
4784-4784 Fixed facilities for vehicles, not elsewhere classified
4785-4785 Motor vehicle inspection
4789-4789 Transportation services
41 Whlsl Wholesale
5000-5000 Wholesale - durable goods
5010-5015 Wholesale - autos and parts
5020-5023 Wholesale - furniture and home furnishings
5030-5039 Wholesale - lumber and construction materials
5040-5042 Wholesale - professional and commercial equipment
and supplies
37 LabEq Measuring and Control Equipment
3811-3811 Engr lab and research equipment
3820-3820 Measuring and controlling equipment
3821-3821 Lab apparatus and furniture
3822-3822 Automatic controls - Envir and applic
3823-3823 Industrial measurement instru
3824-3824 Totalizing fluid meters
3825-3825 Elec meas & test instr
166
5570-5579 Retail - motorcycle dealers
5590-5599 Retail - automotive dealers
5600-5699 Retail - apparel & acces
5700-5700 Retail - home furniture and equipment stores
5710-5719 Retail - home furnishings stores
5720-5722 Retail - household appliance stores
5730-5733 Retail - radio, TV and consumer electronic stores
5734-5734 Retail - computer and computer software stores
5735-5735 Retail - record and tape stores
5736-5736 Retail - musical instrument stores
5750-5799 Retail
5900-5900 Retail - misc
5910-5912 Retail - drug & proprietary stores
5920-5929 Retail - liquor stores
5930-5932 Retail - used merchandise stores
5940-5940 Retail - misc
5941-5941 Retail - sporting goods stores, bike shops
5942-5942 Retail - book stores
5943-5943 Retail - stationery stores
5944-5944 Retail - jewelry stores
5945-5945 Retail - hobby, toy and game shops
5946-5946 Retail - camera and photo shop
5947-5947 Retail - gift, novelty
5948-5948 Retail - luggage
5949-5949 Retail - sewing & needlework stores
5950-5959 Retail
5960-5969 Retail - non-store retailers (catalogs, etc)
5970-5979 Retail
5980-5989 Retail - fuel & ice stores (Penn Central Co)
5990-5990 Retail - retail stores
5992-5992 Retail - florists
5993-5993 Retail - tobacco stores
5994-5994 Retail - news dealers
5995-5995 Retail - computer stores
5999-5999 Retail stores
48 INDUSTRIES (continued)
5043-5043 Wholesale - photographic equipment
5044-5044 Wholesale - office equipment
5045-5045 Wholesale - computers
5046-5046 Wholesale - commercial equip
5047-5047 Wholesale - medical, dental equip
5048-5048 Wholesale - ophthalmic goods
5049-5049 Wholesale - professional equip and supplies
5050-5059 Wholesale - metals and minerals
5060-5060 Wholesale - electrical goods
5063-5063 Wholesale - electrical apparatus and
equipment
5064-5064 Wholesale - electrical appliance TV and
radio
5065-5065 Wholesale - electronic parts
5070-5078 Wholesale - hardware, plumbing, heating
equip
5080-5080 Wholesale - machinery and equipment
5081-5081 Wholesale - machinery and equipment (?)
5082-5082 Wholesale - construction and mining
equipment
5083-5083 Wholesale - farm and garden machinery
5084-5084 Wholesale - industrial machinery and
equipment
5085-5085 Wholesale - industrial supplies
5086-5087 Wholesale - machinery and equipment (?)
5088-5088 Wholesale - trans eq except motor vehicles
5090-5090 Wholesale - misc durable goods
5091-5092 Wholesale - sporting goods, toys
5093-5093 Wholesale - scrap and waste materials
5094-5094 Wholesale - jewelry and watches
5099-5099 Wholesale - durable goods
5100-5100 Wholesale - nondurable goods
5110-5113 Wholesale - paper and paper products
5120-5122 Wholesale - drugs & proprietary
5130-5139 Wholesale - apparel
5140-5149 Wholesale - groceries & related prods
5150-5159 Wholesale - farm products
5160-5169 Wholesale - chemicals & allied prods
5170-5172 Wholesale - petroleum and petro prods
5180-5182 Wholesale - beer, wine
5190-5199 Wholesale - non-durable goods
42 Rtail Retail
5200-5200 Retail - bldg material, hardware, garden
5210-5219 Retail - lumber & other building mat
5220-5229 Retail
5230-5231 Retail - paint, glass, wallpaper
5250-5251 Retail - hardware stores
5260-5261 Retail - nurseries, lawn, garden stores
5270-5271 Retail - mobile home dealers
5300-5300 Retail - general merchandise stores
5310-5311 Retail - department stores
5320-5320 Retail - general merchandise stores
5330-5331 Retail - variety stores
5334-5334 Retail - catalog showroom
5340-5349 Retail
5390-5399 Retail - Misc general merchandise stores
5400-5400 Retail - food stores
5410-5411 Retail - grocery stores
5412-5412 Retail - convenience stores
5420-5429 Retail - meat, fish mkt
5430-5439 Retail - fruit and vegetable markets
5440-5449 Retail - candy, nut, confectionary stores
5450-5459 Retail - dairy product stores
5460-5469 Retail - bakeries
5490-5499 Retail - miscellaneous food stores
5500-5500 Retail - auto dealers and gas stations
5510-5529 Retail - auto dealers
5530-5539 Retail - auto and home supply stores
5540-5549 Retail - gasoline service stations
5550-5559 Retail - boat dealers
5560-5569 Retail - recreational vehicle dealers
43 Meals Restaurants, Hotels, Motels
5800-5819 Retail - eating places
5820-5829 Restaurants, hotels, motels
5890-5899 Eating and drinking places
7000-7000 Hotels, other lodging places
7010-7019 Hotels motels
7040-7049 Membership hotels and lodging
7213-7213 Services - linen
44 Banks Banking
6000-6000 Depository institutions
6010-6019 Federal reserve banks
6020-6020 Commercial banks
6021-6021 National commercial banks
6022-6022 State banks - Fed Res System
6023-6024 State banks - not Fed Res System
6025-6025 National banks - Fed Res System
6026-6026 National banks - not Fed Res System
167
9900-9999 Not classifiable
48 INDUSTRIES (continued)
6027-6027 National banks, not FDIC
6028-6029 Banks
6030-6036 Savings institutions
6040-6059 Banks (?)
6060-6062 Credit unions
6080-6082 Foreign banks
6090-6099 Functions related to deposit banking
6100-6100 Nondepository credit institutions
6110-6111 Federal credit agencies
6112-6113 FNMA
6120-6129 S&Ls
6130-6139 Agricultural credit institutions
6140-6149 Personal credit institutions (Beneficial)
6150-6159 Business credit institutions
6160-6169 Mortgage bankers
6170-6179 Finance lessors
6190-6199 Financial services
45 Insur Insurance
6300-6300 Insurance
6310-6319 Life insurance
6320-6329 Accident and health insurance
6330-6331 Fire, marine, property-casualty ins
6350-6351 Surety insurance
6360-6361 Title insurance
6370-6379 Pension, health, welfare funds
6390-6399 Insurance carriers
6400-6411 Insurance agents
46 RlEst Real Estate
6500-6500 Real estate
6510-6510 Real estate operators
6512-6512 Operators - non-resident buildings
6513-6513 Operators - apartment buildings
6514-6514 Operators - other than apartment
6515-6515 Operators - residential mobile home
6517-6519 Lessors of real property
6520-6529 Real estate
6530-6531 Real estate agents and managers
6532-6532 Real estate dealers
6540-6541 Title abstract offices
6550-6553 Real estate developers
6590-6599 Real estate
6610-6611 Combined real estate, insurance, etc
47 Fin Trading
6200-6299 Security and commodity brokers
6700-6700 Holding, other investment offices
6710-6719 Holding offices
6720-6722 Investment offices
6723-6723 Management investment, closed-end
6724-6724 Unit investment trusts
6725-6725 Face-amount certificate offices
6730-6733 Trusts
6740-6779 Investment offices
6790-6791 Miscellaneous investing
6792-6792 Oil royalty traders
6793-6793 Commodity traders
6794-6794 Patent owners & lessors
6795-6795 Mineral royalty traders
6798-6798 REIT
6799-6799 Investors, NEC
48 Other Miscellaneous (also includes other SIC codes not in a specific
industry)
3990-3990 Misc manufacturing industries
3992-3994 Misc manufacturing industries
3997-3999 Misc manufacturing industries
4950-4959 Sanitary services
4960-4961 Steam, air conditioning supplies
4970-4971 Irrigation systems
4990-4991 Cogeneration - SM power producer
168
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BIBLIOGRAPHICAL SKETCH
Barry Marchman is originally from Macon, Georgia.
He received his
undergraduate degree in Electrical Engineering from Georgia Institute of
Technology and worked as an engineer for several multinational companies
before beginning his doctoral work at Florida State University. He has been
married to Evelyn Steen since March 1995 and together they have had six
children (four while doing doctoral work).
Florida.
176
He currently resides in Tallahassee,