THE CHOICE OF IPO VERSUS TAKEOVER: EMPIRICAL EVIDENCE
James C. Brau
Assistant Professor of Finance
Department of Business Management
Marriott School, TNRB 660
Brigham Young University
Provo, UT 84602
Phone: (801) 378-8952
Fax: (801) 378-5984
E-mail: [email protected]
Bill Francis
Associate Professor of Finance
Department of Finance
University of South Florida
4202 East Fowler Avenue, BSN 3403
Tampa, FL 33647-5500
Phone: (813) 974-6330
E-mail: [email protected]
Ninon Kohers
Assistant Professor of Finance
Department of Finance
University of South Florida
4202 East Fowler Avenue, BSN 3403
Tampa, FL 33647-5500
Phone: (813) 974-6337
E-mail: [email protected]
First: January 2000
Current: June 2001
The authors thank Rob Daines, Hal Heaton, Andrew Holmes, Mike Lemmon, Beverly Marshall,
Grant McQueen, Bill Megginson, Todd Mitton, Mike Pinegar, Mike Schill, Bernell Stone, Steve
Thorley, Keith Vorkink, participants at the BYU and USF finance seminars, participants at the
2000 FMA conference, an anonymous referee, and the editor (Albert Madansky) for helpful
comments. All omissions/errors are the responsibility of the authors.
The Choice of IPO Versus Takeover: Empirical Evidence
Abstract
Private firm owners interested in gaining increased access to public capital, increasing
their liquidity, and/or changing the control of their firms, face a fundamental choice between an
initial public offering (IPO) or a takeover by a public acquirer. Using a sample of over 9,500
U.S. privately held firms, we address the IPO versus takeover issue by examining market-timing,
industry, deal-specific, and fund demand factors of the IPO versus acquisition choice. Our
results show that the concentration of the industry, the high-tech status of the private firm, the
current cost of debt, the ‘hotness’ of the IPO market relative to the takeover market, the
percentage of insider ownership, and the size of the firm are all positively related to the
probability that a firm will conduct an IPO. In contrast, private companies in high market-tobook industries, firms in financial service sectors, firms in highly leveraged industries, and deals
involving greater liquidity for selling insiders show a stronger likelihood for takeovers. Finally,
a quantitative analysis of the premiums associated with the IPO versus takeover decision
provides evidence that a liquidity discount exists in takeovers relative to IPOs.
1
I. Introduction
Recent literature has highlighted a privately held firm's choice of going public via an
initial public offering (IPO) or staying private.1 This literature addresses only two outcomes,
staying private or conducting an IPO; however an important, yet unexplored alternative pathway
exists for private firms wishing to access public equity markets. Agreeing to a takeover by a
publicly traded acquirer is often an attractive opportunity for private firms and presents an
alternative to the IPO route.
In this study, we focus on the following four key factors advanced in the current IPO and
merger and acquisition (M&A) literature that can impact the IPO versus takeover decision: first,
industry characteristics (e.g., Mitchell and Mulherin (1996), Pagano et al. (1998), Maksimovic
and Pichler (2001), Stoughton, Wong and Zechner (2001)); second, the role of market-timing
(e.g., Ritter (1984), DeLong, Shleifer, Summers, and Waldmann (1990), Golbe and White
(1993), and Rajan and Servaes (1997)); third, the demand for funds by private firms (e.g.,
Mikkelson, Partch and Shah (1997) and Lowry (2000)); and fourth, deal-specific factors such as
the size of the firm, insider ownership, and the liquidity effects of the deal. In the extant
literature these four factors are important separately for IPOs and takeovers. However, to our
knowledge no existing study examines the choice of conducting an IPO or agreeing to a
takeover. In this study, we attempt to fill this void by evaluating the impact of industry, markettiming, deal-specific, and fund demand effects on a private firm’s choice of an IPO versus a
takeover.
Anecdotal evidence from the financial press indicates that our extension is not trivial. An
increasing number of articles have drawn attention to the choice between a takeover or an IPO.
For example, a recent Fortune article focuses on the strategic restructuring decisions of serial
2
entrepreneurs. These entrepreneurs create and grow businesses with the express purpose of
selling out to large publicly traded firms in lieu of conducting an IPO. Many of these same
entrepreneurs previously focused on IPOs and have recently changed to the takeover strategy
(Gimein (2/21/2000)). As another example, a recent Wall Street Journal article tells how the
insiders at Cerent deviated from their original IPO plan and sold-out to Cisco instead, in a classic
example of what has become known as a ‘pick-off’ (Thurm (3/1/2000)). Another Wall Street
Journal article entitled, "Many Firms Take Buyouts after Planning IPOs," articulates several of
the ideas that we formally test in our paper (Fitch and Benjamin (2/27/1998)). Specifically, the
article discusses the dual paths to equity markets (i.e., takeover or IPO) and emphasizes how
factors such as market and industry conditions impact the choice of a takeover or an IPO. The
connection between the takeover and IPO markets is further highlighted by the observation that
surges in IPO volume have been associated with downturns in takeover activity (July-August,
1996, Mergers and Acquisitions, p. 5).
The lack of an academic study on this fundamental restructuring choice as well as the
recognition of this issue in the current financial press and among practitioners motivate us to
explore which factors determine whether a privately-held firm reorganizes via an IPO or a
takeover. Although both choices allow firms to access public equity markets (directly with an
IPO and indirectly with a takeover), we conjecture that different motivations and market
conditions exist that impact the choice between an IPO and a takeover.
Two important motives for choosing an IPO or a takeover may be the level of liquidity
and ownership insiders require following the completion of the transaction.
The takeover
arrangement may make cashing out (or significantly increasing liquidity) more efficient than an
IPO for the insiders of the firm. Leland and Pyle (1977) argue that insiders who sell large
3
portions of their firm in the IPO send a signal that the firm is overvalued. Insiders who attempt
to liquidate by selling a large amount of personally owned (i.e., secondary) shares in the IPO
may depress the price of their firm and decrease both the amount raised in an IPO and the
probability of full subscription through the negative signal they convey.
These negative
signaling effects are less likely in takeovers, since acquiring firms might face fewer information
asymmetries relating to the target firm’s value (see Leland and Pyle, 1977). Thus, takeovers
offer selling insiders the ability to divest the entire firm by selling to an existing company that
may not interpret the exit by insiders as a negative signal.
Closely related to the issue of liquidity is that of ownership and control. Insiders who
wish to maintain a controlling ownership in the firm while obtaining access to capital markets
may prefer an IPO. Relative to target insiders, IPO insiders do not have an acquiring firm to deal
with in matters of control, and depending on the proportion of primary to secondary shares may
retain effective ownership after the IPO. In our empirical analysis, we examine the relative
importance of insider ownership in the IPO versus takeover decision to ascertain whether, on
average, differences in control preferences drive private firm owners to choose one type of
restructuring route over the other.
In addition to examining the liquidity and ownership effects of the IPO versus takeover
decision, we investigate external factors that can influence the relative attractiveness of IPOs and
takeovers for private firms. Specifically, we argue that certain macroeconomic, stock market and
industry factors are important determinants in a private firm's restructuring decision.2 Our
investigation of these external influences shows that the degree of concentration of the private
firm’s industry, the high-tech industry affiliation of the firm, the ‘hotness’ of the IPO market
relative to the takeover market, and the current cost of debt are positively related to the
4
probability that a firm will conduct an IPO. Also, an analysis of the influence of certain deal
related factors on the IPO versus takeover decision reveals that larger private firms are more
likely to choose IPOs, and the level of post deal insider ownership tends to be higher for IPOs
than takeovers. In contrast, our results indicate that private companies in high market-to-book
industries, in financial service industries, and in high debt industries show a stronger likelihood
for takeovers. Further, examination of the liquidity versus ownership implications of these two
types of transactions indicates that the level of post deal insider liquidity (ownership) tends to be
higher (lower) for takeovers than for IPOs.
In a separate comparison of premiums earned by insiders of IPOs versus insiders of
takeovers, we find that in the aggregate sample, target insiders receive a takeover payoff that
equals approximately 78 percent of an IPO payoff. Regression analysis suggests that takeover
insiders are willing to accept this 22 percent discount due to the greater liquidity they obtain.
The remainder of this paper proceeds as follows. Section II contains a discussion of the
theoretical underpinnings for our empirical tests.
In Section III, we present the data and
difference testing between samples consisting of takeover and IPO firms. Section IV contains
multivariate tests of the hypotheses and Section V contains an analysis of the premiums received
by issuers in an IPO and sellers in a takeover. We summarize and conclude in Section VI.
II. Factors Influencing the Relative Attractiveness of IPOs versus Takeovers
In examining the IPO versus private target takeover decision, we focus on certain
industry-related characteristics, market-timing factors, deal-specific factors, and fund demand
determinants that can influence the relative attractiveness of one restructuring route versus the
other. We discuss the possible impact of these factors in the following subsections. Most of the
variables we examine are broad market and industry-related factors and, thus, a clear theoretical
5
prediction for each factor's influence on the IPO versus takeover decision is not always available.
For variables with no clear prediction, we analyze both sides of the issue by presenting the
opposing arguments that predict how each factor can influence the decision to choose an IPO or
a takeover. After discussing the potential effect of each factor, we test these empirical issues by
examining the relative importance of each variable on the IPO versus takeover choice. For
convenience, Table I provides a succinct summary of this section. The first column of Table I
lists the variables in question. The second column provides the arguments in favor of an IPO,
while the third column provides the arguments in support of a takeover.
[INSERT TABLE I ABOUT HERE.]
A. Industry Related Factors
Panel A of Table I lists the industry-related factors that are likely to determine the choice
of an IPO or takeover. First, the level of concentration within an industry may influence whether
privately held firms conduct IPOs or instead agree to acquisitions. The likelihood of a takeover,
as opposed to an IPO, can be smaller in relatively high concentration industries because high
concentration industry environments would have less potential for further consolidation.
Further, anti-trust concerns and government scrutiny would be more prevalent in high
concentration industries, leading to greater difficulties in implementing takeovers.
Thus,
mergers, as opposed to IPOs, would be more likely to occur in low concentration (fragmented)
industries.
However, industries with lower concentration may not be inclined towards increased
concentration if the technological dynamics and demand characteristics of the industry make
6
concentration unattractive as an industrial strategy.3 Further, firm survival can be more difficult
in industries that are highly concentrated, making the takeover route an attractive strategy for
smaller, less competitive private firms (see Sharma and Kesner (1996) and Audretsch (1995)).
Thus, given these two counter-arguments, the role of industry concentration in influencing the
relative attractiveness of IPOs versus takeovers becomes an empirical issue. Similar to Pagano
et al. (1998), and others, we employ the Herfindahl index as a measure of the degree of
competition within an industry.4 In essence, this index represents the sum of squared market
shares of all members of a particular industry and is a measure of the degree of concentration
within an industry. Higher index values indicate higher industry concentration. The Herfindahl
index is calculated using sales data obtained from Compustat.
In addition to examining the importance of the degree of concentration within industries,
we also test for the influence of distinct types of industries through the use of industry dummy
variables. Investor enthusiasm towards newly public firms operating in high-tech industries in
recent years illustrates the popularity of these new, emerging fields and technologies among
investors searching for the next super growth IPO (see Business Week, March 31, 1997, p. 58).
For example, the notoriously lofty valuations of many internet companies that are often not
expected to produce positive earnings in the near future, have provided some indication of the
favorable perception of firms involved in certain high-tech pursuits and emerging industries
(Maksimovic and Pichler (2001)). Given the popularity of high-tech IPOs among investors over
the time period examined, privately held high-tech firms may be more likely to capitalize on this
enthusiasm by choosing an IPO instead of agreeing to a takeover (Allen and Gale (1999)).
Further, a firm’s decision to go public provides a signal to customers and investors that the
company is willing to provide the periodical Security and Exchange Commission reporting
7
documents and undergo the scrutiny of outside analysts. This aspect of going public can be of
particular importance to firms in high-tech industries where there is a significant amount of
uncertainty about the quality of its product(s) and where competitive dynamics are an important
consideration for the firm’s longevity (Stoughton, Wong, and Zechner (2001)).
In contrast, Yosha (1995) and Maksimovic and Pichler (2001) argue that the potential
loss of confidentiality can deter high-tech firms from choosing an IPO. Additionally, in recent
years high-tech firms have been attractive takeover targets for acquiring firms in search of
enhanced growth opportunities. In examining this high-tech attraction, Kohers and Kohers
(2000) find that the premiums paid for high-tech targets during the late 80’s through the mid 90’s
were significantly larger than those paid for non-high-tech targets. In our empirical analysis, we
examine which of the predictions is more important in explaining the tendency of private hightech firms to choose an IPO or to agree to a takeover. High-tech industry identifications are
based on classifications made by Securities Data Company (SDC), and include areas in
biotechnology, chemicals, computers, defense, electronics, communications, medical, and
pharmaceuticals, among others.
In addition to isolating high-tech industries, we test for effects associated with private
firms operating in financial service industries. The deregulation occurring in financial services,
in conjunction with the relatively high degree of fragmentation within these industries, has
created an environment that is conducive towards acquisitions and widespread consolidation.
Berger, Kashyap and Scalise (1995) and Berger, Demsetz, and Strahan (1999) present evidence
that supports the consolidation hypothesis. Specifically, they show that consolidation-promoting
bank policy that lifts geographic restrictions (for example, the Riegle-Neal Interstate Banking
and Branching Efficiency Act of 1994) leads to mergers. Institutions that merge often benefit
8
from economies of scale that allow income to grow faster than expenses. Further, although the
firms we examine are privately held companies, public information is often available for
financial firms. As a result, the information acquisition role for conducting an IPO becomes less
important (Shah and Thakor (1988), Holmstrom and Tirole (1993), van Bommel (1997), Maug
(1999), and Stoughton, Wong, and Zechner (2001)). Based on the consolidation and information
acquisition arguments, financial service firms would be more likely to agree to a takeover rather
than to choose an IPO.
However, the consolidation occurring in financial services’ areas might also promote IPO
activity. Because bidders’ stock is often used as a method of payment in these types of mergers,
potential acquirers that are privately held could be constrained from engaging in merger activity
because of their limited financial options. Conducting an IPO would provide the firm with a
publicly traded stock that could be used as a payment method in future acquisitions. This
enhanced ability to undertake acquisitions and adapt to changing industry conditions could, in
turn, increase the firm’s competitiveness within the industry. In a similar view, even if stock is
not needed for acquisitions, the cash raised from public equity sales allow financial service firms
to take advantage of the deregulatory environment.
Our next industry-related factor captures the capital structure characteristics of the private
firm’s industry. Following Pagano et al. (1998), we use the firm's debt level as a proxy for
financial risk. Because leverage ratios are not available for many of the private firms in our
sample, we assume an equilibrium capital structure in industries and proxy for firm leverage with
the industry leverage ratio (measured as debt/assets). Given the data constraints, this proxy
seems reasonable since previous studies such as Bradley, Jarrell, and Kim (1984) have
documented strong intra-industry similarities in individual firm leverage ratios. When evaluating
9
privately-held firms, investors may perceive high debt levels as a potential risk factor, especially
since these investors do not have wide access to other sources of reliable information that would
be useful in evaluating these companies. Thus, private firms from highly levered industries may
be viewed with an extra degree of caution. As such, all else constant, these firms would be more
severely underpriced in an IPO transaction, thereby leaving a significant amount of money on the
table.
Consequently, the more conservative takeover route may be the more appealing
restructuring path for private firms belonging to highly leveraged industries.
However, an alternative is that highly leveraged firms are generally required to undergo
close scrutiny and monitoring by creditors, making the investigation costs lower for these firms
(see Harris and Raviv (1990, 1991)). If these firms have already passed the hurdles of lenders,
potential shareholders may free ride on prior bondholder and bank scrutiny and view more highly
levered firms as attractive IPO candidates. These observations suggest that firms in highly
leveraged industries are likely to use IPOs as their restructuring choice. Overall, in view of these
competing arguments, the effect of the industry’s leverage ratio on the choice of IPO versus
takeover is an issue to be resolved through empirical testing.
We also employ the market-to-book ratio of the private firm’s industry to capture the
potential influence of industry valuations on the IPO versus takeover choice.
In their
examination of the going public decision, Pagano et al. (1998) note that high market-to-book
ratios in an industry may create an environment conducive to IPOs. Alternatively, takeovers
may be attractive for private firms in high market-to-book industries since these multiples can
serve as a basis in the target valuation process (Golbe and White (1993)). Our empirical analysis
is designed to reveal the relative importance of industry valuations, as proxied by the market-tobook ratio, for the IPO and private target takeover decision.
10
B. Market-Timing Factors
Panel B of Table I lists the market-timing variables that are hypothesized to influence the
choice between an IPO and a takeover. Ritter (1984) and Ibbotson, Sindelar, and Ritter (1994),
among others, document the existence of “hot issue” periods in the IPO market. DeLong,
Shleifer, Summers, and Waldmann (1990), Lerner (1994), Loughran, Ritter, and Rydqvist
(1994), Rajan and Servaes (1997), and Pagano, Panetta, and Zingales (1998), among others, also
provide evidence of market-timing behavior by managers and underwriters in the IPO market.
One explanation for hot issue periods is time variation in adverse selection costs that leads to
periods of favorable market environments, or windows of opportunities for issuing equity. This
literature contends that during periods characterized by high information asymmetry, adverse
selection costs are high and, as a result, fewer firms choose to issue equity. Lowry (2000) and
Lowry and Schwert (2000) provide evidence of the impact of adverse selection costs on the
volume of IPOs; while Bayless and Chaplinsky (1996) and Choe, Masulis, and Nanda (1993)
also find that adverse selection costs influence the timing of seasoned equity offerings.
Another important aspect of the IPO timing hypothesis is that of investor sentiment. The
investor sentiment hypothesis argues that periods exist when investors are overly optimistic and
are willing to overpay for IPOs. Thus, during these periods, managers and underwriters are more
likely to bring IPOs to the market. Lee, Shleifer, and Thaler (1991), Helwege and Liang (1996),
Rajan and Servaes (1997), and more recently, Lowry (2000) present evidence of a positive
relationship between IPO volume and investor sentiment in the U.S. market. Pagano, Panetta,
and Zingales (1998) present similar evidence for the Italian market. We use two proxies to
capture the level of investor sentiment. The first is the return on the stock market (Lowry
(2000)). The second is a lagged relative volume variable that measures the quarterly volume of
11
IPOs occurring in a particular industry in the prior quarter, divided by the quarterly volume of
private target takeovers occurring in that industry over the same time frame. This variable is
motivated by the fact that both the theoretical literature (Maksimovic and Pichler (2001),
Hoffmann-Burchardi (1999)) and empirical evidence (Ritter (1984), Lowry (2000)) show that the
clustering of IPOs is predominantly an industry phenomenon.
Mitchell and Mulherin (1996), among others, provide evidence on market-timing in
merger activity by revealing clustering of mergers over time. They show an increase in merger
activity during industry or economic contractions, periods when information asymmetry tends to
be relatively high.
Although not strictly comparable because they focus on mergers and
acquisitions involving publicly held firms, this observed tendency may also be evident in the
private takeover market. The anecdotal evidence cited earlier suggests that IPOs and takeovers
occur in waves that are negatively correlated. If the cycles are actually simultaneous, then the
relative volume of IPOs to takeovers may not have an effect on the choice between conducting
an IPO or takeover.
C. Deal-Specific Factors
Panel C of Table I reports the four deal-specific factors and how they may influence the
choice between an IPO and a takeover. Perhaps the most defining firm-level characteristic is
firm size. Firm size can provide some indication of the private firm’s ability to successfully
compete as an independent publicly traded firm. Holmstrom and Tirole (1993) and Pagano and
Roell (1998), to name a few, argue that IPOs involve high explicit fixed costs. Evidence
provided by Ritter (1987) is consistent with this argument. Thus, for relatively small private
firms, conducting an IPO can be quite costly, and the potential for success as an independent
12
public company may be limited. Whereas small firms may not be equipped to successfully
operate as stand-alone public companies, they can provide value for acquiring firms who could
share their own resources and skills with the target firm. Similar to Pagano et al. (1998), we use
the total assets of the firm as well as the scaled transaction value as proxies for size and as
measures of the potential for independent survival. Based on our preceding arguments, we
predict a positive relation between the size of the firm and the choice of an IPO (and thus a
negative relation with the choice of a takeover).
The level of insider ownership after the reorganization varies when comparing takeovers
and IPOs. The typical IPO allows for gradual changes in ownership for insiders wishing to
relinquish control of their firm slowly over time (see Rydqvist and Hogholm (1995), Zingales
(1995), Mello and Parsons (1998), and Bebchuk and Zingales (1999)). In a takeover, the
controlling stake in the private firm changes hands at the time of the transaction. However, the
target owners can maintain a non-controlling stake in the merged firm, depending on the method
of payment used. For example, the acquisition of a private firm through a stock offer enables its
owners to retain some stake, albeit indirectly, in their firm after a merger. At the other extreme,
cash takeovers provide the most dramatic change in ownership structure, enabling owners to
divest completely and thereby relinquish their stakes in the firm. Since owners can tailor the
design of either an IPO or a takeover to result in various levels of control, the degree of insider
ownership may not be a critical factor in the IPO versus takeover decision. The ownership issue
is thus an empirical one. We measure insider ownership in IPOs as the percentage of the firm
not offered in the IPO (i.e., one minus the ratio of the number of shares offered in the IPO
divided by the total number of shares outstanding after the offer).
For mergers, insider
ownership is the percentage of the stake that target insiders have in the newly combined firm
13
(i.e., the ratio of the target firm value to the combined firm value, all times the proportion of the
takeover paid in stock).
Closely related to the level of insider ownership is the issue of insider liquidity. Private
firm owners may seek a particular level of liquidity, which can be achieved, to varying degrees,
through either an IPO or a private target takeover. For smaller liquidity needs, the owners may
decide on a partial sale of the firm via an IPO, or for maximum liquidity, the private firm owners
can completely cash out by agreeing to acquisition via a cash offer (Dhillon, Raman, and
Ramirez (2000) and Brown, Ditmar, and Servaes (2000)). However, similar to the insider
ownership issue, the proper design of a transaction, whether an IPO or a takeover, could allow
the initial owner to achieve the desired level of liquidity and is, thus, an empirical matter.5 For
takeovers, we measure liquidity as the percentage of the offer that is in cash. The metric is
bounded above by one (for total cash-outs) and below by zero (for total stock deals). For IPOs,
liquidity equals the ratio of secondary shares offered to total shares (also bounded by zero and
one). Proceeds from secondary shares sold in the IPO go directly to selling insiders as cash
payments.
D. Funding Demand Factors
Panel D of Table I lists three demand for fund factors and their potential influence on the
choice between an IPO and takeover. Mikkelson, Partch, and Shah (1997) and Lowry and
Schwert (2000) present evidence that one of the most important reasons for going public is to
raise funds for new investments.
Since information on the demand for capital by private
companies is not available, we use as proxies certain variables that have been shown to be good
indicators of future investment opportunities. Specifically, we use the return on a portfolio that
is long on high book-to-market stocks and short on low book-to-market stocks (HML), and the
14
return on a portfolio that is long on small capitalization stocks and short on large capitalization
stocks (SMB). The choice of these variables as proxies for the demand for funds is based on
evidence provided by Liew and Vassalou (2000). They find that HML and SMB, shown by
Lowry and Schwert (2000) to be good proxies for private firm fund demand, also reliably predict
future gross domestic product. In comparing IPOs to takeovers, the preferable restructuring
decision for private firms wishing to increase their access to financing is not clear. As pointed
out earlier, an IPO is one path to enhanced financing choices; yet a takeover by a publicly traded
acquirer also provides the private target with access to public capital markets, via the acquiring
firm. Thus, our tests are constructed to reveal whether or not the demand for funds plays a
stronger role in either type of transaction.
In addition to HML and SMB, the 3-month T-bill rate is used as a proxy for borrowing
costs at the time of the transaction. Since many acquirers use debt to finance acquisitions,
periods of higher interest rates can result in less attractive takeover environments (Golbe and
White (1993)). The pecking order hypothesis of Myers and Majluf (1984) contends that, for
firms that require external financing, the use of debt is attractive only up to a certain level, after
which it gets prohibitively costly and external equity becomes the chosen alternative. Thus, for
firms that are highly leveraged, equity issues may represent an important source of financing
during periods of high interest rates. Taken together, these arguments suggest that, as interest
rates increase, the likelihood of IPOs relative to takeovers increases. Conversely, a higher
interest rate may indicate lower firm value in IPO pricing.6 Additionally higher interest rates
may also indicate lower future growth opportunities for IPOs. In this case, high interest rate
environments may decrease activity in the IPO market relative to takeovers. Once again, we are
left with an empirical issue.
15
III. Data Description and Difference Tests between IPOs and Takeovers
In this section, we describe the sample used in the subsequent empirical tests.
Additionally, we perform difference tests between IPO firms and takeover firms as initial tests of
our hypotheses outlined in the preceding section.
A. Data
Our aggregate sample consists of two subsamples – an IPO sample and a takeover
sample. The IPO sample is obtained from the SDC Global New Issues database. The initial
sample consists of 7,716 firms that conducted a firm commitment IPO from 1984-1998. We
exclude unit issues (1,025), closed-end funds (505), limited partnerships (85), spinoffs (719)
Previous leveraged buyouts (283), and foreign issuers (384) from this original SDC download to
obtain a preliminary group of 4,715 firms. Next we eliminate firms if there are missing data for
the high-tech indicator, financial services indicator, industry market-to-book ratio, transaction
value, or the Herfindahl index resulting in a sample of 4,683 firms. We use the 4,683 firms for
the majority of our univariate difference tests.7 Also, we require that IPO firms have sufficient
data available for the regression models used to examine the influence of the previously
discussed factors. Because some firms have missing variables, the IPO sample size for the
subsequent regressions is 3,147 firms.
Similarly, the takeover sample is drawn from the SDC Mergers and Acquisitions
database and utilizes all completed deals involving 100 percent acquisitions of U.S. private
targets by U.S public acquirers. Using these screening criteria, our initial sample consists of
19,908 firms. Excluding limited partnerships (198) and leveraged buyouts (9,261) results in a
sample of 10,449. Again we eliminate firms if there are missing data for the high-tech indicator,
financial services indicator, industry market-to-book ratio, transaction value, or the Herfindahl
16
index, resulting in a sample of 4,927 firms. We use all 4,927 takeovers for the majority of the
univariate tests.8 The data requirements for the multivariate model lead to a regression sample of
2,691 privately held target firms.
Other databases employed in this study include Compustat (to construct the Herfindahl
industry index) and the Federal Reserve Economic Data (FRED) provided by the Federal
Reserve Bank of St. Louis (for the 3-month T-bill rates and the consumer price index). To
control for inflation effects, we adjust all dollar values to 1998 dollars using the consumer price
index.
The distribution of takeovers and IPOs in our sample is shown over time in Figure 1,
based on the available observations in SDC.
We construct Figure 1 prior to eliminating
observations with missing independent variables to capture the overall trend of IPOs and
takeovers, which results in a sample size of 15,164 firms (i.e., 4,715 IPOs and 10,449 takeovers).
In earlier years, IPOs occurred more frequently than takeovers, while in more recent years,
takeovers have clearly gained prominence. Figure 2, which reports the frequency distribution for
the IPO and takeover samples after firms with missing independent variables are omitted
(resulting in 4,683 IPOs and 4,927 takeovers), provides further evidence of these trends. The
bars in the graph represent the ratio of the number of firms in a specific year divided by the total
number of firms over all years for a particular sub-sample. For example, in 1984, 4.9 percent of
the IPO sample occurred (i.e., 229/4683). For each year from 1984-1995, we observe a larger
percentage of IPOs relative to takeovers. This trend reverses from 1996-1998, with takeovers
representing a larger percentage. The largest disparities are in 1997 (8.9 percent IPO versus 30.5
percent takeovers) and 1998 (5.7 percent IPOs versus 30.0 percent takeovers). In our subsequent
tests, this intertemporal pattern is controlled for by using constant 1998 dollars, by performing
17
sub-period tests for the pre and post 1996 samples, and by including year dummy variables in the
regressions.
[INSERT FIGURES 1 AND 2 ABOUT HERE.]
In Figure 3 we report the industry classification breakdown used in Figure 2 (4,683 IPOs
and 4,927 takeovers). Each column represents the percentage of the specific sample (i.e., IPO or
takeover) that is in the particular industry grouping. Our industry classifications are taken from
Kahle and Walking (1996).
Based upon the two-digit SIC classifications, takeovers are
relatively more prevalent in the manufacturing and retail trade sectors, with IPOs relatively
higher in the other sectors.
[INSERT FIGURE 3 ABOUT HERE.]
B. Difference Testing between IPO and Takeover Samples
Table II reports the results of difference tests between the IPO sample and the private
target takeover sample. To test for differences in means and differences in medians, we conduct
t-tests and Wilcoxon rank difference tests, respectively. Initial inspection of Table II shows that
the mean and median for each of the industry, market-timing, and deal-specific variables are
significantly different between the IPO and takeover samples (i.e., all the corresponding p-values
are below 1 percent). In the remainder of this section, we examine the relationships between
these factors and the IPO versus takeover decision.
As discussed earlier, if takeovers are less likely to occur in industries that are already
highly concentrated, then the Herfindahl Index for the takeover sample would tend to be less
than that for the IPO sample. Panel A of Table II reveals that the IPO sample mean for the
Herfindahl index is significantly greater than the takeover sample mean (p<0.0001).
This
finding suggests that takeovers are less prevalent in higher concentration industries, where the
18
potential for further consolidation may be limited. The results also provide evidence that hightech firms, with an IPO sample mean of 24 percent versus only 11 percent for takeovers, are
more likely to go public via an IPO.
The soaring popularity of high-tech IPOs with investors
over the time period examined may help explain this result. Table II also shows that owners of
financial service firms are more likely to agree to be acquired rather than to go public via an IPO.
This finding supports the argument that the deregulation occurring in financial service industries
has helped create an environment more conducive to consolidation within these industries.
[INSERT TABLE II ABOUT HERE.]
The findings in Panel A indicate that the IPO sample mean for each firm's industry
leverage ratio is significantly less than that for the takeover sample. If higher financial leverage
serves as a proxy for risk, then private firms from riskier industries have a tendency to take the
more conservative restructuring path, i.e., a takeover. Similarly, an examination of the average
industry market-to-book for the two samples reveals a higher ratio for the takeover sample than
for the IPO sample. Since this ratio can serve as a basis for the valuation of private targets in
takeover transactions, privately held firms may find it advantageous to agree to takeovers when
these industry valuations are relatively high.
Panel B reports the market-timing variables. The relative volume of IPOs to mergers
indicates that during heavy periods of IPO activity, takeover activity tends to be relatively low
and vice versa. This observed clustering of IPOs and takeovers supports the work of Loughran,
Ritter, and Rydqvist (1994) and Mitchell and Mulherin (1996) who analyze IPOs and takeovers
separately. Furthermore, takeovers of privately-held firms tend to occur during periods of
relatively high stock market returns, as evidenced by the higher market return variables for the
19
takeover sample than for the IPO sample.
This result suggests that favorable market
environments encourage bidder activity relative to IPO activity.
In examining deal-specific factors, Panel C indicates that larger firms (measured by total
assets) are more likely to undertake an IPO rather than be acquired by a public company.
Specifically, IPO firms tend to have about 2.5 times the assets of takeover targets ($268 million
for IPOs, versus $111 million for private targets). Consistent with this finding, our second proxy
for size, the average transaction value (i.e., the total amount paid to the private firm owners
scaled by the percentage of the firm sold) is significantly larger for IPOs ($138.18 million) than
for takeovers ($48.25 million).
An examination of the insider ownership and liquidity
characteristics of IPOs and takeovers provides some preliminary evidence on the ownership
versus liquidity tradeoff in these two types of transactions. Specifically, insider ownership after
the deal is more pronounced in IPOs, where insiders retain an average 64 percent of the firm. In
contrast, in mergers, target firm owners average only about five percent ownership in the
combined firm. The liquidity effects of mergers show that, on average, private firm owners
receive about 60 percent of the deal’s value in cash. On the other hand, the liquidity effects of
IPOs tend to be significantly more modest, providing only about 11 percent liquidity for insiders.
Both the ownership and liquidity findings are consistent with the Leland and Pyle (1977)
framework discussed earlier. In Section V, we examine the relationship between liquidity and
the premiums paid in takeovers versus IPOs.
Finally, Panel D provides mixed results for the explanatory power of the demand for fund
factors in the choice of IPOs versus takeovers. The HML and SMB factors tend to be larger for
IPOs, but this is not the case for all the lagged coefficients. In a univariate setting, HML and
SMB are difficult to interpret because, without adequate controls for competing effects, these
20
variables possibly serve as proxies for multiple economic factors.
We therefore defer the
interpretation of HML and SMB to the subsequent multivariate setting. The last demand for
fund factor, the three-month T-bill variable, indicates that takeovers tend to occur during times of
lower interest rates than IPOs. Since acquirers often use debt to finance acquisitions, they may
time their takeovers to correspond with cheaper debt markets.
This section has provided, in a univariate setting, initial empirical evidence on the
determinants of the choice between a takeover or an IPO. In an attempt to provide more rigorous
tests, we estimate several multivariate models in the following section.
IV. Logistic Regression Results
A. Full Sample Model with the Choice of Either an IPO or Takeover as the Binomial Choice
Dependent Variable
The sample under examination includes firms that choose either an IPO or firms that
choose to be acquired by a publicly traded bidder. Firms that remain private or choose any route
other than a) conducting an IPO or b) being acquired by a publicly traded firm are omitted from
the sample. By focusing on this specific sample we are able to examine how different factors
influence the relative attractiveness of IPOs versus takeovers of private firms. We model the
dependent variable as a binomial choice variable of either a) going public via an IPO (in which
case the dependent variable equals zero) or b) agreeing to a takeover by a publicly traded
company (in which case the dependent variable equals one). As a result of the bivariate nature of
the dependent variable, we employ a logistic regression methodology and estimate the following
model:
[0 if IPO or 1 if Takeover] = αi + Σi=1,5βi industry related factors + Σi=6,11βi market-timing
(2)
factors + Σi=12,13βi deal-related factors + Σi=14,22βi demand for fund factors + εi,
21
where the dependent variable equals zero when the transaction is an IPO and one when it is a
takeover.9 The specific independent variables discussed in Section II are listed in the first
column of Table III. The results show that four of the five industry related factors have
coefficients significantly different than zero. In addition, the sign of each variable is consistent
with the univariate findings in Table II, indicating that industry effects play a prominent role in
the choice of an IPO or takeover. These results are consistent with the theoretical arguments of
Maksimovic and Pilcher (2001), Stoughton, Wong, and Zechner (2001), Hoffmann-Burchardi
(1999) and Mitchell and Mulherin (1996), that emphasize the importance of industry factors
when testing for the determinants of IPOs and mergers independently.
[INSERT TABLE III ABOUT HERE.]
The market-timing variable, an extension of the hot issue theories in the IPO and merger
literature, predicts a negative coefficient for the relative volume of IPOs to mergers. The result
in Table III provides support for this prediction, with a negative coefficient that is different from
zero beyond the one percent level. The significance of the relative volume variable suggests that
private firms tend to time, or herd, when they go public via either an IPO or a takeover. The
relative influence of the market return variables, however, is not as clear. Specifically, the
coefficients for MKT and its corresponding lags are insignificant, except at the first and fourth
lags where they are positive and significant at the 10 percent and five percent levels,
respectively.
Turning to the deal-specific factors, the size proxy (the log of transaction value) shows
that larger transactions are more likely to be IPOs. The size variable result supports the use of
size as an indication of the private firm’s ability to stand alone as an independent company. In
addition, the negative coefficient for size supports the conjecture that flotation costs can deter
22
smaller private firms from conducting IPOs. Furthermore, an examination of the liquidity effects
of the two types of transactions confirms the earlier findings of Table I. Private firm owners who
agree to a takeover experience significantly larger liquidity as a result of the transaction, as
shown by the positive sign of the liquidity coefficient, that is significant at the one percent
level.10
Similar to the market-return results, the proxies for the demand for funds also provide
mixed results. The HML factor is negative and significant at lag 2. The SMB factor is also
negative and significant at lag 2, but positive and significant at lag 4. The instability of signs
across both HML and SMB and the lack of significance on the first lags, suggests that HML and
SMB are not significant determinants in the choice of IPO versus takeover. Finally, the 3-month
T-bill rate is inversely related to the probability of a takeover, which supports the prediction that
acquiring firms are more likely to undertake acquisitions when debt is cheaper.
B. Method of Merger Payment Models
In this section, we test the robustness of our full model results by examining the influence
of the method of payment on the decision to conduct a takeover or an IPO. Similar to the
previous analysis, in this section, the sample consists of firms that were either bought by a
publicly traded firm or went public via an IPO. The dependent variable for each of the first three
models in Table IV is a binomial choice indicator variable that takes a value of one when the
firm is taken over with either a 100 percent cash offer (Model 1), a mixed offer (Model 2), or a
100 percent stock offer (Model 3). When the private firm conducts an IPO, the dependent
variable is zero in all three models. Model 4 in Table IV reports the results of an ordered
multinomial logit regression where the dependent variable is: zero for firms conducting IPOs, 1
for 100 percent stock takeovers, 2 for mixed takeovers, and 3 for 100 percent cash takeovers.11
23
The Table IV analysis is undertaken, in part, because numerous studies have shown that the
method of payment in mergers is an important variable in the value effects of takeover activities
(e.g., Travlos (1987), and Amihud, Lev, and Travlos (1990), Martin (1996) and Ang and Kohers
(2001)).
Also, as previously discussed, private firm owners completely relinquish their
ownership of the firm in cash offers, while they still retain some ownership in stock offers.
Thus, from the standpoint of ownership, IPOs and stock offers may be more comparable than
IPOs and cash takeovers.12
[INSERT TABLE IV ABOUT HERE.]
Overall, the results of the method of payment sub-sample analysis are largely consistent
with those found for the full sample of takeovers and IPOs in Table III. There are nevertheless
certain distinctions in the relative influence of the factors on the IPO versus cash offer (Model 1),
mixed offer (model 2), and stock offer (Model 3) decisions. To avoid redundancy, we focus on
the key differences in results between the models. The degree of industry concentration, as
measured by the Herfindahl Index, plays a significant role in influencing the IPO versus stock
offer decision in Model 3, but this factor does not have a distinct impact on the other decisions
captured in Models 1, 2, and 4. Also, the high-tech indicator is insignificant in the IPO versus
stock takeover specification of Model 3, suggesting that high-tech sellers tend not to have a
preference over IPOs or stock takeovers. In contrast, the high-tech indicator is negative and
significant in Models 1, 2, and 4, indicating an increase in the likelihood of an IPO relative to the
types of takeovers captured by these models. This finding suggests that high-tech firm owners,
who often provide a valuable source of human capital in their firms, are more likely to retain
partial ownership in their firm through an IPO instead of completely cashing out via a cash offer.
24
Finally, another difference in the method of payment models involves the role of the Tbill variable, the proxy for the cost of debt. In particular, this factor is negative and significant in
models 2, 3, and 4, indicating that takeovers are more likely than IPOs when the T-bill rate is
lower. In contrast, the T-bill factor loses its significance in Model 1, which captures the IPO
versus cash offer choice. Since private target takeovers paid fully with cash tend to be relatively
smaller transactions, acquirers often do not require significant amounts of debt financing and,
thus, may not be particularly sensitive to the cost of debt. In sum, while the overall results in
Table IV are similar to those reported for the full samples in Table III, this method of payment
subsample analysis provides additional insights on the relative influence of the factors on the
choice between IPOs and different types of takeovers.
D. Further Robustness Tests
Schwert and Lowry (2000) find that IPO volume fluctuates substantially over time.
Figure 1 supports this finding and indicates that the volume of private takeovers also fluctuates
over time. To control for time period differences, we employed yearly dummy variables and reestimated the modified Equation 2.
Estimation results (not reported) showed significant
coefficients for the 1997 and 1998 periods. However, the findings for the previously discussed
variables were not qualitatively different. In further robustness tests, an alternative measure for
firm size, total assets, was employed. With the exception of the average market-to-book of the
firm industry and the three-month T-bill rate, all of the results were robust to this specification.
Because the availability of total asset figures for privately held firms is more limited than
transaction size information, using this measure would decrease the sample size and hence the
power of our tests. We therefore choose to report the transaction size specifications.
25
Another robustness check further addresses the issue of ownership.
As previously
discussed, in contrast to IPOs, insiders of a takeover often lose substantial, if not entire,
ownership of the firm. In a sub-sample analysis, we re-estimated Equation 2 with all of the
takeovers in our sample, but only those IPOs where the insiders lose majority stake control of the
firm (i.e., they own less than 50 percent of the firm after the IPO). Thus, we focused on the set
of firms that had chosen either an IPO or a takeover and, at the same time, had agreed to give up
majority ownership of their firms. The results of the sub-sample analysis were consistent with
the previous logistic regression analyses and, thus, are not shown.
An additional robustness test involved re-estimating Equation 2 to capture possible time
period shifts in the IPO versus takeover decision. As previously reported in Figure 1, the
frequency of takeovers grew significantly in the period from 1996 through 1998. Thus, the first
model was estimated for deals occurring between 1984 and 1995, and the second model covers
deals between 1996 and 1998. In both periods, our results are qualitatively robust, indicating
that time period effects do not drive our findings.
A final robustness test treats for possible industry clustering in the multivariate logit
model (Table IV, Model 4). Moulton (1986) provides evidence that adjusting for the crosscorrelation of the error terms in a regression if clustering exists can increase efficiency of
estimators. Using an adaptation of Diggle, Liang, and Zeger's (1994) method, we construct a
generalized estimating equation that controls for clustering by two-digit SIC code. The results of
this model are nearly identical to the results of Model 4 reported in Table IV.13 Overall, these
alternative tests and different model specifications provide strong validation of the robustness of
the previously examined factors and their role in the IPO versus takeover choice for privately
held firms.
26
V. Seller Premiums to Insiders in IPOs and Takeovers
Thus far, we have not specifically examined the premiums received by selling insiders.
In this section, we explore the pay-offs that selling insiders receive through either the takeover or
the IPO.
Using the argument of Leland and Pyle (1977), insiders who retain a larger stake of a
risky firm issue a costly, and therefore credible, signal of the quality of the firm. This signal of
quality would allow them to obtain a better price for the stake they sell.
However, if a
controlling stake affords the firm’s new owners the ability to extract some private benefits of
control from the company, then a controlling stake may command a premium (Zingales (1994)).
In an attempt to test this issue we analyze the ratio of offer price per share to book value of
equity per share for each firm. By using a measure on a per share basis, we are able to capture
the premium for the portion of the firm sold in the transaction (either IPO or takeover).
Table V reports the results of parametric t-tests (and Wilcoxon rank tests) between the
mean (and median) of the IPO and Takeover samples. The first column of the table reports subsamples based on the conditioning variables of method of payment and high-tech industry status.
[INSERT TABLE V ABOUT HERE.]
The complete sample difference tests indicate that the premiums for IPOs are significantly
greater on average than the takeover premiums (13.3 versus 10.9 mean, 7.1 versus 4.7 median,
both p-values < 0.01). Thus, insiders who choose the IPO route tend to earn a greater premium
(22 percent larger, on average [i.e., (13.3-10.9)/10.9]) than insiders who sell out to acquirers.
One potential reason for the IPO premium may be the existence of a liquidity discount which
private target owners experience in takeovers. We documented earlier that takeovers provide
greater liquidity on average than IPOs for selling insiders. This greater liquidity in takeovers
27
may result in a liquidity discount, as target firm insiders are generally not willing to bear the nonliquidity risk that is associated more with IPOs. The next two rows of Table V indicate that this
relationship holds for non-high-tech firms, but it does not hold statistically for high-tech firms.
The finding that high-tech takeover premiums are not significantly smaller than IPO premiums
extends the work of Kohers and Kohers (2000) who show that high-tech takeover targets
generally receive higher premiums than non-high-tech targets.
The remaining six rows consider the method of payment and compare IPOs with cash and
stock offers, respectively. The premiums paid to IPO insiders are greater than takeover insider
premiums in all scenarios except the stock only high-tech sample. Consistent with the high-tech
indicator result in Model 3, Table IV, the IPO and stock offer high-tech takeover premiums are
not significantly different, supporting the notion that liquidity may be the driving force behind
the premiums. Focusing on the takeover columns, the stock takeover sample average 13.0 is
greater than the cash takeover sample average 9.5 (p-value = 0.0542, not reported in table). The
significant difference between the cash and stock takeover samples also supports the liquidity
discount hypothesis advanced in the paragraph above.
We have suggested that the disparity in premiums reported in Table V may be due to
varying levels of liquidity between the choice of IPO or takeover. To formally test this
conjecture, we estimate the following Tobit regression model:
Offer/book value = 13.87 – 0.04 LIQUIDITY – 0.45 TAKEOVER,
(< 0.0001)
(< 0.0001)
(< 0.6124)
(3)
where the dependent variable is the premium paid to selling insiders (i.e., the offer price per
share to book value of equity per share), LIQUIDITY is the percentage of the takeover or IPO
transaction value received in cash by sellers, and TAKEOVER is an indicator variable that
28
equals one when the firm is a takeover and zero when the firm is an IPO.14
Estimated
coefficients are reported in equation (3) and p-values for each are reported beneath in
parentheses. The regression results confirm our intuition that the disparity in premiums reported
in Table V is driven by the liquidity associated with the deal. Additionally, the negative
coefficient on TAKEOVER suggests that mergers receive a smaller premium than IPOs;
however it is not statistically significant.15 The results of equation (3) indicate that insiders in
takeovers experience a discounted payoff relative to IPOs due mainly to the greater average
liquidity associated with takeovers.
VI. Summary and Conclusions
A fundamental decision facing privately held firms interested in reaching public equity
markets is the choice between an IPO and a takeover by a publicly traded acquirer. Whereas
previous studies have recently highlighted the importance of industry and market-timing factors
in the decision to go public via an IPO versus staying private, none of this research has addressed
alternative corporate strategies for private firms to access public equity markets. Additionally,
the large numbers of privately-held companies undertaking IPOs and takeovers make the
identification of economic factors that influence firms to choose one route versus the other
inherently interesting.
Using a sample of over 9,500 U.S. privately held firms, we address this issue by
examining the determinants of the IPO choice versus the decision to be acquired by a publicly
traded firm. Our results show that four factors – industry, market-timing, deal-specific, and to a
lesser degree demand for funds – play a role in the IPO versus takeover choice. Specifically, the
concentration of the industry, the high-tech industry status of the private firm, the ‘hotness’ of
the IPO market relative to the private target takeover market, the current cost of debt, the
29
percentage of insider ownership maintained in the firm, and the size of the firm are all positively
related to the probability that a firm will conduct an IPO. In contrast, firms in high market-tobook industries, financial service firms, firms in high debt industries, and deals involving greater
liquidity for selling insiders show a stronger likelihood for takeovers. Finally, a quantitative
analysis of the liquidity effects of the IPO versus takeover decision provides evidence that a
liquidity discount of approximately 22 percent exists in takeovers. Overall, in addressing the
IPO versus takeover choice, our study sheds new light on a previously unexplored dimension of
the going public decision for privately-held companies and identifies key determinants that
influence this fundamental restructuring choice for private firms.
30
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Figure 1.
Volume of IPOs and Takeovers of Private Firms from 1984-1998
400
300
250
200
150
100
50
IPO
1988
1997
1966
1955
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
0
1984
Number of Firms Conducting Either an
IPO or Takeover per Month
350
Takeover
The volume of IPOs and the volume of private target takeovers for firms that conducted either an IPO (n
= 4,715) or were taken over (n = 10,449) from 1984-1998.
35
Figure 2.
Frequency Distribution of IPO and Takeover Sample by Year, 1984-1998
35
30
Percent
25
20
15
10
% of IPOs
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
0
1984
5
% of Takeovers
The volume of IPOs and the volume of private target takeovers for the firms in the sample are shown over
time. The dark (light) bar represents the proportion of IPOs (takeovers) for that year relative to the entire
sample of IPOs (takeovers). The total sample contains 4,683 IPOs and 4,927 takeovers.
36
Figure 3.
Frequency Distribution of IPO and Takeover Sample by Industry, 1984-1998
35
30
Percent
25
20
15
10
% of IPOs
I
H
G
F
E
D
C
B
0
A
5
% of Takeovers
SIC Manual Two-Digit
Division
Major Group
Industry description
Agriculture, Forestry, and Fishing
A
01-09
Mining
B
10-14
Construction
C
15-17
Manufacturing
D
20-39
Transportation, Communications, Electric, Gas, and Sanitary Services
E
40-49
Wholesale Trade
F
50-51
Retail Trade
G
52-59
Finance, Insurance, and Real Estate
H
60-67
Services
I
70-89
Public Administration *
* There are no public administration firms in our sample.
J
91-97
Each bar represents the proportion of either IPOs (n = 4,683) or takeovers (n = 4,927) for that industry
relative to the entire sample.
37
Table I.
Summary of Factors of the IPO and Takeover Decision
Variable
Dependent variable = 0
Theories predicting
IPO
Panel A. Industry Related Factors
Herfindahl index for private/issue firm High concentration industries are less
industry
conducive to corporate combinations;
anti-trust concerns make takeovers more
difficult
High-tech indicator
Investors' attraction to high-tech IPOs
may encourage IPO activity
(Maksimovic and Pichler (2001), Allen
and Gale (1999))
Financial services indicator
Deregulation may motivate private
firms to do IPOs to allow for stock
payment in future acquisitions or to
raise cash for future acquisitions
Average debt ratio for private/issue firm High leverage firms have already underindustry
gone the scrutiny of lenders, leading to
lower investigation costs for investors
(Harris and Raviv (1990))
Average market/book for private/issue High industry M/B provides favorable
firm industry
environment for IPOs (Pagano, et al.
(1998))
Relative volume of IPOs to mergers
Market return (MKT)
Total assets
and
Scaled transaction value
Insider ownership after offer (%)
Liquidity
HML
SMB
3-month T-bill rate
Panel B. Market-Timing Variables
Hot issue periods have been found for
IPOs (Ritter (1984), HoffmannBurchardi (1999))
IPOs may be strategically timed during
favorable market environments (Lowry
(2000))
Dependent variable = 1
Theories Predicting
Takeover
Firm survival may be more difficult in
highly concentrated industries (Sharma
and Kesner (1996) and Audretsch (1995)
High-tech targets tend to receive
relatively attractive premiums (Kohers
and Kohers (2000)); ability to maintain
confidentiality (Yosha (1995))
Deregulation of fragmented industry
makes consolidation more feasible
(Berger, Demsetz, and Strahan (1999))
Higher leverage firms may opt for the
more conservative restructuring path,
i.e., a takeover
Target valuation may be based on
industry M/B, leading to attractive
premiums (Golbe and White (1993))
Clustering of merger activity has been
documented (Mitchell and Mulherin
(1996))
Favorable markets can also encourage
bidder activity (Golbe and White
(1993))
Panel C. Deal-Specific Factors
High fixed cost component of the IPO
process may deter smaller private firms from
IPOs (Ritter (1987), Holmstrom and Tirole
(1993))
Owners who wish to maintain control prefer Target owners can structure takeovers
IPOs (Bebchuk and Zingales (1998))
via stock purchases to retain varying
levels of control
IPO insiders may sell secondary shares for Takeovers may be an effective cash-out
liquidity needs (Dhillon, Raman, and
strategy (Brown, Ditmar and Servaes
Ramirez (2000)).
(2000)). IPO insiders selling large
personal shares may devalue firm
(Leland and Pyle (1977))
Panel D. Demand for Fund Factors
Private firms' need for funds leads to
IPOs (Mikkelson, Partch, and Shah
(1997), Lowry and Schwert (2000))
Similar to HML, used as a proxy for
demand for funds (Lowry (2000))
High cost of debt makes IPOs more
attractive via the pecking order (Myers
and Majluf (1984)); high cost of debt
makes takeovers harder to fund (Golbe
and White (1993))
38
A takeover by a public acquirer can
provide private firms with needed
financing as well
Like HML, a proxy for demand for
funds
High cost of debt may decrease the
value of an IPO firm when using
discounted cash flows or considering
future growth opportunities
Table II.
Difference Tests Between IPO and Takeover Sample Occurring from 1984 to 1998
IPO Sample
Mean
Take-over Sample
Mean
Difference
in Means
Parametric
p-value
Wilcoxon
p-value
Herfindahl index for private/issue firm industry
0.09
0.07
0.02
<.0001
<.0001
High-tech indicator
0.24
0.11
0.12
<.0001
<.0001
Financial services indicator
0.17
0.21
-0.04
<.0001
<.0001
Average debt ratio for private firm industry
0.47
0.52
-0.05
<.0001
<.0001
Average market/book for private firm industry
1.48
1.67
-0.20
<.0001
<.0001
3.15
0.45
2.70
<.0001
<.0001
Panel A. Industry Related Factors
Panel B. Market-Timing Variables
Relative volume of IPOs to mergers
Market return (MKT)t
1.44
1.97
-0.53
<.0001
<.0001
MKTt-1
1.55
1.88
-0.33
<.0001
<.0001
MKTt-2
1.65
2.10
-0.45
<.0001
<.0001
MKTt-3
1.54
2.15
-0.61
<.0001
<.0001
MKTt-4
1.65
2.18
-0.53
<.0001
<.0001
Total assets ($ million)
267.76
111.38
156.38
0.0008
<.0001
Transaction value/stake of firm sold ($ million)
138.18
48.25
89.93
<.0001
<.0001
Insider ownership after deal (%)
63.73
4.60
59.13
<.0001
<.0001
Liquidity of deal (%)
10.77
60.40
-49.63
<.0001
<.0001
HMLt-1
0.20
0.28
-0.08
0.1068
0.0639
HMLt-2
0.19
0.16
0.03
0.5395
0.5593
HMLt-3
0.25
0.19
0.05
0.2768
0.3011
HMLt-4
0.25
0.09
0.17
0.0011
0.0013
SMBt-1
-0.17
-0.25
0.09
0.1314
0.0004
SMBt-2
0.08
-0.16
0.24
<.0001
<.0001
SMBt-3
0.02
-0.18
0.20
0.0006
<.0001
SMBt-4
-0.07
-0.06
-0.02
0.7925
0.0022
3-month T-bill rate
5.34
5.00
0.34
<.0001
<.0001
Panel C. Deal-Specific Factors
Panel D. Demand for Fund Factors
The mean values for selected variables are shown for a sample of IPOs (n = 4,683) and a
sample of takeovers involving private targets (n = 4,927). Differences in the mean values for
the two samples are also provided, along with significance levels for parametric t-tests and
non-parametric Wilcoxon rank tests.
39
Table III.
Logistic Regressions on Full Sample to Predict a Takeover versus an IPO
Variable
Wald
Estimate
Standard
Error
Chi-Square
p-value
Intercept
0.71
0.27
7.04
0.0080
Herfindahl index
-2.25
0.51
19.55
<.0001
High-tech indicator
-0.07
0.09
0.67
0.4124
Financial services indicator
0.85
0.11
56.78
<.0001
Average debt ratio for private firm industry
0.37
0.17
4.73
0.0296
Average market/book for private firm industry
0.48
0.08
35.13
<.0001
Relative volume of IPOs to mergers
-0.60
0.04
283.51
<.0001
Market return (MKT)t
MKTt-1
MKTt-2
MKTt-3
MKTt-4
0.01
0.03
-0.01
0.00
0.03
0.01
0.01
0.01
0.01
0.01
1.29
2.91
1.02
0.08
5.42
0.2556
0.0880
0.3114
0.7744
0.0199
Log of transaction value
-0.46
0.03
226.88
<.0001
Liquidity
0.04
0.00
892.64
<.0001
HMLt-1
HMLt-2
HMLt-3
HMLt-4
0.02
-0.05
-0.02
-0.01
0.02
0.02
0.02
0.02
0.53
5.47
0.84
0.12
0.4653
0.0194
0.3585
0.7287
SMBt-1
SMBt-2
SMBt-3
SMBt-4
-0.01
-0.03
-0.02
0.05
0.02
0.02
0.02
0.02
0.34
4.58
1.40
10.68
0.5597
0.0323
0.2366
0.0011
3-month T-bill rate
-0.17
0.04
20.23
<.0001
4646.8
<.0001
-2 Log Likelihood
The dependent variable is an binary variable equal to 1 for private firms taken over by publicly
traded companies and equal to 0 for private firms conducting an IPO. Each of the lagged factors
(SMB, HML, and the market return, MKT) is shown with the corresponding number of lags. The
sample consists of 3,147 IPOs and 2,691 takeovers.
40
Table IV.
Logistic Regressions: IPOs versus Takeovers by Payment Method
Dependent Variable
Variable
1 if CASH
0 if IPO
(1)
Est
Coeff p-value
1 if MIXED
0 if IPO
(2)
Est
Coeff p-value
1 if STOCK
0 if IPO
(3)
Est
Coeff p-value
3 if CASH
2 if MIXED
1 if STOCK
0 if IPO
(4)
Est
Coeff
p-value
Intercept
0.77
0.0135
1.02
0.0079
0.78
0.0138
Herfindahl index
-0.10
0.8390
-0.98
0.1269
-3.39
<.0001
2 of 3 significant
-0.47
0.1814
High-tech indicator
-0.66
<.0001
-0.65
<.0001
0.13
0.1996
-0.42
<.0001
Financial services indicator
0.92
<.0001
0.09
0.6004
0.85
<.0001
0.59
<.0001
Average debt ratio for private firm industry
0.36
0.0457
0.64
0.0012
0.17
0.387
0.27
0.0136
Average market/book for private firm industry
0.18
0.0423
0.07
0.5271
0.42
<.0001
0.04
0.5075
Relative volume of IPOs to mergers
-0.99
<.0001
-0.99
<.0001
-0.65
<.0001
-0.73
<.0001
Market return (MKT)t
MKTt-1
MKTt-2
MKTt-3
MKTt-4
0.03
0.04
0.01
0.02
0.03
0.0461
0.0113
0.5451
0.3021
0.0453
0.05
0.07
0.01
0.00
0.04
0.0072
0.0007
0.7390
0.9709
0.0734
0.00
0.03
0.00
0.01
0.05
0.9758
0.1570
0.7962
0.4455
0.0134
0.03
0.04
0.01
0.01
0.03
0.0014
0.0003
0.2393
0.2343
0.0052
Log of transaction value
-0.53
<.0001
-0.47
<.0001
-0.43
<.0001
-0.32
<.0001
HMLt-1
HMLt-2
HMLt-3
HMLt-4
0.02
-0.01
0.00
0.03
0.5149
0.6644
0.8966
0.2725
0.08
-0.02
-0.01
-0.03
0.0174
0.6257
0.6779
0.3536
0.02
-0.05
-0.01
0.01
0.5782
0.0482
0.8073
0.7604
0.03
-0.01
0.00
0.02
0.1270
0.7321
0.8283
0.1912
SMBt-1
SMBt-2
SMBt-3
SMBt-4
0.00
-0.01
0.02
0.07
0.9564
0.6517
0.2224
0.0001
0.02
0.00
0.01
0.09
0.2378
0.966
0.6725
<.0001
-0.02
-0.03
-0.03
0.07
0.3367
0.1086
0.0924
0.0001
0.00
0.00
0.01
0.05
0.9365
0.9081
0.2510
<.0001
0.00
0.9331
-0.17
0.0035
-0.17
0.0004
-0.07
0.0301
3-month T-bill rate
-2 Log Likelihood
3640.30 <.0001 2520.77 <.0001 3442.01 <.0001 11865.09 <.0001
The dependent variable differs for each model and is indicated at the top of the result columns.
Models 1-3 are binomial logit models, Model 4 is an ordered multinomial logit model. Est Coeff is
an abbreviation for estimated coefficient. Each of the lagged factors (SMB, HML, and the market
return, MKT) is shown with the corresponding number of lags. The cash takeover sample consists
of 1,194 firms, the mixed takeover sample consists of 595 firms and the stock takeover sample
consists of 902 firms. The IPO sample contains 3,185 firms.
41
Table V.
Various Premiums Associated with Takeovers and IPOs
IPO
Takeover
Difference tests
Parametric Non-parametric
p-value
p-value
Mean
Median
Mean
Median
Complete sample
13.3
7.1
10.9
4.7
0.0018
<.0001
High-tech sample
13.7
7.9
13.0
7.6
0.7455
0.2178
Non-high-tech sample
13.2
6.6
10.7
4.5
0.0025
<.0001
Stock only merger sample
13.3
7.1
13.0
4.6
0.8597
0.0060
Cash only merger sample
13.3
7.1
9.5
4.2
0.0044
<.0001
Stock only high-tech sample
13.7
7.9
18.9
12.0
0.1651
0.1170
Stock only non-high-tech sample
13.2
6.6
11.8
3.4
0.3606
0.0008
Cash only high-tech sample
13.7
7.9
4.6
3.2
<.0001
0.0019
Cash only non-high-tech sample
13.2
6.6
9.9
4.4
0.0216
<.0001
The mean and median values are for the ratio of offer price per share to book value of equity per share for each
respective sample. P-values for tests of differences in the mean and median values for the two samples are provided,
parametric t-tests first with non-parametric Wilcoxon rank tests second. The complete sample consists of 4,683 IPOs and
4,927 takeovers.
42
Notes
1
Examples of IPO versus staying private literature include Chemmanur and Fulghieri (1999), Gomes
(1999), Maug (1999), Stoughton, Wong and Zechner (1999), Bolton and von Thadden (1998), Mello and
Parsons (1998), Pagano, Panetta, and Zingales (1998), Pagano and Roell (1998), Stoughton and Zechner
(1998), Brennan and Franks (1997), and Roell (1996).
2
In our analysis, we focus on broad external influences, and not on firm-specific factors, for two primary
reasons. First, we wish to capture those industry and market-timing factors that are able to explain marketwide tendencies for private firms to take one restructuring route instead of the other. As previously
discussed, recent observations reported in the financial press suggest that market-related environmental
factors play a role in determining broad trends in takeover versus IPO activity. Additionally, and perhaps
more importantly, Mitchell and Mulherin (1996), Pagano, et al. (1998), Stoughton, et al. (1999), and
Maksimovic and Pilcher (1999), among others, find that market and industry factors significantly impact
IPOs and takeovers independently. To capture these broad tendencies, we examine larger numbers of
private firms that make the IPO versus takeover decision. Whereas an examination of firm-specific factors
is certainly of interest, the severe lack of available data on private firms would significantly reduce our
sample and compromise the ability to identify the external factors that are thought to influence this choice.
3
Different industries would not necessarily have a tendency to move towards the same level of industry
concentration since increased concentration may not provide the same efficiency effects across all
industries. Thus, in equilibrium, a low concentration industry may have no strong inclination to become
more concentrated in the long run. We appreciate this point provided by our referee.
4
In addition to its common use in the finance and economic literature as a measure of industry
concentration, the Herfindahl index is also utilized by the Justice Department in assessing market power for
antitrust analysis.
5
We thank the referee for highlighting this point.
6
The IPO pricing argument relies on a larger discount rate in a discounted cash flow model. We thank
Mike Pinegar for this point.
7
Variables with less than 4,683 observations include industry leverage ratio (4,417), relative volume of
IPOs to takeovers (3,450), MKT (4,610), total assets (4,187), HML (4,610), and SMB (4,610).
8
Variables with less than 4,927 observations include industry leverage ratio (3,451), relative volume of
IPOs to takeovers (4,095), MKT (4,264), total assets (834), ownership (2,647), HML (4,264), and SMB
(4,264).
9
In the subsequent robustness tests, we estimate a multinomial logit and a multinomial general estimation
equation. These specifications relax the binomial assumption of the dependent variable.
10
Given the tradeoff between liquidity and ownership for private firm owners, the inverse relationship
between these two factors, and the lack of ownership data for takeovers, we include only the liquidity
variable in the reported regression analysis. In a separate regression incorporating the ownership variable,
the findings showed a negative sign for this factor, which was significant beyond the one percent level.
Thus, consistent with the findings of Table II, the level of insider ownership is positively associated with
the IPO choice.
11
We take the format of our Table IV from Martin's (1996) Table II. The number of cash takeovers (Model
1) is 1,194; the number of mixed takeovers (Model 2) is 595; the number of stock takeovers (Model 3) is
902. The number of IPOs in each model is 3,185 firms. Model 4 contains all of the observations from
43
Models 1 through 3. Table IV contains 38 more IPOs than Table III because the liquidity variable is not
required for Table IV and 38 IPO firms have missing data for liquidity.
12
We do include the method of payment as a dummy variable in Equation 2 because it only applies to
takeovers. The nature of the binomial dependent variable creates a quasi-separation that cannot converge
when only one of the dependent variable choices has a variable with observations. We overcome this
obstacle with the analysis reported in Table IV.
13
We thank Rob Daines for pointing out the potential problem of cross correlation of the error terms and
the remedy.
14
We use Tobit methodology because the dependent variable (offer price to book value) is censored on the
left tail of the distribution at zero.
15
We do not include offer to book value as an independent variable in the full model (i.e., Table III)
because we lose a large portion of the merger sample when we do. In unreported testing, we include offer
to book value along with all of the other variables listed in Table III. The coefficient on the offer to book
variable is not statistically different from zero in the regression.
44