Industries, Analysts, and Price Momentum
LESLIE BONI and KENT L. WOMACK*
Preliminary Version 1.02, please do not cite without permission
Comments Welcome
* Boni is at the University of New Mexico and Womack is at the Tuck School of
Business at Dartmouth. We especially acknowledge the expert research assistance of Bob
Burnham and helpful comments by Bhaskaran Swaminathan and seminar participants at
Babson, Colorado and Penn State. We thank Keith Ferry at Validea.com for data on
analyst recommendations. We also appreciate data provided by I/B/E/S and Ken French.
Comments
and
questions
should
be
sent
to
[email protected]
or
[email protected]. An updated version of the paper will be found at
http://mba.tuck.dartmouth.edu/pages/faculty/kent.womack/workpaper.htm
and
http://www.unm.edu/~boni/RPAWP/RPAWP.htm.
i
Industries, Analysts, and Price Momentum
Abstract
This paper examines the competition among sell-side analysts, who are predominately
industry specialists, to provide investment value through their recommendations within
their followed industries. Earlier “unconditional” research using event studies and
consensus levels suggested that modest abnormal returns before transactions costs were
available to investors using recommendation information. Our industry-based analysis
shows the potential for even larger and more consistent abnormal returns. We also
distinguish between analysts’ value for intra-industry stock picking and the value of their
information in identifying industry momentum or sector rotation strategies.
We find that long-short industry-based portfolios using buy-sell consensus levels are
ineffective at garnering significant excess returns, but monthly recommendation changes
(upgrades and downgrades) within industries provide significant market neutral onemonth-ahead returns that are 1.3%, or 18.9% annualized. However, we show that the
value of these analyst opinion changes dissipates rapidly, over about 2 to 3 months.
At an industry level, aggregated analyst information precedes excess industry returns
in the subsequent months. This suggests considerable promise for the use of analyst
recommendations in explaining a substantial portion of price momentum as an industry
phenomenon and in implementing sector rotation strategies.
JEL Classification: G14, G24
Keywords: Sell-Side Research; Value of Brokerage Recommendations; Market
Efficiency; Price Momentum; Sector Rotation.
ii
Brokerage research (often called “sell-side research”) departments are predominantly
organized by industry. Thus, with few exceptions, the Wall Street analysts that write reports,
estimate earnings, help underwrite new issues, and issue buy and sell recommendations are
considered industry specialists. Not surprisingly then, analysts are regularly compared and
evaluated relative to their competitors in the same industry. The Institutional Investor AllStar rankings within each industry published each October are a prime example of the
reputational rewards that accrue to the most highly regarded analysts in each industry.
Krigman, Shaw, and Womack (2001) show that Institutional Investor All-Star reputations of
industry-specific analysts are instrumental in garnering highly profitable investment-banking
business for the star analyst’s firm.
This study examines recommendation upgrades and downgrades of sell-side security
analysts that are one of the most prominent sources of the information flow that investors
appear to use in revising their portfolios. And, they use them with good reason. Earlier
research has shown that trading volume doubles and returns increase 1-3%, on average, on
days when analysts from the major U.S. brokerage firms upgrade their recommendations on
individual stocks. Abnormal volumes and price decreases are even larger when stocks are
downgraded (for example, from “buy” to “hold”). Barber, Lehavy, McNichols, and Trueman
(2001), Stickel (1995), and Womack (1996) provide estimates of the value changes in this
increased activity: returns for stocks upgraded continue to increase (after risk and market
adjustments) and stocks downgraded continue to decrease for a month or more after the
recommendation change. But, it should be noted that this challenge to available-information
market efficiency is not unique to analysts’ recommendations. Bernard and Thomas (1989)
and Gleason and Lee (2002) for example, show that the market is slow to incorporate and
incorrectly interprets the information contained in earnings estimate revisions as well. And,
the literature on price momentum begun by Jegadeesh and Titman (1993) possibly suggests
that the market incorporates “some information” into market prices slowly, with a lag of at
least a few months. “Some information” may be, in part, the opinions of sell-side analysts.
The focus of this paper is the value investors may derive by observing the competition
among industry analysts to provide timely and accurate opinions on the stocks they follow.
For small capitalization companies, this competition may include only one, two, or three
analysts; but, for heavily-traded, large capitalization stocks such as Microsoft and General
Electric, the “herd” may be as many as 30 or more analysts.
Higher reputation and
presumably higher pay reward the winners of this stock-picking competition. Indeed, the AllAmerican research poll conducted by Institutional Investor has a special recognition category
for “stock picking ability”.
We examine two particular questions that have relevance to investors trying to gain a
risk-reward edge. First, we examine whether industry analysts are able to identify future
winners and losers within their industry specializations. Since analysts typically follow a
small number (usually from 5 to 30) stocks in one industry, it would be reasonable to assume
that their stock picking ability, if any exists, should most likely be in ranking the stocks in
their industry sector as winners and losers. Earlier research, which focused primarily on
portfolios and event studies, were not well industry-diversified. Barber, Lehavy, McNichols,
and Trueman (hereafter Barber, et.al., 2001) found excess returns before transactions costs by
constructing unconstrained “buy” and “sell/unattractive” portfolios. Indeed, we find that such
an approach, choosing portfolios based on the highest and lowest absolute consensus levels is
quite industry-unbalanced and loads heavily on momentum. Stickel (1995) and Womack
(1996) identified upgraded and downgrades stocks but did little industry-based analysis.
Hence, earlier analyses did little to answer the question of industry “skill”.
While we do not exhaust all or even the most optimal strategies for maximizing future
return, we find that a simple long-short strategy of buying upgraded firms and selling
2
downgraded firms within each industry in any calendar month yields a substantial 1.3% per
month in the next calendar month and cumulatively 2.4% over 3 months. These returns are
well diversified, computed across all industry-months, and have no large common risk factor
loadings. This is in contrast to earlier portfolio-based attempts that have significant risk factor
loadings and industry imbalances.
The second investment question that we address is whether observing the collective
recommendation changes or consensus levels by all analysts within an industry provides value
in capturing sector rotation or industry momentum. As mentioned earlier, one of the largest
challenges to market efficiency is the phenomenon of price momentum. Moskowitz and
Grinblatt (1999) argue that much of the observed price momentum previously documented by
Jegadeesh and Titman (1993) and others is industry based. Our data provide an indication of
the momentum (correlation) in industry information that may be driving industry price
momentum. We find that conditioning long-short portfolios on the basis of “hot” industries
with recommendation momentum (whether the industry was net upgraded or downgraded in
the formation month) earns 1.56% per month, or about 3% more annually than the “all
industry” case in the previous paragraph. Our findings that future intra-industry and industry
sector returns are predictable and significant when using the information content in brokerage
upgrades and downgrades suggests a possible informational explanation for price momentum
that has not been previously explored.
Our examination, of course, does not exhaust all the potential strategies that investors
might employ using recommendation information, and it does not provide evidence that these
returns are fully realizable after transactions costs. However, the magnitude of our returns,
relative to earlier price momentum and post-earnings announcement drift studies, shows
considerable promise of the potential for predicting future returns from analysts’
pronouncements. We conjecture that recommendations are a proxy for the primary source of
3
information Jegadeesh and Titman (1993) claim as a fundamental explanation for price
momentum.
The rest of the paper proceeds as follows.
Section 1 provides the institutional
background on analysts and their recommendations and Section 2 describes the data and
sample selection methods used in the paper. Section 3 examines the question of analysts’
intra-industry
stock-picking
ability,
and
Section
4
analyzes
analysts’
collective
pronouncements as a mechanism for predicting industry/sector rotation. Section 5 discusses
the implications of our findings and concludes.
1. Analyst Recommendation Activity: The Institutional Setting and Prior Literature
Recommendations are only one of the informational products that analysts provide for
investors. Others include building pro forma valuation models, forecasting future earnings,
cash flows and price targets. The analyst’s specific information snooping and dissemination
tasks can be categorized as 1) gathering new information on the industry or individual stock
from customers, suppliers, and firm managers; 2) analyzing these data and forming earnings
estimates and recommendations; and 3) presenting recommendations and financial models to
buy-side customers in presentations and written reports.
The analyst’s dissemination of information to investment customers is usually
disseminated through a morning research conference call. Such conference calls are held at
most brokerage firms about two hours before the stock market opens for trading in New York.
Analysts and portfolio strategists speak about, interpret, and possibly change opinions on
firms or sectors they follow. Both institutional and retail salespeople at the brokerage firm
listen to this call, take notes, and ask questions. Alternatively, urgent communications may be
made following a surprising quarterly earnings announcement or some type of other corporate
announcement while the market is open for trading.
In both cases, analysts notify the
salespeople at the brokerage firm, who in turn call customers who they believe might care
4
(and potentially transact) on the basis of the change. Once the sales force is notified, the
analyst may directly call, fax, or send e-mail to the firm’s largest customers if the analyst
knows of their interest in the particular stock or sector. The information is sometimes
retransmitted via the Dow Jones News Service, Reuters, CNN, or other news sources,
especially when the price response in the market is significant.
The type of announcement analyzed extensively here is a change of opinion rating
level by an analyst on a stock. New “buy” recommendations are usually scrutinized by a
research oversight committee or the legal department of the brokerage firm before release.
Thus, a new added-to-buy recommendation may have been in the planning stage for several
days or weeks before an announcement. Sudden changes in recommendations (especially,
removals of “buy” recommendations) may occur in response to new and significant
information by or about the company.
Several earlier academic studies form our priors as to how investors and the collective
market respond to recommendation changes. Womack (1996) identified significant price drift
in short time periods following recommendation upgrades and downgrades by the largest US
brokerage firms in the 1989-1991 time period. For upgrades to “buy” or “strong buy”, he
found using an event-study methodology that the market reacted strongly to the news
announcement of the upgrade and continued to drift in the predicted upward direction for
another one to two months. For downgrades from a buy category and for outright sell
recommendations, he documented the negative returns with somewhat higher intensity: larger
immediate reactions and larger amounts of drift over somewhat longer periods, three to six
months. Two issues related to market efficiency were addressed that we examine more
carefully here. First, adjustments for standard Fama-French and momentum risk factors were
used and did not significantly affect the magnitudes of the short-term (1 to 3 month) returns.
Second, in Figure 1, Womack demonstrates the lesser reactions of larger companies relative to
5
smaller ones. Price reactions and drift were almost twice as large in the 5 smallest market
capitalization deciles as in the top two.
Barber, Lehavy, McNichols, and Trueman (2001), take an importantly different
perspective. Using a different dataset (Zacks) of recommendation information, they address
the issue of whether forming portfolios through calendar time can capture the returns
suggested by event study results in Womack (1996) and Stickel (1995).
Two features
distinguish the Barber et.al. results. First, they form portfolios by choosing stocks below and
above two time-invariant cutoff levels (to identify attractive and unattractive stocks). To form
long portfolios, they choose all stocks with analyst consensus level ratings of 1.5 or less. For
the short portfolio stocks, they include stocks with ratings higher than 3.0. Second, they
rebalance these portfolios daily, as stocks move into and out of these cutoff ranges. Their
results suggest significant long-short returns before transactions cost (slightly less than 1% per
month) for the 1986 to 1996 period, but implied transactions costs eat up these “profits”.
Jegadeesh and Titman (1993) and others document price momentum over periods of 3
months to one year and suggest that the evidence is consistent with delayed reactions to firm
specific information. Our paper offers causal confirmation, showing that one type of or proxy
for firm-specific information is analysts’ valuation changes.
Hong, Lim, and Stein (2000) link the phenomenon of price momentum with analyst
following. This gives support to the hypothesis of Hong and Stein (1999) that momentum is a
symptom of investors’ collective underreaction to individual pieces of private information.
They use analysts as a proxy for intensity of information dissemination, and, after controlling
for size, find that price momentum is greater where analyst coverage is lower.
6
2. Data and Sample Selection
The data used in this study were originally developed by Validea.com, a now defunct
Internet startup company that had planned to develop a product for institutional and individual
investors to rank analysts and their recommendations. Validea’s choice of recommendations
data (after searching for the most timely and accurate source with historical data back to at
least 1995) was IBES, which constructs two recommendations databases used here. The first
is a monthly Summary History-Recommendation file that compiles a monthly snapshot of
each company followed by sell-side analysts whose brokerage firms provides data to IBES.
This database tracks at mid-calendar month (similar to the Summary History-EPS file) the
number of analysts following the stock, the average consensus rating level on a 1 to 5 scale
(where 1 is a “strong buy” and 5 is a “sell”) and its standard deviation for the stock, and the
number of analysts upgrading and downgrading their opinion level in the month. The Detail
History-Recommendation file provides a database entry for each recommendation change
made by each analyst. Important variables include the date of the change, the analyst and the
brokerage firm’s name, to what level the change was made. Table 1 shows the brokerage firm
level characteristics of the 150,873 recommendation changes we analyze. There were 7,960
companies followed during at least one month of the January 1996 to June 2001 time frame
that we examine.
[Table 1 about here.]
McNichols and O’Brien (1998) discuss the determinants of analyst coverage,
specifically the question of what would motivate an analyst to initiate coverage of a stock.
While there are of course several answers, one of the most obvious is the potential for
generating trading commissions and investment banking fees from the followed company.
Not surprisingly then, we find and document in Table 2 that there is a significant correlation
between the number of analysts following a company and the market capitalization of that
7
company. For example, we find in the tenth (largest) CRSP size decile the median number of
analysts following a stock is 13. In the 6th decile, by contrast, the median number of analysts
following is 3.1
[Table 2 about here.]
In addition to the data supplied by IBES, Validea merged all companies with the
Morgan Stanley GICS Industry codes after their research concluding that Morgan Stanley’s
industry-coding scheme was most closely aligned to the natural industry distinctions used on
the sell-side. This divides the 7,960 companies in our dataset into 120 distinct industry
groups. The average number of companies in each industry is 66 but the median is 32.
“Banks” is the largest (and unusual) industry comprising 791 companies, which skews the
industry distribution. There are 6 industries with fewer than 10 companies.
To provide an indication of variation within and across sectors, Table 3 shows
descriptive data for all industries along with three representative industries (“Systems
Software”, “Restaurants”, and “Electric Utilities”) and one company in each of the industries.
The average consensus level for all industries is close to a “Buy” (IBES rating level = 2) each
month (with little variation). As shown in Figure 1, the average consensus level for these
industries varies from 2.0 by less than + 0.5. As shown by the spreads between the consensus
levels each month for the “best” company (lowest consensus level) and “worst” company
(highest consensus level), analysts do appear to be willing to differentiate among companies
within the industry they follow.2 Figure 2 provides an example of correlation between price
movement and consensus level both at the industry and company level for the Systems
Software industry and Microsoft.
1
Analyst coverage is defined as the number of analysts that provide earnings forecasts for a stock each month per
the IBES Summary History-EPS file.
2
We update the IBES summary month consensus level numbers, which are usually mid-month based, to
calendar-end basis using the daily recommendation data.
8
[Table 3, Figure 1, and Figure 2 about here.]
The key informational output collected by IBES and examined in this paper are
recommendation changes from one rating level to another. In the 1996 to 2001 timeframe of
our data, there are 83,634 recommendation changes. In addition to changes, where both an
earlier level and the new current level are identified, we also observe 67,239 “first
recommendations”, which are recommendations where we obtain the new current level, but do
not know the previous rating level, either because it did not exist (if an analyst initiates
coverage) or because the company could not be matched to an earlier rating in the database.
Note that known reiterations (common in the communications of analysts to their clients) of
prior ratings levels are not recorded or examined, unlike in the Zacks data examined by
Barber, et.al. The total number of recommendation changes examined is therefore 150,873
over five and one-half years, as shown in Table 4, Panel A.
Although buy and strong buy recommendations grossly outnumber hold and sell
recommendations, interestingly Table 4 shows that downgrades (55.5% of recommendation
changes) actually exceed upgrades (44.5% of recommendation changes). This suggests a
broader universe of stocks is available for portfolio strategies based on going long upgrades /
short downgrades than for strategies based on consensus level cutoffs of going long buy while
shorting underperform and sell categories.
[Table 4 about here.]
It is instructive to note the magnitude of the three-day market-adjusted returns for the
various change categories shown in Table 4, Panel B. When a stock is moved to the strong
buy (IBES Code “1”) category from “buy” or “hold”, the average market response is a sizedecile-adjusted return of 3%. This is significantly higher than the 1.06% to 1.48% range
reported by Barber, et.al. for the 1985 to 1996 time period in the Zacks data. We offer two
possible explanations for the difference. First, the Zacks database, while including most of
9
the significant US brokerage firms, omitted a few large ones. Further, Zacks collected the
data second-hand, so that often the dates of the recommendation changes were the dates of
written reports that may have been several days later than the actual recommendation event.
Hence, the averages reported in Barber, et.al. may have included some “event” returns after
the actual event dates. It is important to note that this issue does not likely affect the
important Barber et.al. portfolio strategy results reported later in that paper.
Second, there is some evidence that market responses to new information may have
been more intense in the more recent time frame analyzed here. In some ways, the late 1990s
was recognized as the advent of the “day trader” due to convenient and inexpensive
transactions services available via the Internet. For example, Busse and Green (2002) show
that traders respond to televised analysts’ recommendations within a minute of their broadcast
in the year 2000.
3. Analysts’ Industry Stock-Picking Expertise
Event-study returns in Table 4 show that the market responds in the direction that
security analysts predict. Upgraded stocks go up and downgraded stocks go down in the
three-day period centered on the recommendation change date in the IBES database. More
importantly, earlier work by Womack (1996) and Stickel (1995) show that highlighted stocks
continue to drift in the direction predicted by the analyst for 1 to 6 months. These analyses
were summaries of all recommendation changes that may have been heavily weighted towards
some well-followed industries. Research by Barber et al (2001, 2002) also suggest profitable
strategies may be derived using consensus level criteria. Therefore, a fundamental question
on analysts’ expertise remains: Can industry analysts identify future winners and losers within
their industry specializations?
To analyze the intra-industry industry stock picking ability of analysts, we examine
four calendar-month long-short portfolio strategies. Strategies A and B define portfolios
10
based on consensus level criteria at month end, while Strategies C and D use recommendation
changes within the “formation” month.
In Strategy A, two stocks (the “best” and the “worst”) in each of the 120 industries are
chosen for a long-short portfolio based on the two extreme (most favorable and least
favorable) average consensus rating levels for each stock at month end. If there are ties in the
highest and lowest level in each industry, the portfolio is formed by equally weighting the
returns of the two or more stocks involved.
We compare this best-worst industry consensus level strategy to a consensus level
strategy (which we refer to as Strategy B) approximating the Barber, Lehavy, McNichols, and
Trueman (2001) strategy (which does not diversify across industries) for the 1996-2001 time
frame of our data. In their strategy, they choose for the long side of the portfolio all stocks
with a consensus rating of 1.5 or lower (predominantly “strong buy” stocks) and then short all
stocks with a level higher than 3.0 (“unattractive” or “sell” stocks). While they rebalance their
portfolio daily, our version of their strategy forms the portfolios using their cutoffs at the end
of the month.
To examine portfolio strategies based on recommendation changes, we define an
aggregate change measure for stocks and also for industry sectors. For each stock each month,
we define an aggregate recommendation change measure (“AgChange”), which is the sum of
the number of analyst recommendations which are upgrades plus the number of first
recommendations that fall into the IBES buy or strong buy category less the number of
downgrades and the first recommendations that are to IBES category hold, underperforform,
or sell. Using this simple measure, we can refer to stocks with AgChange greater (less) than
zero as “net upgraded” (“net downgraded”) stocks each month.
11
For Strategy C, we construct industry-by-industry portfolios that are long (short) the
“best” (“worst”) stock based on most positive (negative) value for AgChange measure.3
Strategy D is a more evenly diversified version of Strategy C. In Strategy D, we construct
industry-by-industry portfolios that are long all net upgraded stocks and short net downgraded
stocks during the month based on the AgChange measure.
Table 5 shows the results of forming industry-based calendar-time portfolios for each
strategy across all 66 months for all industries in our sample. In Panel A, we focus on returns
to a long-short portfolio when stocks across all market capitalizations are available to
investors. Panels B, C, and D provide constrained results, showing the effects of liquidity
constraints proxied for by limiting portfolio inclusion to high market cap and analyst
coverage.
3.1 Portfolio Strategies based on Consensus Rating Levels
Table 5 shows that the mean return from using the consensus level Strategy A is
0.59% in the calendar month following portfolio formation. However, after controlling for
the three risk factors identified by Fama and French (1993), the excess return is 0.41%, and
when momentum is also factored out, the return is only 0.11% per month, not economically or
statistically different from zero. An initial conclusion is that future returns are not very
predictable by conditioning on extreme rating levels.
[Table 5 about here.]
Table 5 documents that Strategy B, which is also industry unconstrained, is not welldiversified. The mean return for the post-formation month is higher than in Strategy A, at
0.92% but, after adjusting for the three Fama-French factors, becomes 0.76% and after
3
When there are ties among stocks in the maximum number of upgrades or downgrades in a month, which is
quite often, we equal weight all qualifying companies’ returns.
12
adjusting for momentum, becomes 0.05%, insignificantly different from zero. Barber et.al.
report significant monthly excess returns (intercepts) of 0.99% for Fama French and 0.75% for
the four-factor model in the earlier 1986 to 1996 time frame. One conclusion that might be
drawn from these differences in our time frame versus that of Barber et.al. (1986-1995) is the
more significant influence of momentum in the late 1990s, a topic that we address more fully
in Section 4.
3.2 Portfolio Strategies based on Individual Rating Changes within Industries
Note that Strategy C reduces the number of industry-month observations by about 22%
relative to consensus level Strategy A (from 7,716 to 6,027) because in 1,689 industrymonths, the industry sector does not have both a net upgraded and net downgraded stock. We
document that this strategy, based on recommendation changes in the calendar formation
month, provides substantially higher returns in the first post-formation month. The market
neutral portfolio return is 1.38% per month (18.9% annualized) before transactions costs.
Controlling for Fama French and momentum factors through calendar time regressions, the
intercept remains 1.12% per month, highly significant both economically and statistically.
The numbers for Strategy D are quite similar to those in Strategy C, the raw return for
the long-short portfolio being 1.30% per month and 1.08% after controlling for the Fama
French factors and momentum. Thus, we conclude that the significant returns subsequent to
the recommendation change information in a month are not caused by a few outliers each
month, but are quite robust to broader portfolios incorporating all net upgrade and net
downgrade information.
The results of the comparison between Strategies A and B, based on consensus levels,
and Strategies C and D, based on recommendation changes, are quite striking and highlight
the dissipating nature of the value of analysts’ information. Consensus levels usually do not
change considerably from month-to-month. Knowledge that analysts are currently rating a
13
stock a “strong buy” does not suggest measurable outperformance in the future, unless the
rating has been recently upgraded to “strong buy”.4
3.3 The Value of Brokerage Information and the Limits of Market Efficiency
Table 5, Panel A allows analysts to choose the most and least attractive stocks in each
industry, unconstrained by liquidity and market capitalization considerations. Hence, the
documented strategy is likely to be unrealizable by large institutional investors, to the extent
that small firms’ returns contribute significantly to the results. Thus, in Panels B, C, and D,
we constrain the allowable stocks available for portfolio formation in three ways.
In Panel B, we eliminate from consideration all stocks in the bottom half of the CRSP
size deciles.
Therefore, the remaining stocks (deciles 6 through 10) have market
capitalizations of approximately $111 million and above in year-end 2000 dollars. Consistent
with Panel A, strategies A and B, based on levels, do not provide much look-ahead value, but
strategies C and D, based on recommendation upgrades and downgrades in the formation
month, are valuable but are of smaller magnitude than the unconstrained case in Panel A. The
intercepts for the long-short portfolios, adjusted for Fama and French factors and momentum,
are 0.88% and 0.79% respectively and have T-statistics above 3.5.
Similarly, in Panel C we report portfolios that are only populated by the largest CRSP
decile (10). This panel represents the ability of analysts to identify mispricing in the largest
and most liquid stocks. Again, we find that the returns on the strategies based on consensus
ratings levels are insignificantly different from zero. In strategies C and D (based on ratings
changes), we find that post-one-month returns are still positive but marginally significant.
Panel D shows a 0.45% per month return for Upgraded minus Downgraded stocks.
4
By forming portfolios at month-end rather than immediately after recommendation changes obviously biases
(lowers) our reported results since recommendation changes occur approximately uniformly distributed within a
calendar month. Hence our approach is conservative, in that it measures a first post-event return approximately a
half-month after the information event.
14
In Panel D, we show a different slice, based on the stocks most followed by sell-side
analysts. Panel D limits portfolio choice to stocks with at least 15 analysts following them.
Table 2 shows that this constraint corresponds to the largest 9% of stocks in the IBES
universe.
The returns for the changes-based strategies are reported as 0.28% and 0.43%, and
are not significant at conventional levels.
Therefore, the exercise documented in Panels C and D begins to show the limits of
analysts’ value and the power of competition in the largest, most-followed stocks to eliminate
ostensible informational inefficiencies. Naturally, we emphasize that these strategies are not
exhaustive and are conservative to one degree in that they wait, on average, a half-month to
exploit apparently valuable and dissipating information. But, we also point out that the
returns documented are before transactions costs.
3.4 Robustness Checks on Analyst Information Portfolios
The previous section begins to show the limits of analysts’ value and the efficiency of
pricing in the most highly followed stocks. On the other hand, Table 6 demonstrates the
robustness of the documented long short (Upgraded minus Downgraded) portfolio strategy in
two ways. First, we observe the decomposition of the long-short portfolio into the long
(Upgraded) component and the short (Downgraded) component.
Not surprisingly, the
calendar month returns in the portfolio formation month are very substantial, 2.97% for
Upgraded stocks (in excess of an equal-weighted benchmark) and minus 4.05% for
Downgraded stocks, consistent with the 3-day event returns documented in Table 4. In the
first month after the recommendation information, the returns to the Upgraded portfolio are
0.64% while the returns to the Downgraded Portfolio are –0.66%. Thus, the net portfolio
return of 1.30%, reported in Table 5, Panel A. If we extend the analysis to the three months
post formation, the cumulative return is 2.39%, and to 6 months, 2.88%, both significantly
different from zero and economically significant. Hence, we observe the majority of the
15
market’s response to analyst information in the 1st three months after recommendation
changes. For the Downgraded portfolios, we observe the continuation of underperformance
even further out than the post-3 month window, consistent with the asymmetry of response to
positive and negative news documented in Womack (1996).
Equally interesting (and
comforting) are the results of all stocks that are not upgraded or downgraded in the portfolio
formation month (the “No Change” portfolio).
The market-adjusted returns are not
significantly different from zero in all post-event months.
[Table 6 about here.]
In Table 6, Panel B, we show the consistency of the results year-by-year. The postone-month overall average of 1.30% ranges from 1.07% to 1.66% in the 6 calendar-year
periods. Likewise in the cumulative post-event three-month period, the results are consistent
in 5 out of six periods with the year 2000 being the only year with results less than 2.2%
(+0.98% for year 2000).
The results industry-by-industry are remarkably consistent as well. Post-event onemonth and three-month post-formation-month returns are positive for 94 and 93 respectively
of the 117 industries for which long-short portfolios can be constructed.5 Our conclusion
from the results in Section 3 is that analysts appear able to distinguish temporarily underversus over-valued stocks within their followed industries, at least in the short term ranging
from 1-3 months.
4. The Value of Analyst Industry Information for Predicting Sector Rotation
The second investment question that we examine is whether observing the collective
recommendation changes or consensus levels by all analysts within an industry provides any
value in explaining sector rotation or industry momentum. Moskowitz and Grinblatt (1999)
5
For 3 of the 120 industries, in no month are there both a net-upgraded stock and a net-downgraded stock.
16
demonstrate that the puzzling anomaly of individual stock momentum is substantially driven
by relative industry momentum.
In this section, we examine whether industry momentum
may have its roots in delayed response to analyst information.
In the earlier section, we used the Aggregate Change Measure for each stock to form
portfolios of net Upgraded and Downgraded stocks within each industry (each industry-month
observation is a within-industry portfolio). In this section, we aggregate (sum) the Aggregate
Change Measure for all stocks in each industry each month to calculate portfolios returns of
Upgraded industries minus Downgrades industries in each month.
Industries are
differentiated by whether the sum of the Aggregate Change Measure for all stocks in the
industry was positive (hence, labeled an In-Favor industry) or negative (and called an Out-ofFavor industry). Then, these strategies calculate an equal-weighted return for long positions
in In-Favor industries, financed by short positions in Out-of-Favor industries in each month.
In essence, do industries where there are, in aggregate, more recommendation upgrades than
downgrades outperform industries where downgrades dominate upgrades over the next
month?
In Table 7, we document two distinct versions of these portfolios. Having shown in
Section 3 that analysts are ostensibly good month-ahead forecasters within their own
industries (Table 5, Strategies C and D), the next logical question is whether investors
selecting the subset of In-favor industries and avoiding Out-of-Favor industries can earn even
higher net returns. We label this as Strategy E in Table 7. The mean return increases from
1.30% to 1.59% in the post-formation month. This implies that a one-month-ahead strategy
using aggregate analyst industry recommendation information adds about 3.5% annually to an
already large naïve all-industry within-industry strategy return.
[Table 7 about here.]
17
In Strategy F, we record the self-financing portfolio return when we equal weight all
companies within In-Favor industries on the long side and equal weight all companies in Outof-Favor industries on the short side. Note that this strategy does not take advantage of the
within-industry expertise documented earlier (since stocks are not differentiated within each
industry).
In sum, it is one simple measure of one-month-ahead industry momentum
attributable to analyst information. The mean return is 0.43% in the post-formation month.
In Table 8, we take a different tack at understanding industry momentum and analysts’
contribution to it. Table 7 suggested that (at least) month-ahead returns are predictable from
the aggregate sum of analysts’ upgrades and downgrades. Earlier work has suggested that
price momentum is most robust at intervals of about 6 months. We now examine the
determinants of industry returns through a series of regressions.
[Table 8 about here.]
We take the industry excess return for 1 month (alternatively, 6 months) as the
dependent variable to be explained. We define industry excess return as the equal weighted
return on an industry sector minus the equal weighted return on the market portfolio in the
calendar month. Each observation is an industry-month. Independent variables that we use to
explain the industry excess return one month (and six months) ahead are:
1)
AgChangeCurrent, the sum of the AgChange measures (as defined in Section
3) for all companies in the industry in the formation month divided by the sum
of the analyst-coverage (i.e., number of analysts) for the companies in the sector
less the mean of this variable for all the industry sectors for that month,
2)
AgChangePrior, same as 1) above but for the month prior to formation,
18
3)
ConsensusLevelCurrent, the consensus level of the industry at the end of the
formation month minus the average consensus level for the all the stocks in our
data for that month,
4)
IndustryExcessReturnCurrent, the equal-weighted return of the industry minus
the return of an equally weighted average of all the companies in our dataset
that have CRSP returns for the formation month,
5)
IndustryExcessReturnPrior, the equal-weighted return of the industry minus
the return of an equally weighted average of all the companies in our dataset
that have CRSP returns for the month prior to the formation month,
6)
IndustryAverageSizeDecile, the average CRSP decile for stocks in the industry
for the month minus the mean decile of all stocks in our data that month, and
7)
IndustryAverageAnalysts, the number of analysts following stocks in the
industry in the month minus the mean analyst coverage of all stocks in our data
that month.
If aggregate industry analyst information has value in predicting future returns, the
coefficient for 1) and possibly 2) will be positive. If a favorable current average rating level
for the industry is predictive, the coefficient on 3) will be negative (since a higher rating
number is a less favorable rating. Variables 4) through 6) are used as control variables for
industry price momentum and market capitalization, and variable 7) shows the impact of
above average analyst following in an industry, controlling for the other variables.
The results in Table 8 show that the industry recommendation AgChangeCurrent
measure is quite significant in explaining the one and six-month ahead returns. For the longer
period only, the AgChangePrior month is also significant.
The ConsensusLevelCurrent
measure also loads positively, but only for the longer period, meaning that returns are higher
19
when the industry consensus level was less favorable (a higher rating number) controlling for
the other factors.
IndustryExcessReturnCurrent and IndustryExcessReturnPrior are also
significant and positive, suggesting substantial industry price momentum, even at short
horizons of one to three months. Finally, industries with lower average market capitalization
have higher returns and, interestingly, industries with higher analyst coverage, all else equal,
have higher returns.
5. Interpretation and Conclusions
We document in this study that analyst information, processed at the industry level, is
quite useful in explaining future returns. First, our evidence suggests that analysts are indeed
able to distinguish between future outperforming and underperforming stocks within their
followed industry, since industry portfolios formed in a variety of ways earn consistent returns
that are significantly higher than comparable price momentum strategies. Using information
obtained over the course of one calendar month, long-short industry portfolios earn an average
of 1.3% in the next month. This return is a conservative estimate of the actual effect, since the
value of analyst information dissipates rapidly and our calculations approximate a half-month
delay in forming portfolios after the information is available.
Our second contribution is to show that aggregated analyst information within an
industry is valuable for explaining relatively under- and out-performing industries for at least
the next six months. This suggests a direct link and partial explanation for the puzzle of price
momentum. Our evidence is consistent with the claim of Moskowitz and Grinblatt (1999)
that price momentum is significantly an industry phenomenon. In fact, we may be able to
show, with future work, that analyst information is an appropriate and accurate proxy for the
firm-specific information that Jegadeesh and Titman (1993) claim investors react to with a
delay.
20
Bibliography
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from the Prophets? Security Analyst Recommendations and Stock Returns. The Journal of Finance
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Reassessing the Returns To Analysts' Stock Recommendations, forthcoming, Financial Analysts
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Finance 54, 1249-1290.
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Financial Analysts Journal Sept-Oct 1995:25-39.
21
Womack, Kent L. 1996. Do Brokerage Analysts' Recommendations Have Investment Value? Journal
of Finance L1 (no.1):137-167.
22
Figure 1
Time Series Variation in Industry and Stock Consensus Rating Levels
Figure 1 shows the consensus (average) level of analyst ratings in 3 example GISC (Morgan Stanley)
industry categories for January 1996 to June 2001, where 1 = strong buy, 2 = buy, 3 = hold, 4 =
underperform, and 5 = sell. The average consensus level for the market as a whole is very close to 2.0 for
the entire time period. The graphs below show the consensus level each month for the "best" and "worst"
company (which changes through time), the average for all companies in the industry, and a sample
company.
5
Systems Software and Microsoft
4.5
Consensus Level
4
Worst Company
3.5
3
Industry Average-60 companies
2.5
2
1.5
Microsoft
1
0.5
Best Company
0
Jan-96
Jul-96
Feb-97
Aug-97
Mar-98
Sep-98
Apr-99
Nov-99
May-00
Dec-00
5
Restaurant Industry and Starbucks
4.5
Worst Company
Consensus Level
4
3.5
3
Starbucks
2.5
2
1.5
1
0.5
Best Company
Industry Average-109 companies
0
Jan-96
Jul-96
Feb-97
Aug-97
Mar-98
Sep-98
Apr-99
Nov-99
May-00
Dec-00
5
Electric Utilities and El Paso Electric
4.5
Worst Company
Consensus Level
4
3.5
3
2.5
2
1.5
Industry Average-97 companies
El Paso Electric
1
Best Company
0.5
0
Jan-96
Jul-96
Feb-97
Aug-97
Mar-98
23
Sep-98
Apr-99
Nov-99
May-00
Dec-00
Figure 2
Comparison of Rating Levels and Price Movements, Jan 1996 to June 2001
Figure 2 shows the consensus (average) level of analyst ratings and a price index for the System Softward GISC (Morgan
Stanley) industry category and for Microsoft Corporation for January 1996 to June 2001, where 1 = strong buy, 2 = buy, 3 =
hold, 4 = underperform, and 5 = sell. The average consensus level for the market as a whole is very close to 2.0 for the entire
time period.
System Software Industry
Consensus Rating Level vs. Monthly Price Index
(Jan 1996 to Jun 2001)
3
Con. Rating Level
1.6
2.8
1.7
2.6
1.8
2.4
1.9
2
2.2
2.1
2
2.2
Log of Monthly Price Index
1.5
1.8
Consensus Rating Level
Log of Monthly Price Index
Microsoft Corporation
Consensus Rating Level vs. Monthly Price Index
(Jan 1996 to Jun 2001)
Con. Rating Level
3
1.6
2.8
1.7
2.6
1.8
2.4
1.9
2.2
2
2
Consensus Rating Level
24
Log of Monthly Price Index
Log of Monthly Price Index
1.5
Table 1
Brokerage Firm Characteristics
The data on analyst recommendation changes shows 150, 873 recommendation changes by sell-side analysts in
the time period January 1996 to Jun 2001. Table 1 shows the number of companies and GISC (Morgan Stanley)
industries followed by the largest 24 U.S. brokerage houses as well as the remaining 409 brokerage firms and
subsidiaries separately listed in IBES. During the time frame above, column 4 gives the total number of analyst
IDs listed in IBES. When an analyst changes firms, he or she may be counted separately by the other brokerage
firm. Hence, the Analyst count is almost surely an overestimate of the actual number of analysts working at sellside firms.
Companies
followed
(2)
2,420
2,124
2,046
1,827
1,812
1,645
1,644
1,620
1,577
1,460
1,452
1,439
1,282
1,265
1,178
1,120
1,076
1,023
996
892
885
841
822
749
Industries
followed
(3)
119
119
112
115
116
116
110
119
102
109
113
111
108
111
83
106
93
103
107
98
85
65
81
101
Remaining 409 Brokers & Subs
7,303
120
Total, all Brokerage Firms
7,960
120
BrokerName
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25+
(1)
Salomon Smith Barney
Merrill Lynch
Deutsche Banc Alex Brown
Morgan Stanley
Credit Suisse First Boston
Goldman Sachs
Lehman Brothers
Donaldson
Banc of America Scurities
UBS Warburg
CIBC World Markets
Bear Stearns
Prudential Securities
J.P. Morgan
Robertson Stephens
Paine Webber
Dain Rauscher Wessels
A.G. Edwards & Sons
ABN AMRO
ING Baring Furman Selz
First Union Securities
Chase H&Q
U.S. Bancorp Piper Jaffr
Schroder
25
Analysts
Number of
1996-2001
Recommendations
(4)
(5)
380
6,532
373
4,967
309
5,328
249
4,768
259
3,692
193
3,720
208
3,091
118
3,672
160
3,428
215
2,877
162
2,816
155
2,870
131
3,121
193
2,329
131
2,890
107
2,558
105
2,415
91
3,203
95
1,911
108
1,867
89
1,817
82
1,657
69
1,832
79
1,601
3,883
75,911
150,873
Table 2
Analyst Coverage by Market Capitalization
Table 2 shows the number of company-month observations cross-tabulated by analyst coverage and CRSP market capitalization decile for
January 1996 to June 2001 (66 months) for all stocks recommended by at least 1 analyst during that period. The number of analysts following a
particular stock may change over time, although such changes are usually not dramatic over a short period of time. Column 1 shows the
NYSE/AMEX/Nasdaq CRSP decile and the market capitalization (MC) represented by that decile as of the end of 2000.
Company's
CRSP Market
Cap Decile
1
MC< $7.6
2
MC< $18.8
3
MC<$36.5
4
MC<$66.1
5
MC<$111
6
MC<$202
7
MC<$384
8
MC<$856
9
MC<$2,575
10
MC>$2,575
All
%
1-2
3-4
Number of Analysts Covering a Company (company/month observations)
5-6
7-8
9-10
11-14
15-20
21-25
26-30
31+
All
%
5,050
457
65
14
.
10
26
.
.
.
5,622
1.7%
12,957
1,395
165
16
2
12
1
.
.
.
14,548
4.4%
17,583
3,610
586
147
46
42
3
.
1
.
22,018
6.7%
18,559
7,041
1,745
480
178
79
38
16
.
.
28,136
8.6%
17,640
10,075
3,371
940
324
208
12
1
1
.
32,572
9.9%
15,039
12,553
5,635
2,028
808
411
60
9
.
.
36,543
11.1%
11,897
13,751
8,151
3,855
1,612
909
239
55
3
.
40,472
12.3%
8,050
13,357
10,764
6,720
3,631
2,525
761
138
46
3
45,995
14.0%
4,063
8,553
10,185
9,166
6,565
7,049
3,097
616
77
11
49,382
15.0%
955
2,038
3,419
4,785
5,679
12,114
13,902
6,019
3,011
1,588
53,510
16.3%
111,793
72,830
44,086
28,151
18,845
23,359
18,139
6,854
3,139
1,602
328,798
100.0%
34.0%
22.2%
13.4%
8.6%
5.7%
7.1%
5.5%
2.1%
1.0%
0.5%
100.0%
26
Table 3
Descriptive Statistics and Examples for Industries
Table 3 shows summary statistics on the number of companies in GISC (Morgan Stanley) industries, as well
as size characteristics and analyst coverage statistics.
Number of
Companies
CRSP
Decile
mean
(min, max)
Analyst
Coverage
per company
mean
(min, max)
Number of
Recommendations
per company
mean
(min, max)
6.8
6.6
10.0
3.7
5.9
5.3
15.8
2.1
24.0
20.0
81.0
8.0
7.4
(2.4, 10.0)
8.0
(1.0, 31.5)
36.2
(1, 192)
10
30
146
5.1
(1.3, 10.0)
4.8
(1.0, 23.8)
17.4
(1, 117)
10
19
117
8.8
(2.0, 10.0)
10.2
(1.0, 24.4)
28.7
(1, 94)
8
2
7
Summary Statistics for All Industries
Mean
Median
Maximum
Minimum
66.3
32.0
791
1
Example Industries:
Systems Software
60
Example Company: Microsoft
Restaurants
109
Example Company: Starbucks
Electric Utilities
97
Example Company: El Paso Electric Co.
27
Table 4
Transition Matrix of Recommendations and Three-Day Event Excess Returns, 1996 to 2001:06
Panel A shows the number of analyst recommendations as categorized by IBES, where 1 = strong buy, 2
= buy, 3 = hold, 4 = underperform, and 5 = sell. Rows 1-5 report recommendations that are changes
from one category to another. Row 6 reports recommendations that are the first observation of an
analyst at a brokerage firm for a particular stock. Panel B reports the means of percentage 3-day event
excess returns for the recommendation categories in Panel A. The three-day event return is the
geometrically cumulated return for the day before, day of, and day after the recommendation. The
excess return is the raw stock return less the appropriate size-decile return of the equal-weighted CRSP
NYSE/AMEX/ Nasdaq index. Excess return means that are significantly different from zero at the 10%
level are shown in bold.
Panel A: Number of Recommendations
To IBES Code
1
From IBES Code
2
3
na
15,590
9,643
197
235
25,665
2
15,015
na
17,742
445
224
33,426
3
6,205
13,290
na
1,315
892
21,702
4
106
314
1,145
na
107
1,672
5-Sell
129
104
826
110
na
1,169
First Recommendations
22,355
28,011
15,761
640
472
67,239
All
43,810
57,309
45,117
2,707
1,930
150,873
2
3
4
5
1-Strong Buy
Panel B: 3-Day Event Mean Returns*
To IBES Code
1
From IBES Code
1-Strong Buy
4
5
All
na
-4.53%
-6.77%
-7.24%
-3.74%
2
3.17%
na
-5.64%
-5.83%
-4.95%
3
2.96%
3.02%
na
-2.30%
-3.60%
4
3.20%
3.06%
1.32%
na
-3.95%
5-Sell
1.13%
0.52%
1.40%
-0.66%
na
1.88%
0.44%
-1.83%
-2.80%
-1.56%
First Recommendations
* Bold numbers indicate significance at the 10% level
28
Table 5
Percentage Monthly Returns and Regression Estimates, 1996 to 2001
This table shows percentage monthly returns (column 1) and estimates from time-series regressions of calendar-time portfolios formed using strategies A, B, C, and D.
Strategy A (C) portfolios are created in each industry by buying the stocks with the most favorable consensus level (aggregate of recommendation changes) and shorting
the stocks with the least favorable consensus level (aggregate of recommendation changes) each month. In Strategy B, a single portfolio is formed each month choosing
from all stocks regardless of industry: stocks with consensus level < 1.5 are purchased (equal-weighted) while stocks with consensus level > 3 are shorted (equalweighted). Strategy D portfolios are long net upgraded stocks (equal-weighted) and short net downgraded stocks (equal-weighted) in the industry each month. Column
2 shows the intercept from the Fama and French 3-factor model. Columns 3-7 show estimates for the 4-factor model. Panel A shows results when stocks can be selected
for portfolios regardless of market cap or analyst coverage. Panels B and C show results when stocks are restricted by CRSP market cap decile to > 6 and 10
respectively. Panel D shows results when stocks are restricted to analyst coverage of 15 and higher. T-statistics in bold indicate estimates that are significant at the
10% level. * and ** in column 8 indicate industry-month and month observations respectively.
Mean
Return
(1)
Intercept from
F&F
4-Factor
(2)
(3)
Coefficient Estimate for the 4-Factor Model
Rm - Rf
SMB
HML
MOM
(4)
(5)
(6)
(7)
Number of
Observations
(8)
Panel A: Portfolios Chosen from All Stocks
Strategy A: Best - Worst (Consensus Levels by Industry)
0.594
2.48
0.416
1.65
0.110
0.43
0.202
2.92
0.002
0.03
0.200
2.37
0.226
5.56
7,716*
Strategy B: Barber et al (Consensus Levels not by Industry)
0.918
1.38
0.760
1.17
0.051
0.10
0.314
2.25
0.186
1.48
0.093
0.54
0.523
6.36
66**
Strategy C: Best - Worst (Rec Changes by Industry)
1.377
6.47
1.366
6.15
1.119
4.92
0.073
1.20
-0.056
-1.02
-0.010
-0.14
0.177
4.80
6,027*
Strategy D: Upgraded - Downgraded (Rec Changes by Industry)
1.305
9.02
1.254
8.33
1.078
6.98
0.099
2.40
-0.026
-0.69
0.025
0.48
0.126
5.06
6,027*
Panel B: Portfolios Chosen from Stocks with Market Capitalization of CRSP Decile 6 or Greater
Strategy A: Best - Worst (Consensus Levels by Industry)
0.651
2.90
0.446
1.89
0.093
0.39
0.301
4.66
0.036
0.63
0.109
1.38
0.259
6.84
7,675*
Strategy B: Barber et al (Consensus Levels not by Industry)
0.977
1.35
0.977
1.78
0.461
0.97
0.284
2.23
0.263
2.30
-0.327
-2.09
0.380
5.07
66**
Strategy C: Best - Worst (Rec Changes by Industry)
1.098
5.11
1.103
4.93
0.881
3.84
0.058
0.95
-0.022
-0.39
-0.031
-0.41
0.161
4.34
5,761*
Strategy D: Upgraded - Downgraded (Rec Changes by Industry)
0.953
6.44
0.947
6.14
0.791
5.01
0.059
1.40
-0.033
-0.85
-0.031
-0.59
0.113
4.43
5,761*
29
Table 5 (continued, see legend on previous page)
Percentage Monthly Returns and Regression Estimates, 1996 to 2001
Mean
Intercept from
Return
F&F
4-Factor
(1)
(2)
(3)
Panel C: Portfolios Chosen from Stocks with Market Capitalization of CRSP Decile 10
Coefficient Estimate for the 4-Factor Model
Rm - Rf
SMB
HML
MOM
(4)
(5)
(6)
(7)
Number of
Observations
(8)
Strategy A: Best - Worst (Consensus Levels by Industry)
0.246
1.30
0.111
0.56
-0.050
-0.24
0.189
3.48
-0.014
-0.29
0.043
0.64
0.116
3.52
6,480*
Strategy B: Barber et al (Consensus Levels not by Industry)
0.003
0.00
0.114
0.16
-0.195
-0.27
0.328
1.69
0.410
2.34
-0.764
-3.20
0.228
1.99
66**
Strategy C: Best - Worst (Rec Changes by Industry)
0.283
1.16
0.337
1.33
0.299
1.15
-0.007
-0.10
0.056
0.87
-0.109
-1.28
0.030
0.70
3,403*
Strategy D: Upgraded - Downgraded (Rec Changes by Industry)
0.420
2.18
0.461
2.28
0.446
2.16
-0.006
-0.11
-0.038
-0.74
-0.098
-1.44
0.012
0.36
3,403*
Panel D: Portfolios Chosen from Stocks with Coverage of at Least 15 Analysts
Strategy A: Best - Worst (Consensus Levels by Industry)
0.138
0.54
0.098
0.37
-0.302
-1.13
0.206
2.85
-0.202
-3.19
-0.055
-0.64
0.288
7.10
4,304*
Strategy B: Barber et al (Consensus Levels not by Industry)
1.100
0.56
0.381
0.19
-0.479
-0.24
1.132
2.13
-0.810
-1.69
0.059
0.09
0.635
2.03
66**
Strategy C: Best - Worst (Rec Changes by Industry)
0.296
0.96
0.461
1.43
0.283
0.86
-0.097
-1.11
-0.097
-1.23
-0.190
-1.82
0.132
2.64
2,338*
Strategy D: Upgraded - Downgraded (Rec Changes by Industry)
0.479
1.89
0.600
2.27
0.427
1.75
-0.051
-0.71
-0.141
-2.19
-0.161
-1.87
0.094
2.30
2,338*
30
Table 6
Further Examination and Robustness Checks, Calendar Monthly Returns, 1996 to 2001
This table shows percentage returns and t-statistics for Strategy D portfolios. A Strategy D portfolio is formed for each industry each month,
consistent with Table 5, Panel A, that is long net upgraded stocks (equal-weighted) and short net downgraded stocks (equal-weighted). Monthly
returns are shown for the month prior to portfolio formation (column 1), month of formation (column 2), and month after formation (column 3).
Columns 4 and 5 show geometrically cumulated returns for 1 to 3 months and 1 to 6 months following portfolio formation. Mean returns for
Strategy D are shown in row 4 of Panel A. Returns are decomposed in row 1 and 3 of Panel A relative to an equal-weighted market portfolio
return each month. Row 2 shows mean returns for industry porfolios of equal-weighted investments in all stocks without net upgrades or
downgrades relative to an equal-weighted market portfolio return. Panel B shows returns for the Strategy D portfolios by year.
1st Mo.
Rec D
Post Mo.
Prior
Mo.
1
1-3
1-6
of Obs
(1)
(2)
(3)
(4)
(5)
(6)
Post-Mos. Post Mos.
Number
Panel A: Upgraded - Downgraded Portfolios--Expansion of Table 5, Panel A, Strategy D--All Stocks
Upgraded Port. minus Market--All Years
Mean
T-Stat
0.619
4.92
2.967
22.42
0.646
5.21
0.989
4.2
0.641
1.84
6,027
No Change Port. minus Market--All Years
Mean
-0.147
-0.178
-0.073
-0.105
-0.250
7,761
T-Stat
-1.72
-2.14
-0.81
-0.59
-0.94
Downgraded Port. Minus Market --All Years
Mean
T-Stat
-0.617
-4.18
-4.053
-25.67
-0.657
-4.86
-1.431
-6.03
-2.243
-6.17
6,027
Upgraded - Downgraded Portfolio--All Years
Mean
T-Stat
1.236
7.91
7.019
40.6
1.303
9.01
2.392
9.5
2.886
7.98
6,027
Panel B: Upgraded - Downgraded Portfolio--Year By Year
Year 1996
Mean
0.67
5.08
1.44
2.72
2.98
1,093
Year 1997
Mean
1.54
5.88
1.21
2.96
3.42
1,080
Year 1998
Mean
1.30
6.44
1.40
2.20
2.71
1,155
Year 1999
Mean
1.01
8.09
1.07
2.81
5.40
1,141
Year 2000
Mean
1.88
9.28
1.22
0.98
0.38
1,040
Year 2001 1:6
Mean
0.87
7.87
1.66
2.86
1.41
518
All Years
Avg. Mean
1.24
7.02
1.30
2.39
2.89
6,027
31
Table 7
Percentage Monthly Returns and Regression Estimates for In-Favor and Out-of-Favor Portfolios, 1996 to 2001
This table shows percentage monthly returns (column 1) and estimates from time-series regressions of calendar-time portfolios formed using In-Favor
and Out-of-Favor strategies E and F. In-Favor (Out-of-Favor) industries are those with sum of aggregate changes for all stocks within that industry > 0
(< 0) for the month. In Strategy E, only net upgraded stocks in In-Favor industries are bought and only net downgraded Out-of-Favor industries are
short. In Strategy F, investors buy all stocks in In-Favor industries and short all stocks in Out-of-Favor industries. Column 2 shows the intercept from
the Fama and French 3-factor model. Columns 3-7 show estimates for the 4-factor model. T-statistics in bold indicate estimates that are significant at
the 10% level. ** in column 8 indicates month observations.
Mean
Return
(1)
Intercept from
F&F
4-Factor
(2)
(3)
Coefficient Estimate for the 4-Factor Model
Rm - Rf
SMB
HML
MOM
(4)
(5)
(6)
(7)
Number of
Observations
(8)
Strategy E: In Favor Industries minus Out-of-Favor Industries
(using only Upgraded (Downgraded) Stocks in Industry)
1.586
4.11
1.508
3.97
1.015
3.92
0.153
2.20
0.129
2.07
0.102
1.19
0.364
8.89
66**
Strategy F: In Favor Industries minus Out-of-Favor Industries
(using all Stocks in both Industries)
0.425
1.43
0.470
1.71
0.135
0.67
0.026
0.49
0.109
2.24
-0.040
-0.60
0.247
7.78
66**
32
Table 8
Regression Results for One-Month and Six-Month Industry Excess Returns, 1996 - 2001:6
This table reports estimates and t-statistics for time-series regressions of one-month and six-month
industry excess returns. Industry excess returns are the equal-weighted return on an industry minus the
equal-weighted return on the market portfolio in the calendar month. Each observation is an industrymonth. Independent variables are:
∙ AgChangeCurrent , the sum of the AgChange measures (as defined in Section 3) for all companies in
the industry in the formation month divided by the sum of the analyst-coverage (i.e., number of
analysts) for the companies in the sector less the mean of this variable for all the industry sectors for
that month,
∙ AgChangePrior , same as above but for the month prior to formation,
∙ ConsensusLevelCurrent , the consensus level of the industry at the end of the formation month minus
the average consensus level for the all the stocks in our data for that month,
∙ IndustryExcessReturnCurrent , the equal-weighted return of the industry minus the return of an
equally weighted average of all the companies in our dataset that have CRSP returns for the formation
month,
∙ IndustryExcessReturnPrior , the equal-weighted return of the industry minus the return of an equally
weighted average of all the companies in our dataset that have CRSP returns for the month prior to the
formation month,
∙ IndustryAverageSizeDecile , the average CRSP decile for stocks in the industry for the month minus
the mean decile of all stocks in our data that month, and
∙ IndustryAverageAnalysts , the number of analysts following stocks in the industry in the month minus
the mean analyst coverage of all stocks in our data that month.
T-statistics for estimates significant at the 10% level are shown in bold.
Dependent Variable
One-month Return
Six-month Return
Independent Variables
Parameter
Estimate
t-stat
Parameter
Estimate
t -stat
Intercept
0.000
-0.14
0.000
-0.05
AgChangeCurrent
0.111
5.65
0.411
6.60
AgChangePrior
0.015
0.76
0.220
3.42
ConsensusLevelCurrent
0.004
0.96
0.032
2.47
IndustryExcessReturnCurrent
0.092
7.85
0.351
9.38
IndustryExcessReturnPrior
0.008
0.67
0.256
7.00
IndustryAverageSizeDecile
-0.002
-2.29
-0.020
-6.19
IndustryAverageAnalysts
0.001
1.75
0.006
5.12
2
R
1.6%
33
3.9%