Herding on Earnings Surprises: The Role of Institutional Investors in
Post-Earnings Announcement Drift
Linda H. Chen, Wei Huang and George J. Jiang☆
March 2015
☆
Linda Chen is from the Department of Accounting, Carson College of Business, Washington State
University, Pullman, WA 99164. Email address: linda.chen@wsu.edu. Wei Huang is from the
Accounting and Finance Department, College of St. Benedict and St. John’s University, Collegeville,
MN, 56321. Email address: whuang@csbsju.edu. George J. Jiang is the Gary P. Brinson Chair of
Investment Management in the Department of Finance and Management Science, Carson College of
Business, Washington State University, Pullman, WA 99164. Email address: george.jiang@wsu.edu.
Tel: (509) 335-4474, Fax: (509) 335-3857. We wish to thank … for helpful comments and suggestions.
The usual disclaimer applies.
Herding on Earnings Surprises: The Role of Institutional Investors in PostEarnings Announcement Drift
Abstract
In this paper, we examine the role of institutional investors underlying post-earnings
announcement drift. We provide evidence that transaction costs or limits to arbitrage do not
fully explain why post-announcement abnormal stock returns are not arbitraged away by
institutional investors. Instead, we find that the drift is only present in the stock sample where
institutional trading herds in the same direction of earnings surprises. That is, institutional
herding is likely a culprit of the stock price continuation. Nevertheless, we show that
institutional herding in the same direction of earnings surprises does not push stock prices
away from fundamental values but represents slow incorporation of information into stock
prices. On the other hand, when institutional investors herd in the opposite direction of
earnings surprises, there is an immediate destabilizing effect on stock prices.
Key words: Post-earnings announcement drift; Transaction costs; Limits to arbitrage;
Institutional herding; Stock price discovery.
JEL Classification:
1 I. Introduction
Existing literature documents a well-known post-earnings announcement drift (PEAD). That is,
firms with positive earnings surprises subsequently outperform those with negative earnings
surprises. Ball and Brown (1968) are the first to document the anomalous pattern in stock
prices using a sample extending back to the 1950s. Subsequent studies confirm the postearnings announcement drift using different samples and methods, see, e.g., Foster, Olsen, and
Shevlin (1984), Bernard and Thomas (1989, 1990), among others. While existing literature has
proposed a number of explanations, from both rational and behavioral perspectives, of the postearnings announcement drift, one important question remains. That is, why are the anomalous
returns not arbitraged away?
In this paper, we examine the role of institutional investors underlying PEAD. Motivated by
the arguments and empirical evidence documented in the existing literature, we are particularly
interested in the following two questions. First, we examine to what extent transaction costs
and limits to arbitrage prevent PEAD to be arbitraged away by institutional investors.
Institutional investors are perceived as sophisticated and skilled. Several studies document that
institutional investors exploit anomalous patterns in stock returns. For instance, Ke and
Ramalingegowda (2005) show that active short-term institutional investors trade to exploit
post-earnings announcement drift and earn positive abnormal returns. If institutional investors
are indeed sophisticated and skilled, then the only reasons that prevent PEAD from being
arbitraged away are transaction costs and limits to arbitrage.
Second, we are interested in to what extent PEAD is associated with institutional herding.
Existing literature documents that institutional investors have the tendency to engage in
herding, that is, they buy or sell the same stocks during the same time period. While such
herding behavior may be driven by access to common information set (Froot, Scharfstein, and
2 Stein (1992); Hirshleifer, Subrahmanyam, and Titman. (1994)), it is also likely that institutions
may herd for reasons unrelated to information, such as the reputational risk of acting
differently from others due to career concerns or preferences for specific stock characteristics
due to investment styles (for detailed surveys of the herding literature, see Scharfstein and
Stein (1990); Banerjee (1992); Devenow and Welch (1996); Falkenstein (1996); Bikhchandani
and Sharma (2001); Gompers and Metrick (2001)). Following earnings announcement,
institutions receive the same information about firm fundamentals. Thus, they may trade
altogether on earnings surprises. If institutional herding incorporates information into stock
prices, then price continuation or drift following earnings announcement represents a slow
discovery process of stock prices. On the other hand, it is also possible that institutional
herding drives stock prices away from their fundamental values if the trading of some
institutions is not driven by information. We examine the effect of institutional herding on
stock prices based on long-run stock returns.
The main data used in our study includes CRSP for stock returns, Compustat for earnings
announcements and other information on firm fundamentals, as well as the Thomason
Financial 13F database for quarterly holdings of institutional investors. Our stock sample is
restricted to common stocks traded on the NYSE, AMEX or NASDAQ. The sample period is
from January 1980 to December 2013.
To examine whether transaction costs and limits to arbitrage prevent PEAD from being
arbitraged away by institutional investors, we use two proxies of transaction costs, namely size
and Amihud (2002) illiquidity ratio, and two proxies of limits to arbitrage, namely
idiosyncratic volatility and short-sale constrain, in our empirical analysis. These measures have
been used in existing studies, such as Bhushan (1994), Christophe, Ferri, and Angel (2004),
Mendenhall (2004), Sadka (2006), Ng, Rusticus and Verdi (2008), Boehmer and Wu (2012),
3 etc. In particular, idiosyncratic volatility and short-sale constrain may explain not only why
post-earnings announcement drift persists but also why it exists. Miller (1977) points out that
stock prices reflect optimism in the presence of short-sale constraints. When investors’ beliefs
are diverse and investors with pessimistic views are kept out of the market due to limits to
arbitrage (as proxied by high idiosyncratic volatility or short-sale constraint), stocks tend to be
over-valued and a future price reversal is likely.
Our results show that consistent with findings in existing literature (Bhushan (1994) and Ng,
Rusticus and Verdi (2008)), PEAD is more pronounced for small stocks and stocks with high
illiquidity ratio. That is, transaction costs contribute to post-earnings announcement anomalous
stock returns. Nevertheless, PEAD remains significant even among large cap stocks, i.e., those
above the 20th percentile of the market cap of NYSE stocks. Similarly, consistent with findings
in existing literature, PEAD is more pronounced for stocks with high idiosyncratic volatility
and those with high short-sale constraint. That is, limits to arbitrage also contribute to postearnings announcement anomalous stock returns. Nevertheless, PEAD remains significant
even among stocks below the 33rd percentile of idiosyncratic volatility or short-sale constraint
of NYSE stocks. These findings suggest that while both transaction costs and limits to
arbitrage attribute to PEAD, neither seems to fully explain why post-earnings announcement
anomalous stock returns are not arbitraged sway by institutional investors.
Next, we examine to what extent PEAD is associated with the herding behavior of
institutional investors. Our results show that institutional investors in general herd in the
direction of earnings surprises, namely institutional investors buy (sell) stocks with positive
earnings surprises during the same time period. Nevertheless, we also note that institutional
investors do not always herd in the same direction as that of earnings surprises. More than one
third of stocks in the top (bottom) decile of earnings surprises are sold (bought) altogether by
4 institutional investors. This presents a nice setting to examine the extent to which post-earnings
announcement drift is attributed by institutional herding and, more importantly, the effect of
institutional herding on stock price discovery.
Dividing stocks based on whether institutional investors herd in the same direction or
opposite direction of earnings surprises, we show that drift following earnings announcement
is significant only when institutional investors herd in the same direction of earnings surprises.
When institutional investors herd in the opposite direction of earnings surprises, there is no
longer significant drift after two weeks following earnings announcement. This is evidence that
institutional herding against earnings surprises reverse market reaction to earnings surprises.
We also use alternative measures of institutional herding in our analysis, namely the change of
the number of institutional investors and the percentage change of the number of institutional
investors. Both measures provide consistent results. In fact, when the percentage change of the
number of institutional investors is used, the results are even stronger. Specifically, when the
number of institutional investors decreases (increases) following positive (negative) earnings
surprises, there is an immediate price reversal following earnings announcement. We confirm
the results based on Fama-MacBeth regressions where we control for other firm characteristics
that are known to be related to stock returns.
Finally, we examine the effect of institutional herding on stock price discovery. As
noted earlier, institutional herding driven by information helps stock price discovery as
information is incorporated into stock prices gradually. On the other hand, institutional herding
unrelated to information my push stock prices away from fundamental values and destabilize
the price discovery process. We examine long-run stock returns up to 5 years following
earnings announcements. Our results show that when institutions herd in the same direction of
earnings surprises, we observe a slow price discovery process as information is slowly
5 incorporated into stock prices. On the other hand, when institutions herd in the opposite
direction of earnings surprises, we observe an immediate short-term reversal following
earnings announcement. This is evidence that institutional herding unrelated to information
destabilizes stock prices and deters stock price discovery process.
The rest of the paper is structured as follows. Section II describes data used in our analysis.
Section III presents post-earnings announcement returns for subsamples of stocks based on
size, illiquidity, idiosyncratic volatility, and short-sale constraint. In Section IV, we examine
the effect of institutional herding on post-earnings announcement drift and stock price
discovery. Section V concludes.
II. Data and Methodology
The main data used in our study include CRSP, Compustat, and Thomason Financial 13F.
Firm characteristics are computed using information from the CRSP daily and monthly data as
well as COMPUSTAT annual financial statements. We obtain quarterly institutional investors’
holdings from Thomason Financial 13F database. All institutions with greater than $100
million of securities under discretionary management are required to report their holdings to
the Securities and Exchange Commission (SEC) within 45 days of the end of a calendar
quarter. They must disclose all common-stock positions greater than $200,000 or 10,000
shares. Short interest data is obtained from NYSE and NASD (National Association of
Securities Dealers) with monthly observations and from COMPUSTAT with semi-monthly
observations. Our stock sample is restricted to common stocks traded on the NYSE, AMEX or
NASDAQ. The sample period is from January 1980 to December 2013.
The key variable used in our empirical analysis is the standardized unexpected earnings (SUE).
Following Foster (1977) and Foster, Olsen, and Shevlin (1984), we measure SUE as follows:
6 ,
,
,
,
,
where
,
,
,
1
,
2
= quarterly earnings of the ith firm in period t,
and
are estimated using the
most recent twenty quarters of data.
Table I reports summary statistics of SUE. At the end of each quarter, we compute the mean,
median, standard deviation, 25th and 75th percentiles of SUE and the number of observations
of SUE in our sample. Table I reports these statistics for selected years in the sample period.
Table I also reports summary statistics of other firm characteristics, including size, book to
market ratio, momentum, Amihud (2002) illiquidity ratio, idiosyncratic volatility, and shortsale constraint. Following Fama and French (1993), market capitalization (SIZE) is calculated
in the end of each June as stock price times the number of shares outstanding. Book-to-market
ratio (BEME) is calculated as book value for the fiscal year ending in calendar year t-1 divided
by market capitalization at the end of December of t-1. As defined in Fama and French (1993),
book value is equal to book value of stockholders’ equity plus balance sheet deferred taxes and
investment tax credits minus book value of preferred stocks. B/M are calculated at the end of
each June, and used for the next four quarters. We exclude firms with negative book values.
Momentum (MOM) is calculated as the cumulative 12-month return from Quarter t-2 to
Quarter t-12. The Amihud illiquidity (ILLIQ) measure is calculated as the ratio of daily
absolute return to dollar trading volume and averaged over the quarter (Amihud, 2002). Since
trading volume in NASDAQ is double counted (Atkins and Dyl, 1997; Nagel, 2005), we adjust
the turnover of NASDAQ stocks by a factor of 1/2. Following Ang, Hodrick, Xing, and Zhang
(2006), we measure idiosyncratic volatility relative to the Fama-French 3-factor model:
ri ,t   i  i , MKT MKTt  i , SMB SMBt  i , HML HMLt  i ,t . The model is estimated using daily
7 returns in the preceding quarter and idiosyncratic volatility (IVOLt) is obtained as var(i ,t ) .
Relative short interest (RSI) is defined as monthly number of shares held short divided by the
number of shares outstanding and averaged over the quarter. Following Asquith, Pathak and
Ritter (2005), we use the difference between relative short interest, a proxy of short sale
demand, and institutional ownership (IO), a proxy of short sale supply, as a measure of shortsale constraint (SSC). Institutional ownership is calculated as the number of shares held by
institutional investors divided by total number of shares outstanding for each stock in the end
of each quarter, i.e.,
IOt =
#
of shares held by institutional investors t
 total
# of shares outstanding t
III. Post-Earnings Announcement Drift: The Effect of Transaction Costs and Limits to
Arbitrage
A. Post-Earnings Announcement Drift
In our empirical analysis, we first replicate post-earnings announcement drift (PEAD) in our
sample period. Each quarter, stocks are sorted into decile portfolios based on SUE using
previous quarter’s SUE breakpoints. For each decile portfolio, we compute equal-weighted
cumulative abnormal returns over different horizons following earnings announcement.
Following Bernard and Thomas (1989, 1990) and Foster, Olsen, and Shevlin (1984), we
calculate the abnormal returns as follows:
ARi ,t  Ri ,t  R p ,t
where ARi ,t = abnormal return from firm i, day t; Ri ,t = raw return from firm i, day t; and R p ,t =
equal-weighted average portfolio return for day t. To obtain equal-weighted average portfolio
8 return, we divide firms into deciles according to previous year December market capitalization
and calculate average daily return for each portfolio.
Table II reports the average abnormal stock returns of all SUE decile portfolios over different
holding periods. It also reports return differentials and the associated Newey-West (1987) tstatistics between top and bottom SUE decides. To ensure that the return differentials are not
just driven by the top and bottom deciles, we also compute and report return differentials
between the average of top two and the average of bottom two SUE decides. Consistent with
exiting studies (Ball and Brown (1968), Foster, Olsen, and Shevlin (1984), Bernard and
Thomas (1989, 1990)), the results show clear and significant drift following earnings
announcements. The return differentials between top and bottom SUE deciles are 0.774%,
1.556%, 2.374%, and 2.902%, respectively, over one-week, one-month, and one-quarter
horizons following earnings announcements. All the differences are highly significant based on
the Newey-West t-statistics.
B. The Effect of Transaction Costs
We use two variables as proxies of transaction costs: SIZE and Amihund illiquidity ratio
(ILLIQ), as defined in Section II. Bhushan (1994) finds that transaction costs positively related
to the magnitude of PEAD. Bhushan (1994) uses firm size as one of the proxies for transaction
costs and shows that size is negatively related to transaction costs. Ng, Rusticus and Verdi
(2008) also find that high transaction costs could weaken the return response to earnings
announcement and enhance the PEAD. They find that transaction costs response for the
existence and persistence of PEAD. Sadka (2006) also uses firm size and Amihud illiquidity
ratio as proxy for liquidity and shows that liquidity risk can serve as an explanation of PEAD.
9 Follow the same procedure in the previous section, each quarter stocks are assigned to deciles
using the SUE breakpoints of the previous quarter. As in Fama and French (2008), stocks in
each decile are then divided into Big, Small, and Micro-cap subsamples using the 20th and
50th percentiles of the market cap for NYSE stocks. That is, we divide each SUE decile
portfolio into three subgroups based on SIZE classification. This is equivalent to form SUE
deciles for each size subgroup since the SUE breakpoints are based on the previous quarter.
Table III reports the average equal-weighted cumulative abnormal returns of SUE decile
portfolios of each size subgroup over different holding periods. As expected, the drift is most
pronounced for micro-cap stocks. While the magnitude of drift for big stocks is much lower, it
remains highly statistically significant. The return differentials between top and bottom SUE
deciles are 0.533%, 1.047%, 0.896%, and 0.910%, respectively, over one-week, one-month,
and one-quarter horizons following earnings announcements. The corresponding Newey-West
t-statistics are 4.87, 5.33, 3.11, and 2.88. Note that this subsample contains stocks in the top
20th percentile of the market cap for NYSE stocks and there are on average only 32 stocks in
the top and bottom deciles.
Similarly, we divide stocks in each SUE decile into three subsamples according to the 33th and
67th percentiles of the ILLIQ of NYSE stocks. Again, this is equivalent to form SUE deciles
for each ILLIQ subgroup since the SUE breakpoints are based on the previous quarter. Table
IV reports the average equal-weighted cumulative abnormal returns of SUE decile portfolios of
each ILLIQ subgroup over different holding periods. Similar to results based on SIZE
subsamples, the drift is most pronounced for high ILLIQ stocks. While the magnitude of drift
for low ILLIQ stocks is much lower, it remains highly statistically significant. The return
differentials between top and bottom SUE deciles are 0.615%, 1.189%, 1.453%, and 1.490%,
respectively, over one-week, one-month, and one-quarter horizons following earnings
10 announcements. The corresponding Newey-West t-statistics are 5.44, 5.79, 5.43, and 4.74. We
interpret the results based on SIZE and ILLIQ as evidence that transaction costs unlikely fully
explain why PEAD is not arbitraged away by institutional investors.
C. The Effect of Limits to Arbitrage
We use two variables as proxies of limits to arbitrage: idiosyncratic volatility (IVOL) and short
sale constraint (SSC). Mendenhall (2004) uses idiosyncratic volatility to measure arbitrage risk
and find that it is significantly positively related to the magnitude of PEAD. Boehmer and Wu
(2012) find that short sales help reduce PEAD. Christophe, Ferri, and Angel (2004) find that
short sales before earnings announcements are negatively related to stock returns after earnings
announcement for NASDAQ firms.
We divide stocks in each SUE decile in Table II into three subsamples according to the 33th
and 67th percentiles of the IVOL for NYSE stocks. For each decile portfolio, we compute
equal-weighted cumulative abnormal returns over different horizons following earnings
announcement. Table V reports the average abnormal stock returns of SUE decile portfolios of
each IVOL subgroup over different holding periods. Similarly, we divide stocks in each decile
into three subsamples according to the 33th and 67th percentiles of the SSC for NYSE stocks.
Table VI reports the average abnormal stock returns of SUE decile portfolios of different SSC
groups over different holding period.
Consistent with existing studies, our results show that stocks with high IVOL or SSC have
higher drift following earnings announcement. Nevertheless, for both the subsample of stocks
with the lowest IVOL and the subsample of stocks with the lowest SSC, there remains strong
and significant post-earnings announcement drift. For the subsample of stocks with the lowest
IVOL, the return differentials between top and bottom SUE deciles are 1.290%, 2.265%,
11 2.720%, and 3.141%, respectively, over one-week, one-month, and one-quarter horizons
following earnings announcements. The corresponding Newey-West t-statistics are 12.42,
13.09, 12.82, and 14.04. For the subsample of stocks with the lowest SSC, the return
differentials between top and bottom SUE deciles are 1.081%, 1.502%, 1.908%, and 2.076%,
respectively, over one-week, one-month, and one-quarter horizons following earnings
announcements. The corresponding Newey-West t-statistics are 6.65, 7.16, 5.63, and 4.95. We
interpret these results as evidence that limits to arbitrage unlikely fully explain why PEAD is
not arbitraged away by institutional investors.
D. Fama-MacBeth Regressions
We perform event-based Fama-MacBeth regressions of post-earnings announcement returns
against SUE and other common firm characteristics. The main difference with the conventional
Fama-MacBeth regression is that in our setting, stock returns and lagged variables are based on
event dates instead of calendar dates. Specifically, we perform cross-sectional regression of
post-earnings announcement returns against SUE with various firm characteristics included as
control variables:
RETi ,[ t 1,t T ]   t  1t SUEi ,t   k 1  kt X ki ,t   i ,t T
K
where RETi ,[t 1,t T ] is post-earnings announcement returns of stock i from day t+1 to day t+τ
with τ=5, 10, 21 and 63. The control variables include lagged market capitalization (SIZE),
book to market ratio (B/M), previous month return (LRET), momentum (MOM), idiosyncratic
volatility (IVOL), Amihund illiquidity ratio (ILLIQ) and relative short interest (RSI).
The above regressions are estimated each quarter at the cross-section and Table VII reports
time series averages of coefficient estimates as well as their Newey-West t-statistics. The
results show that there is a significantly positive relation between SUE and post-earnings
12 announcement returns. More importantly, even after we control for common firm
characteristics, such as SIZE, ILLIQ, IVOL, and ILLIQ, the relation remains positive and
significant.
IV. Post-Earnings Announcement Drift: The Effect of Institutional Herding
A large body of empirical literature shows that institutional investors exhibit a tendency to
herd, that is, they buy or sell the same stocks during the same time period. For example,
Lakonishok, Shleifer, and Vishny (1992), Nofsinger and Sias (1999), Wermers (1999), and
Sias (2004) all provide evidence that institutional investors herd in buying or selling stocks.
The theoretical literature offers two main reasons for why institutions might herd. First, they
might receive similar information and trade based on the new information (Froot, Scharfstein,
and Stein (1992); Hirshleifer, Subrahmanyam, and Titman. (1994)). Second, institutions may
herd for reasons unrelated to information such as the reputational risk of acting differently
from others or preferences for specific stock characteristics (for detailed surveys of the herding
literature, see Scharfstein and Stein (1990); Banerjee (1992); Devenow and Welch (1996);
Falkenstein (1996); Bikhchandani and Sharma (2001); Gompers and Metrick (2001)). In this
paper, we examine institutional herding following earnings announcements. We are interested
in whether institutional investors herd on earnings surprises. More importantly, what is the
association between institutional herding and drift following earnings announcement?
Moreover, what is the effect of institutional herding on the stock price discovery process?
A. Earnings Announcements and Institutional Herding
13 We follow Lakonishok, Shleifer and Vishny (1992), Wermers (1999) and Sias (2004) and
calculate the institutional herding (HERD) measure for each stock in our sample. Specifically,
institutional herding is calculated as follows:
HERDt  pi ,t  E  pi ,t   E NH [ pi ,t  E  pi ,t  ]
where pi ,t is the actual percentage of institutional investors that buy stock i. Those buyers are
defined as institutional investors who increase their ownership over the quarter (IOt > lag IOt).
E  pi ,t  is the expected value of pi ,t ,defined as the average buying percentage of all
institutional investors trading at quarter t. E NH [ pi ,t  E  pi ,t  ] is an adjustment factor which is
the expected value of the first term under the null hypothesis that here is no herding. The
theoretical distribution of pi ,t considering independent and random trades for each manager is a
binomial distribution with mean E  pi ,t 
We follow Brown, Wei and Wermers (2014) and further distinguish herding on the buy and
sell sides by calculate buy-herding ( BHM i ,t ) and sell herding (SHMit):
BHM i ,t  HERDi ,t | pi ,t  E[ pi ,t ] |
SHM i ,t  HERDi ,t | pi ,t  E[ pi ,t ] |
An “adjusted herding measure” is constructed to capture the direction of herding for a specific
stock. Specifically, for each quarter and within each buy herding group (or sell herding group),
the above measure is subtracted by the minimum value of BHM (or SHM) from each stock’s
BHM (or SHM) to obtain a non-negative herding measure. Stocks that are traded by fewer than
five institutional investors during the quarter are excluded in the calculation. We also use two
additional measures of institutional herding in our analysis, namely the change of the number
of institutional investors and the percentage change of the number of institutional investors.
14 Table VIII reports descriptive statistics of institutional ownership change (∆IO), institutional
herding (INST HERD), the change of the number of institutional investors (∆#INST), and the
percentage change of the number of institutional investors (%∆#INST ) each quarter in
different SUE decile groups. For stocks in each decile, we compute the mean, median, 25th,
35th, 65th, and 75th percentiles of institutional ownership change (∆IO),institutional herding
(INST HERD), change of the number of institutional investors (∆#INST), and the percentage
change of the number of institutional investors (%∆#INST ) in each quarter. The table reports
time series averages of these statistics for all SUE deciles. As shown in the table, institutional
investors in general trade and herd in the same direction of earnings surprises. For example, as
earnings surprise increases from SUE decile 1 (negative) to SUE decile 10 (positive), the
average institutional herding increases, almost monotonically. The same patterns are observed
for ∆IO, ∆#INST, and %∆#INST. We also note that there are variations in institutional
herding among stocks within each decile. Specifically, in SUE decile 1 with negative earnings
surprises, institutional investors herd in buying at least 35% of those stocks, whereas in SUE
decile 10 with positive earnings surprises, institutional investors herd in selling at least 35% of
those stocks. This cross-sectional variation presents an opportunity for us to examine the effect
of institutional herding on post-earnings announcement stock returns.
B. Institutional Herding and Post-Earnings Announcement Returns
We divide stocks in each SUE decile into three subgroups based on the relation between
institutional herding and earnings surprises, namely those with strongly positive, weakly
positive, and negative relations between institutional herding and earnings surprises. For each
decile portfolio, we compute equal-weighted cumulative abnormal returns over different
horizons following earnings announcement. We perform similar analysis based on the change
15 of the number of institutional investors (∆#INST) and the percentage change in the number of
institutional investors (%∆#INST).
Table IX reports the average abnormal stock returns of SUE decile portfolios of each
institutional herding (HERD) subgroup over different holding periods. The results show that
when institutional investors herd strongly in the same direction of earnings surprises, there is a
much stronger drift following earnings announcement. More importantly, when institutional
investors herd in the opposite direction of earnings surprises, the drift following earnings
announcement is only short-lived. As shown in Panel C of Table IX, there is a significant drift
over the one-week horizon following earnings announcement. The drift is no longer significant
beyond one-week horizon.
Table X reports the average abnormal stock returns of SUE decile portfolios of different
percentage change in the number of institutional investors (%∆#INST) groups over different
holding period. Results based on the change in the number of institutional investors (∆#INST)
are similar and thus not reported for brevity. Consistent with the result sin Table IX, the results
in Table X show that when there is a high increase (decrease) in the percentage of the number
of institutional investors for stocks with positive (negative) earnings surprises, the drift
following earnings announcement is much stronger in both magnitude and statistical
significance. On the other hand, when there is a high decrease (increase) in the percentage of
the number of institutional investors for stocks with positive (negative) earnings surprises, we
observe an immediate reversal in stock returns following earnings announcement. While
announcement day returns for stocks with positive surprises are significantly higher than for
those with negative surprises, the post-earnings announcement stock returns are quickly
reversed. The return differentials between top and bottom SUE deciles are negative and
16 statistically insignificant over one-week horizon, but negative and highly significant beyond
one-week horizons.
To further control for the effect of other firm characteristics on post-earnings announcement
returns, we perform event-based Fama-MacBeth regressions of post-earnings announcement
returns. Specifically, we regress post-earnings announcement returns against SUE, interactions
of SUE with dummies for institutional herding and percentage change in the number of
institutional investors, and various firm characteristics included as control variables:
RETi ,[ t 1,t T ]   t  1t SUEi ,t   2 t DHERD * SUEi ,t  3t DIO * SUEi ,t   k 1  kt X ki ,t   i ,t T
K
where RETi ,[t 1,t T ] is post-earnings announcement returns of stock i from day t+1 to day t+τ
with τ=5, 10, 21 and 63. DHERD * SUEi ,t is the interaction term of interactions of SUE with
dummies for institutional herding. The institutional herding dummy is set equal to 1 if there is
strongly positive or weakly positive correlations between institutional herding and SUE and
otherwise 0. DIO * SUEi ,t is the interaction term of interactions of SUE with dummies for
percentage change in the number of institutional investors. The percentage change in the
number of institutional investors dummy is set equal to 1 if there is strongly positive or weakly
positive correlations between percentage change in the number of institutional investors and
SUE and otherwise 0. The control variables include lagged market capitalization (SIZE), book
to market ratio (B/M), previous month return (LRET), momentum (MOM), idiosyncratic
volatility (IVOL), Amihund illiquidity ratio (ILLIQ) and relative short interest (RSI). The
above regressions are estimated each quarter at the cross-section and Table XI reports time
series averages of coefficient estimates as well as their Newey-West t-statistics. The results
confirm that the relation between SUE and post-earnings announcement returns is significantly
17 positive only for stock samples where institutional investors herd in the same direction of
earnings surprises.
V. Post-Earnings Announcement Returns Over Long-Run
The finding that post-earnings announcement drift is significant only when institutional
investors herd in the same direction of earnings surprises suggests tow possible roles of
institutional investors. One interpretation of the finding is that institutional investors may be
slow in reacting to earnings information and in incorporating information into stock prices.
This represents a slow process of stock price discovery. The other interpretation of the finding
is that institutional investors may herd beyond the effect of earnings information and as such
drive stock prices away from fundamental values. We examine long run stock returns
following earnings announcement to distinguish the above two hypotheses. Our main premise
is that if institutional herding drives stock prices away from fundamental values, we should
observe return reversals over long run.
Figure I plots post-earnings announcement abnormal stock returns of all stocks in the SUE
deciles. Panel A plots the average abnormal stock returns up to 60 months during the postearnings announcement period for each decile portfolio formed on SUE. Panel B plots return
differentials between the top and bottom deciles (D10-D1) and the average of top two and
bottom two deciles (D10|9-D1|2). The reason to examine returns up to 60 months or 5 years is
that as documented in De Bondt and Thaler (1985), stock returns on average tend to revers
over 3- to 5-year horizons. As shown in Figure I, returns of both top and bottom SUE deciles
tend to be persistent. The differences between D1 and D10 as well as the difference between
the average of D1 and D2 and the average of D10 and D9, as plotted in Panel B, show that
18 there is no obvious reversal of stock returns following earnings announcements even up to 60month horizon.
Figure II plots post-earnings announcement abnormal stock returns for the subsample of stocks
with strongly positive relation between the percentage change of the number of institutional
investors (%∆#INST) and SUE. Panel A plots the average abnormal stock returns up to 60
months during the post-earnings announcement period for all SUE decile portfolios. Panel B
plots return differentials between top and bottom deciles (D10-D1) and the average of top two
and the average of bottom two deciles (D10|9-D1|2). The results show similar patterns as the
full sample results as plotted in Figure I. That is, there is no obvious reversal of stock returns
following earnings announcements even up to 60-month horizon. This is evidence that
institutional herding in the direction of earnings surprises do not destabilize stock prices but
represents a slow process of stock price discovery.
Finally, Figure III plots post-earnings announcement abnormal stock returns for the
subsample of stocks with negative relation between the percentage change of the number of
institutional investors (%∆#INST) and SUE. Panel A plots the average abnormal stock returns
up to 60 months during the post-earnings announcement period for all SUE deciles. Panel B
plots return differentials between top and bottom deciles (D10-D1) and the average of top two
and the average of bottom two deciles (D10|9-D1|2). The plots confirm the results in Table X
that there is an immediate return reversal following earnings announcement when institutional
investors herd in the opposite direction of earnings surprises. However, as illustrated in Panel
B of Figure III, these reversals are soon corrected over longer horizons.
VI. Conclusion
19 In this paper, we examine the role of institutional investors underlying post-earnings
announcement drift. We are mainly interested in the following two questions. First, to what
extent transaction costs and limits to arbitrage prevent institutional investors from arbitraging
away anomalous stock returns following earnings announcement? Second, to what extent the
drift following earnings announcement is associated with the herding behavior of institutional
investors. We provide evidence that transaction costs or limits to arbitrage do not fully explain
why post-announcement abnormal stock returns are not arbitraged away by institutional
investors. In addition, we find that while institutional investors in general herd in the same
direction of earnings surprises, there are more than one third of stocks in the top (bottom)
decile with positive (negative) earnings surprises where institutional investors herd in selling
these stocks. We use these two subsamples to examine how the drift following earnings
announcement is associated with herding behavior of institutional investors and, moreover, the
effect of institutional herding on long-run stock price discovery. Our results show that the drift
is only present in the stock sample where institutional trading herds in the same direction of
earnings surprises. That is, institutional herding following earnings announcements is likely a
culprit of the stock price continuation. Nevertheless, we show that such herding behavior does
not push stock prices away from fundamental values but represents slow incorporation of
information into stock prices. On the other hand, when institutional investors herd in the
opposite direction of earnings surprises, there is clear evidence of immediate price reversal
following earnings announcement.
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22 Table I. Summary Statistics of Firm Characteristics
This table reports summary statistics of firm characteristics of the stock sample at the end of selected years during
our sample period: 1980, 1990, 2000, 2013. Each year, we compute the mean, median, standard deviation (StDev),
5th, 25th, 75th and 95th percentiles and the number of observations of each variable. The variables include market
capitalization (SIZE), book to market ratio (B/M), momentum (MOM), Amihud illiquidity ratio (ILLIQ),
idiosyncratic volatility (IVOL), and relative short interest (RSI). SIZE is calculated at the end of each June as market
capitalization. B/M is calculated at the end of each June using book value for the fiscal year ending in calendar year
t-1 divided by market capitalization at the end of December of year t-1. MOM is the skip one month cumulative 12month return preceding the month. ILLIQ is calculated as the ratio of absolute daily return to dollar trading volume
and averaged over the quarter. IVOL is the standard error of the residual of the Fama-French 3-factor model
estimated for daily return over the quarter. RSI is calculated each month as the number of shares held short divided
by the total number of shares outstanding and averaged over the quarter. The sample period is from January 1980 to
December 2013.
Year
1980
Variable
SIZE
B/M
MOM
ILLIQ
IVOL
RSI
N
2097
1658
2103
1910
2108
2109
5%
55.07
0.0299
-0.3081
0.0049
0.8973
0.00%
25%
251.86
0.0632
-0.0790
0.0360
1.4642
0.00%
Mean
3993.25
0.1170
0.1562
2.9447
2.3168
79.31%
Median
865.62
0.1010
0.0708
0.1994
2.0492
1.43%
75%
3071.98
0.1480
0.2916
1.1624
2.8479
27.36%
95%
14182.53
0.2580
0.8980
12.7410
4.5668
287.94%
StDev
16249.34
0.0845
0.3928
11.2994
1.2276
376.44%
1990
SIZE
B/M
MOM
ILLIQ
IVOL
RSI
3285
2584
3305
3318
3318
3318
54.26
0.0140
-0.6188
0.0018
1.0916
0.00%
233.66
0.0362
-0.3125
0.0349
1.8416
2.64%
7794.01
0.0784
-0.0351
16.7970
3.6304
160.19%
819.26
0.0619
-0.0987
0.5252
2.8433
17.29%
3955.08
0.0964
0.1275
4.8222
4.3385
101.85%
34449.17
0.1896
0.7391
56.9030
9.0274
681.45%
28639.09
0.0870
0.4917
139.5041
2.8618
651.58%
2000
SIZE
B/M
MOM
ILLIQ
IVOL
RSI
4713
3611
4779
4820
4817
4820
88.08
0.0049
-0.6170
0.0005
1.5029
0.15%
409.44
0.0203
-0.2857
0.0085
2.4450
4.05%
25929.36
0.0738
0.2388
3.3921
4.2380
171.96%
1478.28
0.0492
-0.0459
0.1166
3.7097
45.05%
6958.06
0.0966
0.3469
1.4421
5.4594
181.48%
84264.44
0.2171
2.0083
16.7587
8.7359
758.62%
152370.39
0.0849
1.1410
13.5646
2.4163
376.68%
2013
SIZE
B/M
MOM
ILLIQ
IVOL
RSI
2922
2146
2947
2979
2976
2979
205.56
0.0094
-0.3516
0.0001
0.7277
0.00%
1241.99
0.0299
0.0622
0.0006
1.1059
0.00%
44581.81
0.0833
0.3433
4.3723
2.1322
0.00%
5162.70
0.0528
0.2648
0.0046
1.6453
0.00%
21233.08
0.0888
0.4989
0.0576
2.5415
0.00%
174616.54
0.2061
1.1337
10.1769
4.9221
0.00%
182971.60
0.3829
0.8144
41.6818
1.8836
0.00%
23 Table II. Post-Earnings Announcement Abnormal Stock Returns
This table reports the average abnormal stock returns of all SUE decile portfolios over different holding periods. Each
quarter, stocks are assigned to deciles using the SUE breakpoints of the previous quarter. D1 includes firms with the
lowest SUE rank, and D10 includes firms with the highest SUE rank. The average return differentials between top and
bottom deciles (top and bottom two deciles), as well as their Newey-West t-statistics, are also reported. N is the
average number of stocks in each decile. The sample period is from January 1980 to December 2013.
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
348
341
345
347
346
345
344
342
344
346
-1.240
-0.817
-0.537
-0.223
0.071
0.419
0.752
1.084
1.394
1.824
-0.776
-0.592
-0.470
-0.288
-0.189
0.093
0.175
0.303
0.485
0.607
-0.897
-0.768
-0.612
-0.423
-0.327
0.026
0.217
0.362
0.591
0.774
-0.906
-0.906
-0.572
-0.404
-0.337
0.096
0.400
0.528
0.830
0.965
-0.918
-0.863
-0.524
-0.365
-0.293
0.306
0.542
0.714
1.048
1.256
-1.055
-0.858
-0.502
-0.351
-0.162
0.456
0.666
0.895
1.272
1.556
-1.335
-1.019
-0.692
-0.374
0.018
0.828
1.047
1.230
1.787
2.374
-1.424
-1.245
-0.861
-0.357
0.123
1.103
1.255
1.522
2.264
2.902
3.064
(31.32)
2.637
(32.29)
1.383
(12.34)
1.230
(12.80)
1.671
(10.04)
1.515
(11.52)
1.871
(9.39)
1.804
(11.75)
2.175
(9.88)
2.043
(12.41)
2.611
(10.95)
2.370
(13.09)
3.708
(14.74)
3.257
(17.53)
4.326
(16.58)
3.918
(20.93)
D10–D1
NW-t
D10|9–D1|2
NW-t
24 Table III. Post-Earnings Announcement Abnormal Stock Returns – Size Subsamples
Each quarter, stocks are assigned to deciles using the SUE breakpoints of the previous quarter. Stocks in each decile are
then divided into big, small, and micro-cap subsamples using the 20th and 50th percentiles of the market cap for NYSE
stocks. The average abnormal returns for all SUE deciles in each size subsample are calculated and reported. The average
return differentials between top and bottom deciles (top and bottom two deciles) in each size subsample, as well as their
Newey-West t-statistics, are also reported. N is the average number of stocks in each decile. The sample period is from
January 1980 to December 2013.
Panel A: Micro-cap Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
260
257
264
266
264
259
251
249
256
260
-1.514
-0.969
-0.629
-0.275
0.112
0.488
0.920
1.324
1.646
2.212
-0.933
-0.723
-0.570
-0.370
-0.231
0.101
0.202
0.345
0.546
0.713
-1.050
-0.959
-0.739
-0.516
-0.392
0.023
0.256
0.399
0.663
0.903
-1.070
-1.148
-0.693
-0.500
-0.411
0.083
0.471
0.584
0.928
1.094
-1.067
-1.116
-0.635
-0.468
-0.342
0.333
0.659
0.823
1.173
1.424
-1.196
-1.095
-0.618
-0.419
-0.174
0.543
0.825
1.065
1.470
1.759
-1.511
-1.249
-0.823
-0.383
0.133
1.020
1.321
1.545
2.173
2.834
-1.667
-1.514
-0.989
-0.368
0.285
1.444
1.658
1.897
2.806
3.498
D10 –D1
NW-t
D10|9–D1|2
3.726
(28.94)
3.171
1.646
(12.37)
1.458
1.953
(9.81)
1.788
2.163
(9.12)
2.120
2.491
(9.60)
2.390
2.956
(10.26)
2.760
4.345
(14.18)
3.884
5.165
(16.17)
4.742
NW-t
(29.85)
(12.78)
(11.44)
(11.53)
(12.27)
(12.72)
(17.25)
(20.86)
25 Panel B: Small Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
56
53
52
52
52
53
57
57
56
54
-0.568
-0.395
-0.274
-0.082
-0.068
0.237
0.352
0.520
0.761
0.801
-0.264
-0.164
-0.170
0.039
0.006
0.132
0.115
0.211
0.373
0.312
-0.469
-0.185
-0.268
-0.045
-0.010
0.104
0.099
0.264
0.511
0.419
-0.431
-0.209
-0.266
-0.009
-0.007
0.244
0.254
0.376
0.759
0.611
-0.432
-0.161
-0.273
0.047
-0.083
0.398
0.267
0.426
0.953
0.853
-0.551
-0.237
-0.277
-0.083
-0.118
0.364
0.204
0.475
0.961
1.083
-0.790
-0.351
-0.414
-0.246
-0.407
0.503
0.275
0.448
0.984
1.212
-0.711
-0.494
-0.507
-0.164
-0.351
0.323
0.206
0.643
0.994
1.369
D10 –D1
NW-t
D10|9–D1|2
1.369
(12.87)
1.262
0.576
(7.20)
0.557
0.888
(7.62)
0.792
1.043
(6.80)
1.005
1.285
(6.88)
1.199
1.635
(7.55)
1.416
2.002
(7.26)
1.669
2.079
(5.91)
1.783
NW-t
(16.31)
(3.14)
(7.69)
(9.01)
(8.84)
(9.61)
(7.70)
(6.56)
Panel C: Big Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
32
31
28
29
29
33
36
36
32
31
-0.293
-0.394
-0.254
-0.102
-0.070
0.104
0.298
0.414
0.577
0.516
-0.251
-0.127
0.019
-0.063
-0.051
0.015
0.092
0.150
0.160
0.223
-0.263
-0.127
0.043
-0.078
-0.170
-0.015
0.155
0.256
0.208
0.270
-0.279
-0.125
0.114
-0.118
-0.139
-0.048
0.120
0.326
0.239
0.444
-0.424
-0.075
0.176
-0.079
-0.153
-0.013
0.115
0.385
0.328
0.500
-0.521
-0.042
0.212
-0.039
-0.048
-0.018
0.272
0.373
0.373
0.526
-0.460
-0.225
0.066
-0.215
-0.166
-0.064
0.330
0.424
0.312
0.436
-0.463
-0.278
-0.180
-0.297
-0.301
-0.206
0.165
0.440
0.520
0.447
0.810
(8.01)
0.891
(11.07)
0.474
(5.70)
0.381
(6.90)
0.533
(4.87)
0.434
(5.36)
0.722
(4.69)
0.543
(5.33)
0.924
(5.30)
0.664
(5.03)
1.047
(5.33)
0.731
(4.48)
0.896
(3.11)
0.717
(3.30)
0.910
(2.88)
0.854
(3.30)
D10 –D1
NW-t
D10|9–D1|2
NW-t
26 Table IV. Post-Earnings Announcement Abnormal Stock Returns – Illiquidity Subsamples
Each quarter, stocks are assigned to deciles using the SUE breakpoints of the previous quarter. Stocks in each decile
are then divided into three subsamples according to the 33th and 67th percentiles of the Amihund illiquidity ratio
(ILLIQ) for NYSE stocks. The average abnormal returns for all SUE deciles in each illiquidity subsample are
calculated and reported. The average return differentials between top and bottom deciles (top and bottom two deciles)
in each illiquidity subsample, as well as their Newey-West t-statistics, are also reported. N is the average number of
stocks in each decile. The sample period is from January 1980 to December 2013.
Panel A: High ILLIQ Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
208
208
217
221
221
216
208
207
212
217
-1.648
-1.037
-0.653
-0.297
0.142
0.556
1.003
1.417
1.811
2.446
-1.003
-0.787
-0.596
-0.388
-0.270
0.086
0.195
0.374
0.606
0.774
-1.162
-1.030
-0.786
-0.561
-0.446
0.007
0.255
0.410
0.713
0.950
-1.213
-1.267
-0.751
-0.543
-0.489
0.025
0.463
0.588
1.010
1.143
-1.249
-1.268
-0.692
-0.520
-0.441
0.321
0.651
0.827
1.232
1.499
-1.381
-1.217
-0.681
-0.452
-0.229
0.570
0.817
1.084
1.532
1.860
-1.513
-1.244
-0.770
-0.288
0.196
1.174
1.479
1.743
2.398
3.132
-1.681
-1.585
-0.954
-0.240
0.428
1.632
1.913
2.175
3.132
3.881
D10 –D1
NW-t
D10|9–D1|2
4.095
(26.98)
3.472
1.777
(12.61)
1.585
2.112
(9.89)
1.928
2.356
(9.25)
2.317
2.748
(9.79)
2.624
3.241
(10.30)
2.995
4.645
(13.57)
4.144
5.562
(15.09)
5.139
NW-t
(28.74)
(13.13)
(11.31)
(11.59)
(12.19)
(12.32)
(16.46)
(19.13)
27 Panel B: Medium ILLIQ Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
79
76
74
72
71
72
73
73
73
70
-0.893
-0.577
-0.417
-0.150
-0.021
0.164
0.457
0.721
0.868
1.021
-0.530
-0.379
-0.316
-0.156
0.026
0.195
0.185
0.249
0.311
0.385
-0.563
-0.543
-0.399
-0.254
-0.088
0.114
0.220
0.369
0.456
0.609
-0.523
-0.560
-0.387
-0.257
-0.009
0.269
0.460
0.509
0.654
0.894
-0.438
-0.436
-0.363
-0.168
0.057
0.376
0.577
0.697
0.951
1.123
-0.516
-0.528
-0.335
-0.316
0.003
0.363
0.652
0.835
1.148
1.309
-1.103
-0.910
-0.904
-0.757
-0.266
0.331
0.582
0.787
1.249
1.499
-1.145
-0.954
-0.992
-0.748
-0.426
0.410
0.392
0.826
1.338
1.772
D10 –D1
NW-t
D10|9–D1|2
1.914
(16.01)
1.679
0.915
(8.60)
0.803
1.172
(8.18)
1.086
1.417
(8.30)
1.316
1.561
(8.70)
1.474
1.824
(9.31)
1.751
2.602
(9.39)
2.380
2.917
(8.26)
2.604
NW-t
(17.98)
(9.90)
(10.47)
(10.70)
(10.41)
(11.90)
(11.06)
(9.61)
Panel C: Low ILLIQ Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
61
58
54
54
54
58
62
62
59
58
-0.416
-0.394
-0.330
-0.113
-0.087
0.219
0.341
0.491
0.655
0.675
-0.252
-0.131
-0.102
-0.003
-0.086
0.054
0.136
0.155
0.248
0.220
-0.381
-0.094
-0.128
0.009
-0.150
0.058
0.156
0.229
0.341
0.234
-0.310
-0.072
-0.055
0.041
-0.116
0.142
0.184
0.355
0.474
0.369
-0.343
0.003
0.017
0.090
-0.144
0.235
0.223
0.386
0.598
0.522
-0.522
0.008
0.046
0.099
-0.095
0.225
0.289
0.340
0.587
0.667
-0.814
-0.255
-0.062
-0.089
-0.263
0.307
0.279
0.186
0.416
0.639
-0.695
-0.383
-0.219
-0.165
-0.237
0.125
0.257
0.373
0.614
0.795
D10 –D1
NW-t
D10|9–D1|2
1.091
(12.35)
1.070
0.471
(5.94)
0.425
0.615
(5.44)
0.525
0.679
(5.02)
0.612
0.865
(4.77)
0.730
1.189
(5.79)
0.884
1.453
(5.43)
1.062
1.490
(4.74)
1.243
NW-t
(15.48)
(7.44)
(5.86)
(5.91)
(5.81)
(6.13)
(5.71)
(5.68)
28 Table V. Post-Earnings Announcement Abnormal Stock Returns – IVOL Subsamples
Each quarter, stocks are assigned to deciles using the SUE breakpoints of the previous quarter. Stocks in each decile
are then divided into three subsamples according to the 33th and 67th percentiles of the idiosyncratic volatility (IVOL)
for NYSE stocks. The average abnormal returns for all SUE deciles in each IVOL subsample are calculated and
reported. The average return differentials between top and bottom deciles (top and bottom two deciles) in each IVOL
subsample, as well as their Newey-West t-statistics, are also reported. N is the average number of stocks in each
decile. The sample period is from January 1980 to December 2013.
Panel A: High IVOL Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
208
200
205
208
204
196
187
185
195
206
-1.576
-1.028
-0.653
-0.277
0.113
0.538
1.015
1.476
1.778
2.330
-1.026
-0.810
-0.642
-0.443
-0.337
0.098
0.179
0.332
0.509
0.712
-1.146
-1.037
-0.799
-0.651
-0.559
-0.053
0.164
0.293
0.532
0.782
-1.052
-1.193
-0.716
-0.621
-0.581
0.014
0.395
0.515
0.818
0.973
-1.004
-1.124
-0.628
-0.588
-0.523
0.297
0.629
0.804
1.084
1.370
-1.153
-1.087
-0.589
-0.536
-0.327
0.524
0.793
1.064
1.403
1.699
-1.437
-1.246
-0.743
-0.503
0.065
1.079
1.324
1.633
2.131
2.862
-1.426
-1.399
-0.849
-0.325
0.433
1.632
1.859
2.157
2.857
3.706
D10 –D1
NW-t
D10|9–D1|2
3.908
(28.76)
3.356
1.738
(11.43)
1.529
1.928
(8.60)
1.749
2.032
(7.58)
2.018
2.379
(7.86)
2.291
2.870
(8.41)
2.671
4.305
(11.92)
3.838
5.471
(13.54)
4.694
NW-t
(32.41)
(12.14)
(9.88)
(9.73)
(10.24)
(10.76)
(14.63)
(17.82)
29 Panel B: Median IVOL Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
80
81
82
80
81
82
85
85
83
77
-0.889
-0.632
-0.397
-0.208
0.026
0.331
0.528
0.807
1.042
1.367
-0.397
-0.295
-0.240
-0.040
0.074
0.118
0.200
0.332
0.511
0.526
-0.542
-0.421
-0.370
-0.063
0.087
0.144
0.337
0.527
0.752
0.787
-0.626
-0.521
-0.372
-0.068
0.118
0.236
0.493
0.637
0.969
1.050
-0.715
-0.539
-0.405
0.002
0.137
0.408
0.553
0.741
1.157
1.228
-0.807
-0.644
-0.394
-0.003
0.195
0.524
0.625
0.852
1.346
1.489
-1.197
-0.770
-0.626
-0.192
0.063
0.680
0.695
0.964
1.638
1.787
-1.468
-1.111
-0.881
-0.505
-0.129
0.616
0.616
1.008
1.755
1.920
D10 –D1
NW-t
D10|9–D1|2
2.256
(20.65)
1.965
0.923
(9.85)
0.864
1.329
(8.78)
1.251
1.676
(10.15)
1.583
1.943
(10.55)
1.820
2.297
(11.79)
2.143
2.984
(12.21)
2.696
3.388
(12.16)
3.127
NW-t
(21.47)
(10.51)
(10.51)
(12.30)
(12.97)
(13.49)
(15.12)
(14.87)
Panel C: Low IVOL Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
60
61
58
60
62
66
71
72
66
63
-0.611
-0.433
-0.358
-0.177
-0.047
0.129
0.313
0.420
0.672
0.839
-0.395
-0.258
-0.130
-0.069
-0.034
0.065
0.116
0.217
0.325
0.359
-0.572
-0.310
-0.251
-0.079
-0.071
0.141
0.221
0.388
0.600
0.717
-0.800
-0.447
-0.287
-0.046
-0.057
0.192
0.335
0.492
0.790
0.907
-0.971
-0.481
-0.292
-0.077
-0.047
0.249
0.313
0.539
0.901
0.985
-1.095
-0.462
-0.322
-0.108
-0.024
0.232
0.355
0.605
0.926
1.170
-1.246
-0.609
-0.453
-0.100
-0.020
0.369
0.628
0.764
1.104
1.474
-1.653
-0.906
-0.851
-0.312
-0.269
0.141
0.398
0.738
1.128
1.488
D10 –D1
NW-t
D10|9–D1|2
1.450
(16.64)
1.277
0.754
(11.15)
0.668
1.290
(12.42)
1.100
1.707
(11.75)
1.472
1.956
(12.36)
1.669
2.265
(13.09)
1.826
2.720
(12.82)
2.217
3.141
(14.04)
2.588
NW-t
(18.69)
(11.42)
(13.80)
(13.81)
(13.88)
(14.55)
(15.20)
(15.58)
30 Table VI. Post-Earnings Announcement Abnormal Stock Returns – Subsamples of Short Sale
Constraint
Each quarter, stocks are assigned to deciles using the SUE breakpoints of the previous quarter. Stocks in each decile
are then divided into three subsamples according to the 33th and 67th percentiles of the short sale constraint (SSC) for
NYSE stocks. SSC is defined as relative short interest minus institutional ownership. The average abnormal returns for
all SUE deciles in each SSC subsample are calculated and reported. The average return differentials between top and
bottom deciles (top and bottom two deciles) in each SSC subsample, as well as their Newey-West t-statistics, are also
reported. N is the average number of stocks in each decile. The sample period is from January 1980 to December
2013.
Panel A: High Short Sale Constraint Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
192
190
194
195
193
188
187
188
194
197
-1.557
-0.965
-0.628
-0.239
0.151
0.510
0.917
1.323
1.694
2.265
-0.966
-0.758
-0.621
-0.430
-0.298
-0.004
0.082
0.274
0.501
0.680
-1.140
-1.036
-0.805
-0.670
-0.474
-0.105
0.080
0.276
0.584
0.791
-1.193
-1.203
-0.786
-0.612
-0.555
-0.053
0.278
0.435
0.836
1.006
-1.240
-1.223
-0.762
-0.641
-0.480
0.222
0.515
0.669
1.075
1.364
-1.428
-1.175
-0.802
-0.654
-0.360
0.460
0.595
0.836
1.326
1.676
-1.854
-1.339
-0.942
-0.556
-0.067
0.995
1.192
1.399
2.076
2.735
-2.046
-1.632
-1.118
-0.451
0.239
1.524
1.507
1.823
2.768
3.433
D10 –D1
NW-t
D10|9–D1|2
3.821
(27.56)
3.240
1.647
(12.40)
1.453
1.931
(9.35)
1.776
2.199
(8.86)
2.119
2.605
(9.47)
2.451
3.104
(9.98)
2.803
4.589
(13.83)
4.002
5.479
(16.05)
4.939
NW-t
(30.03)
(13.13)
(11.20)
(11.23)
(12.06)
(11.82)
(15.89)
(19.06)
31 Panel B: Median Short Sale Constraint Stocks
Holding Period
SUE Decile
N
[-1, 1]
[2, 2]
[2, 5]
[2, 10]
[2, 15]
[2, 21]
[2, 42]
[2, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
86
83
83
83
84
85
86
86
83
83
-0.964
-0.703
-0.441
-0.244
-0.114
0.266
0.589
0.934
1.060
1.383
-0.642
-0.398
-0.286
-0.088
-0.068
0.176
0.256
0.368
0.426
0.559
-0.648
-0.436
-0.421
-0.094
-0.147
0.136
0.451
0.497
0.594
0.825
-0.667
-0.554
-0.324
-0.069
-0.065
0.198
0.628
0.743
0.856
1.031
-0.680
-0.463
-0.293
0.087
-0.049
0.380
0.677
0.910
1.097
1.288
-0.707
-0.460
-0.219
0.127
0.103
0.356
0.947
1.220
1.241
1.653
-0.719
-0.480
-0.457
-0.083
0.180
0.568
1.180
1.432
1.652
2.311
-0.651
-0.582
-0.541
-0.040
0.093
0.534
1.401
1.621
1.989
2.669
D10 –D1
NW-t
D10|9–D1|2
2.347
(17.40)
2.055
1.200
(10.80)
1.012
1.473
(9.14)
1.251
1.698
(8.43)
1.554
1.968
(9.33)
1.764
2.360
(10.20)
2.030
3.029
(10.67)
2.581
3.320
(8.66)
2.946
NW-t
(18.83)
(11.06)
(9.72)
(10.20)
(10.79)
(11.53)
(12.24)
(10.70)
Panel C: Low Short Sale Constraint Stocks
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
70
67
68
70
69
72
72
69
67
65
-0.679
-0.483
-0.405
-0.133
0.037
0.289
0.520
0.573
0.865
0.966
-0.386
-0.286
-0.188
-0.080
0.050
0.273
0.337
0.338
0.512
0.450
-0.461
-0.314
-0.230
-0.129
-0.018
0.281
0.368
0.551
0.678
0.621
-0.367
-0.351
-0.168
-0.137
0.064
0.412
0.526
0.645
0.863
0.821
-0.292
-0.215
-0.053
-0.017
0.081
0.490
0.537
0.698
1.023
1.003
-0.330
-0.317
0.014
0.008
0.159
0.597
0.626
0.789
1.200
1.173
-0.524
-0.644
-0.279
-0.154
0.087
0.656
0.636
0.615
1.143
1.384
-0.540
-0.739
-0.524
-0.478
-0.116
0.597
0.446
0.704
1.202
1.536
D10 –D1
NW-t
D10|9–D1|2
1.645
(17.31)
1.496
0.836
(7.36)
0.817
1.081
(6.65)
1.037
1.188
(6.05)
1.201
1.294
(6.60)
1.266
1.502
(7.16)
1.510
1.908
(5.63)
1.848
2.076
(4.95)
2.009
NW-t
(17.40)
(8.80)
(8.28)
(8.18)
(8.64)
(9.66)
(8.06)
(7.07)
32 Table VII. Fama-MacBeth Regressions of Post-Earnings Announcement Returns
Each quarter, we perform cross-sectional regressions of post-earnings announcement stock returns over different
holding periods on SUE with various control variables. The control variables include lagged market capitalization
(SIZE), book to market ratio (B/M), previous month return (LRET), momentum (MOM), Amihund illiquidity ratio
(ILLIQ), idiosyncratic volatility (IVOL), and relative short interest (RSI). For details on variable definitions, please
refer to Table I. The table reports time series average of coefficient estimates and their Newey-West t-statistics. *** and
**
indicate significance at the 1% and 5% level, respectively. The sample period is from January 1980 to December
2013.
[1, 5]
[1, 10]
[1, 21]
0.561***
(11.15)
0.000
(0.861)
0.503
(1.83)
-0.130
(-0.60)
-0.350***
(-4.74)
0.016***
(3.95)
-0.168***
(-6.40)
0.008**
(2.09)
0.611***
(12.25)
0.658***
(12.16)
0.000
(0.96)
0.231
(0.55)
-0.330
(-1.06)
-0.525***
(-4.27)
0.016***
(3.84)
-0.189***
(-4.74)
0.013**
(2.35)
0.858***
(14.27)
0.920***
(14.47)
0.000
(0.19)
0.368
(0.54)
-0.691
(-1.46)
-0.475**
(-2.18)
0.012**
(2.05)
-0.161**
(-2.30)
0.021***
(2.78)
1.595***
(21.25)
1.663***
(18.76)
-0.000
(-0.94)
3.844**
(2.22)
-0.153
(-0.15)
-0.255
(-0.46)
0.046***
(4.59)
-0.121
(-0.66)
0.032**
(2.11)
N
0.131
(1.27)
3534
0.494***
(4.91)
2643
0.454**
(2.31)
3534
0.882***
(4.65)
2643
1.218***
(3.46)
3534
1.473***
(4.59)
2643
3.917***
(4.22)
3534
3.210***
(4.46)
2643
Adj. R2 (%)
0.42%
1.37%
0.38%
1.55%
0.41%
2.11%
0.50%
3.16%
SIZE
B/M
LRET
MOM
ILLIQ
IVOL
RSI
Intercept
33 [1, 63]
0.517***
(11.39)
SUE
Table VIII . Institutional Ownership Changes and Institutional Herding Across SUE Deciles
Each quarter, stocks are assigned to deciles using the SUE breakpoints of the previous quarter. For stocks in each decile, we compute the mean, median, 25th, 35th, 65th, and
75th percentiles of institutional ownership change (∆IO),institutional herding (INST HERD), change of the number of institutional investors (∆#INST), and the percentage
change of the number of institutional investors (%∆#INST ) each quarter. The table reports time series averages of these statistics for all SUE deciles. The sample period is
from January 1980 to December 2013.
∆IO
INST. HERD
SUE Decile
25%
35%
Mean
Median
65%
75%
25%
35%
Mean
Median
65%
75%
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
-1.450
-1.261
-1.175
-1.085
-1.078
-1.078
-1.012
-0.975
-0.990
-0.998
-0.676
-0.574
-0.538
-0.485
-0.484
-0.478
-0.441
-0.417
-0.433
-0.422
-0.346
-0.018
0.190
0.267
0.360
0.385
0.482
0.539
0.472
0.516
-0.021
0.019
0.043
0.066
0.098
0.100
0.120
0.149
0.119
0.132
0.623
0.676
0.722
0.762
0.828
0.834
0.835
0.878
0.814
0.860
1.334
1.429
1.480
1.552
1.626
1.594
1.601
1.640
1.592
1.667
-32.920
-31.446
-30.095
-29.764
-29.532
-29.189
-28.862
-29.036
-29.026
-28.442
-21.521
-20.553
-19.678
-18.848
-18.903
-17.933
-18.199
-17.846
-17.262
-16.540
-3.506
-2.476
-2.403
-1.734
-1.568
-0.542
-0.256
0.127
-0.186
0.295
-6.291
-4.704
-5.031
-4.361
-4.486
-3.333
-3.013
-2.504
-2.797
-1.258
7.391
9.671
9.512
10.968
10.859
14.060
15.145
14.658
14.207
14.181
25.233
25.700
24.929
25.979
26.718
27.330
28.728
28.719
28.300
28.347
SUE Decile
t
25 %
35%
Mean
Median
65%
75%
25%
35%
Mean
Median
65%
75%
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
-3.890
-3.438
-3.077
-2.813
-2.669
-2.438
-2.482
-2.265
-2.055
-1.941
-2.015
-1.691
-1.456
-1.324
-1.184
-0.993
-0.989
-0.846
-0.699
-0.574
2.007
2.485
2.527
3.063
3.304
3.803
4.231
4.583
4.499
4.724
-0.202
0.037
0.136
0.371
0.507
0.717
0.805
0.930
1.007
1.107
1.787
2.063
2.096
2.485
2.610
2.982
3.206
3.515
3.522
3.735
4.048
4.301
4.423
4.779
4.963
5.717
6.022
6.566
6.445
6.717
-6.835
-5.720
-5.601
-5.256
-4.873
-4.215
-4.001
-3.826
-3.767
-3.752
-3.669
-2.874
-2.747
-2.298
-2.137
-1.738
-1.551
-1.448
-1.235
-1.210
2.655
3.487
4.443
4.699
5.032
5.422
5.650
5.943
8.623
7.002
-0.391
0.070
0.333
0.581
0.868
1.295
1.426
1.608
1.878
1.925
3.065
3.531
3.974
4.559
4.640
5.097
5.331
5.631
5.940
6.021
6.354
7.225
7.560
8.472
8.408
9.093
8.973
9.854
9.957
10.510
∆#INST
%∆#INST
34 Table IX. Post-Earnings Announcement Abnormal Stock Returns – Subsamples of Institutional
Herding
Each quarter, stocks are assigned to deciles using the SUE breakpoints of the previous quarter. Stocks in each decile
are then divided into three subsamples: as those with strongly positive, weakly positive, and negative correlations
between institutional herding (HERD) and SUE. The average abnormal returns for all SUE deciles in each subsample
are calculated and reported. The average return differentials between top and bottom deciles (top and bottom two
deciles), as well as their Newey-West t-statistics, are also reported. N is the average number of stocks in each decile.
The sample period is from January 1980 to December 2013.
Panel A: Strong Positive Correlation between HERD and SUE
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
111
105
104
103
102
106
107
107
104
105
-1.287
-0.834
-0.637
-0.315
-0.166
0.580
0.729
1.161
1.445
1.803
-1.008
-0.825
-0.772
-0.543
-0.432
0.265
0.424
0.526
0.692
0.816
-1.216
-1.144
-1.099
-0.913
-0.735
0.502
0.700
0.913
1.076
1.410
-1.362
-1.265
-1.179
-1.018
-0.877
0.870
1.121
1.341
1.590
1.908
-1.499
-1.194
-1.177
-1.081
-0.921
1.290
1.350
1.674
1.986
2.465
-1.750
-1.376
-1.284
-1.202
-0.910
1.583
1.703
2.059
2.477
3.008
-2.294
-1.846
-1.764
-1.414
-1.084
2.307
2.416
2.767
3.372
4.104
-2.193
-2.099
-1.981
-1.356
-1.070
2.464
2.643
3.078
4.001
4.681
D10 –D1
NW-t
D10|9–D1|2
3.090
(30.62)
2.685
1.824
(9.99)
1.671
2.626
(9.57)
2.423
3.270
(10.01)
3.062
3.963
(11.30)
3.572
4.758
(12.40)
4.306
6.398
(15.01)
5.808
6.874
(13.85)
6.487
NW-t
(32.26)
(10.34)
(10.33)
(11.13)
(11.82)
(12.87)
(14.79)
(15.29)
35 Panel B: Weak Positive Correlation between HERD and SUE
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
143
141
144
146
143
140
139
139
145
147
-1.371
-0.912
-0.493
-0.202
0.207
0.540
1.025
1.205
1.612
2.207
-0.787
-0.579
-0.483
-0.278
-0.178
0.166
0.227
0.441
0.596
0.720
-0.967
-0.795
-0.694
-0.405
-0.237
0.073
0.323
0.385
0.760
0.810
-0.996
-1.046
-0.677
-0.338
-0.314
0.059
0.448
0.488
0.892
0.896
-0.948
-0.994
-0.715
-0.316
-0.353
0.168
0.633
0.584
1.078
1.192
-1.153
-0.938
-0.633
-0.293
-0.254
0.438
0.690
0.777
1.244
1.436
-1.430
-1.243
-1.085
-0.367
-0.133
0.766
1.260
1.104
1.751
2.496
-1.675
-1.455
-1.331
-0.307
0.029
1.137
1.491
1.193
2.260
2.752
D10 –D1
NW-t
D10|9–D1|2
3.578
(26.50)
3.051
1.506
(11.10)
1.340
1.777
(8.77)
1.666
1.892
(7.41)
1.916
2.140
(7.63)
2.106
2.589
(8.56)
2.386
3.926
(10.41)
3.460
4.426
(10.81)
4.071
NW-t
(26.81)
(12.36)
(11.13)
(10.48)
(10.86)
(11.03)
(13.76)
(14.21)
Panel C: Negative Correlation between HERD and SUE
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
93
95
97
99
100
99
98
96
95
93
-0.837
-0.527
-0.389
-0.106
0.199
0.100
0.419
0.704
0.954
1.111
-0.428
-0.324
-0.097
0.012
0.093
-0.161
-0.190
-0.021
0.109
0.158
-0.367
-0.297
0.013
0.171
0.132
-0.508
-0.433
-0.187
-0.052
0.028
-0.114
-0.291
0.232
0.400
0.353
-0.537
-0.327
-0.159
0.058
0.018
-0.059
-0.149
0.503
0.583
0.598
-0.386
-0.252
-0.042
0.185
0.078
0.015
0.089
0.724
0.786
0.879
-0.468
-0.256
-0.083
0.217
0.247
0.198
0.320
1.036
1.110
1.578
-0.336
-0.387
-0.155
0.193
0.427
0.232
0.223
0.998
1.087
1.650
-0.068
-0.255
0.205
0.473
0.979
D10 –D1
NW-t
D10|9–D1|2
1.948
(17.55)
1.715
0.586
(6.36)
0.509
0.395
(2.55)
0.320
0.132
(0.62)
0.241
0.137
(0.52)
0.236
0.232
(0.70)
0.180
0.228
(0.50)
0.051
0.747
(1.61)
0.498
NW-t
(18.28)
(7.52)
(2.99)
(1.51)
(1.21)
(0.72)
(0.13)
(1.35)
36 Table X. Post-Earnings Announcement Abnormal Stock Returns – Subsamples of Percentage
Change in the Number of Institutional Investors
Each quarter, stocks are assigned to deciles using the SUE breakpoints of the previous quarter. Stocks in each decile
are then divided into three subsamples: as those with strongly positive, weakly positive, and negative correlations
between percentage change in the number of institutional investors (%∆#INST) and SUE. The average abnormal
returns for all SUE deciles in each subsample are calculated and reported. The average return differentials between top
and bottom deciles (top and bottom two deciles), as well as their Newey-West t-statistics, are also reported. N is the
average number of stocks in each decile. The sample period is from January 1980 to December 2013.
Panel A: Strong Positive Correlation between %∆#INST and SUE
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
136
127
125
120
116
155
156
159
163
167
-1.602
-1.286
-1.014
-0.643
-0.313
0.798
1.185
1.618
1.857
2.326
-1.280
-1.054
-0.983
-0.700
-0.638
0.473
0.624
0.735
0.971
1.173
-1.507
-1.449
-1.372
-1.114
-1.040
0.693
0.913
1.151
1.412
1.691
-1.768
-1.885
-1.554
-1.429
-1.259
1.014
1.311
1.599
1.960
2.249
-1.988
-2.031
-1.599
-1.624
-1.433
1.495
1.693
2.047
2.397
2.861
-2.352
-2.246
-1.881
-1.887
-1.560
1.951
2.028
2.592
2.986
3.472
-3.142
-3.062
-2.900
-2.657
-2.066
3.031
3.057
3.643
4.206
5.050
-3.028
-3.240
-3.271
-2.723
-2.216
3.306
3.371
4.165
4.799
5.717
D10 –D1
NW-t
D10|9–D1|2
3.929
(35.74)
3.535
2.452
(10.97)
2.238
3.198
(10.02)
3.030
4.017
(10.70)
3.931
4.849
(11.81)
4.639
5.824
(13.06)
5.528
8.193
(15.87)
7.730
8.745
(14.95)
8.392
NW-t
(39.49)
(11.07)
(11.12)
(12.61)
(14.05)
(15.25)
(17.35)
(17.24)
37 Panel B: Weak Positive Correlation between %∆#INST and SUE
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
78
79
79
80
80
81
83
80
78
77
-0.886
-0.577
-0.351
-0.142
0.035
0.417
0.598
0.845
1.140
1.316
-0.455
-0.371
-0.323
-0.211
-0.150
0.102
0.125
0.304
0.360
0.458
-0.541
-0.541
-0.418
-0.334
-0.353
0.095
0.123
0.258
0.354
0.524
-0.430
-0.588
-0.334
-0.369
-0.337
0.050
0.245
0.389
0.349
0.555
-0.521
-0.588
-0.304
-0.416
-0.391
0.201
0.346
0.448
0.482
0.771
-0.652
-0.608
-0.178
-0.516
-0.394
0.327
0.360
0.471
0.489
0.996
-0.723
-0.765
-0.432
-0.651
-0.360
0.461
0.552
0.358
0.642
1.295
-0.629
-0.916
-0.719
-0.873
-0.439
0.655
0.546
0.482
0.854
1.639
D10 –D1
NW-t
D10|9–D1|2
2.202
(14.81)
1.959
0.913
(9.28)
0.822
1.066
(6.94)
0.980
0.985
(5.46)
0.961
1.292
(6.81)
1.181
1.648
(7.10)
1.372
2.019
(5.84)
1.712
2.268
(5.75)
2.019
NW-t
(15.20)
(11.13)
(8.76)
(7.29)
(8.43)
(8.23)
(6.69)
(7.90)
Panel C: Negative Correlation between %∆#INST and SUE
Holding Period
SUE Decile
N
[-1, 0]
[1, 1]
[1, 5]
[1, 10]
[1, 15]
[1, 21]
[1, 42]
[1, 63]
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
133
136
142
147
150
109
106
103
102
101
-0.803
-0.399
-0.054
0.238
0.465
-0.002
0.334
0.414
0.700
1.155
-0.321
-0.203
-0.013
0.105
0.230
-0.353
-0.314
-0.219
-0.039
-0.099
-0.260
-0.209
0.037
0.205
0.337
-0.780
-0.633
-0.602
-0.307
-0.342
-0.055
-0.119
0.336
0.540
0.512
-0.981
-0.759
-0.829
-0.435
-0.570
0.185
0.156
0.542
0.866
0.872
-1.120
-0.869
-0.915
-0.474
-0.665
0.324
0.422
0.844
1.148
1.357
-1.297
-0.949
-1.062
-0.621
-0.732
0.543
0.927
1.441
1.848
2.201
-1.591
-1.433
-1.580
-0.811
-0.925
0.321
0.603
1.428
1.991
2.574
-1.304
-1.218
-1.542
-0.449
-0.692
D10 –D1
NW-t
D10|9–D1|2
1.958
(14.07)
1.529
0.222
(2.46)
0.193
-0.082
(-0.51)
-0.090
-0.515
(-2.52)
-0.416
-0.850
(-3.71)
-0.740
-1.057
(-3.76)
-1.049
-1.468
(-3.15)
-1.603
-1.013
(-2.09)
-1.032
NW-t
(15.61)
(2.93)
(-0.79)
(-2.75)
(-4.03)
(-4.55)
(-4.04)
(-2.40)
38 Table XI. Fama-MacBeth Regressions of Post-Earnings Announcement Returns on Institutional Herding
Each quarter, we perform cross-sectional regressions of post earnings announcement stock returns over different holding period on SUE, interactions of SUE with dummies
for institutional herding and percentage change in the number of institutional investors (dHERD and d%∆#INST), and various control variables. The institutional herding dummy
is set equal to 1 if there is strongly positive or weakly positive correlations between institutional herding (or percentage change in the number of institutional investors) and
SUE and otherwise 0. The control variables include market capitalization (SIZE), book to market ratio (B/M), previous month return (LRET), momentum (MOM), Amihund
illiquidity ratio (ILLIQ), idiosyncratic volatility (IVOL), and relative short interest (RSI). For details on variable definitions, please refer to Table I. The table reports time
series average of coefficient estimates and their Newey-West t-statistics. *** and ** indicate significance at the 1% and 5% level, respectively. The sample period is from
January 1980 to December 2013.
SUE
dHERD *SUE
***
0.254
(6.40)
0.480***
(8.38)
d%∆#INST *SUE
SIZE
B/M
LRET
MOM
ILLIQ
IVOL
RSI
Intercept
N
Adj. R2 (%)
0.000
(0.83)
0.543**
(1.97)
-0.180
(-0.84)
0.363***
(-4.88)
0.017***
(4.02)
0.158***
(-6.17)
0.008**
(2.20)
0.479***
(4.77)
2641
1.429
[1, 5]
0.182***
(3.54)
***
0.166
(3.08)
0.735***
(10.36)
0.547***
(8.61)
0.000
(1.01)
0.551**
(2.01)
-0.176
(-0.82)
-0.053
(-1.00)
0.395***
(7.63)
0.505***
(8.36)
0.000
(0.98)
0.579**
(2.10)
-0.215
(-1.00)
-0.364***
[1, 10]
0.143**
(2.16)
***
[1, 21]
0.090
(1.01)
-0.000
(-0.96)
4.005**
(2.34)
-0.298
(-0.30)
1.908***
(9.26)
-0.000
(-0.87)
3.903**
(2.26)
-0.294
(-0.30)
-0.530**
-0.299
-0.294
-0.326
(-2.30)
0.012**
(2.02)
(-2.40)
0.013**
(2.23)
(-0.54)
0.048***
(4.82)
(-0.53)
0.045***
(4.58)
(-0.59)
-0.047***
(4.78)
-0.136
-0.153**
-0.133
-0.084
-0.110
-0.082
(-4.32)
0.014**
(2.52)
(-1.94)
0.022***
(2.86)
(-2.19)
0.022***
(2.91)
(-1.90)
0.023***
(2.98)
(-0.46)
0.032**
(2.15)
(-0.61)
0.033**
(2.20)
(-0.45)
0.034**
(2.23)
0.834***
(4.42)
2641
1.727
1.427***
(4.46)
2641
2.230
1.437***
(4.49)
2641
2.264
1.400***
(4.38)
2641
2.352
3.136***
(4.36)
2643
3.258
3.151***
(4.38)
2641
3.311
3.093***
(4.30)
2641
3.386
0.171
(2.20)
1.116***
(9.92)
0.000
(0.88)
0.307
(0.73)
-0.405
(-1.30)
0.749***
(8.91)
0.000
(1.04)
0.281
(0.67)
-0.388
(-1.25)
-0.374***
-0.546***
-0.543***
(-4.91)
0.016***
(3.93)
(-5.01)
0.017***
(3.99)
(-4.40)
0.017***
(3.94)
-0.164***
-0.156***
(-6.29)
0.008**
(2.19)
0.477***
(4.76)
2641
1.458
***
***
[1, 63]
0.345**
(2.22)
-0.479**
(-2.36)
1.394***
(7.18)
1.727***
(9.08)
-0.000
(-0.89)
4.029**
(2.35)
-0.403
(-0.41)
-0.241
(-3.12)
0.648***
(9.98)
0.674***
(8.50)
0.000
(0.96)
0.342
(0.81)
-0.448
(-1.45)
0.564
(4.29)
1.619***
(7.78)
0.000
(0.13)
0.487
(0.72)
-0.800
(-1.70)
1.202***
(10.26)
0.000
(0.25)
0.432
(0.633)
-0.788
(-1.67)
-0.487
(-4.26)
0.976***
(9.48)
1.083***
(10.07)
0.000
(0.20)
0.528
(0.78)
-0.873
(-1.86)
-0.559***
-0.510**
-0.504**
(-4.39)
0.016***
(3.81)
(-4.49)
0.016***
(3.90)
(-2.32)
0.014**
(2.29)
-0.172***
-0.183***
-0.169***
(-6.12)
0.009**
(2.29)
(-4.38)
0.014**
(2.42)
(-4.61)
0.014**
(2.45)
0.465***
(4.64)
2641
1.501
0.854***
(4.53)
2641
1.640
0.856***
(4.52)
2641
1.663
39 **
Figure I. Post-Earnings Announcement Abnormal Stock Returns
Panel A plots the average abnormal stock returns up to 60 months following earnings announcement for each decile
portfolio formed on SUE. Panel B plots return differentials between top and bottom deciles (D10-D1) and top and
bottom two deciles (D10|9-D1|2). The sample period is from January 1980 to December 2013.
CAR(%)
Panel A: Post-Earnings Announcement Abnormal Stock Returns
16
14
12
10
8
6
4
2
0
-2
-4
-6
-8
-10
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Month
Decile 1
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 7
Decile 8
Decile 9
Decile 10
Panel B: Return Differentials between D10 and D1 and (D10+D9)/2 and (D1+D2)/2
20.000
18.000
16.000
CAR(%)
14.000
12.000
10.000
8.000
6.000
4.000
2.000
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Month
D10|9-D1|2
D10 –D1
40 Figure II. Post-Earnings Announcement Abnormal Stock Returns – Strong Positive Correlation
between Percentage Change in the Number of Institutional Investors and SUE
Panel A plots the average abnormal stock returns up to 60 months following earnings announcement for stocks in each
decile portfolio formed on SUE with strongly positive correlations between percentage change in the number of
institutional investors and SUE. Panel B plots return differentials between the top and bottom deciles (D10-D1) and
top and bottom two deciles (D10|9-D1|2). The sample period is from January 1980 to December 2013.
CAR (%)
Panel A: Post-earnings announcement abnormal stock returns
20.00
18.00
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00
-2.00
-4.00
-6.00
-8.00
-10.00
-12.00
-14.00
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Month
Decile 1
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 7
Decile 8
Decile 9
Decile 10
CAR(%)
Panel B: Return differentials between (D10+D9)/2 and (D1+D2)/2
26.000
24.000
22.000
20.000
18.000
16.000
14.000
12.000
10.000
8.000
6.000
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Month
D10|9-D1|2
D10 –D1
41 Figure III. Post-Earnings Announcement Abnormal Stock Returns – Negative Correlation between
Percentage Change in the Number of Institutional Investors and SUE
Panel A plots the average abnormal stock returns up to 60 months following earnings announcement for stocks in each
decile portfolio formed on SUE with negative correlations between percentage change in the number of institutional
investors and SUE. Panel B plots return differentials between the top and bottom deciles (D10-D1) and top and bottom
two deciles (D10|9-D1|2). The sample period is from January 1980 to December 2013.
Panel A: Post-earnings announcement abnormal stock returns
10.000
8.000
CAR(%)
6.000
4.000
2.000
0.000
-2.000
-4.000
-6.000
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Month
Decile 1
Decile 2
Decile 3
Decile 4
Decile 5
Decile 6
Decile 7
Decile 8
Decile 9
Decile 10
Panel B: Return differentials between (D10+D9)/2 and (D1+D2)/2
6.000
CAR(%)
4.000
2.000
0.000
-2.000
-4.000
-6.000
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Month
D10|9-D1|2
D10 –D1
42