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Institutional Trading around Repurchase Announcements:
An Uphill Battle
Pankaj Jain
Suchismita Mishra
Vinh Huy Nguyen
Abstract
Share repurchases create an asymmetric information environment for institutional
investors. The firm and its insiders know the announcement’s timing and enjoy regulatory
exemptions from securities law violations for the timing and pricing of share repurchases
implementation or lack thereof. Institutions do not have this information ex-ante, but do they have
the foresight to trade profitably? We test and cannot reject lack of institutional foresight around
repurchases using quarterly 13F institutional holding changes, daily intermarket sweep orders,
Abel Noser institutional trades, and biweekly short interest data. These results hold with control
sample analyses as well as after addressing potential endogeneity issues.
Key Words: Intermarket sweep order, Abel Noser, 13F, short seller, repurchase announcement,
institutional investor
JEL Classification: D82, G14, G20, G35
Pankaj K. Jain, University of Memphis, Fogelman College of Business and Economics, 3675
Central Avenue, Memphis, TN 38152, [email protected], 901-678-3810
Suchismita Mishra, Florida International University, College of Business Administration, 11200
S.W. 8th Street RB 204BA, Miami, Florida 33199, [email protected], 305-348-4282
Vinh Huy Nguyen, California State University, Fresno, Craig School of Business, 5245 N Backer
Ave. M/S PB7, Fresno, CA 93740, [email protected], 559-278-8214
For helpful comments and suggestions, we thank the seminar participants at the American
University, University of Miami, University of Utah, University of Memphis, Xavier Institute of
Management, Bhubaneswar, Indian Institute of Management, Lucknow, the 2015 Florida Finance
Conference, the 2016 Eastern Finance Association Conference, the 2016 Global Finance
Conference, the 2016 Financial Management Association Conference, and the 2018 Western
Decision Sciences Institute Annual Meeting.
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1. Introduction
In the strategic trading model developed by Holden and Subrahmanyam (1992), there are
multiple informed traders competing aggressively, and in the process, they quickly reveal their
commonly shared information. Their private information is rapidly incorporated into stock prices
and the market is assumed to be strong-form efficient. This means that in a multi-period scenario
traders with private information do not have an information advantage to be able to sustain
profitable trading. We empirically test this model using share repurchase announcements as the
event with multiple periods: the pre-announcement, announcement, and post-announcement
period. The informed agents are the institutional investors, registered insiders, and the firm.
Outside the context of repurchases, several papers document the superior trading profitability
separately for institutions (e.g., Dechow et al., 2001; Ke and Petroni, 2004; and Christophe et al.,
2004; Chemmanur et al., 2009; Chemmanur et al., 2010; Chemmanur et al., 2015; Chemmanur
and He, 2016; Chakravarty et al., 2012; McInish et al., 2014;) and insiders (e.g., Lee et al., 1992
and Alldredge and Cicero, 2015). However, those situations do not create competition among
important informed traders as share repurchases announcements do. Unlike other corporate events
for which the firm itself does not engage in buying its own stocks, all three participants actively
participate around share repurchase announcements. Institutional investors are important because
they hold approximately 75 percent of U.S. stocks (Alexander et al., 2014). Share repurchase
announcements provide a good opportunity for insiders to sell their accumulated shares as the
stock prices increase due to the significant positive announcement effect (Ofer and Thakor, 1987;
Ikenberry et al., 1995; Peyer and Vermaelen, 2009; Bargeron et al., 2011). For the firm, this
corporate event is the only time it can buy back its shares, and it has the timing advantage.
Essentially, this is a battle of champions between the institutional investors, insiders and the firm.
Hoberg et al. (2018) note that funds generate future alpha when they face less competition from
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rival institutions. However, we posit that at the time of share repurchases the institutions face
competition from the corporations themselves and their insiders even if the competition from the
rival institutions is low. Hence, we ask the question if the institutions have the foresight to trade
profitably around repurchase announcements when they are pitted against these other informed
and active players. Alternatively, will it be a three-way tie because in an efficient market all
information advantage is competed away rapidly?
The answers to these questions are further complicated by the repurchasing strategy of the
firm. While other corporate events, such as earnings announcements, tend to resolve uncertainty
(Lee et al., 1993; Jiang et al., 2012), share repurchase announcements seem to complicate matters
further for other market participants because the announcing firms are not required to follow
through with actual repurchases. In our sample, 72% of the announcements are followed through
with the actual implementation of share repurchases within two years, but the other 28% are not.
Some of these announcement-only firms may not need to follow through with actual repurchases
because the announcement may itself result in valuation correction. According to Bhattacharya
and Jacobsen (2016), the announcement can generate enough interest to the firm that retail and
institutional trading will adjust the stock price back to the equilibrium for heavily undervalued
firms. In the case that there is a large mispricing, the firm may choose to repurchase shares, but it
does not necessarily need to buy back the shares. For slightly undervalued firms, the announcement
is not enough, and the firm has to follow through with actual buybacks implementing a costly
signal. With overvalued firms, they should not announce since it may attract unwanted attention
from investors. Yet, the overvalued firms may still announce and repurchase shares for other
reasons highlighted in the literature. Asquith and Mullins (1986), Ikenberry and Vermaelen (1996),
Dittmar (2000), Lee and Rui (2007), Bhana (2007), De Ridder (2009), Lee et al. (2010), and
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Punwasi and Brijlal (2016) indicate that some ulterior motives behind repurchase may include
reducing taxes, distributing excess cash, adjusting capital structure, creating an attractive selling
opportunity for insiders, and eliminating threats of unwanted takeover attempts. Thus, institutions
may not know for sure that the announcement is due to undervaluation or for other purposes. In
contrast, for the insiders and the firm, this uncertainty does not exist. In summary, the share
repurchase environment is quite complicated for institutional traders because the true intention of
the repurchase announcement is not clear. By analyzing this heightened uncertainty around
repurchase announcements, we are able to meaningfully extend the literature on trading activity
around repurchases that has focused mainly on actual repurchase implementations (e.g., De Lisle
et al., 2014).
Using a diverse set of data sources, we find that institutional investors’ trades are not
profitable because they are unable to overcome the information advantage of the firm and its
insiders with little notice about when the announcement will occur1. The timing of the repurchase
announcement appears to create an environment that is difficult for institutional investors to trade
profitably. Institutional investors are selling around the pre-announcement (t=-1) and
announcement (t=0) periods when they should be buying more shares to take advantage of the
post-announcement (t=+1) price run-up. An alternative interpretation of our findings is that
institutional investors sell because they have captured their target profit from a long-term
investment perspective. This explanation assumes that institutional investors have purchased
shares long before the announcement, and they view the event as a good time to capture the
required profit for their investment. Rather than making this assumption in our paper, we analyze
1 Chakrabarty, Moulton, and Trzcinka (2017) evaluate institutional trading performance outside the context of corporate announcements by
examining short-term, round-trip trades of money managers and pension funds. They question the prior literature on short-term trading skills of institutional investors and do not find them to be profitable in the short run due to behavioral biases, such as trading to look active and the
recency bias.
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profitability as the institutional investor’s ability to earn a significant and positive return given the
evidence from prior research of a post-announcement price run-up (Stephens and Weisbach, 1998;
Babenko, 2009).
Our paper differs from prior research on repurchases in several important ways. First, we
focus on the complexity and dual nature of the repurchase signals due to the presence of the firms
and insiders as highly informed participants in the market. In De Cesari et al. (2012), the authors
explain that the repurchasing firms can buy their shares back at a bargain price if the firms have
little institutional interest. Without institutional involvement, the firms can take further advantage
of the information asymmetry by buying back shares from less informed retail traders. Also, Louis
and White (2007) find that insiders use fixed-priced tender offers to signal undervaluation and
Dutch-auction tender offers to reduce the actual repurchasing price. Therefore, we believe the
inclusion of the firm and its insiders will enrich our understanding of institutional trading ability.
Second, we focus on the sophisticated institutional investors’ decisions around the
announcement time, not around the actual implementation of share repurchases. Based on the
findings of De Lisle et al. (2014), institutional investors are active around actual repurchases; they
are net sellers when the firms are implementing repurchases. The authors attribute this trading
strategy mostly to the information asymmetry between institutions and individual investors. Our
contribution is different from theirs as they analyze institutional trading around the repurchase
implementation period, which resolves the uncertainty created by the initial repurchase
announcement. Because institutional investors hold the majority of U.S. equities, they would serve
as liquidity providers and take the opposite side of the firm’s actual repurchases for the right price
at the time of repurchase implementation, especially when individual investors are unwilling to
tender their shares. Also, on the liquidity demand side when institutions need to move large chunks
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of investments, repurchase events often create counterparty trading interest and liquidity that they
need to execute large trades. In a non-repurchase related working paper by Chakravarty and Ray
(2018), the authors explain that liquidity trading is a significant motive for non-profit trading,
which could explain why institutions would sell their shares around actual repurchases.
The third contribution of our paper is that we directly examine institutions’ own
profitability around the announcements, including the announcement period (t=0). In a working
paper closely related to ours, Chemmanur et al. (2018) examine information production by Abel
Noser institutions, including hedge funds around open market share repurchase programs to profit
during the post-announcement quarters, Q+1 to Q+4. Their paper highlights the importance of
examining the role played by institutions in determining the market reaction to corporate
repurchase announcements. We contribute to their line of inquiry in several complementary ways.
Even if institutions produce information, it may be challenging to profit from it at the
announcement (t=0) because of the difficulties in timing the market in relation to price changes
and trading actions of the announcing firms and its insiders. Additionally, institutions face capital
constraints and diversification constraints that may prevent them from buying the stock of an
undervalued repurchase announcer; they also face short-selling constraints which may prevent
them from selling the stock of an overvalued repurchase announcer (Saar, 2001; Chiyachantana et
al., 2017). Such constraints are less likely to apply to repurchasing firm or even its insiders.
Fourth, given that the Abel-Noser institutions cover only about 15% of the overall
institutions (Hu et al. 2018), we broaden the sample of sophisticated institutions to include all 13F
institutions, intermarket sweep order traders in TAQ, and short sellers. Thus, we cover institutional
trading strategies related to both long and short positions as well as long and short horizons. We
also verify that our findings are robust to the inclusion of non-announcing firms in the control
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sample analyses as well as after addressing potential endogeneity issues using instrumental
variables.
Besides adding to the repurchase and institutional investor literature, we believe our
research has important regulatory implications. Given that institutional investors are informed,
their inability to properly time their trades tells us that the firm and insiders may have an unbeatable
strategy. In fact, share repurchases have gained attention from regulators and lawmakers, and they
are considering curbing these advantages. Robert Jackson Jr., a member of the Securities and
Exchange Commission, believes that the exemptions firms have from securities law violations for
the timing and pricing of share repurchases should be reconsidered (Cox, 2018). Our research
supports the idea that in this competitive environment the firm’s advantages prevent investors from
gaining any significant profits. The competition appears to be an uphill battle for institutional
investors.
2. Hypothesis Development & Research Design
In contrast to the monopolistic informed trader model developed by Kyle (1985), the multi-
period model of Holden and Subrahmanyam (1992) relaxes this restriction and allows for at least
two informed agents. In this model, prices rapidly incorporate any information as soon as
competitive informed agents take action. The market is strong-form efficient in which the
advantage of having private information quickly dissipates. We believe this model holds true when
there is no uncertainty surrounding the information signal. This is not the case with share
repurchase announcements because the first source of uncertainty comes from the unknown timing
of the announcement, which has been linked to lower market efficiency (Bagnoli and Watts, 1998).
While there may be a way to identify when the announcement might occur by examining the
average volatility spreads using daily options trading data according to Hao (2016), share
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repurchase announcements are still considered non-routine when compared to other corporate
events, such as earnings and dividend announcements. Secondly, the announcement signal
regarding the value of the firm may not be entirely true. For example, the information signaling
hypothesis in the share repurchase literature posits that if the firm believes its shares are
undervalued, it can signal such information to the market using repurchase announcements (Miller
and Rock, 1985; Vermaelen, 1981). However, the announcement may not be related to
undervaluation signaling. Overvalued firms may engage in share repurchases for other reasons. It
is possible that the announcing firms are employing a “signal-jamming” strategy considering that
a large proportion of announcement events are not followed through with actual share repurchases.
According to Fudenberg and Tirole (1986), firms can interfere with other participants’
information-gathering and decision-making process by providing signals that may not be entirely
true about the firm. It is also possible that the firm’s repurchase decision is related to funding
corporate acquisitions, managing the dilutive effects of employee stock options, boosting the
reported EPS, reducing excess cash available to management, and inhibiting overinvesting
(Jensen, 1986; Bens et al., 2003; Grullon and Michaely, 2004; Skinner, 2008). These disturbances
or noisy information may lead others to make poor decisions with unfavorable outcomes (Mirman
et al., 1993). In the context of share repurchases, the institutional investors are the information
gatherers and their decision-making process is affected by the signals sent by the firm and its
insiders. These dual signals can help the institutions make profitable trades if this information
accurately portrays the status of the firm. While there are papers related to undervaluation signaling
or signal jamming, our paper is unique because we are the first to study the interaction between
the institutions, firm, and insiders around repurchase announcements when all three parties are
actively competing against each other. We fill this gap in the literature by explaining if the
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institutions have the skills to decipher the complex and compounded nature of the information
signaled by the firm and its insiders.
[Insert Figure 1]
For the purpose of developing our formal hypotheses, we consider a four-period timeline
in Figure 1. The time t = 0 represents the repurchase announcement. At the pre-announcement
period, time t = -1, if the firm is undervalued, then it would make more sense to announce share
repurchases. However, depending on the motives of the insiders, the firm may announce its
repurchase event when the firm is overvalued or fairly valued at time t = -1. Perhaps, the decision
to announce share repurchases even when the firm is overvalued may be a result of perceived
undervaluation by the managers (Chen et al., 2018). The other signal about the firm’s
undervaluation or overvaluation is contained in the insider’s decision to buy or sell, respectively,
at time t = -1. We expect prices to appreciate after the announcement at time t = 1 irrespective of
the undervaluation or overvaluation of the firm (Chan et al., 2010). For the overvalued firms, prices
would depreciate at time t = 2 because of the lack of follow through. Although the firm will not
follow through in an overvalued scenario, it may still announce repurchases for other ulterior
motives mentioned previously. However, these reasons are unrelated to undervaluation, and
therefore, the stock price would still adjust back to the pre-announcement equilibrium. For the
truly undervalued firms, prices would continue to appreciate at time t = 2 in response to the actual
implementation of repurchases.
Against the null hypothesis of no effects, if the institutional traders have foresight, then our
alternative hypothesis is that they purchase shares aggressively at time t=-1 or t=0 before the post-
announcement price run-up. Similarly, with good foresight, we expect the institutions to sell shares
of overvalued firms at time t = 1 and sell shares of initially undervalued firms at time t = 2 when
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the stocks have reached full price appreciation due to the actual implementation of repurchases.
While there is ample evidence in the literature that institutions are skilled traders, we question their
ability to replicate the same success around share repurchase announcements when the insiders
and firm, who control the information release, actively participate in the market by executing their
own trades.
Hypothesis 1 (null form): Institutional investors do not trade profitably around share
repurchases announcements. The direction, pricing, and timing of their trades do not result in
significant positive returns.
For the second hypothesis, we turn our attention to the registered insiders. Registered
insiders are defined as individuals who directly or indirectly own more than 10% of the firm’s
equity or who are officers or directors of the company according to Section 16 of the Securities
Exchange Act of 1934. While lawmakers have established key regulations to increase transparency
and reduce market manipulation, these traders are still active and profitable around key corporate
events. For instance, lawmakers established Rule 10b-5, which requires insiders 1) to refrain from
trading the firm’s shares when they have “material” nonpublic information or 2) to disclose the
information. However, to be charged with breaking Rule 10b-5, the insiders have to intentionally
deceive others. Fraud due to negligent behavior will not invoke Rule 10b-5. Furthermore, the
information has to be “material,” giving the insiders an unfair advantage to unduly influence the
market. Otherwise, the insiders are free to trade. The regulation also allows insiders to establish
multiple 10b-5 plans, which facilitate the sales of a predesignated number of shares at regular
intervals. While the intentions are good, the insiders can still work around these restrictions by
canceling the planned sales if they perceive good information is in the near future. This does not
break insider trading laws as no transactions were executed. Hence, no liabilities were created. In
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fact, according to Lee et al. (1992), insiders appear to increase their frequency of buying and
decrease their frequency of selling before the repurchase announcement. In addition to Rule 10b-
5, the insiders need to follow the SEC Section 16(b) short-swing profit rule, which states that the
insiders must return any profit gained from the buying and selling of the company stock within a
six-month period. This regulation is designed to discourage insider trading with non-public
information. Similar to the other regulations, there are ways to get around the short-swing rule.
The insiders can avoid violating Section 16(b) by waiting until the six-month period ends, allowing
them to keep all the profit. For example, insiders can buy shares months before the announcement
and sell these shares during the post-announcement price run-up to maximizing the selling price
or to exploit any mispricing (Louis et al., 2010). Due to their ability to trade profitably, we further
expand the main question to test the relevance of the second signal from the portfolio activities of
insiders around the time of the repurchase announcement.
We determine the insiders’ net trade direction based on their transactions during the
previous six months when they are found to be active (Chan et al., 2012). We believe that insider
trades are a valuable source of information to investors. The more often insiders trade, the more
information is revealed to the public, giving investors more opportunities to reallocate their
resources and potentially make some profits (Manne, 1966; Bernhardt et al., 1995). According to
Bonaime and Ryngaert (2013), actual repurchases accompanied by net insider buying result in
significantly higher and longer-lasting abnormal returns than when insiders are net sellers. We
believe that insider trading during the pre-announcement period could also provide information to
investors as it does during the actual share repurchase, and it would be wise for the institutions to
study the insider’s trading pattern so they can respond profitably.
Hypothesis 2 (null form): Institutional investors do not trade profitably in the complex signaling
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environment of share repurchases taking into account insider trades.
3. Data
According to the amendments to Rule 10b-18 (Release Nos. 33-8335; 34-48766; IC-26252;
File No. S7-50-02) made effective in January 2004, the SEC requires that the announcing firm
discloses its repurchase activities every quarter. The firm must disclose the total number of shares
repurchased during the previous quarter, the average price paid for those shares, the number of
shares that were purchased as a part of a previously announced plan, and the maximum number of
shares that could be repurchased. If the firm has not made any share repurchases after the
announcement, it is not required to disclose any of the information mentioned previously or its
intention to follow through with the repurchase plan. A disclosure is only necessary if the firm
decides to terminate the repurchase program.
The data for share repurchases are from the Securities Data Company (SDC). Our full
repurchases sample has 4,051 repurchase announcements from 2,259 unique firms reported from
September 2007 to December 2013. The firms announce the repurchase of approximately 243
billion shares and actually repurchase 38 billion shares at an average repurchase price of $33.16.
In total, these firms spent $1.2 trillion to repurchase their shares (SDC). We exclude all tender
offers, which are guaranteed to be implemented, to properly assess institutional trading ability in
an environment with added uncertainty.
We evaluate institutional profitability using three different datasets: 1) Thomson Reuters
13F provides quarterly institutional holding which will also capture institutional trading activities
not included in the ISO and short interest data, such as the changes in institutional holding resulting
from trades executed in the upstairs market, 2) TAQ provides intra-day ISO trading activity
including the exact timestamp, price, quantity, and trade condition, 3) Abel Noser provides daily
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institutional trades with exact execution price and quantity, and 4) Compustat provides biweekly
short interest position data. We calculate profit by comparing the actual trade prices to the CRSP
closing prices in a later period.
Our first dataset, Thomson Reuters 13F, provides a big-picture summary of institutional
trading. The 13F data provide required filings of institutional investment managers with over $100
million in assets. We use the quarterly updates to understand long-term institutional trading and to
determine if institutions profit in the quarters around the repurchase announcement. Using the
Bushee (2001) method of classifying the 13F institutions, we are able to study in-depth if
institutions with different investment time horizons and styles are able to profit in the presence of
the firm and its insiders. The first type is the transient institutional investors, who have a high
portfolio turnover and highly diversified portfolio holdings. These are the institutions most likely
to pay close attention to corporate announcements and respond to them by altering their holding
and position frequently. According to Bushee (2001) and Puckett and Yan (2011), this trading
strategy generates significant abnormal returns. The second type is the dedicated institutional
investors, who maintain a very low portfolio turnover and larger average portfolio investments.
Quasi-indexer institutional investors also have low portfolio turnover but highly diversified
portfolio holdings. Both dedicated and quasi-indexer investors have longer investment horizons.
In addition to 13F, we use ISOs, which are limit orders that automatically execute in
designated markets while simultaneously submitting orders in the markets with better prices. ISOs
represent 31% of the volume and 38% of trades in our sample. Fully integrated in September 2007,
ISOs are mainly used by informed institutional traders to sweep liquidity from multiple markets,
although possibly at prices inferior to NBBO (Chakravarty et al., 2012). The authors also provide
evidence that ISO trades, presumably used by buy-side institutions, are associated with larger
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information share than NISO trades, which are mainly used by retail traders. While ISOs can be
used to take liquidity, they can also provide liquidity. Liquidity-providing ISO traders are
incentivized to do so in order to earn rebates to cushion their profitability. Regardless of liquidity
taking or provision, traders use ISOs more for the execution speed and order fulfillment. Faster
execution gives institutional investors more opportunities to trades profitably as price-sensitive
information is released, for example, in repurchase announcements. Because we suspect that ISO
traders will be able to properly time the execution of their orders, we focus on these relatively
more informed trades to determine if they can make a profit using this more aggressive trading
mechanism. An added benefit to using the ISO data is the ability to examine the immediate
response to the announcement at the daily level.
We also use data from Abel Noser to examine institutional trading at the daily level. Abel
Noser collects its data directly from portfolio managers of plan sponsors, who are required to report
their trades to a cost analysis service by the Department of Labor’s Employment Retirement
Income Security Act (ERISA) to ensure that investment portfolio managers demonstrate the best
execution for their clients (Hu et al. 2018). According to Puckett and Yan (2011) and Hu et al.
(2018), the data cover 8% to 15% of CRSP volume. The Abel Noser data have similar advantages
as the ISO data in that we can gather the exact executed trade price and volume to assess
institutional trades around the announcement day. Several leading papers have used the Abel Noser
data to analyze institutional trading behavior around key corporate decisions, such as seasoned
equity offering (Chemmanur et al., 2009), spin-offs (Chemmanur and He, 2016), IPOs
(Chemmanur et al., 2010), and stock splits (Chemmanur et al., 2015). Bhattacharya, Wei and Xia
use same data source in their working paper to determine that institutions can using credit ratings
by investor-paid agencies, specifically Egan-Jones Ratings Company to outperform other investors
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that do not follow the same advice.
Our last data source is Compustat short interest file. We analyze the trading activities of
short sellers, which account for approximately 26% of the daily total trading volume (Alexander
et al., 2014), to determine if these sophisticated traders can benefit from repurchase
announcements. Considering that there is a price appreciation after the announcement, we expect
to see a significant decrease in short selling up to the price run-up associated with repurchase
announcement.
In our efforts to provide a diverse set of institutional data, we do acknowledge that there
are overlaps in the data. For instance, transient 13F institutions can use ISOs to execute their trades
around the announcement, short sellers can also be transient 13F traders, some shorting strategies
could include using ISOs, and Abel Noser trades are also reported in the 13F filings. Without the
information to identify these traders, we are unable to clearly separate the three institutional types
from each other but such a separation is not required to test our hypotheses. Instead, we believe
that our inclusion of these three types can shed light by providing a multiplicity of evidence on
how the ISO sweeping mechanism, Abel Noser portfolio managers’ trades, short seller’s timing,
and overall institution’s strategy can lead to profitability.
Lastly, for control variables, we use analyst forecast data from I/B/E/S, accounting data
from Compustat, and insider trading data from Thomson Reuter TFN U.S. Securities and
Exchange Commission Form 4.
4. Findings
Before we begin our investigation of institutional foresight, we first examine the premise
of the post-announcement price run-up documented in prior research. Using the same estimation
and testing periods as in Stephens and Weisbach (1998) and Babenko (2009), we too find in our
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sample a significant price increase after the repurchase announcement as shown in Table 1. We
also find that the abnormal return exists around repurchase announcements for additional testing
periods. Modifying the testing period in Babenko (2009) to [0, +90] and [-5, +5], we find that the
cumulative abnormal returns are positive and significant in these extended windows as well at
2.42% and 1.76%, respectively.
[Insert Table 1]
4.1 Trade imbalance
Having confirmed the presence of the post-announcement price run-up, we proceed in our
research using the four different institutional datasets. The descriptive statistics are presented in
Table 2, which shows the variables of interest for our multivariate regression models.
[Insert Table 2]
Before we look at the multivariate regression analysis, we first examine the trading activities of
institutional investors at the quarterly level using the 13F data. We analyze institutional trading for
five quarters before and after the announcement quarter. In Table 3 Panel A, we show that the
announcing firms during the non-announcing time, quarters [-5, -1], are associated with significant
positive or neutral institutional trade imbalance. During the repurchase announcement quarter [0]
and the following five quarters [+1,+5] they become net sellers, which means that repurchase
announcements do affect institutional trading and the effects last for many quarters. We believe
that the positive or neutral trade imbalances in quarters [-5, -1] show that the significant
institutional selling or negative imbalance in subsequent quarters is in response to the
announcement and not the other way around. In other words, the firm did not announce share
repurchases because of pre-announcement selling pressure from institutional investors, ruling out
reverse causality as the explanation of our findings. More importantly, we fail to see an increase
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in institutional buying during the repurchase announcement quarter, Q0, which is associated on
average with positive abnormal returns in near future.
To consider the effects of insiders for the dual signal hypothesis, we separate the full
sample based on whether the corporate insiders are net buyers or net sellers in the period
surrounding the repurchase announcement. Our method of classifying insider trade direction is
similar to that of Bonaime and Ryngaert (2013). The announcement event is considered net selling
if insider sales exceed insider purchases by at least 0.01% of the firm’s market capitalization. The
announcement event is considered net buying if purchases exceed sales by the same requirement.
Otherwise, the event is associated with neutral insider trading. These classifications are based on
the transactions of insiders during the previous six months relative to the announcement because
insiders are found to be most active during this period based on the findings of Chan et al. (2012).
When we divide the full 13F sample based on the insider trade direction, we observe similar
significant and negative trade imbalances for quarters [0, +4] following the repurchase
announcement.
[Insert Table 3]
Our examination of the 13F data extends further by breaking down the trading activities of
the institutional investors according to the classification method of Bushee (2001). We observe
that transient, dedicated, and quasi-indexer institutions are all net sellers exhibiting significant
negative trade imbalances from quarters [0, +1] as shown in Figure 2.
[Insert Figure 2]
To observe how institutional investors trade when there is no announcement effect, we
create a matching control sample of non-announcing firms based on closest matches to the industry
SIC, market capitalization, year and quarter to conduct placebo tests. We also impose a strict
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matching requirement that the minimized absolute difference in the market capitalizations of
treatment and control firm be no greater than twenty-five percent of the sample firm’s value. In
the case that there is no matching firm, we drop the treatment firm. If any market-wide omitted
factors or economic reasons other than repurchases cause institutional selling, then the selling
imbalance would also show up in the control sample placebo tests using the treatment firm’s
repurchase announcement date as the pseudo-announcement date for matched control firms.
In sharp contrast to our sample of announcing firm, our control sample of non-announcing
firms shows that there is a positive buying trade imbalance for the quarters around the pseudo-
announcement with varying degrees of significance. The results are shown in Figure 2. This
control sample shows that the firms with no connections to repurchase announcements are
associated with net institutional buying. This is further evidence that there are no omitted market-
wide factors affecting institutional imbalance around repurchase announcements.
In Figure 3, when we examine the activities of the firm and its insiders, we find that they
are not trading in the same direction as institutions. The firm appears to be repurchasing shares
starting at the announcement quarter, and the insiders exhibit neutral or negative trade imbalances.
We attribute the insignificance to a passive buying strategy in which the insiders reduce their sales
significantly more than their purchases to have the appearance of indifference (Agrawal and
Nasser, 2012). While we can assume that their trading activities may be independent of each other,
the appearance of the firm’s repurchases to support insider selling should be acknowledged. For
instance, in Moore (2017), the author finds that there is a positive relationship between share
repurchases and CEO equity sales. Hence, the idea that share repurchases can be used strategically
by insiders is a plausible one.
[Insert Figure 3]
19
Next, we take a much closer look at how institutions trade during the days immediately
around the announcement. The ISO intra-day data allow us to examine how investors promptly
react to the event. Our analysis of daily ISO trading covers three distinct periods similar to Jain
and Wang (2013) and follows the general approach of Irvine et al. (2007): the pre-announcement
period is the five days [-5, -1] window leading up to the announcement date, day 0 is the
announcement day, and the post-announcement period is the five days [+1, +5] after the event
date. Although we are studying the perceptive of institutional investors before and on the
announcement date, we include the post-announcement period to evaluate the institutional
investor’s immediate reaction to the repurchase information.
Based on the well-documented, positive market reaction to the announcement, we expect
institutional investors to be net buyers in these three periods. In theory, purchasing these shares
before the price run-up reaches its peak is a profitable strategy. Rather than observing a net buying
trend, we actually find evidence of significant negative institutional trade imbalance for all 11 days
shown in Table 3 Panel B. We calculate daily ISO trade imbalance as the net shares traded
normalized by the number of shares outstanding in millions. During the pre-announcement period,
we expect institutional investors to take a neutral trading position if they are unable to predict the
timing of the announcement. If they do have the foresight, we expect them to be net buyers.
Therefore, to see that these investors are presenting themselves as sellers is rather surprising. The
statistically significant daily trade imbalance of -75.32 shares per million of shares outstanding for
days [-5, -1] relative to the announcement day shown in Table 3 Panel B suggests that the ISO
traders do not perceive value in these firms before the repurchase event. On the announcement day
and during the post-announcement period, institutional investors would still be profitable if they
are net buyers. Yet, we observe statistically significant daily trade imbalances of -87.06 and -75.86
20
for the announcement day and post-announcement period. The net selling trend suggests that
institutional investors do not foresee the possible gains that follow repurchase announcements and
are selling to the better-informed traders. Additionally, we find that institutional investors do not
take cues from insiders; institutional investors are still net sellers with statistically significant daily
trade imbalances ranging from -50.72 to -141.40 when insiders are net buyers as shown in Table
3 Panel B.
In our third measure using the Abel Noser data, we examine how institutions trade daily in
the thirty days before and after the announcement. The daily Abel Noser trade imbalance is
calculated as the actual number of buy shares minus the number of sell shares, normalized by the
number of shares outstanding in millions. In Table 3 Panel C, we show that institutional traders do
not exhibit a positive trade imbalance before, during and after the announcement. The average
daily trade imbalance during the thirty days before the announcement is -281.50. Similarly, after
the announcement, the average daily trade imbalance is -653.50. These institutional traders
actually exhibit the highest net selling trend on the day of the announcement with a trade imbalance
of -9,485.10. Furthermore, when we divide the full sample based on insider trading trends, we
find that institutional traders do not exhibit any significant buying. Once again, we have
confirmation that institutional traders are not buying more shares around the announcement to
resell at a higher price.
Lastly, we turn our attention to the short position traders. We expect the short sellers to
refrain from trading around the announcement period as stock prices have not fully appreciated.
Any abnormal shorting during this period could result in significant losses. Abnormal short interest
is calculated as the average number of shares sold short in the test period divided by the average
number of shares sold short in the benchmark period minus one (Christophe et al., 2004). The
21
benchmark period is six biweekly periods before the announcement period, ending approximately
one quarter before the announcement. We find the highest abnormal short selling in the immediate
five biweekly periods after the repurchase announcement as shown in Table 3 Panel D. The
statistically significant abnormal short interests in periods [+1, +5] relative to the benchmark are
18.01%, 19.00%, 18.40%, 17.23, and 16.89%. Again, we find evidence of an increase in selling
around the announcement period before stock prices have reached their peaks. Interestingly, when
the insiders are net buyers we see a significant reduction in short selling during the five periods
before the announcement. The abnormal short selling in during period [-5, -1] are -21.31%, -
21.59%, -20.75%, -20.85%, and -19.47%. From these numbers, which are all statistically and
economically significant, we see that short-sellers are taking cues from the insiders’ net buying
signal, and, consequently, reduce their shorting activities before the announcement.
4.2 Profitability
Next, we evaluate the profitability of institutional investors. We calculate institutional
profitability based on the percentage change in quarterly closing prices. For negative trade
imbalances, we calculate profitability as the difference between the closing prices of the
announcement quarter and the following quarter (to cover the position). Analogously, the opposite
difference is used for positive trade imbalances. The decision to sell by the institutions is dominant
during the announcement quarter and closing such positions by quarter +4 results in an average
loss of 0.75% as shown in Table 4.
[Insert Table 4]
Next, we evaluate if the ISO trades executed during the days around the announcement are
profitable. Daily institutional profit is determined using the exact ISO trade prices in each of the
[0, +5] days and CRSP daily closing prices on day +5 relative to the announcement. For sell trades,
profitability is calculated as the actual trade price minus the imputed closing price divided by the
22
trade price. For buy trades, profitability is calculated as the imputed closing price minus the actual
trade price divided by the trade price.
In Table 4, we find that the institutional ISO trades opened during day [0, +5] result in
significant losses if the positions are closed +5 days after the announcement. In the full sample of
all repurchase announcements, the average daily loss of the trades initiated during days [+0, +5] is
0.04%, statistically significant at one percent. When we examine institutional profitability
considering insiders’ buy versus sell signal, we find that the institutions are not profitable in either
category even with dual signals. For instance, the trades initiated in the subsample with net insider
buying has a daily loss of 0.07% also significant at the one percent level.
When we examine the daily profitability of institutional traders using the Abel Noser data,
we find that they are not profitable. From days [0, +30], the average daily institutional profitability
is 0.00%. We find that in the sample where the insiders are net buyers the institutions incur a daily
loss of 0.02%. This is most likely due to the non-positive trade imbalance during the [0, +30] day
period. Selling even a few shares at the announcement combined with insider buying appears to
be an unprofitable strategy for the institutions, due to large adverse price moves.
Finally, we determine if the short sellers’ poor timing relative to the repurchase
announcements adversely affects their profitability. We measure profit as the difference between
the proceeds from the sale and the cost to close the position using CRSP closing prices at the end
of the biweekly period [0, +8]. Table 4 shows that significant abnormal short selling in the post-
announcement biweekly periods leads to a significant loss because the cost to close the short
position is higher than the revenue from the opening trades. We find that by the time these traders
cover their short positions in the eighth biweekly period they have cumulated loss of 2.15%
significant at one percent. We also examine other time frames, such as [0, +4], [0,+5], [0,+6],
23
[0,+7], and find that short sellers are not profitable in any of these periods around repurchase
announcements. Similar to the ISO results, we conclude that short sellers are not able to trade
profitably around repurchase announcements regardless of the insider trade directions. Our
findings are different from the literature citing short seller’s foresight (Chakrabarty and Shkilko,
2013; Christophe et al., 2004) because we specifically study the short seller’s trading performance
in an environment with heightened uncertainty and multiple highly informed and actively trading
participants.
4.3 Robustness tests
To ensure that both institutional trading and profitability are not influenced by other
corporate events, we create two sub-samples with the ISO data. In one sample, the share repurchase
announcements do not coincide with other corporate news, such as dividend announcements,
earnings announcements, merger and acquisition announcements, stock dividends and stock splits.
In the second sample, all the share repurchase announcements coincide with at least one of the
corporate events listed. The results are shown in Table 5. Like before, we calculate the daily ISO
trade imbalance as the actual number of buy shares minus the number of sell shares, normalized
by the number of shares outstanding in millions. We calculate ISO profitability using the exact
trade prices and CRSP daily closing prices on day +5. First, we find that the ISO traders exhibit
statistically significant negative trade imbalances in both subsamples. Second, the trades result in
significant losses in both samples. Thus, the effects of the repurchase announcement on
institutional trading strategy and its profitability are independent of other corporate events.
[Insert Table 5]
Then, we analyze the profitability of institutional trades opened during the days around the
announcement and closed 5 days later in the full sample adjusting for quarter-end effects. Since
24
some institutions engage in quarter-end window dressing (He et al., 2004), we eliminate all
announcements and institutional trades during the last three days of each quarter using the ISO
data so our results are independent of these decisions, and we find that these sell trades still result
in significant losses. The average loss is statistically significant at 0.05% as shown in Table 6. In
summary, our findings using the ISO data reveal that daily institutional trades are not profitable.
[Insert Table 6]
Additionally, we entertain the possibility that a repurchase announcement may be a strategy
to counter institutional selling pressure. In this sense, institutional trade imbalance and the
announcement event may be endogenously determined. To examine this potential endogeneity
issue, we perform two tests. In our first test, we examine the differences between the institutional
trade imbalances in the shares of announcing firms around the repurchase announcement date
versus that in matching non-announcing firms around the matching pseudo-event date. We find
that in quarter [-5, -1] before the announcement, the differences are statistically insignificant.
Although we do not show the results in a table format, the similarity between the institutional trade
imbalances for the announcing and non-announcing firms before the event can also be seen in
Figure 2. Thus, institutional trading does not appear to be the catalyst for the repurchase
announcement.
In our second test, we formally evaluate if the institutional trade imbalance during the
announcement quarter and announcement signal can be endogenously determined by estimating
the following linear model:
25
𝑇𝑅𝐴𝐷𝐸𝐼𝑀𝐵𝐴𝐿𝐴𝑁𝐶𝐸 = 𝛼 + 𝛽1𝐴𝑁𝑁𝑂𝑈𝑁𝐶𝐸𝑀𝐸𝑁𝑇 + 𝛽2𝐼𝑁𝑆𝐼𝐷𝐸𝑅𝐵𝑈𝑌 +
𝛽3𝐼𝑁𝑆𝑇. 𝐻𝑂𝐿𝐷𝐼𝑁𝐺(𝑄−1) + 𝛽4𝐸𝑃𝑆𝑆𝑈𝑅𝑃𝑅𝐼𝑆𝐸 + 𝛽5𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸 + 𝑢 (1)
The dependent variable, TRADEIMBALANCE, is the quarterly institutional trade
imbalance of the announcement quarter, Q0. ANNOUNCEMENT, the endogenous regressor, is a
dummy variable set to 1 if the firm announces share repurchases. INSIDERBUY is a dummy
variable set to 1 if insider purchases exceed insider sales during the six months before the
announcement day by at least 0.01% of the firm’s market capitalization. INST.HOLDING Q-1 is
calculated based on the reported holdings on Form 13F normalized by the number of shares
outstanding. EPS SURPRISE measures the difference between the actual reported EPS and the
mean analyst EPS forecast divided by the share price. FIRMSIZE is the natural log of the firm’s
market capitalization. u is the error term.
To determine if endogeneity exists meaning that the ANNOUNCEMENT is correlated with
the error term, u, we use the Hausman Test. The test requires two stages. In the first stage, we
regress the endogenous regressor, ANNOUNCEMENT, on the instrumental variables (IVs), which
are ΔCASH, DILUTION, and TAXGAP, to find the residuals, v. ΔCASH is the change in
EBITDA/Total Assets over the previous quarter. DILUTION is calculated as one minus the ratio
of diluted EPS/ basic EPS. TAXGAP is calculated as the difference between the average tax rate
on dividends and the average tax rate on capital gains. For these instruments, we follow the method
outlined in De Lisle et al. (2014).
ANNOUNCEMENT= 𝛼 + 𝛽1𝛥𝐶𝐴𝑆𝐻 + 𝛽2𝐷𝐼𝐿𝑈𝑇𝐼𝑂𝑁 + 𝛽3𝑇𝐴𝑋𝐺𝐴𝑃 + 𝑣 (2)
In the second stage, we include the residuals, v, from the first stage as an independent
26
variable in the following model:
𝑇𝑅𝐴𝐷𝐸𝐼𝑀𝐵𝐴𝐿𝐴𝑁𝐶𝐸 = 𝛼 + 𝛽1𝐴𝑁𝑁𝑂𝑈𝑁𝐶𝐸𝑀𝐸𝑁𝑇 + 𝛽2𝐼𝑁𝑆𝐼𝐷𝐸𝑅𝐵𝑈𝑌 +
𝛽3𝐼𝑁𝑆𝑇. 𝐻𝑂𝐿𝐷𝐼𝑁𝐺(𝑄−1) + 𝛽4𝐸𝑃𝑆𝑆𝑈𝑅𝑃𝑅𝐼𝑆𝐸 + 𝛽5𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸 + 𝛽6𝑣 + 𝜀 (3)
where the error term, 𝑢 = 𝛽6
𝑣 + 𝜀. This means that the error term, u, depends on v and some other
residual, 𝜀. Therefore, we test the null hypothesis that the residual coefficient, 𝛽6, is statistically
equal to zero. If 𝛽6 = 0, then we can conclude that institutional trading during the announcement
quarter and the announcement signal from the firm are not endogenously determined (Hausman,
1978; Nakamura and Nakamura, 1981; Abrevaya et al., 2010). However, if 𝛽6 ≠ 0, then
endogeneity exists and OLS regressions will provide biased estimates. In Table 7, we show that
the residual, v, with a coefficient of -0.0006 is not statistically different from zero. From this result,
we conclude that institutional trading and the firm’s repurchase announcements are not
endogenously determined.
[Insert Table 7]
4.4 Factors affecting institutional profit
To understand the various factors that may affect how institutions perform around the
announcement, we model institutional profit as a function of the firm’s repurchase announcement
and the insiders’ trade direction controlling for institutional holdings, EPS surprise, firm size, beta,
SMB, HML, industry fixed effects, and year fixed effects.
𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑎𝑙 𝑃𝑟𝑜𝑓𝑖𝑡 = 𝛼 + 𝛽1𝐴𝑛𝑛𝑜𝑢𝑛𝑐𝑒𝑚𝑒𝑛𝑡 + 𝛽2𝐼𝑛𝑠𝑖𝑑𝑒𝑟 𝐵𝑢𝑦 𝑆𝑖𝑔𝑛𝑎𝑙 +𝛽3𝐴𝑛𝑛𝑜𝑢𝑛𝑐𝑒𝑚𝑒𝑛𝑡 ∗ 𝐼𝑛𝑠𝑖𝑑𝑒𝑟 𝐵𝑢𝑦 𝑆𝑖𝑔𝑛𝑎𝑙 + 𝛽4𝐼𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛𝑎𝑙 𝐻𝑜𝑙𝑑𝑖𝑛𝑔𝑠𝑄−1 +
𝛽5𝐸𝑃𝑆 𝑆𝑢𝑟𝑝𝑟𝑖𝑠𝑒 + 𝛽6𝐹𝑖𝑟𝑚 𝑆𝑖𝑧𝑒 + 𝛽7𝐵𝑒𝑡𝑎 + 𝛽8𝑆𝑀𝐵 + 𝛽9𝐻𝑀𝐿 +𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝑌𝑒𝑎𝑟 𝐹𝑖𝑥𝑒𝑑 𝐸𝑓𝑓𝑒𝑐𝑡𝑠
(4)
27
Institutional profit for all three institutional types is calculated as presented previously in
Table 4. We calculate profits for trades in stocks of repurchase announcers (treatment stocks) and
matching non-announcer control stocks. ANNOUNCEMENT is a dummy variable set to 1 if the
firm announces share repurchases. INSIDER BUY is a dummy variable set to 1 if insider purchases
exceed insider sales during the six months before the announcement day by at least 0.01% of the
firm’s market capitalization. INSTITUTIONAL HOLDING Q-1 is calculated based on the
reported holdings on Form 13F and normalized across firms by dividing by the number of shares
outstanding. We include institutional holdings to account for the liquidity provision role that
institutions may play around repurchases (De Lisle et al., 2014). EPS SURPRISE measures the
difference between the actual reported EPS and the mean analyst EPS forecast for the
announcement quarter divided by the share price. We also include other common control variables,
such as the firm size, market risk premium, SMB, and HML. To address multicollinearity issues
when fitting our regression models, we examine VIF scores and find that all of them are below 10.
In Table 8, we report how the announcement and the insider buy direction affect
institutional profit from Q0 to Q+4 using the quarterly 13F data. We use a sample that includes
both announcing and non-announcing control firms, which are matched by industry and size. The
evidence shows that the announcement and the insider buy direction alone do not significantly
affect long-term profit. However, when the insiders are net buyers during the six months before
the announcement and then the firm actually announces buybacks, institutional profit significantly
decreases by 12.31%. These two signals from the firm and insiders suggest that institutions should
buy more shares, but from the evidence previously presented in Table 3 institutions exhibit
significant negative trade imbalances starting in Q0, ultimately resulting in trading losses for
institutions now reported in Table 8.
28
[Insert Table 8]
To examine how the announcement and the insider buy direction affect institutional profit
in the short term, we use the ISO data. Table 9 shows that the insider buy direction appears to have
a significant and negative relationship with short-term profit, reducing it by 0.14% daily or 40.03%
annualized with daily compounding. Instead, the overall institutional holdings in Q-1 appears to
be more significant in determining profit. Higher institutional holdings in the pre-announcement
period appear to be a profitable strategy, which aligns with our belief that buying before the price
run-up and selling during the price appreciation can be beneficial to institutional investors.
[Insert Table 9]
In Table 10, we show the results for the regression of profitability using the Abel Noser
data on the announcement and insider signal. We find that on average a repurchase announcement
has a negative impact on institutional profit. The impact on daily profit range from -0.0129% to -
0.0121%, significant at the five and ten percent levels, respectively.
[Insert Table 10]
Finally, we analyze the profitability of short sellers from biweekly 0 to +8 period shown in
Table 11, and we find that the announcement and insider buy direction do not help short-sellers
generate any statistically significant profit, either. In summary, all of the profitability regressions
indicate institutional investors’ lack of foresight around share repurchase announcement and the
insider buy direction fails to produce abnormal long- or short-term profit. Instead, some of the
institutional selling trends are counterintuitive, especially when there are buy signals from other
informed players.
[Insert Table 11]
29
5. Institutional Foresight Potential Explanations
While we acknowledge that prior research explains that institutional investors are informed
relative to average market participants (Daniel et al., 1997; Baker et al., 2010; Hoberg et al., 2018),
we find that in the battle of champions around a share repurchase announcement, there are several
factors working against these sophisticated traders. First, a repurchase announcement may or may
not be driven by undervaluation; other motives may include tax benefits, distributing excess cash,
adjusting capital structure, creating opportunities for insiders to sell, and eliminating threats of
unwanted takeover attempts according to Asquith and Mullins (1986), Ikenberry and Vermaelen
(1996), Dittmar (2000), Lee and Rui (2007), Bhana (2007), De Ridder (2009), Lee et al. (2010),
and Punwasi and Brijlal (2016). Second, share repurchase announcements do not occur at a
predetermined time interval. The timing of these announcements are unpredictable, and it leaves
the institutional investors little time to respond to the information. Third, the decision to follow
through with actual share repurchases and the timing of these buyback programs are also
unpredictable. Once announced, the firm has no legal obligations to follow through with actual
implementation nor does it have to buy back shares within a certain time period. This flexibility is
one of the main reasons for using repurchases rather than dividends as the main payout method.
Fourth, around share repurchase announcements, institutional investors are competing against the
firm and its insiders, who arguably are more informed than the institutional investors, at least,
when it comes to matters related to the valuation of the company. In this scenario of a share
repurchase announcement, both of these informed players are also actively trading their company’s
stocks. When we examine the dual signals given by these two players, it appears that they provide
mixed messages to outsiders. Our analysis shows that approximately 50% of the follow-through
sample is associated with net insider sell, 31% is associated with net insider buying, and 19% with
neutral insider trading. This contradiction between the firm and the insiders implies that the
30
insiders may be using the firm’s money to support insider selling. In fact, the Wall Street Journal
has taken notice of this strategy and has published articles explaining that repurchases can be used
to support sales, install an artificial price floor, and most interestingly, to help executives and board
members earn abnormal returns (Browning, 2015; Driebusch and Eisen, 2016; Strumpf, 2014;
Waggoner, 2015). The insiders can benefit from the initial post-announcement price run-up and
then from the actual implementation of the repurchase programs that could take years to complete.
The long-term strategy of using repurchase events from the announcement to the follow through
to manage price impact is completely plausible. Klein et al. (2017) find that when there are multiple
insiders trading simultaneously managing price impact and market liquidity is the main concern.
Because of these aforementioned reasons, it may be difficult for institutions to trade profitably
around the announcement.
6. Conclusion
Share buyback or repurchase announcements create heightened information asymmetry
about the motives and the final outcome of such announcements. We analyze the institutional
foresight about the dual signals from the actions of repurchase announcing firms and its insiders,
for various types of institutions. We find that institutions—13F institutional investors, ISO traders,
Abel Noser institutions and short sellers—sell their shares around the announcement. This is a
rather unprofitable strategy because of the poor timing and execution price of institutional trades
around repurchase announcements. Our paper provides evidence that although institutional
investors are sophisticated traders they may not be profitable when better-informed agents such as
the announcing firms and their insiders are actively pursuing their own interests.
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38
Figure 1: Timeline of Possible Actions Taken by Institutional Investors, the Firm, and its Insiders around Share Repurchase
Announcement and Actual Repurchases
Overvalued
Fairly Valued
Undervalued
Announcement
No Announcement
Institutions Should Sell
Actual Repurchases
No Repurchases
Insider Sell
Announcement
No Announcement
Announcement
No Announcement
Insider Buy
Institutions Should Buy
InstitutionsShould Sell
Announcement Period (t= 0)
Pre-Announcement Period (t=-1)
Post-Announcement Period (t=1)
Repurchasing Period (t=2)TIMELINE
InstitutionsShould Sell
No Repurchases Price Stabilizes
Actual Repurchases
Price Holds due to Firm's Commitment
Institutions Should Buy
Actual Repurchases
No Repurchases
Price Increases as Investors Discover Value
39
Table 1: Evidence of Post-Announcement Price Run-Up for Repurchase Announcements
For all 4,051 stock repurchase announcements from 2,259 unique firms from September 2007 to December 2013, we calculate the
cumulative total return (CRET) and cumulative abnormal return (CAR) using the Fama-French factors plus momentum risk model for
the estimation and testing periods corresponding to the periods used in the studies cited below. Date 0 is the repurchase announcement
day. Repurchase announcements are from SDC. Returns are from CRSP. The estimation and testing periods in the first two rows are
from Stephens and Weisbach (1998) and Babenko (2009). The testing periods in the last two rows are our modifications of Babenko
(2009) to include longer testing periods. ***, **, * stand for statistical significance at the 1%, 5%, and 10% level, respectively.
Method Corresponds to: Estimation
Period
Testing
Period CRET CAR
Obs = (4,051
Announcements) Mean p-value Mean p-value
Stephens and Weisbach (1998) [-165, -65] [-1, +1] 1.89%*** <.0001 1.75%*** <.0001
Babenko (2009) [-250 -50] [-1, +1] 1.89%*** <.0001 1.75%*** <.0001
Babenko (2009) w/new testing period [-250 -50] [0, +90] 6.06%*** <.0001 2.42%*** <.0001
Babenko (2009) w/new testing period [-250 -50] [-5, +5] 1.83%*** <.0001 1.76%*** <.0001
40
Table 2: Descriptive Statistics for Final Regression Datasets
This table reports the descriptive statistics for variables used in the next set of regression analysis. Profitability is measure as in Table
4. ANNOUNCEMENT is a dummy variable set to 1 if the firm announces share repurchases. INSIDER BUY is a dummy variable set
to 1 if insider purchases exceed insider sales during the six months before the announcement day by at least 0.01% of the firm’s market
capitalization. EPS SURPRISE measures the difference between the actual reported EPS and mean analyst EPS forecast divided by the
share price. INST. HOLDING Q-1 is calculated based on the reported holdings on Form 13F normalized by the number of shares
outstanding. EPS SURPRISE measures the difference between the actual reported EPS and mean analyst EPS forecast divided by the
share price. We also include other common control variables, such as the natural log of the firm’s market capitalization or the natural
log of the firm’s total assets as the FIRM SIZE during the announcement quarter, market risk premium (BETA), SMB and HML. The
final sample sizes based on data availability for all regression variables are 875 repurchase announcements for quarterly 13F institutional
holding changes regression, 679 announcements for intra-day ISO analysis regression, 1,154 announcements for Abel Noser institutional
trading imbalance regression, and 715 announcements for bi-weekly short seller regressions.
Panel A: Quarterly 13F
Mean Maximum Minimum Standard Deviation
PROFIT Q0 to Q+4 1.9730 105.0000 -93.4860 28.8940
ANNOUNCEMENT 0.5550 1.0000 0.0000 0.4970
INSIDER BUY 0.2180 1.0000 0.0000 0.4130
INST. HOLDING Q-1 0.2850 0.7580 0.0000 0.2930
EPS SURPRISE -0.0032 0.0663 -0.4754 0.0255
FIRM SIZE 19.0610 25.4058 12.6115 1.9957
BETA 1.6922 16.5500 -22.1900 10.2498
SMB 0.8097 12.0100 -6.0300 3.4163
HML -0.1512 23.8500 -13.6200 7.1290
41
Panel B: Daily Intermarket Sweep Order
Panel C: Daily Abel Noser
Mean Maximum Minimum Standard Deviation
PROFIT D0 to D+5 -0.0610 2.2410 -3.6960 0.5140
ANNOUNCEMENT 0.5270 1.0000 0.0000 0.5000
INSIDER BUY 0.3170 1.0000 0.0000 0.4660
INST. HOLDING Q-1 0.0390 0.3810 0.0000 0.0980
EPS SURPRISE -0.0004 1.9186 -1.8256 0.1277
FIRM SIZE 6.6190 13.2280 0.0000 2.6330
BETA -0.0622 11.3500 -8.9500 1.8483
SMB 0.0170 4.2900 -3.7900 0.7103
HML -0.0623 3.9500 -2.8000 0.7095
Mean Maximum Minimum Standard Deviation
PROFIT D0 to D+30 0.0000 0.2900 -0.3100 0.1000
ANNOUNCEMENT 0.5000 1.0000 0.0000 0.5000
INSIDER BUY 0.1900 1.0000 0.0000 0.4000
INST. HOLDING Q-1 0.5600 2.4000 0.0000 0.5000
EPS SURPRISE 0.0000 4.9700 -1.0200 0.1500
FIRM SIZE 6.4500 11.5900 1.3200 1.9900
BETA 0.0107 4.9700 -8.9500 1.5419
SMB 0.0064 3.6100 -3.6700 0.6961
HML 0.0476 3.0800 -3.5200 0.7216
42
Panel D: Biweekly Short Seller
Mean Maximum Minimum Standard Deviation
PROFIT BW0 to BW+8 -1.9200 26.1270 -46.8950 10.2720
ANNOUNCEMENT 0.5080 1.0000 0.0000 0.5000
INSIDER BUY 0.3100 1.0000 0.0000 0.4630
INST. HOLDING Q-1 0.2990 1.2470 0.0000 0.3880
EPS SURPRISE 0.0065 1.9186 -0.2371 0.0785
FIRM SIZE 7.2030 13.4670 2.4860 1.8460
BETA -0.0368 11.3500 -7.3600 1.7184
SMB 0.0398 4.2900 -3.4600 0.6835
HML -0.0221 3.5000 -2.6200 0.6516
43
Table 3: Institutional Trade Imbalance for 13F, ISO, Abel Noser and Short Selling
This table reports the trade imbalance for all four institutional types around stock repurchase announcements from September 2007 to
December 2013. The quarterly 13F institutional trade imbalance is calculated as the net shares purchased, the number of shares bought
minus the number of shares sold, in the quarter normalized by the number of shares outstanding in millions for 836 unique announcing
firms found in the 13-F dataset. The daily ISO trade imbalance is calculated as the actual number of buy shares (inferred by Lee and
Ready (1991) mechanism) minus the number of sell shares, normalized by the number of shares outstanding in millions, for all 1870
unique firms in the final sample. The daily Abel Noser trade imbalance is calculated as the actual number of buy shares minus the
number of sell shares, normalized by the number of shares outstanding in millions, for 543 unique firms found in Abel Noser dataset.
Abnormal short interest is calculated as the average number of shares sold short in the test period divided by the average number of
shares sold short in the benchmark period minus one (Christophe et al., 2004), for 1,753 unique firms found in short selling dataset. The
benchmark period is six biweekly periods before the announcement period, approximately one quarter before the announcement. Net
insider trading direction is separated into three categories: net buying, net selling and neutral broadly following Bonaime and Ryngaert
(2013). The announcement event is considered net selling if insider sales exceed insider purchases by at least 0.01% of the firm’s market
capitalization. The announcement event is considered net buying if purchases exceed sales by the same requirement. Otherwise, the
event is defined as neutral insider trading. These classifications are based on the transactions of insiders during the previous six months
relative to the announcement because insiders are found to be most active during this period based on the findings of Chan, Ikenberry,
Lee, and Wang (2012). ***, **, * stand for statistical significance at the 1%, 5%, and 10% level, respectively.
Panel A: Quarterly 13F Trade Imbalance Implied for Changes in Institutional Holdings
13F
Quarterly
Trade
Imbalance
Full
Sample
(Obs=1,265
Announcements )
Insider Buy
Subsample
(Obs=353
Announcements)
Insider Sell
Subsample
(Obs=403
Announcements)
Neutral Insider
Subsample
(Obs=509
Announcements)
Mean p-value Mean p-value Mean p-value Mean p-value
-5 1,100** 0.0351 -700 0.4461 4,700*** <.0001 -400 0.6478
-4 200 0.8276 -2,200* 0.0795 1,300 0.2312 900 0.4878
-3 500 0.4181 800 0.5494 200 0.8778 600 0.5014
-2 2,400*** <.0001 1,900** 0.0351 4,100*** <.0001 1,500** 0.0364
-1 -300 0.5440 -1,700 0.1072 1,600 0.1539 -900 0.1772
0 -4,600*** <.0001 -5,600*** <.0001 -3,300*** 0.0017 -5,000*** <.0001
+1 -4,700*** <.0001 -3,100*** 0.0008 -4,100*** <.0001 -6,300*** <.0001
44
Panel B: Intra-day ISO Trade Imbalance from TAQ
ISO Daily
Trade
Imbalance
Full
Sample
(Obs=3,334
Announcements)
Insider Buy
Subsample
(Obs=1,131
Announcements)
Insider Sell
Subsample
(Obs=1,576
Announcements)
Neutral Insider
Subsample
(Obs=627
Announcements)
Mean p-value Mean p-value Mean p-value Mean p-value
-5 to -1 -75.32*** <.0001 -68.46*** 0.0010 -87.85*** <.0001 -56.16*** 0.0097
0 -87.06** 0.0183 -141.40* 0.0828 -76.50 0.1139 -15.64 0.7344
+1 to +5 -75.86*** <.0001 -50.72*** 0.0007 -98.08*** <.0001 -65.36** 0.0461
Panel C: Daily Abel Noser Institutional Trade Imbalance
Abel Noser
Daily Trade
Imbalance
Full
Sample
(Obs=702
Announcements)
Insider Buy
Subsample
(Obs=192
Announcements)
Insider Sell
Subsample
(Obs=235
Announcements)
Neutral Insider
Subsample
(Obs=275
Announcements)
Mean p-value Mean p-value Mean p-value Mean p-value
-30 to -1 -281.50 0.6100 -1,980.40 0.1347 83.30 0.9350 595.30 0.3196
0 -9,485.10 0.1609 -28,524.90 0.2428 -1,377.60 0.5332 -3,107.50 0.1641
+1 to +30 -653.50 0.1672 -198.60 0.7114 -500.20 0.3988 -1102.00 0.2859
+2 -2,700*** <.0001 -3,000*** 0.0018 -1,700* 0.0565 -3,200*** <.0001
+3 -3,500*** <.0001 -3,700*** <.0001 -3,300*** 0.0002 -3,600*** <.0001
+4 -3,300*** <.0001 -2,900*** 0.0009 -2,400*** 0.0032 -4,200*** <.0001
+5 -1,500*** 0.0004 -1,600** 0.0416 0 0.9936 -2,700*** 0.0001
45
Panel D: Biweekly Abnormal Short Selling
Biweekly
Abnormal
Short Selling
Full
Sample
(Obs=3,196
Announcements)
Insider Buy
Subsample
(Obs=1,024
Announcements)
Insider Sell
Subsample
(Obs=1,465
Announcements)
Neutral Insider
Subsample
(Obs=707
Announcements)
Mean p-value Mean p-value Mean p-value Mean p-value
-5 7.23%* 0.0860 -21.31%** 0.0389 11.80%*** 0.0051 39.11%*** <.0001
-4 6.87%* 0.0950 -21.59%** 0.0306 11.31%*** 0.0060 38.90%*** <.0001
-3 7.23%* 0.0875 -20.75%** 0.0452 10.75%*** 0.0085 40.45%*** <.0001
-2 7.44%* 0.0801 -20.85%** 0.0459 11.52%*** 0.0052 39.96%*** <.0001
-1 8.13%* 0.0648 -19.47%* 0.0799 13.00%*** 0.0019 38.00%*** <.0001
0 15.55%*** 0.0019 -15.59% 0.1761 18.29%*** <.0001 54.99%*** <.0001
+1 18.01%*** 0.0002 -14.23% 0.2007 21.23%*** <.0001 58.05%*** <.0001
+2 19.00%*** 0.0001 -13.84% 0.2255 23.38%*** <.0001 57.47%*** <.0001
+3 18.40%*** 0.0003 -13.46% 0.2684 21.73%*** <.0001 57.65%*** <.0001
+4 17.23%*** 0.0005 -13.53% 0.2602 20.50%*** <.0001 55.02%*** <.0001
+5 16.89%*** 0.0006 -13.60% 0.2582 21.64%*** <.0001 51.19%*** <.0001
46
Figure 2: Quarterly Institutional Trade Imbalance Separated by Investment Behavior
This graph shows the cumulative trade imbalance of all institutional investors as well as separately for the transient, quasi-indexer, and
dedicated institutional investor around the announcement quarter (t=0). The trade imbalance is calculated as the net shares traded, the
number of shares bought minus the number of shares sold, in the quarter normalized by the number of shares outstanding. We separate
institutional trading by three types according to Bushee (2001). Transient institutional investors have high portfolio turnover and highly
diversified portfolio holdings. Transient investors are more focus on short-term gains. Dedicated institutional investors have very low
portfolio turnover and larger average portfolio investments. Quasi-indexer institutional investors also have low portfolio turnover but
highly diversified portfolio holdings. Both dedicated and quasi-indexer investors have longer investment horizons. The Institution line
below is the total institutional trade imbalance that can be separated into transient, dedicated, and quasi-indexer trade imbalances using
the method discussed in Bushee (2001), which means that cumulative Institution = TRA + DED +QIX. Our control firm quarterly
institutional trade imbalance is calculated using non-repurchase-announcing firms matched by industry and size.
-5,000
-4,000
-3,000
-2,000
-1,000
0
1,000
2,000
3,000
4,000
5,000
-5 -4 -3 -2 -1 0 1 2 3 4 5
Trad
e im
bal
ance
Quarter
Institution TRA DED QIX Control
Negative Trade Imbalance
Positive Trade Imbalance
47
Figure 3: Quarterly Trade Imbalance for the Firm, Insiders, and Institutions
This graph shows the trade imbalance of the firm, insiders and institutions around the announcement quarter (t=0). The trade imbalance
for the firm is calculated as the actual share repurchased normalized by the number of shares outstanding. The firm’s repurchases before
the announcement quarter (t=0) are from previous uncompleted repurchase programs. The trade imbalance for both the insiders and the
institutions is calculated as the net shares traded, the number of shares bought minus the number of shares sold, in the quarter normalized
by the number of shares outstanding.
-6,000
-4,000
-2,000
0
2,000
4,000
6,000
8,000
10,000
12,000
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
Trad
e im
bal
ance
Quarter
Institution Insider Firm
Positive Trade Imbalance
Negative Trade Imbalance
48
Table 4: Profitability for 13F, ISO, Abel Noser and Short Selling
This table reports the profitability for all four institutional types around stock repurchase announcements from September 2007 to
December 2013. For negative changes in Quarterly 13F holdings trade imbalances, we calculate profitability as the difference between
the closing prices of repurchase quarter and following quarter (to cover the position). Analogously, the opposite difference is used for
positive changes in holdings. For ISOs, daily institutional profit is determined using the exact ISO trade prices in each of the [0, +5] day
and CRSP daily closing prices on day +5 relative to the announcement: ISO Profitability = 𝑇𝑟𝑎𝑑𝑒 𝑝𝑟𝑖𝑐𝑒𝑡−𝐶𝑙𝑜𝑠𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒𝑡+5
𝑇𝑟𝑎𝑑𝑒 𝑝𝑟𝑖𝑐𝑒𝑡 for sells,
and ISO Profitability = 𝐶𝑙𝑜𝑠𝑖𝑛𝑔 𝑝𝑟𝑖𝑐𝑒𝑡+5−𝑇𝑟𝑎𝑑𝑒 𝑝𝑟𝑖𝑐𝑒𝑡
𝑇𝑟𝑎𝑑𝑒 𝑝𝑟𝑖𝑐𝑒𝑡 for buys. For Abel Noser, daily institutional profit is determined using the exact trade
prices in each of the [0, +30] day and CRSP daily closing prices on day +30 relative to the announcement. Similar to the ISO calculation,
the Abel Noser profitability is calculated according to the trade direction. Short-selling profit is calculated as the difference between the
proceeds from the sale and the cost to close the position using CRSP closing prices. Net insider trading direction is separated into three
categories: net buying, net selling and neutral broadly following Bonaime and Ryngaert (2013). The announcement event is considered
net selling if insider sales exceed insider purchases by at least 0.01% of the firm’s market capitalization. The announcement event is
considered net buying if purchases exceed sales by the same requirement. Otherwise, the event is defined as neutral insider trading.
These classifications are based on the transactions of insiders during the previous six months relative to the announcement because
insiders are found to be most active during this period based on the findings of Chan, Ikenberry, Lee, and Wang (2012). ***, **, * stand
for statistical significance at the 1%, 5%, and 10% level, respectively.
Institutional
Profitability
Full
Sample
Insider Buy
Subsample
Insider Sell
Subsample
Neutral Insider
Subsample
Mean Obs Mean Obs Mean Obs Mean Obs
13F: Q0 to Q+4 -0.75% 2,246 -0.79% 715 -1.05% 1,005 -0.12% 526
(0.1196) (0.3593) (0.1652) (0.8914)
ISO: D0 to D+5 -0.04%*** 3,174 -0.07%*** 935 -0.01% 1,580 -0.04%*** 659
(<.0001) (<.0001) (0.2158) (0.0005)
Abel Noser: 0.00% 702 -0.02% 192 0.00% 235 0.01% 275
D0 to D+30 (0.8522) (0.2034) (0.9268) (0.1984)
Short Seller: -2.15%*** 3,169 -2.81%*** 1,017 -2.27%*** 1,455 -0.94%** 697
BW0 to BW+8 (<.0001) (0.0005) (<.0001) (0.0114)
49
Table 5: ISO Trade Imbalance and Profitability around Share Repurchase Announcements and Contemporaneous Events
The table shows the trade imbalance and profitability of ISOs around share repurchase announcements on days with and without other
types of corporate events, such as dividend announcements, earnings announcements, M&A announcements, stock dividends and stock
splits. The daily ISO trade imbalance is calculated as the actual number of buy shares minus the number of sell shares, normalized by
the number of shares outstanding in millions. ISO profit is determined using the actual ISO buy or sell price for each given trade during
days [0, +5] as the opening price. We assume that the position is closed at the CRSP daily closing price on day t+5. ***, **, * stand for
the statistical significance of difference from zero at the 1%, 5%, and 10% level, respectively.
Panel A: Daily ISO trade imbalance around repurchase announcements:
ISO
Trade Imbalance
No Other Contemporaneous
Corporate Events
(Obs=1,259)
With Contemporaneous
Corporate Events
(Obs=853 )
Mean p-value Mean p-value
-5 to -1 -118.10*** <.0001 -77.99*** <.0001
0 -64.99 0.1664 -110.50 0.1008
+1 to +5 -82.86*** <.0001 -136.90*** 0.0006
Panel B: Profitability of ISO trades around repurchase announcements:
ISO
Profitability
No Other Contemporaneous
Corporate Events
(Obs=1,259)
With Contemporaneous
Corporate Events (Obs=853)
Mean p-value Mean p-value
0 to +5 -0.05%*** 0.0014 -0.03%* 0.0568
50
Table 6: ISO Trade Imbalance and Profitability around Share Repurchase Announcements Adjusting for Quarter Ends
The table shows the trade imbalance and profitability of ISOs around share repurchase announcements adjusting for quarter ends.
Announcements and trades during the last three days of each quarter are eliminated from the sample. The daily ISO trade imbalance is
calculated as the actual number of buy shares minus the number of sell shares, normalized by the number of shares outstanding in
millions. ISO profit is determined using the actual ISO buy or sell price for each given trade during days [0, +5] as the opening price.
We assume that the position is closed at the CRSP daily closing price on day t+5. ***, **, * stand for the statistical significance of
difference from zero at the 1%, 5%, and 10% level, respectively.
Panel A: Daily ISO trade imbalance around repurchase announcements:
ISO Trade Imbalance Adjusting Quarter Ends (Obs=2,078)
Mean p-value
-5 to -1 -100.20*** <.0001
0 -79.74** 0.0423
+1 to +5 -106.20*** <.0001
Panel B: Profitability of ISO trades around repurchase announcements:
ISO Profitability Adjusting Quarter Ends (Obs=2,078)
Mean p-value
0 to +5 -0.05%*** <.0001
51
Table 7: Test of Endogeneity
This table reports the results of the Hausman Test for endogeneity. Using this test, we can
determine if the announcement signal is indeed endogenous to the institutional trade imbalance
during the announcement quarter. First, we regress the endogenous variable on the instrumental
variables (IVs). The endogenous variable is ANNOUNCEMENT, which is a dummy variable set
to 1 if the firm announces share repurchases. We use three IVs, ΔCASH, DILUTION, and TAXGAP,
in our test. We calculate ΔCASH as the change in EBITDA/Total Assets over the previous quarter.
DILUTION is calculated as one minus the ratio of diluted EPS/ basic EPS. TAXGAP is calculated
as the difference between the average tax rate on dividends and the average tax rate on capital
gains. We follow the method outlined in De Lisle et al. (2014) for these IVs.
ANNOUNCEMENT= 𝛼 + 𝛽1𝛥𝐶𝐴𝑆𝐻 + 𝛽2𝐷𝐼𝐿𝑈𝑇𝐼𝑂𝑁 + 𝛽3𝑇𝐴𝑋𝐺𝐴𝑃 + 𝑣
Dependent Variable:
𝐴𝑁𝑁𝑂𝑈𝑁𝐶𝐸𝑀𝐸𝑁𝑇
Coefficient
Estimate t-statistic p-value
𝛥𝐶𝐴𝑆𝐻 -1.4051* -1.77 0.0779
𝐷𝐼𝐿𝑈𝑇𝐼𝑂𝑁 -0.8587 -1.23 0.2175
𝑇𝐴𝑋𝐺𝐴𝑃 -0.2801*** -7.37 <.0001
INTERCEPT 4.6132*** 8.42 <.0001
Adj. R-Square 0.0727
Observations 714
52
In the second stage, we save the residuals, v, and include the residuals as an extra variable in the
second estimation.
𝑇𝑅𝐴𝐷𝐸𝐼𝑀𝐵𝐴𝐿𝐴𝑁𝐶𝐸 = 𝛼 + 𝛽1𝐴𝑁𝑁𝑂𝑈𝑁𝐶𝐸𝑀𝐸𝑁𝑇 + 𝛽2𝐼𝑁𝑆𝐼𝐷𝐸𝑅𝐵𝑈𝑌 +𝛽3𝐼𝑁𝑆𝑇. 𝐻𝑂𝐿𝐷𝐼𝑁𝐺(𝑄−1) + 𝛽4𝐸𝑃𝑆𝑆𝑈𝑅𝑃𝑅𝐼𝑆𝐸 + 𝛽5𝐹𝐼𝑅𝑀𝑆𝐼𝑍𝐸 + 𝛽6𝑣 + 𝜀
In the second estimation, the dependent variable is the quarterly institutional trade imbalance in
the announcement quarter, TRADEIMBALANCE, which is calculated as the net shares purchased,
the number of shares bought minus the number of shares sold, in the quarter normalized by the
number of shares outstanding in millions. ANNOUNCEMENT is a dummy variable set to 1 if the
firm announces share repurchases. INSIDERBUY is a dummy variable set to 1 if insider purchases
exceed insider sales during the six months before the announcement day by at least 0.01% of the
firm’s market capitalization. INST. HOLDING Q-1 is calculated based on the reported holdings on
Form 13F normalized by the number of shares outstanding. EPSSURPRISE measures the
difference between the actual reported EPS and mean analyst EPS forecast divided by the share
price. FIRMSIZE is the natural log of the firm’s market capitalization. Finally, we examine the t-
statistics of 𝛽6 to determine if endogeneity exists. If 𝛽6 is equal to zero, the there is no endogeneity.
If 𝛽6 is not equal to zero, then endogeneity exists. Our data is Winsorized at the 1% and 99%
levels. ***, **, * stand for statistical significance of difference from zero at the 1%, 5%, and 10%
level, respectively.
Dependent Variable: TRADEIMBALANCE
Coefficient
Estimate t-statistic p-value
ANNOUNCEMENT -0.0081 -0.50 0.6201
INSIDERBUY 0.0032 0.69 0.4891
INST. HOLDING Q-1 -0.0116 -0.89 0.3749
EPSSURPRISE 0.0340 0.49 0.6229
FIRMSIZE 0.0013 0.99 0.3224
v -0.0006 -0.04 0.9687
INTERCEPT -0.0121 -0.43 0.6709
Adj. R-Square 0.0247
Observations 714
53
Table 8: Regression Analysis of 13F Profitability on the Announcement and Insider Signal
This table reports the regression results where the dependent variable is the quarterly institutional trade profit from Q0 to Q+4. Our
sample consists of 875 events from 307 announcing firms and 307 matching firms between 2007 and 2013. ANNOUNCEMENT is a
dummy variable set to 1 if the firm announces share repurchases. INSIDER BUY is a dummy variable set to 1 if insider purchases
exceed insider sales during the six months before the announcement day by at least 0.01% of the firm’s market capitalization. INST.
HOLDING Q-1 is calculated based on the reported holdings on Form 13F normalized by the number of shares outstanding. EPS
SURPRISE measures the difference between the actual reported EPS and mean analyst EPS forecast divided by the share price. We also
include other common control variables, such as the natural log of the firm’s market capitalization as the firm size during the
announcement quarter, market risk premium, SMB, and HML. To address multicollinearity issues when fitting our regression models,
we eliminated any variable with a VIF score higher than 10. Our data is Winsorized at the 1% and 99% levels. ***, **, * stand for the
statistical significance of difference from zero at the 1%, 5%, and 10% level, respectively.
Dependent Variable:
Quarterly 13F Profit
Announcement Signal
(Obs=875)
Insider Buy
Signal (Obs=875)
Announcement &
Insider Buy Interaction
(Obs=875)
Coefficient
Estimate p-value
Coefficient
Estimate p-value
Coefficient
Estimate p-value
ANNOUNCEMENT -10.2299 0.1621 -7.8186 0.2937
INSIDER BUY -0.2883 0.9290 4.6278 0.2738
ANNOUNCEMENT*
INSIDER BUY
-12.3125* 0.0504
INST. HOLDING Q-1 6.7484 0.5364 -5.0710 0.4677 5.7101 0.6007
EPS SURPRISE 115.5757 0.2214 125.6476 0.1837 112.3733 0.2342
FIRM SIZE -1.1022 0.4042 -0.1490 0.8959 -1.4240 0.2896
BETA 0.1484 0.5823 0.1905 0.4802 0.1097 0.6861
SMB -0.4073 0.6180 -0.4229 0.6062 -0.3177 0.6979
HML 0.2034 0.5739 0.1650 0.6483 0.1852 0.6082
INDUSTRY FIXED EFFECTS YES YES YES
YEAR FIXED EFFECTS YES YES YES
R-SQUARE 0.3949 0.3926 0.3995
54
Table 9: Regression Analysis of ISO Profitability on the Announcement and Insider Signal
This table reports the regression results where the dependent variable is the average daily ISO profit from day 0 to day +5. Our sample
consists of 679 events from 255 announcing and 255 matching firms between 2007 and 2013. ANNOUNCEMENT is a dummy variable
set to 1 if the firm announces share repurchases. INSIDER BUY is a dummy variable set to 1 if insider purchases exceed insider sales
during the six months before the announcement day by at least 0.01% of the firm’s market capitalization. INST. HOLDING Q-1 is
calculated based on the reported holdings on Form 13F normalized by the number of shares outstanding. EPS SURPRISE measures the
difference between the actual reported EPS and mean analyst EPS forecast divided by the share price. We also include other common
control variables, such as the natural log of the firm’s total assets as the firm size during the announcement quarter, market risk premium,
SMB, and HML. To address multicollinearity issues when fitting our regression models, we eliminated any variable with a VIF score
higher than 10. Our data is Winsorized at the 1% and 99% levels. ***, **, * stand for the statistical significance of difference from zero
at the 1%, 5%, and 10% level, respectively.
Dependent Variable:
Daily ISO Profit
Announcement Signal
(Obs=679)
Insider Buy
Signal (Obs=679)
Announcement & Insider
Buy Interaction (Obs=679)
Coefficient
Estimate p-value
Coefficient
Estimate p-value
Coefficient
Estimate p-value
ANNOUNCEMENT -0.1154 0.1092 -0.1030 0.2143
INSIDER BUY -0.1404* 0.0848 -0.1208 0.2414
ANNOUNCEMENT*
INSIDER BUY
-0.0450 0.7394
INST. HOLDING Q-1 0.8887** 0.0278 0.6943* 0.0601 0.9802** 0.0163
EPS SURPRISE -0.0679 0.8581 -0.1649 0.6666 -0.1654 0.6659
FIRM SIZE 0.0125 0.3696 0.0059 0.6768 0.0070 0.6206
BETA 0.0272 0.1692 0.0280 0.1562 0.0288 0.1451
SMB 0.0039 0.9280 0.0060 0.8906 0.0047 0.9136
HML 0.0604 0.2433 0.0510 0.3259 0.0529 0.3083
INDUSTRY FIXED
EFFECTS YES YES YES
YEAR FIXED EFFECTS YES YES YES
R-SQUARE 0.6174 0.6181 0.6223
55
Table 10: Regression Analysis of Abel Noser Profitability on the Announcement and Insider Signal
This table reports the regression results where the dependent variable is the average daily institutional profit from day 0 to day +30. Our
sample consists of 1,154 events from 577 announcing and 577 matching firms between 2007 and 2013. ANNOUNCEMENT is a dummy
variable set to 1 if the firm announces share repurchases. INSIDER BUY is a dummy variable set to 1 if insider purchases exceed insider
sales during the six months before the announcement day by at least 0.01% of the firm’s market capitalization. INST. HOLDING Q-1
is calculated based on the reported holdings on Form 13F normalized by the number of shares outstanding. EPS SURPRISE measures
the difference between the actual reported EPS and mean analyst EPS forecast divided by the share price. We also include other common
control variables, such as the natural log of the firm’s market value as the firm size during the announcement quarter, market risk
premium, SMB, and HML. To address multicollinearity issues when fitting our regression models, we eliminated any variable with a
VIF score higher than 10. Our data is Winsorized at the 5% and 95% levels. ***, **, * stand for the statistical significance of difference
from zero at the 1%, 5%, and 10% level, respectively.
Dependent Variable:
Daily Abel Noser Profit
Announcement Signal
(Obs=1,154)
Insider Buy
Signal (Obs=1,154)
Announcement & Insider
Buy Interaction
(Obs=1,154)
Coefficient
Estimate p-value
Coefficient
Estimate p-value
Coefficient
Estimate p-value
ANNOUNCEMENT -0.0129** 0.0222 -0.0121* 0.0678
INSIDER BUY -0.0058 0.4942 0.0014 0.9272
ANNOUNCEMENT*
INSIDER BUY
-0.0036 0.8493
INST. HOLDING Q-1 0.0222*** 0.0090 0.0189** 0.0245 0.0221*** 0.0095
EPS SURPRISE 0.0107 0.6153 0.0103 0.6278 0.0104 0.6239
FIRM SIZE -0.0003 0.9290 -0.0001 0.9598 -0.0003 0.9185
BETA -0.0017 0.6147 -0.0018 0.5914 -0.0017 0.6114
SMB 0.0080 0.2205 0.0083 0.2046 0.0080 0.2215
HML 0.0128* 0.0673 0.0126* 0.0725 0.0128* 0.0678
INDUSTRY FIXED EFFECTS YES YES YES
YEAR FIXED EFFECTS YES YES YES
R-SQUARE 0.3963 0.3925 0.3963
56
Table 11: Regression Analysis of Short Sales Profitability on the Announcement and Insider Signal
This table reports the regression results where the dependent variable is the biweekly short selling profit from biweekly 0 to biweekly
+8. Our sample consists of 715 events from 274 announcing and 274 matching firms between 2007 and 2013. ANNOUNCEMENT is a
dummy variable set to 1 if the firm announces share repurchases. INSIDER BUY is a dummy variable set to 1 if insider purchases
exceed insider sales during the six months before the announcement day by at least 0.01% of the firm’s market capitalization. INST.
HOLDING Q-1 is calculated based on the reported holdings on Form 13F normalized by the number of shares outstanding. EPS
SURPRISE measures the difference between the actual reported EPS and mean analyst EPS forecast divided by the share price. We also
include other common control variables, such as the natural log of the firm’s total assets as the firm size during the announcement
quarter, market risk premium, SMB, and HML. To address multicollinearity issues when fitting our regression models, we eliminated
any variable with a VIF score higher than 10. Our data is Winsorized at the 1% and 99% levels. ***, **, * stand for the statistical
significance of difference from zero at the 1%, 5%, and 10% level, respectively.
Dependent Variable:
Biweekly Short Seller Profit
Announcement Signal
(Obs=715)
Insider Buy
Signal (Obs=715)
Announcement & Insider Buy
Interaction (Obs=715)
Coefficient
Estimate p-value
Coefficient
Estimate p-value
Coefficient
Estimate p-value
ANNOUNCEMENT -1.5684 0.3526 -1.2119 0.5180
INSIDER BUY -0.5184 0.7092 0.0129 0.9942
ANNOUNCEMENT*
INSIDER BUY
-0.9153 0.6878
INST. HOLDING Q-1 2.9686 0.2129 1.2225 0.4214 2.7965 0.2465
EPS SURPRISE -5.4594 0.4141 -6.0541 0.3637 -5.3208 0.4283
FIRM SIZE 0.0302 0.9449 0.0449 0.9200 -0.0093 0.9835
BETA -0.3446 0.3501 -0.3329 0.3681 -0.3398 0.3592
SMB 1.7243** 0.0340 1.6479** 0.0421 1.7266** 0.0344
HML -0.5654 0.5579 -0.6589 0.4978 -0.5955 0.5415
INDUSTRY FIXED EFFECTS YES YES YES
YEAR FIXED EFFECTS YES YES YES
R-SQUARE 0.5828 0.5818 0.5831