Information Leakage and Wealth Transfer in a Connected World
Wenli Huang
Assistant Professor of Accounting
School of Management
Boston University
wlhuang@bu.edu
Hai Lu*
Associate Professor of Accounting
Rotman School of Management
University of Toronto
hai.lu@rotman.utoronto.ca
Xiaolu Wang
Assistant Professor of Finance
College of Business
Iowa State University
xiaoluw@iastate.edu
This version: February 3, 2013
*Corresponding author. We appreciate the helpful comments of Kevin C.W. Chen, Zhihong
Chen, Agnes Cheng, Yuyan Guan, Allen Huang, Minyi Hung, Krish Menon, Eddie Riedl, Kumar
Sivakumar, Michael Smith, Kam-Ming Wan, Peter Wysocki, Haifeng You, Guochang Zhang,
Yong Zhang, and the seminar participants at Boston University, Chinese University of Hong
Kong, City University of Hong Kong, Hong Kong Polytechnic University, Hong Kong
University of Science and Technology, Iowa State University, and Peking University. We
gratefully acknowledge the financial support from the Social Sciences and Humanities Research
Council of Canada.
Information Leakage and Wealth Transfer in a Connected World
Abstract
We examine whether information leakage occurs and persists between institutional investors and
firms. We document that institutions trade ahead of other investors before the public revelation
of option backdating and gain significantly. Based on these trades, we develop a proxy for the
likelihood of information leakage for each institution-firm pair and examine institutional trades
on connected firms outside the backdating setting. We find that institutions trade more actively
on connected firms, trade in the same direction as the upcoming earnings surprises prior to
earnings announcements, and consistently perform better on connected firms than on nonconnected firms. Our results are consistent with information leakage resulting in wealth transfer.
Key words: Information leakage, private connections, option backdating, wealth transfer.
JEL Classification: M41, K22, G14, G38
Information Leakage and Wealth Transfer in a Connected World
1. Introduction
Information disclosures contribute to the efficiency of capital markets but they are also
alleged to lead to unfair wealth transfer among investors if those disclosures are selective (De
Franco, Lu, and Vasvari, 2007). In 2000, the Securities and Exchange Commission (SEC)
introduced Regulation Fair Disclosure (Reg FD) barring companies from selectively disclosing
material information to favored investors and analysts, which eliminates an important source of
information to these investors. However, investors remain hungry for information. An
unintended consequence of Reg FD is believed to be a surge in insider trading in U.S. financial
markets, including new ways non-public information is passed to traders through “expert
network” (Zuckerman and Pulliam, 2010).
Insider trading generally refers to buying or selling a security based on material, non-public
information. Anecdotal evidence and academic research suggest that the leakage of such
information before the public disclosure is rampant in financial markets. Prior event studies
document significant changes in stock price and trading volume in the days before corporate
events (see, e.g., Cornell and Sirri, 1992; Meulbroek, 1992; Irvine, Lipson, and Puckett, 2007;
Christophe, Ferri, and Hsieh, 2010; Khan and Lu, 2012). While the changes could be due to the
rational expectations of investors that are related to forthcoming information, it is also possible
that private information has been leaked prior to public announcements.
Because insider trading undermines investor confidence in the fairness and integrity of the
securities markets, the SEC has treated the detection and prosecution of insider trading violations
as a high priority area for its enforcement program. Motivated by the significance of insider
1
trading, in this study, we search for evidence of information leakage using a unique corporate
event, the stock option backdating announcement.1 We define information leakage as a loss or
breach of private and confidential information, usually resulting in information disclosed to
selected market participants before disclosed to the general public. Our objective is two-fold.
First, we examine the occurrence and persistence of information leakage between backdating
firms and institutional investors in the financial markets with private connections. Second, we
analyze the welfare allocation between general investors and early-informed institutional
investors to evaluate information leakage and insider trading more explicitly. Our results show
that some institutions repeatedly benefit from their trades on the same set of firms, suggesting
that these institutions and the firms are most likely to be privately connected, and that wealth
transfer from other investors to these institutions is likely attributable to the connections.
We examine the public revelation of stock option backdating based on the following reasons:
First, it is a corporate event associated with a significant loss of shareholder wealth indicating
that material information is involved in this event. Second, the backdating investigation
announcement is an unexpected event. Unlike earnings announcements or some other corporate
events that are typically scheduled well in advance, scandals related to stock-option grants
shocked corporate America and the investment community, making it hard to attribute investors’
advanced trading to their rational expectations of a forthcoming firm-specific announcement.
Third, Bernile and Jarrell (2009) document that a significant loss of market value occurs within
20 trading days prior to backdating announcements, so we can reasonably conjecture that some
1
Lie (2005) is the first study reporting many companies setting the option grant date retroactively to an earlier date
when the stock price was lower than that on the date when the option was awarded. Backdated grants spread across a
variety of firms, industries, and exchanges, including S&P firms such as Microsoft, Apple, and small OTC firms.
This practice quickly attracted a great amount of attention from the media, regulators, and investors. The SEC and
Department of Justice investigated over a hundred firms by the end of 2006 (Wall Street Journal, 2006. Perfect
Payday: Option Scorecard, at www.wsj.com).
2
investors in the market have early access to non-public information and use that information to
their benefit in trading ahead of other investors. Fourth, the practice of backdating options is an
indicator of poor corporate governance and of a severe agency problem.2 We expect that private
information is more likely to be leaked from these firms. Finally, many firms took actions to
improve their corporate governance after the revelation of backdating, which potentially affected
the degree of connectedness between institutions and firms. In that sense, the backdating
revelation is an external shock to the private institution-firm connections rather than a shock to
institutions’ ability to acquire and process information pertinent to value of the stock. Hence, it
provides us a nice setting to better understand information leakage by analyzing and comparing
institutional trading in the pre- and post-backdating scandal period.
We focus on institutional trading data to seek evidence of information leakage based on the
following reasons: First, it is reasonable to expect that some institutions are the beneficiaries of
information leakage. For example, De Franco et al. (2007) document a wealth transfer effect
from individuals to institutions that is due to misleading behavior by analysts. Second, prior
studies have shown that institutional investors closely monitor firm behavior (see, e.g., Gillan
and Starks, 2000; Hartzell and Starks, 2003) and that they are more likely to be connected to
firms—either through direct access to corporate insiders or informed third parties (e.g., analysts
and consultants). Third, managers of the firms or those informed third parties have incentives to
leak information to institutional investors with whom they are connected for personal benefits.
Butler and Gurun (2012) analyze educational connections between fund managers and firm
managers and find that mutual funds with managers in the same educational network as the
firm’s CEO are more likely to vote against shareholder proposals limiting executive
2
For example, Bernile and Jarrell (2009) suggest that the revelation of backdating changes investor confidence in
management’s credibility and alters their perception of the agency problem within the backdating firms.
3
compensation. They also find that CEOs of the connected firms receive higher compensation
than their unconnected counterparts. Meanwhile, the Galleon Hedge Fund (see Appendix 1 for
detailed discussion) and other recent expert network cases investigated by the SEC and federal
prosecutors provide direct evidence that informed third parties leak private information for
personal benefits. Finally, private information tends to be short term,3 so using quarterly holdings
data to search for evidence of information leakage is likely to be unsuccessful. Using transaction
level institutional trading data enables us to better identify information leakage.
We first compare the trading behavior of an average fund,4 estimated from the institutional
intraday trading database, with that of general investors, estimated from the Trade and Quote
(TAQ) database, prior to the first firm-specific backdating announcements to discern whether or
not institutions have information advantage. We find that funds liquidate shares of backdating
firms several weeks, rather than days, before the public revelation. Such advanced trading
generates significant economic gain to institutional investors compared to general investors. Our
results are consistent with the conjecture that information leakage exists prior to the revelation of
backdating and that there is wealth transfer associated with the leakage.
However, it is unlikely that each fund obtains private information from each backdating firm.
To identify potentially connected fund-firm pairs, we propose a measure, labeled as Leakage
Score (L-score), to proxy for the likelihood of leakage. This measure is constructed for each
fund-firm pair based on the volume and timing of the trades prior to backdating announcements
(see Section 3 for details). The intuition to infer private connections from trading data is that as
3
Puckett and Yan (2011) show that an institution’s intra-quarter trades consistently earn significant abnormal
returns.
4
In this paper, we use the words institution(s) and fund(s) interchangeably.
4
long as investors want to take advantage of the private information, they need to trade to realize
the benefit. Therefore, connections inferred from trading tend to be comprehensive.
We then examine whether the effect of private connections is persistent. The primary purpose
for institutional investors to develop private connections with firms is to gain an investing edge
on an ongoing basis. In other words, if private connections have already been established,
information is expected to consistently flow through those connections and institutions are
expected to consistently gain from their trades on connected firms. 5 We use three different
approaches to test these predictions. First, we investigate the relationship between L-score and a
measure capturing how actively a fund trades the stock of a backdating firm in the year prior to
the scandal. We expect that a fund would more actively trade the stock of the firms with which it
has private connections and information advantage. Test results are consistent with our
prediction.
Second, we examine institutional trading prior to earnings announcements of backdating
firms. If a fund has private connections with a firm, we expect information leakage also to occur
prior to other corporate events such as earnings announcements. Test results show that this is
indeed the case during the pre-backdating-scandal period. More specifically, during 2002-2005,
those funds benefiting from their trades prior to backdating announcements are also likely to
trade in the same direction as the upcoming earnings surprises. This phenomenon, however,
disappears after the scandal. This finding supports our argument that the backdating revelation is
an external shock to the private connections. The likelihood of information leakage is thus
expected to decrease after the scandal.
5
These predictions are intuitive. To provide direct support for these predictions, we analyze the case of Galleon,
which is considered to be one of the biggest insider trading cases in U.S. history. See Appendix 1 for details.
5
Third, we compare annual fund performance on connected firms with that on non-connected
firms. If L-score indeed captures the long-term, stable connection between funds and firms, we
expect connected firms to give higher returns to the funds than non-connected firms. Test results
show that fund performance is significantly better on connected firms only in the pre-backdatingscandal period. The evidence is again consistent with the expectation that the revelation reduces
the likelihood of information leakage.
Our study contributes to the literature in multiple ways and has important implications for
financial regulations and corporate governance. First, we add to the understanding about
information flow in capital markets by showing that relatively stable private channels exist
between institutional investors and firms. We also show that information consistently flows from
firms to funds through these channels. Existing studies show that social networking facilitates
information dissemination (see, e.g., Cohen, Frazzini, and Malloy, 2008, 2010), but at the same
time the effect of the network is quite problematic if the information is private and advanced
trading on private information is illegal.
Second, we document a significant wealth transfer consequence of information leakage (i.e.,
a group of institutional investors systematically benefiting from private networks that they have
established with firms in which they invest).6 Some prior studies argue that insider trading leads
to more rapid price discovery (see, e.g., Cornell and Sirri, 1992; Meulbroek, 1992; Chakravarty
and McConnell, 1997); however, the unfair wealth transfer due to insider trading would erode
the confidence of investors in capital markets. Maintaining equal access to information is a
primary objective of introducing Regulation Fair Disclosure. If leakage is prevalent through
many private connections, the effectiveness of current regulations is questionable. Understanding
6
While our evidence is consistent with improper activity by market participants, providing definitive proof is
beyond the scope of the data available to us.
6
the underlying mechanisms of information flow would aid in the development of more effective
regulations by paying attention to the hidden leakage channels.
Third, our study contributes to the literature on the role of institutional investors in the equity
market. Some studies provide evidence that a subset of institutional investors have superior
investment performance, arguably due to their active information search (see subsection 2.1 for a
review of related literature). Our study implies that their timely trading might be partially due to
private networks that institutions have established. While we cannot exclude the possibility that
certain institutions use the leaked information to conduct a diligent analysis of a firm’s value,
trading on such private information ahead of public announcements about corporate events
would certainly impose negative effect on the quality of capital markets.
Our study differs from other studies that examine explicit forms of networks established ex
ante in that we infer private connections and identify connected fund-firm pairs through
institutional investors’ trading behavior ex post. Information can be leaked through various
channels such as meeting, conferences, education network (Cohen et al., 2008, 2010), and local
connections (Hong, Kubik, and Stein, 2004, 2005), etc. In many studies that focus on one or
more explicit types of network, the information network is likely to be incomplete and there is a
possibility of hidden channels driving the results. Our analysis is based on institutional trading
behavior, which reflects an institution’s informational advantage obtained through all possible
private channels. This allows us to estimate the aggregate level of information leakage in a
connected world.
The next section reviews related literature and institutional background. Section 3 describes
our sample and defines the variables. In Section 4, we present the main empirical results and
discuss additional analyses. Section 5 concludes.
7
2. Literature Review and Institutional Background
Our study is related to three themes of research: Institutional trading, information networks,
and information leakage and insider trading. We briefly discuss each of these themes and the
related institutional background that place our study in context.
2.1. Institutional trading
A large body of research on institutional investors and mutual funds focus on whether or not
their trading has information content. Some studies suggest that institutions influence price
discovery and formation process (e.g., Bartov, Radhakrishnan, and Krinsky, 2000; Piotroski and
Roulstone, 2004). However, the evidence showing institutions producing superior investment
performance is mixed.7 Recent studies find that empirical results are sensitive to the measure of
institutional trading (Bennett, Sias, and Starks, 2003; Cremers and Petajisto, 2009), style of
trading (Yan and Zhang, 2009), or data availability (Puckett and Yan, 2011). Yan and Zhang
(2009) find that the trades from the institutions that trade more actively (short-term institutions)
forecast future returns while the trades from less active traders (long-term institutions) do not.
The study concludes that the gain of short-term institutions is due to better information rather
than short-term pressure as proposed in Bushee (1998, 2001).8 Ke and Ramalingegowda (2005)
also find that transient institutions trade to exploit the post-earnings-announcement-drifts.
Moreover, using transaction level data to examine the performance of intra-quarter roundtrip
trades for each institution or portfolio manager, Puckett and Yan (2011) find that some
institutional portfolio managers possess superior and persistent interim trading skills.
7
See, for example, Jensen, 1968; Lakonishok, Shleifer, and Vishny, 1992; Grinblatt, Titman, and Wermers, 1995;
Nofsinger and Sias, 1999; Chen, Jegadeesh, and Wermers, 2000; Wermers, 2000; Gompers and Metrick, 2001;
Griffin, Harris, and Topaloglu, 2003; Cohen, Coval, and Pastor, 2005; Kacperczyk, Sialm, and Zheng, 2005.
8
Bushee (1998, 2001) suggest that firms with higher transient institutional ownership are more likely to underinvest
in long term projects such as R&D and overweight near term expected earnings.
8
While these recent studies reveal some predictability of returns of institutional traders, we
know little about why transient investors or some fund managers have information advantage.
There are several possible scenarios: institutional investors actively search for firm-specific
information and monitor firm activities on an ongoing basis, financial analysts’ decisions to
follow companies are positively related to the demand from institutional investors (see, e.g.,
DeFond and Hung, 2003), and analysts may tip off their clients (De Franco et al., 2007; Irvine et
al., 2007). In this study, we examine a different scenario where institutional investor’s
information advantage comes from private connections and results in wealth transfer.
2.2. Information networks
The role of the connections among participants in the financial markets has not been
explored until recently. Academic researchers have come to realize that, in a connected world,
the behavior of investors and their information sources are not isolated. How information
disseminates through social networking has become an important research topic. Recent studies
approach the problem in different ways. Theoretical studies typically use the network concept
and a measure of social networking to study the effect of networks on asset pricing (see, e.g.,
Ozsoylev and Walden, 2011; Han and Yang, 2012). Most empirical studies identify a specific
form of network among agents in the financial markets and examine its effect on market
participation and stock prices. For example, Hong et al. (2004) find that social interactions, such
as interacting with neighbors or attending church, positively affect stock market participation.
Ivkovic and Weisbenner (2007) find a positive correlation between stock purchases made by
households and those made by their neighbors. Such word-of-mouth communication effects are
also found in Hong et al. (2005) who examine fund manager’s trading behavior. Focusing on
educational connections between mutual fund managers, financial analysts, and boards of
9
directors, Cohen et al. (2008) show that fund managers place larger bets on connected firms and
perform significantly better on these holdings relative to non-connected holdings. Cohen et al.
(2010) find that analysts perform significantly better on their stock recommendations when they
have an educational connection to senior executives. Their collective evidence suggests
information transfer through educational networks.
In this study, we do not focus on a specific form of network such as a local or educational
connection. Rather, we attempt to infer private networks via trading data and show that
information leakage and the resulting economic gain of institutional investors could be due to
private connections.
2.3. Information leakage and insider trading
It is commonly recognized that information leakage and insider trading erode public
confidence in capital markets. The Securities and Exchange Act of 1934 and the subsequent
amendments9 state that it is illegal to pass on to others material, non-public information or to
enter into transactions while in possession of such information. The financial industry has also
formed its own code of conduct. The National Association of Securities Dealers (NASD)10 Rule
2110, “Standards of Commercial Honor and Principles of Trades,” explicitly prohibits trading by
its member brokerage firms before the release of its own analyst’s research reports. Many firms
also establish internal policies that address insider trading. Bettis, Coles, and Lemmon (2000)
show that 92% of firms in their sample have policies restricting insider trading and 78% have
blackout periods of insider trading.
9
For example, the Insider Trading Sanctions Act of 1984, the Insider Trading and Securities Fraud Enforcement Act
of 1988, the SEC Rules 10b5-1 and 10b5-2 of 2000, Regulation Fair Disclosure.
10
NASD’s successor is now the Financial Industry Regulatory Authority (FINRA).
10
Despite the intensified efforts from regulators, industry organizations, and firms to reign in
insider trading, information leakage and illegal insider trading persist. For example, Seyhun
(1992) shows that both profitability and the volume of insider trading increased significantly (by
a factor of 4 to 6) during the 1980s when the SEC increased its enforcement efforts. The
evidence suggests that the cost of insider trading is low, potentially due to the very low
likelihood of being caught and prosecuted. Given the inherent difficulties in investigating and
proving insider trading cases, the reality can be that there is a significant amount of clearly
illegal activity that goes undetected or unpunished.
As a result, prior empirical studies on information leakage either focus on insider trading
cases (Cornell and Sirri, 1992; Meulbroek, 1992) or infer the existence of information leakage
(Irvine, et al., 2007; Christophe et al., 2010; Khan and Lu, 2012). For example, Irvine et al.
(2007) find that institutions trade ahead of analyst’s buy recommendations. Christophe et al.
(2010) provide evidence that short-sellers short ahead of analyst sell recommendations using data
from the period after the adoption of the SEC Rule 10b5-1 and 10b5-2 in 2000. Recent work by
Khan and Lu (2012) shows that front-running is facilitated by leaked information.
3. Sample and Variable Definitions
3.1. Backdating stock sample
As of December 31, 2006 there were 136 firms identified by the Wall Street Journal (WSJ)
to have disclosed investigations by the SEC or the Department of Justice, or admitted to having
backdated stock options.11 Bernile and Jarrell (2009, hereinafter BJ) collect news stories related
to options backdating using a keyword search of all available sources in English on Factiva for
these firms. We use the first date of these firm-specific news events as recorded in their paper.
11
See the “Perfect Payday: Option Scorecard” at www.wsj.com for the list of affected companies.
11
For several firms which do not have the dates in BJ, we use the first date of investigation
reported on WSJ website.
For all backdating (hereinafter BD) firms, we collect intra-day institutional trading data from
the Ancerno database (introduced in the next subsection), stock returns and other stock
characteristics from the Center for Research in Security Prices (CRSP) database, intra-day
trading data from the TAQ database, and quarterly institutional holdings data from the Thomson
13F holdings database. Four firms have all daily returns missing around the BD announcement
date and are excluded from our sample. Because we are interested in understanding the trading
behavior of institutional investors around BD announcements, we only include BD stocks that
are traded by at least five funds from the Ancerno database over the window of [-60, +1] days
relative to the announcement day. This reduces our sample to 126 stocks.
Table 1 presents summary statistics of stock characteristics of these 126 firms as of the end
of 2005. The average and median firm size are $7,640 and $1,617 million, respectively. The
book-to-market ratio is 0.4 on average. Annual stock turnover, calculated as the total trading
volume divided by shares outstanding, is 3.47 on average (2.80 in median). Average annual
cumulative return in 2005 is 11.1%, and that of the second half of the year is 12.4%.
Bernile and Jarrell (2009) provide evidence that investors react negatively to BD revelation
and that the negative returns start at more than 20 days prior to the announcements. In Table 2,
we replicate their tests and confirm their findings. Cumulative abnormal returns (CAR) with
respect to the CRSP market return, to the equal-weighted size portfolio, and to the equalweighted size-B/M-momentum benchmark portfolio in various windows around BD
announcements are reported. Significantly negative CAR is identified for the windows of [-20, -2]
12
and [-1, +1] relative to the announcement day no matter which benchmark return is used, with
day 0 being the BD announcement day.
Table 2 suggests that the BD scandal significantly changes investor’s valuation of the firms.
The more interesting feature of the return pattern is that investors, at least some, start to adjust
their valuation a couple of weeks before the announcements as shown by the significantly
negative CAR over the window [-20, -2]. As the firm-specific announcement dates are not prescheduled, it is difficult for investors to predict the timing of such events by researching public
information. A more plausible explanation for the return pattern is private information leakage.
3.2. Institutional trading sample
We obtain institutional trading data for the period from January 2002 to December 2010 from
Ancerno Ltd., a widely recognized consulting firm that provides trading cost analysis to
institutional investors. Ancerno covers high-frequency transaction-level data for a large sample
of institutional investors, which offers significant advantages over quarterly disclosed holdings
data in 13F filings to understand institutional investor trading behavior.12 When an institution
buys service from Ancerno, this institution’s trading data will enter Ancerno’s database. The
names of all institutions are removed from the data set due to confidentiality. While the Ancerno
data capture the activities of a subset of pension and mutual funds, the subset represents a
significant fraction of total institutional trading volume.
13
Ancerno assigns a unique
identification code to each institution in the data set. In addition, unique identification codes are
also provided for different funds within those institutions, enabling us to reliably track each fund
12
This data set is used in several other papers (see, e.g., Chemmanur, He, and Hu, 2009; Puckett and Yan, 2011) to
understand institutional investor behavior and skills.
13
Puckett and Yan (2011) shows that, on average, the trading activity of institutions in the Ancerno database
accounts for approximately 8% of the dollar value of CRSP trading volume from 1999-2005.
13
in the data set. For each transaction, we collect the identification code of the institution and the
fund, date of the transaction, stock traded (symbol and CUSIP), buy or sell indicator, number of
shares traded, price per share, and transaction costs (i.e., commissions and fees paid).
We extract trading data for all the funds that have traded BD stocks in the [-60, +1] window
around BD announcement dates. To be included in our sample, we require a fund to exist in the
Ancerno data set from month t-4 to t+1 with t being the month of BD announcement, which
guarantees all the transactions of BD stocks in the event window by a fund are in our sample.
Our final sample contains 957 distinct funds with 800 pension funds and 157 mutual funds.
While the number of mutual funds is small relative to pension funds, mutual funds are
responsible for more than half of the trading volume in the database, consistent with the finding
in Puckett and Yan (2011).
Panel A of Table 3 reports summary statistics of the trading behavior on BD stock by sample
funds in the [-60, +1] event window. A typical fund trades 8.8 BD stocks in the [-60, +1] event
window on average and 4.0 in median. Mutual funds, in general, trade more BD stocks than
pension funds. The total dollar amount traded by a typical fund for all BD stocks in the event
window is $59.88 million on average and $2.00 million in median. For a given BD firm, 67
funds trade the stock in the event window on average. Trading by sample funds constitutes 4.67%
of all trading volume for the BD stocks in the event window.
3.3. Variable definition
3.3.1. Economic gains
We compute both raw economic gains and benchmark adjusted economic gains for each
fund-stock pair to proxy for the wealth transfer effect. Raw economic gains for trades on stock i
14
made by fund m from day t1 to t2 is calculated as follows. First, we compute dollar raw
economic gains for the fund during the period from day t1 to day t2 following the approach in De
Franco et al. (2007):
=
∑
ℎℎ × −
∑( × ℎℎ + −
).
(1)
The first term in the above equation computes the value of fund m’s holding in stock i at the end
of the period where Pt2 is the closing price for that day. The second term summarizes the actual
net dollar amount paid by the fund for stock i during the period. The number of shares is adjusted
for stock splits and other share adjustments in the period. The difference between the two terms
represents net profit in dollar amount earned by the fund from trading the stock during the period.
Second, we divide the dollar raw economic gains by the total purchase in dollars to obtain
the raw economic gains which reflects return on investment of the fund on the stock:14
Raw economic gains = ∑24
"#$$%&&%'()#*#+,)-%,*.
2526(%)/%$0&,)(0%,12 ×.3%&(.0/&)3%.(12 7&%*.%),#*)#..2 )
(2)
The calculation of raw economic gains does not take into consideration opportunity costs of
money. To account for the opportunity costs to investors, we also calculate economic gains
adjusted for various benchmark returns. The dollar benchmark adjusted economic gains is
calculated using the following equation:
14
Note that if the net holding at the end of day t2 is negative, the market value of the holdings (i.e., Pt2 * net shares)
at the time needs to be included in total purchase to calculate economic gains. For example, consider that a fund
purchases 50 shares of stock A at the price of $1/share at the beginning of the period. In the middle of the period, the
fund sells 100 shares of A at the price of $2/share. The price of stock A drops to $1.5/share at the end of the period.
Assuming no transaction costs or dividends, the total raw economic gains are: 1.5 * (-50) – (1 * 50 – 2 * 100) = 75.
The raw economic gains are calculated as: 75 / (1 * 50 + 1.5 * 50) = 60%.
15
8
ℎ9:
= ∑
ℎℎ × −
∑
( × ℎℎ + −
) × ∏
.7<1 + >,. @,
(3)
where rb,s is the benchmark return on day s. We assume that all trades occur at the end of the
trading day and adjust the opportunity cost starting from the following day. Two benchmarks are
used in the paper: the value-weighted CRSP market portfolio (market adjusted) and the 5 x 5 x 5
equal-weighted size-B/M-momentum benchmark portfolios (style adjusted). Similarly, we divide
the dollar benchmark adjusted economic gains by the total purchases in dollars to obtain the
benchmark adjusted economic gains.
3.3.2. Leakage score
We propose a new measure, labeled as Leakage Score (L-Score), for each BD firm-fund pair
to capture the likelihood of information leakage between the firm and the fund based on the
transactions made by the fund in the [-60,-2] window before the BD announcement. The leakage
score is calculated based on the following intuition: If a fund obtains leaked information earlier
than other investors and reacts quickly, we expect the fund to sell before the public
announcement and in higher volume relative to other investors.15 For fund m and BD stock i:
A − (, ) = ∑F4
25FGH<.($$,*-.3%&(.B,C,2 D>/E,*-.3%&(.B,C,2 @×
∑F4
25FGH<.($$,*-.3%&(.B,C,2 7>/E,*-.3%&(.B,C,2 @
.
(4)
The above expression suggests that L-score is typically negative for funds with leaked
information. When a fund sells instead of buys, the first term in the numerator is positive, but t is
negative. As a result, L-score is negative. The more the fund sells and/or the earlier the fund sells,
15
The leaked information is likely to be short-term, so any strategic trading behavior related to long-term
information is ignored in our calculation. We also recognize that funds will be prompted to capture trading profits
only if the value of leaked information is large enough to overcome the liquidity costs associated with these BD
stocks.
16
L-score becomes more negative. Therefore, L-score is an inverse indicator of information
leakage (i.e., the more negative L-score is, the more likely information is leaked between the BD
firm and the fund).
3.3.3. Other variables
We calculate trading characteristics of sample funds, including round-trip trade percentage,
stock concentration, stock size rank, and BD stock rank, based on the trading in 2005, the year
prior to the BD announcements. We require at least 10 months of trading data in 2005 to
compute these characteristics.
Round-trip trade percentage. When value-relevant private information is short-term in nature,
such as information about the upcoming BD announcement, profitable trading opportunities will
dissipate quickly. Therefore, we expect a fund in possession of this information to make roundtrip trades (i.e., reverse their trading) to lock in economic gains while assuming that this
information does not change the long-term portfolio allocation of the fund. Furthermore, if
round-trip trades occur within a quarter, they cannot be observed in the quarterly holding data.
We take advantage of our intra-day trading data and construct the round-trip percentage measure
for each fund to capture the fund’s tendency to act on short-term information (Bushee, 1998,
2000; Chen et al., 2000; Puckett and Yan, 2011). Round-trip percentage for fund m and stock i is
computed as follows:
Round-trip percentage (m, i) =
+,*(>/E,*-I#$/+(,.($$,*-I#$/+()∗
./+(>/E,*-I#$/+(,.($$,*-I#$/+()
.
(5)
This measure captures the proportion of trading volume that is round-trip. Round-trip
percentage for the fund is calculated as the total trading value (buying and selling value)
weighted fund-stock round-trip percentage.
17
Stock concentration. When a fund is privately connected with some firms and is able to obtain
preferential information about these firms, we expect the fund’s trading to be more likely to
concentrate on these connected firms (Cohen et al., 2008). We construct a stock concentration
measure for each fund to capture this effect. Stock concentration of a fund is calculated similarly
to the Herfindahl index:
(>/E,*-I%$/(C 7.($$,*-I%$/(C )
9
() = ∑*, K∑L
M ,
C56(>/E,*-I%$/(C 7.($$,*-I%$/(C )
(6)
where n is the number of stocks that fund m traded in 2005.
Size rank. Each transaction made by a fund is assigned to a size decile based on the market value
of the stock at the beginning of the trading month using the NYSE breakpoints. Size rank of a
fund is calculated as the trading value weighted size decile of the individual transaction.
BD stock rank. For fund m, we rank all the stocks that fund m traded in 2005 based on the total
trading value and assign a rank between 0 (not equal to 0, least traded) and 1 (most traded) to
each stock. BD stock rank is obtained for each BD stock-fund pair and is equal to the rank of the
BD stock traded by a given fund m. If a fund did not trade a BD stock in 2005, then the BD stock
rank is set to zero. A higher BD stock rank suggests the fund heavily traded the stock and is
expected when the fund has private information on the BD stock.
Panel B of Table 3 presents the characteristics of fund trading defined above. Most of these
characteristics capture how actively the funds trade and thus will be used as control variables in
our regression analysis in subsection 4.3.
18
4. Empirical Results
4.1. Investor trading and economic gains surrounding BD announcements
If the knowledge that a firm backdates its stock options affects institutional investor’s
valuation of the firm, then we expect these investors to lower their holdings in BD stocks once
they become informed about the backdating practice. Using quarterly institutional ownership
data, we analyze the quarterly change in institutional holdings before and surrounding BD
announcements. Untabulated results suggest that institutional ownership of BD stocks decreases
in the BD announcement quarter, a finding also shown in Bernile and Jarrell (2009). However,
whether institutions react to BD announcements or trade before the announcements is difficult to
discern using only quarterly data. In this subsection, we resort to the intra-day institutional
transaction data to understand the trading behavior of our sample funds in the days surrounding
BD announcements.
Table 4 presents buy-sell order imbalances (BSI) for sample funds in various windows of
time near BD announcements. BSI for a given fund and a given BD stock is calculated as net
buying volume (i.e., buy – sell) in the window divided by total trading volume (i.e., buy + sell).
Average BSI is obtained for each BD stock. Cross-stock averages are reported in the table. For
comparison, we also include the BSI of general investors, which is inferred from the TAQ data
based on the Lee-Ready algorithm (Lee and Ready, 1991).
The results in Table 4 suggest that sample funds start to sell BD stocks as early as 60 days
before the announcements while the general investors buy before the announcements. Over the
window of [-60, +1], from 60 days before to one day after the announcement, the BSI for sample
funds is -7.14% and that for the general investors is 2.07%. The difference is statistically
19
significant. This finding is consistent with our conjecture that institutional investors have early
access to private information and trade before public announcements to leverage their
information advantage.
Next, we investigate the economic gains of the trades conducted by sample funds in the
window of [-60, +1] days relative to the BD announcements. 16 Raw economic gains, CRSP
market return adjusted (market-adjusted) economic gains, and the equal-weighted size-B/Mmomentum portfolio return adjusted (style-adjusted) economic gains are calculated for each BD
stock-fund pair. Cross-fund averages are obtained for each BD stock. Cross-stock averages and
the corresponding t-values are reported in Table 5. Similarly, we include the economic gains of
general investors (inferred from the TAQ dataset) for comparison.
Table 5 shows that trades made by sample funds generate significantly positive economic
gains. Raw, market-adjusted, and style-adjusted economic gains are 1.14%, 0.99%, and 0.92%,
respectively and are all statistically significant. Trades by the general investors, however, lead to
significantly negative economic gains (i.e., economic losses) in the same window regardless of
the economic gain measure used. Note that the computation of economic gains for the general
investors does not include transaction costs as we do not have access to such information. Even
excluding transaction costs, the performance of the general investors is still poorer than that of
the sample funds.
To summarize, Tables 4 and 5 provide evidence that sample funds sell BD stocks prior to
BD announcements and generate significantly positive economic gains from their trading. The
evidence is consistent with some institutional investors having early access to private
16
The significance of order imbalance starts from the window [-60, -41], so we use the window [-60, +1] in the
calculation of economic gains. We test the robustness of our results using an alternative window [-40, +1] in
subsection 4.4.
20
information before public disclosure of BD investigations and benefiting from this preferential
information.
4.2. Leakage score
In the previous subsection we provide evidence in support of the information leakage
hypothesis for sample funds on average. However, the probability of obtaining private
information for a given stock varies across funds. We calculate the leakage score (L-score) for
each fund-stock pair to capture the likelihood of information leakage based on trades conducted
in the window from 60 days to two days prior to BD announcements. Our L-score has two
characteristics. First, recall that L-score tends to be an inverse indicator of information leakage.
If a fund sells a BD stock in the window, L-score is negative. A lower L-score (i.e., sell more
and/or sell earlier) indicates a higher likelihood of information leakage. Note that it is possible
for a fund to have a low L-score on one BD stock but a high L-score on another BD stock.
Second, we posit that L-score also reflects the private connection between funds and BD firms.
The more closely a fund manager is connected with a BD firm, the more likely it is for him to
obtain private information about the firm such as the upcoming BD revelation and to trade prior
to the public announcement, therefore the lower the L-score.
We analyze economic gains for different levels of L-score and the results are reported in
Table 6. For each BD stock, we sort all funds that trade this stock in the event window into three
groups based on L-score. The descriptive statistics of L-score and average economic gain for
each L-score group are reported in the table. The average L-score for the low group is -36.14,
suggesting that on average funds in this group start to sell BD stocks 36.14 days before public
announcements. The L-scores for the medium and the high groups are -3.18 and 30.93,
respectively. Not surprisingly, economic gains for the low L-score group are the highest (raw,
21
market-adjusted, and style-adjusted economic gains are 10.03%, 8.96%, and 8.65%,
respectively), and those for the high L-score group are lowest and all negative (raw, marketadjusted, and style-adjusted economic gains are -7.21%, -6.49%, and -6.35%, respectively). The
difference in economic gains between the low L-score group and the medium L-score group and
that between the low L-score group and the high L-score group are both statistically significant
(untabulated). These results suggest that a higher likelihood of information leakage (i.e., lower
L-score) is associated with greater economic gains.
4.3. Private connections
A low L-score between a fund and a BD firm suggests that the fund trades well ahead of
other investors prior to the BD revelation. This is consistent with the hypothesis that the fund and
the firm are privately connected and that information is leaked through the private channels. The
next interesting questions are: Are these connections stable? Is information persistently leaked
through the connections?
We use three different approaches to investigate this issue. Our first approach is to examine
whether funds actively trading on BD stocks during 2005 (the pre-announcement year) are more
likely to have a low L-score (i.e., high likelihood of leakage). Cohen et al. (2008) show that
funds are more likely to place larger bets on firms with whom they are connected. Therefore, it
is reasonable to expect that funds tend to trade more actively the stock of connected firms. We
use BD stock rank to proxy how actively a fund trades a BD stock. We then conduct crosssectional regression with L-score being the dependent variable for each BD stock. Cross-BD
stock average coefficients and the corresponding t-values are reported in Table 7.
Table 7 shows that higher BD stock rank is significantly negatively associated with L-score,
which is consistent with our conjecture. Relative to a fund that does not trade a given BD stock
22
during 2005, the fund that heavily trades the stock tends to sell nearly eight days earlier in the
BD scandal. We also include some other fund trading characteristics in the regression. Test
results are also consistent with our intuition. Round-trip trade percentage is negatively related to
L-score, suggesting that funds trading on short-term information are more likely to obtain the
leaked information about BD announcements. In addition, funds with higher stock concentration
(i.e., funds more likely to be connected with firms) are more likely to have a low L-score. In
terms of stock size, there is no clear evidence which type of stocks (small or large) that funds
with access to leaked information tend to trade, which is also consistent with what we observe in
the BD revelation.17
Our second approach is to explore whether funds tend to obtain preferential information
from connected firms before public disclosure of other major corporate events. As discussed in
the introduction section, institutions and firms have incentives to build and maintain private
connections with each other for mutual benefit. For institutions specifically, their primary
objective is to seek an investing edge on a continuing basis. The level of information asymmetry
between insiders and the public is believed to be higher before major corporate events. Therefore,
private information is more likely to be leaked during this period. To test this conjecture, we
consider the case of earnings announcements and examine the trading behavior of sample funds
before quarterly earnings announcements of BD stocks from 2002 to 2010, excluding 2006. We
expect a fund to trade in the same direction as earnings surprise when the fund and the stock are
connected.
Table 8 reports the results. The dependent variable is the pre-announcement trade measured
by the net purchase (scaled by shares outstanding) of a BD stock by a given fund from the last
17
The backdating sample includes both small and large firms. For either type, there is evidence that some funds
trade ahead of others.
23
fiscal quarter-end to two days prior to quarterly earnings announcements. Earnings surprise (UE)
is calculated as the difference between the actual earnings announced and the most recent mean
analyst forecast scaled by quarter-end stock price. Med L-score D (High L-score D) is an
indicator variable, equal to one when the L-score falls in the medium (high) tercile (as defined in
subsection 4.2), and zero otherwise. Market value (MV), book to market ratio (B/M), and 6month stock cumulative return are obtained as of the last fiscal quarter-end prior to earnings
announcements and are included as control variables. Quarter-fund fixed effects are included in
the regression.
The results confirm that funds trade privately connected stocks in the same direction as
earnings surprises prior to earnings announcements as shown by the significantly positive
coefficient of UE (3.82 with t-value = 4.09 for the full sample period). For non-connected stocks
(medium or high L-score), no clear pattern is identified. The coefficients for medium and high Lscore group are -0.06 (= 3.82 - 3.88) and -0.14 (= 3.82 - 3.96), respectively and none are
statistically significant. We then divide the full sample period into two sub-periods, pre-scandal
(2002–2005) and post-scandal (2007–2010),18 and run the same regression in each sub-period.
The comparison of patterns between the two sub-periods is instructive, because it shows that the
significantly positive relation between UE and pre-announcement trades only exists for
connected stocks (low L-score) in the pre-scandal period. Thus, it appears that BD revelation
weakens the private fund-firm connection and as a result, information leakage became less likely
to occur.
The findings presented above are consistent with information leakage hypothesis rather than
monitoring hypothesis. Under the latter hypothesis, institutional investors would expend effort to
18
We exclude year 2006 because it is the event year.
24
acquire firm-specific information and monitor firm activities on an ongoing basis. Investors’
demand for public information can be reflected in their search activities through channels such as
the Internet. For example, Drake, Roulstone, and Thornock (2012) employ novel data to show
that abnormal Google search increases about two weeks prior to earnings announcements. In our
setting of unexpected BD revelation, the monitoring hypothesis cannot explain the null relation
between pre-earnings announcement trading and UE for medium and high L-score groups nor
can it justify the different results in pre- and post-scandal periods because there is no clear reason
why fund managers’ information search abilities would change dramatically after the scandal.
Our last approach is to compare fund trading performance on connected stocks versus nonconnected stocks. We expect that funds consistently perform better on connected stocks than on
non-connected stocks because they have superior access to corporate insiders for private
information. Following the same procedure as described in the previous subsection, for each BD
stock, we rank L-score into three groups. When the L-score falls into the lowest tercile, we
classify this fund-stock pair as closely connected. All other fund-stock pairs are classified as
non-connected. In Table 9, we report performance differences between connected and nonconnected stocks in the full sample period, pre-scandal, and post-scandal periods, respectively.
Funds that trade both connected stocks and non-connected stocks are included in the test.19
For the full sample period, we find that the difference in economic gains between connected
stocks and non-connected stocks is significant no matter which economic gain measure is used.
Sub-period tests indicate that this significance mainly comes from the pre-scandal period.
During 2002-2005, the difference in raw economic gains is 1.99% at the 1% significance level.
19
We have also included non-BD stocks in these tests. It is possible that a firm is privately connected with some of
the non-BD firms. Classifying such stocks as non-connected tends to work against finding significant results in our
tests.
25
After adjusting for benchmark returns, the differences remain significantly positive. Following
the backdating revelation, however, this pattern changes dramatically. During the post-scandal
period of 2007-2010, economic gains for connected stocks and those for non-connected stocks
are no longer significantly different from each other across all three measures of economic gains.
This finding, again, indicates that the backdating revelation either cuts off the private fund-firm
connections or, at the minimum, reduces the likelihood of information leakage through private
network.
Taken together, the results in both Tables 8 and 9 suggest that empirical evidence is
consistent with the information leakage story. The revelation of option backdating is a shock to
the private connection but it is not a shock to the trading skills of fund managers. Therefore, the
change of trading patterns and economic gains after the scandal is unlikely due to the change of
management ability.
4.4. Additional Analyses
4.4.1. Timing of BD announcements
Although the first article highlighting the backdating scandal appeared in the Wall Street
Journal in November 2005, it was not until 2006 that BD announcements for all our sample
firms were made. The clustering of the BD revelations may raise the concern that some
institutional investors may have learned from those firms that announced BD earlier and, as a
result, traded on other potential BD firms who have not yet made such announcements. The
argument implies that buy-sell order imbalances related to the late announcers may have been
driven by investor responses to early announcements rather than leaked information regarding
the late announcers. To address this concern, we separate our BD stock sample into two subsamples. Half of our sample firms announced BD news before June 19, 2006, which we call
26
early announcers. The other half, which we call late announcers, announced BD news after June
19, 2006. If the above argument holds, one would expect the differences in buy-sell order
imbalance and economic gain between institutional investors and general investors (TAQ) to
exist only for late announcers or at least be stronger for late announcers than for early
announcers.
We repeat our analyses reported in Tables 4 (order imbalance) and 5 (economic gain) for the
two sub-samples separately. We find that all the results are robust to both sub-samples. The
differences in buy-sell order imbalance and economic gain between institutional and general
(TAQ) investors exist for both BD stock sub-samples (results are untabulated but available upon
request). Specifically, the differences in the buy-sell order imbalance between institutional and
general investors is -8.71% (t = -3.11) for the early announcers and -9.71% (t = -3.24) for the late
announcers. The difference in style-adjusted economic gains between institutional and general
investors is 1.39% (t = 3.33) for the early announcers and 1.10% (t = 2.61) for the late
announcers. These results further confirm that institutional investors sell earlier and gain more
relative to general investors regardless of the timing of BD announcements.
4.4.2. Estimations over alternative windows around BD announcements
In our main analysis, we choose windows [-60, +1] to estimate the economic gains. We
repeat our analysis on economic gains based on an alternative window [-40, +1]. The results
(untabulated) are robust. Institutional investors make significantly more profit than general
investors. For example, the difference in style-adjusted economic gains between institutional
traders and TAQ traders is 0.75% (t = 2.24).
27
5. Concluding Remarks
Taking advantage of an unexpected significant corporate event, namely the public revelation
of stock-option backdating, and a proprietary intraday institutional trading database, we examine
whether information leakage occurs between institutional investors and backdating firms. We
also investigate whether information is repeatedly leaked through these channels due to the
persistent institution-firm connections.
Our empirical results show that some institutions sold shares of backdating stocks prior to
the public revelation of backdating and, as a result, wealth is transferred from other investors to
these early-informed institutions. Further analyses suggest that information is persistently
disseminated through private connections. Specifically, we find that funds actively trading on
backdating firms in the year prior to the revelation are more likely to benefit in the event.
Moreover, funds benefiting in the backdating revelation tend to gain from their trades prior to
earnings announcements of connected firms, and funds consistently perform better on trades of
connected firms than on those of non-connected firms. These results disappear after the scandal,
consistent with the argument that the backdating revelation is an external shock to the private
connections.
The SEC has been expanding the investigations of so-called expert network where insiders
with access to private information are hired as consultants by hedge funds or mutual funds.
Information leakage through such networks is a recent phenomenon and considered by some as
an unintended consequence of regulations such as Regulations Fair Disclosure. Our large-sample
evidence supports the ongoing investigations of the SEC of insider trading involving expert
network. If information leakage is associated with many mini-networks, then what has been
revealed is just the tip of the iceberg. The unfair wealth transfer would erode the confidence of
28
investors in the capital markets. In that sense, our study indicates that future efforts should be
made to develop more effective regulations by paying attention to the hidden leakage channels in
the connected world.
29
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Appendix 1
The Effect of Expert Network on Hedge Fund’s Performance: The Case of Galleon
Based on the evidence documented in our main analysis, we infer that information leakage is
likely attributable to private connections in the setting of an unexpected firm-specific event. We
draw this inference because we find that funds with private connections consistently outperform
other funds across different information events such as backdating revelations and earnings
announcements. We also find that these funds consistently perform better on trades of connected
firms than on those of non-connected firms. We believe that the connections implicitly inferred
from a fund’s trading behavior tend to be more comprehensive than other explicit forms of
connections such as school ties or social interactions. While one may argue that a limitation of
the above analysis is that the connection is inferred ex post rather than observed ex ante, in this
Appendix, we conduct a case study on the Galleon hedge fund to further strengthen our analysis.
The Galleon case is considered to be one of the biggest insider trading cases in U.S. history.
It provides a perfect example of information leakage within an explicitly identified private
network. Galleon’s manager, Raj Rajaratnam, built a network through which he obtained and
used private information to trade and consequently made significant illicit gains during the
seven-year period (2002–2008) before he was arrested in 2009. We use this special case to verify
two of our assertions: (1) Galleon outperformed other institutions on the connected firms; (2)
Among its own portfolios, Galleon performed better on the connected firms than on nonconnected firms. The advantage of the case study is that we are able to explicitly identify the
firms who are privately connected with Galleon and document the subsequent wealth transfer
effect. The limitation is that we have to use quarterly data to conduct the analysis because we are
unable to identify Galleon in our intra-day institutional trading database.
34
Based on the SEC’s investigations, the Wall Street Journal identified 16 companies that
were connected directly or indirectly to Raj Rajaratnam. Figure A represents his network of
connections. The square icons stand for the 16 firms connected to Galleon. These firms include
IBM, Intel, P&G, etc. Table A presents the trading performance of Galleon versus other funds on
stocks of these 16 connected firms (Panel A) and the trading performance of Galleon on the
stocks of the 16 connected versus other non-connected firms (Panel B). Economic gain (EG) is
calculated for each fund-stock pair for each year in the seven year period (2002–2008) during
which Raj Rajaratnam was convicted for engaging in conspiracy to trade on inside information.
All calculations of EG are based on 13F institutional holdings data but in the spirit of the EG
calculation explained in subsection 3.3.
In Panel A, the mean and median of EG for other institutions are computed for each
connected stock and each year. Performance differences and corresponding t-values are reported.
The results suggest that Galleon outperformed other institutions on trades of the 16 connected
stocks. In Panel B, EG is computed for each stock that Galleon traded each year. The average EG
as well as the difference between connected and non-connected stocks is reported. The results
suggest that Galleon performed better on trades of the 16 connected stocks than on those of nonconnected stocks. Both results are consistent with our argument that information leakage through
private network results in economic gains for institutional investors who are closely connected
with the firms in which they invest. Galleon is thus a special case supporting our conclusion that
information leakage occurs from private networks and leads to wealth transfer.
35
Appendix 1 (Cont.)
Figure A: Galleon’s Information Leakage Network (Source: Wall Street Journal).
36
Appendix 1 (Cont.)
Table A: Economic Gain of Galleon
*, **, ***
indicate significance levels of 10%, 5%, and 1% (one-tailed), respectively.
Panel A. Galleon vs. other institutions—performance on the 16 connected stocks
Period: 2002–2008
Raw-EG
N = 81
mean
Mkt-adj EG
N = 81
median
mean
Style-adj EG
N = 69
median
median
Galleon
6.43%
Other institutions
2.99%
0.57%
2.11%
0.17%
-0.34%
-1.58%
Difference
3.43%
5.86%
1.98%
3.91%
4.17%
5.57%
t-value
1.77
**
4.09%
mean
2.42
***
1.29
*
3.67%
2.05
**
1.62
*
1.88**
Panel B. Galleon’s performance on the 16 connected vs. non-connected stocks
Period: 2002–2008
Raw-EG
Est.
N
Mkt-adj EG
Est.
N
Style-adj EG
Est.
N
Connected stocks
6.43%
81
4.09%
81
3.67%
69
Non-connected stocks
1.32%
9,110
-0.38%
9,110
-0.38%
6,948
Difference
5.10%
t-value
1.64
**
4.47%
1.56
*
4.04%
1.46*
37
Table 1: Summary Statistics
The table presents summary statistics of our sample consisting of 126 backdating (BD) firms as of the end
of 2005. We only include those firms on which at least five funds traded in our event window [-60, +1].
Mean
Median
Q1
Q3
Firm Characteristics (126 BD firms)
Size ($ millions)
BM ratio
7,640
0.40
1,617
0.30
809
0.21
5,541
0.48
Annual stock turnover
3.47
2.80
1.91
4.53
Return from month t-12 to t-1
0.111
0.034
-0.180
0.313
Return from month t-7 to t-1
0.124
0.067
-0.077
0.251
38
Table 2: Cumulative Abnormal Returns Around Backdating News Announcements
This table presents the cumulative abnormal returns around backdating news announcements in different
windows for the full sample of 126 BD firms. *** indicates a significance level of 1%.
Days to BD
announcement
Mkt-adj
CAR
t-value
Size-adj
CAR
t-value
Size/BM/MMTadj CAR
t-value
[-60,-41]
-0.0078
-0.94
-0.0095
-1.17
-0.0105
-1.23
[-40,-21]
-0.0001
-0.01
-0.0002
-0.02
0.0014
0.15
[-20,-2]
-0.0576
-5.71***
-0.0503
-5.04***
-0.0512
-5.26***
[-1, +1]
-0.0217
-3.87***
-0.0228
-4.21***
-0.0245
-4.63***
0.0052
0.58
0.0071
0.82
0.0096
1.08
[2,20]
39
Table 3: Trading Characteristics of Sample Funds
Panel A reports the summary of the trades of BD firms by sample funds over the [-60, +1] event window
around BD announcements. Panel B reports summary statistics of fund trading characteristics in 2005.
Panel A: Summary of the trades of BD firms
# BD firms traded / fund
Value traded / fund ($ millions)
# Funds / BD firm
Volume traded by funds / total
trading volume (%)
Panel B: Characteristics of fund trading in 2005
# stocks traded
Total trading value ($M)
Stock concentration
Round-trip percentage
Size rank
Average BD stock rank
Mean
8.8
59.88
67
Median
4.0
2.00
58
Q1
2.0
0.49
32
Q3
9.0
7.22
88
4.67
4.17
2.51
5.59
290
2,924
0.017
0.360
7.582
0.383
141
128
0.013
0.371
8.449
0.402
81
49
0.008
0.242
5.805
0.233
250
435
0.020
0.477
9.386
0.520
40
Table 4: Trading Behavior of Institutional Investors and General Investors
This table compares the Buy-Sell Order Imbalance (BSI) of sample funds with the BSI of general
investors (TAQ) in the different windows around BD announcements. Day “0” is BD announcement day.
Sell or buy transaction is either from institutional trading database (Ancerno) or estimated from intraday
TAQ data using Lee and Ready algorithm (Lee and Ready, 1991). BSI (in percentage) is the difference
between buy and sell volume divided by the sum of these two. Institutional BSI for a BD stock is the
average BSI of all funds which trade the stock. The sample only consists of those BD stocks with both
Ancerno and TAQ data available. *, **, *** indicate significance levels of 10%, 5%, and 1%, respectively.
Buy-Sell Order Imbalance (BSI)
Windows
[-60,-41]
[-40,-21]
Inst. trade
-2.97%
-2.64%
TAQ
2.54%
2.37%
Difference
-5.51%
-5.00%
t-value
-2.22**
-1.79*
[-20, -2]
-1.10%
1.20%
-2.30%
-0.83
[-1, +1]
-4.75%
0.12%
-4.87%
-1.38
[-60,+1]
-7.14%
2.07%
-9.21%
-4.50***
[-60, -2]
-6.19%
2.23%
-8.42%
-4.06***
41
Table 5: Economic Gain of Institutional Trading
This table compares economic gain of institutions and general investors for their trading on BD stocks in
window [-60, +1]. Only BD stocks with at least five funds trading are included in the sample. Economic
gain is the profit from per purchasing dollar traded, i.e., total economic gain divided by total purchasing
value traded within the window by each fund and then the gain is averaged across funds and BD stocks.
Total economic gain is calculated following De Franco et al. (2007). *** indicate significance levels of 5%
and 1%, respectively.
Raw
Inst. trade
TAQ
Difference
t-value
Market-adj
***
0.99%
-0.34%
-3.14***
1.48%
4.38***
1.14%
3.66
Size/BM/MMT
-adj
t-value
t-value
***
0.92%
3.44***
-0.37%
-3.57***
-0.33%
-3.32***
1.36%
4.30***
1.25%
4.22***
3.38
42
Table 6: Leakage Score (L-Score) and Economic Gain
This table reports the descriptive statistics of the leakage score (L-score) and economic gains (EG) for
funds with low, medium, and high L-score. L-score is defined as follows for each fund (m)-stock (i) pair:
A − score(m, i) = ∑F4
`5FGH<UVWWXYZU[\]VU^,_,` DabcXYZU[\]VU^,_,` @×d
F4
∑`5FGH
<UVWWXYZU[\]VU^,_,` 7abcXYZU[\]VU^,_,` @
.
Funds trading the same BD stock are ranked into low, medium, and high groups based on the value of Lscore. Average L-score is obtained for each BD stock.
L-score rank
L-score
Economic Gain
Raw
Mkt-adj
13.06
10.03%
8.96%
Size/BM/MMTadj
8.65%
-3.60
13.71
0.43%
0.14%
0.19%
30.14
13.87
-7.21%
-6.49%
-6.35%
Mean
Median
Std Dev
Low
-36.14
-36.00
Medium
-3.18
High
30.93
43
Table 7: Leakage Score (L-Score) and Fund Trading Characteristics
This table examines the association between leakage score and fund characteristics. Cross-sectional OLS
regression is conducted for each BD stock with L-score being the dependent variable. Cross-BD stock
average coefficients and the corresponding t-values are reported. We require at least seven observations
for a cross-sectional regression. All independent variables are computed based on fund trades in 2005 and
we only include funds with at least 10 months of trading data in 2005. The definition of the independent
variables can be found in subsection 3.3. *, **, *** indicate significance levels of 10%, 5%, and 1%,
respectively.
Estimate
t-value
-7.51
-11.85
-3.36***
-1.97**
-406.70
-1.86*
Size rank
-0.16
-0.32
Avg. R-sq.
0.18
Number of BD stocks
119
BD stock rank
Roundtrip
Stock concentration
44
Table 8: Information Leakage and Institutional Trading Before Earnings Announcements
This table presents the results from a panel regression examining the relation between institutional trading
before earnings announcement and earnings surprises. The dependent variable is the net purchase of a BD
stock by a given fund over the window from the last fiscal quarter-end to two days prior to earnings
announcement. Net purchase is scaled by the number of shares outstanding. The results are reported for
the full sample period (2002–2010, excluding 2006), pre-scandal (2002–2005) and post-scandal (2007–
2010) periods. The sample consists of all quarterly earnings announcements for 126 BD firms in the
sample period. UE is calculated as the difference between the actual earnings announcement and the most
recent mean analyst forecast scaled by quarter-end stock price. Med L-score D (high L-score D) is a
dummy variable, equal to one if the L-score of the fund-stock pair is in the medium (high) tercile, and 0
otherwise. Market size, B/M, and 6-month cumulative stock returns are computed as of the last fiscal
quarter-end prior to earnings announcements. *, **, *** indicate significance levels of 10%, 5%, and 1%,
respectively.
UE
UE * Med L-score D
UE * High L-score D
log(MV)
log(B/M)
CR (-6,-1)
Fixed Effects
R-Sq (%)
N
Full Sample Period
(2002–2010, excluding 2006)
Coeff
t-value
3.82
4.09***
-3.88
-3.56***
-3.96
-3.89***
-0.02
-0.00
0.12
-1.46
-0.18
2.45**
qtr x fund
8.72
43,489
Pre-Scandal Period
(2002–2005)
Coeff
t-value
9.71
5.17***
-5.01
-0.87
-9.22
-3.38***
-0.06
0.03
0.13
-3.67***
1.02
1.96**
qtr x fund
8.95
20,341
Post-Scandal Period
(2007–2010)
Coeff
t-value
1.64
1.57
-1.79
-1.52
-1.82
-1.62
0.02
-0.01
0.05
1.66*
-0.58
0.77
qtr x fund
8.56
23,148
45
Table 9: Trading Performance Comparison Between Connected and Non-connected Stocks
This table reports the difference in the performance of funds trading on connected (i.e., low L-score)
stocks vs. non-connected (i.e., medium and high L-scores) stocks in the full sample period (2002–2010,
excluding 2006), pre-scandal period (2002–2005), and post-scandal period (2007–2010). *** indicates
significance level of 1%.
Sample Period
Difference in
Raw EG (%)
Estimate t-value
Full Sample Period
Difference in
Mkt-adj EG (%)
Difference in Size
/BM/MMT-adj EG (%)
Estimate
t-value
Estimate
t-value
1.16
3.95***
0.83
3.48***
0.76
3.18***
1.99
5.54***
1.27
4.02***
1.21
3.91***
0.35
0.72
0.38
1.04
0.26
0.69
(2002–2010,
excluding 2006)
Pre-Scandal Period
(2002–2005)
Post-Scandal Period
(2007–2010)
46