Shadow Trading:
Do Insiders Exploit Private Information About Other Firms?
Mihir N. Mehta
Temple University & MIT Sloan
David M. Reeb*
National University of Singapore
Wanli Zhao
Southern Illinois University
June 10, 2014
ABSTRACT
US regulations prohibit a firm’s employees from exploiting their private information
about the firm and mandate that senior executives report their trades in their firm. Private
information, however, often exhibits spillover effects and implications for a firm’s customers,
suppliers, and competitors. We label the potential opportunities for employees at various levels
of the firm (source firms) to profitably trade or disseminate private information about their
business partners or competitors (target firms) as shadow trading. Our evidence indicates that
target firms experience a 43% to 77% increase in symptoms of informed trading activity prior to
the release of private information by a business partner or competitor. Furthermore, although
business partners and competitors both exhibit similar return correlations with source firms,
symptoms of shadow trading occur more extensively in business partners than in competitors.
Additional tests reveal that shadow trading symptoms arise more readily when source firms (1)
have no explicit policy restricting shadow trading by their employees; (2) employ CEOs with
poorly diversified portfolios; or (3) internally promote their CEOs. Finally, we document an
increase in shadow trading after increased regulatory attention on “conventional” insider trading.
In sum, this analysis provides evidence inconsistent with the notion that shadow trading is an
inconsequential concern.
Key Words: Business Partners; Informed Trading; Private Information; Supply Chain
JEL Codes: D4, G14, K22
_____________________
*Corresponding Author. NUS Business School, MRB BIZ1 07-65. National University of Singapore, Mochtar Riady
Building, BIZ1 Level 7, 15 Kent Ridge Drive, Singapore 119245. Email: [email protected]. We thank Renee
Adams, Ron Anderson, Sudipta Basu, Larry Brown, Steve Dimmock, Huasheng Gao, Gerald Hoberg, Steve
Huddart, Angie Low, Randall Morck, Lalitha Naveen, Srini Sankaraguruswamy, Sheridan Titman, Bernie Yeung, and
seminar participants at the 2013 Singapore Scholars Symposium, Hong Kong University of Science and Technology,
and Temple University for helpful comments. Mehta and Reeb acknowledge financial support from the Fox School
of Business Young Scholars Fund and the Singapore Ministry of Education AcRF Tier 1 research fund respectively.
1. Introduction
The U.S. Department of Justice routinely seeks to prosecute managers for trading in their
own-firm stocks or for breaching their fiduciary trust by leaking valuable information to
outsiders. Yet, corporate events often exhibit spillover effects and implications for related firms,
creating opportunities for employees with stakeholder-relevant private information (“source
firm” insiders) to profitably make trades in, or leak information about, their business partners or
competitors (“target firms”). Government regulations prohibit the “tipping” of material, nonpublic information, and also require a firm’s senior employees to report their trades in their own
firms but these disclosure rules do not apply to other employees, or for trading activity in a
firm’s business partners (suppliers and customers) or competitors. Disparities in government
scrutiny of insider trading provide incentive for a firm’s employees to exploit their private
information about business partners or competitors by trading or leaking the information to
other investors, activities that we term “shadow trading”.
US regulations penalize illegal insider trading but fail to explicitly define it, relying on
case law and targeted prosecutions to deter such activity (Dolgopolov, 2012). With limited
prosecutions against shadow trading, source firm employees arguably face low detection, few
penalties, and limited deterrence against trading in their business partners or competitors.
Moreover, source firm employees may rationalize shadow trading based on its lack of an explicit
definition, regulatory restrictions on own-firm trades, and regulatory burdens to only disclose
own-firm trades. Yet, US regulatory policy suggests that shadow trading remains a minor
concern, with a substantive focus on direct insider trading. Disclosure requirements for officers
and directors of the firm only apply to own firm trades and implicitly exclude trades in the firm’s
business partners or competitors, or the leakage of information about these stakeholders. The
potential for the immateriality or irrelevancy of shadow trading activity provides a likely
explanation for this limited regulatory attention.
Publicly traded firms often provide extensive codes and rules on trading based on private
information from the firm or its business partners. In a broad sub-sample of US publicly listed
firms that publicly disclose their corporate ethics and rules, we find that over half of the sample
explicitly prohibits shadow trading by their managers and employees. The other firms mainly
provide general proscriptions against conventional insider trading. Thus, extensive overall
1
regulatory scrutiny, broad internal company proscriptions, and the coarse nature of private
information regarding business partners and competitors suggest that shadow trading could
remain an inconsequential concern. 1 In this context, it is possible that regulators do not require
managers to disclose shadow trading because insiders rarely engage in such avaricious activity.
We explore whether information spillovers represent an important, undocumented, and
material channel that affects trading activity. We use the term “source firm” to denote the
company from which private information potentially emerges, while “target firm” refers to the
business partner or competitor for whom the private information could be price relevant. Our
research strategy to examine shadow trading employs two distinct steps: 1) establish whether
information leakage from source firms affects trading in target firms; and 2) determine if this
leakage appears attributable to source firm employees. In the first step, we identify situations
where it appears feasible to accurately attribute abnormal trading activity in a target firm to an
affiliated source firm’s private information. In the second step, we determine if source firm
employees plausibly serve as an origin of this information leakage by exploiting cross-sectional
variation in the costs and benefits of shadow trading. We identify four distinct situations where
source firm employees face differing shadow trading costs that should not influence trading
activity by sophisticated market traders or by target firm insiders.
Our primary tests exploit a unique dataset from Standard & Poor’s Capital IQ database
that allows us to precisely identify firm-specific material business partners (customers and
suppliers) and competitors for a large sample of US firms. The firm-specific supplier data allows
us to overcome limitations inherent in reverse engineering the supplier data from material
customer data disclosures available in Compustat. Rather than focusing on all suppliers and
customers, we isolate a subsample that consists of target-source firm pairs in which the target
firm only has one source firm that makes an earnings announcement during a given window.
While this requirement significantly reduces the sample size, it facilitates identification by limiting
1 In a rare case against shadow trading, in 2011 the SEC brought action against employees of Flextronics for leaking
information about business partner component orders (including Apple and Research in Motion) to hedge fund
managers. In a related case, the SEC and DOJ initiated enforcement actions against mid-level managers for leaking
information about their business partners to Primary Global Research LLC, an expert-network firm. Two associated
indictments against insider trading using confidential business partner data include actions against executives at
Advanced
Micro
Devices
and
Taiwan
Semiconductor
Manufacturing
Company
(http://www.sec.gov/news/press/2011/2011-38.htm).
2
the sources of potential private information, providing clearer attribution as to the information
source.
The intuition of our test strategy is straightforward: if a source firm insider has private
information about an own-firm forthcoming earnings shock, two avenues for monetization prior
to the release of this information to the public seem plausible. First, the source firm employee
with private information could trade in their own firm, for instance, by short selling the stock in
the case of an impending negative earnings shock. Second, the employee could leak information
or trade in a business partner’s or competitor’s stock (shadow trading). We focus on the latter
case and examine whether private information held by a source firm employee is related to
abnormal informed trading in target firms. 2
While it is possible that our results are affected by investing activities as a result of
information production by unrelated market participants, we find that stock return correlations
between source firms and their business partners (competitors) is .33 (.37). The similar return
correlations across stakeholder groups suggests market participants have incentives to actively
follow both types of affiliated stakeholders. Furthermore, given the profusion of industry based
market pundits and the return correlations, one would expect that information production
focuses on a firm’s competitors, implying that symptoms of shadow trading in competitors
should be at least equal to or possibly greater than that for business partners. In contrast, our
insider-trading arguments suggests higher quality firm-specific information results in greater
shadow trading in business partners rather than source firm competitors, providing a differential
predication than expected if our results are attributable to external sources of information
production.
We test for the presence of shadow trading using two proxies of informed trading
commonly used in the literature on private information based trading - abnormal short sales and
abnormal Amihud (Desai et al., 2002; Christophe et al., 2004; Diether et al., 2009; Anderson et
An example from our sample provides a simple illustration of this finding: On May 4, 2010, The Proctor &
Gamble Company (NYSE: PG) announced unexpectedly low quarterly earnings (based on both earnings
expectation models and analyst consensus forecasts). Sally Beauty Holdings Inc. (NYSE: SBH), a material customer
for Proctor & Gamble, experienced a 25% increase in short sales in the 30-day window prior to the date of Proctor
& Gamble’s earnings announcement. Sally Beauty Holdings did not experience any single-day stock price shocks
during that 30-day window, did not provide any new disclosures, and nor did they have any other material suppliers
(i.e., for which Sally Beauty Holdings is a material customer) release earnings information during that window.
2
3
al., 2012). The univariate tests provide evidence consistent with the presence of shadow trading.
Specifically, we find that abnormal short selling in a target firm increases (decreases) by 23.5%
(5%) in the 30-day window leading up to a business partner’s future negative (positive) quarterly
earnings announcement shock. For the negative earnings surprise sample, we find that suppliers
(customers) of the target firm experience a 15.8% (35.6%) increase in abnormal short selling.
Hence, prior to the public disclosure of an earnings announcement shock in a source firm, we
observe symptoms of informed trading in that source firm’s customers and suppliers. We also
find evidence of abnormal trading activity in a source firm’s competitors prior to the source
firm’s public disclosure of its private information. Specifically, we find source firm competitors
experience a 5% increase in abnormal short selling prior to a negative earnings announcement
shock from the source firm.
Multivariate test results are consistent with the univariate findings: abnormal short sales
in target firms are statistically and economically related to the magnitude of future earnings shocks
from both competitor and business partner source firms. In total, target firms experience more
than double the increase in abnormal short selling preceding negative earnings shocks from
business partner source firms than from competitor source firms, suggesting extensive shadow
trading. Our analysis also indicates that the abnormal short selling preceding negative earnings
shocks from business partner stakeholder firms occurs across both upstream (customer) and
downstream (supplier) business partners. Furthermore, target firms sustain less abnormal short
selling prior to positive earnings surprises from business partners than from competitor source
firms. The results are robust to the use of the abnormal Amihud proxy to identify shadow
trading. We also find that the magnitude of informed trading sustained by target firms is
increasing in tandem with the size of the source firm’s earnings shock. Target firm abnormal
short sales (abnormal Amihud) are 22 (3.7) times more sensitive to business partner source firm
shocks than to competitor source firm shocks. These differences in magnitude between business
partner and competitor symptoms of informed trading appear inconsistent with the notion that
experienced market participants deduce this information.
Our next series of tests focus on identifying whether the documented results above are
attributable to information leakages by source firm employees. First, we examine whether
shadow trading varies with cross-sectional differences in firm-level restrictions on shadow
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trading among source firms. Using a sub-sample of firms with publicly-disclosed internal insider
trading policies, we compare the presence and magnitude of shadow trading across firms with
and without explicit restrictions on shadow trading. As the public disclosure of corporate rules
represents a conscious choice made by a firm, our tests do not allow us to draw inferences about
the veracity of such rules. Yet, this empirical test allows inferences about the role of source firm
employees in the documented abnormal trading of their business partners. We find that the
subsample of source firms that do not explicitly ban trading in the firms’ stakeholders display
approximately four times more shadow trading than firms with bans on such activity.
Our second test exploits a shock to insider trading regulations in Section 32(a) of the
Securities Exchange Act of 1934 as amended by the Sarbanes-Oxley Act of 2002, which
increased the disclosure requirements for insider trades and the magnitude of the penalty for
“willful” violation of the act and regulations. 3 We examine the prevalence of shadow trading
around the regulatory change using Compustat data for customers and suppliers and Hoberg and
Phillips (2010a, 2014) competitor data that allow for tests over a longer horizon (but with less
precise supplier data). We find evidence of a significant increase in shadow trading activity
following these regulatory changes that increased government scrutiny of conventional “insider
trading” of own-firm shares. This finding appears inconsistent with the hypothesis that our
results are driven by trading activity from target company insiders or by sophisticated market
participants, rather than by source firm employees.
The next two sets of tests focus on cross-sectional variation in source firm CEO
characteristics. Our first test strategy focuses on the source firm managers’ incentives to engage
in shadow trading, which are presumably unrelated to trading decisions by sophisticated market
participants. The test centers on the notion that the concentration of managerial compensation
and human capital in a single firms imparts managers with an under-diversified portfolio. Insider
trading (and shadow trading) provides rents to offset the under-diversification problem faced by
managers. Intuitively, CEOs with greater wealth under-diversification have stronger incentives to
engage in shadow trading.
The current mandated penalty for individuals is a maximum of 20 years in prison and/or a maximum fine of $5
million. Corporations face penalties of up to $25 million.
3
5
We measure CEO incentives to diversify using a factor score based on the total
compensation the CEO has accumulated in the position, her stock ownership value, and her
tenure at the firm. If our main findings merely capture trading by sophisticated investors rather
than by source firm employees, we expect to observe similar results across partitions of CEOs
with based on high or low incentives to diversify personal wealth portfolios. The empirical
results indicate that shadow trading is concentrated amongst the subsample of source firm CEOs
with the lowest levels of wealth diversification (i.e., those CEOs with the highest incentives to
diversify). In contrast, we find statistical evidence of decreased abnormal trading when source
firms CEOs have relatively low incentives to diversify their wealth.
Our final identification test focuses on the firm-specific knowledge of the source firm
CEO who exhibits the potential to engage in shadow trading based on their private information.
We expect that internally promoted CEOs, relative to outside CEOs, are more familiar with the
firm’s operations and governance practices, especially early in the CEO’s tenure (Murphy and
Zábojník, 2004). Thus, internally promoted CEOs are arguably better placed to evaluate the
effect of the firm’s operations on business partners and competitors and thus profitably trade or
leak relevant information. Empirical results indicate that shadow trading is concentrated in firms
that have appointed an internally promoted CEO during the sample period. While the tests
exploiting variations in CEO characteristics provide evidence that is inconsistent with arguments
that sophisticated market participants simply decode this information on their own, we cannot
rule out the possibility that our results capture opportunistic trading activity by market
participants. Furthermore, we find that the extent of shadow trading from business partners is
more pronounced in less competitive industries. Finally, a closing series of robustness tests
consider alternative approaches to identify information shocks, a variety of proxies for informed
trading, and a battery of controls and sensitivity checks.
2. Legal Issues in Trading in Corporate Stakeholders
The separation of ownership and control in publicly traded firms generates incentives for
employees to exploit their private information, allowing profitable trade strategies to be
implemented at the expense of outside shareholders (Cheng and Lo, 2006; Huddart et al., 2007).
Banerjee and Eckard (2001) document how managers gain substantive profits on their insider
6
trading activity, while Ke et al. (2003) report that employees trade upon their private information
up to two years prior to the corporate disclosure. DeMarzo et al. (1996) note how regulators
create strict rules, with substantial penalties and well-publicized enforcement actions, to limit
managerial use of this private information for personal gain. Generally, US regulations and case
law stipulate that trading as an employee while in possession of material private information
creates an illegal breach of fiduciary responsibility (Acharya and Johnson, 2010). 4 A stream of
empirical studies document a substantial reduction in trading based on private information over
time due to greater regulatory scrutiny by the SEC and DOJ (Brochet, 2010; Del Guercio et al.,
2013; Anderson et al., 2013).
One concern centers on expert networks gathering information for their hedge fund
clients by focusing on information procurement from mid-level managers of a target firm’s
suppliers and customers. A recent well-publicized example involves a business development
manager at Flextronics (a camera component supplier) who is charged with supplying
information about a client’s (Apple) sales forecasts and upcoming product releases (Yin, 2010).
Furthermore, in contrast to the extensive SEC and Department of Justice prosecutorial efforts
against own-firm insider trading, very few prosecutions are based on insider trading using
information that indirectly affects a firm’s suppliers or customers. Existing prosecution of insider
trading activity requires the navigation of a loosely defined scope of insider trading (Anderson et
al., 2012), but the legality of shadow trading imposes an additional hurdle because it requires an
assessment as to whether the trading creates a breach of fiduciary responsibility.
The extensive debate and attention on insider trading stems from concerns about
outsider’s incentives to engage in trading activities (Bozanic et al., 2012). US insider trading rules
and regulations have progressed from a landmark decision by the US Supreme Court in United
States v. O’Hagan (1997). In that decision, the Supreme Court ruled that securities laws meant to
limit insider trading “inspire investor confidence”, thereby promoting market pricing and the
efficient allocation of resources. Senior SEC regulators note “…insider trading is legally
An extensive empirical literature documents information externalities for competitors and vertical stakeholders in
earnings announcements, management forecasts, and news releases (e.g., Foster 1981; Olson and Dietrich, 1985;
Clinch and Sinclair, 1987; Baginski, 1987; Han et al., 1989; Han and Wild, 1990; Pyo and Lustgarten, 1990; Slovin et
al., 1991; Freeman and Tse, 1992; Shahrur, 2005; Raman and Shahrur, 2008; Hertzel et al., 2008; Cohen and
Frazzini, 2008; Pandit et al., 2011).
4
7
forbidden. It is morally wrong. And it is economically dangerous. Moreover, the SEC has zero
tolerance for the crime of insider trading” (Levitt, 1998).
While insider trading has garnered much regulatory attention, its legal definition provides
limited guidance about how a firm’s boundaries are defined, and therefore the legality with which
insiders can use own-firm private information that affects stakeholders to trade through them. 5
Beeson (1996) notes that the US Congress deliberated over whether to include a definition of
this in the Insider Trading Sanctions Act of 1984, but ultimately decided that extensive case law
provided sufficient guidance. The SEC provides guidance in interpreting case law on insider
trading. For instance, SEC Rule 10b-5, implementing Section 10(b) of the Securities Exchange
Act of 1934, states, in part, that an insider must disclose material inside information or refrain
from trading when in possession of it. Ausubel (1990) notes that in order to violate Rule l0b-5,
the insider must be linked with the firm whose security is traded or with the nonpublic
information in such a way that the use of the information in his trading is deemed to breach
some fiduciary duty. Misappropriation theory suggests that an individual who trades on the basis
of material nonpublic information that is from their employer and about another firm breaches
their fiduciary duty of trust or confidence and has committed fraud (Salbu, 1992).
The SEC and Department of Justice largely focus on insider trading prosecutions and
enforcement actions regarding illegal trades by managers in their own firms or by financial
market participants (e.g., hedge fund managers) rather than on managers or employees who trade
in related firms, i.e., business partners or competitors. Academic research also appears to focus
on understanding the implications and consequences of insider trading activity by corporate
insiders in their own firm’s stock. We consider a different type of insider trading activity that
appears to provide an opportunity for managers and employees of other firms in the value chain
to use their private information to profit from trading activity with seemingly limited risk of
regulatory prosecution.
In this context, employees’ private information about their firm’s
customers, suppliers, and competitors arguably creates an opportunity for either direct or
indirect shadow trading.
Insiders include managers, employees and anyone else who obtains material, nonpublic information from a
corporate insider or from the issuer, or anyone who expropriates such information from another source. Material
information occurs if a “substantial likelihood exists that a reasonable investor would consider it important in
making his or her investment decisions” and insider trading requires “an intent to deceive, manipulate, or defraud”.
5
8
3. Data and Empirical Framework
3.1 Sample Selection
Our central research objective is to examine whether a firm’s employees opportunistically
use their stakeholder-relevant private information before the public release of that information.
Our empirical strategy centers around establishing causal links between employees that have
stakeholder-relevant private information (i.e., managers and employees of “source firms”) and
abnormal trading activity in the source firm’s stakeholders (“target firms”) before the public
release of the source firm’s private information via an earnings announcement. Intuitively, if the
source firm’s earnings announcement contains stakeholder-relevant news, source firm employees
who have access to relevant information can use this information to profitably trade in the target
firm, resulting in abnormal trading activity in the target firm prior to the source firm’s earnings
announcement.
For our empirical tests, we begin with all non-utility, non-financial US stock exchange
listed firm observations (target firms) in Standard & Poor’s Capital IQ database for the 2010
fiscal year for which Capital IQ identifies material US publicly listed stakeholders (source firms). 6
Given that stakeholders are likely to be relatively stable in the short run, we apply our 2010 data
to the 2009 and 2011 fiscal years, which increases our sample by threefold. The Capital IQ data
provides a unique opportunity to accurately identify stakeholders for a large sample of public US
firms, in contrast to other methods that are typically subject to limitations. For instance, studies
that use SIC (Standard Industrial Classification) codes to identify competitors are susceptible to
fairly wide and potentially inaccurate categorizations of competitors since the classifications were
designed to facilitate sector evaluation (Hoberg and Phillips, 2010a, 2014).
The Capital IQ firm-specific supplier data allows us to overcome another data limitation
for the majority of our tests. Existing research typically has to use material customer data
disclosures available in Compustat to reverse engineer and thus “proxy” a firm’s suppliers. This
approach results in a coarse measure of total suppliers and thus, could result in a
6 We classify non-US domiciled stakeholders as all firms not listed on the NYSE, NASDAQ, or AMEX. To account
for the possibility that firms classified as US listed firms are actually foreign firms with ADRs, we obtain headquarter
location from Compustat and manually re-code all suspect firms as non-US firms.
9
misunderstanding of the importance of a supplier. To illustrate this reverse engineering issue,
consider Caterpillar Inc. (NYSE: CAT), a manufacturer of construction and mining equipment.
Reverse engineering the supplier count from the Compustat customer data results in the
identification of five “suppliers”. These five firms have identified Caterpillar as a major
customer, but they may not necessarily be material suppliers for Caterpillar. Intuition suggests
that manufacturers of complex equipment like Caterpillar are likely to have a relatively large
number of material suppliers. Consistent with this expectation, Capital IQ reports that
Caterpillar has 27 material suppliers. In sum, our Capital IQ stakeholder dataset allows us to
more accurately identify target firm stakeholders relative to conventional approaches. 7
We merge the Capital IQ data with earnings announcement data from the Institutional
Brokers’ Estimate System (I/B/E/S) and impose two filters to improve our identification of the
information source driving abnormal trading in target firms. First, we require that for each
source firm’s earnings announcement at day t, none of the target firm’s other stakeholders
release an earnings announcement during the 60-day window around the source firm’s
announcement (i.e., from t-30 to t+30) in order to alleviate the possibility that abnormal trading
in target firms occurs for reasons unrelated to information from the source firm’s earnings
announcement. Second, we require that each target firm’s earnings announcement does not
occur within the same window in order to isolate the informed trading from its own
informational shock. Figure 1 presents a graphical illustration of the timeline.
We then merge daily short sale volume and total trading volume from NYSE,
NASDAQ, and FINRA; institutional holdings data from Thomson Reuters 13F filings; and
details of insider trades from Thomson Reuters Insider Filing. We obtain stock price data from
the Center for Research in Security Prices (CRSP), firm specific financial data from Compustat,
board information from RiskMetrics database, and firm proxy statements from the Electronic
Data-Gathering, Analysis, and Retrieval system (EDGAR). Finally, we procure insider ownership
data from firm proxy statements, ExecuComp, RiskMetrics, Capital IQ, and BoardEx. We delete
observations for which we cannot obtain all the required data. Our final sample consists of 2,330
The Capital IQ data is subject to two limitations that arise because the data is provided in real time without access
to archival records. First, we are subject to the use of a limited window to conduct tests. Second, while all
stakeholders captured by Capital IQ are classified as “material”, we cannot ascertain the magnitude or duration of
the relationship between the firm and a given stakeholder.
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10
target firm quarter observations, of which 836 represent negative earnings shocks and 1,494
represent positive shocks. The sample consists of 504 unique target firms and 346 unique source
firms (221 competitors, 63 customers, and 92 suppliers). We consider both positive and negative
earnings shocks in our empirical tests. 8
3.2 Primary Variable Measurement
Our main test specification is as follows:
ShadowTrading = α + β1 Business Partner + β2 EarnShock + β3 Business Partner * EarnShock + ε
(1)
The primary dependent variable, ShadowTrading, is defined as one of two measures: 1)
abnormal short sales and 2) abnormal Amihud. Abnormal short sales for each target firm are
computed as the average of the following firm-specific measure: ([target firm i’s average daily
short sales prior to source firm A’s quarterly earnings announcement (day t-30 to t-1) divided by
target firm i’s average daily short sales for the year outside of A’s earnings announcement
windows] − 1). 9 To measure daily short sales, we use the daily short sale volume divided by the
daily share trading volume.
Our second measure is based on the “Amihud” measure from Amihud (2002), calculated
as the absolute value of daily stock returns scaled by the daily dollar volume. This measure of
informational asymmetry builds on the idea that market makers cannot distinguish between
order flow that is generated by informed traders and that generated by noise traders; thus,
market makers set prices that are an increasing function of the imbalance in the order flow,
which may indicate informed trading (Kyle, 1985). The Amihud measure does not utilize detailed
order flow information but it is positively and strongly related to the microstructure estimate of
Kyle’s measure (Amihud, 2002; Brennan and Subrahmanyam, 1996), and it has been shown to
perform well when compared to measures using intraday data (Goyenko et al., 2009). Greater
values of Amihud indicate higher information asymmetry and more severe informed trading.
Our measure, labeled “Abnormal Amihud”, is calculated as: ([target firm i’s average daily
Amihud prior to source firm A’s quarterly earnings announcement (day t-30 to t-1) divided by
The results are qualitatively similar if we exclude repeated target source firm pairs.
Our results are qualitatively similar if we the target firm i’s earnings announcement dates to determine the nonevent window.
8
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11
target firm i’s average daily Amihud for the year outside of A’s earnings announcement
windows] - 1).
The next variable, Business Partner, is an indicator variable that captures whether informed
trading in the target varies based on the type of source firm with private information. Business
Partner is set to 1 when the source firm represents a target firm’s supplier or customer, and 0 if
the source firm represents a target firm competitor of. EarnShock is the unexpected quarterly
earnings in quarter q for source firm A, measured as the residual term from the following
regression:
EPSA,q = α + β1EPSA,q-1 + β2EPSA,q-4 + β3EPSA,q-8 + εA,q
(2)
where EPSA,q is reported earnings per share in announcement quarter q, and historical EPS is
reported earnings per share in the prior quarter (q-1), four quarters ago (q-4), and eight quarters
ago (q-8) using quarterly earnings announcements from I/B/E/S. 10 Business Partner * EarnShock is
the interaction term between those two variables.
3.3 Control Variables
Controls represents the following control variables for target firm i. Firm Size is the log of
total assets. Larger firms are likely to have greater transparency and liquidity, reducing the ability
to profit from informed trading. ROA is return on assets, measured as income before
extraordinary items divided by total assets. Leverage is measured as long-term liabilities divided by
total assets. Market-to-Book is measured as the market value of equity scaled by the book value of
equity. Volatility measures stock volatility and is captured as the standard deviation of monthly
stock return over the prior five years. Institution% is institutional ownership in firm i as a
percentage of common outstanding shares. Sias and Whidbee (2010) note that greater
institutional investor attention in a stock increases liquidity and potentially facilitates insider
trading. Bid-Ask Spread is the daily bid-ask spread averaged over the year. Trading Volume is the
log of the average daily trading volume over the year. Turnover captures the stakeholder’s stock
liquidity, calculated as the log of the prior year’s trading volume scaled by total shares
In additional tests presented in the Appendix D, we use an alternative measure of an earnings announcement
shock, calculated as the difference between the consensus analyst quarterly earnings forecast and the corresponding
reported quarterly earnings. Our results are qualitatively similar when using this alternative measure.
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outstanding at the start of the year. We also control for Analyst Following, the log of the number
of analysts following the firm (Frankel and Li, 2004). Forecast Dispersion is the standard deviation
of the analysts’ quarterly EPS forecasts divided by the prior quarter-end stock price, averaged
over the year. Finally, we control for total opportunistic trades reported by source firm
employees during the prior year (Opportunistic Trade), calculated following the method of Cohen
et al. (2012). Opportunistic Trade allows us to control for the degree or magnitude of the
informed trading of the target firm related to the target firm employees. All the aforementioned
variables are defined in Table 1.
4. Descriptive Statistics
We present summary statistics in Table 2. Panel A presents characteristics for sample
target firms. First, we observe that in the 30-day window leading up to a source firm’s negative
(positive) earning shock, target firms experience abnormal short sales of 0.075 (-0.054), or in
other words, a 7.5% increase (5.4%) in short selling activity relative to the activity during
nonevent windows. This finding is consistent with source firm employees using their
stakeholder-relevant private information to profitably trade in their stakeholders. The evidence
for the abnormal Amihud measure is consistent with the short sale results: on average, abnormal
Amihud for target firms is 0.118 in the 30-day window leading up to a source firm’s earnings
shock. The average target firm has total assets of $4.4 billion (lower quartile = $334 million;
upper quartile = $2.5 billion). Mean ROA is 1.23% and mean Market-to-Book is 1.98. The
average institutional ownership of the target firms is 53% and the mean bid-ask spread is 0.043.
On average, about seven analysts follow each target firm.
Panel B presents a summary of abnormal trading activity in target firms by different
types of source firms. For source firm negative earnings shocks observations, we find that target
firms experience the greatest amount of abnormal short selling activity (35.6%) leading up to
customer earnings announcement shocks (that is, when the source firm is the target’s customer),
relative to when the source firm is the target’s competitor (5%) or supplier (15.8%). Results from
a t-test of differences indicate that the variations between pairs of competitors, customers and
suppliers are all statistically significant at the 5% level or greater. Results for the source firm
positive earnings shock sample indicate a similar inference: abnormal short sales have the
13
greatest decreases when the stakeholder firm is a customer. Finally, results using abnormal
Amihud, which incorporates both positive and negative earnings announcement shocks, indicate
that shadow trading in the target firm is significantly higher when the source firm is a customer
relative to when the source firm is a competitor. Overall, univariate statistics suggest that shadow
trading seems to be driven by information from business partner source firms. 11
Panel C presents the proportion of target and source firms for the top ten industries
represented in the respective groups. Sample target firms represent a large distribution of
industries, with no single industry representing more than 9% of the total sample. These top ten
industries represent about 56% of the entire sample. In addition, the sample source firms appear
to have similar levels of industry dispersion. No single industry represents more than about 10%
of the source firm sample, and the top ten industries represent approximately 55% of the entire
source firm sample. These statistics indicate our results are unlikely to be affected by industry
biases.
5. Multivariate Tests
5.1 Main Results
Our primary objective is to examine whether employees with private information about
their stakeholders opportunistically use that information to trade in those stakeholders (target
firms) or disseminate the information prior to the public release of the information. We use the
period leading up to source firm earnings announcement shocks as a setting for trading
opportunities using informational advantages and expect that if source firms employees use their
stakeholder-relevant private information to trade in those stakeholders, we should observe
abnormal trading activity in the respective target firms prior to the source firm earnings
announcement. In contrast, if source firm employees do not use their private information to
opportunistically trade in their stakeholders, then we should not observe a systematic relation
between abnormal trading activity in a target firm prior to the source firm’s release of private
information. Table 3 presents the results of regression (1). Columns 1 (2) present results for the
11 Figure 2 presents the pattern of abnormal trading activities in target firms around source firm earnings
announcement. The evidence suggests that 1) informed trading occur in target firms prior to earnings shocks in
source firms; and 2) shadow trading in business partners is more prominent than in competitors.
14
effect of negative (positive) earnings surprises on short selling, and in Column 3, we present
results from a regression in which the dependent variable is the Amihud measure of informed
trading, which is based on both positive and negative earnings shocks. 12
In the first two columns, the dependent variable is the abnormal short sales for the
stakeholder during the 30-day window prior to the source firm earnings announcement. In
Column 1 (2) the coefficient on EarnShock is positive (negative) and is statistically significant at
the 10% level, indicating that as the magnitude of the negative (positive) earnings shock
increases, abnormal short sales for stakeholder firms increase (decrease) irrespective of
stakeholder type. The standalone Business Partner variable captures whether short sales in business
partner stakeholders increase (decrease) prior to the source firm’s negative (positive) earnings
announcement shock, irrespective of the size of the shock, and incremental to the effect for the
competitor source firms (which is captured in the intercept term). Results in Column 1 (2) show
positive (negative) coefficient estimates on Business Partner, which indicates that target firms
experience increased (decreased) abnormal daily short selling activity in the 30-day window prior
to negative (positive) earnings shocks by business partner source firms. In economic terms,
abnormal short selling in the target firm increases (decreases) by 43% (22%) leading up to a
negative (positive) earnings announcement shock from a business partner source firm,
incremental to the effect for negative (positive) earnings announcement shocks from a
competitor source firm. 13
Short sales in target firms also appear to be much more sensitive to the size of negative
earnings shocks from business partner source firms than from competitor source firms. The
coefficient on Business Partner * EarnShock (Column 1) captures the incremental abnormal short
sales in target firms when the source firms are business partner stakeholders (relative to when
source firms are competitor stakeholders) based on the magnitude of the negative earnings
shock. As an example, assuming a $0.10 negative earnings shock and holding all other factors
constant, daily abnormal short selling in a target firm increases by 20% (1.5%/7.5%) following
Unlike short sales, Amihud is increasing with both positive and negative earnings shocks. As such, we combine
the positive and negative shocks together. For exposition purposes, we take the absolute value of the earnings shock
in the regression. We do the same to the negative earnings shock in Column 1.
13 We calculate the economic significance as the coefficient divided by the mean abnormal short sales with negative
shocks (7.5%) and positive shocks (5.4%), respectively.
12
15
an earnings shock from a competitor source firm. The same earnings shock when the source
firm is a business partner results in an increase in the target firm’s daily abnormal short sales by
77% (5.8%/7.5%) in the 30-day window prior to the source firm’s negative earnings
announcement. 14 Target firm short sales are over 20 times more sensitive to the size of business
partner source firm negative earnings shocks than to competitor source firm negative earnings
shocks. 15 In Column 2, we examine the effect on abnormal short sales prior to positive earnings
shocks for source firms. The coefficient on Business Partner is negative and significant, which
suggests that the target firm’s abnormal short sales decrease more when the positive earnings
shock is from a business partner source firm relative to when the positive earnings shock is from
a competitor source firm. The negative coefficient on Business Partner * EarnShock in Column 2
indicates that daily abnormal short sales for the target firms are significantly smaller prior to
positive earnings shocks from business partner source firms relative to competitor source firms.
In economic terms, a $0.10 positive earnings shock from a competitor source firm results in a
20% (1.1%/5.4%) decrease in abnormal short sales for the target firms. In contrast, when the
earnings shock is from a business partner source firm, the target firm’s abnormal short sales
decrease by 58% (3.13%/5.4%) prior to the earnings announcement. In sum, the evidence using
short sales to proxy for insider trading is consistent with the argument that shadow trading in
target firms occurs in the lead up to source firm earnings shocks, and that the effect is greater
when the source firm is a business partner rather than a competitor.
In Column 3, we present results for tests using the abnormal Amihud measure of
informed trading. The evidence using this measure is consistent with that presented in Columns
1 and 2. The positive coefficient on EarnShock is consistent with greater informed trading in
target firms prior to source firm earnings announcements. On average, a $0.10 increase in a
competitor source firm’s earnings shock is associated with a 57% [(0.071 * $0.10 + 0.06)/0.118]
increase in daily abnormal Amihud in target firms. The stand-alone business partner stakeholder
term (Business Partner) displays a positive and significant coefficient, which indicates that
informed trading in the pre-announcement period increases by 23% when the source firm is a
Calculated as the sum of the coefficients on EarnShock and Business Partner * EarnShock, all multiplied by the $0.10
earnings shock: (0.005 + 0.105) * 0.10 + 0.015 + 0.032 = 5.8%.
15 Calculated as the sum of the coefficient estimates on EarnShock and Business Partner * EarnShock, scaled by the
coefficient estimate on EarnShock: [(0.005 * 0.10 + 0.105 * 10)/(0.005 * 0.10) = 22].
14
16
business partner stakeholder (relative to when the source firm is a competitor stakeholder).
Furthermore, informed trading in target firms is increasing in terms of the size of the business
partner source firm shock, as evidenced by the positive coefficient on Business Partner *
EarnShock. Insider trading in target firms is over 3.7 times more sensitive to the magnitude of
earnings shocks from business partner stakeholders than from the magnitude of earnings shocks
from competitor stakeholders.
In sum, the preceding analysis indicates that: (1) target firms experience significant
informed trading in advance of their stakeholders’ earnings announcement, and the results hold
across both measures of informed trading; (2) target firms experience greater (lower) daily
abnormal short sales prior to business partner source firm negative (positive) earnings
announcement surprises than for corresponding competitor source firm earnings announcement
surprises; (3) target firm daily abnormal short sales are economically more sensitive to the
magnitude of business partner stakeholder negative earnings announcements shocks than to the
magnitude of competitor stakeholder negative earnings announcements shocks (over 20 times
more sensitive); and (4) the results are consistent for both negative and positive earnings shocks.
Columns 4 through 6 of Table 3 examine whether informed trading in target firms
differs across upstream and downstream business partner source firms. We replace Business
Partner with indicator variables to capture source firms classified as the target’s Customers or
Suppliers, and also include interaction terms to allow informed trading to vary with the magnitude
of the earnings shock. In Column 4, we consider the effect of negative earnings shocks when the
dependent variable is abnormal short sales. The results indicate that informed trading in the
target firm increases prior to earnings announcement shocks by either the supplier or customer
source firms. In economic terms, abnormal short selling in the target increases by 59% (39%)
prior to earnings announcements by Customers (Suppliers) source firms.
The interaction terms between the magnitude of the earnings shock and types of source
firms (Customer*EarnShock and Supplier*EarnShock) bear positive coefficients, which is consistent
with informed trading in target firms increasing with the magnitude of the earnings shock. In
Column 5, we find evidence of decreases in target firm abnormal short sales prior to both
customer and supplier source firms earnings shocks. Column 6 of Table 3 presents results from
a test where the abnormal Amihud measure is the dependent variable. We find that target firm
17
informed trading increases prior to earnings shocks for business partner source firms. Finally, we
observe that the coefficients on control variables are generally consistent with our expectations.
5.2 Identification Strategy for Attribution of Information Source
Our research methodology and analysis present results consistent with the presence of
shadow trading activity but do not allow us to rule out the possibility that our findings simply
capture trading activity by sophisticated investors who have used superior data collecting and
forecasting skills to anticipate the source firm earnings shocks and related implications for
stakeholders. In an ideal setting, we would identify the individual parties engaging in trading
activity in the preannouncement period, but as such data is not publicly available, we undertake a
series of tests to identify the source of insider trading activity. Our first test exploits crosssectional variation in source firms’ insider trading restrictions as disclosed in each firm’s
Corporate Code of Conduct. Our second test examines the effect of changes in insider trading
laws in the early 2000s on shadow trading. The final two tests examine cross-sectional variation
in the source firm managers’ incentives to engage in shadow trading. 16
5.2.1 Firm-Specific Insider Trading Policies and Shadow Trading
Our first identification strategy focuses on source firms’ corporate restrictions on
shadow trading. Bettis et al. (2000) find that firms with corporate policies to restrict insidertrading exhibit lower instances of such activity. We hand collect and read insider trading policy
disclosures from the Code of Ethics statement for each source firm in our sample. We determine
whether a firm’s insider trading policy explicitly prohibits insiders from trading in business
partners or competitors, or whether the insider trading policy restricts exclusively on their own
firm. We present examples of these two types of insider trading policies in Appendix A. After
excluding source firms that do not publicly disclose their insider trading policies, we are able to
obtain data for 267 unique source firms. 17 The evidence suggests substantial variations in the
16 These tests provide indirect evidence to address an alternative explanation for our results, namely that the fiscal
years ends for most US firms are clustered on December 31. If our primary findings represent information spillover
from disclosures by firms other than the source firm, we should observe no cross-sectional variation or time-series
differences in shadow trading activity as evidenced in Section 4.2.
17 A number of firms do not make the details of their insider trading policies publicly available. In such cases, the
firms usually state that the insider trading policy is a component of the employee handbook or another non-public
document.
18
insider trading policy restrictions: approximately 53% of the source firms explicitly prohibit firm
insiders from using private information to trade in their own firms or in their business partners,
whereas the remaining 47% only prohibit firm insiders from using private information to trade
in their own firms. Additional analyses also indicate that firms with explicit prohibitions against
shadow trading have significantly greater analyst following than firms without an explicit policy
(significant at the 5% level), consistent with Bushman et al. (2005). We find no statistical
evidence that the average Amihud differs between the explicit policy and no-explicit policy
groups.
Table 4 presents results from empirical tests. Columns 1 and 2 (3 and 4) present results
for the partition of source firms that explicitly restrict (do not explicitly restrict) shadow trading
by insiders. 18 The dependent variable is again set to either abnormal short sales or abnormal
Amihud. In the interest of brevity, we only present results for abnormal short sales related to
negative earnings shocks. In the first two columns, EarnShock is positive and statistically
significant at the 10% level, indicating that informed trading in target firms increases with the
magnitude of the source firm’s negative earnings shock, irrespective of the stakeholder type. The
positive coefficient estimates on Business Partner indicate that target firms experience increased
levels of abnormal daily informed trading activity leading up to a business partner source firm’s
earnings shock, but the effect is only marginally significant for abnormal short sales and is
statistically insignificant when the dependent variable is abnormal Amihud. We find no statistical
evidence that informed trading in target firms is more sensitive to the size of the negative
earnings shocks from business partner source firms than from competitor stakeholder source
firms.
In Columns 3 and 4, the coefficients on EarnShock are positive and statistically significant
at the 10% level, indicating that informed trading in target firms increases with the magnitude of
the source firm’s negative earnings shock, irrespective of the stakeholder type. This result is
In Appendix B, we present an amended specification of Equation (1) using the full sample and include a dummy
variable labeled Policy to capture the two different types of firm-specific insider trading classifications, as well as
additional interaction terms, in order to allow the effect of different insider trading policies on informed trading to
vary across source firm types. The variable Policy is set to one if the source firm explicitly prohibits shadow trading,
and set to zero otherwise. An advantage of this approach is that it allows us to observe whether there is a statistical
difference in shadow trading across firms that explicitly prohibit shadow trading versus firms that do not. A
disadvantage of this approach stems from the difficulty in estimating the economic significance from different
policies.
18
19
economically and statistically similar to the results in Columns 1 and 2. The coefficient estimates
on Business Partner, however, are positive and statistically significant at the 5% level or better for
tests using either abnormal short sales (for negative earnings shocks) or abnormal Amihud. In
terms of magnitude, the increase in shadow trading for the subsample of source firms that have
no explicit restriction on shadow trading is between four to six times larger relative to the
increase for the subsample of source firms with explicit restrictions on shadow trading. We also
find statistical evidence that informed trading in target firms is increasing in terms of the size of
the business partner source firm’s negative earnings shock. Coefficients on control variables are
consistent with our expectations.
In sum, the evidence in Table 4 suggests that the prevalence of shadow trading varies
with firm-specific policies. Our findings are inconsistent with arguments that our primary results
are attributable to trading activity by sophisticated traders who do not have access to the private
information released via the earnings announcement.
5.2.2 The Impact of Regulatory Changes on Shadow Trading
Our next test exploits a shock to insider trading regulations in Section 32(a) of the
Securities Exchange Act of 1934, as amended by the Sarbanes-Oxley Act of 2002. Specifically,
the act increased the disclosure requirements for insider trades and the magnitude of the penalty
for “willful” violation of the act and its regulations. 19 We examine the prevalence of shadow
trading around the 2002 regulatory change using alternative data sources to test the time interval
around the regulatory change. For a large sample of Compustat firms over the ten-year window
between 1997 and 2007 (excluding all observations in the regulatory change year), we identify
competitors using the industry classification data from Hoberg and Phillips (2010a, 2014) and
identify customers and suppliers from the Compustat Segment database using the methodology
in Ellis et al. (2012). Our data collection yields 10,465 unique supplier-customer pairs.
20
We
apply the same procedure and restrictions used for our primary sample as for the identified
stakeholder firms to generate a final sample of 14,885 observations. The test focuses on shadow
The current mandated penalty for individuals is a maximum of 20 years in prison and/or a maximum fine of $5
million. Corporations face penalties of up to $25 million.
20 In the Compustat Segment files some source firms report generic customer names that cannot be matched with
Compustat data. Additionally, we obtain a list of suppliers for the sample firms by reverse engineering the material
customer disclosure data. This method results in a less precise identification of a firm’s actual suppliers than the
proprietary Capital IQ firm-specific material suppliers.
19
20
trading before and after the regulatory shock. An increase in regulation on own firm trading
based on private information should lead to greater trading in business partners and/or
competitors.
We present the regression results in Table 5. The dependent variable in all tests is
abnormal Amihud since daily short sale data is only publicly available from 2005 onwards. The
variable EarnShock remains as previously defined. Post is a dummy variable set to one for all years
after the passage of the 2002 laws. The variable allows the level of abnormal short sales to vary
after the passage of the regulation in 2002. Post*EarnShock captures whether the relation between
the magnitude of the earnings shock and abnormal Amihud changes after the passage of the
regulation. The overall evidence suggests that target firms experience significant increases in
informed trading following the regulatory shift from both competitor and business partner
source firms. First, we find that EarnShock is positive across all three partitions and is statistically
significant at the 5% level when the source firm is a business partner stakeholder. The coefficient
on Post is positive and statistically significant at the 5% level across all partitions, which indicates
that informed trading in both competitor and business partner stakeholders increases following
the change in insider trading restrictions in 2002. We find evidence of a significant increase in
shadow trading activity following the regulatory changes, which is consistent with the argument
that the passage of SOX and specifically, the change and restrictions in insider trading, resulting
in an increase in shadow trading. These results appear consistent with the notion that a significant
portion of trading in target firms based on private information stems from source firm insiders.
5.2.3 Managerial Diversification Incentives
Managers of modern corporations typically receive incentive compensation, including
stock ownership, in order to mitigate agency problems, such as shirking. This firm-specific
wealth concentration often leads to managers holding under-diversified wealth portfolios.
Holding a poorly diversified portfolio is rational if the managers can obtain private rents, such as
perquisite consumption or insider trading. Consequently, standard economic theory suggests
that managers who are more poorly diversified have greater incentive to engage in insider trading
to off-set or justify their portfolio position (Ofek and Yermack, 2000). Thus, informed trading
by a manager can serve as a tool for hedging against ownership risks or as a mechanism to
mitigate the underinvestment problem imbedded in their compensation contract.
21
We measure managerial wealth diversification and the incentive to diversify using a factor
score based on three proxies: the CEO’s aggregate compensation since becoming a top executive
in the company, the CEO’s prior fiscal year-end stock ownership multiplied by the year-end
stock price, and the CEO’s tenure at the company. 21 The analysis is performed for the entire
population of firms for which we can obtain executive data from ExecuComp, RiskMetrics,
Capital IQ, and BoardEx. Source firms with CEOs who score in the top quartile in each fiscal
year are classified as “High Incentive” firms. All other source firm CEOs are classified as “Low
Incentive” CEOs for that fiscal year. In untabulated tests, we replicate our analysis for both the
CEO and the CFO, because the CFO is also likely to be privy to earnings information, and find
qualitatively similar results to those presented below. 22
We present empirical results in Table 6. In the interest of brevity, we only present results
for negative earnings shocks for our short sale test. In Columns 1 and 2, the dependent variable
is set to abnormal short sales, and in Columns 3 and 4, the dependent variable is abnormal
Amihud. We perform our tests separately based on whether the source firm is a competitor or a
business partner of the target firm. We use two dummy variables, High Incentive and Low Incentive
to indicate if the source firm CEO belongs in the top quartile or bottom quartile of our measure
of CEO diversification incentives. If our findings above are attributable to shadow trading by
source firm managers, we expect to observe a positive coefficient on High Incentive. Furthermore,
source firm managers with relatively lower incentives to diversify their wealth are likely to be less
inclined to engage in informed trading, which indicates that we should observe negative
coefficients on Low Incentive. In contrast, if our findings are instead due to trading by
sophisticated investors who are not privy to the source firm’s earnings shock information,
abnormal informed trading in the target firms should not be affected by cross-sectional variation
in the trading incentives of the source firm managers.
The results in Table 6 indicate that source firm managerial wealth diversification
incentives affect the relation between target firm informed trading and source firm earnings
We find that the three measures are all positively correlated and the factor analysis suggests all three measures load
on a single common factor with an Eigen value of 1.828, which explains 55% of the common variance.
22 Recent scholarship documents that both offers and directors have access to, and can trade on private information
(Fidrmuc et al. 2006; Ravina and Sapienza, 2010). Jagolinzer et al. (2011) find that firms that require executives to
obtain general counsel approval before purchasing their own firm shares exhibit lower opportunistic insider trading.
Rogers (2008) documents that managerial incentives for insider trading appear related to corporate disclosure policy.
21
22
shocks. Furthermore, we find that the results are more pronounced for business partner source
firms relative to competitor source firms. Results in Column 1 indicate there is no statistical
relation between informed trading in the target firm and source firm managerial incentives for
the competitor source firm sample. The coefficient on EarnShock is positive and insignificant,
and coefficients for High Incentive and Low Incentive capture variation in competitor source firm
managerial incentives to diversify on target firm abnormal short sales. The evidence again
provides no support for the presence of such a relation. In Column 2, we present coefficient
estimates for similar tests but using the business partner source firm sample. The coefficient on
EarnShock is positive and significant, suggesting that informed trading in target firms increases
prior to business partner source firm earnings shocks, irrespective of managerial incentives. The
standalone High Incentive and Low Incentive terms show positive (negative) coefficients which
indicates that informed trading in target firms is greater (smaller) when business partner source
firm managers have greater (smaller) incentives to trade.
In economic terms, when the target’s shock arises from a business partner source firm
with a High Incentive (Low Incentive) manager, we find abnormal short sales in the target firm
increase (decrease) by 24% (13%) during the preannouncement period. Next, the interaction
terms High Incentive*EarnShock and Low Incentive*EarnShock capture the incremental effect of the
magnitude of the earnings shock on the trading incentives across high and low incentive source
firm managers. The results indicate that the magnitude of the earnings shock increases
(decreases) for business partner source firm managers with high (low) incentives to engage in
shadow trading. As an example, consider a $0.10 earnings shock. Holding all other factors
constant, the presence of a High Incentive (Low Incentive) business partner source firm executive
results in an increase (decrease) in the daily abnormal short sales for a target firm by 35% (24%)
prior to the source firm earnings shock.
Columns 3 and 4 present corresponding results when the dependent variable is abnormal
Amihud. The evidence in both columns suggests that the target firms experience significant
increases in informed trading prior to earnings shocks from either competitor or business
partner source firms. First, we find positive and significant coefficients on EarnShock for both
the competitor (business partner) source firm samples, which indicate that informed trading in
the target firm is increasing with the magnitude of the earnings surprise. We also find that source
23
firm managerial incentives to diversify affect informed trading. High trading incentives are
positively associated with abnormal Amihud but are statistically insignificant, and low incentives
are negatively associated with abnormal Amihud and significant at the 10% level. Furthermore, the
effect of managerial incentives on shadow trading is magnified in terms of the size of the
earnings shock. As the magnitude of the earnings shock increases, we find that both high and
low source firm CEO incentives to diversify result in greater informed trading in the target firm,
indicating a greater sensitivity between shadow trading and earnings shocks. We present results
for business partner stakeholders in Column 4 and find similar results. More specifically, we
observe that the presence of a High Incentive (Low Incentive) business partner source firm executive
results in an increase (decrease) in the target firm’s daily abnormal Amihud by 17% (12%). For a
$0.10 earnings shock, and holding all other factors constant, the presence of a High Incentive (Low
Incentive) source firm executive results in a 25% (4.2%) additional increase (decrease) in abnormal
Amihud in target firm stock.
The preceding analysis and results are inconsistent with arguments that our findings are
attributable to trading done by sophisticated market participants who use superior forecasting
and analysis to make investment decisions (but who are not privy to the private information held
by the source firm). In contrast, the findings are consistent with our primary hypothesis that
source firm managers use their private information to trade in their stakeholders’ stocks prior the
release of material private information.
5.2.4 Managerial Knowledge
Our final identification test relies on the notion that managerial ability to engage in
shadow trading derives from their firm-specific knowledge of the firm and its operating
environment. The greater the firm-specific knowledge, the greater the likelihood that the source
firm manager will engage in shadow trading based. We predict that an internally promoted
source firm CEO, relative to an externally hired CEO, is presumably more familiar with the
firm’s operations and customer-supply chain and is therefore more likely to engage in shadow
trading. Furthermore, a new externally-hired CEO has relatively less of an under-diversification
problem than an internally hired CEO, suggesting reduced incentives to engage in shadow
trading in the short run, following the initial appointment.
24
We use a subsample of source firms that experience CEO turnover during the sample
period and partition firms based on whether the replacement CEO is promoted from within the
firm or externally recruited. We obtain CEO information based on a firm’s proxy statements and
news announcements to determine whether the new CEO is hired from inside or outside. We
identify 61 CEO turnover events among the source firms over our three-year sample period.
Thirty-three of the CEO dismissals result in external successor replacement and the remainder
denotes internal replacements (roughly 17% of the sample experiences CEO turnover).
Two potential issues arise from this test. First, CEOs engaging in shadow trading appear
more likely to be replaced by the source firm due to diligent corporate governance, with a
replacement explicitly aware of the firm’s actions against shadow trading. The ambiguity of the
legal boundaries and limited enforcement regarding shadow trading, however, suggest CEO
turnover occurs for other reasons (Farrell and Whidbee, 2003). A second issue arises from the
possibility that new CEOs change the firm’s business partners, creating a bias in our sampling.
However, in untabulated tests using an alternative data source (Compustat) to identify material
stakeholders, we find no evidence of abnormal supply chain changes in the period immediately
following CEO appointments.
We present our results in Table 7. In Columns 1 and 2 (3 and 4) the dependent variable
is set to abnormal short sales (abnormal Amihud). We partition tests by competitor and business
partner source firms for each of our two measures of informed trading. The evidence in
Columns 1 and 2 continues to indicate that earnings shocks from both competitor and business
partner source firms are associated with greater abnormal short sales in target firms during the
preannouncement period. We use two dummy variables to represent the new CEO types, Insider
and Outsider. In Columns 1 and 2, the results indicate that when the new CEO is an insider, the
target firms experience a significant increase in abnormal short selling prior to source firm
negative earnings shocks, irrespective of the source firm type. Furthermore, the effect is
increasing in terms of the size of the negative earnings shock. In economic terms, the presence
of an inside CEO in the competitor (business partner) source firm results in a 17% (37%)
increase in abnormal short sales in the target firms. Given a $0.10 increase in the magnitude of
the earnings shock, abnormal short sales in the target firm increase by 31% (40%) if the
competitor (business partner) source firm CEO is an insider.
25
On the other hand, we observe that the presence of an outside CEO in either a business
partner or competitor source firms is associated with a reduction in informed trading in a target
firm prior to the source firm’s negative earnings announcement shock. The effect is only
statistically significant for business partner source firms. The magnitude of the reduction in
abnormal short sales is also significantly increasing with the size of the negative earning shock,
but only for business partner source firms. In economic terms, the presence of an outsider CEO
in a business partner source firm reduces abnormal short sales in the target firm by about 27%
relative to the average short sales.
Columns 3 and 4 present results from similar regressions except we measure informed
trading using the abnormal Amihud measure. We find similar results to those presented in
Columns 1 and 2. The presence of a business partner or competitor source firm insider
(outsider) CEO is associated with an increase (decrease) in preannouncement informed trading
in the target firm, and the effect is increasing in the magnitude of the source firm’s earnings
shock. In economic terms, a $0.10 increase in the earnings shock when the CEO is an insider
(outsider) among competitor source firms is associated with a 23% (21%) increase (decrease) in
abnormal Amihud in the target firms. For business partner source firms, the same earnings
shock results in an increase (decrease) of abnormal Amihud in the target firm of 47% (14%) for
an inside (outside) CEO.
Overall, the results in Table 7 are consistent with our primary hypothesis that shadow
trading activity is attributable to source firm employees. While we cannot definitively rule out the
possibility that shadow trading may be driven by sophisticated market participants who are
trading without access to the source firm’s private information, our test results are inconsistent
with such an argument.
5.3 Stock Return Methodology to Identify Shadow Trading
In this section, we present evidence on shadow trading using an alternative approach to
capture informational shocks that rely on stock price reactions, rather than earnings shocks, to
identify periods during which source firms have non-public information about stakeholders.
More specifically, we use a sample of source firms and target firms that experience consecutive
stock price shocks to identify when the source firms have incentives to engage in shadow
26
trading. An advantage of this alternative approach is that it provides a robust setting to identify
events that are unexpected by the market.
We construct our sample using the following conditions: (1) we require that both a
source firm and an affiliated target firm experience price shocks, which allows us to focus on
cases where the source firm shock affects the target firm; (2) a stock price shock for a target firm
must follow such a shock for a source firm and occur within one trading day. This requirement
allows us to more accurately identify which source firm stock price shocks are relevant for a
given target firm; (3) we require that the shocks to the source firm and target firm are at least 5%
to ensure that the stock return shocks are material and create incentives for source firm insiders
to engage in shadow trading; (4) we delete source firms with highly volatile stock returns,
measured as firms with more than 50 days over the three-year sample window in which the stock
price changes by more than 5% in a trading day; (5) we eliminate observations where more than
one stakeholder for a source firm experiences a large stock price shock within one trading day
after the source firm shock in order to ensure our sample consists of observations where we can
reliably assume that the effect of a source firm shock is applicable to a particular target firm; and
(6) we also eliminate observations for which multiple stakeholder firms affiliated with a given
source firm experience price shocks on the same day in order to reduce the possibility of
confounding events making it difficult to assess the net effect on the trading activity in the target
firm. Note that these restrictions yield more conservative results than if we do not impose such
conditions.
These restrictions result in a sample of 355 cases where we can match a source firm with
at least one target firm, of which 163 cases involve negative shocks from a source firm. We then
examine trading patterns in the target firms from t-30 to t-1 to identify the presence of shadow
trading (where t represents the date of the source firm shock).
We present regression coefficients in Appendix C. Columns 1 and 2 present results for
tests of insider trading using abnormal short sales and abnormal Amihud measure, respectively.
The overall inferences are largely consistent with our primary findings. In Column 1, we find
evidence of a significant increase in abnormal short sales in the target firm prior to negative
abnormal stock return from the stakeholder firm. The positive coefficient estimates present on
Business Partner indicate that a target firm experiences increased abnormal short sales activity in
27
the 30-day window prior to business partner stakeholder firms experiencing negative abnormal
stock returns. In economic terms, abnormal short selling in target firms prior to a business
partner stakeholder firm’s negative shock increases by 19% more than corresponding competitor
stakeholder shocks. 23 Short sales in target firms also appear to be much more sensitive to the size
of business partner source firm negative shocks relative to competitor source firm negative
shocks. The positive coefficient on Business Partner * StockShock suggests that a 10% negative
shock in the business partner source firm is associated with the target firm’s daily abnormal short
selling increase of 40%, a higher value than if the source firm is a competitor stakeholder. 24
Target firm short sales are over two times more sensitive to the size of business partner source
firm negative shocks than to the size of competitor source firm shocks. 25
The result in Column 2 when the dependent variable is abnormal Amihud is similar to
the result where the dependent variable is abnormal short sales. More specifically, we find that a
10% increase in unexpected abnormal stock return surprise in competitor (business partner)
source firms is associated with an increase in abnormal Amihud by 10.4% (28.7%) for target
firms. 26 Furthermore, the sensitivity of trading in the target firms is 1.9 times greater when the
shock arises from a business partner stakeholder stock price shock relative to that of a
competitor stakeholder.
In Columns 3 and 4, we partition business partner stakeholder firms into customers and
suppliers. The results indicate that shocks from both types of business partner stakeholders have
economic and statistical effects on trading in the target firms. Overall, our insider trading results
using a stock return approach yield very similar inferences to our tests using earnings
announcement shocks. A key advantage of sampling based on stock market reactions is that we
can better isolate the origin and the magnitude of the source firm shock for the target firm. As
such, we are better able to provide implication about causality between source firm shocks and
informed trading activity in a target firm. In sum, our stock return test results strengthen our
primary findings. Earnings shocks and market reaction shocks may reflect different information
Average abnormal short sales are 9% higher than short sales during the non-event window.
Calculated as the sum of the coefficients on Business Partner and Business Partner * StockShock: 0.017 + 0.1*0.190 =
3.6%.
25 Calculated as the sum of the coefficient estimates on stakeholder Shock and Business Partner * StockShock, scaled by
the coefficient estimate on stakeholder Shock: [(0.158 + 0.190)/0.158 = 2.2].
26 The average abnormal Amihud is 0.136.
23
24
28
sources, natures and origins, and as such, our findings suggest that shadow trading activities are a
prominent form of insider trading that receives relatively little attention.
5.4 Industry Competitiveness and Shadow Trading
In a final test, we consider whether industry competition influences shadow trading. We
use the Herfindahl-Hirschman index of industry competitiveness from Hoberg and Phillips
(2010b) to classify source firms into high competition or low competition industries. In
untabulated results, we find evidence of increased (decreased) shadow trading in competitors
when those target firms operate in relatively more (less) competitive industries, which suggests
that industry peer relevant information acquisition activities may curb opportunities for shadow
trading gains in competitive industries. Empirical results for business partner target firms provide
different results to those for competitor target firms. We find evidence of increased (decreased)
shadow trading in business partners when source firms operate in relatively more (less)
competitive industries.
Shocks from competitor source firms are less informative than shocks from those in less
competitive industries; and inversely 2) in less competitive industries, shadow trading in business
partners are more pronounced (i.e., shocks are more informative in business partners) than
business-partner shadow trading in more competitive industries.
6. Concluding Remarks and Policy Implications
In this study, we examine and find evidence that corporate insiders use their stakeholderrelevant private information to trade in, or leak information about, those very stakeholders, prior
to the public release of the private information – a behavior that we call “shadow trading”. We
use the period immediately preceding earnings announcement shocks to identify periods in
which a source firm’s employees have private information, and can likely recognize the impact of
that private earnings information on stakeholders. Our results are robust to the use of multiple
measures of insider trading and hold across different groups of stakeholders. Even though data
limitations prevent us from identifying the individuals engaging in opportunistic trading or
information leakage, our findings imply that source firm employees actively participate in
shadow trading.
29
We also undertake a series of tests to rule out the possibility that our findings merely
capture trading activity conducted by sophisticated investors who are not privy to any private
information and who anticipate the earnings shock. Our approach involves examining whether
shadow trading varies with source firm characteristics, including: 1) firm-specific restrictions on
shadow trading; 2) CEO wealth diversification incentives to engage in shadow trading; and 3) the
CEO’s knowledge about the firm’s operations and the implications of shocks for stakeholders.
Our empirical results suggest the presence of shadow trading varies according to firm and CEO
characteristics, which is inconsistent with alternative arguments that target firm insiders or
sophisticated investor activity drives this information leakage. In our final test, we exploit a
regulatory shock in the early 2000’s via the Sarbanes-Oxley Act, which increased the penalties for
traditional insider trading, effectively increasing incentives for insiders to engage in shadow
trading, and we find evidence of an increase in shadow trading following the regulatory shock.
The Department of Justice routinely seeks to prosecute managers for trading in their
own-firm stocks or for breaching their fiduciary trust by leaking valuable information to
outsiders. Yet the notion of exploiting private information by trading in corporate business
partners or competitors receives limited consideration. Existing prosecution of insider trading
activity requires the navigation of a loosely defined scope of insider trading (Anderson et al.,
2012), but the legality of shadow trading requires an assessment as to whether the trading creates
a breach of fiduciary responsibility.
The extensive debate and attention on insider trading stems from concerns about
outsider incentives to participate in the market, while the US stock market trading activity is at
nearly $15 trillion (Bozanic et al., 2012). US insider trading rules and regulations have progressed
from a landmark decision by the US Supreme Court in United States v. O’Hagan (1997). In that
decision, the Supreme Court ruled that securities laws meant to limit insider trading “inspire
investor confidence”, thereby limiting market pricing and the efficient allocation of resources.
SEC regulators note “insider trading is legally forbidden. It is morally wrong. And it is
economically dangerous”. Moreover, the SEC has “zero tolerance for the crime of insider
trading” (Levitt, 1998).
While insider trading has garnered much regulatory attention, its legal definition provides
limited guidance about how a firm’s boundaries are defined, and therefore the legality with which
30
insiders can use own-firm private information that affects stakeholders to trade through them. 27
Beeson (1996) notes that the US Congress deliberated over whether to include a definition of
this in the Insider Trading Sanctions Act of 1984, but ultimately decided that extensive case law
provided sufficient guidance. Furthermore, in 1991, the SEC announced that creating a
definition of insider trading would be “too daunting a task” (Fisch, 1991). The SEC argued that
creating a definition would limit its enforcement powers when responding to future
unanticipated situations. In this context, extensive case law exists to indicate that managers that
use private information to trade in their employer’s business partners create a breach of fiduciary
responsibility (limited case law appears directly applicable to trading in competitors).
The SEC provides guidance in interpreting case law on insider trading. For instance, SEC
Rule 10b-5, implementing Section 10(b) of the Securities Exchange Act of 1934, states, in part,
that an insider must disclose material inside information or refrain from trading when in
possession of it. Ausubel (1990) notes that in order to violate Rule l0b-5, the insider must be
linked with the firm whose security is traded or with the nonpublic information in such a way
that the use of the information in his trading is deemed to breach some fiduciary duty. Insider
trading prosecution often relies on the theory of misappropriation, which stems from
employment outside the firm and in a capacity that creates an obligation to the firm’s
shareholders. Misappropriation theory suggests that an individual who trades on the basis of
material nonpublic information that is from their employer and about another firm breaches
their fiduciary duty of trust or confidence and has committed fraud under Rule 10b-5,
promulgated by authority of Section 10(b) of the 1934 Securities Exchange Act (Salbu, 1992).
Our study has an important policy implication. The vast majority of regulatory (and
academic) attention on insider trading has centered on managers who use private information to
trade in their own firms as opposed to other firms. Misappropriation theory indicates that an
insider of a source firm who trades or sells their private information about suppliers, customers,
or competitors creates a breach of fiduciary responsibility. Our findings suggest a need for
Insiders include corporate officers and directors and anyone else who obtains material, nonpublic information
from a corporate insider or from the issuer, or anyone who expropriates such information from another source.
Material information occurs if a “substantial likelihood exists that a reasonable investor would consider it important
in making his or her investment decisions” and insider trading requires “an intent to deceive, manipulate, or
defraud”.
27
31
regulatory attention and possibly for the development of additional disclosure requirements
imposed on source firm managers regarding their trading activity business partners and
competitors.
32
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35
Table 1. Variable Definitions
Short Sales: daily short sale volume divided by total stock trading volume.
Amihud: daily absolute return divided by daily dollar trading volume.
Business Partner: a dummy variable set to 1 if the stakeholder is a customer/supplier and 0 if the
stakeholder is a competitor.
Customer: a dummy variable set to 1 if the shock source firm is a customer and 0 otherwise.
Supplier: a dummy variable set to 1 if the shock source firm is a supplier and 0 otherwise.
EarnShock: unexpected earnings surprise calculated as the residual term of regression (2).
Stockshock: the absolute stock return on day t of the stakeholder firm.
StakeholderX_ret: a dummy variable set to 1 if the stakeholder firm experiences a significant
shock (excess stock return of 5% or greater) on day t and 0 otherwise, and where
StakeholderX represents a business partner, customer, or supplier.
StockShock: the absolute daily excess stock return of the target firm, when the target firm
experiences a significant shock (excess stock return of 1% or greater) between day t+1 and
t+5, where t represents the day of the source firm shock.
Policy: a dummy variable equal to 1 if the source firm has an explicit insider trading policy
prohibiting the firm’s employees and directors from engaging in shadow trading and 0
otherwise.
Post: a dummy variable set to 1 if the year is after 2002 and 0 otherwise.
Target firm variables
Size: log of total assets.
ROA: return on assets.
Leverage: long-term debt divided by assets.
Market-to-book: market value of common equity scaled by book value of common equity.
R&D: R&D expenditure scaled by total assets.
Institutional%: proportion of common equity ownership by institutional investors.
Forecast_error: based on annual EPS forecast, calculated as the standard deviation of forecast
errors scaled by the absolute value of actual EPS.
Turnover: daily stock trading volume divided by common shares outstanding, averaged over the
current year.
Volatility: standard deviation of daily stock return over a year.
Opportunistic Trade: reported opportunistic insider trading activity by managers as measured in
Cohen et al. (2012).
36
Table 2. Summary Statistics
Table 2 presents summary statistics for sample target and source firms. Panel A presents descriptive data for sample
target firms. Panel B displays abnormal trading data for sample target firm observations partitioned by the type of
source firm. Panel C outlines the top ten industries represented for sample target firms and source firms. All
variables are defined in Table 1.
Panel A: Target Firm Descriptive Statistics
Mean
Median
Abnormal short sales
following a source firm’s:
Negative EarnShock
0.075
0.020
Positive EarnShock
-0.054
-0.012
Abnormal Amihud
0.118
0.002
Total Assets ($billion)
4,434.53
814.97
ROA (%)
1.23
1.33
Market-to-book
1.98
1.45
Institutional%
0.53
0.56
Volatility
0.14
0.13
Bid-Ask Spread
0.043
0.040
Trading Volume
12.12
12.12
Turnover
13.93
14.17
Analyst Following
7.05
5.00
Forecast Dispersion
0.012
0.004
Opportunistic Trade
9.54
0.00
Std. Dev.
Lower Quartile
Upper Quartile
0.325
0.312
0.864
16,039.91
12.029
4.33
0.29
0.07
0.019
1.78
1.78
6.64
0.031
37.87
-0.123
-0.245
-0.180
334.86
0.00
0.99
0.30
0.09
0.030
10.95
13.37
2.00
0.002
0.00
0.204
0.107
0.406
2,529.50
6.38
2.34
0.77
0.18
0.053
13.41
14.80
9.00
0.010
5.00
Panel B: Source firm stakeholder characteristics (mean test)
Competitor Customer Supplier Comp. – Cust.
Abnormal Short Sales
Negative EarnShock
0.050
0.356
0.158
-4.52***
Positive EarnShock
-0.010
-0.054
-0.022
2.20**
Abnormal Amihud
0.069
0.193
0.122
-2.16**
Panel C: Industry Representation
Industry Name
% of Target Firms
Drugs
8.91
Energy
8.51
Business Services
8.24
Meals
6.68
Machinery
5.74
Health
3.85
Building Materials
3.71
Retail
3.51
Transportation
3.51
Wholesale
3.31
Total
55.97
Industry Name
Drugs
Business Services
Energy
Chemicals
Telecommunications
Retail
Machinery
Health
Electric Equipment
Building Materials
Total
37
Cust. – Supp.
Comp. – Supp.
2.50**
-1.76*
1.24
-1.99**
1.26
1.16
% of Source Firms
10.02
9.71
7.27
5.25
4.34
4.22
3.73
3.60
3.54
3.48
55.16
Table 3. Earnings Shock and Shadow Trading
This table presents coefficients from regressions testing whether employees with stakeholder-relevant private
information opportunistically trade through those stakeholders prior to publicly disclosing their private information.
We measure shadow trading using two proxies: 1) abnormal short sales and 2) abnormal Amihud, and we use the
30-day window prior to earnings announcement shocks as a proxy for the presence of private information. All
variables are defined in Table 1. t-values are reported in parentheses based on Huber-White Sandwich standard
errors clustered at the firm level. All specifications include year and industry fixed effects. Statistical significance at
the 1%, 5%, and 10% level is denoted by ***, **, and *, respectively.
Dependent variable:
Abnormal Short Sales
Shock type:
Constant
Negative
0.015
(0.32)
0.005*
(1.82)
0.032**
(2.38)
0.105**
(2.04)
-
Positive
-0.011**
(-2.33)
-0.003*
(-1.74)
-0.012*
(-1.72)
-0.080*
(-1.75)
-
Abnormal
Amihud
All
0.060**
(2.01)
0.071*
(1.83)
0.027**
(2.34)
0.197*
(1.90)
-
Customer*EarnShock
-
-
-
Supplier
-
-
-
Supplier*EarnShock
-
-
-
-0.013
(-0.64)
0.123
(1.49)
-0.081*
(-1.86)
0.007
(1.03)
0.054
(1.26)
-0.028
(-1.35)
0.953
(0.77)
-0.032
(-1.43)
-0.009
(-1.27)
-0.037
(-1.37)
0.584
(1.41)
0.001
(1.05)
Yes
639
0.132
0.012
(0.92)
0.018
(1.30)
0.115***
(2.63)
-0.001
(-1.29)
-0.157
(-0.92)
0.063
(1.20)
-0.919
(-1.01)
0.019
(1.12)
0.043*
(1.72)
0.002
(1.11)
-0.353
(-1.12)
-0.001
(-1.37)
Yes
1,111
0.055
-0.008
(-1.31)
0.222
(1.29)
-0.122
(-1.28)
0.002
(1.49)
0.141
(1.27)
-0.175
(-1.19)
5.784**
(2.44)
-0.019
(-0.68)
-0.118*
(-1.91)
-0.058
(-0.97)
1.309**
(2.35)
0.001*
(1.91)
Yes
1,786
0.093
EarnShock
Business Partner
Business Partner*EarnShock
Customer
Firm Size
ROA
Leverage
Market-to-Book
Volatility
Institution%
Bid-Ask Spread
Trading Volume
Turnover
Analyst Following
Forecast Dispersion
Opportunistic Trade
Year & Industry Dummy
Observations
Adjusted R2
38
Abnormal Short Sales
Negative
0.013
(0.36)
0.008*
(1.85)
-
Positive
-0.016**
(-2.39)
-0.002*
(-1.85)
-
Abnormal
Amihud
All
0.058**
(1.97)
0.071*
(1.82)
-
-
-
-
0.044*
(1.87)
0.141**
(2.02)
0.021*
(1.92)
0.074**
(2.18)
-0.018
(-0.91)
0.097
(1.19)
-0.071*
(-1.74)
0.007
(1.01)
0.080
(1.39)
-0.021
(-1.26)
0.898
(0.72)
-0.039*
(-1.73)
-0.016
(-1.48)
-0.039
(-1.45)
0.402
(1.04)
0.001
(1.14)
Yes
639
0.140
-0.022*
(-1.75)
-0.101*
(-1.82)
-0.005*
(-1.74)
-0.090*
(-1.81)
0.014
(1.03)
0.014
(0.24)
0.111**
(2.54)
-0.001
(-1.34)
-0.156
(-0.90)
0.059
(1.13)
-0.903
(-0.99)
0.022
(1.26)
0.046*
(1.82)
0.002
(1.11)
-0.389
(-1.24)
-0.001
(-1.36)
Yes
1,111
0.065
0.028*
(1.83)
0.221**
(2.06)
0.025*
(1.91)
0.175
(1.10)
-0.007
(-1.27)
0.219
(1.27)
-0.121
(-1.27)
0.003
(1.54)
0.158
(1.30)
-0.177
(-1.19)
5.864**
(2.48)
-0.017
(-0.56)
-0.118*
(-1.89)
-0.061
(-1.01)
1.336**
(2.43)
0.001*
(1.83)
Yes
1,786
0.093
Table 4. Source Firm Shadow Trading Policy and Target Firm Trading
This table presents coefficients from regressions examining whether cross-sectional variation in source (stakeholder)
firm insider trading policies affects shadow trading. We measure shadow trading using two proxies: 1) Abnormal
Short Sales; and 2) Abnormal Amihud, and use the 30 day window prior to earnings announcement shocks as a
proxy for the presence of private information. All variables are defined in Table 1. t-values are reported in
parentheses based on Huber-White Sandwich standard errors clustered at the firm level. All specifications include
year and industry fixed effects. Statistical significance at the 1%, 5%, and 10% level is denoted by ***, **, and *,
respectively.
Dependent variable:
Shock type:
Source Firm Policy:
Constant
EarnShock
Vertical
Vertical*EarnShock
Firm Size
ROA
Leverage
Market-to-Book
Volatility
Institution%
Bid-Ask Spread
Trading Volume
Turnover
Analyst Following
Forecast Dispersion
Opportunistic Trade
Year & Industry Dummy
Observations
Adjusted R2
Abnormal
Amihud
Negative Positive
All
Policy Prohibits Shadow Trading
0.010
-0.011
0.036*
(0.66)
(-1.03)
(1.87)
0.007*
-0.003*
0.163*
(1.78)
(-1.90)
(1.85)
0.019*
-0.003
0.010
(1.70)
(-1.30)
(1.11)
0.069
-0.005
0.042
(0.98)
(-1.28)
(1.28)
-0.029
0.010
-0.017
(-0.86)
(0.16)
(-1.28)
0.279
0.024
0.056
(1.43)
(1.20)
(1.08)
-0.006
0.256
-0.207
(-1.04)
(0.89)
(-1.36)
0.011
-0.017
0.001
(1.38)
(-0.88)
(1.15)
0.096
-0.220
1.157
(0.73)
(-0.47)
(0.79)
-0.150
0.142
-0.276
(-1.03)
(1.60)
(-0.69)
1.131
-1.855
1.488
(1.47)
(-0.44)
(1.36)
-0.038
0.097
-0.016
(-0.88)
(1.42)
(-0.14)
-0.062
0.047
-0.054*
(-1.30)
(1.39)
(-1.67)
-0.062
0.010
-0.103
(-1.15)
(1.40)
(-0.71)
1.356**
-1.111
1.468
(2.13)
(-1.52)
(1.43)
0.001**
-0.001
0.001*
(2.11)
(-1.31)
(1.86)
Yes
Yes
Yes
226
212
591
0.179
0.135
0.141
Abnormal Short Sales
39
Abnormal
Amihud
Negative
Positive
All
No Policy Prohibits Shadow Trading
0.017*
-0.019**
0.088**
(1.86)
(-2.23)
(2.12)
0.018*
-0.004**
0.170*
(1.84)
(-2.06)
(1.86)
0.069***
-0.043**
0.311*
(2.70)
(-2.11)
(1.93)
0.202*
-0.039*
0.118*
(1.87)
(-1.90)
(1.90)
-0.033
0.035
-0.036
(-1.11)
(1.24)
(-1.57)
0.031
0.060
0.306
(1.17)
(0.84)
(1.21)
-0.028
0.171**
-0.097
(-1.28)
(2.26)
(-1.36)
0.002
-0.005
0.002
(1.05)
(-1.52)
(1.08)
0.050
-0.398
0.171
(1.15)
(-1.29)
(1.22)
-0.034
0.111
-0.570
(-1.35)
(0.99)
(-1.54)
0.843
-0.498
11.498**
(0.69)
(-1.28)
(2.13)
-0.040
0.039
-0.034
(-1.10)
(1.20)
(-0.41)
-0.043
0.050
-0.271*
(-1.10)
(1.20)
(-1.81)
-0.045
0.031
-0.194
(-1.19)
(0.85)
(-1.10)
0.205
-0.817
0.554**
(1.45)
(-1.21)
(2.19)
0.001
-0.001
0.001*
(1.42)
(-1.51)
(1.90)
Yes
Yes
Yes
360
399
626
0.234
0.386
0.150
Abnormal Short Sales
Table 5. The Effect of an Exogenous Regulatory Shock on Shadow Trading
This table presents coefficients from regression of the effect of a regulatory change in insider trading laws enacted in
2002 on shadow trading, using an alternative sample over the 10 year period from 1997 to 2007 (excluding the year
of the regulatory shock). All variables are defined in Table 1. In all columns, t-values are reported in parentheses
based on Huber-White Sandwich standard errors estimation clustered at the firm level. All specifications include
year and industry fixed effects. Statistical significance is denoted by *, **, and ***, for significance at the 10%, 5%,
and 1% levels, respectively.
Dependent variable:
Source firm type:
Constant
EarnShock
Post
Post*EarnShock
Firm Size
ROA
Leverage
Market-to-Book
Volatility
Institution%
Bid-Ask Spread
Trading Volume
Turnover
Analyst Following
Forecast Dispersion
Opportunistic Trade
Year & Industry Dummy
Observations
Adjusted R2
Abnormal Amihud
Competitor
1.448***
(6.81)
0.035
(1.48)
0.101**
(2.07)
0.055
(0.68)
0.027***
(2.86)
-0.031
(-1.26)
0.112*
(1.90)
-0.005**
(-2.40)
-0.616*
(-1.80)
-0.851***
(-8.44)
-5.725***
(-2.97)
0.042**
(2.31)
0.091***
(8.72)
-0.081***
(-3.75)
1.029*
(1.66)
-0.000***
(-3.26)
Yes
7,532
0.072
All stakeholders
0.941***
(6.94)
0.082*
(1.84)
0.144***
(3.24)
0.102*
(1.68)
0.017**
(2.56)
-0.105*
(-1.80)
0.090**
(2.32)
-0.006***
(-4.22)
-0.321*
(-1.72)
-0.488***
(-8.81)
-4.736***
(-4.36)
0.033**
(2.37)
0.048***
(7.72)
-0.057***
(-3.72)
1.229*
(1.76)
-0.000***
(-3.30)
Yes
14,885
0.120
40
Business partner
0.340***
(2.86)
0.117**
(2.36)
0.301***
(5.08)
0.124
(1.37)
0.010*
(1.89)
-0.166**
(-2.47)
0.087*
(1.69)
-0.006***
(-3.04)
-0.137*
(-1.66)
-0.237***
(-4.09)
-3.306***
(-2.76)
0.026**
(2.20)
0.021***
(2.96)
-0.020*
(-1.92)
1.540*
(1.78)
-0.000**
(-1.98)
Yes
7,353
0.252
Table 6. Stakeholder Firm Managerial Diversification and Shadow Trading
This table presents coefficients from regressions testing whether managers with stakeholder-relevant private
information opportunistically trade in those stakeholders prior to publicly disclosing their private information. All
specifications include interaction terms (labeled High Incentive and Low Incentive) to capture cross-sectional differences
in managerial incentives to trade, measured using a factor score that includes CEO total wealth, tenure and equity
ownership. High Incentive (Low Incentive) is set to one for source firm managers in the top quartile (managers not
in top quartile) of scores each year, and zero otherwise. We measure shadow trading using two proxies: 1) abnormal
short sales and 2) abnormal Amihud, and we use the 30-day window prior to earnings announcement shocks as a
proxy for the presence of private information. All variables are defined in Table 1. t-values are reported in
parentheses based on Huber-White Sandwich standard errors clustered at the firm level. All specifications include
year and industry fixed effects. Statistical significance at the 1%, 5%, and 10% level is denoted by ***, **, and *,
respectively.
Dependent variable:
Shock type:
Source Firm type:
Constant
EarnShock
High Incentive
High Incentive*EarnShock
Low Incentive
Low Incentive*EarnShock
Firm Size
ROA
Leverage
Market-to-Book
Volatility
Institution%
Bid-Ask Spread
Trading Volume
Turnover
Analyst Following
Forecast Dispersion
Opportunistic Trade
Year & Industry Dummy
Observations
Adjusted R2
Abnormal Short Sales
Negative
Competitor
Business partner
0.022
-0.036
(1.49)
(-1.59)
0.064
0.380***
(1.23)
(2.66)
0.025
0.018**
(0.23)
(2.30)
0.083
0.256***
(0.85)
(3.05)
-0.155
-0.010**
(-1.30)
(2.52)
-0.242
-0.181***
(-1.79)
(-4.83)
-0.006
-0.030*
(-1.20)
(-1.81)
0.047
0.363**
(1.47)
(2.02)
-0.020
-0.064
(-1.17)
(-1.36)
0.001
0.027**
(1.17)
(2.21)
0.010
0.350
(1.04)
(0.85)
-0.091
-0.089
(-0.91)
(-1.44)
0.083
0.572
(1.05)
(1.59)
-0.018
-0.035
(-0.62)
(-0.89)
-0.007
-0.048
(-1.16)
(-0.74)
-0.015
-0.004
(-1.37)
(-1.10)
0.549
0.009
(1.11)
(1.01)
0.001
-0.001
(1.14)
(-1.32)
Yes
Yes
382
257
0.127
0.390
41
Abnormal Amihud
All
Competitor Business partner
0.051**
0.060**
(2.05)
(2.19)
0.088*
0.320*
(1.67)
(1.86)
0.020**
0.017
(2.18)
(1.18)
0.302**
0.017**
(2.30)
(2.13)
-0.014*
-0.166*
(-1.76)
(-1.77)
-0.051*
-0.246*
(-1.88)
(-1.92)
-0.041
-0.044
(-1.02)
(-0.85)
0.531***
0.037
(2.79)
(1.14)
-0.145
-0.159
(-0.91)
(-1.20)
0.007
0.011***
(0.75)
(2.67)
0.786
0.174
(1.10)
(1.25)
-0.218
-0.071
(-0.91)
(-0.41)
0.821
1.848**
(0.87)
(2.18)
-0.042
-0.054
(-1.31)
(-1.00)
-0.172**
-0.244**
(-2.02)
(-2.24)
-0.120
-0.014
(-1.39)
(-1.22)
0.622
2.738**
(0.73)
(2.25)
0.001
-0.001
(0.98)
(-1.42)
Yes
Yes
718
1,068
0.182
0.096
Table 7. CEO Knowledge and Shadow Trading
This table presents coefficients from regressions testing whether managers with stakeholder-relevant private
information opportunistically trade in those stakeholders prior to the public release of their private information. The
specification includes interaction terms to capture cross-sectional differences in managerial knowledge, as measured
based on whether the current source firm CEO has been internally promoted (Insider) or externally recruited
(Outsider). We measure shadow trading using two proxies: 1) abnormal short sales and 2) abnormal Amihud, and use
the 30-day window prior to earnings announcement shocks as a proxy for the presence of private information. All
variables are defined in Table 1. t-values are reported in parentheses based on Huber-White Sandwich standard
errors clustered at the firm level. All specifications include year and industry fixed effects. Statistical significance at
the 1%, 5%, and 10% level is denoted by ***, **, and *, respectively.
Dependent variable:
Shock type:
Stakeholder type:
Constant
EarnShock
Insider
Insider*EarnShock
Outsider
Outsider*EarnShock
Firm Size
ROA
Leverage
Market-to-Book
Volatility
Institution%
Bid-Ask Spread
Trading Volume
Turnover
Analyst Following
Forecast Dispersion
Opportunistic Trade
Year & Industry Dummy
Observations
Adjusted R2
Abnormal Short Sales
Negative
Competitor
Business partner
0.193
-0.996
(1.41)
(-1.07)
0.020*
0.061*
(1.81)
(1.91)
0.013**
0.020***
(2.52)
(2.19)
0.229**
0.297**
(2.46)
(2.59)
-0.017
-0.020**
(-1.24)
(-2.53)
-0.028
-0.265*
(-1.32)
(-1.90)
-0.007
-0.020
(-1.26)
(-1.51)
0.061
0.218
(0.62)
(0.95)
-0.023
-0.083
(-0.20)
(-0.47)
0.001
0.029**
(1.16)
(2.39)
0.077
0.179
(1.28)
(1.47)
-0.114
-0.012
(-1.12)
(-1.06)
0.187
0.702
(1.12)
(1.64)
-0.015
-0.021
(-0.51)
(-0.57)
-0.006
-0.020
(-1.13)
(-1.30)
-0.016
-0.017
(-1.41)
(-1.36)
0.492
0.026
(1.00)
(1.03)
0.001
-0.001
(0.95)
(-1.39)
Yes
Yes
382
257
0.128
0.352
42
Abnormal Amihud
All
Competitor
Business partner
2.391**
2.337*
(2.05)
(1.94)
0.047*
0.039**
(1.89)
(1.99)
0.012**
0.023*
(2.01)
(1.89)
0.108**
0.282**
(2.15)
(2.27)
-0.011
-0.011**
(-1.31)
(-2.08)
-0.179**
-0.098**
(-2.20)
(-2.42)
-0.047
-0.046
(-1.18)
(-0.89)
0.548*
0.052
(1.85)
(1.20)
-0.165
-0.146
(-1.03)
(-1.09)
0.009*
0.012*
(1.85)
(1.76)
0.852
0.254
(1.16)
(1.36)
-0.159
-0.029
(-1.34)
(-1.17)
0.768
1.546
(1.00)
(1.20)
-0.041
-0.055
(-1.22)
(-0.98)
-0.178**
-0.241**
(-2.03)
(-2.14)
-0.117
-0.055
(-1.39)
(-1.01)
0.579*
2.879**
(1.67)
(2.26)
0.001
-0.001
(1.22)
(-0.46)
Yes
Yes
718
1,068
0.181
0.097
Appendix A. Examples of Firm Insider Trading Disclosures
No Explicit Restriction on Shadow Trading
“No Insider shall engage in any transaction involving a purchase or sale of the Company’s
securities, including any offer to purchase or offer to sell, during any period commencing with
the date that he or she comes into possession of Material Nonpublic Information (as defined
below), and ending at the close of business on the second Trading Day following the date of
public disclosure of such information; or, alternatively, the date on which such information is no
longer material…This prohibition includes transactions in the Company’s 401(k) Plan, such as
elections to redirect future contributions or realign existing account balances, that result in a
purchase or sale (intentional or otherwise) of Company stock.”
~ Excerpt from Alliance One International Inc.’s Code of Business Conduct and Ethics
Explicit Restriction on Shadow Trading
“It is both illegal and against Company policy for any employee or director who is aware of
material nonpublic information relating to the Company, any of its customers, suppliers, service
providers or other business partners, or any other company to buy or sell any securities of those issuers
or to pass on the information to anyone else under circumstances that suggest the other person
may buy or sell securities based on the information.”
~ Excerpt from Angiodynamics Corporation’s Code of Business Conduct and Ethics
(emphasis added)
43
Appendix B. Source Firm Shadow Trading Policy and Stakeholder Trading
We present results using an alternative specification examining whether a source (stakeholder) firm’s explicit insider
trading policy prohibiting shadow trading has an effect on shadow trading activities. We measure insider trading
using two proxies: 1) abnormal short sales and 2) abnormal Amihud, and we use the 30-day window prior to
earnings announcement shocks as a proxy for the presence of private information. All variables are defined in Table
1. t-values are reported in parentheses based on Huber-White Sandwich standard errors clustered at the firm level.
All specifications include year and industry fixed effects. Statistical significance at the 1%, 5%, and 10% level is
denoted by ***, **, and *, respectively.
Dependent variable:
Shock type:
Constant
Policy
EarnShock
Business Partner
Policy*EarnShock
Policy*Business Partner
Business Partner*EarnShock
Policy*Business Partner*EarnShock
Firm Size
ROA
Leverage
Market-to-Book
Volatility
Institution%
Bid-Ask Spread
Trading Volume
Turnover
Analyst Following
Forecast Dispersion
Opportunistic Trade
Year & Industry Dummy
Observations
Adjusted R2
Abnormal Short Sales
Negative
Positive
0.818
-0.747**
(1.48)
(-2.18)
-0.047*
0.069
(-1.84)
(1.47)
0.017*
-0.003*
(1.94)
(-1.74)
0.047*
-0.088**
(1.74)
(-2.09)
-0.077*
0.108*
(-1.89)
(1.69)
0.092
-0.134
(1.10)
(-1.12)
0.055*
-0.032
(1.79)
(-1.37)
-0.347**
0.181*
(-2.05)
(1.40)
-0.014
0.014
(-0.58)
(0.74)
-0.022
0.022
(-0.24)
(0.36)
0.147
0.148***
(1.30)
(2.75)
0.010
-0.005
(1.40)
(-1.01)
0.290
-0.107
(1.21)
(-0.53)
-0.025
0.072
(-0.26)
(1.14)
0.199
-0.739
(0.10)
(-0.74)
-0.051
0.034
(-1.49)
(1.37)
-0.007
0.054*
(-0.18)
(1.74)
-0.093**
0.041
(-2.50)
(1.58)
1.028*
-0.843**
(1.91)
(-2.06)
0.001*
-0.001*
(1.84)
(-1.66)
Yes
Yes
438
759
0.220
0.096
44
Abnormal Amihud
All
1.711
(1.60)
0.071*
(1.79)
0.168*
(1.82)
0.211*
(1.84)
0.005
(1.04)
0.226
(1.45)
0.042
(1.05)
0.005**
(2.03)
-0.014
(-1.42)
0.304
(1.53)
-0.156
(-1.23)
0.001
(1.10)
0.402
(0.64)
-0.410*
(-1.76)
1.793**
(2.17)
-0.008
(-0.19)
-0.097
(-1.27)
-0.115
(-1.26)
1.561*
(1.85)
0.001*
(1.71)
Yes
1,217
0.119
Appendix C. Stock Return Methodology to Identify Shadow Trading
In this table, we repeat tests in Table 2 using abnormal stock return, an alternative measure of stakeholder shock.
Sample firms cover Compustat and the Capital IQ database during 2009-2011. All the variables are defined in Table
1. In all columns, t-values are reported in parentheses based on Huber-White Sandwich standard errors estimation
clustered at the firm level. Statistical significance is denoted by *, **, and ***, for significance at the 10%, 5%, and
1% levels, respectively.
Abnormal Short
Sales
Negative
0.014
(1.00)
0.158**
(2.19)
0.017**
(2.41)
0.190**
(2.36)
-
Abnormal
Amihud
All
0.021
(1.34)
0.142*
(1.71)
0.015**
(2.26)
0.126*
(1.89)
-
Customer*StockShock
-
-
Supplier
-
-
Supplier*StockShock
-
-
Control Variables
Year & Industry Dummy
Observations
Adjusted R2
Yes
Yes
163
0.136
Yes
Yes
355
0.153
Dependent variable:
Shock type:
Constant
StockShock
Business Partner
Business Partner*StockShock
Customer
45
Abnormal
Short Sales
Negative
0.011
(1.20)
0.119**
(2.15)
-
Abnormal
Amihud
All
0.018
(1.32)
0.095*
(1.89)
-
-
-
0.015*
(1.86)
0.168*
(1.81)
0.019***
(3.46)
0.229*
(1.79)
Yes
Yes
163
0.121
0.009**
(2.50)
0.072*
(1.90)
0.017*
(1.88)
0.213**
(2.05)
Yes
Yes
355
0.151
Appendix D. Alternative Measure of Earnings Shock
In this table, we repeat tests in Tables 2-5 using an alternative measure of earnings shock, i.e., the difference
between actual EPS and analysts’ consensus forecast. Sample firms cover Compustat and the Capital IQ database
during 2009-2011. All the variables are defined in Table 1. In all the regressions, t-values are reported in parentheses
based on Huber-White Sandwich standard errors estimation clustered at the firm level. Statistical significance is
denoted by *, **, and ***, for significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Main Results
Dependent variable:
Shock type:
Constant
EarnShock
Business Partner
Business Partner*EarnShock
Customer
Abnormal
Short Sales
Negative Positive
0.909**
-0.676*
(2.36)
(-1.91)
0.249*
-0.132*
(1.86)
(-1.83)
0.072**
-0.024*
(2.13)
(-1.90)
0.516**
-0.268
(1.99)
(-1.23)
-
Abnormal
Amihud
All
1.353*
(1.65)
0.388*
(1.91)
0.112**
(2.19)
0.447*
(1.92)
-
Customer*EarnShock
-
-
-
Supplier
-
-
-
Supplier*EarnShock
-
-
-
Control Variables
Year & Industry Dummy
Observations
Adjusted R2
Yes
Yes
438
0.152
Yes
Yes
959
0.107
Yes
Yes
1,589
0.126
46
Abnormal
Short Sales
Negative Positive
0.475
-0.683*
(1.19)
(-1.94)
0.230*
-0.134*
(1.74)
(-1.84)
-
Abnormal
Amihud
All
1.298
(1.60)
0.379*
(1.88)
-
-
-
-
0.063*
(1.90)
-0.222
(-1.46)
-0.135**
(-2.22)
-1.065**
(-2.57)
Yes
Yes
438
0.165
-0.003**
(-2.07)
-0.186*
(1.69)
-0.034*
(-1.84)
-0.294**
(-2.21)
Yes
Yes
959
0.107
0.139***
(2.71)
0.258*
(1.92)
0.097*
(1.78)
0.258**
(2.10)
Yes
Yes
1,589
0.051
Panel B: Source Firm CEO Diversification Incentive
Dependent variable:
Shock type:
Stakeholder type:
Constant
EarnShock
High Incentive
High Incentive*EarnShock
Low Incentive
Low Incentive*EarnShock
Control Variables
Year & Industry Dummy
Observations
Adjusted R2
Abnormal Short Sales
Negative
Customer &
Competitor
Supplier
0.699
0.526
(1.12)
(0.80)
0.474*
1.524*
(1.76)
(1.70)
0.087*
0.058*
(1.74)
(1.87)
0.290
1.023*
(1.34)
(1.88)
-0.098*
-0.156*
(-1.76)
(-1.91)
-0.717
-1.930**
(-1.49)
(-2.03)
Yes
Yes
Yes
Yes
260
178
0.187
0.403
Panel C: Source Firm Successor CEO Type
Dependent variable:
Shock type:
Stakeholder type:
Constant
EarnShock
Insider
Insider*EarnShock
Outsider
Outsider*EarnShock
Control Variables
Year & Industry Dummy
Observations
Adjusted R2
Abnormal Amihud
Negative & Positive
Customer &
Competitor
Supplier
1.597
2.498***
(1.17)
(2.93)
1.028*
0.266**
(1.89)
(2.28)
0.087*
0.009**
(1.79)
(2.11)
0.370
0.134**
(1.59)
(2.13)
-0.111
-0.162*
(-0.78)
(-1.75)
-0.359*
-0.555**
(-1.86)
(-2.07)
Yes
Yes
Yes
Yes
731
858
0.224
0.131
Abnormal Short Sales
Negative
Competitor
Customer & Supplier
0.590
0.546
(0.94)
(0.82)
0.246*
0.553*
(1.80)
(1.73)
0.007**
0.211*
(2.04)
(1.87)
0.087*
0.724**
(1.87)
(2.60)
-0.035
-0.039*
(-1.48)
(-1.84)
-0.424
-0.879**
(-1.58)
(-2.23)
Yes
Yes
Yes
Yes
260
178
0.186
0.400
47
Abnormal Amihud
Negative & Positive
Competitor Customer
1.673
2.552***
(1.21)
(2.86)
0.339*
0.192*
(1.78)
(1.81)
0.111**
0.128**
(2.16)
(2.23)
1.478**
1.007**
(2.27)
(2.29)
-0.135
-0.073*
(-1.35)
(-1.69)
-0.199
-0.679*
(-1.26)
(-1.94)
Yes
Yes
Yes
Yes
731
858
0.225
0.130
Panel D: Regulatory Shock and Shadow Trading
Dependent variable:
Source firm type:
Constant
EarnShock
Post
Post*EarnShock
Controls, Year & Industry Dummy
Observations
Adjusted R2
All stakeholders
-1.283***
(5.99)
0.097*
(1.72)
0.060***
(10.10)
0.510*
(1.80)
Yes
13,839
0.166
48
Abnormal Amihud
Competitor
-1.801***
(-4.55)
0.072*
(1.83)
0.250**
(3.38)
0.373
(0.62)
Yes
6,373
0.192
Business partner
-1.073***
(-3.99)
0.018**
(2.34)
1.040***
(9.64)
0.474
(1.54)
Yes
7,466
0.170
Figure 1 Timeline of Events
Source firm information release via
earnings announcement
t-30
t-365
Non Event Window; (non
earnings announcement
windows only)
t
Event Window. Source firm insiders
use stakeholder relevant private
information to trade in stakeholders
49
t+30
Figure 2 Trading in Target Firms Around Source Firm Earnings Announcement
Panel A: Abnormal Amihud for Source Firm Competitors
Mean
Lower Bound
0.35
Upper Bound
0.3
0.25
0.2
0.15
0.1
0.05
-30
-28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
0
2
4
0
2
4
-0.05
-0.1
-0.15
-0.2
Panel B: Abnormal Amihud for Source Firm Business Partners
Mean
Lower Bound
0.6
Upper Bound
0.5
0.4
0.3
0.2
0.1
-30
-28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
-0.1
-0.2
50
Figure 3 Target Firms Shadow Trading Restriction Policy
Panel A: Source Firms without Policy: Abnormal Short Sales in Competitors (Negative
earnings shock)
Mean
Lower Bound
0.4
Upper Bound
0.3
0.2
0.1
-30
-28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
0
-2
0
2
4
-0.1
-0.2
Panel B: Source Firms with Policy: Abnormal Short Sales in Competitors (Negative
earnings shock)
Mean
Lower Bound
0.25
Upper Bound
0.2
0.15
0.1
0.05
-30
-28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
0
-2
0
2
4
-0.05
-0.1
-0.15
Panel C: Source Firms without Policy: Abnormal Short Sales in Business Partners
(Negative earnings shock)
Mean
Upper Bound 0.7
Lower Bound
0.6
0.5
0.4
0.3
0.2
0.1
-30
-28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
0
-0.1
2
4
-0.2
Panel D: Source Firms with Policy: Abnormal Short Sales in Business Partners (Negative
earnings shock)
Mean
Lower Bound
Upper Bound
0.4
0.3
0.2
0.1
-30
-28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
-0.1
-0.2
51
0
2
4