The Sound of Silence:

What Do We Know When Insiders Do Not Trade?*

George P. Gao and Qingzhong Ma

Cornell University

This draft: October 24, 2013

* We are grateful to Ferhat Akbas, Warren Bailey, Sanjeev Bhojraj, Walter Boudry, Linda Canina, Matt Clayton, Zhi

Da, Frank D'Souza, Julia D. D'Souza, David Feldman, Vyacheslav Fos, Pengjie Gao, Ross Garon, John Griffin,

Levon Goukasian, Yaniv Grinstein, Omrane Guedhami, Michael Johnson, G. Andrew Karolyi, Jan Katz, Hyunseob

Kim, Heather Knewtson, Paul Koch, Piotr Korczak, Dima Leshchinskii, Claire Liang, Bob Libby, Crocker Liu,

Edith Liu, Peng Liu, Xiaomeng Lu, Ken Merkley, Roni Michaely, Pam Moulton, David Ng, Dominik Rösch,

Gideon Saar, Rick Sias, Richard Smith, Mike Sturman, Andrey Ukhov, Abhishek Varma, Samuel Weaver, David

Whidbee, Jinghua Yan, Eric Yeung, Xiaojing Yuan, Kelvin Chunhui Zhang, Wei Zhang, and seminar participants at

Cornell University (Johnson School accounting), Cornell finance brownbag seminars, the 2012 Northern Finance

Association conference, SAC Capital quant seminar, the 2013 Midwest Finance Association conference, the 2013

Applied Finance Conference at St. John’s University, the 2013 SFS Finance Cavalcade, and the 2013 Financial

Management Association (FMA) annual meeting in Chicago (a top ten session) for helpful suggestions, and Irene

Kim and Douglas Skinner for help with constructing the litigation risk data. All errors are our own.

Gao: 314 Sage Hall, Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New

York, 14853. Email: pg297@cornell.edu, phone: (607) 255–8729. Ma (corresponding author): 435 B Statler Hall,

Cornell University, Ithaca, New York, 14853. Email: qm26@cornell.edu, phone: (607) 255–8140.

The Sound of Silence:

What Do We Know When Insiders Do Not Trade?

Abstract

This paper examines stock returns following insider silence, periods of no insider

trading. We hypothesize that, for fear of litigation risk, rational insiders do not sell

own-company shares when they withhold bad news; neither would they buy,

given unfavorable prospects; thus they keep silent. By contrast, insiders sell

shares when they do not anticipate significant bad news. Consistent with this

hypothesis, future returns are significantly lower following insider silence than

following insider net selling. Further, the silence-sell return difference is wider

among firms with worse information environment and firms at higher litigation

risk. In sum, insider silence sounds bad news.

Keywords: Insider trading, Insider silence, Information environment, Litigation

risk

JEL Classification: G12, G14, G18

1

1. Introduction

It is well documented that managers tend to withhold bad news about their firms.1 This

paper examines managers’ insider trading decisions and subsequent stock returns when they

withhold bad news. We hypothesize that, when managers (and insiders in general) withhold bad

news, their rational decision is not to sell own-company shares on the bad news, as doing so

involves high levels of litigation risk. This is because shareholders launch securities class action

lawsuits following large stock price drops on the basis of 10b-5, mostly alleging that insiders

knew the adverse information but failed to disclose it to the market.2 Insider selling prior to the

disclosure of the information would be taken as evidence that insiders knew the information.3 By

contrast, lack of insider selling undercuts plaintiffs’ allegation that insiders knew the

information. Thus, for fear of shareholder litigation, rational insiders do not sell own-company

shares while possessing significant negative information. Neither would they buy, given the

unfavorable prospects. Hence, insider silence, a period of no insider trading, is associated with

bad news.4 On the other hand, insiders selling shares indicates that they do not expect as bad

news in the future.5 This litigation risk hypothesis thus implies that insider silence is associated

with significant negative future returns, and insider net selling is associated with not-so-negative

1 See, for example, empirical evidence in Kothari, Shu, and Wysocki (2009) and survey evidence in Graham,

Harvey and Rajgopal (2005). Sletten (2012) and Tse and Tucker (2010) provide empirical evidence of clustering in

bad news announcements, which is predicted if firms withhold bad news (Dye and Sridhar, 1995; Acharya,

DeMarzo, and Kremer, 2011); Rogers, Shrand, and Zechman (2013) provide evidence of managers tacitly

withholding industry-wide bad news. Theories on managers withholding bad news include Dye (1985), Jung and

Kwon (1988), Dye and Sridhar (1995), Shin (2003, 2006), and Acharya, DeMarzo, and Kremer (2011). 2 See Arshadi (1998) and Bainbridge (2007) for overviews of insider trading laws and enforcement strategies. For a

recent example of securities class-action lawsuits following large stock price declines, see the case involving

Expedia, http://securities.stanford.edu/1050/EXPE00_01/index.html. 3 See, e.g., Johnson, Nelson, and Pritchard (2007), Rogers (2008), Rogers, Van Buskirk, and Zeckman (2011).

4 One might argue that insider silence could also be related to good news, such as a takeover offer. Such a possibility

adds noise to the relation between insider silence and negative future returns and biases against us in empirical tests.

Section 4.2 examines this possibility in greater detail and shows that, with a six-month window to measure insider

trading activity, takeover cases do not significantly contaminate insider silence as a negative signal. 5 Numerous studies show that insider sales are inferior as a return predictor (Jaffe, 1974; Finnerty, 1976; Seyhun,

1986; Rozeff and Zaman, 1988; Lin and Howe, 1990; Seyhun, 1998; Noe, 1999), or insider sales have no predictive

ability (Chowdhury, Howe, and Lin, 1993; Lakonishok and Lee, 2001; Jeng, Metrick, and Zeckhauser, 2003).

2

(or non-negative) future returns. That is, the spread in future returns between insider silence and

insider net selling, or the silence-sell spread, is negative.

Further, the hypothesis has two cross-sectional implications. First, when the information

environment of a firm is worse, it is more likely that insiders possess important information that

outsiders do not know. Thus, we expect the silence-sell spreads are wider among firms with

worse information environment. Second, if litigation risk drives insiders’ decision not to sell on

negative information, the silence-sell spreads are wider among firms at higher litigation risk.

We test these implications in a sample of NYSE/ASE/NASDAQ common stocks from

1990 to 2011. Each month we form portfolios of stocks based on their cumulative insider trading

activity over the trailing six months. Stocks whose insiders do not trade over the past six months

form the “silence” portfolio; stocks that insiders net buy and net sell over the past six months

form the “buy” and “sell” portfolios, respectively. We then examine their future returns.

We report the following findings. First, the silence portfolio on average earns a

significant negative abnormal return (adjusted by size and book-to-market ratio) over the

subsequent 12-month period, which is significantly lower than the “sell” portfolio. The silence-

sell spread is a significant -2.50%. This result is consistent with the litigation risk hypothesis.

Second, the silence-sell spread in future returns varies systematically in the cross-section.

It is wider for growth stocks, stocks of younger firms, and more volatile stocks. These results are

consistent with the hypothesis that this litigation risk effect is stronger among firms with worse

information environment. Using a composite measure of litigation risk developed by Kim and

Skinner (2012), industry membership (following Francis, Philbrick, and Schipper, 1994), and

sales growth as proxies for litigation risk, we find that the silence-sell spread is wider among

firms with higher ex-ante litigation risk, further supporting the litigation risk hypothesis.

3

The main findings survive numerous robustness checks. The significant negative silence-

sell spread survives alternative estimation methodologies (size and B/M adjusted returns, Fama

and French three-factor alphas); the silence-sell spread also arises in subsequent quarterly

earnings announcement period abnormal returns. The silence-sell spread is not explained by the

underperformance of firms that have just conducted initial public offering (IPO); it is not

explained by a broad list of anomaly variables that predict future returns in the cross section; it is

not explained by insider trading activity prior to the six-month measuring period. Furthermore,

while insiders might also abstain from trading prior to releasing extremely positive news (e.g., a

takeover offer), there is no evidence that insiders abstain from trading over the six-month period

prior to takeover announcements. As a result, takeover cases do not materially contaminate

insider silence as a negative signal. Last, the silence-sell spread does not reverse in the long term

and in fact it continues to widen for at least another two years.

The paper contributes to the insider trading literature. The existing literature focuses

exclusively on insiders’ buying and selling activities and no study examines the phenomenon of

the lack of insider trading. In addition, while it is well known that litigation risk reduces insider

trading during information rich events (e.g., Seyhun, 1992; Jagolinzer and Roulstone, 2009;

Piotroski and Roulstone, 2008), there is no systematic study on how the reduction in insider

trading is related to stock returns. Our paper fills this gap. We show that insider silence, periods

of no insider trading, signals bad news as it predicts significant negative future returns, which are

even lower than when insiders net sell. We further show that this effect is systematically related

to litigation risk and information environment. Our findings offer a potential explanation for the

puzzling results documented in the literature that insider sales, on average, are not informative.

In recent work, Cohen, Malloy, and Pomorski (2012) find that some insider trades (the

4

opportunistic ones) are informative for up to six months. Our paper is related to theirs in that we

find another significant information signal from insider trading activity: the lack of it.

This paper is also broadly related to several recent papers on the price implication of lack

of activity. Bagnoli, Kross, and Watts (2002) show that delays in earnings reports precede price

drops. Marin and Olivier (2008) report that lower insider selling volume over the recent past and

heavy insider selling volume in the distant past predicts higher probability of price crash. Akbas

(2013) reports that stocks experiencing unusually low trading volume over a week before

earnings announcements tend to have more unfavorable earnings surprises. Giglio and Shue

(2013) find that the passage of time after merger announcement is related to the probability of

merger completion. Our paper shows that insider silence, the lack of insider trading, sounds bad

news.

We describe the sample and data in Section 2, report main findings in Section 3, discuss

alternative hypotheses and robustness checks in Section 4, and conclude in Section 5.

2. Sample and data

The sample is based on all NYSE/Amex/NASDAQ common stocks (share code 10 or 11)

covered in CRSP/Compustat merged database from January 1990 to December 2011, a total of

264 year/month cross-sections. The sample starts from 1990 when insider trading data is

available and ends in 2011 so that we have at least one-year future return data. We exclude

stocks whose prior month-end price is lower than $5 and stocks that would be classified into the

lowest NYSE market capitalization decile.6 We also exclude firms with missing or non-positive

book value of equity. Firms are required to be at least one year old.7

6 These two restrictions eliminate low-priced and small stocks, which are likely to have low past returns. As a result,

the average trailing 12-month return of the remaining stocks is 25.5% (see Table 1). Without these restrictions the

average would be around 15.5%. Further, since insiders’ buying and selling activities are more informative among

small firms (e.g., Lakonishok and Lee, 2001; Sias and Whidbee, 2010), excluding these firms reduces the return

5

Stock return data are obtained from the Center for Research in Security Prices (CRSP) at

the University of Chicago and accounting data from Compustat. We follow Fama and French

(1992) to construct firm size and B/M ratio, and Jegadeesh and Titman (2001) to calculate past

returns. Insider trading data are from Thomson Reuters Insider Filing Data Feed. The Securities

and Exchange Commission (SEC) mandates that officers and directors, large shareholders (those

who own 10% or more of the outstanding shares), and affiliated shareholders report their

transactions to the SEC by the 10th

of the month following the transactions (prior to August

2002) or within two days (since August 2002). The database cleaning process largely follows

recent studies (e.g., Lakonishok and Lee, 2001; Piotroski and Roulstone, 2005; Sias and

Whidbee, 2010).8 Only open-market transactions are considered. Defined in equation (1), the net

insider demand (NID) for month j is the total number of shares insiders buy minus the total

number of shares insiders sell over the past six months, normalized by the total number of shares

outstanding at the end of month j-1.

(1)

Portfolios are formed on past insider trading activity. Stocks with no insider trading

activity reported over the past six months form the “silence” portfolio; stocks with positive and

non-positive NID form the “buy” and “sell” portfolios, respectively. Future returns start from

predictability following insiders’ buying and selling activities. In our main analysis we choose to exclude these

stocks to focus on economically significant stocks. Our main findings hold as strongly when low-priced and small

stocks are included (see Section 4.5). 7 For firms that just conducted initial public offerings, this filter allows a six-month period for insiders of these firms

to make insider trading decisions if the IPO lockup period is shorter than six months; for IPO firms with a lockup

period longer than 180 days (see Brav and Gompers, 2003), this filter is not sufficient. We address this issue in

robustness section 4.1. 8 We follow the literature (e.g., Lakonishok and Lee, 2001; Sias and Whidbee, 2010) to “clean” the insider trading

data. Specifically, we use the following filters. We delete duplicate and amended records and records with cleanse

code of “S” or “A” are deleted. Transaction price must be available, and we delete records if the number of shares in

a transaction is below 100. The transaction code is either “P” or “S” for stock transactions. We delete transactions

that involve more than 20% of total shares outstanding, and delete records if the transaction price is outside the

80%–120% range of the CRSP end-of-day stock price.

6

month j. It is worth noting that since we use report date to measure past insider trading activity

(see Lakonishok and Lee, 2001, p.97), such information is publicly available by the time the

portfolios are formed.9

Figure 1 presents, month by month from January 1990 to December 2011, the proportion

of firms with insider net selling, net buying, and insider silence over the trailing six-month

period. The proportion of insider silence is around 35% during early years and generally declines

over time, except for a spike around the end of 2010. The average proportion of insider silence is

24%.10

Figure 1 also shows that insider net selling is more frequent than net buying.

[Insert Figure 1 about here]

Table 1 shows summary statistics of the whole sample and portfolios formed on past

insider trading. The sample average NID over the trailing six months is -0.418%, consistent with

insiders on average being net sellers. The average six-month NID is also consistent with the

literature.11

Compared to stocks in the overall sample, stocks in the silence group tend to be

smaller, have higher B/M ratio, have lower institutional ownership, are less likely followed by

analysts, have fewer analysts following, are slightly younger, and are a bit more volatile. There is

no apparent difference in terms of past stock issuance. Stocks in the silence group tend to have

lower past return, lower accruals, lower past asset growth, and are less profitable. The silence

group also has lower values of KS, FPS, and sales growth, proxies we use for litigation risk (see

section 3.2.2).

9 Our main findings are robust to alternative choices in defining net insider demand. Detailed are in Section 4.5.

10 By definition, firms that fall in the “silence” group in a month are more likely to fall in the “silence” group over

the subsequent months. That is, the “silence” portfolio membership is sticky over time. Indeed, with a probability of

48%, a firm in the “silence” group will remain in the “silence” group six months later. This number indicates that on

average over half of the “silence” portfolio members are replaced during a half-year period. 11

The average quarterly NID (unreported) for our sample is -0.246, slightly more negative than the corresponding

number reported in Sias and Whidbee (2010, p.1551), which is -0.145%. Because we exclude the very small stocks,

which tend to have more positive (or less negative) net insider demand, the average NID of our sample is larger (in

magnitude) than that reported in Sias and Whidbee (2010).

7

Note that for all stocks, the average return over the trailing 12 months is 25.5%. This

average return is higher than one would expect, mainly because the sampling procedure

eliminates low-priced and small stocks, which are likely to have low past returns.12

Stocks that

insiders net buy have lower returns than stocks that insiders net sell, consistent with the literature

that the contemporaneous correlation between net insider demand and stock returns is negative

(e.g., Sias and Whidbee, 2010). In addition, stocks in the “buy” portfolio are smaller with higher

B/M ratios, consistent with the notion that insiders of larger firms tend to sell and insiders are

contrarian (Seyhun, 1986; Rozeff and Zaman, 1998; Piotroski and Roulstone, 2005).

[Insert Table 1 about here]

3. Results

3.1. Insider silence and future returns

We take three approaches to examining future returns following insider trading decisions.

First is the cumulative returns over a holding period of one, six, and 12 months, both raw returns

and cumulative abnormal returns adjusted by size and B/M; second is the abnormal returns based

on Fama and French (1993) three-factor regressions; and the third is the abnormal returns during

subsequent earnings announcement periods.

3.1.1. Cumulative returns, both raw and adjusted

We first examine the subsequent raw returns and returns adjusted by size and B/M over

the one-, six-, and 12-month holding periods. The methodology of calculating the cumulative

abnormal returns are described in the Appendix. Table 2 presents the raw (Panel A) and adjusted

(Panel B) returns for the silence, buy, sell portfolios, as well as the silence-sell and buy-sell

12

Without excluding the low-priced small stocks, the average 12-month return for the period of 1990-2011 is

15.5%. Our main conclusion is robust with or without excluding these stocks. See Section 4.5 for detail.

8

spreads. All statistical tests (this table and all other tables throughout this paper) are based on

Newey-West standard errors.

In Panel A of Table 2, the silence portfolio on average earns raw returns of 0.90%,

5.34%, and 11.08% over the one-, six-, and 12-month holding period, respectively. The sell

portfolio earns raw returns of 1.01%, 6.26%, and 13.10% over the one-, six-, and 12-month

holding period, respectively. The silence-sell spreads are all negative over these holding periods,

and significantly so for the longer holding periods. There is some evidence that the buy portfolio

earns higher returns than the sell portfolio in the one-month holding period.

Because firm characteristics are different among these portfolios, it is more informative

to examine abnormal returns. Panel B of Table 2 presents such results, where we adjust the

returns by size and B/M. Later in calendar-time portfolio regressions and Fama-MacBeth

regressions we also control for momentum and other potentially relevant variables. Panel B

shows that the silence portfolio earns abnormal returns of -0.15% (t=-2.41), -0.87% (t=-3.60),

and -1.74% (t=-4.29) over the one-, six-, and 12-month holding periods, respectively. By

comparison, the sell portfolio earns 0.00% (t=0.01), 0.30% (t=0.87) and 0.75% (t=1.26) over the

one-, six-, and 12-month holding periods, respectively.13

As a result, the silence-sell spreads are -

0.15% (t=-2.77), -1.17% (t=-4.19), and -2.50% (t=-4.10) over the one-, six- and 12-month

holding periods, respectively. These results support the litigation risk hypothesis.

The two panels also present the returns following insider net buying and the buy-sell

spreads. In the very short holding period (one month), returns are higher (with marginal

13

Note it is well documented in the literature that insider sales do not predict significant negative returns. For

example, Lakonishok and Lee (2001, p.102), in their comprehensive study of insider trades, report a small one-year

abnormal return of -0.5% for low net purchase ratio (a measure similar to net insider demand in our paper); the

abnormal return following low net purchase ratio is a positive 1.6% for larger firms; even more telling, Lakonishok

and Lee (2001, p.106-7) find that a strong insider selling signal actually predicts positive, instead of negative, future

returns. Similarly, Jeng, Metrick, and Zeckhauser (2003, p.456) report that there is no noticeable abnormal returns

over the 100 days after insider selling; Sias and Whidbee (2010, 1575) find that the 6-month abnormal returns

following net insider selling are 0.717% for medium-cap stocks and 0.045% for large-cap stocks.

9

statistical significance) following insider net buying than following net selling. For longer

holding periods, however, returns following insider net buying and following net selling are

largely indistinguishable.14

Note that in our sample the very small stocks are excluded. In

unreported analysis when these (many) stocks are added back in the sample, we find that the

buy-sell spreads are positive and more significant. These results are consistent with the prior

literature that insider trading activities predict future returns mostly among small firms (e.g.,

Lakonishok and Lee, 2001; Sias and Whidbee, 2010).15

[Insert Table 2 about here]

3.1.2. Monthly alphas

Alternatively, we examine the future returns following insider trading decisions using

monthly alphas from asset pricing models such as the one by Fama and French (1993).

Specifically, Each month j from January 1989 to December 2012, portfolios are formed on

preceding insider trading activity over the trailing six months. Stocks with no insider trading

activity over the prior six-month period form the “silence” portfolio; stocks with positive and

non-positive net insider demand (NID) over the prior six-month period form the “buy” and “sell”

portfolios, respectively. The portfolios are held over months j+1 to j+k (k=1, 6, or 12). Portfolio

returns are equal weighted (value weighted) across their constituent stocks. The overall portfolio

return for month j is the equal-weight average month-j returns of the strategy implemented in the

prior month and strategies formed up to k (k=1, 6, or 12) months earlier. These monthly

portfolios (from January 1990 to December 2012) are then regressed on the Fama and French

14

Forming finer portfolios within the net selling and net buying groups does not reveal any significant differences

either. The lack of significance of the buy-sell spread is largely due to our sampling procedure in which we exclude

the very small stocks. Untabulated analysis shows that adding back these stocks into the sample does generate a

positive and marginally significant buy-sell spread in adjusted returns over the one-year period (0.83% with t=1.52). 15

It is worth noting that a silence-sell return spread of 2.50% does not appear attractive for an aggressive trading

strategy. In this paper we simply point out that insider silence has information content, which is negative. For

potential trading strategies that utilize this phenomenon, we refer readers to contemporaneous work by Ma (2013)

and Ma and Ukhov (2013).

10

(1993) three-factor model. Table 3 presents the equal-weight (Panel A) and value-weight (Panel

B) monthly alphas for the holding period of one, six, and 12 months, respectively. Adding the

momentum factor, Pastor and Stambaugh (2003) liquidity factor, and Sadka (2006) liquidity

factor generates similar results and are thus not reported.

Panel A shows that the silence portfolio generates equal-weight monthly alphas of -

0.15% (t=-2.54), -0.19% (t=-2.95) and -0.21% (t=-2.77) for the strategy of holding one, six, and

12 months, respectively. By contrast, the monthly alphas of the sell portfolio are much smaller

and not significant. The silence-sell spreads are -0.16% (t=-2.62), -0.24% (t=-4.23) and -0.26%

(t=-4.89) for the strategy of holding one, six, and 12 months, respectively. These results are

consistent with those presented in Table 2 based on the size and B/M adjusted cumulative

abnormal returns. The value-weight results are statistically weaker. At the value-weight base, the

silence-sell spreads are -0.13% (t=-1.42), -0.14% (t=-1.78) and -0.13% (t=-1.85) for the strategy

of holding one, six, and 12 months, respectively. Since value-weight results are dominated by the

largest stocks, the results suggest that the silence-sell spread is weaker among the largest stocks.

On the other hand, in both equal- and value-weight bases, the buy-sell spreads are not significant

in any holding period. This result is also consistent with that in Table 2.

[Insert Table 3 about here]

3.1.3. Earnings announcement abnormal returns

The results so far suggest that investors fail to fully incorporate the information contained

in preceding insider trading activity. To further investigate whether investors fail to incorporate

the negative information in insider silence and whether they are systematically surprised when

the relevant information is subsequently disclosed to the market, we examine the earnings

announcement period abnormal returns. Specifically, we extract quarterly earnings

11

announcement dates from Compustat and calculate three-day announcement period abnormal

returns adjusted by CRSP equal-weight daily market returns (i.e., an event window [-1, +1]

covering one trading day before to one day after the earnings announcement date). Within an

insider trading portfolio, we then calculate the average three-day abnormal returns of its

constituent firms for the first four quarterly earnings announcements following portfolio

formation. Table 4 presents the time-series average of the abnormal returns for the whole sample

and the three portfolios (“silence,” “buy,” and “sell”) for each of the four quarterly

announcements and the four-quarter average.16

Due to data overlapping, tests on the time-series

average earnings announcement abnormal returns are Newey-West adjusted with two lags for

quarterly data and 11 lags for the four-quarter average.

Table 4 shows that the silence portfolio is associated with negative earnings

announcement returns during each of the four announcements, with an average return of -0.08%,

which is significant at the 5% level. By contrast, the sell portfolio is associated with positive

announcement period abnormal returns, with an average of 0.06%. As a result, the silence-sell

spreads are negative and significant for all four quarters, with an average of -0.15% (t=-5.06).

In sum, the results in Table 4 provide further support for the litigation risk hypothesis that

insider silence is associated with negative information. Further, the evidence suggests that

investors fail to incorporate the insider silence information into stock prices.

[Insert Table 4 about here]

3.2. Cross-sectional variation

The litigation risk hypothesis posits that when insiders possess significant negative

information, for fear of shareholder litigation they keep silent by abstaining from trading shares.

16

We follow the methodology developed in Chopra, Lakonishok, and Ritter (1992), which is used in Jegadeesh and

Titman (1993), La Porta, Lakonishok, Shleifer, and Vishny (1997), and Titman, Wei, and Xie (2004), among others.

12

The hypothesis has two cross-sectional implications. First, when the information environment of

a firm is worse, it is more likely that insiders possess important information that outsiders do not

know. Thus, we expect the silence-sell spreads are wider among firms with worse information

environment. Second, if litigation risk drives insiders’ decision not to sell on negative

information, the silence-sell spreads are wider among firms at higher litigation risk. This section

examines these two cross-sectional implications. Here we focus on the one-year cumulative

abnormal return.

3.2.1. The role of information environment

We choose book-to-market (B/M) ratio, firm age, and stock volatility as proxies for

information environment (e.g., Zhang, 2006; Choi and Sias, 2012). Information about firms with

low B/M is more ambiguous (Daniel and Titman, 1999); younger firms have worse information

environment; and volatile stocks have worse information environment. We do not use firm size,

institutional ownership, or the number of analysts following as proxies for information

environment here, because these firm characteristics are also related to litigation risk and because

there are opposite implications on the silence-sell spread. For instance, on one hand, smaller

firms have a worse information environment, implying a wider silence-sell spread. On the other

hand, smaller firms are at lower litigation risk (e.g., Kim and Skinner, 2012), implying a

narrower silence-sell spread. These two opposite effects partially cancel out. Thus, the cross-

sectional implication on these variables is unclear ex ante and becomes an empirical issue.

To explore the role of information environment on the silence-sell spread, we first form

terciles on one of the proxies (B/M, age, and volatility), and within each tercile we form

portfolios on preceding insider trading activity and examine their future returns. Table 5 reports

the results of cumulative abnormal returns over a one-year holding period. Results with one- or

13

six-month holding periods lead to the same conclusions. Panel A shows that the silence-sell

spreads are -4.29% (t=-4.85) among growth stocks (the bottom 1/3 stocks based on B/M) and -

1.30% (t=-2.73) among value stocks (the top 1/3 stocks based on B/M). The growth-value

difference between the two spreads is -2.98% (t=-4.30). Note all t-statistics throughout this paper

are based on Newey-West standard errors. To the extent that lower B/M indicates worse

information environment, the result in Panel A is consistent with our hypothesis that the silence-

sell spread is wider among firms with worse information environment.

In Panel B of Table 5 we form subsamples on firm age. As mentioned earlier, younger

firms have worse information environment. Thus we expect wider silence-sell spreads among

younger firms. Indeed, Panel B shows that the silence-sell spreads are -3.58% (t=-4.77) among

younger firms (lower 1/3 based on firm age) and -1.19% (t=-2.17) among the older firms (top 1/3

based on firm age), leading to a significant young-old difference of -2.39% (t=-3.82). Similarly,

Panel C shows that the silence-sell spreads are -4.76% (t=-4.83) among the most volatile stocks

(top 1/3 on stock volatility) and -1.49% (t=-3.47) among the least volatile stocks (bottom 1/3 on

stock volatility), leading to a significant high-low difference of -3.27% (t=-3.70). To the extent

that firm age and volatility proxy for information environment, these results are consistent with

the hypothesis that firms with worse information environment have a wider silence-sell spread.17

[Insert Table 5 about here]

3.2.2. The role of litigation risk

17

In untabulated analysis, we examine the silence-sell spreads among subsamples formed on firm size. Stocks with

market cap below the NYSE 20th

percentile form the “small” group; stocks with market cap above the NYSE 50th

percentile form the “large” group; all other stocks form the “medium” group. The silence-sell spreads are -1.95%

(t=-2.23) among small firms, -1.69% (t=-2.08) among large firms, and -3.54% (t=-5.46) among medium-sized firms.

Consistent with the conjecture that the relation is ex-ante ambiguous between firm size and the silence-sell spread in

future returns, there is no significant difference between the small and large groups (0.26% with a t=0.29).

14

We use three proxies for litigation risk: KS, FPS, and sales growth. Recent literature on

litigation risk suggests a composite measure developed by Kim and Skinner (2012, KS).18

Note

that the KS composite (see Appendix for details) is built upon FPS (following Francis, Philbrick,

and Schipper, 1994), firm size, sales growth, past stock returns, return skewness, volatility, and

trading volume, some of which are also related to information environment. We then choose two

of the components (FPS and sales growth) as relatively “pure” proxies for litigation risk.19

If

litigation risk is behind the spread between insider silence and net selling, one expects that the

spread is wider among firms with higher level of litigation risk.

Table 6 presents the results. In Panel A, KS is the proxy for litigation risk. Among firms

with low KS (bottom 1/3) the silence portfolio earns abnormal returns of -2.05% (t=-1.91) while

the return for the sell portfolio is 0.04% (t=0.03), resulting in a silence-sell spread of -2.09% (t=-

4.94). Among firms with high KS (top 1/3) the silence-sell spread is -3.91% (t=-4.07). The high-

low difference is a significant -1.82% (t=-2.30). Thus, silence-sell spread is wider among firms at

higher litigation risk as measured by KS, consistent with the litigation risk hypothesis.

In Panel B the litigation risk proxy is FPS (1994), which is based on industry

membership. Among firms that do not belong to the industries with higher litigation risk, the

silence-sell spread is -1.96% (t=-2.94), while the spread is -3.88% (t=-4.18) among firms that

belong to the industries with higher litigation risk. The difference between the two groups is a

significant -1.92% (t=-1.96). In Panel C the proxy for litigation risk is sales growth. Firms with

low sales growth (bottom 1/3) have a silence-sell spread of -1.66% (t=-2.56) while firms with

high sales growth (top 1/3) have a silence-sell spread of -4.12% (t=-4.21), resulting in a high-low

18

We gratefully appreciate the advice from Professors Kim and Skinner on constructing this variable (KS). 19

We do not choose the others because they are derived from past stock returns and proxy for the size of damage in

shareholder litigation. Such measures are sensitive to how the measuring window overlaps with the class period. By

contrast, FPS is simply industry membership and sales growth is based on past two years’ sales data, both relatively

less sensitive to how closely their measuring window overlaps with the class period.

15

difference of -2.45% (t=-2.69). This result provides further evidence that firms at higher

litigation risk are associated with a wider silence-sell spread.

[Insert Table 6 about here]

3.2.3. Fama-MacBeth regressions

This section presents Fama-MacBeth regressions. Compared to portfolio analysis, linear

regressions are more flexible to control for other firm/stock characteristics. The dependent

variable is the one-year abnormal returns following portfolio formation. The main independent

variable of interest is a binary variable “silence,” which is equal to one if for a stock/month

insiders have kept silent over the past six months, and zero otherwise. We control for firm size,

book-to-market, and past stock returns, in addition to a binary variable “buy,” which is equal to

one if insiders of the firm have net bought over the past six months. The benchmark case is when

insiders net sell. Thus, the coefficient of the “silence” variable represents the silence-sell spread

after controlling for other variables.

Table 7 presents seven regression models. Each column shows the time-series averages

of 264 cross-sectional regression coefficients with t-statistics presented in parentheses, which are

based on Newey-West standard errors. Model 1 shows a significant coefficient of -2.69% (t=-

4.66) on the “silence” variable, representing the silence-sell spread after controlling for size,

B/M, and past return. The coefficient on “buy” variable is not statistically significant.20

These

results reconfirm the portfolio results as presented in Table 2.

In Model 2, we add the interaction term between “silence” and B/M. The variable B/M

itself is already in the list of control variables. In this and all subsequent regressions in this table

that have interaction terms with the “silence” variable, all the interacted variables are demeaned

20

In an unreported regression, in which a continuous variable of net insider demand is added as a control variable ,

the “silence” variable has a coefficient of -2.68% (t=-4.46), similar to that in Model 1 of Table 7.

16

so that the coefficient on “silence” itself means the silence-sell spread when the interacted

variable is at the sample mean. In Model 2, the coefficient on this interaction term represents

how the silence-sell spread changes with B/M. The coefficient is a significant positive 1.70%

(t=6.14). This result further confirms that the silence-sell spread is wider (more negative) for

firms with lower B/M ratio (growth firms), a result consistent with the hypothesis that the

silence-sell spread is wider among firms with worse information environment. This conclusion is

further supported in Models 3 and 4, where we add the interaction term between “silence” and

firm age (model 3) or volatility (model 4). Both coefficients on the interaction terms are

significant with the expected signs. That is, the silence-sell spread is wider (more negative) for

younger firms and for stocks that are more volatile. In sum, Models 2 to 4 reconfirm the portfolio

results in Table 5 that the silence-sell spread is wider with worse information environment.

In Model 5, we add an interaction term between “silence” and KS, the composite proxy

for litigation risk. If the silence-sell spread is due to insiders’ fear for shareholder litigation risk,

this spread should be wider for firms with higher levels of ex-ante litigation risk. That is, the

interaction term should have a significant negative coefficient. Model 5 shows that the

interaction term silence * KS indeed has a significant negative coefficient of -0.53 (t=-2.17). In

Models 6 and 7, we replace KS with FPS and sales growth, respectively, and find that both

interaction terms have significant negative coefficients. In sum, the regression models 5 to 7

reconfirm that the silence-sell spread is wider among firms at higher litigation risk.21

21

Note that among the three proxies for information environment and three proxies for litigation risk, some are

closely related. For example, the composite measure of litigation risk (KS) has a component of volatility, a

component of FPS, and a component of sales growth. Thus, a regression model with all interaction terms included

could be difficult to interpret. Indeed, when all interaction terms are included (together with the six proxy variables

themselves), the coefficient on KS becomes positive, which is weakly significant. All other interaction terms still

have the expected signs, and the coefficients on silence*volatility and silence*ln(B/M) remain statistically

significant. Unreported for brevity, these results are available upon request.

17

Note that in all seven models, the coefficients on “silence” are all significant at the 1%

level, ranging from -2.52% to -2.85%. These results suggest that when the proxy variables take

the sample mean values, the silence-sell spreads are all significant. Overall, Table 7 provides

further results, in a regression setting, that the silence-sell spread is negative, which

systematically varies with proxies for information environment and proxies for ex-ante litigation

risk. These results are all consistent with the litigation risk hypothesis that, for fear of litigation

risk, insiders abstain from trading on negative information.

[Insert Table 7 about here]

4. Alternative hypotheses and robustness checks

4.1. Other information signals

The evidence so far indicates that insider silence is a signal of negative information that

predicts future returns. It is important to check whether it is actually just a proxy for other

information signals. In this section we consider three types of signals.

First, we check whether insider silence is a proxy for underperformance of firms that

have just conducted initial public offering (IPO). Note that we require sample firms at least one-

year old, so that most sample firms have passed the IPO lockup period (see Brav and Gompers,

2003, p.7) and insiders of the sample firms have at least six months to make insider trading

decisions without the IPO lockup restriction (see section 2). For the remaining IPO firms that

have lockup periods longer than six months, their insiders are still restricted to sell shares during

our six-month window to measure insider trading. Thus, it is important to check whether our

results are driven by the underperformance of these IPO firms (e.g., Ritter, 1991). To account for

this possibility, we add in the Fama-MacBeth regressions an IPO dummy variable which is equal

18

to one if the stock is younger than three years (relative to its first appearance in the CRSP

database).

Second, we check whether it is likely that insiders keep silent in response to some

publicly available signals that predict future returns. For example, firms that have issued stocks

in the past tend to underperform (Loughran and Ritter, 1995), and firms that have higher levels

of accruals tend to underperform (Sloan, 1996). If insiders abstain from trading as a response to

past stock issuance or accruals, then insider silence should not have any information content after

controlling for these other public information signals. We choose the five return anomaly

variables examined in Fama and French (2008) as proxies for the public information signals.

They are net stock issues, momentum (already in our original regressions), accruals, asset

growth, and profitability.

Third, in our hypothesis we implicitly assume that insiders have shares available to sell

but, for fear of shareholder litigation, choose not to. It is possible, however, that the outcome of

“no trading” is due to portfolio constraints as discussed in Marin and Olivier (2008). That is,

insiders cannot short sell their own company shares, insiders have concerns about corporate

control, or the shares insiders hold are restricted and not available for sale. In the framework of

Marin and Olivier (2008), uninformed investors, when observing reduced insider trading

following a period of heavy selling, infer that insiders possess bad news and update their beliefs

on expected payoffs and risk, leading to a price crash.22

To address this possibility, we control

for the net insider demand over the six-month period prior to the current time period. That is, we

control for net insider demand over months -12 to -7 relative to portfolio formation.

22

Strictly speaking, the insider silence phenomenon we examine is fundamentally different from the reduced insider

selling volume studied in Marin and Olivier (2008). In addition, Marin and Olivier (2008) model the probability of

price crash only but do not predict subsequent stock returns, which are the focus of our paper.

19

The Fama-MacBeth regression results are presented in Table 8. Based on the first model

in Table 7, Model 1 in Table 8 adds a dummy variable “IPO firm,” which is equal to one if the

stock is younger than three years. This variable has a significant negative coefficient of -2.49%

(t=-2.43), consistent with IPO underperformance. The coefficient on “silence” remains negative

and significant, with a magnitude similar to that in the first model of Table 7. Thus, Model 1 of

Table 8 suggests that IPO underperformance does not explain the insider silence effect.

In Model 2, we add the anomaly variables: net stock issues (NS), accruals (Ac/B), asset

growth (dA/A), and profitability (Y/B) in the regression. Note that momentum is already in the

list of control variables. Consistent with the literature, firms that issue stock in net underperform

(Ikenberry, Lakonishok, and Vermaelen, 1995; Loughran and Ritter, 1995; Daniel and Titman,

2006; Pontiff and Woodgate, 2008; McLean, Pontiff, and Watanabe, 2012); higher accruals are

associated with lower returns (Sloan, 1996); firms that grow faster in the past tend to earn lower

returns (Fairfield, Whisenant, and Yohn, 2003; Titman, Wei, and Xie, 2004; Cooper, Gulen, and

Schill, 2008); and higher profitability is associated with positive returns (Haugen and Baker,

1996; Cohen, Gompers, and Vuolteenaho, 2002). These results are largely consistent with Fama

and French (2008). Nevertheless, the coefficient on the “silence” dummy remains negative and

significant at -2.77 (t=-4.53), even after controlling for these anomaly variables. Although the

five anomaly variables (with past return included) do not represent all publicly available

information signals, the evidence in Model 2 is clear that these important publicly available

informational signals do not explain the underperformance associated with insider silence.

In Model 3, we add past trades by insiders (NID over months -12 to -7). The coefficient

on past trades is not significant. By contrast, the coefficient on “silence” is virtually unchanged.

Thus, insider silence contains new information beyond what is contained in earlier insider

20

trading activities. Model 4 adds all aforementioned variables in the regression. The coefficient on

“silence” remains negative and significant with a similar magnitude (-2.69% with t=-4.43).

Further, in an untabulated analysis, we add short interest as a control and find that the coefficient

on “silence” remains significant and negative (-2.11% with t=-3.70) and that the short interest

variable carries a significant negative coefficient, consistent with the literature.

In sum, the evidence in Table 8 suggests that the information contained in insider silence

is not explained by IPO underperformance, a broad list of anomaly variables, and insider trading

activity of earlier time period.

[Insert Table 8 about here]

4.2. Insider silence and positive information: the takeover cases

We motivate the phenomenon of insider silence from the perspective of shareholder

litigation, which represents the primary enforcers of insider trading regulations. Because

shareholders are more likely to launch lawsuits following large price drops, the litigation risk is

higher when insiders sell on negative news than when insiders buy on positive news.23

This

argument establishes that insider silence is more likely associated with negative information. The

empirical evidence presented so far confirms this hypothesis. There are, however, situations in

which insiders also abstain from trading when anticipating large stock price jumps. After all, the

regulation on insider trading, in particular 10b-5, applies to both buying and selling on

information. Thus, it is important to consider the potential relation between insider silence and

positive informational events. Of interest is the insider trading decisions prior to takeover offers,

as stock prices on average experience significant jumps at the public announcement. Indeed,

23

See Bettis, Coles, and Lemmon (2000, p.208), Ke, Huddart, and Petroni (2003, p.316), Cheng and Lo (2006, p.

821), Piotroski and Roulstone (2008, p.410), Rogers (2008, p. 1269), Lee, Lemmon, Li, and Sequeira (2012),

Cohen, Malloy, and Pomorski (2012, p. 1040, model 4, Panel B of Table IX), among others.

21

Agrawal and Nasser (2012) document that insiders of takeover targets scale back from selling

shares prior to the public announcement.

In this section we reconcile the average negative relation between insider silence and

future returns as we have shown so far in this paper with a possible positive relation in the case

of takeover offers. Specifically, we examine how frequent takeovers are, how likely insiders

abstain from trading over a six-month period prior to takeover announcements, and the extent to

which takeover events contaminate the average negative signal in insider silence. We first

classify every firm/month as either targeted or non-targeted. A firm/month is targeted if this firm

receives a takeover offer over the subsequent 12-month period; otherwise, it is non-targeted. We

then form portfolios based on both preceding insider trading (silence, buy, and sell) and

subsequent takeover status (targeted vs. non-targeted).

Table 9 presents the average numbers of stocks in the portfolios and their future one-year

returns. For convenience we also present the whole sample data. For the whole sample of

average 2,487 stocks, 141 (5.7%) are classified as targeted during the subsequent 12-month

period. These targeted firms receive an annual return of 20.58% on average, which is consistent

with the literature on mergers and acquisitions (e.g., Andrade, Mitchell, and Stafford, 2001).

Accordingly, the non-targeted portfolio earns a significant negative return of -1.18% (t=-2.64).

Interestingly, among the firms that are targeted, insiders net sell in more than half (74/141) of the

firms over the past six months, net buy in about 20% (28/141) of the firms, and keep silent in

only 28% (39/141) of them. Thus, there is no evidence that insiders predominantly abstain from

trading own-company shares over the six-month period prior to the public announcement.24

After

24

This result is probably related to how long it takes for a takeover deal from deal initiation to the public

announcement. The merger literature seems to use a two-month (Schwert, 1996) or three-month (e.g., Boone and

Mulherin, 2007) period to measure the price runup. If the start of the stock price runup period is somewhere near the

start of the time point insiders refrain from buying shares, it is not surprising that insider silence does not emerge as

22

excluding these targeted firms, the silence portfolio earns a return of -3.10% over the subsequent

12-month period, and the sell portfolios earns -0.39%, resulting in a silence-sell spread of -2.72%

(t=-4.42). This return spread is comparable to the whole sample spread of -2.50% (t=-4.10).

The evidence in Table 9 suggests that for the sample we examine in the paper, the

probability of receiving a takeover offer over a one-year period is not trivial but relatively low

(5.7%); more importantly, there is no evidence that insiders predominantly keep silent over the

six-month period prior to the public announcement; the silence-sell spreads are comparable with

or without excluding these takeover cases. Therefore, the conclusion is in line with our

hypothesis: Insider silence on average signals negative insider information.

[Insert Table 9 about here]

4.3. Long term performance

The results so far suggest that insider silence is associated with negative returns over the

future one, six, and 12 months. In this section we study the long-term stock performance

following preceding insider trading activity. We have two goals in mind. First, following the

litigation risk hypothesis, if insiders keep silent because they possess negative information, a

priori it is unclear whether the negative information is completely incorporated into stock prices

over a one-year period. An investigation of the long-term performance indicates how long it

takes for the negative information to be incorporated into prices. Second is to exclude the

possibility that the results are just by chance. If the relation between insider silence and negative

future returns is by chance, it is expected that the negative returns reverse over the long term.

Table 10 presents the returns of the silence and sell portfolios over the 2nd

& 3rd

years

following portfolio formation. The silence firms earn an average of -1.65% (t=-4.53) and -0.64%

an overwhelming phenomenon during a six-month period prior to the public announcement. After all, the insider

trading pattern of the targeted firms over the first three or four months of the six-month period is no different from

that of a typical firm.

23

(t=-1.86) over the 2nd

and 3rd

subsequent years, respectively. Thus, there is no reversal over the

long term, and it takes more than a year for the negative information to be incorporated into

prices. By comparison, the sell portfolio earns positive returns over each of the two subsequent

years. As a result, the silence-sell spreads are negative and significant over the two years. The

evidence clearly rejects the possibility that the main results in the paper are by chance.

It is worth noting that the long-term returns following insider net selling are not negative.

These results are consistent with the existing literature. For example, Lakonishok and Lee (2001,

p.102) report abnormal returns to a portfolio of low net purchase ratio (more likely net selling)

are 1.0% and 1.2% for the second and third years, respectively, following portfolio formation.

By contrast, the corresponding returns to a portfolio of high net purchase ratio (more likely net

buying) are 0.1% and -0.1% over the second and third years, respectively. Similarly, Sias and

Whidbee (2010, p.1575) report a two-year abnormal return of 1.93% and 0.28% for mid- and

large-cap stocks, respectively, following net insider selling.

The result that insider net selling is associated with non-negative (or even positive) long-

term returns appears counterintuitive to conventional wisdom, which suggests that insider net

selling is motivated, at least partly, by bad news and thus predicts negative future returns. If one

takes into account the litigation risk involved in insider selling, however, it is not surprising non-

negative long-term returns are associated with insider net selling. Despite the high-profile cases

that some insiders commit illegal insider trading by selling on significant negative news, the

result indicates that, on average, insiders do not sell on significant negative information. The fact

that a small number of insiders who committed apparently illegal insider trading were

subsequently exposed to the media and prosecuted by legal forces also helps remind all corporate

24

insiders the litigation risk involved in insider trading.25

Such legal actions help maintain the

equilibrium outcome in which corporate insiders as a whole do not sell shares on significant

negative information. Indeed, Cohen, Malloy, and Pomorski (2012, p. 1039) show that

opportunistic insider trading, which predicts returns up to six months, is sensitive to the number

of SEC sanctions and that opportunistic trades (especially sales) attract subsequent SEC activity.

The results on insider trading in this paper suggest that corporate insiders on average sell shares

while anticipating not so negative (or mildly positive) returns in the future and abstain from

trading while anticipating significant negative future returns.

[Insert Table 10 about here]

4.4. Database quality

In our analysis we classify a firm to the “silence” group if we do not observe any insider

trading activity as recorded in the database. The validity of this definition depends on the quality

of the database, Thomson Reuters. Several other possibilities could lead to observing no insider

trading. First, insiders have traded but failed to report to the SEC or the SEC’s record of insider

trades is incomplete.26

Thus, a researcher could mistakenly assign stocks to the “silence”

portfolio while they actually belong to the “buy” or “sell” portfolio. If these unfiled or

unrecorded trades are not driven by private information, the incompleteness of the database

simply adds random noise to the data and works against our empirical testing.

Second, insiders have traded on private information but decided not to report to the SEC

due to fear of regulatory and legal action. Seyhun and Bradley (1997, p. 200) discuss this

25

For recent legal cases against such apparent illegal insider trading, see Pulliam and Barry (2012), “Executives’

good luck in trading own stock,” The Wall Street Journal, November 22, 2012. 26

The insider trading data we use are made available through Wharton Research Data Services (WRDS). The

WRDS web has the following description of the database: “The Insider Filing Data Feed (IFDF) is designed to

capture all U.S. insider activity as reported on Forms 3, 4, 5, and 144 in line-by-line detail.” (See http://wrds-

web.wharton.upenn.edu/wrds/ds/tfn/index.cfm)

25

possibility on a sample of firms that filed bankruptcy. Meulbroek (1992) examines episodes of

illegal insider trading. Without a full list of such illegal insider trading we cannot distinguish

whether our “insider silence” cases are due to insiders having not traded on information or

insiders having traded on information but having hidden the trades from regulators. Either way,

however, the observed insider silence is consistent with our hypothesis that the fear for

regulatory and legal actions is at work when insiders make trading decisions.

Third, insiders might engage in off-market transactions instead of outright open-market

transactions.27

Jagolinzer, Matsunaga, and Yeung (2007) report that insiders use prepaid variable

forward (PVF) transactions for hedging purposes and find that such transactions typically follow

strong performance and precede degraded accounting and stock performance.28

Jagolinzer,

Matsunaga, and Yeung (2007, p. 1056) point out that these PVF transactions are alternative to

open-market sales that allow insiders to avoid downside loss and mitigate litigation risk. While

beyond the scope of the paper, it is interesting for future research to examine whether insider

silence and off-market transactions are somehow related. Regardless, insiders’ incentives to

engage in off-market transactions are also consistent with avoiding litigation risk.

4.5. Time-series variation

Two time points over the sample period are potentially important for understanding the

evolving litigation environment. One is the Private Securities Litigation Reform Act of 1995, or

PSLRA, and the other is the Sarbanes-Oxley of 2002 (SOX).

The PSLRA erects a series of procedural barriers designed to discourage frivolous

lawsuits. On the face value, firms would face lower litigation risk post-PSLRA. The number of

suits being filed Post-PSLRA, however, has not declined (Johnson, Nelson, and Pritchard 2007).

27

We thank P. Eric Yeung for pointing out this possibility and for detailed discussions on this point. 28

See also Bettis, Bizjak, and Lemmon (2001), who examine zero-cost collars transacted between insiders and

investment banks.

26

The upsurge in filings probably reflects a massive expansion in the amount of fraud being

committed, perhaps due to reduced exposure to liability (Bernardo, Talley, and Welch, 2000).

The notorious fraudulent cases such as Enron and WorldCom over this time period are examples.

Another issue is that plaintiff lawyers have stronger incentives to file a larger number of

lawsuits, in the hope they eventually win at least some. Thus, it is not all clear whether firms face

higher or lower litigation risk post-PSLRA.

The litigation environment may have shifted again due to the SOX, passed in 2002. The

direction on the change of litigation risk, however, is again unclear. On one hand, SOX imposes

more stringent requirements on governance and accounting control on public companies in an

effort to deter fraud. These mechanisms may lower firms’ litigation risk by lowering the overall

incidence of fraud. On the other hand, SOX also requires the chief executive and financial

officers to certify their company’s financial results, which could expose these executives and

their companies to additional litigation risk because it provides additional evidence of scienter if

those financial results are subsequently found misstated.

Because the implications of the two time points (PSLRA and SOX) on litigation risk are

not clear, our analysis on the time variation of the silence-sell spread is mostly descriptive in

nature. To simply take a look at the time series, we first draw the month-by-month silence-sell

spread in Figure 2. As one can see, the silence-sell spread is negative for majority of the months.

The recent financial crisis period is the obvious exception. It might be that insiders behave

differently during the recent economic turmoil, that insiders’ information is not as predictive as

in normal time periods, or that the stock prices do not reflect as much the fundamentals. To avoid

the impact of this turbulent period, in our subsequent exploratory analysis on time-varying

litigation risk, we exclude the time period after the end of 2007.

27

[Insert Figure 2 about here]

Table 11 presents the one-year portfolio cumulative abnormal returns and silence-sell

spreads for three periods: pre-PSLRA (1990-1995), post-PSLRA but pre-SOX (1996-2002), and

post-SOX (2003-2007). Pre-PSLRA, the silence portfolio earns a return of -2.54% (t=-3.18) and

the sell portfolio earns 0.75% (t=2.32), resulting in a silence-sell spread of -3.28% (t=-4.05).

Post-PSLRA but pre-SOX, the silence-sell spread is -4.46% (t=-3.96). The difference between

these two periods is an insignificant -1.18% (t=-0.85). Post-SOX, the silence-sell spread is -

1.36% (t=-3.38). Compared to the second period (post-PSLRA but pre-SOX), the silence-sell

spread post-SOX is significantly lower by 3.10% (t=2.59).29

The overall results appear to be

consistent with the view that SOX, by imposing stringent governance and accounting control

requirement, has reduced the overall incidence of fraud.30

[Insert Table 11 about here]

4.6. Other robustness checks

Additional robustness checks are conducted. Specifically, we check robustness by a)

using 3-month and 12-month periods to measure past insider trading activity; b) focusing on

trading activity by managers and directors only; c) measuring insider trading activity based on

transaction dates instead of report dates; d) adding back the low-priced and very small stocks

into the sample. We conduct these robustness checks using the Fama-MacBeth regression as in

Model 1 of Table 7. Results are presented in Table 12.

The first two regressions in Table 12 show that when insider trading information is

measured over the preceding three and 12 months, the regression coefficients on the “silence”

29

Unreported for brevity, none of the buy-sell spreads over the three periods is statistically significant; likewise,

none of the differences in the buy-sell spreads between the periods is statistically significant. 30

Alternatively, the narrower silence-sell spread over the more recent time period is also possible if the information

environment facing the firms has in general improved over time.

28

variable are -2.20% (t=-4.64), and -4.89% (t=-6.73), respectively. These results suggest that our

main results are robust to the window of measuring past insider trading activity. The third and

fourth models also produce significant negative coefficients on the “silence” variable, suggesting

that our main results are robust to focusing on trades by managers and officers only or to using

transaction dates instead of report dates to define the measuring period. In the last model, we

expand the sample by adding back the low-priced and small stocks (we require minimum stock

price of $1). The coefficient on “silence” is -2.39% (t=-3.88), indicating that our main results are

robust to including the very small and low-priced stocks in the sample.

[Insert Table 12 about here]

5. Conclusion

This paper examines stock returns following insider silence, periods of no insider trading.

We hypothesize that, for fear of litigation risk, rational insiders do not sell own-company shares

when they withhold bad news. Neither would they buy, given the unfavorable prospects. Thus

they keep silent. By contrast, rational insiders sell shares when they do not anticipate significant

bad news to come. Consistent with this hypothesis, we find that future returns are significantly

lower following insider silence than following insider net selling. Further, the silence-sell spread

is wider among firms with worse information environment and firms at higher litigation risk.

This paper fills a gap in the insider trading literature. The extant literature focuses

exclusively on the buying and selling activities by corporate insiders. This paper shows that

insider silence, a period of no insider trading, signals significant negative information.

29

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33

Table 1: Summary statistics

Past insider trading

Variables All stocks Silence Buy Sell

Avg. N 2487 596 453 1438

NID -0.418

N/A 0.227 -0.613

Ln(Size) 6.686

6.377 6.415 6.891

Ln(B/M) -0.770

-0.610 -0.607 -0.890

Institutional ownership 0.517

0.482 0.462 0.548

Followed 0.927

0.867 0.910 0.957

Num. analysts 8.831

7.005 7.518 9.981

Firm age 17.525

17.158 17.399 17.617

Sigma 0.058

0.061 0.058 0.058

Net stock issues (NS) 0.036

0.036 0.042 0.036

Past year return (Momentum) 0.255

0.215 0.128 0.317

Accruals (Ac/B) 0.010

0.008 0.007 0.011

Asset growth (dA/A) 0.099

0.082 0.082 0.111

Profitability (Y/B) 0.065

0.036 0.043 0.082

KS 7.009

7.002 6.891 7.072

FPS 0.269

0.253 0.201 0.299

Sales growth 0.143 0.115 0.102 0.169

The sample covers 1990-2011. This table shows the summary statistics of the whole sample and

subsamples formed on preceding insider trading activity. The silence portfolio includes firms

whose insiders do not trade over the trailing six months; the buy and sell portfolios consist of

firms whose insiders net buy and net sell over the trailing six months, respectively. The table

shows the time-series averages of cross-sectional means. All variables are defined in the

Appendix.

34

Table 2: Insider trading decisions and future returns

Cumulative returns over a holding period of

Portfolios One month Six months One year

Panel A: Raw return

Silence

0.90a 5.34a 11.08a

(2.67) (3.29) (3.90)

Buy

1.22a 6.98a 14.64a

(3.68) (4.04) (4.70)

Sell

1.01a 6.26a 13.10a

(3.05) (3.91) (4.51)

Silence – Sell

-0.11 -0.93b -2.01a

[-1.51] [-2.46] [-2.63]

Buy – Sell

0.21c 0.72 1.55

[1.82] [1.16] [1.22]

Panel B: Returns adjusted by size and B/M

Silence

-0.15b -0.87a -1.74a

(-2.41) (-3.60) (-4.29)

Buy

0.12c 0.23 0.45

(1.76) (0.70) (0.77)

Sell

0.00 0.30 0.75

(0.01) (0.87) (1.26)

Silence – Sell

-0.15a -1.17a -2.50a

[-2.77] [-4.19] [-4.10]

Buy – Sell

0.12 -0.07 -0.30

[1.44] [-0.22] [-0.60]

The sample covers 264 monthly cross-sections from 1990 to 2011. This table presents the raw

(Panel A) and cumulative abnormal returns adjusted by size and B/M (Panel B) of portfolios

formed on preceding insider trading activity over a holding period of one, six, and 12 months,

respectively. The silence portfolio includes firms whose insiders do not trade over the trailing six

months; the buy and sell portfolios consist of firms whose insiders net buy and net sell over the

trailing six months, respectively. The rows “silence – sell” and “buy – sell” show the spreads

between the portfolios. T-statistics, shown in parentheses and square brackets, are based on

Newey-West standard errors. Superscripts a,

b, and

c denote statistical significance at the 1%, 5%,

and 10% levels, respectively. The construction of cumulative abnormal returns is described in

the Appendix.

35

Table 3: Fama-French three-factor monthly alphas

Monthly alpha with holding months

Portfolios One Six Twelve

Panel A: Equal weight

Silence

-0.15b -0.19a -0.21a

(-2.54) (-2.95) (-2.77)

Buy

0.10 0.01 -0.00

(1.09) (0.09) (-0.05)

Sell

0.01 0.05 0.05

(0.18) (0.82) (0.74)

Silence – Sell

-0.16a -0.24a -0.26a

[-2.62] [-4.23] [-4.89]

Buy – Sell

0.09 -0.04 -0.06

[1.06] [-0.65] [-1.01]

Panel B: Value weight

Silence

-0.08 -0.08 -0.09

(-1.01) (-1.09) (-1.20)

Buy

-0.06 -0.09 -0.01

(-0.62) (-0.99) (-0.15)

Sell

0.05b 0.06b 0.05

(2.18) (2.06) (1.14)

Silence – Sell

-0.13 -0.14c -0.13c

[-1.42] [-1.78] [-1.85]

Buy – Sell

-0.11 -0.15 -0.06

[-1.02] [-1.57] [-0.68]

Each month j from January 1989 to December 2012, portfolios are formed on preceding insider

trading activity over the trailing six months. Stocks with no insider trading activity over the prior

six-month period form the “silence” portfolio; stocks with positive and non-positive net insider

demand (NID) over the prior six-month period form the “buy” and “sell” portfolios, respectively.

The portfolios are held over months j+1 to j+k (k=1, 6, or 12). Portfolio returns are equal

weighted (value weighted) across their constituent stocks. The overall portfolio return for month

j is the equal-weight average month-j returns of the strategy implemented in the prior month and

strategies formed up to k (k=1, 6, or 12) months earlier. These monthly portfolios are then

regressed on the Fama and French (1993) three-factor model. The table presents the equal-weight

(Panel A) and value-weight (Panel B) monthly alphas for holding period of 1, 6, and 12 months,

respectively. Rows “silence-sell” and “buy-sell” show the return spread between the

corresponding portfolios. Superscripts a,

b, and

c denote statistical significance at the 1%, 5%, and

10% levels, respectively

36

Table 4: Earnings announcement abnormal returns

Quarters

Portfolios 1st 2

nd 3

rd 4

th

Average

All

0.03 0.02 0.02 0.03

0.03

Silence

-0.03 -0.10b -0.11b -0.09c

-0.08b

Buy

0.04 -0.01 -0.01 0.01

0.01

Sell

0.05 0.07c 0.07c 0.08b

0.06

Silence – Sell

-0.08b -0.16a -0.18a -0.17a

-0.15a

[-2.15] [-4.05] [-4.93] [-4.17]

[-5.06]

Buy – Sell

-0.01 -0.07 -0.08 -0.07

-0.06

[-0.14] [-1.56] [-1.52] [-1.39] [-1.51]

Monthly portfolios are formed from January 1990 to December 2011, based on preceding insider

trading information. Portfolio “All” represents the whole sample; stocks with no insider trading

activity over the prior six-month period form the “silence” portfolio; stocks with positive and

non-positive net insider demand (NID) over the prior six-month period form the “buy” and “sell”

portfolios, respectively. The rows “silence-sell” and “buy-sell” represent return spreads between

the corresponding portfolios. Every column presents the time-series averages of cross-sectional

mean abnormal returns (in %) over the three-day window earnings announcement period. The

abnormal returns are adjusted by CRSP equal-weight daily market returns. Columns “1st”

through “4th

” refer to the 1st to 4

th subsequent quarterly earnings announcements, respectively,

following portfolio formation, and “average” represents the average over the four quarters. The t-

statistics in square brackets are based on Newey-West standard errors with 2 lags for each

quarterly earnings announcement returns and 11 lags for the average. Superscripts a,

b, and

c

denote statistical significance at the 1%, 5%, and 10% levels, respectively.

37

Table 5: The role of information environment

Panel A: By B/M

Value Mid Growth

Growth – Value

Silence -0.94 -1.60a -3.13a

-2.18b

(-1.47) (-4.73) (-4.33)

[-2.42]

Sell 0.36 0.56 1.16c

0.80

(0.55) (0.76) (1.89)

[1.35]

Silence – Sell -1.30a -2.16a -4.29a

-2.98a

[-2.73] [-2.77] [-4.85] [-4.30]

Panel B: By firm age

Old Mid Young

Young – Old

Silence -0.94 -1.08b -3.01a

-2.08b

(-1.31) (-2.02) (-5.40)

[-2.25]

Sell 0.25 1.44c 0.56

0.31

(0.31) (1.92) (0.92)

[0.33]

Silence – Sell -1.19b -2.52a -3.58a

-2.39a

[-2.17] [-3.58] [-4.77] [-3.82]

Panel C: By volatility

Low Mid High

High – Low

Silence -1.73 -0.38 -3.08a

-1.35

(-1.56) (-0.51) (-2.70)

[-0.66]

Sell -0.23 0.91 1.68

1.92

(-0.18) (1.10) (1.32)

[0.83]

Silence – Sell -1.49a -1.29b -4.76a

-3.27a

[-3.47] [-2.03] [-4.83] [-3.70]

The sample covers 264 monthly cross-sections from January 1990 to December 2011. This table

presents the cumulative abnormal returns (adjusted by size and B/M) of portfolios formed on

preceding insider trading activity and B/M (Panel A), firm age (Panel B), and volatility (Panel C)

over a one-year holding period. The silence portfolio includes firms whose insiders do not trade

over the trailing six months; the buy and sell portfolios consist of firms whose insiders net buy

and net sell over the trailing six months, respectively. The rows “silence – sell” show the spreads

between the two portfolios. T-statistics are based on Newey-West standard errors and presented

in parentheses and square brackets. Subsamples on B/M, firm age, and volatility (Panels A to C)

are formed on simple terciles. Each panel also presents the differences between the two extreme

subsamples (e.g., value vs. growth in Panel A) for the silence, sell, and silence-sell portfolios.

Superscripts a,

b, and

c denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Variables B/M, firm age, and volatility, and the construction of cumulative abnormal returns are

described in the Appendix.

38

Table 6: The role of litigation risk

Panel A: By KS

Low Mid High

High – Low

Silence -2.05c -0.74 -2.60b

-0.54

(-1.91) (-1.27) (-2.52)

[-0.29]

Sell 0.04 1.13 1.32

1.28

(0.03) (1.28) (1.40)

[0.69]

Silence – Sell -2.09a -1.87b -3.91a

-1.82b

[-4.94] [-2.15] [-4.07] [-2.30]

Panel B: By FPS

Non-FPS

FPS

FPS – Non

Silence -2.31a

-0.52

1.80

(-3.85)

(-0.44)

[1.16]

Sell -0.35

3.37b

3.72b

(-0.42)

(2.51)

[2.06]

Silence – Sell -1.96a

-3.88a

-1.92c

[-2.94]

[-4.18] [-1.96]

Panel C: By sales growth

Low Mid High

High – Low

Silence -1.39c -0.60 -3.04a

-1.66

(-1.86) (-0.97) (-3.66)

[-1.31]

Sell 0.28 1.15 1.07c

0.80

(0.31) (1.45) (1.88)

[0.90]

Silence – Sell -1.66b -1.75b -4.12a

-2.45a

[-2.56] [-2.35] [-4.21] [-2.69]

The sample covers 264 monthly cross-sections from January 1990 to December 2011. This table

presents the cumulative abnormal returns (adjusted by size and B/M) of portfolios formed on

preceding insider trading activity and KS (Panel A), FPS (Panel B), and sales growth (Panel C)

over a one-year holding period. The silence portfolio includes firms whose insiders do not trade

over the trailing six months; the buy and sell portfolios consist of firms whose insiders net buy

and net sell over the trailing six months, respectively. The rows “silence – sell” show the spreads

between the two portfolios. T-statistics are based on Newey-West standard errors and presented

in parentheses and square brackets. Subsamples on KS (Panel A) and sales growth Panel C) are

formed on simple terciles. In Panel B, subsample “Non-FPS” includes firms that are not in the

industries defined as at high litigation risk and “FPS” includes firms that are in the industries at

high litigation risk. Each panel also presents the differences between the two extreme subsamples

(e.g., value vs. growth in Panel A) for the silence, sell, and silence-sell portfolios. Superscripts a,

b, and

c denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variables KS,

FPS, and sales growth, and the construction of cumulative abnormal returns are described in the

Appendix.

39

Table 7: Fama-MacBeth regressions

Variables Model1 Model2 Model3 Model4 Model5 Model6 Model7

Silence -2.69a -2.80a -2.66a -2.55a -2.78a -2.52a -2.85a

(-4.66) (-4.81) (-4.60) (-4.81) (-4.82) (-4.50) (-4.79)

Silence*Ln(B/M)

1.70a

(6.14)

Silence*Age

0.93a

(3.65)

Silence*Sigma

-69.26a

(-4.16)

Silence*KS

-0.53b

(-2.17)

Silence*FPS

-2.37b

(-2.38)

Silence*Sales growth

-3.17a

(-2.62)

Age

0.05

(0.12)

Sigma

9.57

(0.22)

KS

0.08

(0.16)

FPS

4.50b

(2.34)

Sales growth

-1.46

(-0.87)

Buy -0.58 -0.43 -0.60 -0.45 -0.67c -0.20 -0.76c

(-1.50) (-1.11) (-1.54) (-1.24) (-1.87) (-0.57) (-1.81)

Ln(size) -0.01 -0.03 -0.05 -0.07 -0.03 0.07 -0.10

(-0.07) (-0.18) (-0.20) (-0.20) (-0.16) (0.40) (-0.52)

Ln(B/M) 0.16 -0.34 0.11 0.05 -0.22 0.77b -0.24

(0.61) (-1.30) (0.37) (0.09) (-0.57) (2.26) (-0.69)

Momentum -0.15 -0.16 -0.12 0.52 0.05 -0.24 -0.38

(-0.07) (-0.07) (-0.06) (0.26) (0.03) (-0.11) (-0.17)

Intercept 0.89 0.96 1.26 1.41 1.04 0.19 1.66

(0.71) (0.75) (0.71) (0.55) (0.74) (0.14) (1.12)

The sample covers 264 monthly cross-sections from 1990 to 2011. The dependent variable in

each model is the one-year cumulative abnormal return, adjusted by size and B/M. Each model

presents the time series averages of cross-sectional regression coefficients, with t-statistics in

parentheses. The t-statistics are Newey-West adjusted. All continuous variables are winsorized at

the 1st and 99

th percentiles. All variables interacted with the variable “silence” are demeaned. All

variables are defined in the Appendix. Superscripts a,

b, and

c denote statistical significance at the

1%, 5%, and 10% levels, respectively.

40

Table 8: Alternative hypotheses

Variables Model1 Model2 Model3 Model4

Silence -2.68a -2.77a -2.65a -2.69a

(-4.67) (-4.53) (-4.56) (-4.43)

IPO firm -2.49b

-0.72

(-2.43)

(-0.79)

NS

-14.44a

-14.41a

(-6.64)

(-6.64)

Ac/B

-2.29b

-2.35b

(-2.42)

(-2.43)

dA/A

-3.92b

-3.86b

(-2.47)

(-2.50)

Y/B

2.61

2.60

(1.39)

(1.40)

Past trades

-0.16 -0.28

(-0.91) (-1.48)

Buy -0.59 -0.47 -0.55 -0.41

(-1.53) (-1.17) (-1.30) (-0.94)

Ln(size) -0.08 -0.37 -0.00 -0.36

(-0.42) (-1.63) (-0.03) (-1.55)

Ln(B/M) 0.05 -1.01b 0.17 -0.98b

(0.17) (-2.21) (0.66) (-2.10)

Momentum -0.16 -0.91 -0.14 -0.93

(-0.08) (-0.43) (-0.06) (-0.43)

Intercept 1.60 3.45b 0.80 3.33c

(1.09) (2.01) (0.60) (1.85)

Four Fama-MacBeth regressions are presented. The sample covers 264 monthly cross-sections

from January 1990 to December 2011. The dependent variable in each model is the cumulative

abnormal return over the one-year holding period, adjusted by size and B/M. Each column shows

the time-series averages of the cross-sectional regression coefficients with t-statistics in

parentheses. The t-statistics are based on time-series standard errors with Newey-West

adjustment. All continuous variables are winsorized at the 1st and 99

th percentiles. The variable

“Past trades” is the net insider demand over the period from months -12 to -7 relative to the

portfolio formation month; the variable “IPO firm” is equal to one if the firm is younger than

three years, and zero otherwise. All other variables are defined in the Appendix. Superscripts a,

b,

and c denote statistical significance at the 1%, 5%, and 10% levels, respectively.

41

Table 9: Insider silence and takeover offers

Whole sample Targeted Non-targeted

Portfolios Avg. N Return

Avg. N Return

Avg. N Return

All

2487 0.02

141 20.53a

2346 -1.18a

(0.05)

(19.16)

(-2.64)

Silence

596 -1.74a

39 17.53a

557 -3.10a

(-4.29)

(9.94)

(-8.08)

Buy

453 0.45

28 21.18a

425 -0.79

(0.77)

(17.14)

(-1.22)

Sell

1438 0.75

74 22.67a

1364 -0.39

(1.26)

(20.33)

(-0.65)

Silence – Sell

-2.50a

-5.15a

-2.72a

[-4.10] [-3.15] [-4.42]

This table shows the average number of stocks in the portfolio and average one-year cumulative

abnormal returns for the portfolios formed on past insider trading activity (All, Silence, Buy, and

Sell), and the silence-sell portfolio. The portfolios are based on the whole sample, the targeted

subsample, or the non-targeted subsample. The targeted subsample includes firms that are to

receive a takeover offer over the subsequent 12 months, and the non-targeted subsample includes

firms that do not receive a takeover offer over the subsequent 12 months. The silence portfolio

includes firms whose insiders do not trade over the trailing six months; the buy and sell

portfolios consist of firms whose insiders net buy and net sell over the trailing six months,

respectively. The row “silence – sell” shows the spread between the portfolios. T-statistics are

based on Newey-West standard errors and presented in parentheses or brackets. Superscripts a,

b,

and c denote statistical significance at the 1%, 5%, and 10% levels, respectively. The

construction of cumulative abnormal returns is described in the Appendix.

42

Table 10: Evidence over the long term

Portfolios Yr 2 Yr 3

Silence -1.65a -0.64c

(-4.53) (-1.86)

Buy 0.49 0.83

(0.84) (1.37)

Sell 1.36a 1.93a

(2.71) (3.13)

Silence – Sell -3.01a -2.57a

[-4.57] [-3.33]

Buy – Sell -0.87 -1.10c

[-1.64] [-1.91]

The sample covers 264 monthly cross-sections from January 1990 to December 2011. This table

presents the cumulative abnormal returns (adjusted by size and B/M) of portfolios formed on

preceding insider trading activity over the second (Yr 2) and third (Yr 3) years subsequent to

portfolio formation, respectively. The silence portfolio includes firms whose insiders do not trade

over the trailing six months; the buy and sell portfolios consist of firms whose insiders net buy

and net sell over the trailing six months, respectively. The rows “silence – sell” and “buy – sell”

show the spreads between the portfolios. T-statistics are based on Newey-West standard errors

and presented in parentheses or brackets. Superscripts a,

b, and

c denote statistical significance at

the 1%, 5%, and 10% levels, respectively. The construction of cumulative abnormal returns is

described in the Appendix.

43

Table 11: The time series variation

Period 1: Period 2: Period 3:

Period

Period

Pre-PSLRA Post-PSLRA, Pre-SOX Post-SOX

2 vs. 1

3 vs. 2

Silence

-2.54a -2.31a -1.26a

0.23

1.05c

(-3.18) (-5.65) (-2.73)

[0.26]

[1.70]

Sell

0.75b 2.15c 0.11

1.41

-2.05

(2.32) (1.87) (0.16)

[1.17]

[-1.53]

Silence – Sell

-3.28a -4.46a -1.36a

-1.18

3.10b

[-4.05] [-3.96] [-3.38] [-0.85] [2.59]

The table presents the one-year cumulative abnormal returns of the silence, sell, and silence-sell

portfolios over the three sub-periods. Period 1 (Pre-PSLRA) refers to 1990 to 1995; period 2

(Post-PSLRA, Pre-SOX) refers to 1996 to 2002; and period 3 (Post-SOX) refers to 2003 to 2007.

In this analysis we exclude years from 2008 to 2011, the recent financial crisis period. The last

two columns list the differences between periods 2 and 1 and between 3 and 2, and the t-statistics

in these two columns are based on simple two-sample t-tests assuming independence. The

silence portfolio includes firms whose insiders do not trade over the trailing six months; the buy

and sell portfolios consist of firms whose insiders net buy and net sell over the trailing six

months, respectively. The row “silence – sell” shows the spread between the portfolios. T-

statistics in the first three columns are based on Newey-West standard errors and presented in

parentheses or brackets. Superscripts a,

b, and

c denote statistical significance at the 1%, 5%, and

10% levels, respectively. The construction of cumulative abnormal returns is described in the

Appendix.

44

Table 12: Additional robustness checks

Variables NID3 NID12 MD Transaction Large Sample

Silence -2.20a -4.89a -3.20a -3.05a -2.39a

(-4.64) (-6.73) (-4.97) (-5.57) (-3.88)

Buy -0.50 -1.10b -0.84c -0.62 0.69

(-1.13) (-2.56) (-1.83) (-1.16) (1.34)

Ln(size) 0.04 0.08 0.03 0.06 0.05

(0.22) (0.46) (0.18) (0.32) (0.23)

Ln(B/M) 0.12 0.27 0.21 0.21 -0.12

(0.47) (0.98) (0.77) (0.78) (-0.50)

Momentum -0.16 -0.16 -0.21 -0.23 -0.72

(-0.07) (-0.07) (-0.10) (-0.11) (-0.39)

Intercept 0.93 0.88 1.16 0.73 -1.21

(0.80) (0.80) (0.97) (0.69) (-0.56)

Each model in the five regressions represents some deviation from Model 1 of Table 7. In

Models NID3 and NID12, insider trading activity is measured over the past three- and 12-month

periods, respectively; in Model MD, only the insider trading activity by managers and directors

are considered; in Model Transaction, the transaction dates are used to define the six-month

measuring period; in Model Large Sample, the sample also includes small and low-priced stocks

(minimum of $1 stock price is still required). The sample covers 264 monthly cross-sections

from 1990 to 2011. The dependent variable in each model is the one-year cumulative abnormal

return, adjusted by size and B/M. Each model presents the time series averages of cross-sectional

regression coefficients, with t-statistics in parentheses. The t-statistics are Newey-West adjusted.

All continuous variables are winsorized at the 1st and 99

th percentiles. All variables are defined in

the Appendix. Superscripts a,

b, and

c denote statistical significance at the 1%, 5%, and 10%

levels, respectively.

45

Figure 1: Insider buying, selling, and silence

Every month from January 1990 to December 2011, we calculate the cross-sectional proportion

of firms with insider net selling (Sell), net buying (Buy), and no trading (Silence). Stocks with no

insider trading activity over the prior six-month period form the “silence” portfolio; stocks with

positive and non-positive NIDs form the “buy” and “sell” portfolios, respectively. NID is defined

in the Appendix.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%1

99

001

199

101

199

201

199

301

199

401

199

501

199

601

199

701

199

801

199

901

200

001

200

101

200

201

200

301

200

401

200

501

200

601

200

701

200

801

200

901

201

001

201

101

Sell

Buy

Silence

46

Figure 2: The time-series of silence-sell spreads

Every month from January 1990 to December 2011 we construct the silence and sell portfolios

based on preceding insider trading activity. Stocks with no insider trading activity over the prior

six-month period form the “silence” portfolio; stocks that insider net sell form the “sell”

portfolio. For each portfolio we estimate their average cumulative abnormal returns (adjusted by

size and B/M) over the subsequent 12-month period and calculate the silence-sell spread. The

Figure shows the month-by-month silence-sell spread (in %).

-15

-10

-5

0

5

10

199

001

199

101

199

201

199

301

199

401

199

501

199

601

199

701

199

801

199

901

200

001

200

101

200

201

200

301

200

401

200

501

200

601

200

701

200

801

200

901

201

001

201

101

Silence-Sell Spread (%)

47

Appendix

The data sources are the Center for Research in security Prices (CRSP), Compustat,

Thomson Reuters Insider Filing Data Feed, and 13f. Time t in Compustat refers to fiscal year end

in calendar year t. The main variables are defined below.

Firm characteristics

Ln(Size): Market capitalization, the natural log of price times number of shares

outstanding at the end of June of year t, from CRSP.

Momentum: The buy-and-hold return from month j-12 to j-1, where j-1 is the month of

portfolio formation and j is the first month of forecasted stock returns. This

variable is monthly rebalanced.

Insider trading variable

NID: Net insider demand, NID of month j is defined as the number of shares that

insiders buy minus the number of shares that insiders sell over the past six

months, normalized by the total number of shares outstanding at the end of

month j-1.

Silence: Equal to one if there is no insider trading activity over the past six months,

and zero otherwise.

Buy: Equal to one if NID is positive, zero otherwise.

Information Environment

Ln(B/M): Book to market ratio, the natural log of the ratio of the book value of

equity to the market value of equity. Book value B is total assets

(Compustat item AT) for year t-1, minus liabilities (LT), plus balance sheet

deferred taxes and investment tax credit (TXDIC) if available, minus

preferred stock liquidating value (PSTKL) if available, or redemption

value (PSTKRV) if available, or carrying value (PSTK). Market value M is

price times share outstanding at the end of December of t-1, from CRSP.

Age: Number of months since first appearing on CRSP.

Sigma: Weekly return standard deviation over the past 52 weeks.

Anomaly variables

NS: Net stock issues, the natural log of the split-adjusted shares outstanding at

the fiscal year end in t-1 minus the natural log of the split-adjusted shares

outstanding at the fiscal year end in t-2. The split-adjusted shares

outstanding is CSHO times AJEX in Compustat.

Ac/B: Accruals, the change in operating working capital per share (split-adjusted)

from fiscal year t-2 to t-1, divided by book equity per share (split-adjusted)

in year t-1. Operating working capital is current assets (Compustat item

ACT) minus cash and short-term investment (CHE) minus current

liabilities (LCT) plus debt in current liabilities (DLC).

dA/A: Growth in assets, the natural log of assets per share (split-adjusted) at the

fiscal year end in t-1, minus the natural log of assets per share (split-

adjusted) at the fiscal year end in t-2.

Y/B: Profitability, equity income (IB), minus dividends on preferred (DVP), if

available, plus income statement deferred taxes (TXDI), if available of

fiscal year end in t-1, normalized by book value of equity in fiscal year t-1.

48

Future return variable

Size and B/M

adjusted abnormal

return:

We construct abnormal returns adjusted by size and B/M. Specifically, at

the end of June of year t, we independently form NYSE size and book–to–

market (B/M) quintiles to extract the breakpoint values, and assign AMEX

and NASDAQ stocks to the 10 x 10 portfolios according to their size and

B/M values. The equal–weight portfolio return serves as the benchmark

return for the stock in the same size and B/M portfolio for the months

starting from July of year t to June of year t+1. Portfolio assignment is

rebalanced every year. The monthly abnormal return for a stock is its raw

return minus the benchmark portfolio return. The monthly abnormal

returns are then accumulated for the designated holding period. If a stock is

delisted before the holding period, the delisting return is used for the

delisting month.

Litigation risk

KS: KS is based on Model 3 in Table 7 of Kim and Skinner (2012, page 302).

KS=

FPS * 0.566

+ LNASSETS * 0.518

+ SALES_GROWTH * 0.982

+ RETURN * 0.379

+ SKEWNESS * (–0.108)

+ STDDEV * 25.635

+ TURNOVER * 0.00007/1000,

where the right–hand–side variables are defined below.

FPS: Defined below;

LNASSETS: Natural log of total assets at the end of year t–1;

SALES_GROWTH: Defined below (Sales growth);

RETURN: Market–adjusted 12–month stock return. The accumulation

period ends with year t–1 fiscal year–end month;

SKEWNESS: Skewness of the firm’s 12–month return for year t–1;

STDDEV: Standard deviation of the firm’s 12–month returns for year t–1;

TURNOVER: Trading volume accumulated over the 12–month period

ending with the fiscal year–end before year t–1 fiscal year–end month

scaled by beginning of year t–1 shares outstanding.

FPS: FPS is equal to 1 if the firm is in the biotech (SIC codes 2833–2836 and

8731–8734), computer (3570–3577 and 7370–7374), electronics (3600–

3674), or retail (5200–5961) industry, and 0 otherwise.

Sales growth: Year t–1 sales less year t–2 sales scaled by beginning of year t–1 total

assets.

Other variables

Institutional

ownership:

The percent ownership by institutional investors. Data is from 13f.

Followed: Dummy variable equal to one if the stock is followed by analysts.

Num. analysts: The number of analysts following the stock over the past six months.