an investigation of causality between insider transactions and stock returns

An investigation of causality between insider transactionsand stock returns

Zahid Iqbala, Shekar Shettyb,*aSchool of Business, Texas Southern University, Houston, TX 77004, USA

bSchool of Business, Western New England College, Springfield, MA 01119, USA

Received 24 May 2000; received in revised form 18 June 2001; accepted 19 July 2001

Abstract

Prior research shows that corporate insiders engage in profitable transactions by trading securitiesof their own firms. The main purpose of this study is to examine whether insider transactions and stockreturns have causality relationships at the firm level for a sample of 2,521 firms during the period 1988to 1998. We find a large impact of stock returns on subsequent insider transactions at both theaggregate and firm levels. The impact appears to be negative which suggests that insiders buy afterstock price decreases and sell after stock price increases. Our findings on the predictive content ofinsider transactions for subsequent stock returns are primarily consistent with prior literature. Weobserve a positive but weak relationship between insider transactions and future stock returns. © 2002Board of Trustees of the University of Illinois. All rights reserved.

1. Introduction

There is empirical evidence that corporate insiders utilize private information to strate-gically trade their own stocks for personal gain. For example, (Seyhun & Bradley, 1997)observe that insiders of firms that file for bankruptcy protection sell their shareholdingsbefore stock prices fall and buy after prices have fallen. (Karpoff & Lee, 1991) provideevidence of insider sales prior to new stock offerings which generally elicit negative stockprice reactions. (Lee, Mikkelson, & Partch, 1992), (Penman, 1982), (Seyhun, 1990), (John &Lang, 1991), and (Lamba & Khan, 1999) arrive at similar conclusions that insiders are ableto trade profitably around major corporate events.

* Corresponding author. Tel.: �1-413-782-1708; fax: �1-413-796-2068.E-mail address: [email protected] (S. Shetty).

The Quarterly Review of Economics and Finance 42 (2002) 41–57

1062-9769/02/$ – see front matter © 2002 Board of Trustees of the University of Illinois. All rights reserved.PII: S1062-9769(01)00114-4

Another line of research by (Seyhun, 1986, 1992) and (Rozeff & Zaman, 1988) examinesabnormal stock returns in fixed time intervals surrounding insider trading activity, where theevent is the trade itself. These studies observe positive returns following insider purchasesand negative returns following sales. Also, negative returns precede purchases and positivereturns precede sales. Insiders are able to trade profitably because they possess privilegedinformation as a result of their association with the firm’s day-to-day operations.

The main purpose of this paper is to provide evidence of time-series relationships betweeninsider transactions and stock returns at the firm level. With this approach, we are able toobserve whether insider trading is related to firm-specific information, without examiningany specific corporate event. We posit that insider transactions based on firm-specificinformation will be reflected in the time-series relationship between insider transactions andstock returns. A positive association between insider transactions and subsequent stockreturns will indicate that insiders purchase (sell) before stock prices increase (decrease). Anegative association between stock returns and subsequent insider transactions will indicatethat insiders sell (purchase) after stock prices increase (decrease).

(Chowdhury, Howe & Lin, 1993) use a similar approach at the aggregate level. Whileinsider trading at the aggregate level occurs due to macroeconomic factors, our analysissheds light on insider trading associated with firm-specific information for a large sample offirms. Moreover, our study updates (Chowdhury, Howe & Lin’s, 1993) analysis using recentaggregate data on insider transactions. All the insider transactions in our study occur after theInsider Trading Sanctions Act of 1984. Although this Act is intended to discourage insidertrading, (Seyhun, 1992) argues that the insider trading regulations defined a safe harbor forinsider trading and have led to more, not less, insider trading profits. However, because ofincreased scrutiny by the regulators, insider transactions may not occur immediately prior toa significant corporate event.

Our causality analysis provides evidence that stock returns have a strong effect onsubsequent insider transactions at both the aggregate and firm levels. The findings onregression coefficients and impulse response indicate that insiders purchase shares of theirown firms following a decrease in stock price and sell shares following an increase in stockprice. Our findings on the relationship between insider transactions and future stock returnsare largely consistent (although weaker) with prior literature. We observe a positive rela-tionship between net insider transactions and subsequent stock returns for roughly sixtypercentage of the sample firms.

The remainder of this paper is organized as follows. Section II describes the sample, data,and measurement of insider transactions; Section III explains the methodology; the findingsof this study are given in Section IV; and concluding remarks are provided in Section V.

2. Sample, data, and measurement of insider transactions

The data on monthly insider transactions are obtained from the CDA Investnet databasefrom January 1988 to December 1998. Following (Lamba & Khan, 1999), we include openmarket transactions of 100 shares or more because such transactions are likely to beinformation-motivated trades by the insiders. Also, we include trades by Chairmen of the

42 Z. Iqbal, S. Shetty / The Quarterly Review of Economics and Finance 42 (2002) 41–57

Board, Presidents, Vice-Presidents, and all officers and directors of the firm. There are 16,675firms on the CDA Investnet database that have at least one open market purchase or saletransaction in the sample period meeting these requirements. To ensure that the time-seriesanalysis for individual firms have enough monthly data points, we include firms with at least 30months of insider transactions (either consecutive or nonconsecutive) in the sample period.No-trading case during a given month is set to zero value. A total of 2,725 firms meet thisrequirement. Of these 2,725 firms, monthly stock returns are obtained for 2,521 firms from theStandard & Poor’s Research Insight database from January 1988 to December 1998.

Table 1 provides descriptive statistics of the 2,521 firms in the final sample. For each firm,we average the annual financial data over 1988 to 1998 and report the mean and median ofthese average values in Panel A. The mean and median total assets are $3,914 million and$460 million, respectively, and the mean and median sales are $1,888 million and $269million, respectively. Our sample firms are larger than the rest of the firms in the Standard &Poor’s Research Insight database. For example, the mean and median total assets for the rest ofthe firms are $1,641 million and $56 million, respectively (not reported in Table 1). The samplefirms have mean and median net income of $94.99 million and $12.13 million, respectively.The mean and median debt-to-assets ratio are 21.36% and 18.97%, respectively.

The industry distribution, reported in Panel B, shows that the largest number of casesoccur among the national commercial banks followed by state commercial banks and savingsinstitutions. Thus, the financial sector is the largest industry in our sample.

Panel C provides estimates of annual stock returns and insider transactions from 1988 to1998. The stock returns were generally positive during this period which is also confirmedin Fig. 1. The insider transactions data show that the average number of purchases increasedonly slightly while the average number of sales doubled from 1988 to 1998. The averagenumber of shares purchased decreased while the average number of shares sold quadrupledover the same period.

We use four indexes to measure the degree of insider transactions per month. The first index,used by (John & Lang, 1991) and (Yur-Austin, 1998), is the net number index (NNI) defined as:

NNI ��P � S�

�P � S�(1)

where P�aggregate number of insider purchase transactions in a given month and S�aggregatenumber of insider sale transactions in a given month. The net number index reflects tradingactivities from two directions. A favorable news report can result in more purchase transactionsor fewer sale transactions. Either action results in a higher value of net number index.

The second measure, which controls for the volume of transactions, is the net share index(NSI) computed as:

NSI ��PV � SV�

�PV � SV�(2)

where PV�aggregate number of shares purchased by insiders in a given month andSV�aggregate number of shares sold by insiders in a given month.

43Z. Iqbal, S. Shetty / The Quarterly Review of Economics and Finance 42 (2002) 41–57

The next two indexes examine insider purchases and sales separately. Separate examina-tions of purchases and sales are considered because (Chowdhury, Howe & Lin, 1993) claimthat insider purchases have more information content than insider sales. Also, (Seyhun, 1986,1992) find that abnormal stock returns are higher for purchases than they are for sales. Theinsider purchase index (PNI) is defined as:

PNI �P

�P � S�(3)

and the insider sale index (SNI) is defined as

Table 1Descriptive statistics of the 2,521 firms in the sample

Panel A (financial data) Mean Median

Total assets (millions of $) $3,914.08 $460.04Sales (millions of $) $1,888.72 $269.57Net income (millions of $) $94.99 $12.13Debt-to-assets ratio 21.36% 18.97%

Panel B (Industry Distribution)

SIC Industry # of firms

6021 National Commercial Banks 1336022 State Commercial Banks 986035 Savings Institutions 512834 Pharmaceutical Preparations 427372 Prepackaged Software 42Other (356) Industries 2,155Total 2,521

Panel C (Stock Returns and Insider Transactions: Mean values across firms are reported)

# of transactions # of shares Insider trading indexes

Year Return Purchases Sales Purchases Sales NNI NSI PNI SNI

1988 0.1327 3.795 5.191 38,432 63,124 �0.234 �0.290 1.558 1.7911989 0.1146 4.071 6.277 57,558 79,640 �0.439 �0.527 1.665 2.1051990 �0.2813 6.957 6.023 40,266 67,675 0.599 �0.520 2.402 1.8031991 0.3517 4.433 11.261 58,393 170,737 �1.124 �1.230 1.663 2.7871992 0.1696 4.388 12.851 35,069 179,689 �1.319 �1.452 1.709 3.0271993 0.1259 4.369 12.047 45,864 209,861 �1.387 �1.533 1.686 3.0731994 �0.0661 4.993 9.242 57,700 135,861 �0.566 �0.649 1.972 2.5081995 0.2075 4.037 11.248 26,351 261,177 �1.204 �1.347 1.637 2.8411996 0.1191 3.840 10.883 33,159 266,326 �1.109 �1.209 1.542 2.6511997 0.1645 3.551 11.449 27,985 205,931 �1.364 �1.472 1.329 2.6921998 �0.1602 4.340 10.561 32,049 255,228 �0.608 �0.708 1.294 1.901

Panel D (Correlations between monthly stock returns and monthly insider transactions)

Purchase Index (# of transactions) �0.390***Sales Index (# of transactions) 0.324***Net Number Index �0.468***Net Shares Index �0.455***

*,**,*** Significant at the 10%, 5%, and 1% percent levels.


SNI �S

�P � S�. (4)

PNI and SNI indexes are based on the number of transactions during a given month.1

Panel C in Table 1 shows that the indexes of net transactions are negative and that salesindexes are higher than the purchase indexes during the period 1988 to 1998. Also, Fig. 2 showsthat the indexes of the net number of transactions and net shares of transactions have declinedsince the beginning of 1991. Overall, the data in Panel C suggest that while stock prices wereincreasing, corporate insiders were selling more than they were buying during the sample period.

Finally, the contemporaneous correlations between monthly stock returns and insidertrading indexes are reported in Panel D. The correlations indicate that stock returns have anegative association with purchases and a positive association with sales. Furthermore, stockreturns have negative associations with net insider transactions.

Fig. 1. Cumulative monthly stock returns (average across firms) from January 1988 to December 1998.

Fig. 2. Cumulative net insider transactions (average across firms) from January 1988 to December 1998.


3. Methodology

We employ a Granger causality procedure (Granger, 1969) to examine the time-seriesrelation between the monthly insider trading measures and stock returns (R). Monthly stockreturn is defined as log(Pt/Pt-1), where Pt is the month-end stock price. We use stock returnsinstead of stock prices because both Dickey-Fuller and Phillips-Perron unit-root tests fail toreject stationarity of the price series at the aggregate level. The stock returns as well as allfour insider transaction indexes are stationary series which meet the requirements of theGranger causality test.

The causality models are given by the following autoregressive representations

Rt � � � �k�1

K

�kRt�k � �k�1

K

�kXt�j � �t (5)

Xt � � � �k�1

K

�kXt�k � �k�1

K

�kRt�j � �t (6)

where K is the lag length in months and X is an insider transaction index (NNI, NSI, PNI,or SNI). Causality from X to R is tested in Eq. (5) and causality from R to X is tested in Eq.(6). �k is the coefficient of the lagged dependent variable and �k is the coefficient of thelagged explanatory variable. To better compare results across firms, we use constant laglengths, K, of one, three, and twelve months.

Causality from one variable to the other is tested by an F-statistic. In the case of testingcausality from X to R, the FX3R indicates whether the residual variance of Eq. (5) with Xomitted is significantly greater than the residual variance of the full equation. If so, the lagsof X add to the explanatory power of the equation, that is, Granger causality test indicatesthat insider transactions affect stock returns. Causality from R to X is examined in a similarway using the FR3X value.

4. Empirical findings

4.1. Findings at the aggregate level

Tables 2, 3, 4, and 5 report empirical results of the Granger causality tests at the aggregatelevel. For each month, we aggregate data by averaging the monthly stock returns and theindex values across the sample firms. According to Table 2, the F-statistics for all three laglengths (one month, three months, and twelve months) are significant when causality runsfrom stock returns to net number index (NNI), but not from net number index to stockreturns. The regression coefficients indicate that stock returns have significant and negativerelationship with net number index in the next month. This relationship is true for all threelag lengths. None of the other coefficients are significant. The findings in Table 3 on net shareindex (NSI), which accounts for the volume of insider trading, show similar results. Stockreturns are significantly negatively related to the next month’s share index.


The relationship between stock returns and net number index is shown graphically in Figs.3. Specifically, we measure the direction and magnitude of the impact of innovation of onevariable on the other variable over a period of twelve months using impulse analysis. Theresults in Fig. 3, which are standardized by the standard errors of the residual variances, showthat a positive innovation in the net number index results in a very small response by thestock return variable.2 The magnitude of the response of net number index to a positive shockin stock returns, on the other hand, is large. The direction of the response is negative overthe entire twelve-month horizon suggesting that a decrease (an increase) in stock returns isfollowed by net insider purchases (sales). The strongest negative response occurs at about thesecond month (about a -0.80 decrease in NNI due to a standard deviation shock in stockreturns) and then the response decreases towards zero.

Table 2Coefficient values and F-statistics using the Granger causality model for the aggregate data of 2,521 firms.The results for the coefficients of the lagged dependent variables are not reported. Insider transactions aremeasured by the index of net number of transactions (NNI) per month

Dependentvariable

Explanatoryvariable

Lag Explanatory variableCoeficient � (t-statistic)

F-statistic

1-lag length Return NNI 1 0.057 (0.949) 0.902NNI Return 1 �0.416 (4.056)*** 16.454***

3-lag length Return NNI 1 �0.040 (�0.338) 0.8962 �0.073 (�0.633)3 0.155 (1.529)

NNI Return 1 �0.748 (�6.331)*** 13.712***2 �0.106 (�0.839)3 0.176 (1.594)

12-lag length Return NNI 1 �0.098 (�0.603) 0.4222 �0.152 (�0.908)3 0.160 (0.953)4 �0.032 (�0.175)5 �0.003 (�0.016)6 �0.124 (�0.671)7 0.127 (0.711)8 �0.082 (�0.456)9 0.001 (0.008)

10 �0.017 (�0.108)11 0.185 (1.183)12 0.014 (0.099)

NNI Return 1 �0.772 (�4.973)*** 2.773***2 �0.051 (�0.265)3 0.187 (0.948)4 0.121 (0.609)5 �0.040 (�0.195)6 0.044 (0.207)7 0.043 (0.207)8 0.176 (0.877)9 0.044 (0.221)

10 �0.083 (�0.439)11 0.207 (0.155)12 0.016 (0.109)



In Table 4, we present the findings of the causality tests between stock returns and insiderpurchase index (PNI). The F-statistics for all three lag-lengths indicate that past stock returns havea significant impact on the purchase transactions by the insiders. The purchase index, on the otherhand, has little predictive content for subsequent stock returns. As in Tables 2 and 3, the stockreturn coefficients for the first lag period are significantly negative in all three lag-length modelssuggesting that insiders tend to purchase stock one month after prices drop. The stock returncoefficients for three, six, and eleven lags are significantly positive, however, which suggest thathigher stock returns lead to more insider purchases over a longer horizon. One other result inTable 4 is worth mentioning. The purchase index coefficient for lag nine is significant with apositive sign which indicates that insider purchases have predictive content for stock returns nine

Table 3Coefficient values and F-statistics using the Granger causality model for the aggregate data of 2,521 firms.The results for the coefficients of the lagged dependent variables are not reported. Insider transactions aremeasured by the index of net shares traded (NSI) per month

Dependent Explanatory Lag Explanatory variable F-statisticvariable variable coefficient � (t-statistic)

1-lag length Return NSI 1 0.053 (0.902) 0.813NSI Return 1 �0.408 (�3.896)*** 15.176***

3-lag length Return NSI 1 �0.049 (�0.433) 0.8882 �0.061 (�0.552)3 0.150 (1.535)

NSI Return 1 �0.742 (�6.255)*** 13.362***2 �0.120 (�0.949)3 0.168 (1.590)

12-lag length Return NSI 1 �0.103 (�0.664) 0.4082 �0.136 (�0.848)3 0.152 (0.948)4 �0.029 (�0.169)5 0.001 (0.008)6 �0.107 (�0.603)7 0.127 (0.735)8 �0.087 (�0.503)9 �0.005 (�0.029)

10 �0.012 (�0.076)11 0.174 (1.155)12 0.009 (0.069)

NSI Return 1 �0.768 (�4.912)*** 2.648***2 �0.062 (�0.328)3 0.167 (0.851)4 0.122 (0.616)5 �0.049 (�0.238)6 0.031 (0.144)7 0.029 (0.141)8 0.152 (0.754)9 �0.011 (�0.053)

10 �0.122 (�0.641)11 0.028 (0.163)12 0.035 (0.244)



months in advance. One plausible explanation is that the insiders may purchase several monthsbefore the stock prices increase to avoid possible scrutiny by the federal regulators.

Table 5 presents results of causality flow between stock returns and insider sales index (SNI).The F-statistic results indicate that stock returns Granger cause insider sales when one- andthree-lag lengths are used, but not when twelve-lag length is used. Positive and significant stock returncoefficients for lag one indicate that insiders sell their holdings one month after stock prices increase.

The results of the impulse analysis for stock returns and insider purchases and sales arepresented in Fig. 4. According to Fig. 4, an innovation in either the purchase or sale indexresults in a small shock in stock returns. In contrast, stock returns have a strong impact onboth purchase and sale indexes in subsequent months. For the purchase index, the impact isnegative, suggesting more purchases after stock prices drop. For the sale index, the impactis negative, indicating more sales after prices increase.

As in (Chowdhury, Howe & Lin, 1993), our findings on aggregate data suggest that, ingeneral, stock returns have a significant negative impact on subsequent insider trading activities.(Chowdhury, Howe & Lin, 1993) argue that this relation, in which aggregate returns predictaggregate insider transactions, would be expected due to noise trading. If a stock declines (rises)significantly from its intrinsic value, insiders may perceive the stock to be undervalued (over-valued) and buy (sell) it. Another plausible explanation is that the insiders, who are relativelyundiversified, tend to sell (buy) shares after stock prices of their own firm increase (decrease) torebalance their portfolios. Although the predictive ability of aggregate insider transactions in ourstudy is consistent with the evidence in (Seyhun, 1988), the relationship appears to be weak.

4.2. Findings at the individual firm level

Aggregate data convey information about insider trading activities associated with mac-roeconomic factors. Firm-specific data, on the other hand, provide insights on insidertransactions that are based on private information about the individual firm.

Fig. 3. Impulse response—aggregate return and net number index.


In Table 6, we present summary results of the firm-specific causality flows between stockreturn and net insider transactions for the sample of 2,521 firms.3 The percentage of positiveregression coefficients and percentage of significant F-statistics for one-, three-, and twelve-month lag lengths are reported. When causality runs from net number index to stock returns,14.19%, 13.60%, and 10.93% of the sample F-statistics are significant for lag-lengths one, three,and twelve months, respectively. The percentages of positive coefficients for the net numberindex are more than fifty percentage for all the lags which suggest that, for the majority of thesample firms, stock returns increase after net purchases increase. Identical results are obtainedwhen causality is tested from net shares index to stock returns. These results at the firm level areconsistent with the event study analysis by (Seyhun, 1986) and (Rozeff & Zaman, 1988) thatabnormal returns are positive (negative) following insider purchases (sales).

Table 4Coefficient values and F-statistics using the Granger causality model for the aggregate data of 2,521 firms.The results for the coefficients of the lagged dependent variables are not reported. Insider transactions aremeasured by the index of number of purchases (PNI) per month

Dependent Explanatory Lag Explanatory variable F-statisticvariable variable coefficient � (t-statistic)

1-lag length Return PNI 1 0.179 (1.538) 2.365PNI Return 1 �0.224 (�3.926)*** 15.413***

3-lag length Return PNI 1 0.144 (0.811) 1.4622 �0.193 (�0.936)3 0.270 (1.512)

PNI Return 1 �0.283 (�4.754)*** 13.600***2 �0.026 (�0.401)3 0.261 (4.496)***

12-lag length Return PNI 1 0.180 (0.734) 1.4382 �0.353 (�1.305)3 0.113 (0.399)4 �0.014 (�0.049)5 0.041 (0.143)6 �0.183 (�0.628)7 0.055 (0.019)8 �0.234 (�0.821)9 0.676 (2.357)***

10 �0.457 (�1.476)11 0.413 (1.549)12 �0.005 (�0.027)

PNI Return 1 �0.359 (�5.026)*** 4.732***2 �0.012 (�0.142)3 0.278 (3.231)***4 0.065 (0.719)5 0.134 (1.497)6 0.180 (1.987)**7 0.110 (1.216)8 0.063 (0.711)9 �0.028 (�0.321)

10 0.098 (1.131)11 0.147 (1.704)*12 0.064 (0.827)



Much higher percentages of the sample F-statistics are significant, however, when causalityflows from stock returns to net number index or net shares index. For causality flow from stockreturn to net number index, 33.69%, 32.71%, and 22.11% of the sample F-statistics are significantfor one-, three-, and twelve-month lag lengths, respectively. For causality flow from stock returnto net shares index, 33.17%, 32.14%, and 22.61% of the sample F-statistics are significant forone-, three-, and twelve-month lag lengths, respectively. Higher percentages of negative regres-sion coefficients indicate that insiders of a large majority of the firms become net buyers (netsellers) following a decrease (an increase) in stock price.

The findings of the firm-specific impulse responses between stock returns and net numberindex over a twelve-month horizon are presented in Fig. 5.4 The results confirm theconclusions obtained earlier that stock returns have a much greater impact on net insiders

Table 5Coefficient values and F-statistics using the Granger causality model for the aggregate data of 2,521 firms.The results for the coefficients of the lagged dependent variables are not reported. Insider transactions aremeasured by the index of number of sales (SNI) per month

Dependent Explanatory Lag Explanatory variable F-statisticvariable variable coefficient B (t-statistic)

1-lag length Return SNI 1 �0.015 (�0.199) 0.039SNI Return 1 0.194 (2.109)** 4.449**

3-lag length Return SNI 1 0.070 (0.667) 0.3182 0.004 (0.032)3 �0.095 (�0.901)

SNI Return 1 0.366 (4.346)*** 6.364***2 �0.016 (�0.184)3 0.016 (0.186)

12-lag length Return SNI 1 0.169 (1.122) 0.4422 0.126 (0.646)3 �0.156 (�0.808)4 0.025 (0.128)5 �0.007 (�0.034)6 0.049 (0.231)7 �0.071 (�0.329)8 0.037 (0.171)9 0.232 (1.146)

10 �0.145 (�0.799)11 �0.114 (�0.613)12 �0.032 (�0.193)

SNI Return 1 0.272 (2.762)*** 1.3372 �0.152 (�1.319)3 �0.009 (�0.076)4 �0.010 (�0.078)5 0.201 (1.490)6 0.029 (0.213)7 0.054 (0.392)8 �0.126 (�0.927)9 �0.052 (�0.376)

10 0.151 (1.164)11 0.152 (1.288)12 �0.057 (�0.503)



transactions and that the impact is substantially negative during the first few months anddecreases afterward. The response of stock return to a shock in net number index is positive,but small, and eventually decreases to zero.

The results of separate analyses of insider purchases and sales using firm-specific data aregiven in Table 7. The F-statistics show that the purchase and sales indexes have smallerimpacts on stock returns when compared to the impacts of stock returns on purchase and saleindexes. For example, for one-month lag length, the percentage of positive F-statistic is 13.90when causality runs from purchase index to stock returns while the percentage of positiveF-statistic is 28.30 when causality runs from stock return to purchase index.

The coefficient summaries indicate that, for a majority of the sample firms, stock pricedecreases are followed by more insider purchases, and price increases are followed by moreinsider sales. For example, about eighty percentage of the firms have more purchases withina month after stock prices decrease and about the same percentage of the firms sell morewithin a month after prices increase. Also, there appears to be no noticeable differencebetween the results of insider purchases and sales.

Impulse response results for stock returns and indexes of insider purchases and sales arepresented in Fig. 6. The results show that a positive shock in stock return results in a negativeresponse by the purchase index variable and a positive response by the sales index variable.This suggests that higher stock returns result in less purchases and more sales in subsequentmonths. The responses of stock returns to shocks in purchase and sales indexes, on the otherhand, are positive, but small. Also, insider purchases are larger than sales, consistent withprior empirical studies.

4.3. Findings by firm size

(Seyhun, 1986) and (Rozeff & Zaman, 1988) find that insider trading behavior depends onfirm size. Insider purchases tend to be concentrated in small company stocks and insider sales

Fig. 4. Impulse response—aggregate stock return, purchase and sale indexes.


in large company stocks. To examine whether our results vary by firm size, we ran separateanalyses for large and small firms in the sample. Firms above (below) the median value ofassets are defined as large (small) firms. Overall, the results based on firm size indicate thatinsider trading has more predictive content in small firms than in large firms. For example,at the aggregate level, the purchase index (PNI) of the small firms predicts stock returns witha Granger F-statistic of 8.488 (significant at the 1% level) and a coefficient value of 0.411(t-statistic � 2.913) when lag-length K�1. At the firm level, 68.53% of the PNI coefficientsare positive in the small firm sample, while 61.88% of the PNI coefficients are positive in thelarge firm sample when lag-length K�1.

Table 6Percentage of positive coefficients using the Granger causality model and percentage of significant F-statisticsfor exogeneity of variables. Total sample size is 2,521 firms. We do not report coefficient results for the lagsof the dependent variable. Insider transactions are measured by the index of net number of transactions (NNI)and index of net shares traded (NSI) per month

Lag Dependent variable: Return Dependent variable: ReturnExplanatory variable: NNI Explanatory variable: NSI

% Positive % Positive % Positive % Positive % Positive % Positive

1 65.81 62.76 59.59 65.53 62.25 58.842 59.63 56.63 59.42 56.993 58.67 55.07 58.75 55.154 54.19 53.835 55.02 54.516 52.54 52.557 53.22 53.998 51.78 51.789 51.26 50.94

10 53.74 54.3111 53.42 53.0212 57.30 56.79Significant F (%) 14.19 13.60 10.93 14.08 13.17 10.46

Lag Dependent variable: NNI Dependent variable: NSIExplanatory variable: Return Explanatory variable: Return


1 14.82 13.43 14.15 14.50 13.71 14.862 26.67 26.51 27.02 26.693 38.12 35.87 37.94 35.604 40.22 39.075 40.46 40.556 41.86 41.197 41.94 42.558 42.82 41.959 43.62 43.51

10 43.50 43.4711 46.02 45.4312 45.30 45.23Significant F (%) 33.69 32.71 22.11 33.17 32.14 22.61


4.4. Findings for nonfinancial firms

To verify the robustness of our results, we also examine the data using a separate sampleof nonfinancial firms. The results are essentially the same as those for the entire sampleindicating that the relationship between insider transactions and stock returns does notdepend on whether the firm is in the financial or nonfinancial sector.

4.5. Findings on stock returns relative to insider trading month

To further examine how our results compare with prior research, we perform ananalysis similar to that in (Seyhun, 1986). We identify “net purchase month” and “netsale month” for each of the 2,521 firms in our sample. We define net purchase (sale)month as the month when the aggregate number of insider purchase transactions isgreater (less) than aggregate number of sale transactions. There are 44,183 net purchasemonths and 66,900 net sale months in our sample. Months with no transactions or anequal number of purchase and sale transactions are ignored. In Fig. 7, we report averagemonthly stock returns from twelve month before to twelve months after the net purchaseand sale months. Fig. 7 shows that stock returns increase prior to the month of net insidersales and stock returns decrease prior to the month of net purchases. This confirms ourearlier results that stock returns have a negative relationship with subsequent insidertransactions. Fig. 7 also shows that stock returns increase slightly after net purchasessuggesting that insider sales and future stock prices are not positively related. Insiderbuying seems to predict stock returns in the expected direction, however. That is, insiderpurchases predict an increase in stock return. In sum, these results indicate that insidertrading activity only weakly predicts future stock returns.

Fig. 5. Impulse response—firm-specific return and net number index. Mean responses of individual firms are plotted.


5. Conclusions

In this paper, we examine the association between stock returns and insider transactionsusing monthly time-series data from 1988 to 1998. We report results for both aggregate andfirm-specific data for a sample of 2,521 firms. The findings on aggregate data show a strongand negative impact of stock returns on subsequent insider transactions. However, therelationship between insider transactions and subsequent stock returns appears to be weak.

The findings on firm-specific data are consistent with the conclusions obtained on aggregatedata. We find that the insiders of a large majority of sample firms purchase stocks after prices

Table 7Percentage of positive coefficients using the Granger causality model and percentage of significant F-statisticsfor exogeneity of variables. Total sample size is 2,521 firms. We do not report coefficient results for the lagsof the dependent variable. Insider transactions are measured by the index of number of purchase transactions(PNI) and index of net number of sale transactions (SNI) per month

Lag Dependent variable: Return Dependent variable: ReturnExplanatory variable: PNI Explanatory variable: SNI


1 65.02 63.00 61.11 41.19 44.82 47.692 57.46 56.37 42.62 47.413 57.30 55.56 44.54 47.734 52.97 51.135 55.56 48.226 53.66 50.367 52.81 48.388 52.61 50.659 54.43 52.87

10 52.21 47.9811 55.56 51.1012 56.90 47.37Significant F (%) 13.90 14.45 13.11 12.85 12.52 11.70

Lag Dependent variable: PNI Dependent variable: SNIExplanatory variable: Return Explanatory variable: Return


1 21.33 20.26 22.33 80.54 82.10 81.202 32.35 32.59 70.35 70.713 46.36 44.43 65.09 67.294 46.08 64.345 47.81 63.536 45.19 60.677 48.05 60.798 47.85 60.549 47.36 59.06

10 47.04 58.8111 49.38 57.7212 50.10 57.08Significant F (%) 28.30 26.82 20.24 23.96 22.43 18.96


decrease and sell stocks after prices increase. The causality flow from stock returns to insidertransactions is stronger than the causality flow from insider transactions to stock returns.

The evidence presented in this paper at both the aggregate and firm levels seems to be atodds with the conjecture that, on average, insiders profit from mispricing of firm’s stockassociated with macroeconomic and firm-specific information. The findings that insidertrading behavior does not have a strong relationship with subsequent stock returns appear tobe inconsistent with those of (Seyhun, 1988), (Seyhun & Bradley, 1997), (Karpoff & Lee,1991) and a few others who find that insiders are able to trade profitably around specificcorporate announcements. In future research, it would be interesting to learn whether insidertransactions around these corporate announcements depend on past stock returns.

Fig. 6. Impulse response—firm-specific return, purchase and sale indexes. Mean responses of individual firms areplotted.

Fig. 7. Cumulative monthly average stock returns around insider trading month (defined as 0).


(Chowdhury, Howe & Lin, 1993) claim that a negative effect of stock returns on subsequentinsider transactions can be expected because of noise trading. Insiders buy (sell) when marketreturns fall (rise) significantly due to noise trading which drive market prices away from intrinsicvalue. Another plausible explanation is that the insiders rebalance their portfolios by selling(buying) shares after stock price of their own firm increases (decreases). (Sivakumar & Waymire,1994) observe a similar negative relationship around earnings announcements and conclude thatinsiders buy when stock returns are abnormally low and sell when the returns are high.

Notes

1. To verify the robustness of our results, we examine net number of insider transactionsand insider purchase and sales indexes based on shares. The results obtained arequantitatively very similar to those reported in this study.

2. We do not plot the results of the impulse response flow between stock returns and netshare index because they are very similar to the results shown in Fig. 3.

3. Summary results are reported since reporting the results for each firm-equation wouldrequire a separate table for each of the 2,521 firms.

4. Mean impulse responses of individual firms are presented. We obtain similar resultsusing median values.

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an investigation of causality between insider transactions and stock returns

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