insider trades and demand by institutional and individual...

Insider Trades and Demand by Institutional and Individual Investors

Richard W. Sias and David A. Whidbee*

September 24, 2008

Abstract

We investigate whether insider trading is related to net demand by institutional versus individual investors. Our tests reveal a strong inverse relation between insider trading and institutional demand the same quarter and over the previous year. Additional tests suggest a combination of factors contributes to this relation. First, institutional investors are more likely to provide the liquidity necessary for insiders to trade. Second, insiders are more likely to buy low valuation and low lag return stocks while institutions are attracted to the opposite security characteristics. Last, the results are consistent with the hypothesis that insiders are more likely to view their securities as overvalued following a period when institutions were net buyers and undervalued following a period when institutions were net sellers.

* Both authors are from Department of Finance, Insurance, and Real Estate, Washington State University, Pullman, Washington, 99164-4746. Sias: (509) 335-2347, [email protected]; Whidbee: (509) 335-3098, [email protected]. Whidbee appreciates the financial support provided by the Brinson Summer Fellowship Program. The authors thank Terry Odean for providing the discount broker data on individual investors‘ transactions, Mark Edwards, Wayne Wagner and the Plexus Group for allowing us to use the Plexus institutional transaction data, Marc Lipson for his extensive assistance in working with the Plexus data, Brian Bushee for providing institutional investor style classifications, Abhishek Varma for his assistance in working with the Odean data, and Donna Paul and Swami Kalpathy for their helpful comments. The authors especially thank Tobias Moskowitz and two anonymous referees for their extensive comments and many helpful suggestions.

Insider Trades and Demand by Institutional and Individual Investors

This study examines the relation between insider (officers and directors) transactions and

trading between institutional and individual investors to better understand what motivates insiders to

trade, the role of institutional investors in providing liquidity to insiders, and how insiders‘

perception of mispricing is related to net demand by individual investors versus institutional

investors. We hypothesize that insider trading will be related to institutional and individual investors‘

demand for three reasons. First, because the typical insider transaction is relatively large, institutional

investors are better suited (than individual investors) to provide liquidity to insiders (the ―liquidity

hypothesis‖). As a result, institutional demand will be inversely related to contemporaneous insider

demand as institutions will be the usual counterparty to insider trades. Moreover, given insider

demand is positively serially correlated, the liquidity hypothesis implies insider demand will be

inversely related to lag institutional demand.

Second, we hypothesize that insider trading will be inversely related to institutional investor

demand measured over the same quarter because the security characteristics that attract insiders

repel institutional investors (the ―characteristics hypothesis‖). Specifically, insiders are more likely to

buy value stocks and stocks that have recently declined in value [e.g., Rozeff and Zaman (1998),

Lakonishok and Lee (2001), Piotroski and Roulstone (2005), Jenter (2005)]. In contrast, institutional

investors, in aggregate, favor growth stocks [e.g., Chen, Jegadeesh, and Wermers (2000), Dasgupta,

Prat, and Verardo (2007)] and stocks that have recently increased in value [e.g., Grinblatt, Titman,

and Wermers (1995), Sias (2007), San (2007)]. Moreover, a number of previous studies [e.g.,

Wermers (1999), Sias, Starks, and Titman (2006), and Gibson and Safieddine (2003)] demonstrate

that there is strong positive correlation between institutional investor demand and returns measured

over the same period. This correlation, combined with insiders‘ attraction to low lag return stocks,

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suggests an inverse relation between insider trading and lag institutional demand, i.e., lag institutional

demand proxies for lag returns.

Last, a growing literature [e.g., Rozeff and Zaman (1998), Piotroski and Roulstone (2005),

Jenter (2005)] suggests that insiders‘ trades are at least sometimes motivated by their perception that

their security‘s price differs from its fundamental value. Thus, if insiders are more likely to believe

their securities are overvalued following a period of institutional net buying and undervalued

following a period of institutional net selling, insiders will tend to trade opposite of same quarter and

lag institutional demand. Alternatively, if insiders believe their securities are more likely to be

overvalued (undervalued) following net buying (selling) by individual investors, then insiders will

tend to trade opposite of same quarter and lag individual investors‘ demand (the ―perceived

mispricing hypothesis‖).

Our empirical tests reveal that insider trading is inversely related to institutional investors‘

demand the same quarter and over the previous year. Because there is a buyer for every seller,

institutional investors‘ net demand must be offset by individual investors‘ net supply.1 Thus, our

results can be equivalently expressed as either: (1) an inverse relation between insider demand and

same quarter and lag institutional demand, or (2) a positive relation between insider demand and

same quarter and lag individual investor demand. For ease of exposition, however, the balance of

the paper primarily refers to the relation between insiders, institutions, and individuals as the relation

between insiders and institutions.

Further tests suggest that although institutional investors providing liquidity to insiders

accounts for a substantial portion of the inverse relation between insider trading and institutional

demand, the liquidity hypothesis fails to fully explain the relation. Even when we assume that

institutional investors are the counterparty to every insider trade, we still document an inverse

1 We define net institutional demand for security i as positive (negative) if institutional investors, in aggregate, are net buyers (sellers) and individual investors are net sellers (buyers).

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relation between insider trading and institutional demand the same quarter and over the previous

year. Moreover, evidence from two transaction databases (the Plexus institutional investor

transaction database and Terry Odean‘s discount broker individual investor transaction database)

demonstrates that institutional investors are sellers and individual investors are buyers in the days

and weeks surrounding an insider purchase (and vice-versa for an insider sale).

Consistent with previous findings, we find insiders favor low valuation and low lag return

securities, while institutional investors (in aggregate) favor high valuation and high lag return

securities. Moreover, analysis by institutional investor ―style‖ reveals that the inverse relation

between insider demand and institutions is much stronger for institutions with a ―growth‖ focus

than those with a ―value‖ focus. Nonetheless, although these preferences help explain the inverse

relation, insider trading and aggregate institutional demand (both the same quarter and over the

previous year) remain inversely related even when controlling for valuation levels and returns.

The inverse relation between insider and institutional demand, even after controlling for the

liquidity explanation and return and valuation characteristics, is consistent with the hypothesis that

insiders are more likely to view their securities as overvalued (undervalued) following a period of

institutional buying (selling). Thus, we next examine returns in the six months and two years

following trading by insiders and institutions to investigate whether insiders‘ trades are motivated by

―actual mispricing‖ or whether insiders mistakenly believe their securities are mispriced when the

market price is appropriate. Specifically, if securities are more likely to be overvalued following a

period of institutional net buying and undervalued following a period of institutional net selling, and

insider trading is motivated by this divergence between fundamental and market valuations, then: (1)

subsequent returns should be positively related to net insider demand, (2) subsequent returns should

be inversely related to net institutional demand, and (3) subsequent returns on securities insiders buy

(sell) should be stronger (weaker) when institutions are selling (buying).

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Consistent with most previous work, outside of insider small stock purchases, there is

relatively little evidence that insiders are trading against actual mispricing. Further, we find little

evidence that, on average, institutional investors systematically drive prices from fundamental values.

Moreover, the average profitability of insider trades is largely independent of whether they trade

against institutions or individuals. Consistent with Jenter (2005), our results suggest that despite

insiders‘ perceptions, there is relatively little evidence their securities are, in fact, mispriced.

Last, we estimate the relative importance of each of three hypotheses in explaining the

inverse relation between institutional demand and insider trading. We begin by estimating how

insider trading is related to a one standard deviation change in institutional demand. We then

examine changes in this relation after accounting for liquidity and insiders‘ and institutions‘

attraction to the opposite security characteristics. We estimate the relation between insider trading

and institutional demand the same quarter is partitioned as follows: 48% is attributed to institutions

providing insiders liquidity, 26% is due to insiders‘ and institutions‘ attraction to the opposite

security characteristics, and the remaining 26% is attributed to insiders‘ belief that their security is

more likely to be overvalued (undervalued) following intra-quarter institutional buying (selling). We

estimate the relation between insider trading and institutional demand the previous year is partitioned

as follows: 29% due to institutions providing liquidity to insiders over the previous year, 43% due to

insiders‘ and institutions‘ attraction to opposite security characteristics, and the remaining 28%

attributed to insiders‘ belief that their security is more likely to be overvalued (undervalued)

following institutional buying (selling) over the previous year.

If, in contrast to arguments made by Rozeff and Zaman (1998), Jenter (2005), and Piotroski

and Roulstone (2005), insider trades are unrelated to their mispricing perceptions, then the portion

of the relation between insiders and institutions not accounted for by the liquidity and characteristics

hypotheses remains unexplained. Regardless, our results indicate that whatever motivates insiders to

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trade is inversely related to institutional demand. As such, our findings also relate to a large body of

work that posits individual investors act as irrational noise traders.2 Specifically, evidence that

insiders tend to trade in the same direction as individual investors (both the same quarter and over

the previous year) is inconsistent with the joint hypothesis that insiders trade against mispricing and

individual investors are noise traders whose collective action drives prices from fundamentals.

The balance of the paper is organized as follows. Section 1 presents the data and Section 2

presents our initial empirical tests. Sections 3, 4, and 5 provide tests of the liquidity, characteristics,

and perceived mispricing hypotheses, respectively. We estimate the relative importance of each of

three hypotheses in explaining the inverse relation between insider trading and institutional demand

in Section 6. Section 7 provides conclusions.

1. Data

1.1. Insider Trading Data

The insider trading data are drawn from two sources: the SEC‘s Ownership Reporting

System (ORS) database (1984-1995) and Thomson Financials‘ Value-Added Insider Data Feed

(1996-2003).3 In addition to officers and directors, large shareholders (those that own more than

10% of the outstanding shares) and affiliated shareholders (e.g., an officer of an investment advisor)

2 See, for example, Cohen, Gompers, and Vuolteenaho (2002), Hribar, Jenkins, and Wang (2004), Gibson, Safieddine, and Sonti (2004), Field and Lowry (2005), Mikhail, Walther, and Willis (2005), Kumar (2005), Poteshman and Serbin (2003) Barber, Odean, and Zhu (2006a, 2006b), Hvidkjaer (2007), and Kumar and Lee (2006). 3 Both data sources provide the number of shares traded by company insiders as reported on SEC Form 4. Because most of the SEC‘s ORS and Thomson Financial‘s Insider Filing data do not distinguish between open-market and private transactions, our data include both. Specifically, prior to April 10, 1991, we include transaction codes P (open market purchase), J (private purchase), S (open market sale), and K (private sale). After April 10, 1991, we include transaction codes P (open market or private purchase) and S (open market or private sale). Further, we redo the analysis to include other purchase and sale transaction types and find qualitatively identical results. We exclude duplicate filings, transactions with missing price data, transactions involving fewer than 100 shares, transactions with prices that deviate from CRSP prices by more than 20%, transactions involving more than 20% of the shares outstanding and stock-quarters with quarterly absolute net insider demand exceeding 20% of the shares outstanding.

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must also report their transactions to the SEC.4 We limit most of our analysis to officers and

directors to allow comparisons to previous studies [e.g., Rozeff and Zaman (1998), Piotroski and

Roulstone (2005)] and because both previous work [e.g., Lakonishok and Lee (2001)] and our

(untabulated) analysis demonstrate that the informational content of ‗insider‘ trades is largely limited

to trades of officers and directors. For ease of exposition we refer to officers and directors as

―insiders.‖

We measure insider demand as the net fraction of shares of security i purchased by officers

and directors in quarter t (henceforth, net insider demand):

.&&

,ti

ti,ti,ti,

gOutstandinShares#

SellDirectorsOf f icersShares#BuyDirectorsOf f icersShares#DemandInsiderNet (1)

1.2. Quarterly Institutional Ownership and CRSP/Compustat Data

The number of shares held by institutional investors is derived from their 13(f) reports. We

measure ―net institutional demand‖ as the net fraction of firm i‘s shares moving to (or from)

institutional investors over quarter t:

.,ti

ti,ti,ti,

gOutstandinShares#

SellnsInstitutioShares#BuynsInstitutioShares#DemandnalInstitutioNet (2)

Because there is a seller for every buyer, the fraction of outstanding shares purchased by

institutional investors filing 13(f) reports is equivalent to the fraction of shares sold by non-13(f)

investors.5 Following the literature [e.g., San (2007), Gibson, Safieddine, and Sonti (2004), Cohen,

4 Exchange Act Rule 16a-1(f) defines the term ―officer‖ to mean ―an issuer‘s president, principal financial officer, principal accounting officer (or, if there is no such accounting officer, the controller), any vice-president of the issuer in charge of a principal business unit, division or function (such as sales, administration or finance), any other officer who performs a policy-making function, or any other person who performs similar policy-making functions for the issuer. Officers of the issuer‘s parent(s) or subsidiaries shall be deemed officers of the issuer if they perform such policy-making functions for the issuer…‖ 5 The 1978 amendment to the Securities and Exchange Act of 1934 requires all professional investors with at least $100 million in equity securities under management to file reports of their positions in each security within 45 days of the quarter-end. Institutions with less than $100 million in assets are not required to file 13(f) reports. In addition, institutions are allowed to exclude small positions from 13(f) reports (less than 10,000 shares and $200,000). We exclude observations when institutional ownership exceeds 100% of shares

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Gompers, and Vuolteenaho (2002), Gompers and Metrick (2001), Nofsinger and Sias (1999)], we

use non-13(f) investors as the measure of ownership by ―individual investors.‖ Because the net

demand by non-13(f) investors includes some small institutions (and small institutional positions)

and insiders‘ net demand, the institutional and individual investors‘ demand metrics contain

measurement error. Moreover, the 13(f) data do not capture inter-corporate holdings (which can be

large) and other employee ownership that is not required to file with the SEC. It is possible that

these mismeasurements are correlated with firm characteristics (including insider ownership),

liquidity, and firm performance for which we do not control. In addition, the SEC requires broker-

dealers who trade for their own account and/or act as a market maker to file 13(f) reports (assuming

they meet the $100 million criteria) regardless of whether they are a registered investment advisor.

As a result, large market makers are classified as institutional (rather than individual) investors.

We use the (updated) institutional investor style classifications from Abarbanell, Bushee, and

Raedy (2003) to partition institutions into four capitalization/valuation style dimensions: small-cap

growth mangers, small-cap value managers, large-cap growth managers, and large-cap value

managers. The classifications are based on the managers‘ portfolio weighted average of 15 firm

specific characteristics, e.g., size, E/P, B/P (see Appendix A for classification details).

In addition to insider trading and institutional ownership data, we collect the set of variables

(except restricted stock granted to insiders) used by Piotroski and Roulstone (2005) to explain

insider trading.6 Return data are from the Center for Research in Security Prices (CRSP). We

compute quarterly book to market ratios from Compustat book value data and CRSP capitalization

outstanding or the beginning and end of quarter cusips do not match. The data are cleaned of obvious reporting errors (usually associated with delays in adjusting for stock splits). The data were purchased from CDA Spectrum/Thomson Financial. 6 We do not include restricted stock grants because we focus on the quarterly relation between institutions and insiders and executive ―grant‖ data are only available annually. Piotroski and Roulstone‘s (2005) results, however, suggest that such grants have a small role in explaining insiders‘ demand (i.e., the increase in R2 in their study is very small and the coefficient associated with grants is only significant at the 5% level).

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data (based on end of quarter book value and market capitalization).7 We compute the change in

return on assets over the previous year (i.e., the return on assets this quarter less the return on assets

four quarters prior, ΔROAi,t), as well as the change in the return on assets over the following year

(i.e., the return on assets four quarters forward less the return on assets this quarter, ΔROAi,t+4)

based on Compustat data. Appendix A provides expanded data descriptions.

We require securities to have the complete set of Piotroski and Roulstone (2005) variables to

be included in the sample. We exclude non-ordinary securities (i.e., we require a CRSP share code of

10 or 11), low-priced securities (less than $1), and securities with negative book values from the

sample. Following previous work [e.g., Lakonishok and Lee (2001), Piotroski and Roulstone (2005)],

we limit the sample to stock-quarters that have insider trading. The final sample consists of an

average of 1,944 securities each quarter between June 1983 and March 2003 for a total of 155,495

firm-quarter observations.

Table 1 reports descriptive statistics pooled over all firm-quarter observations. Although our

sample period is somewhat longer and we focus on quarterly rather than annual insider trading, the

descriptive statistics in Table 1 are nearly identical to those reported by Piotroski and Roulstone

(2005). The last five rows in Table 1 report descriptive statistics of the level of intuitional ownership

in aggregate and by manager style.

[Insert Table 1 about here]

Because 13(f) reports are quarterly snapshots of institutional ownership, we do not know if

the institutional trading in that quarter occurred prior to the insider transaction, was the

counterparty to the insider trade, or occurred after the insider transaction. We address this issue by:

(1) examining both institutional demand the same quarter (t=0) and institutional demand over the

7 We use end of quarter book to market ratios to allow direct comparison with Rozeff and Zaman (1998) and Piotroski and Roulstone (2005). We find qualitatively identical results, however, when we use beginning of quarter book to market ratios.

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prior year (t=-1 to t=-4), and (2) using samples of individual investors‘ and institutional investors‘

transactions to evaluate intra-quarter patterns around insider trades (discussed in the next section).

1.3. Institutional and Individual Investor Transaction Data

The advantage of the 13(f) data is that they provide an estimate of net aggregate demand by

institutional investors (and, by inference, net aggregate demand by individual investors). The

limitation is the coarseness of the data, i.e., quarterly observations of trading between institutional

and individual investors. To better understand intra-quarter patterns, we examine two additional

datasets that provide daily samples of institutional and individual investors‘ trades: (1) the Plexus

institutional investor transaction database, and (2) Terry Odean‘s discount broker individual investor

transaction database.

The Plexus institutional transaction database covers January 1993 through March 1998 and

then January 2000 through March 2001.8 The sample consists of 8.6 million transactions from a total

of 125 institutional investors ranging from a minimum of four institutions the first quarter of the

sample (but moving to 15 institutions the following quarter) to a maximum of 76 institutions during

the second quarter of 2000. On average, there are 44 institutions each quarter. Analogous to net

institutional demand [equation (2)], we compute, each day, the net fraction of outstanding shares

purchased by institutional traders in the Plexus data (henceforth denoted ―Plexus institutional

demand‖). To ensure our results are not driven by observations with only a few institutional

transactions, we limit the sample to those securities that have at least 10 days of Plexus institutional

transactions in the month prior to the insider trade, the month of the insider trade, and the month

following the insider trade. The final sample consists of daily institutional trading records around

40,673 insider sales and 10,212 insider purchases. Plexus includes a client style code for most of the

8 The Plexus Group (acquired by ITG in 2006) is a consulting firm that evaluates trade execution quality and costs for institutional investors. These data (or subsets of these data) have been used in a number of previous studies, e.g., Keim and Madhavan (1995), Jones and Lipson (1999), Conrad, Johnson, and Wahal (2001), Barber and Odean (2006), Burns, Kedia, and Lipson (2005), and Irvine, Lipson, and Puckett (2007).

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trading records that partitions clients into momentum investors (accounting for 22% of Plexus

volume), diversified investors (accounting for 36% of Plexus volume), and value investors

(accounting for 12% of Plexus volume). We group the remaining investor classifications

(international and sponsor, who, in total, account for less than 5% of the Plexus volume) with

unidentified investors (who accounting for the balance of the Plexus volume).

The discount broker data identify transactions of approximately 78,000 individual investors

at a large discount brokerage firm between January 1991 and November 1996.9 Again, analogous to

equation (2), we compute, each day, the net fraction of outstanding shares purchased by individual

investors included in the discount broker data (henceforth denoted ―individual investor brokerage

demand‖). As above, we limit the sample to those securities that have at least 10 days with individual

investor transactions (recorded in the discount broker data) in the month prior to the insider trade,

the month of the insider trade, and the month following the insider trade. The final sample consists

of individual investor transactions around 12,025 insider sales and 3,258 insider purchases.

2. Insider Demand and Institutional Trading

Because insiders‘ trading patterns are related to capitalization, we begin by sorting securities

into small-, medium-, and large-capitalization groups. Following Lakonishok and Lee (2001), we use

NYSE capitalization breakpoints (updated quarterly), and define securities in the bottom three

deciles as small, the next four deciles as medium-sized, and those in the top three capitalization

deciles as large. Within each capitalization trecile, securities are further sorted into four portfolios

based on net insider demand. We then compute, each quarter, the cross-sectional average net insider

demand for that quarter (i.e., the sorting variable), net institutional demand over the same quarter

9 These data (or subsets) have also been used in a number of previous studies, e.g., Odean (1998), Barber and Odean (2006), Barber, Odean, and Zhu (2006a, 2006b), Kumar (2005), Kumar and Lee (2006), Seasholes and Zhu (2007). Barber and Odean (2000) provide a detailed description of the data.

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(quarter t=0), and net institutional demand over the previous year (quarters t=-1 to -4) for securities

within each group. Table 2 reports the time-series average (over the 80 quarters) of these cross-

sectional means and the p-value from an F-test (based on the 80 time-series observations for each

group) of equality across the insider demand groups.


The results in Table 2 reveal a strong inverse relation between net insider demand (top row

of each panel) and institutional investors‘ demand over the same quarter (second row) and the

previous year (third row). We can reject the null hypothesis that net institutional demand the same

quarter, or over the previous year, are equal across the insider demand quartiles at the 1% level in

every case.

Given the growth in institutional ownership over the sample period, institutional investors

are, on average, buying securities in each of the insider demand quartiles. Thus, the quartile of

securities that insiders most heavily purchase also experienced net buying by institutional investors

the same quarter (for medium and large capitalization securities). Although the liquidity hypothesis

suggests that insiders and institutions cannot both be net buyers, the characteristics and perceived

mispricing hypotheses only suggest that insider trading should be inversely related to institutional

demand. The balance of the paper focuses on understanding the inverse relation between insider

trading and institutional demand.

3. The Liquidity Explanation

The median insider sale in our sample is $80,010 and the median insider purchase is $12,880.

Given the size of the typical insider transaction, institutional investors (which includes market

makers) are in a much better position (relative to individual investors) to provide the liquidity

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necessary for insiders to trade.10 We begin by testing whether the liquidity hypothesis can fully

explain the inverse relation between insiders and institutions. Specifically, we assume that

institutional investors are the counterparty to every insider trade and define ―Adjusted Net Institutional

Demand‖ as the sum of net institutional demand and net insider demand.11 Thus, for example, if

officers and directors sell 0.5% and institutions purchase 1% of the company‘s outstanding shares this

quarter, adjusted net institutional demand is 0.5% (i.e., 1% + (-0.5%)). Analogously, adjusted net

institutional demand over the previous year is computed as the sum of net institutional demand over

the previous year and net insider demand over the previous year.

This approach has two limitations. First, it is possible that insiders‘ trades are positively

correlated with the trades of other employees who do not perform ―policy-making‖ functions (and

therefore are not required to file SEC insider trading reports). If institutional investors provide

liquidity to these investors, then this method will underestimate the importance of the liquidity

explanation. Second, even if institutional investors are the counterparty to every insider transaction,

this method will overestimate the importance of the liquidity explanation to the extent that

institutions subsequently adjust their inventories by taking the other side of trades with individual

investors within the same quarter. Consider a simple example: A company has 100,000 shares

outstanding and a market maker (who files a 13(f) report) buys 10,000 shares from an insider today

(thus, net insider demand is -10,000/100,000 = -0.10). Over the next week (still within the same

quarter), the market maker sells 6,000 shares to other institutions and 4,000 shares to individual

10 Barber and Odean (2000) report that the median individual investor‘s purchase is for $5,738 (based on their discount broker data). The median individual investor‘s sale is slightly smaller at $4,988. In contrast, Conrad, Johnson, and Wahal (2002) report the median institutional order size is $60,690 for their sample of institutional trades. 11 Institutions providing liquidity to insiders may help, or hurt, the speed of price adjustment. First, assuming other traders do not know the trade is an insiders‘ until sometime after the trade occurs (i.e., when the insider files their SEC report), illiquidity may increase the price adjustment process because the price impact of any trade (including the insiders‘) will be larger in a less liquid market. If illiquidity discourages insiders from trading, however, then markets will not learn insiders‘ beliefs and therefore slow the price adjustment process.

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investors. Assuming no other trading within the quarter, net institutional demand is 0.06 ((10,000-

10,000+6,000)/100,000) while adjusted net institutional demand is -0.04 (0.06-0.10). This bias will

likely be most severe in the observations with the most extreme net insider trading, e.g., a very large

insider purchase will result in a very large adjustment to net institutional demand. Therefore, to

minimize the effect of this bias and ensure outliers do not drive our results, most our subsequent

tests of adjusted institutional demand exclude the extreme observations where the absolute insider

demand this quarter exceeds 1% of the outstanding shares (about 7% of the observations).

The fourth and fifth rows in each panel of Table 2 report adjusted net institutional demand

over the same quarter (quarter t=0) and over the previous year (quarters t=-1 to -4). The results

indicate that although institutional investors providing liquidity to insiders can explain a substantial

portion of the inverse relation between insider trading and institutional demand, it cannot fully

explain it. Both same quarter and lag adjusted institutional demand are substantially larger in stocks

insiders are selling than stocks insiders are buying even if we assume institutions are the counterparty

to every trade made by an insider.

As noted above, in addition to officers and directors, large shareholders and affiliated

shareholders must also report their transactions to the SEC. If these traders‘ transactions are

positively correlated with officers and directors‘ transactions, then institutional investors providing

liquidity to these traders may help explain the inverse relation between net insider demand and

institutional trading. To assess this possibility, we define ―All Adjusted Net Institutional Demand‖ as the

sum of net institutional demand in the quarter and net demand by officers and directors, large

shareholders, and affiliated shareholders the same quarter. We analogously define adjusted net

institutional demand over the previous year. The sixth and seventh rows of each panel in Table 2

report ―All Adjusted Net Institutional Demand‖ over the same quarter (quarter t=0) and over the

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previous year (quarters t=-1 to -4). The results are nearly identical to the previous results that adjust

only for officers‘ and directors‘ trades.12

3.2. Liquidity and Trade Size

If institutional investors providing liquidity to insiders contributes to the inverse relation

between insider trading and institutional demand, then the relation between insider trading and net

institutional demand should be stronger for large insider trades.13 We begin by defining the ―relative

size‖ of each insider sale as the number of shares sold by insiders on the transaction date divided by

the total volume of shares traded that day. We then partition daily insider sales, each quarter, within

each size decile (based on NYSE breakpoints), into two equal-size groups—those above the median

are classified as large insider trades and those below the median are classified as small insider trades.

We analogously classify insider buys for each quarter within each size group.14

Table 3 reports the analysis for those stock-quarters that have at least one large insider trade

that quarter (Panel A) and those that have no large insider trades that quarter (Panel B).15 Consistent

with the liquidity hypothesis, the difference in net institutional demand, both the same quarter and

over the previous year, for the extreme insider quartiles are greater for the large insider trade sample

than the small insider trade sample. In fact, we find little relation between small insider trades and

12 Adjusting for large and affiliated shareholders has two limitations. First, this method generates ‗double counting.‘ For example, Merrill Lynch held more than 10% of the outstanding shares of BorgWarner in the last quarter of 1994. Thus, Merrill Lynch‘s sale of BorgWarner stock in the fourth quarter of 1994 is counted twice in All Adjusted Net Institutional Demand—once in Merrill‘s 13(f) report and once in Merrill‘s ‗insider‘ trading report to the SEC. Second, for those observations where officers and directors trade in the opposite direction of large and affiliated shareholders, this analysis biases the results away from the liquidity explanation. For these reasons, and because the results are largely insensitive to using either Adjusted Net Institutional Demand or All Adjusted Net Institutional Demand, we focus on Adjusted Net Institutional Demand throughout the balance of the paper. 13 It is also possible, of course, that large insider trades may be more strongly related to institutional demand as a result of security characteristics or perceived mispricing. 14 Because insider sales tend to be larger than insider purchases, we define small and large for sales and purchases separately. Following previous literature we divide volume by two prior to computing relative trade size for Nasdaq securities [Atkins and Dyl (1997)]. 15 Because a stock-quarter can have both large and small insider trades, the number of observations in Panel A (at least one large insider trade that quarter) is greater than the number of observations in Panel B (no large insider trades that quarter).

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institutional demand in the largest stocks. Nonetheless, for most small insider trades (i.e., small and

medium capitalization stocks), we still document an inverse relation between insider trading and

institutional demand. Moreover, inconsistent with the hypothesis that the liquidity explanation fully

explains the inverse relation between institutions and insiders, adjusted net institutional demand

(both the same quarter and over the previous year) remains inversely related to net insider demand

in both large and small insider trades (except for small insider trades in large capitalization stocks).16

As noted above, because adjusted net institutional demand is simply the sum of net

institutional demand and net insider demand, the adjustment to institutional demand will be large

whenever net insider demand is large. Thus, to examine whether the results are sensitive to the most

extreme observations, we next partition the sample into stock-quarters where the absolute value of

net insider demand is greater than 1% of the outstanding shares (Panel C) and those less than 1%

(Panel D).

The results in Panels C reveal that institutional investors providing liquidity to insiders could

explain the inverse relation between insider trades and institutional demand the same quarter when

limited to the very largest insider trades. Specifically, the relation between insider trading and

adjusted institutional demand the same quarter becomes positive for small capitalization securities

and no longer statistically significant (at traditional levels) for medium and large capitalization

securities when limiting the sample to the very largest insider trades. Nonetheless, even when

limiting the sample to the most extreme insider trading observations and assuming institutions are

the counterparty to every insider trade over the previous year, insider trades remain inversely related

to institutional demand over the previous year for small and medium capitalization stocks.

Moreover, for the 93% of the sample observations where absolute net insider demand is less than

16 Results are similar when assuming institutional investors are the counterparty to every trade by an officer or director, a large shareholder, or an affiliated shareholder. We do find, however, that there is little relation between ―All Adjusted Net Institutional Demand‖ and insider demand the same quarter for large trades in small stocks.

16

1% of the outstanding shares (Panel D), insider trading is inversely related to net institutional

demand the same quarter and over the previous year even when we assume institutions are the

counterparty to every insider trade (statistically significant at the 1% level in all cases).17

3.3. Liquidity Explanation: Evidence from Transaction Data

Although the results in Tables 2 and 3 suggest that institutional investors serving as the

counterparty to insider trades contributes to the inverse relation between institutional and insider

demand, the analysis is limited by the coarseness of the data. In this section, we use the Plexus

institutional data and the Odean discount broker data to evaluate daily samples of institutional and

individual investors‘ demand around insider trades. If institutional investors providing liquidity to

insiders fully drives the inverse relation between insider trades and institutional demand, then the net

fraction of shares purchased by Plexus institutions should not be related to insider trading (except

possibly on the day of insider transaction). Alternatively, the characteristics and mispricing

perceptions hypotheses predict that Plexus institutional demand should be inversely related to

insider trading in the days and weeks surrounding the insider transaction.

Figure 1 presents cross-sectional average daily cumulative net Plexus institutional demand (in

percent) in the 61 days (t=-30 to +30 days) surrounding insider sales (solid line) and insider

purchases (broken line). For the purposes of this analysis, we define insider sales (purchases) as days

in which net insider demand is less than (greater than) zero. Consistent with the charactersicis and

perceived mispricing hypotheses, the figure reveals that Plexus institutions are selling in the days and

weeks surrounding insider purchases and buying in the days and weeks surrounding insider sales.

[Insert Figure 1 about here]

Panel A in Table 4 reports tests of the null hypothesis that Plexus institutional demand is

equal for insider sales and purchases. Specifically, the first row in Panel A reports the mean

17 We find qualitatively identical results when assuming institutions are the counterparty to every trade by an officer or director, large shareholders, and affiliated shareholders.

17

cumulative Plexus institutional demand for stocks with insider sales for periods prior to the day of

the insider sale, the day of the insider sale, and periods following the insider sale. The first cell, for

example, indicates that in the 30 days prior to an insider sale, Plexus institutions purchased, on

average, 0.31% of the security‘s outstanding shares. The second row reports analogous figures for

Plexus institutional demand around insider purchases. The third row reports the mean difference in

Plexus institutional demand around insider purchases and sales and associated t-statistics (based on a

t-test for difference in means). In all cases, we can reject the null hypothesis of equal institutional

demand at the 1% level. Note also that insider demand is inversely related to Plexus institutional

demand the day of the insider transaction consistent with the hypothesis that institutions in the

Plexus dataset may help provide liquidity for these insider trades.18


A limitation of the analysis in Panel A is that a given stock can have multiple insider trades.

Therefore it is possible that institutional trades in the 30 days prior to a given insider trade may

reflect, at least in part, institutional investors providing liquidity to earlier insider trades. To

investigate this possibility, we repeat the analysis, but limit the sample to observations where there

are no other insider trades in the stock over the prior 30 trading days (which results in samples of

6,586 insider sales and 3,229 insider purchases). Results, reported in Panel B, are fully consistent

with those reported in Panel A.

We next use the discount broker data to evaluate a sample of individual investors‘ trades

around insider purchases and sales. Figure 2 presents the cumulative net fraction of shares

purchased by investors in the discount broker data in the 61 days around insider purchases (broken

line) and insider sales (solid line). Consistent with our previous results, the figure reveals that

18 This does not require that Plexus institutions directly transact with the insider. Consider, for example, an insider who trades with a market maker and then the market maker unwinds at least a portion of the trade sometime later the same day with a Plexus institution.

18

individual investors are net buyers in the days and weeks surrounding insider purchases, and net

sellers in the days and weeks around insider sales.

[Insert Figure 2 about here]

Panel C in Table 4 reports net demand by individual investors (in the discount broker data)

in the 61 days around insider sales (first row) and purchases (second row). The third row reports the

mean difference in net individual investor brokerage demand around insider purchases and sales and

associated t-statistics. We can reject the null hypothesis at the 1% level for all cases except individual

investors‘ demand the day following an insider transaction.

In sum, the results in Tables 2, 3, and 4 indicate that institutions providing liquidity for

insiders contributes to the inverse relation between institutional and insider demand. The analysis,

however, also reveals that the liquidity hypothesis cannot fully explain the inverse relation between

insider trading and institutional demand the same quarter or over the previous year.

4. The Characteristics Explanation

The last two rows in each panel of Table 2 report average (time-series mean of the 80 cross-

sectional averages) book to market ratios and same quarter returns for securities in each insider

demand quartile. Consistent with the characteristics explanation, the results confirm that insiders

favor securities with low returns and value stocks.19 (Note that although buying value stocks and low

lag return stocks are not mutually exclusive, an investor can do one without doing the other.)

We begin to examine the characteristics explanation by evaluating the relation between

insider trading and institutional demand holding book to market ratios and returns approximately

constant using independent two-pass sorts. Specifically, each quarter we independently sort

securities into book to market quintiles and quarterly return treciles resulting in 15 book to market-

19 In untabulated analysis, we sort observations by net institutional demand and confirm that net institutional demand is positively related to returns and inversely related to book to market ratios.

19

quarterly return groups.20 Within each of these 15 groups that hold book to market ratios and

returns approximately equal, securities are further sorted, each quarter, into four groups based on

net insider demand.21 The first two columns in Panel A of Table 5 report the average (time-series

mean of the 80 cross-sectional averages) net fraction of shares purchased by insiders in the one-

quarter of stocks most heavily purchased by insiders (i.e., highest net insider demand) and the one-

quarter of stocks most heavily sold by insiders. The next two columns report the average net

institutional demand the same quarter for securities in the top and bottom insider demand quartiles.

The fifth column reports a t-statistic from a paired t-test (based on the time-series of the 80 cross-

sectional means) of the null hypothesis that the average net institutional demand does not differ

between the top and bottom insider demand quartiles within each of the 15 book to market-return

groups. The last three columns report analogous figures for net institutional demand over the

previous year.


The results in Table 5 demonstrate that, even when controlling for valuation and return,

institutional demand remains inversely related to insider demand. The differences in institutional

demand, both same quarter and over the previous year, between the quartile of stocks most heavily

purchased by insiders and the quartile most heavily sold are statistically significant at the 1% level in

every case.

We next consider the possibility that the combination of attraction to opposite

characteristics and institutions providing insiders liquidity can fully explain the inverse relation

between institutional and insider demand. Specifically, we repeat the analysis in Panel A, but once

20 Because the book to market and quarterly return sorts are independent, the number of securities in each group differs. Nonetheless, each group maintains a reasonable number of securities—the time-series average number of securities in each group ranges from 84 to 181 securities each quarter. 21 For stocks in our sample that have data over adjacent quarters we also examined the extent to which securities change portfolios from one quarter to the next. On average, 72% of the observations remain in the same book-to-market quintile and 36% of the observations remain in the same return trecile.

20

again assume that institutional investors are the counterparty to every transaction by an officer or

director, i.e., we evaluate adjusted net institutional demand rather than net institutional demand. As

discussed above, we exclude the extreme observations where the absolute insider demand this

quarter exceeds 1% of the outstanding shares to minimize the impact of outliers.

The results, reported in Panel B of Table 5, provide evidence that the characteristics and

liquidity explanations help explain the inverse relation between insider trading and institutional

demand the same quarter. Specifically, five of the 15 differences in adjusted same quarter

institutional demand between stocks insiders strongly buy and those they sell are no longer

statistically significant (at traditional levels). Nonetheless, ten of the 15 differences remain statistically

significant at the 5% level or better. Moreover, insider trading remains strongly inversely related to

net institutional demand over the previous year even when controlling for book to market ratios and

returns and assuming institutions are the counterparty to every insider transaction in the previous

year.22

4.1. Manager Style and Characteristics

In this section we examine the relation between insider trading and demand by institutional

manger style. If institutional investors‘ attraction (in aggregate) to growth stocks and high lag return

stocks contributes to the inverse relation between insiders‘ and institutional investors‘ demand, then

the relation between insiders and institutions should be stronger for institutions that focus on

growth stocks than institutions that focus on value stocks. Analogous to Table 2, Table 6 reports net

institutional demand by investor type (large-cap value, large-cap growth, small-cap value, small-cap

growth) for the insider demand quartiles. Thus, summing over rows in Table 6 yields the results in

22 Results are slightly weaker when assuming institutions are the counterparty to every trade by an officer or director, a large shareholder, or an affiliated shareholder, e.g., six (rather than five) of the same quarter differences are no longer statistically significant at traditional levels.

21

Table 2.23 To gauge the relative importance of each investor type, the first column in Table 6 reports

the time-series mean of the cross-sectional average fraction of shares held by each investor type at

the beginning of quarter of the insider trade (first row) or one year prior (second row).24


The results in Table 6 support the characteristics hypothesis—the relation between insider

trading and investor type is much stronger for institutions focusing on growth stocks than those

focusing on value stocks. In medium capitalization securities (Panel B), for example, ownership

levels at the beginning of the quarter (first column) are similar for large-cap value investors and

large-cap growth investors. The difference in average institutional demand between the extreme

insider demand quartiles, however, is twice as large for large-cap growth investors as large-cap value

investors. Except for investors with a large-cap focus in small stocks (i.e., the first four rows of

Panel A), the difference in institutional demand for the extreme insider demand quartiles divided by

beginning of period levels (first column) is greater for large-cap growth investors than large-cap

value investors and greater for small-cap growth investors than small-cap value investors. Excluding

the first four rows of Panel A, these differences are statistically significant at the 5% level in all cases

(specific results results are not reported to converve space).25

23 Because a few managers lack sufficient data for classification, this holds approximately. 24 In untabulated analysis, we also examine the relation between insider trading and demand by each type of institutional investor (banks, insurance companies, mutual funds, independent advisors, and others). Consistent with the aggregate results shown in Table 2, the results reveal evidence that insider trading is inversely related to demand by each institutional investor type the same quarter and over the previous year. 25 Each quarter we compute the difference between mean institutional demand in the top and bottom insider demand quartiles for each investor type for small, medium, and large capitalization securities. We then compute the ratio of this difference to the mean beginning of quarter fraction of shares held by that institution type for small, medium, and large capitalization securities, respectively. Next, each quarter, we calculate the difference in this ratio for investors with a large-cap value focus and investors with a large-cap growth focus. Analogously, we compute the difference in the ratio for investors with a small-cap value focus and investors with a small-cap growth focus. We then compute a t-statistic from the time series of the difference in these ratios (n=80 quarters for same quarter institutional demand, n=76 quarters for lag institutional demand) associated with the null hypothesis that the difference is zero.

22

In fact, in some cases, the relation between insider demand and institutional demand turns

positive for managers with a value focus. Specifically, on average, small-cap value managers buy

more shares in stocks insiders buy than in stocks insiders sell in medium capitalization stocks (same

quarter demand) and large capitalization stocks (both same quarter and lag year demand). In the case

of large stocks, the difference is statistically significant at the 1% level.

We next examine the relation between insider trades and institutional demand by investor

type for the Plexus data. Table 7 reports net Plexus institutional demand for diversified institutions

(Panel A), momentum institutions (Panel B), value institutions (Panel C) and unidentified/other

institutions (Panel D). Summing over the panels in Table 7 yields the results in Panel A of Table 4.

The analysis in Table 7 reveals little evidence that demand by value institutions is inversely related to

insider trading any time in the 30 days prior to, the day of, or in the 30 days following the insider

trade. Rather, on average, demand by ‗value‘ institutions is positively related to insider demand. In

contrast, insider demand is inversely related to demand by diversified, momentum, and

unidentified/other institutions in the Plexus data.


In sum, the analyses of both the 13(f) and Plexus investor type data reveal that, consistent

with the characteristics hypothesis, the inverse relation between insider trading and institutional

demand is stronger for institutions with a growth focus and weaker for those with a value focus.

5. The Perceived Mispricing Hypothesis

A number of recent studies [e.g., Rozeff and Zaman (1998), Jenter (2005), Piotroski and

Roulstone (2005)] propose that insider trades are motivated, at least in part, by insiders‘ perception

that their security‘s price has moved away from its fundamental value. To the extent that insiders

trade against perceived mispricing, our results indicate that insiders tend to trade against institutional

23

demand rather than individual investors‘ demand. This pattern is consistent with two explanations.

First, insiders may mistakenly believe their securities are mispriced when the market price is

appropriate. Second, institutional investors may be more likely to drive prices from fundamentals

than individual investors and insiders are trading against ‗actual‘ mispricing. Consistent with this

view, Jackson (2003) demonstrates that both volatility and correlation between stocks rise following

an increase in institutional ownership and Pirinsky and Wang (2004) document an increase in

comovement between security prices following a rise in institutional ownership. Gompers and

Metrick (2001) also propose that institutional demand shocks sometimes drive prices from

fundamental values.26

In this section we examine whether insiders‘ mispricing perceptions are correct by evaluating

returns subsequent to insiders‘ trading, institutions‘ trading, and the intersections of these samples.

Specifically, we examine three issues. First, if institutional investors are more likely than individual

investors to drive prices from fundamentals, then subsequent abnormal returns should be inversely

related to net institutional demand. Second, if insiders are trading against actual mispricing, then

subsequent abnormal returns should be positively related to insider trading. Third, if insiders trade

against actual mispricing and institutional investors are more likely to drive prices from

fundamentals, then the relation between insider trading and subsequent return patterns should be

stronger when insiders trade against institutions than when they trade against individuals.

A number of previous studies examine the first prediction and find that when limiting the

sample to NYSE securities over the 1980s and early 1990s, there is some evidence that net

institutional demand forecasts subsequent returns [e.g., Nofsinger and Sias (1999), Parrino, Sias, and

26 A number of studies point out that institutional investors need not behave irrationally to drive prices from fundamentals. Institutions, for instance, may herd to the same stocks (and drive prices from fundamentals) because they face a reputational cost for acting different than the herd [Scharfstein and Stein (1990), Trueman (1994)], or because smart managers may ―ride‖ bubbles and therefore contribute to, rather than mitigate, deviations from fundamentals [e.g., Abreu and Brunnermeier (2002), DeLong, Shleifer, Summers, and Waldmann (1990), Griffin, Harris, and Topaloglu (2003), Brunnermeier and Nagel (2004)].

24

Starks (2003), San (2007)]. Once extending the period and adding non-NYSE securities to the

sample, however, there is little evidence of a significant relation between net institutional demand

and returns over the following quarter or year [Gompers and Metrick (2001), Bennett, Sias, and

Starks (2003), Cai and Zheng (2004), Sias, Starks, and Titman (2006), Bushee and Goodman (2007)].

Consistent with these results, San (2007) reports that institutional demand is positively related to

subsequent returns over the 1981-1992 period, but inversely related to subsequent returns over

1993-2004 period.27

Previous studies have also examined the relation between insider trading and subsequent

returns. Lakonishok and Lee (2001), in the most comprehensive study of insider trading, find that

firms with the greatest insider buying subsequently outperform those with the greatest insider

selling. Moreover, most insider trading studies (including Lakonishok and Lee) find the

informational content of insider trades is largely driven by their purchases of small stocks.

5.1. Net Institutional Demand and Subsequent Returns

To isolate the effects of institutional demand, we form institutional demand portfolios

controlling size and book to market ratios. Specifically, each quarter, all securities with adequate data

are sorted independently into size deciles (based on NYSE breakpoints) and book-to-market deciles

(based on NYSE breakpoints). Securities within each of the 100 size and book-to-market groups are

further sorted into four groups by net institutional demand in quarter t. Securities in the top

27 Although our discussion of this literature focuses on studies that use 13(f) data, related studies using mutual fund data from the mid-1970s to mid-1990s find a similar pattern in that mutual fund demand forecasts returns, e.g., Wermers (2000), Chen, Jegadeesh, and Wermers (2000). Kosowski, Timmermann, Wermers, and White (2006), however, report that mutual fund performance in the 1996-2002 period was substantially worse than in earlier periods. Specifically, in the post-1996 period, an equal-weighted portfolio of all mutual funds garnered negative alphas. In addition, Yan and Zhang (2006) find that net institutional demand by short-term institutions predicts returns the following year, but net institutional demand by long-term institutions does not. Their sub-period analysis, however, reveals that the relation between subsequent annual returns and short-term institutions‘ net demand is much stronger in the early period (1980-1991) and only marginally significant in the later period (1992-2003) (and negative, albeit not statistically significant for long-term institutions in the later period).

25

institutional demand quartile are denoted securities ―institutions buy‖ and those in the bottom

quartile are denoted securities ―institutions sell.‖

Panel A in Table 8 reports the average (time-series mean of the 80 cross-sectional averages)

subsequent (i.e., portfolios are formed at the end of quarter t) six-month and two-year abnormal

return from an equally-weighted portfolio of securities in the top institutional demand quartile, an

equally-weighted portfolio of securities in the bottom institutional demand quartile, and their

difference. As before, we denote securities in the bottom three capitalization deciles (based on

NYSE breakpoints) as ―small,‖ those in the middle four capitalization deciles as ―medium,‖ and

those in the top three deciles as ―large.‖ Abnormal buy and hold returns for each security are

computed as the difference between the security‘s return over the holding period and the cross-

sectional average return for securities in the same beginning of holding period size and book-to-

market deciles.28 Given previous studies suggest the relation between institutional demand and

subsequent returns has changed over time, we also partition the sample into two equal periods: June

1983-March 1993 and June 1993-March 2003 (reported in Panels B and C, respectively).


The results in Table 8 are largely consistent with the literature. Over the entire sample

period, there is little evidence of a systematic relation between net institutional demand and

subsequent returns. In the first half of the sample period (Panel B), small, medium, and large stocks

institutions buy generally outperform those they sell, on average, in the following six months and

two years. The results are statistically significant (at the 5% level or better), however, only for small

28 Following Lakonishok and Lee (2001), size and book to market breakpoints are updated quarterly and based on independent sorts of NYSE ordinary securities (CRSP share code of 10 or 11) with prices of at least $1 and sufficient CRSP and Compustat data. For securities that drop from CRSP during the holding period, we substitute NYSE/AMEX/Nasdaq value-weighted index returns for the missing returns. Lakonishok and Lee examine returns from six months to three years following insider trades. They find, however, little difference between two and three year returns (see their Table 6). As a result, we focus on six month and two year returns.

26

stocks institutions buy or sell and large stocks institutions buy. Further consistent with previous

results, institutional demand tends to be inversely related to subsequent returns in the second half

(Panel C) of the sample period although the results are only statistically significant (at the 5% level or

better) for large capitalization securities over the two-year holding period. Overall, however, the

results in Panels A, B, and C reveal little evidence that institutional (or individual) investors

systematically drive prices from fundamental values.29

5.2. Net Insider Demand and Subsequent Returns

Panel D of Table 8 reports the average subsequent abnormal returns (time-series mean of

the 80 cross-sectional averages) from an equally-weighted portfolio of securities purchased by

insiders (net insider demand greater than zero), an equally-weighted portfolio of securities sold by

insiders (net insider demand less than zero), and their difference for small, medium, and large stocks

(as defined above). Consistent with most previous work [e.g., Seyhun (1986), Rozeff and Zaman

(1988), Lakonishok and Lee (2001)], the results suggest that small stocks insiders purchase

subsequently garner positive abnormal returns and significantly outperform those they sell. In

addition, the six-month and two-year abnormal returns for medium capitalization stocks insiders buy

are also statistically significant (at the 1% level), although not significantly larger than those they

sell.30 Panels E and F in Table 8 partition the insider transactions into the two sample periods. In

both periods, the strongest results are from insiders‘ small stock purchases. Overall, the results

reveal weak evidence supportive of the second prediction—subsequent abnormal returns are related

to insider trading, but the results are largely driven by insider purchases of small stocks.

29 The difference in institutional investors‘ performance during the first and second halves of the sample period raises the question of whether the relation between insider trading and institutional demand is also period-specific? Although we do not report specific results (to conserve space), the sub-period analysis reveals that insider demand is strongly inversely related to institutional demand the same quarter and over the previous year in both the first and second halves of the sample period. 30 Although not reported in the table (to reduce clutter), similar to previous work [e.g., Lakonishok and Lee (2001)], we find that the difference in raw returns for stocks insiders buy versus those they sell are even greater.

27

5.3. Subsequent Returns, Net Insider Demand, and Net Institutional Demand

We next examine the intersection of securities traded by both insiders and institutions to

determine if the profitability of insider trades is related to institutional demand. Panel A in Table 9

partitions the securities in top and bottom institutional demand quartiles into those not traded by

insiders, those purchased by insiders, and those sold by insiders. The first column in Panel A reveals,

for instance, that of the 791 small stocks in the top institutional demand quartile (on average),

insiders did not trade 437, purchased 139, and sold the remaining 215. The second column reports

analogous figures for small-capitalization securities in the bottom institutional demand quartile.


The second to last row in Panel A reports the time-series average of the ratio of the number

of securities purchased by insiders to the number of securities traded by insiders when the sample is

limited to securities in the top institutional demand quartile (first, third, and fifth columns) or

securities in the bottom institutional demand quartile (second, fourth, and sixth columns). The last

row in Panel A reports a t-statistic from a paired t-test (based on the time-series of the ratios over

the 80 quarters) of the null hypothesis that the ratio is equal for stocks in the top institutional

demand quartile and those in the bottom institutional demand quartile. Consistent with our previous

results, the tests in Panel A confirm that insiders are more likely to buy when institutions are selling

the same quarter (differences are significant at the 1% level in every case).

Panels B and C report the average buy-and-hold subsequent six-month and two-year

abnormal returns (time-series average of the 80 cross-sectional means), respectively, for a strategy of

investing in an equal-weighted portfolio conditional on institutional demand and insider trading. The

first column of Panel B reveals, for example, that investing in all small-capitalization securities in the

top institutional demand quartile yields a six-month abnormal return of 0.702% (which matches

Panel A of Table 8). Limiting the sample to those small-capitalization securities in the top

28

institutional demand quartile that are not traded by insiders yields an average six-month abnormal

return of 0.007% (second row of Panel B in Table 9). The last two rows reveal that those small-

capitalization securities in the top institutional demand quartile purchased (sold) by insiders average

a subsequent six-month abnormal return of 4.210% (-0.051%).

The results in Panels B and C reveal little evidence that insiders‘ trades are generally more

profitable when they trade against institutional investors. For small stocks, insiders do better, on

average, when trading with, rather than against, institutional investors (at least during the first six

months). For large stocks, on the other hand, insiders do better, on average, when trading against

institutional investors.

Because the relation between institutional demand and subsequent returns is sensitive to the

sample period, we repeat the subsequent two year return analysis for the first and second halves of

the sample period. Results are presented in Panels D (first half of sample period) and E (second half

of sample period) of Table 9. Consistent with the sub-period examination of institutional trades, in

the first half of the sample period, except for large stocks insiders buy, abnormal returns associated

with insider trades are generally greater when insiders and institutions trade in the ‗same direction.‘

In the second half of the sample period, when institutional demand is inversely related to subsequent

returns, insiders do better when trading against institutional investors.

In sum, we find little evidence to support the hypothesis that insiders are trading against

actual mispricing and institutional investors are more often responsible for that mispricing.

Consistent with previous work, the informativeness of insider trading is largely limited to small

stocks they buy and there is little evidence that institutions systematically drive prices from

fundamentals.

5.4. The Liquidity Explanation, Informed Trading, and Trade Size

29

If insiders trade more aggressively when the deviation between their security‘s price and

value is greater, and institutions are more likely to be the counterparty to large insider trades, larger

insider trades should be associated with large abnormal returns and greater levels of trading against

institutions. To investigate this possibility, we repeat the analysis of the intersections of institutional

demand, insider demand, and subsequent returns for stock-quarters that contain at least one large

insider transaction and those that contain only small insider transactions. Small and large insider

transactions are as defined before [by the ratio of insider sales (or purchases) to daily volume]. The

results are reported in Table 10.


Consistent with the liquidity explanation, the results suggest that institutions are more likely

to be the counterparty to large insider trades. In the case of small stocks with large insider trades, for

example, insiders purchase 33.6% of stocks institutions buy and 47.4% of the stocks institutions

sell—a difference of 13.8% (Panel A). For small stocks with small insider trades, insiders buy 53.9%

of the stocks institutions buy and 60.6% of the stocks institutions sell—a difference of 6.7% (Panel

C). (Although this pattern also holds for medium capitalization stocks, it does not hold for large

capitalization stocks.)

The results reveal some evidence that abnormal returns following insider purchases tend to

be larger when insiders buy more aggressively. The two-year abnormal return for small stocks that

both insiders and institutions purchase, for example, averages 9.301% (Panel B) for stocks with large

insider trades versus 3.775% for small insider trades (Panel D). Again, however, the evidence is

mixed. For small capitalization stocks insiders sell, their small trades appear more informative than

their large trades.31

31 In untabulated results, we repeat the analysis of large and small insider trades in the early (1983:06-1993:03) and later (1993:06-2003:03) halves of the sample period. Consistent with the analysis in Table 8, we find that

30

5.5. Do Insider React to Institutional Demand or Associated Price Changes?

Insider demand is inversely related to same quarter and lag annual institutional demand even

when controlling for the liquidity and characteristics hypotheses. What is not clear is whether

insiders: (1) observe and react to institutional demand, or (2) react to price changes driven by

institutional demand and insiders‘ mispricing perceptions are not fully captured by returns and

valuation levels. If, for example, insiders believe ‗markets‘ have pushed prices too high over the past

year and institutions are the price-setting marginal investors responsible for driving prices higher

over the past year, then insiders‘ trading will be inversely related to institutional demand over the

past year. A number of recent studies (based on transaction data) suggest that institutional investors

are the marginal traders who set prices [e.g., Chakravarty (2001), Froot and Teo (2004), Linnainmaa

(2006), Kaniel, Saar, and Titman (2008), Campbell, Ramadorai, and Schwartz (2007), and Stoffman

(2007)].32

Insiders may have some indication of same quarter and lag institutional demand through

feedback from their investor relations departments (that primarily focus on institutional investors),

investor relations consultants, and interaction with security analysts. Because institutions must file

their 13(f) ownership reports within 45 days of the quarter-end, however, insiders can directly

measure net institutional demand over the most recent quarter by the middle of the current quarter.

Thus, if institutions are reacting to institutional demand rather than price changes induced by

institutional demand, the relation between institutional demand in quarter t-1 and insider demand in

when insiders buy small stocks, they do better when trading with institutional investors in the early period and better when trading against institutional investors in the later period. 32 This view is also consistent with recent survey evidence that managers believe institutional investors play a more important role than do individual investors in driving share values [Graham, Harvey, and Rajgopal (2005)]. Griffin, Harris, and Topaloglu (2003) also report evidence that institutions are short-term (intra-day) positive feedback traders. In contrast, Barber, Odean, and Zhu (2006b) find that when focusing on small orders on the ―active‖ side of the trade, individual investors‘ demand drives the prices of small stocks. Similarly, Kumar and Lee (2006) find that individual investor buy-sell imbalance helps explain security returns for small capitalization, low-priced, low-institutional ownership securities.

31

the last half of quarter t should be substantially stronger the relation between institutional demand in

quarter t-1 and insider demand in the first 45 days of quarter t.

To examine this issue, each quarter we sort securities within each size trecile into quartiles

based on net institutional demand in quarter t-1. We then examine the relation between institutional

demand in quarter t-1 and insider demand in the first 45 days of quarter t and the last half of quarter

t. Although we do not report specific results (to conserve space), we find no evidence that the

relation between institutional demand in quarter t-1 and insider demand in the second half of quarter

t is stronger than the relation between institutional demand in quarter t-1 and insider demand in the

first 45 days of quarter t. In short, the evidence suggests that insiders are responding to price

changes driven by institutional investors rather than net institutional demand per se.

6. The Relative Importance of Liquidity, Characteristics, and Perceived Mispricing

Our results suggest that the liquidity, characteristics, and perceived mispricing hypotheses all

play a role in explaining the inverse relation between insider trading and net institutional demand. In

this section, we estimate the relative importance of each of these factors in driving the relation

between insider trading and institutional demand.

We begin by regressing insider demand on the fraction of shares moving from individual

investors to institutional investors the same quarter (Net Institutional Demandi,t) and over the previous

year (Net Institutional Demandi,t-1 to t-4). As before, to ensure the results are not driven by outliers, we

exclude the most extreme observations where insider trades exceed more than 1% of the

outstanding shares. To allow direct comparisons to subsequent regressions and across variables, we

standardize (rescale to unit variance and zero mean) all variables each quarter and require

32

observations to have data over the previous year (the asterisks indicate the variables are

standardized):33

.,*

41,2*,1

*, tittotititi DemandnalInstitutioNetDemandnalInstitutioNetDemandInsiderNet (3)

The regression is estimated each quarter and the time-series average coefficients for the 76

cross-sectional regressions are reported in the first column (Regression 1) of Table 11. (Because all

variables are standardized the intercept is zero.) The t-statistics (reported in parentheses) are

computed from the time-series standard error of the 76 coefficient estimates. Because the variables

are standardized, the coefficients have a straightforward interpretation—they represent the expected

standard deviation change in net insider demand given a one standard deviation change in the

independent variable. Thus, a one standard deviation increase in same quarter net institutional

demand is associated with a 0.115 standard deviation decrease in net insider demand. And a one

standard deviation increase in net institutional demand over the previous year is associated with a

0.126 standard deviation decrease in net insider demand this quarter.


We next estimate how much of the relation between insider and institutional demand is

accounted for institutions providing liquidity to insiders. Because (1) institutions are better suited to

provide liquidity, (2) market makers (who meet the SEC‘s 13(f) criteria) are included in our measure

of institutional demand, and (3) the evidence that institutions provide liquidity to insiders (Tables 3,

5, and 10), we estimate the relative importance of the liquidity hypothesis by assuming that

institutions are the counterparty to all insider trades. Thus, we compare the coefficients estimated in

33 Note that the R2 from the standardized regression can be written in terms of the standardized beta

coefficients and the correlations. Specifically, given J explanatory variables: .1 1

1

,1

,22

J

j

J

j

J

jkk

kjkjjR

33

the above regression to the coefficients from a regression of standardized net insider demand on

standardized same quarter and previous year adjusted net institutional demand:

*,1

*, titi DemanditutionalInstNetAdjustedDemandInsiderNet

.,*

41,2 tittotiDemandnalInstitutioNetAdjsted (4)

The second column (Regression 2) in Table 11 reports the results. Comparing coefficients

across the first two columns, reveals a one standard deviation increase in institutional demand results

in a 0.060 standard deviation decrease in net insider demand (second column) after accounting for

liquidity, versus a 0.115 standard deviation decrease before accounting for liquidity (first column).

Thus, we estimate that institutions providing liquidity to insiders explains 48% of the relation

between insider demand and institutional demand the same quarter, i.e., after accounting for the

liquidity explanation the standardized coefficient associated with institutional demand falls 48% (1-

0.060/0.115). Analogously, we estimate that institutional investors providing liquidity to insiders

explains 29% of the relation between insider demand and net institutional demand over the previous

year, i.e., the standardized coefficient associated with lag institutional demand falls 29% between

Regressions 1 and 2.

To estimate how much of the relation between insider demand and institutional demand is

driven by insiders‘ and institutions‘ attraction to the opposite characteristics, we regress standardized

net insider demand on standardized adjusted institutional demand the same quarter, standardized

adjusted institutional demand the previous year, and other standardized variables known to explain

insider demand. The regressions closely follow those reported by Piotroski and Roulstone (2005),

except: (1) we add adjusted net institutional demand (the same quarter and over the previous year) as

independent variables, (2) we use net insider demand as the dependent variable (rather than the ratio

of the number of shares insiders buy to the number of shares they trade), (3) we add the stock‘s

return over the previous 12 months (t-1 to t-4) to ensure that net institutional demand over the

34

previous year is not proxying for return over the previous year, (4) we add the stock‘s net insider

demand over the previous 12 months (t-1 to t-4) to account for autocorrelation in insider demand,

and (5) we standardize all variables (each quarter):

*41,2

*,1

*, ttotititi DemanditutionalInstNetAdjustedDemanditutionalInstNetAdjustedDemandInsiderNet

*,5

*4,4

*4,3 .. tititi ROAROAReturnAdjMarketAnn

*,11

*,10

*,9

*,8

*,7

*,6 4321 titititititi MRETHRETBMBMBMBM

.' ,*,14

*41,13

*41,12 titittotittoti ExercisedOptionsInsidersReturnDemandInsiderNet (5)

Piotroski and Roulstone include: (1) the annual subsequent market adjusted stock return

(Ann. Market Adj. Returni,t+4), (2) the change in return on assets over the next year (ΔROAi,t+4), and

(3) the change in return on assets over the last year (ΔROAi,t) because previous work demonstrates

that insider trading is motivated, in part, by insiders‘ superior ability to forecast future performance

[e.g., Ke, Huddart, and Petroni (2003)].34 Piotroski and Roulstone include four dummy variables for

book to market quintiles (where BM1i,t equals 1 if stock i is in the bottom book to market quintile in

quarter t and 0 otherwise) and dummy variables for membership (each quarter) in the top third of

this quarter‘s cross-sectional return distribution (HRETi,t) and the middle third of this quarter‘s

return distribution (MRETi,t) to capture insiders‘ preference for low lag return and value stocks.35

Piotroski and Roulstone use the dummy variable specification to account for outliers and non-

34 Because this quarter‘s return on assets is unknown until this quarter is over, the change in return on assets over the past year (ΔROAi,t) also captures insiders‘ informational advantage [see Piotroski and Roulstone (2005) for additional discussion]. 35 The coefficient associated with an unstandardized dummy variable is the expected response variable difference between a category and the baseline category. Because the dummy variables are standardized, the standardized dummy variable coefficients cannot be interpreted in this manner. For the book to market dummy variables, the standard deviation of each is 40% (i.e., for each variable, one in five observations takes a value of 1 and four of five take a value of 0). As a result, the coefficients associated with the unstandardized dummy variables can be ―backed out‖ by multiplying each coefficient by 2.5, i.e., 1 unit is 2.5 standard deviations (i.e., 1/0.40=2.5). Similarly, given the standard deviation of the unstandardized return dummy variables (MRET and HRET) is 0.4714, the unstandardized coefficient is 2.12 times each standardized coefficient (i.e., 1/0.4714=2.12).

35

normal distributions and to allow direct comparisons with earlier work [e.g., Rozeff and Zaman

(1998)]. Because insiders are more likely to sell when they have recently exercised options, the

authors include the natural logarithm of one plus the number of options exercised by insiders as a

fraction of shares outstanding. Note, however, that prior to May 1991, insiders were required to

hold stock acquired through stock options for at least six months (Piotroski and Roulstone‘s sample

begins in 1992). Therefore, prior to May 1991 (June 1984-March 1991, n=28 quarters) we use the

options exercised by insiders two quarters prior (Insiders’ Options Exercisedi,t-2). And following

Piotroski and Roulstone, we use options exercised the same quarter (Insiders’ Options Exercisedi,t) for

June 1991-March 2003 (n=48 quarters).

The time-series average of the 76 cross-sectional regressions is reported in the last column

(Regression 3) of Table 11. Consistent with Piotroski and Roulstone (2005, Table 3), the coefficients

associated with the book to market quintile and return trecile dummy variables confirm that insiders

prefer value stocks and stocks with low contemporaneous quarter returns. Consistent with insiders

possessing an informational advantage, they also exhibit a tendency to buy prior to an increase in the

return on assets (i.e., the positive coefficients associated with ΔROAi,t and ΔROAi,t+4) or the stock

price (i.e., the positive coefficient associated with Ann. Market Adj. Returni,t+4). It is also possible that

insider trading may be related to future returns because of endogeneity. That is, insider transactions

may impact managerial effort.

Comparing the average coefficients associated with adjusted net institutional demand across

the three regressions, we estimate that insiders‘ and institutional investors‘ attraction to the opposite

security characteristics explains 26% of the relation between insiders and institutional demand the

same quarter, i.e., the average standardized coefficient falls another 26% (0.060/0.115 - 0.030/0.115)

between Regressions 2 and 3. In sum, our estimates suggest that a one standard deviation increase in

same quarter institutional demand is associated with a 0.115 standard deviation decrease in insider

36

trading the same quarter (Regression 1). Institutions providing liquidity to insiders accounts for 48%

of this relation; institutions and insiders attraction to the opposite security characteristics account for

26%; and the remaining 26% is attributed to the relation between insiders‘ mispricing perception

and institutional demand.

Analogously, comparing the lag institutional demand coefficients across the three regressions

reveals that insiders‘ and institutional investors‘ attraction to the opposite security characteristics

explains 43% of the relation between insider demand and institutional demand over the previous year,

i.e., the standardized coefficient falls another 43% (0.089/0.126-0.035/0.126). In sum, we estimate

that 29% of the relation between insider trading and institutional demand over the previous year

results from institutions providing liquidity to insiders, 43% is due to insiders‘ and institutions‘

attraction to the opposite security characteristics, and the remaining 28% is due to the explation that

insiders are more likely to view their security as overvalued (undervalued) following institutional

buying (selling) over the previous year.

7. Conclusions

Insider trading is inversely related to demand by institutional investors (and positively related

to demand by individual investors) the same quarter and over the previous year. Institutional

investors providing liquidity to insiders and institutions‘ and insiders‘ attraction to opposite security

characteristics account for most of these relations.36 Nonetheless, we estimate that a little over one-

36 A fourth potential explanation for the inverse relation between insider trading and institutional demand the same quarter is that individual investors are more strongly attracted (than institutional investors) to stocks insiders buy. Because most insider trades will be revealed within the quarter they occur, this could contribute to the inverse relation between insider trading and institutional demand the same quarter. This explanation, however, also suggests that individual investors‘ demand should be positively related to lag insider demand. In untabulated analysis, we repeat the regression in Table 11 but use net institutional demand as the dependent variable. Contrary to this explanation, we find that institutional investors‘ demand is positively related to net insider demand over the previous year. The result is inconsistent with the hypothesis that individual investors are more strongly attracted to shares insiders buy (and away from those they sell) than are institutional

37

quarter of the relation is not accounted for by the liquidity and characteristics explanations. If

insiders are trading against perceived mispricing, then our results suggest that insiders‘ are more

likely to believe their securities are overvalued after a period of institutional buying (individual

investor selling) and undervalued after a period of institutional selling (individual investor buying).

If, in contrast to Rozeff and Zaman (1998), Jenter (2005), and Piotroski and Roulstone (2005),

insiders‘ trading is unrelated to their perception that their security‘s fundamental value differs from

its market price, then the inverse relation between insider trading and institutional demand

documented here remains unexplained. Regardless, our results indicate that whatever motivates

insiders to trade is inversely related to institutional demand.

If insiders‘ trading against same quarter and lag institutional demand is motivated by insiders‘

perception that their security‘s fundamental value differs from its price, we find little evidence that

their perceptions are generally correct. Consistent with previous work, the relation between insider

trades and subsequent returns is primarily driven by insider purchases of small stocks. Also

consistent with previous work, there is little evidence that institutions systematically drive prices

from fundamentals. Moreover, we find little evidence that profits from following insider trades are

consistently related to whether insiders trade with or against institutions.

We find no evidence that insiders‘ trading behavior is in response to mispricing caused by

individual investors. Thus, our results are inconsistent with the hypothesis that individual investors

are noise traders who collective actions drive prices from values and insiders trade against such

mispricing.37 In sum, while the liquidity and characteristics hypotheses account for most of the

investors. The result is consistent, however, with the liquidity hypothesis if institutional investors providing liquidity to insiders in quarter t unwind those positions over the following year. 37 A number of studies specifically identify individual investors as those investors whose trades are motivated by sentiment rather than fundamentals. Such an argument underlies a substantial body of work that uses closed-end fund discounts as a sentiment index, e.g., Lee, Shleifer, and Thaler (1991), Bodurtha, Kim, and Lee (1995), Neal and Wheatley (1998), Gemmill and Thomas (2002), Burch, Emery, and Fuerst (2003), Baker and Wurgler (2006). For additional evidence of the naïveté or irrationality of individual investors, see Shiller

38

inverse relation between insider trading and institutional demand the same quarter and over the

previous year, the results are also consistent with the hypothesis that insiders are more likely to

believe their security is overvalued (undervalued) following institutional buying (selling). If insiders

are trading against perceived mispricing, they are more often trading against institutional demand

than individual investors‘ demand.

(1984), Cohen, Gompers, and Vuolteenaho (2002), Hribar, Jenkins, and Wang (2004), Burns, Kedia, and Lipson (2005), Gibson, Safieddine, and Sonti (2004), Field and Lowry (2005), Mikhail, Walther, and Willis (2005), Barber and Odean (2000, 2006), Goetzmann and Kumar (2004), Kumar (2005), Poteshman and Serbin (2003) Barber, Odean, and Zhu (2006a, 2006b), Hvidkjaer (2007), and Kumar and Lee (2006).

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Appendix A—Variable Definitions

Variable Definition

Net Insider Demandi,t The net fraction of firm i shares purchased by officers and directors in quarter t. Net Institutional Demandi,t

The net fraction of firm i shares moving from individual investors to institutional investors over quarter t.

Adjusted Net Inst. Demandi,t

The net fraction of firm i shares moving from individual investors to institutional investors over quarter t assuming institutions are the counterparty to every trade by an officer or director (i.e., Adjusted Net Inst. Demandi,t= Net Inst. Demandi,t+Net Insider Demandi,t)

All Adjusted Net Inst. Demandi,t

Net Insider Demand over Previous Yeari,t

The net fraction of firm i shares moving from individual investors to institutional investors over quarter t assuming institutions are the counterparty to every trade by an officer and director, large (greater than 10% ownership) shareholders, and affiliated shareholders required to file trade reports with the SEC. The net fraction of firm i shares purchased by officers and directors over quarters t-1 to t-4.

Net Inst. Demand over Previous Yeari,t

The net fraction of firm i shares moving from individual investors to institutional investors over quarters t-1 to t-4.

Adjusted Net Inst. Demand over Previous Yeari,t

The net fraction of firm i shares moving from individual investors to institutional investors over quarters t-1 to t-4 assuming institutions are the counterparty to every trade by an officer or director (i.e., Adjusted Net Inst. Demand over Previous Yeari,t = Net Inst. Demand over Previous Yeari,t+Net Insider Demand over Previous Yeari,t)

All Adjusted Net Inst. Demand over Previous Yeari,t

The net fraction of firm i shares moving from individual investors to institutional investors over quarters t-1 to t-4 assuming institutions are the counterparty to every trade by an officer and director, large (greater than 10% ownership) shareholder, and affiliated shareholder required to file trade reports with the SEC.

%Shares Held by Institutionsi,t

The percentage of outstanding shares of security i held by institutional investors at the end of quarter t.

Ann. Market Adj. Returni,t+4

The return of security i over quarters t+1 to t+4 less the return on the CRSP value-weighted index over the same period.

ΔROAi,t+4 Return on assets in quarter t+4 less return on assets in quarter t. Return on assets is

annualized, i.e., 4*net income before extraordinary items/average total assets. ΔROAi,t Return on assets in quarter t less return on assets in quarter t-4. Return on assets is

annualized, i.e., 4*net income before extraordinary items/average total assets. Book to Market Ratioi,t

Book value of equity for security i at the end of quarter t divided by the market value of equity for security i at the end of quarter t. Following Jenter (2005) and others, book value is computed as Compustat‘s shareholder‘s equity plus balance sheet deferred taxes and investment tax credits less book value of preferred stock [where book value of preferred stocks is estimated with redemption, liquidation, or par value (in that order)].

Market Capitalizationi,t

The number of shares outstanding of security i at the end of quarter t multiplied by the price per share of security i at the end of quarter t.

BMki,t A dummy variable that equals one if security i is in the kth book to market quintile in quarter t. Book to market quintiles are updated quarterly and based on book and market value of equity at the end of quarter t [following Rozeff and Zaman (1998) and Piotroski and Roulstone (2005)].

HRETi,t A dummy variable that equals one if security i‘s return in quarter t is in the top

third of sample firms‘ quarter t returns. MRETi,t A dummy variable that equals one if security i‘s return in quarter t is in the

middle third of sample firms‘ quarter t returns. Insiders’ Options Exercisedi,t Institutional Investor Types

The natural logarithm of the one plus the percentage ratio of the number of options exercised by insiders in quarter t to security i‘s shares outstanding at quarter t. Our institutional investor classifications are updated versions of the classifications from Abarbanell, Bushee, and Raedy (2003). They were graciously provided by Brian J. Bushee. The classifications are based on factor analysis and cluster analysis similar to the methodology used by Bushee (1998). For the factor analysis, Abarbanell, Bushee, and Raedy construct average annual values for the following 15 variables for each manager: Weighted average market capitalization Percent holdings in the S & P 500 Weighted average time listed Weighted average price per share Weighted average earnings growth Weighted average sales growth Weighted average beta Weighted average standard deviation of returns Weighted average earnings-to-price ratio Weighted average dividend yield Weighted average book-to-price ratio Percent holdings in firms with 5 consecutive years of earnings growth Weighted average S&P stock rating Percent holdings in firms with positive earnings Weighted average debt-to-equity ratio Through principal factor analysis (with an oblique promax rotation) these variables reduce to a set of four unobserved common factors that explain the variables‘ common variation. The four factors in Abarbanell, Bushee, and Raedy‘s analysis indicate the extent of preference for large and mature firms (FSIZE), high risk and growth firms (PGROW), high book-to-market, high earnings-price, and high dividend yield firms (VALUE), and high stock ratings, steady earnings growth, low leverage, and positive earnings (FIDUC). Finally, they perform cluster analysis on the factor scores to classify institutions into four groups based on combinations of preferences for firm size and expected growth. The four institutional investor type categories are large-cap value, large-cap growth, small-cap value, and small-cap growth.

Table 1 Descriptive Statistics

This table provides descriptive statistics for the sample of 155,495 security-quarter observations between June 1983 and March 2003 that have complete data. Net insider demand is defined as the net fraction of shares of firm i purchased by insiders in quarter t. Net institutional demand is the net fraction of firm i shares moving from individual investors to institutional investors over quarter t. Institutional investor types are based on classifications scheme from Abarbanell, Bushee, and Raedy (2003). Appendix A provides full variable definitions and details regarding investor type classifications.

Mean Std. Dev. 5th Percentile 25th Percentile

Median 75th Percentile

95th Percentile

Net Insider Demandi,t (in percent) -0.145 0.900 -1.053 -0.146 -0.013 0.013 0.319 Book to Market Ratioi,t 0.699 0.632 0.120 0.328 0.564 0.895 1.661 Market Capitalizationi,t ($Millions) 2060.560 11523.700 12.050 54.200 200.450 867.940 7050.850 Annual Market-Adj. Returni,t+4 0.027 0.699 -0.678 -0.315 -0.059 0.216 0.943 ROAi,t 0.014 0.226 -0.268 0.007 0.037 0.086 0.188 ΔROAi,t (=ROAi,t – ROAi,t-4) -0.004 0.238 -0.192 -0.025 0.000 0.019 0.169 ln(1+Insiders‘ Options Exercisedi,t) 0.005 0.056 0.000 0.000 0.000 0.000 0.001 Net Institutional Demandi,t (in percent) 0.805 4.730 -4.717 -0.704 0.291 1.922 7.620

%Shares Held by Institutions (Aggregate and by Investor Style) All Institutions (in percent) 33.877 23.090 1.719 13.791 31.578 51.805 73.958 Large-cap Value Focus 8.374 8.890 0.000 1.644 5.177 12.621 26.706 Large-cap Growth Focus 8.266 8.480 0.000 1.559 5.600 12.618 25.047 Small-cap Value Focus 6.786 7.870 0.000 1.085 4.266 9.690 22.701 Small-cap Growth Focus 9.964 10.610 0.000 1.943 6.576 14.562 32.006

Table 2 Insider Trading and Institutional Demand

Each quarter, small (bottom three capitalization deciles based on NYSE breakpoints), medium (middle four capitalization deciles), and large (top three capitalization deciles) securities are further sorted into four portfolios by the fraction of outstanding shares purchased by insiders (Net Insider Demand). We then compute the cross-sectional average net insider demand, net institutional demand (net fraction of outstanding shares moving to institutional investors) over the same quarter as the insider trading (quarter t) and over the previous year (quarters t-1 to t-4), book to market ratio, and same quarter market-adjusted return for securities within each insider demand quartile. We also compute net institutional demand assuming institutional investors are the counterparty to every trade by an officer or director (Adjusted Net Institutional Demand) and assuming institutional investors are the counterparty to every trade by an officer or director, a large shareholder, and an affiliated shareholder (All Adjusted Net Institutional Demand). The last column provides the p-value from an F-test of the null hypothesis that the values do not differ across the insider demand quartiles (computed from the time-series of the 80 quarterly cross-sectional means).

Insiders Selling

Quartile 2 Quartile 3 Insiders Buying

p-value

Panel A: Small Capitalization Securities (n=86,824 stock-quarters) Net Insider Demand -0.99% -0.09% 0.01% 0.51% <0.01 Net Institutional Demand 1.64% 0.55% 0.16% -0.17% <0.01 Net Inst. Demand over Previous Year 4.31% 2.81% 1.82% 0.83% <0.01 Adjusted Net Institutional Demand 0.65% 0.46% 0.17% 0.34% <0.01 Adj. Net Inst. Demand over Previous Year 3.15% 2.31% 1.65% 1.08% <0.01 All Adjusted Net Institutional Demand 0.54% 0.43% 0.18% 0.38% <0.01 All Adj. Net Inst. Demand over Previous Year 2.88% 2.19% 1.60% 1.09% <0.01 Book to Market Ratio 60.76% 74.32% 89.49% 100.68% <0.01 Market-adjusted Quarterly Return 6.04% 0.60% -2.07% 0.14% <0.01

Panel B: Medium Capitalization Securities (n=42,720 stock-quarters) Net Insider Demand -0.78% -0.08% -0.01% 0.08% <0.01 Net Institutional Demand 2.88% 1.23% 0.69% 0.45% <0.01 Net Inst. Demand over Previous Year 9.66% 5.34% 3.55% 3.58% <0.01 Adjusted Net Institutional Demand 2.10% 1.15% 0.68% 0.53% <0.01 Adj. Net Inst. Demand over Previous Year 8.18% 4.74% 3.26% 3.41% <0.01 All Adjusted Net Institutional Demand 1.96% 1.13% 0.66% 0.54% <0.01 All Adj. Net Inst. Demand over Previous Year 7.75% 4.60% 3.19% 3.35% <0.01 Book to Market Ratio 41.63% 53.72% 68.24% 74.12% <0.01 Market-adjusted Quarterly Return 9.58% 3.84% 0.73% -0.71% <0.01

Panel C: Large Capitalization Securities (n=25,951 stock-quarters) Net Insider Demand -0.29% -0.03% 0.00% 0.02% <0.01 Net Institutional Demand 1.25% 0.56% 0.51% 0.45% <0.01 Net Inst. Demand over Previous Year 4.63% 2.80% 2.23% 2.69% <0.01 Adjusted Net Institutional Demand 0.96% 0.54% 0.50% 0.47% <0.01 Adj. Net Inst. Demand over Previous Year 3.92% 2.59% 2.11% 2.62% <0.01 All Adjusted Net Institutional Demand 0.90% 0.52% 0.50% 0.47% <0.01 All Adj. Net Inst. Demand over Previous Year 3.75% 2.51% 2.10% 2.60% <0.01 Book to Market Ratio 39.31% 50.88% 61.11% 69.75% <0.01 Market-adjusted Quarterly Return 7.76% 3.02% 1.11% -1.16% <0.01

Table 3 Insider Trading and Institutional Demand for Large and Small Insider Trades

Each quarter (between June 1983 and March 2003), small (bottom three capitalization deciles based on NYSE breakpoints), medium (middle four capitalization deciles), and large (top three capitalization deciles) securities are further sorted into four portfolios by the fraction of outstanding shares purchased by insiders (Net Insider Demand). We then compute the cross-sectional average net insider demand and net institutional demand (net fraction of outstanding shares moving to institutional investors) over the same quarter as the insider trading (quarter t) and over the previous year (quarters t-1 to t-4) for securities within each insider demand quartile. We also compute net institutional demand assuming institutional investors are the counterparty to every insider trade (Adjusted Net Institutional Demand) over the same quarter and the previous year. Panels A and B report the analysis for the sample limited to firm-quarter observations that include at least one large insider trade in the quarter and those that only have small insider trades. For Panels A and B, the relative size of each insider purchase (sale) is measured as the number of shares bought (sold) divided by the total volume of shares traded that day. Within each capitalization decile, each quarter, insider trades are partitioned into two equal-size groups—those where the relative size is above the median are defined as large trades and those below the median are defined as small trades. Panel C presents the results for the sample limited to those observations where the absolute value of net insider demand is greater than 1% of the outstanding shares. Panel D presents the results for the sample limited to those observations where the absolute value of net insider demand is less than 1% of the outstanding shares. The last column in each panel provides the p-value from an F-test of the null hypothesis that the values do not differ across the insider demand quartiles (computed from the time-series of the 80 quarterly cross-sectional means).

Table 3 Insider Trading and Institutional Demand for Large and Small Insider Trades (continued)

Panel A: Large Insider Trades (n=115,384)

Panel B: Small Insider Trades (n=40,111)

Insiders Sell

Quartile 2

Quartile 3

Insiders Buy

p-value Insiders Sell

Quartile 2

Quartile 3

Insiders Buy

p-value

(n=63,969) Small Capitalization Stocks (n=22,855)

Net Insider Demand -1.24% -0.16% 0.01% 0.64% <0.01 -0.21% -0.01% 0.01% 0.15% <0.01 Net Institutional Demand 1.89% 0.69% 0.25% -0.17% <0.01 0.78% 0.30% 0.07% -0.39% <0.01 Net Inst. Demand over Previous Year 4.63% 2.96% 1.76% 0.68% <0.01 3.09% 2.40% 2.28% 1.27% <0.01 Adjusted Net Institutional Demand 0.65% 0.53% 0.26% 0.47% <0.01 0.57% 0.28% 0.09% -0.24% <0.01 Adj. Net Inst. Demand over Previous Year 3.34% 2.41% 1.64% 1.03% <0.01 2.34% 2.02% 2.09% 1.10% <0.01 (n=31,828) Medium Capitalization Stocks (n=10,892)

Net Insider Demand -0.98% -0.13% -0.03% 0.11% <0.01 -0.07% -0.01% 0.00% 0.01% <0.01 Net Institutional Demand 3.25% 1.42% 0.77% 0.48% <0.01 1.53% 0.78% 0.55% 0.30% <0.01 Net Inst. Demand over Previous Year 10.45% 6.08% 3.76% 3.33% <0.01 6.24% 3.77% 3.37% 4.30% <0.01 Adjusted Net Institutional Demand 2.27% 1.29% 0.75% 0.59% <0.01 1.47% 0.77% 0.55% 0.31% <0.01 Adj. Net Inst. Demand over Previous Year 8.78% 5.35% 3.44% 3.21% <0.01 5.46% 3.43% 3.19% 3.94% <0.01 (n=19,587) Large Capitalization Stocks (n=6,364)

Net Insider Demand -0.36% -0.04% -0.01% 0.03% <0.01 -0.01% 0.00% 0.00% 0.00% <0.01 Net Institutional Demand 1.36% 0.66% 0.47% 0.46% <0.01 0.73% 0.56% 0.43% 0.48% <0.33 Net Inst. Demand over Previous Year 4.96% 3.03% 2.37% 2.46% <0.01 2.98% 2.58% 2.09% 3.26% <0.03 Adjusted Net Institutional Demand 1.00% 0.62% 0.46% 0.48% <0.01 0.72% 0.56% 0.43% 0.48% <0.38 Adj. Net Inst. Demand over Previous Year 4.15% 2.76% 2.24% 2.39% <0.01 2.70% 2.48% 2.04% 3.14% <0.06

Table 3 Insider Trading and Institutional Demand for Large and Small Insider Trades (continued)

Panel C:|Net Insider Demand| > 1% Outstanding Shares (n=10,956)

Panel D: |Net Insider Demand|≤ 1% Outstanding Shares (n=144,539)

Insiders Sell

Quartile 2

Quartile 3

Insiders Buy

p-value Insiders Sell

Quartile 2

Quartile 3

Insiders Buy

p-value

(n=8,160) Small Capitalization Stocks (n=78,664)

Net Insider Demand -4.21% -1.58% -0.36% 2.84% <0.01 -0.41% -0.06% 0.02% 0.23% <0.01 Net Institutional Demand 4.55% 2.62% 1.22% -0.38% <0.01 1.04% 0.48% 0.15% -0.13% <0.01 Net Inst. Demand over Previous Year 6.05% 4.95% 3.55% 0.90% <0.01 3.75% 2.70% 1.77% 0.92% <0.01 Adjusted Net Institutional Demand 0.35% 1.04% 0.86% 2.46% <0.01 0.63% 0.42% 0.17% 0.10% <0.01 Adj. Net Inst. Demand over Previous Year 4.05% 3.29% 2.69% 1.63% <0.01 2.87% 2.26% 1.61% 1.08% <0.01 (n=2,405) Medium Capitalization Stocks (n=40,315)

Net Insider Demand -4.68% -2.04% -1.26% 0.33% <0.01 -0.36% -0.06% -0.01% 0.05% <0.01 Net Institutional Demand 8.42% 5.16% 3.99% 2.42% <0.01 2.13% 1.12% 0.66% 0.45% <0.01 Net Inst. Demand over Previous Year 14.00% 14.00% 13.13% 9.20% <0.01 8.31% 5.05% 3.42% 3.61% <0.01 Adjusted Net Institutional Demand 3.74% 3.11% 2.72% 2.75% <0.17 1.77% 1.06% 0.65% 0.50% <0.01 Adj. Net Inst. Demand over Previous Year 10.67% 11.31% 10.89% 8.13% <0.01 7.15% 4.50% 3.16% 3.42% <0.01 (n=391) Large Capitalization Stocks (n=25,560)

Net Insider Demand -4.01% -2.91% -1.31% -0.17% <0.01 -0.18% -0.02% 0.00% 0.01% <0.01 Net Institutional Demand 4.69% 3.63% 2.91% 1.58% <0.03 1.10% 0.57% 0.49% 0.46% <0.01 Net Inst. Demand over Previous Year 8.06% 8.99% 7.20% 6.60% <0.63 4.36% 2.79% 2.22% 2.70% <0.01 Adjusted Net Institutional Demand 0.68% 0.72% 1.60% 1.42% <0.65 0.92% 0.55% 0.49% 0.47% <0.01 Adj. Net Inst. Demand over Previous Year 5.36% 6.53% 5.46% 5.41% <0.90 3.75% 2.57% 2.10% 2.62% <0.01

Table 4 Daily Institutional and Individual Investor Demand Around Insider Purchases and Sales

Panel A reports the net fraction of shares (in percent) purchased by institutional investors included in the Plexus institutional investor transaction database around insider sales (first row) and insider purchases (second row). The insider transaction occurs on day t=0 and is defined as an insider sale (purchase) if net insider demand on that day is less than (greater than) zero. The third row reports the difference between the first two rows and associated t-statistic based on a t-test for difference in means. Panel B repeats the analysis but excludes any observation where there is another insider transaction in the prior 30 days (t=-1 to -30). Panel C reports the net fraction of shares (in percent) purchased by individual investors included in the discount broker transaction database around insider sales and insider purchases. The last row reports the difference and associated t-statistic. ** indicate statistical significance at the 1% level.

Period t=-30 to -1 t=-20 to -1 t=-10 to -1 t=-1 t=0 t=1 t=1 to 10 t=1 to 20 t=1 to 30

Panel A: Mean Fraction of Shares Purchased by Plexus Institutions Around Insider Transactions (in %)

Insider Sales (n=40,673)

0.3123 0.2385 0.1411 0.0189 0.0259 0.0185 0.1062 0.1476 0.1581

Insider Purchases (n=10,212)

-0.1272 -0.1188 -0.0779 -0.0123 -0.0091 -0.0048 -0.0231 -0.0458 -0.0664

Difference (t-statistic)

0.4395 (24.47)**

0.3573 (24.53)**

0.2190 (21.52)**

0.0312 (13.97)**

0.0350 (13.46)**

0.0233 (11.07)**

0.1293 (14.53)**

0.1933 (14.32)**

0.2245 (13.18)**

Panel B: Mean Fraction of Shares Purchased by Plexus Institutions Around Insider Transactions (in %)

No Insider Trades in Days t=-30 to -1


0.1780 0.1329 0.0850 0.0165 0.0333 0.0180 0.1069 0.1511 0.1663


-0.1277 -0.1307 -0.0894 -0.0170 -0.0083 -0.0013 -0.0137 -0.0264 -0.0561


0.3056 (9.19)**

0.2637 (9.93)**

0.1744 (9.46)**

0.0335 (7.58)**

0.0416 (7.06)**

0.0193 (3.63)**

0.1206 (7.02)**

0.1776 (6.62)**

0.2225 (6.56)**

Panel C: Mean Fraction of Shares Purchased by Discount Broker Individual Investors Around Insider Transactions (in %)


-0.0010 -0.0014 -0.0011 -0.0001 -0.0002 -0.0001 -0.0008 -0.0012 -0.0007


0.0059 0.0042 0.0024 0.0003 0.0001 0.0002 0.0006 0.0016 0.0019


-0.0069 (-9.06)**

-0.0055 (-9.34)**

-0.0035 (-9.21)**

-0.0004 (-3.25)**

-0.0004 (-3.06)**

-0.0002 (-1.88)

-0.0014 (-3.53)**

-0.0029 (-5.31)**

-0.0026 (-3.86)**

Table 5 Insider Trading and Institutional Demand Controlling for Valuation Levels and Returns

Securities are independently sorted, each quarter, into book to market quintiles and return treciles (based on returns that quarter). Within each of these 15 groups, securities are sorted (each quarter) into four groups based on Net Insider Demand (the fraction of outstanding shares purchased or sold by insiders). The first (second) column reports the time-series average (across the 80 quarters) net insider demand for the one-quarter of securities in that book to market and return group that experience the greatest insider demand (sales). The next two columns in Panel A report the time-series average of the cross-sectional mean net institutional demand for those securities in the top and bottom insider demand quartiles, respectively, within each book to market-return group. The fifth column reports a t-statistic from a paired t-test (based on the 80 cross-sectional averages) of the null hypothesis that same quarter net institutional demand for securities in the top insider demand quartile does not differ from that for securities in the bottom insider demand quartile. The last three columns report analogous statistics for net institutional demand measured over the previous year. Panel B reports analogous statistics for Adjusted Net Institutional Demand, which assumes institutions are the counterparty to every insider trade (i.e., adjusted net institutional demand=net institutional demand + net insider demand). Panel B excludes observations when the absolute value of net insider demand in quarter t is greater than 1%. ** and * indicate statistical significance at the 1% and 5% levels, respectively.

Panel A: The Relation between Insider Demand and Net Institutional Demand Controlling for Return and Valuation

Book/Market Quintile

Return Trecile

Net Insider Demand Same Quarter Net Institutional Demand Net Institutional Demand over Previous Year Securities

Insiders Buy Securities

Insiders Sell Securities


Insiders Sell t-statistic Securities


Insiders Sell t-statistic

Losers 0.23% -1.05% -0.26% 1.28% -10.35** 3.45% 8.50% -9.86** Growth Stocks Trecile 2 0.14% -1.04% 0.61% 2.29% -11.80** 2.28% 8.61% -16.49** Winners 0.23% -1.63% 1.32% 4.26% -17.09** 3.15% 9.23% -18.31** Losers 0.23% -0.89% -0.56% 0.84% -8.11** 4.07% 8.59% -10.60** BM2 Trecile 2 0.13% -0.83% 0.56% 2.01% -11.53** 3.26% 6.90% -9.82** Winners 0.21% -1.25% 1.17% 3.81% -13.38** 2.23% 6.53% -12.57** Losers 0.28% -0.72% -0.70% 0.39% -6.66** 3.53% 6.31% -7.41** BM3 Trecile 2 0.21% -0.56% 0.34% 1.67% -10.16** 2.10% 4.59% -9.29** Winners 0.29% -0.83% 0.73% 2.76% -10.66** 1.35% 4.04% -9.98** Losers 0.36% -0.58% -0.58% 0.17% -4.94** 2.50% 4.11% -4.66** BM4 Trecile 2 0.30% -0.46% 0.33% 1.07% -7.32** 1.50% 2.81% -5.45** Winners 0.41% -0.59% 0.32% 1.66% -8.31** 0.32% 2.31% -6.44** Losers 0.56% -0.40% -0.79% -0.30% -3.76** 0.20% 1.92% -6.76** Value Stocks Trecile 2 0.47% -0.42% -0.14% 0.65% -6.86** -0.16% 1.36% -6.77** Winners 0.77% -0.54% -0.31% 1.08% -7.89** -1.42% 0.92% -6.93**

Table 5 Insider Trading and Institutional Demand Controlling for Valuation Levels and Returns (continued)

Panel B: The Relation between Insider Demand and Adjusted Net Institutional Demand Controlling for Return and Valuation

Book/Market Quintile

Return Trecile

Net Insider Demand Same Quarter Adjusted Net Institutional Demand

Adjusted Net Institutional Demand over Previous Year

Securities Insiders Buy

Securities Insiders Sell

Securities Insiders Buy


t-statistic Securities Insiders Buy


t-statistic

Losers 0.11% -0.47% -0.15% 0.19% -2.48* 3.12% 6.22% -6.44** Growth Stocks Trecile 2 0.07% -0.45% 0.69% 1.12% -3.37** 1.98% 5.91% -9.64** Winners 0.08% -0.56% 1.44% 2.38% -6.40** 3.01% 6.90% -12.25** Losers 0.11% -0.41% -0.48% -0.11% -2.31* 3.76% 6.40% -6.69** BM2 Trecile 2 0.07% -0.37% 0.63% 1.09% -3.86** 3.17% 5.25% -5.78** Winners 0.10% -0.46% 1.29% 2.23% -6.10** 2.30% 4.77% -7.15** Losers 0.15% -0.34% -0.54% -0.38% -1.07 3.29% 4.78% -4.24** BM3 Trecile 2 0.10% -0.30% 0.47% 0.95% -3.85** 2.17% 3.48% -4.90** Winners 0.13% -0.36% 0.92% 1.80% -6.31** 1.44% 2.97% -6.00** Losers 0.18% -0.29% -0.32% -0.27% -0.36 2.45% 2.98% -1.56 BM4 Trecile 2 0.14% -0.24% 0.51% 0.60% -1.01 1.69% 2.21% -2.23* Winners 0.18% -0.30% 0.55% 1.09% -3.76** 0.55% 1.57% -3.42** Losers 0.26% -0.23% -0.51% -0.54% 0.20 0.52% 1.34% -3.27** Value Stocks Trecile 2 0.20% -0.22% 0.15% 0.29% -1.36 0.38% 0.95% -2.73** Winners 0.27% -0.26% 0.08% 0.73% -4.21** -0.78% 0.62% -3.93**

Table 6 Insider Trading and Institutional Demand by Investor Type

Each quarter, small (bottom three capitalization deciles based on NYSE breakpoints), medium (middle four capitalization deciles), and large (top three capitalization deciles) securities are further sorted into four portfolios by the fraction of outstanding shares purchased by insiders (Net Insider Demand). We then compute the time-series mean of the cross-sectional average net insider demand, net institutional demand by investor type (net fraction of outstanding shares moving to each institutional investor type) over the same quarter as the insider trading (quarter t) and over the previous year (quarters t-1 to t-4). The last column provides the p-value from an F-test of the null hypothesis that the values do not differ across the insider demand quartiles (computed from the time-series of the 80 quarterly cross-sectional means). The first column reports the time-series mean of the cross-sectional average fraction of shares held by each investor type at the beginning of quarter t=0 (first row) or the beginning of quarter t=-4 (second row). Investor classifications are provided by Brian Bushee based on the classification method in Abarbanell, Bushee, and Raedy (2003) (see Appendix A for additional detail).

Table 6 Insider Trading and Institutional Demand by Investor Type (continued)

Beginning Fraction

Insiders Selling Quartile 2 Quartile 3 Insiders Buying p-value

Panel A: Small Capitalization Securities (n=86,824 stock-quarters) Net Large-cap Value Demand 3.82% 0.25% 0.11% 0.07% -0.03% <0.01 Net Large-cap Value Demand over Previous Year 3.63% 0.71% 0.53% 0.37% 0.19% <0.01 Net Large-cap Growth Demand 4.26% 0.28% 0.07% -0.02% -0.05% <0.01 Net Large-cap Growth Demand over Previous Year 4.40% 0.69% 0.38% 0.21% 0.07% <0.01 Net Small-cap Value Demand 5.60% 0.27% 0.16% 0.12% 0.01% <0.01 Net Small-cap Value Demand over Previous Year 5.17% 0.83% 0.72% 0.58% 0.36% <0.01 Net Small-cap Growth Demand 8.18% 0.83% 0.21% 0.01% -0.09% <0.01 Net Small-cap Growth Demand over Previous Year 8.19% 1.96% 1.17% 0.70% 0.26% <0.01

Panel B: Medium Capitalization Securities (n=42,720 stock-quarters)

Net Large-cap Value Demand 10.46% 0.53% 0.40% 0.20% 0.19% <0.01 Net Large-cap Value Demand over Previous Year 9.55% 1.87% 1.36% 1.03% 1.02% <0.01 Net Large-cap Growth Demand 10.74% 0.68% 0.28% 0.12% -0.01% <0.01 Net Large-cap Growth Demand over Previous Year 9.78% 2.02% 1.03% 0.61% 0.44% <0.01 Net Small-cap Value Demand 8.72% 0.25% 0.23% 0.26% 0.30% <0.31 Net Small-cap Value Demand over Previous Year 7.96% 1.09% 0.97% 1.00% 1.07% <0.67 Net Small-cap Growth Demand 13.76% 1.40% 0.31% 0.12% -0.04% <0.01 Net Small-cap Growth Demand over Previous Year 13.13% 4.35% 1.85% 0.86% 0.96% <0.01

Panel C: Large Capitalization Securities (n=25,951 stock-quarters)

Net Large-cap Value Demand 19.16% 0.40% 0.23% 0.29% 0.37% <0.12 Net Large-cap Value Demand over Previous Year 18.49% 1.54% 1.23% 1.17% 1.32% <0.06 Net Large-cap Growth Demand 16.60% 0.47% 0.18% 0.09% -0.03% <0.01 Net Large-cap Growth Demand over Previous Year 15.81% 1.38% 0.62% 0.43% 0.50% <0.01 Net Small-cap Value Demand 6.54% -0.01% 0.02% 0.09% 0.20% <0.01 Net Small-cap Value Demand over Previous Year 6.50% 0.20% 0.35% 0.28% 0.54% <0.01 Net Small-cap Growth Demand 9.18% 0.40% 0.14% 0.04% -0.09% <0.01 Net Small-cap Growth Demand over Previous Year 9.42% 1.39% 0.57% 0.32% 0.30% <0.01

Table 7 Daily Institutional Demand By Investor Type Around Insider Purchases and Sales

This table reports the net fraction of shares (in percent) purchased by institutional investors included in the Plexus institutional investor transaction database around insider sales (first row) and insider purchases (second row) by Plexus Client Style code. The insider transaction occurs on day t=0 and is defined as an insider sale (purchase) if net insider demand on that day is less than (greater than) zero. The third row reports the difference between the first two rows and associated t-statistic based on a t-test for difference in means. ** indicate statistical significance at the 1% level.

Period t=-30 to -1 t=-20 to -1 t=-10 to -1 t=-1 t=0 t=1 t=1 to 10 t=1 to 20 t=1 to 30 Panel A: Diversified Institutions-Mean Fraction of Shares Purchased by Plexus Institutions Around Insider Transactions (in %)

Insider Sales 0.1320 0.1005 0.0593 0.0077 0.0102 0.0071 0.0426 0.0613 0.0645 Insider Purchases -0.0414 -0.0417 -0.0273 -0.0041 -0.0035 -0.0019 -0.0077 -0.0200 -0.0263 Difference (t-statistic)

0.1733 (15.96)**

0.1423 (16.36)**

0.0866 (14.75)**

0.0118 (9.90)**

0.0137 (10.86)**

0.0089 (8.64)**

0.0504 (9.98)**

0.0814 (10.05)**

0.0908 (8.68)**

Panel B: Momentum Institutions-Mean Fraction of Shares Purchased by Plexus Institutions Around Insider Transactions (in %) Insider Sales 0.0832 0.0636 0.0389 0.0059 0.0073 0.0054 0.0280 0.0388 0.0397 Insider Purchases -0.0496 -0.0452 -0.0302 -0.0053 -0.0050 -0.0016 -0.0090 -0.0252 -0.0433 Difference (t-statistic)

0.1327 (15.65)**

0.1088 (16.09)**

0.0691 (14.18)**

0.0111 (9.67)**

0.0123 (9.58)**

0.0070 (6.69)**

0.0370 (7.69)**

0.0640 (9.34)**

0.0830 (9.40)**

Panel C: Value Institutions-Mean Fraction of Shares Purchased by Plexus Institutions Around Insider Transactions (in %) Insider Sales 0.0095 0.0083 0.0068 0.0010 0.0014 0.0010 0.0066 0.0095 0.0107 Insider Purchases 0.0217 0.0156 0.0088 0.0019 0.0026 0.0028 0.0135 0.0272 0.0364 Difference (t-statistic)

-0.0122 (-2.20)*

-0.0073 (-1.57)

-0.0020 (-0.64)

-0.0010 (-1.30)

-0.0012 (-0.98)

-0.0018 (-1.68)

-0.0069 (-2.58)*

-0.0177 (-4.33)**

-0.0257 (-4.89)**

Panel D: Unidentified/Other-Mean Fraction of Shares Purchased by Plexus Institutions Around Insider Transactions (in %) Insider Sales 0.0877 0.0661 0.0362 0.0044 0.0070 0.0051 0.0289 0.0379 0.0432 Insider Purchases -0.0579 -0.0475 -0.0291 -0.0049 -0.0033 -0.0041 -0.0198 -0.0278 -0.0333 Difference (t-statistic)

0.1456 (15.93)**

0.1136 (14.79)**

0.0653 (11.63)**

0.0093 (6.69)**

0.0102 (7.27)**

0.0092 (8.28)**

0.0487 (11.13)**

0.0656 (9.77)**

0.0764 (9.18)**

Table 8 Abnormal Returns for Strategies of Following Institutions or Following Insiders

Each quarter (between June 1983 and March 2003) securities are sorted independently into capitalization deciles (based on NYSE breakpoints) and book-to-market ratio deciles (based on NYSE breakpoints). Securities within each of the resulting 100 portfolios are sorted into four groups based on the fraction of outstanding shares purchased by institutional investors in quarter t=0 (net institutional demand). Securities within the top institutional demand quartile within each group are denoted ―institutions buy‖ securities and those within the bottom quartile are denoted ―institutions sell‖ securities. Panel A reports the subsequent mean abnormal six-month (t=1 to 6) and 2-year (t=1 to 24 months) return from investing in an equal-weighted portfolio of securities in the top institutional demand quartile (Institutions buy), investing in an equal-weighted portfolio of securities in the bottom institutional demand quartile (Institutions sell), and their difference for the following six months (first row) or following 24 months (second row). Results are presented for small (bottom three capitalization deciles), medium (middle four capitalization deciles), and large (top three capitalization deciles) securities. Abnormal returns are calculated for each security by subtracting the equally-weighted average return of firms in the same size and book-to-market deciles. Panels B and C partition the results in panel A into two equal periods. Panels D, E, and F report analogous results from a strategy of buying an equally-weighted portfolio of securities that insiders purchased (Net Insider Demand > 0) in quarter t, buying an equally-weighted portfolio of securities that insiders sold in quarter t (Net Insider Demand < 0), and their difference. The last row in each panel reports the time-series average of the number of securities in the portfolio. ** and * indicate statistical significance at the 1% and 5% levels, respectively.

Table 8 (continued) Abnormal Returns for Strategies of Following Institutions or Following Insiders

Small Capitalization Stocks Medium Capitalization Stocks Large Capitalization Stocks

Following Institutions Institutions

buy Institutions

sell Difference Institutions

buy Institutions

sell Difference Institutions

buy Institutions

sell Difference

Panel A: Following Institutions – All Periods (1983:06-2003:03)

6-month abnormal ret. 0.702** -0.837* 1.538** -0.054 -0.106 0.053 -0.208 0.372 -0.581 2-year abnormal ret. 0.879 0.902 -0.023 -0.415 0.201 -0.615 -0.028 1.358 -1.386 Ave. no. of securities 791 783 247 237 119 112

Panel B: Following Institutions – Early Period (1983:06-1993:03)

6-month abnormal ret. 0.707 -0.743* 1.451** 0.233 -0.318 0.551 0.022 0.097 -0.075 2-year abnormal ret. 1.801** -0.569 2.370** -0.158 -0.929 0.772 1.804* -0.665 2.469 Ave. no. of securities 644 635 213 203 99 92

Panel C: Following Institutions – Late Period (1993:06-2003:03)

6-month abnormal ret. 0.696 -0.930 1.626* -0.340 0.106 -0.446 -0.439 0.648 -1.087 2-year abnormal ret. -0.042 2.374 -2.416 -0.672 1.330 -2.002 -1.860 3.381** -5.241* Ave. no. of securities 938 931 281 271 138 131

Following Insiders Insiders

buy Insiders

sell Difference Insiders

buy Insiders

sell Difference Insiders

buy Insiders

sell Difference

Panel D: Following Insiders – All Periods (1983:06-2003:03)

6-month abnormal ret. 3.218** -0.474 3.692** 1.081** 0.194 0.887 0.639 -0.019 0.658 2-year abnormal ret. 6.729** -0.342 7.071** 2.504** 0.952 1.552 1.499 0.117 1.381 Ave. no. of securities 620 686 182 422 77 266

Panel E: Following Insiders – Early Period (1983:06-1993:03)

6-month abnormal ret. 3.520** -0.502 4.022** 1.362** -0.328 1.690** 1.025** -0.084 1.109* 2-year abnormal ret. 5.600** -0.609 6.209** 3.052** -0.026 3.078** 1.009 -0.046 1.055 Ave. no. of securities 457 581 168 357 73 220

Panel F: Following Insiders – Late Period (1993:06-2003:03)

6-month abnormal ret. 2.916** -0.445 3.361** 0.799 0.717* 0.083 0.253 0.045 0.208 2-year abnormal ret. 7.858** -0.076 7.934** 1.955 1.930* 0.025 1.988 0.280 1.708 Ave. no. of securities 784 791 196 488 81 312

Table 9 Sample Sizes and Abnormal Returns for Securities Conditional on Insider Trading and Institutional Demand

Each quarter (between June 1983 and March 2003) securities are sorted independently into capitalization deciles (based on NYSE breakpoints) and book-to-market ratio deciles (based on NYSE breakpoints). Securities within each of the resulting 100 portfolios are sorted into four groups based on the fraction of outstanding shares purchased by institutional investors in quarter t=0 (net institutional demand). Securities within the top institutional demand quartile within each group are denoted ―institutions buy‖ securities and those within the bottom quartile are denoted ―institutions sell‖ securities. Securities in the top and bottom institutional demand quartiles are then partitioned into those that insiders did not trade, those that insiders purchased (Net Insider Demand > 0), and those that insiders sold (Net Insider Demand < 0). Results are presented for small (bottom three capitalization deciles), medium (middle four capitalization deciles), and large (top three capitalization deciles) securities. Panel A reports the time-series average sample size for securities in each classification. The fifth row in Panel A reports the time-series average of the ratio of the number of securities insiders are buying to the number of securities insiders are trading for securities purchased (first, third, and fifth columns) or sold (second, fourth and sixth columns) by institutions. The last row in Panel A reports t-statistics from paired t-tests of the hypothesis that the time-series average of the ratio of the number securities insiders buy to the number they trade are equal for securities purchased by institutions and securities sold by institutions. Panels B and C report subsequent abnormal buy-and-hold six month and 2 year returns, respectively, for an equal-weighted portfolio of the securities in that group. Abnormal returns are estimated as the difference between the security‘s return and the average return for securities in the same size and book-to-market deciles. ** and * indicate statistical significance at the 1% and 5% levels, respectively.

Table 9 (continued) Sample Sizes and Abnormal Returns for Securities Conditional on Insider Trading and Institutional Demand

Small Capitalization Securities Medium Capitalization Securities Large Capitalization Securities

Institutions buy

Institutions Sell

Institutions buy

Institutions sell

Institutions buy

Institutions sell

Panel A: Average Number of Securities in Portfolio All Securities 791 783 247 237 119 112 No Insider Trades 437 449 90 97 34 34 Insiders Buy 139 175 37 49 17 19 Insiders Sell 215 159 120 91 68 59 #Sec. Ins. Buy/#Sec. Ins. Trade 39.00% 51.54% 24.29% 34.80% 20.54% 25.57% t-statistic -22.60** -13.77** -6.15**

Panel B: Average Six-Month Abnormal Return (in percent) All Securities 0.702** -0.837* -0.054 -0.106 -0.208 0.372 No Insider Trades 0.007 -1.422** -0.601 -0.265 -0.236 0.169 Insiders Buy 4.210** 2.032** 1.212* 0.654 0.744 0.922 Insiders Sell -0.051 -2.167** 0.255 -0.157 -0.154 0.325

Panel C: Average Two-Year Abnormal Return (in percent) All Securities 0.879 0.902 -0.415 0.201 -0.028 1.358 No Insider Trades -0.612 -1.004 -2.969** -1.001 -1.065 0.627 Insiders Buy 7.153** 6.299** 3.835* 1.940 1.935 4.468** Insiders Sell -0.416 0.387 0.888 -0.077 0.718 0.751

Panel D: Average Two-Year Abnormal Return—Early Period (1983:06-1993:03) All Securities 1.801** -0.569 -0.158 -0.929 1.804* -0.665 No Insider Trades 0.734 -1.265 -2.572* -1.448 1.500 -1.523 Insiders Buy 7.722** 2.585* 6.086* 1.247 1.706 2.711 Insiders Sell 0.783 -0.993 0.372 -2.575* 2.157 -1.187

Panel E: Average Two-Year Abnormal Return—Late Period (1993:06-2003:03) All Securities -0.042 2.374 -0.672 1.330 -1.860 3.381** No Insider Trades -1.957 -0.743 -3.366* -0.554 -3.629 2.777 Insiders Buy 6.583** 10.012** 1.584 2.633 2.165 6.225* Insiders Sell -1.614 1.768 1.405 2.422 -0.721 2.689

Table 10 Two-year Abnormal Returns for Securities Conditional on Insider Trading and Institutional Demand by Insider Trade Size

Each quarter (between June 1983 and March 2003) securities are sorted independently into capitalization deciles (based on NYSE breakpoints) and book-to-market ratio deciles (based on NYSE breakpoints). Securities within each of the resulting 100 portfolios are sorted into four groups based on the fraction of outstanding shares purchased by institutional investors in quarter t=0 (net institutional demand). Securities within the top institutional demand quartile within each group are denoted ―institutions buy‖ securities and those within the bottom quartile are denoted ―institutions sell‖ securities. Securities in the top and bottom institutional demand quartiles are then partitioned into those that insiders did not trade, those that insiders purchased (Net Insider Demand > 0), and those that insiders sold (Net Insider Demand < 0). Results are presented for small (bottom three capitalization deciles), medium (middle four capitalization deciles), and large (top three capitalization deciles) securities. Panels A and B report the analysis for the sample limited to firm-quarter observations that include at least one large insider trade in the quarter and Panels C and D report the analysis for those that only have small insider trades. The relative size of each insider purchase (sale) is measured as the number of shares bought (sold) divided by the total volume of shares traded that day. Within each capitalization decile, each quarter, insider trades are partitioned into two equal-size groups—those where the relative size is above the median are defined as large trades and those below the median are defined as small trades.

Small Capitalization Securities Medium Capitalization Securities Large Capitalization Securities

Institutions buy

Institutions Sell

Institutions buy

Institutions Sell

Institutions buy

Institutions sell

Panel A: Average Number of Securities in Portfolio – Large Insider Trades Insiders Buy 88 111 23 29 11 12 Insiders Sell 171 117 96 67 54 45 #Sec. Ins. Buy/#Sec. Ins. Trade 33.63% 47.40% 19.93% 30.35% 18.01% 21.94% t-statistic -22.03** -12.59** -4.75**

Panel B: Average Two-Year Abnormal Return (in percent) – Large Insider Trades Insiders Buy 9.301** 8.570** 4.345* 3.739* 0.893 1.811 Insiders Sell -0.768 1.907 1.956 -0.134 1.450 1.215

Panel C Average Number of Securities in Portfolio – Small Insider Trades Insiders Buy 51 65 15 20 6 7 Insiders Sell 44 42 24 23 14 14 #Sec. Ins. Buy/#Sec. Ins. Trade 53.91% 60.57% 38.53% 45.62% 30.45% 35.38% t-statistic -9.13** -4.84** -3.47**

Panel D: Average Two-Year Abnormal Return (in percent) – Small Insider Trades Insiders Buy 3.775 2.640 3.293 -1.311 4.450 9.033* Insiders Sell -0.163 -5.261* -3.349 -1.534 -1.159 1.680

Table 11 Standardized Regression of Net Insider Demand on Institutional Demand,

Adjusted Institutional Demand and Share Characteristics

The first column reports the time-series average coefficients from 76 quarterly regressions of the fraction of outstanding shares purchased or sold by insiders in quarter t=0 (Net Insider Demand) on the fraction of shares moving from individual investors to institutional investors the same quarter (Same Qtr. Net Institutional Demand), and the fraction of shares moving from individual investors to institutional investors the previous year (Lag 12 Mo. Net Institutional Demand). All variables are standardized (rescaled to unit variance, zero mean) each quarter. The second column reports average coefficients from quarterly regression of standardized Net Insider Demand on standardized institutional demand the same quarter and the previous year assuming institutional investors are the counterparty to every insider trade in quarter t and over the previous 12 months (Same Qtr. and Lag 12 Mo. Adjusted Net Institutional Demand). Adjusted Net Institutional Demand is calculated as the sum of Net Institutional Demand and Net Insider Demand. The third column adds the (standardized) variables used by Piotroski and Roulstone (2005): Annual subsequent market-adjusted return (Ann. Market Adj. Returni,t+4), the change in return on assets over the following year (ΔROAi,t+4), the change in return on assets over the previous year (ΔROAi,t), four dummy variables for book to market quintiles (where BM1i,t equals 1 if the stock i is in the bottom book to market quintile in quarter t and 0 otherwise), dummy variables for membership (each quarter) in the top third of this quarter‘s cross-sectional return distribution (HRETi,t) and the middle third of this quarter‘s return distribution (MRETi,t), and the natural logarithm of one plus the number of options exercised by insiders as a fraction of shares outstanding (Insiders’ Options Exercised). We also include returns over the previous year (Lag 12 Month Return) and insider demand over the previous year (Lag 12 Month Net Insider Demand). Prior to May 1991, insiders were not allowed to sell stocks until six months following option exercise. After May 1991, insiders could sell immediately. Thus, prior to May 1991, we use insiders options exercised two quarters prior (Insiders’ Options Exercisedi,t-2). From June 1991 onward, we use insider options exercised the same quarter (Insiders’ Options Exercisedi,t-2). Results are based on samples of 76 cross-sectional regressions limited to observation where absolute net insider demand in quarter t is less than 1%. All variables are as defined in Appendix A. The number of observations each quarter averages 1,694. The t-statistics (reported in parentheses) are generated from the time-series standard error of the 76 quarterly regression coefficients between June 1984 and March 2003. ** and * indicate statistical significance at the 1% and 5% levels, respectively.

Table 11 (continued) Regression of Net Insider Demand on Institutional Demand, Adjusted Institutional Demand

and Share Characteristics

Regression 1 Regression 2 Regression 3

Same Qtr. Net Institutional Demand -0.115 (-23.56)**

Lag 12 Mo. Net Institutional Demand -0.126 (-20.55)**

Same Qtr. Adjusted Net Institutional Demand -0.06

(-11.17)** -0.030

(-7.66)** Lag 12 Mo. Adjusted Net Institutional Demand -0.089

(-14.14)** -0.035

(-9.60)**

Ann. Market Adjusted Returnt+4

0.033 (8.87)**

ΔROAt+4

0.008 (2.25)*

ΔROAt

0.007 (1.95)

BM1

-0.159 (-32.76)**

BM2

-0.122 (-25.32)**

BM3

-0.076 (-21.59)**

BM4

-0.043 (-14.06)**

HRETt

-0.063 (-11.21)**

MRETt

-0.026 (-6.20)**

Lag 12 Month Net Insider Demand

0.215 (42.35)**

Lag 12 Month Return

-0.086 (-14.36)**

Insiders’ Options Exercisedi,t-2 (198406-199103, n=28)

-0.012 (-2.05)*

Insiders’ Options Exercisedi,t (199106-200303, n=48)

-0.030 (-6.10)**

Average R2 3.55% 1.75% 13.07%

-0.3

-0.2

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0.6

-30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

Cu

mu

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itu

tion

al

Dem

an

d (

in %

)

Cumulative Institutional Demand

Around Insider Sales

Cumulative Institutional Demand

Around Insider Purchases

Figure 1 Plexus institutional demand around insider sales and purchases This figure reports the mean cumulative institutional demand for institutions in the Plexus database from 30 trading days prior to the insider transaction to 30 trading days following the insider transaction. The insider transaction occurs on day t=0 and is defined as an insider sale (purchase) if net insider demand on that day is less than (greater than) zero.

-0.004

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0

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0.004

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-30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

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Demand Around Insider Purchases

Cumulative Individual Investor

Demand Around Insider Sales

Figure 2 Discount broker individual investor demand around insider sales and purchases This figure reports the mean cumulative individual investor demand for individuals in the discount broker database from 30 trading days prior to the insider transaction to 30 trading days following the insider transaction. The insider transaction occurs on day t=0 and is defined as an insider sale (purchase) if net insider demand on that day is less than (greater than) zero.

insider trades and demand by institutional and individual...

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