Illegal Insider Trading

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The Impact of Illegal Insider Trading in Dealer and Specialist Markets:

Evidence from a Natural Experiment

Raymond P.H. Fishe and Michel A. Robe

School of Business Administration Kogod School of BusinessUniversity of Miami American University

P.O. Box 248126 4400 Massachusetts Avenue, NWCoral Gables, FL 33124 Washington, DC 20016

[email protected] [email protected](305) 284-4397 (202) 885-1880

January 2002

The authors thank the officials at the Securities and Exchange Commission and the U.S. Attorney’s Office in NewYork for assistance with the study. The authors thank Jim Angel, Henk Berkman, Graeme Camp, Jeff Harris, KrisJacobs, Tim McCormick, Ron Melicher, Albert Minguet, David Reeb, and seminar participants at the NASD, theUniversity of Auckland, McGill University, and the 2001 Meetings of the European Finance Association(Barcelona) and of the Financial Management Association (Toronto) for helpful comments. They are grateful toTim McCormick for help in obtaining aggregate depth data for Nasdaq-listed stocks. This work began while PatFishe was a Visiting Academic Scholar at the Securities and Exchange Commission. The Securities and ExchangeCommission, as a matter of policy, disclaims responsibility for any private publication or statement by any of itsemployees. The views expressed herein are those of the authors and do not necessarily reflect the views of theCommission or the authors’ colleagues upon the staff of the Commission. Michel Robe would like to acknowledgethe support received as a Kogod Endowed Research Fellow. Xinxin Wang provided valuable research assistance.The authors are responsible for all errors and omissions.



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The Impact of Illegal Insider Trading in Dealer and Specialist Markets:

Evidence from a Natural Experiment

Abstract

This paper provides direct evidence on market makers’ reaction tounambiguously informed trading in specialist versus dealer markets. Usingthe trades of stockbrokers who had advance copies of a stock analysis

column in Business Week magazine, we document that increases in price andvolume occur after informed trades and before public release of theinformation, especially for Nasdaq stocks. Both quoted and effective bid-ask spreads are unaffected by informed trades. Instead, market makers adjust thedepth at the ask quotes as the information leads to more buys. Quoted depthfalls once insider trading begins and then rebounds after it ends, generally toabove its initial level. Ask depth falls relatively more on the NYSE andAMEX than on the Nasdaq, which suggests that specialist markets detectinformed trading more readily. None of these pre-release changes areobserved in a control sample of stocks that were mentioned in the column butnot traded by these stockbrokers. Overall, our results show that illegal insider trading has a negative impact on market liquidity and that market makers use

depth as the tool to manage asymmetric information risk during unexpectedinsider trading episodes.

JEL-Classification: G12, G14, K22, D82

Keywords: Insider Trading, Asymmetric Information, Depth, Liquidity,Specialist vs. Dealer Market, Business Week



I. Introduction

Insider trading in financial markets has been the focus of extensive study for many years.

Central to the legal and economic debates surrounding this issue are key empirical questions.

What is the impact of insider trading on stock prices and trading volume? Can market makers

detect the presence of privately informed traders? If so, how does market liquidity—not only

spreads but also depth—react to the onset of insider trading?

There exist, however, very few empirical studies of unambiguously informed trading.

Most studies rely on the position of a trader (e.g., company official or board member) to infer

insider information. This paper contributes to the debates by analyzing a recent insider trading

case involving information on 116 stocks obtained by five stockbrokers. Their “inside”

knowledge was gained by acquiring copies of Business Week ’s “Inside Wall Street” (IWS)

column in advance of public release. These stockbrokers were not privy to any news emanating

directly from the corporations whose shares they traded. In effect, they were outsiders trading on

second-hand, non-public, short-lived information about specific firms. Nevertheless, trades based

on advance knowledge of the column yielded significant abnormal returns. Because the

“insiders" (the stockbrokers and their associates) traded only a third of the stocks mentioned in

the column over an eight-month period, this episode offers a unique natural experiment to study

the impact of genuinely informed trading. Furthermore, because the stocks involved were listed

on the Nasdaq, NYSE and AMEX, the data provide the first opportunity to contrast how illegal

insider trading by the same parties affects specialist and dealer markets.

We find strong evidence that illegal insider trading has a negative impact on liquidity and

that market makers adjust depth —not bid-ask spreads—to manage the risk presented by privately

informed traders.1 These results hold for both specialist and dealer markets. The difference

between market structures arises in the magnitude of the depth response. Specialist markets

reduce depth much more than dealer markets in response to insider trading, which suggests that

1 Throughout the paper, we use the term “market makers” to denote all liquidity providers, including specialists,dealers and limit-order traders.



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specialist markets are better equipped to detect such trading (cf., Heidle and Huang, 2002).

The events analyzed here were publicly revealed in January 1999, when the SEC charged

five stockbrokers with insider trading on misappropriated nonpublic information from Business

Week magazine.2 The SEC alleged that one of the brokers, Larry Smath, paid foremen of the

local Business Week distributor, Hudson News Co., to fax him advance copies of Gene Marcial’s

IWS column. He obtained this information in the early afternoon on Thursdays, before the public

release of portions of the magazine over news wire (typically at 5:15 PM the same day) and

electronic distribution on America Online (at 7 PM). Smath was able to forward it to the other

brokers and they were able to enter trades before the markets had closed. The SEC charged that

this scheme involved trades in at least 39 different stocks between June 1995 and January 1996,

and apparently ended only because officials at Business Week noticed unusual trading in some of

the recommended stocks before the magazine’s release.3

In all, the defendants, members of their

families and some of their clients bought $7.73 million worth of securities mentioned in the IWS

column, accounting for about 5 percent of total Thursday trading in the affected stocks.4

This case is of general interest because it offers a close-up view of market making during

numerous episodes of unambiguously informed trading. For every stock traded by the insiders,

as well as for most of the stocks mentioned in the IWS column that the insiders did not trade, we

have data about all transactions (trade time, volume and price, execution market) and quotes (bid

and ask prices, quoted bid and ask depths) for three days around the insider trading day, which

was always a Thursday. Court records from the civil and criminal cases brought against the

brokers identify their trades within the transaction stream. By aggregating and analyzing the

trade and quote data in 15-minute intervals, we obtain a detailed picture of investors’ and market

makers’ behavior during, and immediately following, periods of insider trading activity.

2 See e.g. “Group of Brokers is Facing Charges of Insider Trading,” The New York Times, January 28, 1999, p. C-21.3 See “Is Someone Sneaking a Peek at Business Week? Early Trading of a Few Inside Wall Street Stocks Raises aRed Flag,” by Chris Welles, Business Week , February 5, 1996.4 Smath and two other brokers pled guilty to one felony count each. Another broker, Joseph Falcone, was convictedof insider trading on November 9, 1999, following a 1 1/2-week trial. The fifth broker cited in the SEC complaintwas never criminally charged; neither were the brokers' associates.



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A key question examined in this study is how informed trades affect market liquidity. A

basic tenet of market microstructure theory is that liquidity partially reflects the information

asymmetry created by informed traders (Madhavan, 2000). Most microstructure models focus on

bid-ask spreads as the tool to react to informed trading (e.g., Glosten and Milgrom, 1985;

Glosten, 1989; Easley and O’Hara, 1992). Only recently have models explored whether market

makers may change quoted depth as well as spreads in response to perceived increases in insider

trading (Kavajecz, 1998; Dupont, 2000).

Recent empirical work indicates that both spreads and depth are affected by expected

information events.5 A natural question, however, is whether spreads or depth also react to

unexpected changes in informed trading. To date, the sole evidence comes from case studies of

two NYSE-traded stocks that were targeted by corporate insiders in the early 1980’s (Cornell and

Sirri, 1992; Chakravarty and McConnell, 1997). Those authors find that liquidity, if anything,

improves during insider trading episodes. Our findings are the first broader-sample evidence that

genuinely informed trading has a negative impact on market liquidity. Further, our results

indicate that market makers do not (or perhaps cannot) increase spreads in response to informed

trading but do have the wherewithal to decrease depth. This result, which holds for Exchange-

listed and (to a lesser extent) Nasdaq stocks, provides partial support for recent theoretical results

(Dupont, 2000) that, in a specialist market, depth should react proportionally more than spreads

to changes in informed trading.6

Specifically, we document that neither quoted nor effective spreads are affected by the

arrival of informed trades, and establish this result for both specialist and dealer markets. Instead,

we find that market makers limit their exposure to informed traders by reducing quoted depth.

The data show that depth at the asked quote decreases during intervals of insider buying activity,

5 Liquidity falls in anticipation of and immediately following earnings announcements (e.g., Lee, Mucklow andReady, 1993; Kavajecz, 1999), dividend announcements (Koski and Michaely, 2000) and takeover announcements(Foster and Vishwanathan, 1994; Jennings, 1994). See Kim and Verrecchia (1994) and Krinsky and Lee (1996) for discussions of earlier empirical studies analyzing spread behavior around such information events.6 Kavajecz (1998) also explicitly models quantities and prices as separate choice variables. He forecasts that depthshould fall and spreads should widen around an increase in the amount of adverse selection.



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with Nasdaq depth declining less than Exchange-listed depth. Once insider trading ends, depth

rebounds. Relative to the average quoted depth on the previous day, we find that ask depth is 38

percent lower for NYSE and AMEX stocks during insider trading intervals. In sharp contrast,

after controlling for lower Nasdaq depth, the quoted ask depth for Nasdaq stocks falls by only 3

percent during insider intervals.7

The results are even stronger when we exclude nine traded

stocks featured in non- Business Week news stories on the day before, or the morning of, the

insider trading day. After removing those stocks, we find larger ask depth reductions, with a

similar gap between the insider-related depth decreases for Exchange-listed stocks (-59 percent)

versus their Nasdaq-listed counterparts (-19 percent).

A salient feature of the present study is that it does not involve corporate insiders trading

vast numbers of shares based on internal information. Instead, the “insiders” in the case were

singling out firms after obtaining advance copies of a magazine column and buying a relatively

small number of shares of each company selected on the basis of that short-lived information.

This raises the questions of whether and how the private information gets impounded into prices.

The private information involved had a very short useful life, so insiders were pressed

into action in a relatively short trading window. We find that Thursday volume is not unusual up

to the time of the first insider trade. During intervals when the brokers traded, however, there are

significant increases in the number of trades, and there are further volume increases after the

brokers are done trading. The insider-day volume increase is large (almost two-thirds of the

previous day’s total volume), but the brokers’ trades only account for a small part of the increase.

Court records imply that the IWS information was shared beyond the group of defendants

charged by the SEC, but trades by the brokers’ associates only explain a fraction of the

additional trading. Altogether, the trades of all the individuals identified by the SEC as possibly

privy to some IWS information make up no more than 9.2 percent of the volume increase for

insider-traded stocks. There is no reason to believe that, although it did not prosecute all of them,

7 For Nasdaq stocks, ask (bid ) depth quotes are aggregated across all market makers quoting the best ask (bid ) price,so that our Nasdaq depth figures are comparable to their counterparts for Exchange-listed stocks.



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source, Business Week . Those individuals traded only a third of the stocks mentioned in the IWS

columns — with the other two thirds forming a unique, ideal control sample. Our study is likewise

the first to contrast the impact of illegal insider trading in specialist and dealer markets.

The pioneering study of Meulbroek (1992) uses SEC case files on illegal insider trading

during the 1980-1989 period to determine if stock prices reacted to informed trading. Those files

provide information on securities traded, volume and date of trades for 320 defendants and 218

different companies. Her final sample comprises 183 different cases of insider trading. She finds

that the average cumulative abnormal return per insider trading episode is large (6.85 percent)

and amounts to 47.6 percent of the abnormal return on the day the inside information becomes

public.

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She also documents that the median insider’s trading represents only 11.3 percent of the

affected stock’s total trading volume. Meulbroek makes a case, however, that insiders’ trades

account for most of the extra trading volume on insider days, and hypothesizes that insider trade-

specific characteristics — rather than volume per se — bring about the impounding of the inside

information into security prices. Using intra-day data, we are able to document that trades are

indeed different during insider purchasing intervals — they are much more numerous, yet smaller

in size, than at other times and are overwhelmingly buyer-initiated. In contrast to Meulbroek’s

findings, we find that insiders’ trades do not account for the major fraction of the trading volume

increase on insider days. Overall, our evidence suggests that the latter is in large part due to a

concomitant increase in noise trading by “falsely informed” or mimicking traders.

While Meulbroek (1992) and the present paper deal with a cross section of insider trading

cases, Cornell and Sirri (1992) and Chakravarty and McConnell (1997, 1999) analyze illegal

trading by corporate insiders during two takeover attempts. Those case studies extend

8 Similarly, we find that the Thursday (i.e., insider-day) price increase for stocks traded by our five brokers is only afraction of the total price increase following the release of the IWS column on Friday. Still, it is difficult to comparethis result directly with Meulbroek (1992). On the one hand, it may be that the additional trading and related priceincrease on Thursday led naive IWS readers to believe that the column was all the more relevant, which should bring about an even bigger jump on Friday if readers traded on that basis. Indeed, we find a bigger overnight price jump for stocks traded by the brokers than for comparable IWS stocks that they did not trade. On the other hand, itmay simply be that smaller firms, which make up a majority of the stocks traded by the brokers, routinelyexperience larger Friday IWS “bounces” than other firms in our control sample.



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Meulbroek's work by analyzing the impact of insider trading not only on daily or hourly share

prices, but also on bid-ask spreads and market liquidity.

Specifically, Cornell and Sirri (1992) analyze 124 trades made by Paul Thayer, a director

of Anheuser-Busch, and his accomplices during that company’s 1982 acquisition of Campbell-

Taggart. In all, 38 insiders bought a total of 265,600 shares over 23 trading days, which is

equivalent to more than the monthly trading volume in six of the previous seven months and

amounts to an “extraordinary” 29 percent of the target’s trading volume during that period.

Unlike Meulbroek (1992), but consistent with our evidence, Cornell and Sirri document a large

increase in non-insider, falsely informed trading over the same period. Nevertheless, they find

that the abnormal returns on Campbell-Taggart stock occurred only on insider trading days.

Using intra-day data, we show that the statistically significant positive returns on insider days

actually arise after insiders finish trading.

Cornell and Sirri’s most striking proposition is that insiders obtain superior execution for

their trades, with the estimated bid-ask spread seemingly unchanged by insider trading. Further,

they argue that liquidity improved while insiders were active, with liquidity measured as the cost

of adding an additional share to an order rather than as changes in depth. We find support for the

finding that spreads are not significantly affected by the onset of insider trading, and show that it

generalizes to dealer markets. At the same time, however, we find little evidence of improved

liquidity during insider trading episodes. To the contrary, we find that quoted depth falls sharply,

with the strongest decrease affecting NYSE-listed stocks.

The best-known insider case ever is arguably Ivan Boesky’s illegal purchase of 1,731,200

Carnation shares based on advance knowledge of Nestlé's 1984 acquisition of that firm.

Chakravarty and McConnell (1997, 1999) analyze Boesky’s 366 trades, which were distributed

over 24 days during 11 weeks. He accumulated almost 5 percent of Carnation’s outstanding

shares. These authors document that Boesky’s trades made up half of the incremental volume,

and that significant price increases took place both during and following hours when Boesky

traded. Consistent with Cornell and Sirri (1992), they find that bid-ask spreads were hardly



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affected by Boesky’s transactions, even in the hours when he traded the most often or entered the

largest trades. They also report that depth was unchanged or even improved during hours when

Boesky bought shares, with quoted depth changes during “Boesky hours” greater on the bid side

than on the ask side. They wonder, however, if “[those] results can or should be generalized to a

larger population or to a different time period.”

More generally, as pointed out in Chakravarty & McConnell (1997), a drawback of case

studies is the uncertainty as to whether the results can be generalized. A key contribution of our

paper is to demonstrate that, while many of the extant results can be reproduced in the context of

a cross-section of insider trading episodes, some important results are not general in nature. In

particular, we show that unambiguously informed trading has a negative impact on market

liquidity, and that the magnitude of this impact depends on the type of financial market

(specialist or dealer) where the trades are carried out.

Our finding that the effects of illegal informed trading on market quality depends on

market structure presents a counterpoint to Garfinkel and Nimalendran (2001). Those authors

find that relative effective spreads are wider on days when insiders (legally or not) carry out

trades than on non-insider days, and that the spread increase is stronger for NYSE stocks than for

their Nasdaq counterparts. Such evidence is consistent with informed traders’ being more easily

recognized by NYSE specialists than by Nasdaq dealers. In contrast, we find no consistent

change in quoted or effective bid-ask spreads during (illegal) insider trading episodes, regardless

of the market of execution. At the same time, we do find that such trades decrease quoted ask

depth, and that these depth declines are also much stronger in specialist than dealer markets.

One of our key objectives is to contrast the impact of (private) information in dealer and

specialist markets and, in particular, the extent to which specialists and dealers detect the

presence of informed traders. Hence, the present paper is also related to Corwin and Lipson

(2000), Christie, Corwin and Harris (2002) and Heidle and Huang (2002). For the NYSE,

Corwin and Lipson (2000) find that trading halts are sufficient to resolve price uncertainty

because there are no systematic changes in price after the market reopens. In contrast, Christie,



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Corwin and Harris (2002) find that such halts do not resolve price uncertainty in a sample of

Nasdaq stocks. In particular, spreads more than double following Nasdaq halts and only decrease

after 20 to 30 minutes following the resumption of trading. Based on these findings, they argue that

Nasdaq dealers, faced with incomplete knowledge of aggregate order flow, may be at a

disadvantage to better-informed investors following halts. That conclusion is consistent with

evidence that specialist markets appear better equipped to detect insider trades (Heidle and Huang,

2002). Our results provide further support for that interpretation, based on evidence from actual

insider trades.9

Finally, to the extent that we focus on illegal trades based on positive news from advance

copies of print media, our paper is also related to a sizable literature on the stock market impact

of financial columns. The columns that have attracted academic attention are the Wall Street

Journal ’s “Heard on the Street” (e.g., Lloyd-Davis and Canes, 1979; Liu, Smith and Syed, 1990;

and Beneish, 1991) and “Dartboard” columns (e.g., Barber and Loeffler, 1993; Greene and

Smart, 1999; and Liang, 1999), as well as — the focus of the present paper — Business Week ‘s

“Inside Wall Street” column (e.g., Sant and Zaman, 1996). All studies of financial columns find

significant positive excess returns when good news is reported.10 For favorable mentions in

“Inside Wall Street,” average abnormal returns on the publication day ranged from 1.2 to 1.9

percent during the 1980’s. Using more recent data, we find abnormal returns more than twice

that size, both six months before and during the insider-trading period.

Sant and Zaman (1996), however, show that a favorable mention yields significant

abnormal returns only for stocks that are followed by fewer than 21 analysts and that, for such

stocks, the magnitude of the returns increases as the analyst following decreases.11 A key 9 Many other studies document differences in trading conditions between dealer and specialist markets. Mostconcentrate on differences in trading costs or items directly related to such costs. Examples include Huang and Stoll(1996), Barclay (1997), Bessembinder (1997, 1999), Bessembinder and Kaufman (1997a,b), Clyde, Schultz andZaman (1997), LaPlante and Muscarella (1997), Barclay et al. (1999), Stoll (2000), Weston (2000), Chung,VanNess and VanNess (2001) and references cited in those papers.10 The U.S. evidence presented in those papers is consistent with that from other countries. See, e.g., Wijmenga(1990) in the case of the Netherlands.11 It is unlikely that the stockbrokers knew about Sant and Zaman’s research, as it was not published until 1996.However, they may have known of an earlier, well-publicized case of insider trading involving the same IWScolumn. In 1988, several security breaches occurred at Business Week . A number of people obtained advance copies



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question, tackled by Sant and Zaman for the IWS column and by Greene and Smart (1999) and

Liang (1999) for “Dartboard” picks, is whether that positive impact is long-lived. The answer is

negative. In particular, Sant and Zaman (1996) find that the initial IWS announcement effect is

negated after 26 trading days and that, within six months of a positive recommendation, these

same stocks earn large negative abnormal returns.12

For stocks with the smallest analyst

following (0 or 1-to-5 analysts), the loss exceeds 15 percent. Overall, making illicit gains based

on advance access to the IWS column requires trading in stocks that have little analyst following

and closing positions quickly.

III. Data

According to the criminal case filed by the U.S. Attorney and to the civil case filed by the

SEC, the scheme to obtain advance copies of Business Week ’s IWS column started in June 1995

and ended with the February 5, 1996 issue.13 A total of 116 firms were mentioned in the column

during that eight-month period. Court records provide information on securities traded by the

five brokers and their associates; the date, volume and cost of each insider trade; and, for the

brokers, the time of each trade and the profits earned from each transaction. Of the 116 firms, the

stockbrokers did not trade in 76 companies, leaving 40 traded firms. We remove 10 companies

from the traded sample: nine that were traded by a broker’s customer and but not by the brokers,

which are missing time stamps, and one that had only stock options traded by one broker. 14 The of the magazine from printing plants owned by R.R. Donnelley & Sons, and information was also leaked fromwithin the company. Eleven individuals were convicted or settled charges of insider trading, including threestockbrokers and S.G. Ruderman, Business Week’s radio broadcaster, who went to prison for his participation.12 Consistent with that result, Greene and Smart (1999) find that statistically and economically significant negativeabnormal returns in the 29 days following publication of the “Dartboard” column erase all of the positive initial

effect, leaving no evidence of persistent abnormal returns. Liang (1999) also finds strong mean reversion during the15 days following publication, sufficient to erase the initial announcement effect for “Dartboard pro picks.”13 See United States v. Joseph Falcone, 99 Cr. 332 (TCP) and SEC v. Smath et. al ., 99 CV 523 (TCP).14 The court records contain no information about the timing of the trades made by the brokers’ customers (whowere never charged) or about the profits they made. Most of the customers’ IWS-based transactions were small.The main exception is a set of trades made by a named customer of two of the brokers. In addition to trading 21 of 30 stocks also purchased by the brokers, this customer traded nine IWS stocks that the brokers did not trade. Five of those additional trades were relatively small (ranging from 1,000 to 4,000 shares), and the other four trades (up to8,000 shares) involved large companies mentioned in the IWS column: Conrail, MCI, American Express and OlinCorp. These nine stocks are removed from the sample.



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focus is on the remaining 30 stocks that were traded by the five brokers, and for which there is

complete data.

A. Characteristics of the Traded Companies

Some key characteristics of the firms traded by the brokers are summarized in Table 1.

The table also contrasts those firms with companies that were mentioned in the IWS columns but

not traded by the stockbrokers. Data on the rates of return on assets and on equity, sales level and

growth rate, assets, and growth rate of net income are from the 1994, 1995 and 1996 Compustat

tapes.15 Information about company listing and the sentiment of the column (“Buy”, “Neutral” or

“Sell”) is also included. Finally, the Dow Jones News Retrieval service (searching “all

publications”) is used to determine whether featured firms were mentioned in other news articles

on the Wednesday or Thursday preceding the public release of the IWS column.

Table 1

Table 1 shows that the IWS column offered a favorable sentiment for the vast majority of

stocks mentioned. In addition, most of these stocks were not mentioned in another news source.

Thus, the Business Week column provides unexpected publicity for most of these companies. In

the empirical analysis, we distinguish between companies with and those without other news to

avoid the confounding effects that other news may cause. In addition, 45 percent of the traded

firms were listed on the NYSE or AMEX, compared to 55 percent on the Nasdaq. Nearly the

reverse of these listing proportions held for the non-traded firms.

The Compustat data show that the companies traded by the stockbrokers were smaller

than those not traded. The firms selected by the stockbrokers were also less profitable, although

there is little difference in the growth rate of sales between traded and non-traded companies.

The most striking differences are found in the size of these companies. The average sales of

traded firms were less than one-half, and their average asset size was about one-fourth, of those

15 No Compustat data could be found for nine traded companies traded and 16 non-traded companies.



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of their non-traded counterparts (median differences were somewhat smaller). The stockbrokers

most likely anticipated that mention in the IWS column would have the largest impact on these

smaller companies.

B. Transaction and Quote Data

For all 116 stocks mentioned in the IWS column, we collect transaction and quote data

from the Securities Industry Automation Corporation (SIAC) for three days beginning on

Wednesday (the day before the IWS column was leaked), Thursday (the day of the leak), and

Friday (the first day that the general public could trade on the information). The transaction data

include time, volume and price, as well as the market on which each transaction was executed.

The quote data include the bid and asked prices and depth, that is, the number of round lots for

which each quoted price is “good.” The depth data for Nasdaq stocks is for all market makers

quoting the best bid or ask price, which makes it comparable to exchange-listed depth figures.

Trade direction is determined, for every transaction during each three-day period, with the Lee

and Ready (1991) algorithm. For the regression analysis and figures, the data are summarized

into 15-minute intervals, which smoothes the data and reduces the effect of larger trades and

asynchronous trading on the results.

The brokers’ trades are manually matched in the SIAC transactions stream. For most of

the stocks traded, the information contained in court records (time, quantity, total cost)

unambiguously identifies the brokers’ trades. Because some brokers’ orders were broken into

smaller trades, the information from the court records does not always uniquely identify the

trades. To address this problem, we identify all possible trade sequences that match the brokers’

trades around the time stamp and analyze the data in 15-minute intervals. It is rare for any trade

sequence to cross between two 15-minute intervals. Even so, the statistical analyses are

conducted across all sequences of insider trading intervals. The conclusions reported are robust

across these alternative choices. Hence, in the remainder of the paper, we will only report results

for regressions on the most likely candidate sequence.



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Table 2

Table 2 presents descriptive statistics of the SIAC data. The transaction information is

reported in three panels. Panel (a) shows summary information for all 30 stocks traded by

stockbrokers, Panel (b) shows information for 21 traded stocks that had no other news on either

Wednesday or Thursday, and Panel (c) provides information on 44 non-traded stocks without

any other news. Panels (a) and (b) show fairly similar statistics for most variables. Specifically,

the average traded stock price is a little less than $20 with a quoted spread of about $1/4.

Effective spreads range from $.12 to $.16 for these stocks. Across all three days, there are on

average about 12 trades per 15-minute interval for traded stocks, with an average trade size of

1,550 to 1,750 shares. The average number of trades increases substantially from Wednesday

(8.3 or 6.7) to Friday (17.1 or 15.1), but the average trade size shows a downward trend. This

result is consistent with publicity created by mention in Business Week and with the findings of

Sant and Zaman (1996) about the volume impact of the IWS column. The volume impact here is

found in more, not larger trades, which is evidence that smaller investors are reacting to the IWS

news.

Changes in average depth across these days for traded stocks are shown in Panels (a) and

(b) as well. In Panel (a), average ask depth is 8,600 shares on Wednesday, 8,000 shares on

Thursday, and 10,000 shares on Friday. The bid depth shows a similar pattern. For all 30 traded

stocks, both the bid and ask depth displays a “U-shaped” pattern between Wednesday and

Friday, suggesting that market makers are lowering their commitment to sell shares, particularly

on Thursday—the day when the insiders were trading. However this pattern does not hold for the

no-news sample in Panel (b), where average ask depth increases over these three days. Thus,

these univariate results are generally ambiguous as to whether market makers are reacting to

informed trading by adjusting ask depth.

Additional evidence on the reaction of market makers comes from the results on average

quoted and effective spreads. These average quoted spreads show no significant change from one

day to the next in both Panels (a) and (b). The average effective spreads show a slight decrease



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across days. If market makers detected informed trading, then the extant asymmetric information

theories of the bid-ask spread would predict that spreads would increase to discriminate against

informed traders. These unconditional averages suggest that market makers may not have

detected the informed traders, or they may have reacted to them in another manner.

Average returns for traded stocks over these 15-minute intervals vary widely across days

in Panels (a) and (b). Returns are positive on Wednesday, increase significantly on Thursday,

and then are nearly zero on Friday. The Friday results stand out and can be explained by the fact

that the information in the IWS column is impounded into the opening price or the first few

trades on Fridays. Thus, the intra-day returns show no impact of the IWS column’s release.

To measure the degree of buying pressure in the market, we develop a “Buyside” index

based on Lee-Ready signed trades. Using the Lee-Ready algorithm, a trade is given the value +1

if it is buyer initiated, –1 if it is seller initiated, and zero if it cannot be signed. These values are

summed for all trades in each 15-minute interval to develop the Buyside index value for that

interval. As Table 2 shows, buying pressure increased from an average index value of 1.22 on

Wednesday to 7.20 on Friday for all traded stocks in Panel (a). The results in Panel (b) show the

same pattern of increased demand for these traded stocks.

The transaction data results for 44 non-traded stocks are found in Panel (c). Some results

are similar to those for traded stocks. Average spreads remain fairly constant across all three

days, the volume and number of trades increase, particularly on Friday, and the Buyside index

shows increasing buyer interest. Interval volume and Buyside interest show the biggest

difference from the earlier results: whereas those two variables increase sharply on Thursday (the

insider day) for traded stocks, they actually fall Thursday for the 44 non-traded stocks. In

addition, average trade size decreases from 1,998 shares on Wednesday to 1,355 shares on

Friday. The relative size of this drop compared to trade size changes in panels (a) and (b)

suggests that there was more public interest in these non-traded stocks than in the sample traded

by the five stockbrokers. Finally, depth at the ask quotes is increasing and follows a “U-shaped”

pattern, which is similar to that found for all 30 traded stocks.



- 15 -

C. Price and Volume Impact

Additional information on how the market reacts to stockbroker trading and to the IWS

column is shown in Figures 1 and 2. These figures depict the volume and stock price changes in

fifteen-minute intervals, from the open on Wednesday to the close on Friday. They plot, for 21

traded and 44 non-traded stocks with no non-IWS news, the median price and volume changes

relative to Wednesday opening prices (Figure 1) and average volumes (Figure 2).

Figures 1 and 2

Both Figures 1 and 2 show that trading by the brokers led to increases in volume and

price for the affected stocks. Figure 1 shows that, in many of the intervals that follow the onset of

insider trading (typically between 1:00 PM and 2:00 PM on Thursdays, indicated by the arrow on

the graphs), the median trading volume for those stocks is more than double the average 15-

minute trading volume on the previous day. In contrast, there is no discernible increase in

volume for the stocks mentioned in the IWS column that the brokers did not trade.

Consistent with the volume increase, Figure 2 shows a sharp rise in the price of the traded

shares, while there is no significant price change for the non-traded stocks. The increase on

Thursday occurs mostly after the stockbrokers traded. Thus, supporting the findings of Cornell

and Sirri (1992), insiders appear to help start the price discovery process with their trades. The

median price increase relative to the average price on Wednesday exceeds 6 percent.

Interestingly, the overnight price impact (between Thursday and Friday) of the IWS publication

is much stronger for traded stocks (median jump of more than 4 percent) than for non-traded

stocks (median jump of just over 2 percent). Figure 2 also shows that, following the large price

jump at the open on Friday, there is little price movement during the rest of Friday for traded

stocks; in contrast, there is a further 2 percent upward drift for the non-traded stocks.



- 16 -

D. Returns to Insider Trading

Figure 2 suggests that private knowledge of the IWS column may have generated sizable

returns. To investigate this possibility, data are obtained from the Center for Research in Security

Prices (CRSP) on the high, low and closing prices of all stocks mentioned in Business Week’s

IWS column. For each stock, these data are gathered for a 4-month interval surrounding its

mention in the IWS column. The CRSP data are used to estimate a market model and to compute

abnormal returns for the stocks, each of the three days surrounding their mention in the IWS

column. Those results allow us to determine whether featured stocks experienced abnormal

returns, both in the months immediately before, and during the period when, the insiders traded

on their advance Business Week information.

IV. Abnormal Returns

After Sant and Zaman’s (1996) results were circulated, the market may have adjusted to

the temporary “bump” in prices generated by the IWS column. In which case, the abnormal

returns on Fridays may have diminished or disappeared. This section examines whether

abnormal returns existed both before these insiders gained access to the IWS column and during

their period of trading.

A. The Before and During Periods

The IWS column first was obtained prior to its public release in June 1995. To examine

abnormal returns before this period, IWS columns are searched for all issues published from

November 7, 1994 to May 29, 1995. In those seven months, the IWS column mentioned 107

companies. A total of 26 of these companies are excluded from our analysis: 11 because there is

incomplete or missing data in the CRSP files, and 15 because the Dow-Jones News Retrieval

service mentioned the company in another news story on Wednesday or Thursday. A sample of

81 companies remains after these exclusions, which is called the “Before” sample.



- 17 -

The same procedures are applied to companies mentioned during the period of

stockbroker trading. This period covers the IWS columns published between June 5, 1995 and

February 5, 1996. There are a total of 116 companies mentioned during this period. A total of 47

companies must be eliminated from the final sample. News articles about the company rules out

38 companies. The remaining 9 companies are excluded because daily CRSP data are incomplete

or missing for the estimation period. These eliminations leave a sample of 69 companies, which

we refer to as the “During” sample.

B. An Event Study with Closing Prices

Business Week magazine is generally released to newsstands early Friday morning. Some

of the information is available on America Online the night before, but this information is posted

after the close of trading in the U.S. Thus, if the information is valuable, its impact on stock

prices is expected during trading on Friday. To measure this impact, the event study

methodology discussed by Campbell, Lo and MacKinlay (1997) is used for both “Before” and

“During” data samples. Specifically, stock returns are computed from the closing price on

Thursday (before release of the information) and the closing price on Friday (after the market has

had access to the information). These returns are adjusted for market effects by estimating a

market model using 90-days of close-to-close returns beginning 10 days before the Wednesday

of the announcement week. The Wednesday was chosen as a reference point in order to compute

average abnormal returns on Wednesday, Thursday, and Friday. The ten-day gap was used to

clearly separate the event being analyzed from the estimates provided by the regression. The

market model is estimated using both equal-weighted and value-weighted market indices

computed by the CRSP. The results do not change with the choice of index. This procedure was

followed for each stock in the sample.

Average abnormal returns are computed for Wednesday, Thursday, and Friday of the

week that IWS mentioned the company, and two tests are used to determine statistical

significance. The J2 test described in Campbell, Lo and MacKinlay (1997) gives better power



- 18 -

when the average abnormal return is constant across securities. Given that the potential source of

these returns is the same source, this is a reasonable assumption. The second test evaluates the

likelihood that more than 50 percent of the abnormal returns in the sample are positive. Table 3

presents the results of these tests.

Table 3

Table 3 is divided into two panels. Panel (a) shows the results for the Before sample,

which is November 7, 1994 – May 29, 1995. Panel (b) shows the results for the During sample,

which includes June 5, 1995 – February 5, 1996. In the Before sample, the average abnormal

return is 4.75 percent for Friday (the release day), which is statistically different from zero at the

99-percent level of confidence. There is no evidence of statistically significant abnormal returns

for Wednesday or Thursday stock returns. In this sample, 70.4 percent of the abnormal returns

are positive, which is also statistically different from 50 percent (the expected level if the column

has no effect) at the 99-percent level of confidence. In addition, 75 percent of the “raw” stock

returns on Friday are positive for the companies mentioned in the IWS column. In other words,

despite the existence of the Sant and Zaman (1996) study, the IWS column still had an influence

on the prices of featured stocks. The percent positive abnormal returns are not statistically

different from 50 percent for Wednesday or Thursday.

The results for the During sample are shown in Panel (b). There is an average abnormal

return of 3.87 percent for the public release day, which is statistically significant. In contrast with

Panel (a), there are also statistically significant average abnormal returns on Thursday, although

at 1.51 percent they are less than one-half the Friday abnormal returns. This result is likely due to

leakage of the Business Week information into the market. In this sample, 78.3 percent of the

abnormal returns on Friday are positive, which is also statistically significant. In addition, 78

percent of the “raw” stock returns on Friday are positive for companies mentioned in the IWS

column. Again, the positive price response on Friday was widespread.



- 19 -

These results show that the stockbrokers could have had a reasonable expectation of

profiting from advance access to the IWS column, particularly if their holding period was a

single day. As Sant and Zaman (1996) show, the returns from trading on this column are short-

lived, so these insiders should be expected to have closed their positions fairly quickly.

C. The Holding Period

The stockbrokers had an incentive to close their positions quickly, rather than risk losing

their gains to market adjustments or mean-reversion after the Business Week information loses its

audience. Offsetting this incentive is that rapid turnover may arouse suspicion from exchange

authorities or the SEC. Figure 3 shows that these stockbrokers slowly reduced their trading

horizon over the eight months that they traded.

Figure 3

In the first two months of trading, insiders held the stocks they had purchased for a period

of about a week. This period reduces by two days in the next two months, and by the end of the

eight-month period to only one and one-half days. Overall, Figure 3 suggests that insiders may

have placed less concern on detection after evading discovery for several months and thus began

to seek greater profits by shortening their holding period.

V. Analysis of Stockbroker Trades

In this section, we conduct a formal analysis of the impact of the insiders’ trades. The

analysis focuses on stocks traded by the five stockbrokers, with results also provided for a

control group of 44 non-traded stocks with no other news on Wednesday or Thursday.

A. Buying Interest and Interval Returns

The abnormal return results show that there is a significant price impact for companies

mentioned in the IWS column. Because the column is publicly available after-hours on



- 20 -

Thursday, this impact is generally concentrated at the open on Friday. Even though there is

significant buyside interest throughout Friday, the provision of liquidity is high enough that the

price impact is negligible after the open. These results are illustrated in Table 4 for all 30

companies traded by the stockbrokers and the 21-company subset that did not have any other

news announcements on Wednesday or Thursday.

Table 4

For each panel, Table 4 estimates two regression models to explain the buyside index and

interval returns using data summarized in 15-minute intervals.16 All regressions are corrected for

heteroskedasticity using White’s correction method. The first version (Models 1 and 3 in Panel

(a); Models 5 and 7 in Panel (b)) includes dummy variables to capture Thursday and Friday

effects relative to Wednesday, which is captured in the constant term. The “Insider Trading

Period” dummy variable captures the effects during the intervals in which the stockbrokers were

trading. Typically, trading was completed within at most two 15-minute intervals. The “Nasdaq”

dummy captures the effect of Nasdaq versus Exchange-listed companies.17

An interaction term is

also included to capture the differential effects of insider trading on Nasdaq companies. The

second version (Models 2 and 4 in Panel (a); Models 6 and 8 in Panel (b)) omits the “Insider

Trading Period” dummy, but includes a dummy variable that captures this period plus the

remaining periods in the day. The idea is that this variable captures the effects of other market

participants learning of, or reacting to, the informed trading—possibly causing a cascade of

buying activity in the market. These other participants may be friends, relatives, customers or

associates of the stockbrokers named in the SEC complaint. Also, there may be mimicking or

momentum traders noticing the presence of the privately informed traders. Because the “Insider

Trading Period” and the “Insider & Remaining Day” variables are highly correlated, they are not

16 Not all 15-minute intervals in a day are included in this sample. An interval is excluded if it contains only zero or one trade.17 Two companies were listed on the AMEX. These are combined with NYSE companies to form the set of exchange-listed companies.



- 21 -

included in the same regressions. Lastly, an interaction term is included to capture the effects of

Nasdaq on the “Insider & Remaining Day” variable.

The results from Table 2 (Models 1 and 5) show there is significant buyside interest on

both Thursday and Friday relative to Wednesday. Model 1 suggests that buying interest on

Friday is over three times the interest on Thursday (6.4 versus 1.9). This diminishes somewhat in

Model 5 for the 21 traded stocks without other news (5.4 versus 2.7). Models 2 and 6 show that

buyside interest on Thursday is actually confined to the trading intervals following the first

insider trade. The earlier part of Thursday shows no significant change relative to Wednesday.

Because the “Insider Trading Period” dummy is not significant in these models, one might

conjecture that the stockbrokers are successfully disguising their buying interest relative to all

order flow in these intervals. However, for the subset of 21 stocks, it appears that the market

becomes aware of the higher buying interest after these insiders have completed their trades.

These results also show that Nasdaq stocks exhibit significantly higher buying interest than

exchange-listed securities. Certainly, there is a preference for Nasdaq stocks by these brokers (55

percent of the companies they traded were Nasdaq-listed). The reason for this may be that the

IWS column may have a greater effect on Nasdaq stocks, which are often smaller than exchange

listed companies.

The regressions for interval returns provide a quantitative summary of the graphical

analysis in Figure 2. Figure 2 showed that returns increase toward the end of the day on

Thursday and then take a discrete jump between the Thursday close and the open on Friday.

After that, Friday returns are volatile, but include a number of periods with negative returns.

Models 3, 4, 7, and 8 in Table 4 show that the Thursday dummy variable is not significant.

However, the “Insider Period and Remaining Day” dummy variable is significant, which shows

that positive returns on Thursday arise after these stockbroker trades. The Friday dummy

variable is negative and statistically significant in all of these regressions, which implies that all

of the gain from this information is impounded at the open on Friday. The Nasdaq dummy gives

consistent results, showing that Nasdaq stocks offer higher returns over these days than



- 22 -

exchange-listed stocks. The interaction terms between Nasdaq companies and the time period

dummies are not significant. Because the “Insider Trading Period” dummy variable is not

significant, these results suggest that it is the mimicking behavior of uninformed traders (or the

trades of the brokers’ associates, if they traded after the stockbrokers did) that cause market price

impacts. This is a refinement of Meulbroek (1992) and Cornell & Sirri (1992), who show that

there are abnormal returns for the day that insiders illegally trade.

B. Price and Volume Effects

To explore further how the brokers’ trades affect the price process, we examine the

number of trades and trade size over these 15-minute intervals. To make results comparable

across companies, the data are standardized relative to their respective averages across 15-minute

intervals on Wednesday, and then Wednesday is omitted from the analysis. Table 5 presents

these regression results using the set of explanatory variables examined in Table 4.

Table 5

The relative number of trades is explained in Models 1, 2, 5 and 6. These results show

significant increases in trading on Thursday and Friday, with the number of trades on Friday over

two and one-half times Wednesday’s trading. The number of trades is higher for Nasdaq versus

Exchange-listed, and during the insider trading period. For example, Model 1 indicates that the

number of trades is nearly four times greater for Nasdaq stocks during the insider-trading period

on Thursday relative to the average on Wednesday. In addition, the number of trades increases

relative to Wednesday both during and after insider trading. This is notable because these

stockbrokers do not trade a large fraction of the volume on Thursday, which contrasts with

Meulbroek’s (1992) findings on trading effects; that is, even a relatively low volume of trading

can initiate price effects as shown in Figure 2.

The results for relative trade size across traded stocks are explained in Models 3, 4, 7, and

8. Because the regressions for all 30 traded stocks are marginally significant, we will focus this



- 23 -

discussion on results for the 21 traded stocks with no news. Models 7 and 8 indicate that relative

trade size decreases on Thursday and Friday (coefficient less than one), but increases when only

Nasdaq stocks are examined. These results are consistent with the stockbrokers and their

followers trading more frequently in smaller sizes on Thursday and the public investors

following the same pattern after the news is known on Friday.

C. Insider Trades and Market Making

The key question in this analysis is whether market makers respond to insiders by

changing the bid-ask spreads or the depth offered to the market. Both Cornell and Sirri (1992)

and Chakravarty and McConnell (1997) find no significant effect on spreads from actual insider

trading. Lee, Mucklow and Ready (1993), Kavajecz (1999) and Koski and Michaely (2000)

document that NYSE specialist depth decreases around expected information events, which may

signal that informed traders are in the order flow. With a cross-section of companies, we can

investigate whether these results generalize beyond NYSE-listed stocks, to specific periods in

which traders had valuable information with a limited life that could not be anticipated by the

market. Table 6 shows the results of this analysis for quoted spreads, effective spreads, and ask

and bid depth using 15-minute interval data.

Table 6

As in the analysis of price and volume, the dependent variables in Table 6 are standar-

dized relative to the average value of these variables on Wednesday. Also, the volume of trading

(versus Wednesday) is included to control for the effect of volume on spreads and depth. Panel

(a) shows the results for all stocks traded by these stockbrokers, and Panel (b) shows the results

for the sub-sample of 21 companies without any other news. The spread results are shown in

Models 1 to 4 and Models 9 to 12. For Panel (a), the coefficients on the Thursday and Friday

dummy variables show that both quoted and effective spreads are slightly lower on these days

than their average value on Wednesday. This result, however, only holds for quoted spreads in



- 24 -

Panel (b), as effective spreads are nearly equal to the averages reported on Wednesday. These

results also depend on whether the company is Nasdaq- or exchange-listed. When the Thursday

and Friday dummies are combined with the Nasdaq dummy, the spreads are not statistically

different than on Wednesday in both panels. Importantly, quoted spreads show no response to

trading by these informed stockbrokers in both panels; only effective spreads in Panel (a) show a

slight significant negative response. The latter, of course, is not the anticipated response of

market makers who detect informed traders; that is, they would increase not decrease spreads.

These results generalize the findings of Cornell and Sirri (1992) and Chakravarty and

McConnell (1997) to a larger number of Nasdaq- and Exchange-listed stocks. The only evidence

that insiders may be affecting spreads comes from the effective spread regressions for all 30

stocks. Similar to the startling results in Cornell and Sirri, this evidence suggests that, if

anything, informed trades possibly face slightly lower execution costs.

Table 6 also shows how insider trading affects market depth. Models 5, 6, 13 and 14

reveal that insider trades significantly affect depth on the ask side. Because these insiders were

buying stock, ask depth is the side most likely affected. Models 7, 8, 15 and 16 confirm that

these trades do not affect depth on the bid side. The results on the ask side offer several insights

into how market makers adjust to insiders. Models 5 and 13 show that ask depth is lower during

the insider period. Hence, even though the bid-ask spread does not generally change, the market

maker reduces its risk by offering a smaller quantity at the posted price. This extends to episodes

in which insider trading is unexpected, using data in which informed trading is exactly identified

and using both Nasdaq and NYSE stocks, the results of Lee, Mucklow and Ready (1993),

Kavajecz (1999) and Koski and Michaely (2000) that market makers use quoted depth to manage

asymmetric information risk.

Note that the depth decrease on the ask side is not dependent on whether it is a Nasdaq or

Exchange-listed stock; depth decreases for both trading venues. The results in Models 5 and 13

show that the Nasdaq/Insider Period interaction term is positive and that its size is larger than the

Nasdaq dummy variable effect. As such, Nasdaq depth does not decrease as much as NYSE



- 25 -

depth during the insider-trading period measured relative to average depth on Wednesday. This

result is understandable in a dealer versus specialist market because the diffuse nature of a dealer

market makes it more difficult for a given dealer to determine the information content of the

order flow. Thus, it is more difficult for a given dealer to avoid being “picked off” by informed

traders.

The results for ask depth with the intervals during and after insider trading combined are

mixed in Models 6 and 14. Model 6 (all 30 traded stocks) shows that depth increases following

the insider-trading period, but this dummy variable is not significant in Model 14 (21 traded

stocks with no news). Because the fit is significantly better in Model 14, this regression is likely

more conclusive. As such, we may conclude that ask depth decreases during the insider-trading

period, but rebounds later in the day, possibly because the price moves have attracted other

liquidity providers to the market or because the dealers/specialists have returned after the

insiders have departed.

D. A Comparison with Non-traded Stocks

These stockbrokers chose to trade only selected stocks mentioned in the Business Week

column. We know that these stocks are different from the stocks not traded in size and listing

venue, but the question remains as to whether they are different in market making characteristics.

To explore this question, regressions are estimated for 44 non-traded companies that had no

other news announcement on Wednesday or Thursday. Two “hypothetical” dummy variables are

constructed to capture the time that the insiders would most likely have traded these stocks. This

variable is one during the period 1:00 to 1:30 PM in the first set of regressions and zero

otherwise. In the second set of regressions, the time period slides one-half hour to 1:30 to 2:00

PM. Table 7 presents the results of this analysis.

Table 7



- 26 -

Table 7 reports regressions for the number of trades, trade size, quoted and effective

spreads, and bid and ask depth. These variables are all measured relative to their average values

on Wednesday. The results for the Thursday and Friday dummy variables in the number of trades

regressions are similar to those in Table 5; that is, there are relatively more trades on these days.

Interestingly, there are significantly fewer trades during the hypothetical insider trading periods.

In addition, the Nasdaq dummy variable is not significant. The results for trade size contrast with

Table 5 in that trade sizes for these stocks tend to be relatively higher on Thursday and Friday.

None of the other variables are significant in the trade size regressions. The quoted and effective

spread results are similar to those reported in Table 6, except that quoted spreads are

significantly lower for these Nasdaq stocks versus the exchange-listed stocks. Importantly, the

hypothetical insider trading period dummy variables are not significant, so that there is not

anything unusual about spreads on these non-traded stocks during the time that the stockbrokers

were in the market. Finally, the depth regressions confirm that depth is not changing for these

stocks during the hypothetical insider periods.

In general, the results for the non-traded stocks are a good control group for the effects

that may influence market making in the stocks mentioned by Business Week . That there is no

indication that ask depth is changing for these non-traded stocks further increases confidence that

the relative decrease in ask depth for the trading stocks is due to these stockbroker trades.

E. Unbundling Liquidity Providers

The data available for this study do not show who is trading with whom. As such, it is not

possible to separate liquidity providers into market makers and limit order traders to determine

which group is adjusting most to these informed trades. It is possible that the informed trades

exhaust the inside limit orders and that market makers are left quoting their own commitment,

which may be unchanged. In this event, spreads may not change if market makers (particularly

specialists) have not detected the informed trading, but depth will decrease. Although we cannot



- 27 -

completely rule out this possibility, the evidence between dealer and specialist markets makes it

less likely.

For these stockbrokers, the average trade size is 5,320 shares in Nasdaq stocks and 4,683

shares in exchange-listed stocks. The average depth at the ask is 2,809 shares on Nasdaq and

16,395 shares on the exchanges. With depth for exchange-listed stocks about 5.8 times the depth

of Nasdaq stocks and the stockbrokers’ trade size is smaller on the exchanges, we would expect a

greater reaction in depth on the Nasdaq if the stockbrokers’ trades were only exhausting inside

limit orders on the book. However, Table 6 shows that depth on the exchanges reacts more than

depth on the Nasdaq. Thus, it appears that specialists on the exchanges are playing an active role

in managing quoted depth during these insider periods.

18

This view is also consistent with

Kavajecz (1999), who finds that depth provided by both specialists and limit orders decreases

prior to earnings announcements for NYSE stocks.

VI. Conclusions

Using a unique episode of repeated insider trading by outsiders across a group of stocks,

we show that liquidity providers do not adjust spreads during periods of genuinely informed

trading. Instead, they adjust offered depth to reduce the risk of transacting with informed traders.

This result holds regardless of the type of market where companies traded, although the

magnitude of the depth decrease is less for Nasdaq-listed stocks compared to exchange-listed

companies. In addition, we show that, during and immediately following periods when insiders

are buying shares, trades are much more numerous—yet smaller in size—than at other times.

The results show that trades during those periods are overwhelmingly buyer-initiated. In contrast

to earlier findings, however, insiders’ trades do not account for the major fraction of the trading

volume increase. Our evidence suggests that the volume increases may reflect an increase in

noise trading by “falsely informed” or mimicking traders.

18 The median results provide the same conclusions. The median trade size by stockbrokers is 4,000 shares in Nasdaq stocks and 3,500 shares in exchange-listed stocks. The median depth is 2,732 on Nasdaq and 10,915 on theexchanges.



- 28 -

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Business Week Sentiment Buy Sell Buy Sell

Count 30 0 72 4

Percent 100.0% 0.0% 94.7% 5.3%

Mentioned in Another News Source? Yes No Yes No

Count 9 21 22 54

Percent 30.0% 70.0% 28.9% 71.1%

Exchange Listed NYSE / AMEX Nasdaq NYSE / AMEX NasdaqCount 12 / 2 16 38 / 6 32

Percent 45.2% 54.8% 57.9% 42.1%

Rate of Return on Assets 1994 1995 1994 1995

Median (Average) 1.6% (-3.5%) 1.8% (-0.8%) 3.6% (3.1%) 3.8% (-1.2%)

Standard Deviation 18.4% 10.1% 12.5% 44.0%

Rate of Return on Equity

Median (Average) 4.7% (-1.1%) 3.6% (4.6%) 11.2% (13.8%) 11.5% (-22.4%)

Standard Deviation 24.8% 17.6% 55.2% 221.2%

Sales (millions of dollars)

Median (Average) 218.3 (1529.9) 364.3 (1713.4) 261 (3022.2) 297.3 (3383.2)Standard Deviation 3604.1 4066.6 8925.1 10184.0

Total Assets (millions of dollars)

Median (Average) 374.2 (2294.6) 357.7 (2401.8) 413.9 (8825.8) 406.5 (8978.4)

Standard Deviation 6055.6 6220.0 29279.4 32960.4

Growth Rate of Sales (1994-1995)

Median (Average)

Standard Deviation

Growth Rate of Net Income (1994-95)

Median (Average)

Standard Deviation

Characteristics of Traded and Non-traded Companies Mentioned by Business Week

Table 1

17.5% (33.2%)

Traded Non-traded

Summary data are presented for 30 companies traded by stockbrokers and 76 not-traded companies mentioned in

Business Week's "Inside Wall Street" column. The Dow Jones News Retrieval Service was searched for mention of these

stocks in another news source. The financial data are from Compustat. Compustat does not cover all of the firms in our

sample. Specifically, the financial characteristics are for 20 traded firms and 60 non-traded firms. Generally, the omitted

firms are smaller, which gives an upward bias to sales and total assets information.

58.9%

21.3% (32.7%)

49.1%

-58.3% (280.8%)

1377.2%

16.6% (-26.6%)

416.9%



Variable Mean Median Mean Median Mean Median Mean Median

Stock Price 19.75 14.50 19.26 13.42 19.55 14.00 20.62 15.22

Quoted Spread 0.25 0.20 0.25 0.19 0.25 0.19 0.26 0.21

Effective Spread 0.13 0.10 0.15 0.11 0.13 0.10 0.12 0.09

Bid Depth (100s) 65 35 66 34 63 40 66 34

Ask Depth (100s) 89 38 86 41 80 35 100 39

Trade Count 12.6 7.0 8.3 5.0 10.5 7.0 17.1 9.0

Trade Size 1664 1589 1735 1591 1704 1679 1550 1519

Interval Volume 20147 9500 14153 7200 18345 9000 25616 11854

Interval Returns (%) 0.1021 0.0233 0.0831 0.0000 0.2139 0.0998 0.0063 -0.0312

Buyside Index 3.80 2.25 1.22 1.11 3.09 2.54 7.20 4.15

Stock Price 17.93 13.13 17.39 12.62 17.83 13.13 18.57 13.35

Quoted Spread 0.28 0.22 0.28 0.23 0.27 0.21 0.28 0.23Effective Spread 0.15 0.11 0.16 0.13 0.15 0.11 0.13 0.10

Bid Depth (100s) 46 27 42 27 40 26 54 29

Ask Depth (100s) 60 32 51 31 54 28 76 36

Trade Count 11.6 6.0 6.7 4.0 10.8 6.0 15.1 8.0

Trade Size 1712 1618 1751 1393 1771 1728 1615 1577

Interval Volume 19979 9696 11005 6000 19445 9900 25610 11904

Interval Returns (%) 0.1469 0.0496 0.1044 0.0000 0.3494 0.2598 -0.0132 -0.0377

Buyside Index 3.90 2.75 1.45 1.13 3.74 3.33 6.50 4.61

Stock Price 22.63 16.93 22.36 16.65 22.45 16.71 23.08 17.28

Quoted Spread 0.26 0.20 0.26 0.19 0.25 0.20 0.27 0.21

Effective Spread 0.14 0.09 0.14 0.10 0.15 0.10 0.13 0.09

Bid Depth (100s) 68 10 60 11 71 10 71 10

Ask Depth (100s) 73 14 74 15 64 13 81 15

Trade Count 17.1 8.0 13.2 7.0 12.9 7.0 23.2 11.0

Trade Size 1584 1305 1998 1437 1399 1165 1355 1364

Interval Volume 29150 9605 27529 8602 22126 8498 35490 12200

Interval Returns (%) 0.0576 0.0020 0.0328 0.0000 0.0673 -0.0043 0.0727 0.0676

Buyside Index 4.14 1.60 2.30 1.15 2.06 1.15 8.07 3.10

Table 2

Panel (a): 30 Stocks Traded by Insiders

All Days Wednesday Thursday Friday

Panel (c): 44 Non-Traded Stocks Without Any Other News

Panel (b): 21 Traded Stocks Without Any Other News

This table presents the average and median values for variables and samples examinedin thisstudy. Thesedata are computed

for all trading daysand for Wednesday, Thursday, and Friday separately. The transaction data are summarized into 15-minute

intervals with the averageshown computed across these intervals. Panel (a) summarizesinformation for all 30 stocks tradedby

the five brokerswith access to advance copiesof BusinessWeek magazine. Panel (b) isfor a sub-set of 21 traded stocks that had

no other news announced on Wednesday or Thursday. Panel (c) shows information for 44 non-traded companies mentioned

in BusinessWeek that did not have anyother news on Wednesday or Thursday. Missing transaction data for an additional four

non-traded, no-news companies ruled out their inclusion in Panel (c).

Descriptive Statistics of the Transaction D ata



Wednesday Thursday Friday

Average Abnormal Return 0.64% 0.27% 4.75%

Asymptotic Normal J2 Test 1.62 0.68 12.05**

Percent Positive Abnormal Returns 53.09% 56.79% 70.31%

Asymptotic Normal Z-test 0.56 1.22 3.67**

Average Abnormal Return 0.77% 1.51% 3.87%

Asymptotic Normal J2 Test 1.99 3.90** 10.01**

Percent Positive Abnormal Returns 46.38% 47.83% 78.26%Asymptotic Normal Z-test -0.61 -0.36 4.69**

Panel (b): "During" Period Sample of 69 Companies

Table 3

Average Abnormal Returns on Wednesday, Thursday, and Friday (Release D ay)

for Companies Mentioned in Business Week's "Inside Wall Street" Column

Average abnormal returns are computed using a market model for stocks mentioned in Business Week

between November 7, 1994 to May 29, 1995, which is before the Inside Wall Street column became

available ahead of publication, and for the period June 5, 1995 to January 29, 1996, which is during the

period of insider trading. Stock with other news announcements on Wednesday or Thursday are excluded

from these samples. The results shown use the equal-weighted CRSP index to measure overall market

returns. Results that are statistically significant at the 99-percent level of confidence are shown with an

"**".

Panel (a): "Before" Period Sample of 81 Companies



Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Mo

Constant -0.077 0.012 0.001 0.001 0.655 0.997 0.

(0.851) (0.976) (0.115) (0.052) (0.156) (0.039) (0.

Thursday 1.936 0.204 0.000 -0.001 2.747 0.519 0.

(0.000) (0.667) (0.846) (0.160) (0.000) (0.356) (0.

Friday 6.423 6.443 -0.002 -0.002 5.389 5.408 -0

(0.000) (0.000) (0.008) (0.009) (0.000) (0.000) (0.

Insider Trading Period 2.608 0.005 4.596 0.

(0.300) (0.175) (0.399) (0.

Insider Period and Remaining Day 3.299 0.003 2.158

(0.149) (0.013) (0.023)

Nasdaq Dummy 4.752 4.553 0.002 0.002 2.803 2.266 0.00

(0.000) (0.000) (0.002) (0.013) (0.000) (0.000) (0.

Nasdaq*Insider Period -0.093 0.001 -2.136 -0.00

(0.761) (0.898) (0.710) (0.

Nasdaq*Insider & Remaining Day 1.801 0.003 4.465

(0.229) (0.145) (0.006)

Adjusted R-Squared 0.086 0.094 0.013 0.019 0.056 0.076 0.

F-test of Regression 29.34 32.49 4.94 6.71 12.86 17.42 4

Observations 1515 1515 1515 1515 996 996 9

Table 4

Buying Sentiment and Interval Returns on Wednesday, Thursday and Friday

Buyside IndexInterval ReturnsBuyside Index

Panel (a): 30 Stocks Traded by Insiders Panel (b): 21 Traded Stocks Withou

Regressions are run for 30 stockst raded by brokers for all companies combined (Panel a) and excluding 9 companies that had othernews

Wednesday or Thursday (Panel b). Transactions data are analyzed in 15 minute intervals on Wednesday, Thursday and Friday. Trade

Lee-Ready algorithm (+1 for buyer initiated and -1 for seller initiated). The"Buyside Index" measuresbuying sentiment asthe sum of the

interval. The larger the sum, the more buyer-initiated trades. "Interval Returns" are computed from the last trade in the previous interval t present interval. The independent variables are as follows. "Thursday" and "Friday" are dummy variables for these trading days. "Insider

dummy variablefor intervals of insider trading. "InsiderPeriod and Remaining Day" equals one on Thursday for all intervals after the fi

"Nasdaq" dummy equals one for Nasdaq stocks; zero for exchange-listed stocks. The two interaction terms measure the effect of inside

stocks. Regressions are corrected for heteroscedasticity using White's method. p-values are shown in parentheses below the coefficients.



Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Thursday 1.068 0.668 0.960 0.906 1.603 1.155

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Friday 2.670 2.673 0.894 0.920 2.683 2.790(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Insider Trading Period 1.747 0.437 3.754

(0.067) (0.093) (0.048)

Insider Period and Remaining Day 1.028 0.106 0.771

(0.000) (0.294) (0.012)

Nasdaq Dummy 1.188 1.183 0.119 0.071 0.371 0.213

(0.000) (0.000) (0.038) (0.255) (0.061) (0.357)

Nasdaq*Insider Period -0.129 -0.315 -1.666

(0.932) (0.298) (0.470)

Nasdaq*Insider & Remaining Day 0.204 0.226 1.065(0.674) (0.119) (0.048)

Adjusted R-Squared 0.070 0.074 0.005 0.010 0.044 0.051

F-test of Regression 21.61 23.05 2.31 3.68 9.45 10.92

Observations 1101 1101 1101 1101 739 739

Table 5

Effects of Insider Trades on Number of Trades and Trade Size

Number of TradesTrade Size Number of Trades

Panel (a): 30 Stocks Traded by Insiders Panel (b): 21 Traded Stoc

Regressions are computed for 30 companies traded by insiders using transactions data analyzed in 15 minute intervals o

insider trading day) and Friday. Panel (a) shows the results with all 30 stocks and Panel (b) shows results excluding 9

announcements on either Wednesday or Thursday. All dependent variables are relative to the average over 15-minute interv

of Trades" is the total number of transactions during the interval and "Trade Size" is the average volume of shares trade

dependent variables are measured relative to their average on Wednesday, the day before any inside information was obtained

are defined as in Table 2. All regressions are corrected for heteroscedasticity using White's correction method. p-values are

each estimated coefficient.



Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Thursday 0.893 0.913 0.972 0.976 1.259 1.096

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Friday 0.890 0.885 0.995 0.987 1.732 1.724(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Insider Trading Period -0.027 -0.061 -0.634

(0.586) (0.034) (0.000)

Insider Period and Remaining Day -0.041 -0.002 0.326

(0.291) (0.942) (0.074)

Nasdaq Dummy 0.082 0.088 0.031 0.044 -0.437 -0.402

(0.001) (0.001) (0.052) (0.011) (0.000) (0.004)

Nasdaq*Insider Period -0.156 -0.015 0.785

(0.176) (0.767) (0.003)

Nasdaq*Insider & Remaining Day -0.067 -0.072 -0.022

(0.239) (0.045) (0.917)

Relative Volume 0.048 0.051 0.053 0.054 0.099 0.087

0.008 0.006 0.004 0.003 0.247 0.310



Observations 1101 1101 1101 1101 1101 1101

Effects of Insider Trades on Market Makers Spreads and D epth

Table 6

Panel (a): 30 Stocks Traded by Insiders

Regressions are computed for 30 stocks traded by insiders using transactions data analyzed in 15 minute intervals on Wedne

Panel (a) shows the results with all 30 stocks while Panel (b) shows results excluding 9 stocks that had other news announceme

Thursday. "Quoted Spread" is the average bid-ask spread during the interval, "Effective Spread" equals two times the abso

between price and the midpoint of the bid-ask spread, averaged over the interval. "Depth" is reported at the best bid and offer av Nasdaq stocks, depth is aggregated across all market makersquoting at the best bid or ask. All dependent variables are measured

15-minute intervals on Wednesday, the day before the inside information was obtained. The independent variables are all

"Relative Volume", which equals the volume in the trading interval relative to the average volume across all 15 minute interv

method is used to correct for heteroscedasticity. p-values are shown in parentheses below each estimated coefficient.

Ask DepthEffective SpreadQuoted Spread



Model 9 Model 10 Model 11 Model 12 Model 13 Model 14

Thursday 0.849 0.833 0.990 1.015 1.737 1.7600.000 0.000 0.000 0.000 0.000 0.000

Friday 0.921 0.907 1.032 1.029 2.132 2.190

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Insider Trading Period 0.035 -0.040 -1.327

(0.678) (0.325) (0.000)

Insider Period and Remaining Day 0.047 -0.040 -0.214

(0.394) (0.329) (0.520)

Nasdaq Dummy 0.149 0.168 0.245 0.026 -1.188 -1.264

(0.000) (0.000) (0.214) (0.265) (0.000) (0.000)

Nasdaq*Insider Period -0.189 -0.206 1.591

(0.184) (0.733) (0.000)

Nasdaq*Insider & Remaining Day -0.105 -0.036 0.477

(0.114) (0.351) (0.176)

Relative Volume 0.021 0.023 0.048 0.050 0.272 0.263

(0.354) (0.321) (0.007) (0.005) (0.046) (0.053)



Observations 739 739 739 739 739 739

Panel (b): 21 Traded Stocks Without Any Other News

Quoted Spread Effective Spread Ask Depth



Variables 1:00-1:30 1:30-2:00 1:00-1:30 1:30-2:00 1:00-1:30 1:30-2:00 1:00-1:30 1:30-2:00 1:00-1

Thursday 1.100 1.101 1.125 1.124 0.963 0.963 0.946 0.945 1.05

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.00

Friday 2.309 2.312 1.078 1.069 0.998 0.999 0.962 0.962 1.05

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.00

Hypothetical Insider Period -0.373 -0.307 -0.275 -0.036 -0.361 -0.045 0.049 0.035 0.21

(0.002) (0.005) (0.274) (0.897) (0.594) (0.672) (0.274) (0.663) (0.49

Nasdaq Dummy 0.030 0.023 -0.134 -0.116 -0.049 -0.052 0.036 0.035 -0.179

(0.762) (0.816) (0.062) (0.111) (0.015) (0.009) (0.003) (0.004) (0.00

Nasdaq*Insider Period 0.214 0.292 0.487 -0.232 0.022 0.100 -0.061 0.008 -0.070

(0.369) (0.371) (0.169) (0.432) (0.851) (0.540) (0.466) (0.933) (0.84

Relative Volume 0.043 0.043 0.045 0.045 0.15

(0.004) (0.004) (0.000) (0.000) (0.00

Adjusted R-Squared 0.093 0.093 0.000 0.000 0.028 0.028 0.071 0.071 0.05

F-test of Regression 39.78 39.74 1.06 0.91 9.63 9.73 24.1 24.16 18.3

Observations 1510 1505 1510 1510 1510 1510 1510 1510 151

Table 7

Comparison of "Insider Effects" for Non-Traded Stocks

Number of Trades Effective Spread

All Dependent Variables are Relative to the Average Over 15 Minute Intervals on W

Regressionsare computed for 44 companies, mentioned in Business Week issues from June 15 1995 to February 6 1996 but not traded by insiders. Th

announcement on either Wednesday or Thursday, the day the inside information was obtained. Transactions data are analyzed in 15 minute in

dependent variables relatives to the average on Wednesday. The "Hypothetical Insider Period" dummy is set equal to unity in two periods, 1:00

correspond to the period that insiders traded other stocks mentioned by Business Week . The results are shown for each period separately. Dependent

revious tables. White's method is used to correct for heteroscedasticit . -values are shown in arentheses below each estimated coefficient.

AQuoted SpreadTrade Size



Figure 1

Three-Day Median Volume Changes

This figure shows the median percentage trading volume from the open on the Wednesday precedingthe release of the relevant IWS column until the close on the Friday when the magazine is publiclyreleased. These data are measured in 15-minute intervals relative to the average volume on Wednesday.

This plot is drawn for the 21 stocks that at least one of the brokers traded (“traded”) and 44 that noinsider traded according to the SEC complaint (“not traded”), using transactions data summarized in 15-minute intervals. Only stocks not mentioned in another news source on the insider trading day (Th) or the day before (W) are included. The two vertical lines represent the end of the first (W) and second(Th) trading days. The arrow indicates the 15-minute interval ending at 1:00 PM on Thursday, theearliest starting time for insider trades in the sample.

-100%

100%

300%

500%

700%

900%

1100%

M e d i a n C h a n g e v . A v g . W e

d n e s d a y

traded

non-traded

FridayThursdayWednesday↑



Figure 2

Three-Day Median Price Changes

This figure shows the median percentage price change from the open on the Wednesday preceding therelease of the relevant IWS column until the close on the Friday when the magazine is publicly released.These data are measured in 15-minute intervals relative to the average price on Wednesday. This plot isdrawn for the 21 stocks that at least one of the brokers traded (“traded”) and 44 that no insider tradedaccording to the SEC complaint (“not traded”), using transactions data summarized in 15-minuteintervals. Only stocks not mentioned in another news source on the insider trading day (Th) or the day before (W) are included. The two vertical lines represent the end of the first (W) and second (Th)trading days. The arrow indicates the 15-minute interval ending at 1:00 PM on Thursday, the earlieststarting time for insider trades in the sample.

-1%

1%

3%

5%

7%

9%

11%

13%

M e d i a n C h a n g e v . A v g . W e d n e s d a y

traded

non-traded

Wednesday Thursday Friday↑



The average holding period is computed for stocks traded by stockbrokers who had advance copies of

the "Inside Wall Street" column in Business Week magazine. The holding period decreasedsignificantly over the period of trading, reflecting learning on the part of these brokers about the

temporary nature of the Business Week "bounce."

6.73

4.67

3.17

1.67

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

D a y s

June/July Aug/Sept Oct/Nov Dec/Jan

Figure 3

Average Holding Period for "Inside Wall Street" Stocks Traded by Brokers

Illegal Insider Trading

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