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Market Misvaluation and Merger Activity: Evidence from Managerial Insider Trading

Mehmet Engin Akbulut1

Marshall School of Business University of Southern California

ABSTRACT

This paper tests the empirical predictions of the market misvaluation theory of mergers advanced by Rhodes-Kropf and Vishwanathan (2004) and Shleifer and Vishny (2003) using data on managerial insider trading around merger announcements. I first present evidence of opportunistic insider trading by acquirer-firm managers prior to mergers. Acquirer-firm managers abnormally increase their selling prior to stock mergers suggesting those deals might be overvalued. I then construct a misvaluation measure based on the magnitude and direction of managerial trades prior to the merger and use it to test the empirical predictions of the misvaluation theory. To the extent that managerial trading is motivated by misvaluation, I find consistent differences in the acquisition characteristics and acquisition decisions of overvalued and undervalued firms. Overvalued firms are more likely to conduct stock mergers, have high pre-merger but negative post-merger excess long-run returns and receive negative market reaction to their acquisition announcements. My results support the theory that market misvaluation affects merger activity.

First Version: 01/17/2004

Last Revised: 01/10/2005 I thank Harry DeAngelo, John Matsusaka, Kevin Murphy, Micah Officer, Oguzhan Ozbas and seminar participants at USC for helpful suggestions and USC for financial support.

1 Ph.D. candidate in Finance, Marshall School of Business, University of Southern California, 701 Hoffman Hall, Los Angeles, CA 90089, Phone: 213-740-6884, Fax: 213-740-6650, E-mail: akbulut@usc.edu

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…"Sometimes there is confusion between write-offs and the creation and destruction of

value. Given that the acquisitions were virtually [all] paid in shares, not cash, this non-

cash charge does not represent any value destruction.”

Jean-Marie Messier, chief executive of Vivendi Universal, announcing a €15.7bn write-off, mostly linked

to its $34bn acquisition of Seagram and €12.5bn purchase of Canal Plus.

1. Introduction

Do managers engage in costly acquisition sprees for stock when they perceive their own

company stock as overvalued? While academics and investors alike started entertaining this

possibility heavily after witnessing the giant stock-merger deals of late 1990s, Jean-Marie

Messier, who as the CEO of the French water utility company Vivendi engaged in a $100 billion

acquisition spree that almost ended in bankruptcy, became -to best of my knowledge- the first to

publicly admit it.

A particularly famous stock-merger from the market boom of late 1990s is the AOL-Time

Warner merger, which, with a deal value of $156 billion was the biggest merger in corporate

history. AOL paid a 70% premium for Time Warner using its stock as the acquisition currency.

Despite this large premium, the many observers believe today that AOL got an excellent deal as

its stock was overvalued: “...in early 2002 AOL Time Warner said it would take a $54 billion

charge—the largest in U.S. business history —because the value of the stock it used to buy Time

Warner had plunged. That was a tacit admission that the deal had been overvalued…

(Washington Post, 7/19/2002)”. Ex-post the stock has fallen from $73 the day before the

announcement (1/7/2000) to $31.5 in just two years.

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The positive relationship between high stock market valuations and merger activity is well-

documented: it has been known as early as Nelson (1959) that merger activity peaks in periods of

high market valuations. More recently Andrade et al. (2001), confirms Nelson's findings and

further shows that the preponderance of stock acquisitions is greater in higher valuation markets.

The eagerness of acquirers to use their stock as “acquisition currency” during the times of high

market valuation led researchers to argue that they might be overvalued. For example, Loughran

and Vijh (1997) and Rau and Vermaelen (1998) present evidence from acquirer long-run returns

and argue that overvalued bidders pay with equity to acquire targets at a bargain price whereas

undervalued bidders choose cash.

Two recent theories attempt to model the effect of stock market valuations on merger activity.

Shleifer and Vishny (2003) propose a model with rational managers and an irrational stock

market where mergers are driven purely by investor misvaluation and can occur even in the

absence of any synergies. In their model, target managers maximize their own short run private

gain instead of long-term shareholder value, making them likely to accept stock bids by the

overvalued acquirers. Rhodes-Kropf and Vishwanathan (2004) propose a model where markets

are rational but potential market value deviations from fundamental values on both sides of the

transaction can rationally lead to a correlation between stock merger activity and market

valuation.

Although different in their assumptions about market rationality, both Shleifer and Vishny

(2003) and Rhodes-Kropf and Vishwanathan (2004) models yield the same empirical

predictions. However any test of these empirical predictions first has to deal with the problem of

measuring misvaluation. One way of measuring whether the acquirer was overvalued at the

acquisition date is to look at post-event long-run abnormal stock returns.2 However as Dong et

al. (2003) note, there has been much debate about whether evidence of abnormal long-run post-

event average returns implies stock market inefficiency with respect to the event (see Fama

(1998), Loughran and Ritter (2000), Daniel, Hirshleifer and Teoh (2002), Mitchell and Stafford

(2001)). Also in long-run event studies the results are highly sensitive to the choice of “normal”

return benchmarks and the method for compounding returns; see e.g. Barber and Lyon (1997).

2 See Frank, Harris and Titman (1991), Loughran and Vijh (1997) and Rau and Vermaelen (1998)

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Two recent studies attempt to test the misvaluation theories by measuring misvaluation using

accounting multiples. Dong et al. (2003) define market misvaluation as the discrepancy between

the market price and a contemporaneous measure of the fundamental value. To measure the

fundamental value they use the ratio of book value of equity to price (B/P) and the ratio of

residual income model value to price (V/P). Rhodes-Kropf et al. (2004) use market-to-book

(M/B) as a measure of misvaluation. They decompose M/B into a “misvaluation component” and

a “growth component” and calculate measures of fundamental value by running cross sectional

regressions of market values on accounting fundamentals each year. While both studies find

supporting evidence for the misvaluation theory, their dependence on accounting variables to

measure misvaluation raises some concerns. First, accounting variables are easily distorted by

non-economic events like management manipulation and changes in reporting requirements.

Second, they might be proxying for effects other than misvaluation. For example the ratio of

book value of equity to price (B/P) may also capture risk (see Daniel, Hirshleifer, and

Subrahmanyam (2001) and Barberis and Huang (2001)), growth opportunities, information

asymmetry or managerial discipline. Dong et al. (2003) acknowledge this and use V/P as a

supplementary measure. However they note that there is still the possibility that V/P might be

proxying for risk. Rhodes-Kropf et al. (2004) criticize Dong et al. (2003) for not separately

identifying changes in growth opportunities, expected returns and misvaluation. The variable

they use is M/B, which is a well-known proxy for future growth opportunities. They attempt to

purify it by subtracting the growth component from M/B. However their fundamental value

measures are derived from accounting variables and to the extent they cannot capture the true

value, their measures of misvaluation will also reflect time-varying risk premia and growth

opportunities.

In this paper, I test the market misvaluation theory of mergers using the information contained in

the managerial insider trades prior to the mergers. My analysis consists of two main parts: First,

I present evidence on the information content of managerial insider trading and show that

managers trade opportunistically prior to merger announcements. Second, I construct a

misvaluation measure using the level and direction of managerial insider trading prior to mergers

and use it to test the empirical predictions of the market misvaluation theory. Shleifer and Vishny

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(2003) explicitly mention managerial insider trading as a possible consequence of being

overvalued and to the best of my knowledge this is the first paper which uses managerial insider

trading to test the market misvaluation theory of mergers. Unlike the misvaluation measures used

by Dong et al. (2003) and Rhodes-Kropf et al. (2004), my measure does not require the

calculation of the “true value” of the firm. Instead, I measure the “perceived misvaluation” by

the managers. I argue that firms where managers are net-sellers in company stock in their private

portfolios are perceived to be overvalued by their managers whereas firms where managers are

net-buyers are perceived to be undervalued. Accordingly, higher levels of net-selling relative to

the common stock holdings of the managers represent higher levels of overvaluation and higher

levels of net-buying represent higher levels of undervaluation. Finally, firms with no managerial

insider trading activity are perceived to be fairly valued by the managers.

My misvaluation measure is nevertheless closely related to pricing multiples like B/P and M/B

used by Dong et al. (2003) and Rhodes-Kropf et al. (2004), but it provides a more direct way of

measuring misvaluation. Recent research shows that managerial trading activity is not randomly

distributed among value and growth stocks. Rozeff and Zaman (1998) show that managers in

growth firms tend to sell more equity than managers in value firms, i.e. they have “contrarian”

views about their firms. They interpret this as evidence that the market overvalues growth stocks

and undervalues value stocks. Jenter (2003) finds evidence for the contrarian nature of

managerial trading even after controlling for non-information motives for trading by keeping

managerial ownership levels and compensation grants constant. In short, Rozeff and Zaman

(1998) and Jenter (2003) document the relation between managerial trading and pricing

multiples, while Dong et al. (2003) and Rhodes-Kropf et al. (2004) use pricing multiples to

measure market misvaluation. My approach measures market misvaluation directly from

managerial trades without the intermediate step of using the pricing multiples. This results in a

more natural measure which does not suffer from the limitations of price multiples described

above.

Central to my measure is the information content in the insider trades of the managers. The

information content of insider trading has been well documented (see Jaffee 1974, Finnerty 1976

and Seyhun 1986, 1988). Seyhun (1986) shows that insiders earn an average 3% abnormal return

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on their transactions. Studies examining insider trading around important corporate

announcements indicate significant changes in trading patterns before the public announcement.

Penman (1982) shows that corporate insiders time their trades relative to announcements of

earnings forecasts and earn positive abnormal returns. Elliot et al. (1984) find evidence of

increased buying prior to extremely favorable earnings announcements and of increased selling

prior to extremely unfavorable earnings announcements. Lee et al. (1992) find increased buying

and reduced selling prior repurchase tender offers. Karpoff and Lee (1991) find increased selling

prior to seasoned offerings of common stock. Seyhun (1990b) analyzes the trades of acquirer

firm managers in their own companies around mergers and tender offers and finds that managers

increase their net purchases prior to good acquisitions (as characterized by announcement

abnormal return) and prior to bad acquisitions there is an insignificant increase in net selling. He

interprets this as evidence that there are some top executives who sell stock in their own firms

prior to value-decreasing acquisitions.

In the light of these findings in the insider trading literature, I first establish that insider trades by

the managers are informed trades and reflect the managers’ perceptions about true firm value:

managers earn significant positive abnormal returns (up to 6.81% in 15 trading days) after

purchase transactions and avoid significant losses (up to -6.15% in 15 trading days) after sale

transactions. Second, I find that managers in acquirer firms trade opportunistically around the

merger announcements by abnormally increasing their selling prior to stock mergers and “bad”

mergers as characterized by having a 4-day announcement abnormal cumulative return of less

than -5%. Moreover, there is evidence of increased managerial buying and no significant

changes in managerial selling prior good mergers. I interpret these results as evidence that

managers trade opportunistically prior to the mergers in order to take advantage of the insider

information they possess about the value of their firm. These findings also confirm Shleifer and

Vishny (2003)’s prediction that there would be increased insider selling prior to stock

acquisitions, because those deals are more likely to be overvalued.

I then sort the acquirer and target firms into different valuation groups based on the level and

direction of net trading by the managers in the year prior to the merger announcement. To the

extent that managerial trading is motivated by misvaluation, I find persistent differences in

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acquisition characteristics, methods of payment used, announcement abnormal returns and long

run abnormal stock returns of the firms perceived as overvalued and undervalued by their

managers. First, consistent with the market misvaluation theory, overvalued acquirers prefer

stock as the method of payment whereas undervalued acquirers prefer cash. Moreover, acquirers

are more likely to use stock when the target is overvalued. There is an abnormal increase in

insider selling by the managers prior to the stock mergers, further suggesting that acquirers in

stock mergers are more likely to be overvalued.

Second, acquirer and target announcement period abnormal returns are negatively correlated

with the degree of acquirer and target overvaluation. The most overvalued acquirers have 2.3%

lower announcement abnormal returns than the most undervalued ones. Similarly, the most

overvalued targets have 4.7% lower announcement abnormal returns than the most undervalued

ones. Furthermore, target overvaluation greatly affects acquirer announcement abnormal returns;

acquirers that acquire the most overvalued targets have 2.7% lower announcement abnormal

returns than those that acquire the most undervalued targets. These results suggest that market at

least partially corrects for preexisting acquirer and target misvaluation at the merger

announcement.

Third, overvalued acquirers have higher pre-merger but lower post-merger excess returns than

undervalued acquirers and this effect is strongest in stock mergers. The most overvalued

acquirers underperform the most undervalued acquirers by 9.4% (0.78% per month) in all

mergers and 19% (1.58 % per month) in stock mergers in one year after the merger, using

calendar time portfolio regressions method. Moreover, the underperformance in stock mergers is

robust to using value-weighted returns instead of equal-weighted returns, using a different asset

pricing model and using Buy-and-Hold abnormal returns method instead of calendar-time

portfolio abnormal returns. These findings document an eventual correction of the pre-merger

misvaluation in the long run.

Finally, consistent with the predictions of the Shleifer and Vishny (2003) model, overvalued

acquirers make more diversifying acquisitions because when they are overvalued, there is a good

chance that their industry is also overvalued. Targets require higher bid premiums when they are

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undervalued and are more likely to resist offers from undervalued acquirers confirming the

prediction of Shleifer and Vishny (2003) that target resistance to takeover bids benefits target

shareholders. Overall, my results are supportive of the market misvaluation theory of mergers.

The rest of the paper is organized as follows. Section 2 describes the data and method. Section 3

presents the findings from univariate and multivariate analyses. Section 4 concludes.

2. Data and Method

I searched the Securities Data Corporation (SDC) Platinum Mergers & Acquisitions database for

completed mergers between public companies from January 1984 to December 2000 where:

• The acquirer owns less than 5% of the target prior to the acquisition and buys the rest

with the acquisition

• Data on method of payment, whether the deal was hostile or not and bid premium is

available.

• There is price and return data for both acquirer and the target in the University of

Chicago’s Center for Research in Security Prices (CRSP) database

• There are no other corporate announcements like share repurchases, stock splits etc.

concurrent with the merger announcement

These requirements result in an initial sample of 2,314 mergers. Next I search for the insider

trades of the acquirer and target firms’ managers in the Thomson Financial Insiders Database

(IDF). To ensure that each firm has enough insider data coverage prior to the merger I require the

first entry in the IDF database for a firm to be at least 13 months prior to the merger

announcement. As a result, 178 mergers are eliminated due to insufficient IDF coverage of the

acquirer firm. Out of the 2,136 remaining mergers, 132 are eliminated due to insufficient IDF

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coverage of the target firm. The final sample has 1,970 acquisitions, for which insider trading

data for both acquirer and target firms is available.3

Table 2a presents the summary statistics for the merger data. Acquirers are substantially bigger

than targets and heavily use stock to finance their mergers as opposed to cash, 50.4% versus

24.4% of the time. While acquirers earn a negative announcement abnormal return of -1.35%,

acquisitions on average create value, the average 4-day announcement abnormal return for the

combined firm is 1.24%. Figures 1a and 1b show the annual distribution of merger activity from

1984 through 2000. The stock-merger wave of the late 1990s is clearly visible; from 1995 to

2000, not only the majority of mergers are stock mergers, but these mergers also represent the

highest-value deals measured as a percentage of the total market capitalization of all public firms

in the CRSP database.4

The insider trading data comes from the IDF database, which lists the amount, type and date of

each trade as well as the title of the insider from January 1984 to December 2000. To focus on

information-related trades, I analyze the direct open market sale and purchase transactions of the

managers involving at least 100 shares.5 Using the managerial position descriptions in IDF

database, I categorize the managers in to seven disjoint groups ranked in the order of importance:

chief executive officers (CEOs), chairpersons of the board of directors (CBs), persons who are

both chief executive officers and chairpersons of the board of directors at the same time (CEO-

CBs), directors, persons who are both officers and directors, high-level officers and mid-level

officers. If a person appears in more than one group, I include him only in the one which has the

3 When examining the abnormal acquirer insider trading activity around the mergers, I use the entire 2,136 mergers

for which acquirer insider trading data is available. For the remaining of the paper, the final sample of 1,970 mergers

is used.

4 For each year from 1984 to 2000, the percentage of total market capitalization acquired is calculated by dividing

the total market value (measured at 3 days prior to the merger announcement) of the target firms acquired in that

year by the total market value at the beginning of that year of all public firms available in the CRSP database.

5 Nevertheless, I also present results for the value of stocks purchased through option exercises from time to time for

information, since most of the stock sold on the open market comes from purchases through option exercises.

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highest ranking. Since I am only interested in the managers’ evaluation of their firms, I exclude

institutional shareholders and large individual shareholders who are not managers. Finally I

exclude the firms in IDF database which could not be matched to CRSP database based on the

CUSIP code.

Table 2b describes the overall trading activity for 15,298 firms in the IDF database from January

1984 to December 2000. Panel A shows average annual trading activity by firm size. The figures

are calculated as follows. First, net purchases by the managers as a whole are calculated for each

firm in each year. Then all firm-years are pooled and sorted into ten size deciles. The figures

represent the average annual trading activity for a typical firm in a given size decile. Except for

the smallest firms, managers as a whole are net-sellers in all firms. The magnitude of net-selling

grows monotonically with firm size; with the largest firms having $14.3 million of net-selling.

Percentage of managers who are net sellers in a given year also increases with firm size; only

29% of the managers are net sellers in small firms, compared to 79% in the largest firms. The

main reason for the preponderance of sales in large firms is because managers in large firms

usually receive part of their compensation as stock options, exercise the options to acquire

shares, and then sell the shares. For information, I also report the value of stock purchased

through option exercises in Table 2b. As firm size gets larger, so does the value of options

granted and hence the value of purchases through option exercises. For example managers in the

smallest firms buy just $2,008 worth of shares through option exercises whereas managers in the

largest firms buy $8 million worth of shares which eventually get sold on the open market.

Panel B of Table 2b shows the average annual trading activity for different management groups.

Without exception, all management groups are heavy net-sellers in own company stock on the

open market. The highest ranked managers like CEOs, chairpersons of the board and persons

who are both CEOs and chairpersons of the board are the heaviest net-sellers in absolute dollar

terms, not surprising, since they also purchase the highest amounts of company stock through

option exercises. However in relative terms, mid-level officers seem to be the heaviest net

sellers, selling an average of 28.6% of their common share holdings every year on average. They

also have the highest percentage of net sellers; 75% of mid-level officers are net sellers in a

given year on average. One possible explanation is that the trades of mid-level officers are less

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under the spotlight than those of CEOs and chairpersons of the board, hence enabling mid-level

officers to trade more liberally to take advantage of any insider information that they might have.

The question whether these trading patterns represent informed-trading will be addressed later in

the paper.

2.1 Motivation for and Calculation of the Misvaluation Proxy

My main aim in this paper is to understand how market misvaluation influences merger activity.

Market misvaluation can drive merger activity if the managers are opportunistic and take

advantage of the discrepancy between their valuation and market’s valuation of their firms. If the

acquirer is overvalued, then the opportunistic managers will try to use company stock as

overvalued acquisition currency and buy other firms with it, before the market finds out about

the acquirer’s true value. This is not the only opportunistic behavior triggered by overvaluation;

as long as the managers believe their firm is overvalued, they will try to liquidate their personal

holdings of own company stock at the overvalued market prices and refrain from buying it on the

open market. This will lead to an increase in managerial sales and a decrease in purchases during

the periods when the firm is believed to be overvalued by its managers. As a result, the managers

in overvalued firms will be more likely to be net sellers than net buyers in their firms’ stock.

However, managers can be trading for a variety of reasons like portfolio rebalancing,

diversification or liquidity needs. In order to make sure that the trades I am looking at represent

significant changes in holdings rather than marginal portfolio readjustments, I need to control for

the wealth of the managers. Since it is impossible to calculate the actual wealth of managers, the

best I can do is to come up with a reasonable proxy. I use the value of common share holdings as

a proxy for wealth. For each transaction, IDF database reports the number of shares held after

that transaction. Using this data, I construct the number of common shares held at one-year prior

to the merger announcement date. My measure does not include the value of option holdings,

stock grants or salaries and bonuses, since IDF database doesn’t have this data. While it is

possible to obtain data on the top five paid executives from EXECUCOMP database, doing so

would cover only 5% of all the managers in my data. Since I would like to use the information

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contained in the trades of all managers, not just the highest ranked ones, I decided to use the

common shares held as an imperfect proxy for wealth.

I measure the firm-level misvaluation at the merger date by calculating the ratio of total dollar

net purchases by the managers during the one-year period prior to the merger to total value of

their common stock holdings one year prior to the merger announcement date. I name this

variable as NET. Negative values for this variable mean managers as a whole are net-sellers in

company stock whereas positive values mean they are net-buyers. By using the ratio of trading to

existing stockholdings rather than absolute dollar values, I am able to capture the importance of

the trades relative to the managers’ existing common share holdings. I then classify firms based

on the direction and intensity of managerial trading using the variable NET. Firms for which

NET is less than zero are net-seller firms, firms with NET equal to zero are no-trading firms

(NO) and firms with positive NET are net-buyer firms. I then sort net-seller firms by the values

of NET and label the bottom one-third as high net-sellers (HS), the middle one-third as medium

net-sellers (MS) and the top third as low net-sellers (LS). I label the firms with no trading

(NET=0) as NO. Similarly, I sort net-buyer firms by the values of NET and label the bottom one-

third as low net-buyers (LB), middle one-third as medium net-buyers (MB) and top one-third as

high net-buyers (HB). My final classification from highest net-sellers to highest net-buyers is as

follows: HS, MS, LS, NO, LB, MB and HB. If managerial trading activity indeed reflects

managers’ own beliefs about firm value, then managers in HS firms will have the highest degree

of perceived overvaluation whereas managers in HB firms will have the highest degree of

perceived undervaluation. Finally I define two summary groups, total net-sellers (TS) and total

net-buyers (TB) which include HS, MS, LS and LB, MB, HB groups respectively.

Table 3a shows the summary statistics for NET across different trading activity categories6.

During the one-year period prior to the merger, managers in HS firms sell on average 143.7% of

their beginning common share holdings in acquirer firms and 114.6% in target firms7. These

highly negative values for NET seem to suggest that at least for some firms, there is a serious 6 There were a total of 6 observations where the value of the beginning-of-the-period was very small, resulting in very large positive or negative values for NET. To prevent this, those observations were assigned a NET of 2000% or -2000% depending on the sign. 7 It is possible to have NET smaller than -100% because managers might purchase stock through options exercises and sell them on the open market, driving NET below -100%.

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attempt by managers to abandon stock prior to acquisitions. Whether or not this represents an

abnormal pattern of trading will be addressed later.

Table 3b presents the pairwise correlations of NET variable with firm characteristics for both

acquirer and target firms. While most of the correlation coefficients are significant, none of them

is higher than 0.15 in absolute value, and the correlations get much smaller if we look at the

targets subsample, suggesting that NET is not proxying for firm characteristics. Correlations

between NET and other potential misvaluation measures like book-to-price ratio (B/P) and past

returns (RET1 and RET2) are relatively high at 0.14, -0.13 and -0.10 respectively for the

acquirer firms suggesting that it is crucial to control for these variables when assessing the

strength of NET in measuring misvaluation.

There are two main methodological concerns with my method of measuring misvaluation. First, I

must establish that managerial trades are indeed informed trades. My approach relies heavily on

the information content of managerial trades. If the managers are trading due to non-

informational reasons, then my measure will not capture their beliefs about the true value of the

firm. Second, I must show that the managers behave opportunistically in their personal trades

and make use of their insider information. Until now, I implicitly assumed that the managers will

act opportunistically to take advantage of any valuation discrepancies they can identify.

Opportunistic managers will decrease their purchases and increase their sales if they believe that

their firm is overvalued. If the managers are optimistic however, they may expect their firm’s

value to increase further. Therefore they may increase their purchases and decrease their sales.

As a result my misvaluation measure will incorrectly identify these overvalued firms as

undervalued. I deal with both these issues in the next two subsections.

2.2 Information Content of Managerial Trading

A crucial assumption of my analysis is that the managers’ trades in their own firm’s stock are

motivated by their beliefs about the true value of their firm. However, the managers might be

trading for various other reasons like liquidity concerns, rebalancing and diversifying their

portfolios after stock price run-ups, none of which has anything to do with their beliefs about the

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true value of the firm. For example, most of the sell trades in large firms occur because the

managers receive a great part of their compensation in stock options, and they sell them in the

open market for liquidity purposes. Hence it is crucial to first establish that managerial trades are

indeed informed trades.

In order to see whether managerial trades are informed trades, I calculate short-run cumulative

abnormal returns for each managerial trading date for event windows up to 15 trading days

starting from the transaction date. If the managers purchase stock prior to announcement of

favorable information, then their purchases will be followed by positive abnormal returns. If the

managers sell stock prior to announcement of unfavorable information, then their sales will be

followed by negative abnormal returns. I calculate the average cumulative abnormal returns for

each managerial position as follows: First for each insider, I calculate the net value of all open

market transactions in each trading day. If sales are higher than purchases, I label the net

transaction on that day as a sale, if purchases are higher than sales, I label the net transaction on

that day as a purchase. Taking the trading day as the event date (day 0) I calculate the cumulative

abnormal return on that firm’s stock for 3, 5, 10, and 15 trading-day windows starting at the

event date. I calculate the abnormal return as the return in excess of the return on the CRSP value

weighted index8. I do not use CAPM or a more complicated asset pricing model to generate the

expected returns because as Brown and Warner (1985) and Fama (1991) have noted, with the

relatively short event windows of 3 to 15 days, the way expected returns are estimated when

calculating abnormal returns has little effect on inferences. Finally for each managerial position I

calculate the average cumulative abnormal return by averaging across the cumulative abnormal

returns following all sales and purchases of the managers in that managerial position. The results

are presented in Table 4.

Table 4 shows the cumulative abnormal returns for different event windows starting at the event

day. Managerial insiders as a whole are able to earn significant positive abnormal returns ranging

from 0.74% in 3 days to 6.81% in 15 days on their purchases which suggests that these trades are

on average represent informed trading. Officers (officer-directors, high and mid–level officers),

seem to benefit the most from their inside information whereas CEOs and CEO-CBs do not seem

8 Results are also robust to using equal-weighted returns, not reported for brevity.

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to benefit at all, they even have negative abnormal returns on their trades; 5-day abnormal return

for the CEO group is a significant -1.77% which further plunges down to -15.09% in 15 days.

One can argue that the open market purchases of the CEO are not motivated by inside

information; instead CEO might be engaging in costly signaling to investors that he expects the

stock price to increase in the future. Another reason might be that CEO trading will draw more

attention from investors and regulatory agencies alike which will in turn limit CEOs willingness

to use inside information. Evidence of informed trading is observed in sale transactions as well.

This time all management groups, including the CEOs, are able to avoid significant negative

returns by selling. In fact CEOs avoid the largest negative abnormal returns, -23.37 % for CEOs

and -9.75% for persons who are both CEOs and chairpersons of the Board. Overall, the

managers avoid a loss of -6.15% in 15 trading days after the sale.

The evidence in Table 4 suggests that managerial trades are on average informed trades and are

motivated by managers’ inside information about the true value of the firm. Although most of the

sell trades are done to liquidate the stock options received as part of compensation, the fact that

they are followed by an abnormal return of -6.15% in 15 days is a clear indication that the

timing of those trades is not random, and reflects managerial information about the value of the

firm.

2.3 Managerial Opportunism or Optimism?

My analysis depends on managers being rational and opportunistic. However this may not

necessarily be the case. Managers themselves can believe in the euphoric assessment of the

market about their firms. In that case these optimistic managers can undertake acquisitions

believing that they will further improve their firms’ value. Therefore their trading patterns prior

to the merger will be different from the rational and opportunistic managers. They may end up

increasing their purchases and decreasing their sales before the value decreasing acquisitions. If

this is the case, my method of measuring misvaluation may label overvalued firms with

optimistic managers as undervalued. Therefore, it is essential to first establish the opportunistic

behavior of managers in the context of mergers.

16

In order to understand whether the managers are behaving opportunistically or optimistically in

their personal trades prior to the mergers, one needs to measure the abnormal changes in the

managerial trading activity around the mergers. For this purpose, I adapt the method of Seyhun

(1990b) with some changes. Seyhun (1990b) defines the abnormal trading activity both relative

to the managerial trades of the same firm outside the takeover period (time-series control sample)

and relative to the managerial trades of size-matched non-acquirer firms inside the takeover

period (cross-sectional control sample). The takeover period is defined as the 19 months from 12

months prior to 6 months after the merger announcement month. Due to the data limitations with

the time-series control sample method, I only use the cross-sectional control sample method

here. In order to improve the matching quality of the cross-sectional control sample, I make two

important changes to Seyhun (1990b)’s methodology. First, to control for industry specific

effects in managerial trading, I match acquirers to control firms in the same 2-digit SIC major

industry. Second, in order to control for firm size, I match acquirers to control firms using the

book value of assets instead of market value. Unlike book value, market value is affected by the

degree of market misvaluation. Therefore matching based on market value may match an

acquirer which had a temporary price run-up with bigger control firms which may have

fundamentally different insider trading patterns. As a result, the abnormal managerial trading

activity would be difficult to identify. To circumvent this problem, my cross-sectional control

sample consists of book value and industry matched control firms. I construct the expected levels

of insider trading using the cross-sectional control sample. I then compare the actual and the

expected levels of trading and compute the significance of the difference. The null hypothesis is

that merger activity does not affect normal insider trading patterns during the takeover period. In

my empirical tests, I follow Seyhun (1990b) and use the bootstrap randomization method. As

Seyhun (1990b) shows, the parametric tests such as the two-sample means test or the

nonparametric tests such as the medium test or the sum of the ranks tests have low power since

insider trading activity is non-normal, infrequent and highly skewed in magnitude.

The bootstrap randomization method works as follows. For the cross-sectional control sample,

first I randomly match each acquirer firm with a non-acquirer firm which is in the same 2-digit

SIC industry and book value decile as the acquirer firm during the merger year. The book value

for merger year t is measured as the book value belonging to the fiscal year ending in calendar

17

year t-1. I then record the managerial trading activity for the matching firms and compute the

overall sample statistics. I repeat this process 1,000 times and obtain the empirical distribution of

the sample statistics under the null hypothesis. The expected value for a sample statistic is the

median of the empirical distribution of that statistic under the null hypothesis. In order to test for

the significance of the difference between the realized value and the expected value of a statistic,

I compare the realized value with the empirical distribution under the null. The results are

shown in Table 5.

Table 5 lists the value of open market sales, open market purchases and shares obtained through

option exercises for the acquirer firms in different subperiods during the takeover period. The

first entry is the average value of shares traded, followed by the expected value, computed as the

median (in 1,000 replications) of the empirical distribution of the average number of shares

traded for a random sample of matching control firms. There is a highly significant increase in

sales in the 12 months prior to the merger month; the sales of $19.6 million are more than five

times the expected value. These results stand in sharp contrast to Seyhun (1990b) who found

insignificant decreases in sales in his sample of 393 acquirers from 1975 to 1986. My analysis

covers an entirely different period, where stock options became an important part of managerial

compensation. Indeed, the abnormal increase in open market sales is accompanied by a similar

increase in purchases through option exercises. Therefore sales events will be more frequent than

Seyhun’s sample. However this does not explain why the managers should be increasing their

sales or purchases through option exercises prior to the merger. Nevertheless, the abnormal

increase in sales show the first traces of opportunistic trading activity. In order to elaborate on

this point more, I look at different subsamples next.

2.3.2 Good Mergers versus Bad Mergers

A good way of detecting whether managers behave opportunistically or optimistically in their

trades prior to the merger is to examine the managerial trades prior to “good” and “bad” mergers.

Opportunistic managers will alter their trading patterns prior to the merger if they are able to

anticipate the market’s reaction to the merger announcement. Namely, they will increase their

sales and decrease their purchases before the announcement of bad mergers and decrease their

18

sales and increase their purchases before the good mergers. On the other hand, if they are

optimistic, they will increase their purchases and decrease or not increase their sales before the

bad mergers.

Mergers are classified as “good” mergers if their announcement abnormal returns are higher than

5%. Those mergers with announcement abnormal returns less than -5% are classified as “bad”

mergers. The announcement abnormal returns are calculated using a four day window starting at

day -2 and ending at day +1 relative to the announcement date. Abnormal (or “excess”) returns

are calculated by subtracting the return on the value weighted market index during the event

window from the firm returns. Once the acquisitions are classified this way, I repeat the analysis

in Table 5. Table 6 presents the results.

Columns (1) and (4) of Table 6 show the average dollar value of shares purchased and sold by

the managers between months -12 to -1 relative to the merger announcement month (month 0)

followed by the expected values computed using size and industry matched control firms. The

results reveal that managers trade completely differently prior to good and bad mergers; there is

a significant increase in managerial insider selling prior to bad mergers, $22.3 million versus the

expected value of $3.5 million, whereas no such significant increase is observed prior to good

mergers. Overall, managers in bad mergers sell $12 million more than managers in good

mergers, a highly significant amount; given the expected value of the difference is just $0.7

million.

Figure 2 provides a closer look at how managers trade around good and bad merger

announcements using 3-month sub-periods. In almost all sub-periods prior to the announcement

month, managers in bad mergers sell significantly more shares than both their normal levels of

selling and managers in good mergers. Once the merger is announced however, this picture is

completely reversed; in months 0 to 3 managers in good mergers increase their sales above its

normal levels and sell significantly more than managers in bad mergers. This suggests that the

managers in good mergers cash in their positive announcement gains after the merger by selling

their shares. When it comes to purchases, there are no pronounced differences in the trading

behavior of managers in good mergers and those in bad mergers. Figure 2 shows that while there

19

is a slight increase in purchases prior to both good and bad mergers, the difference is usually

insignificant.

Non-informational Motives for Trading

Before concluding that these abnormal changes in trading patterns are due to insider information,

I need to control for non-informational motives for trading like managers’ incentives to diversify

away from company stock and to rebalance their holdings after substantial price movements and

stock and option grants (Rozeff and Zaman (1998)). Since I do not have data for stock and option

grants for the entire sample, I am able to only control for the impact of substantial price

movements on trading behavior. After periods of high stock price increases, the value of

company stock in manager’s personal portfolio will increase substantially leaving him with an

underdiversified portfolio. Therefore managers will increase their sales to properly diversify their

portfolios. In order to control for the diversification motive for portfolio rebalancing, I match

acquirers to control firms based on prior stock return. In addition to size and industry matching, I

further require that the control firms have a prior stock return within 5% of that of the merger

firms for the one year period between month -24 and month -13 relative to the merger month

(month 0). This additional matching requirement ensures that the increased sales during the one-

year period prior to merger announcement are not due to a recent run-up in stock price. This

added benefit comes with a cost: my sample size is reduced from 2,136 mergers to 1,302 due to

lack of an eligible control firm for many of the acquirers. As a result, I end up with 252 bad

mergers and 126 good mergers. Columns (2) and (5) of Table 6 presents the results.

The results from columns (2) and (5) confirm my earlier findings. Even after controlling for prior

return on the company stock, managers still significantly increase their sales prior to bad mergers

from an expected value of $4.1 million to $9.7 million, whereas no such increase is observed

prior to good mergers. This suggests that the observed difference in selling behavior is not

simply due to different prior stock returns. Overall managers in bad mergers sell a significant

$4.4 million more worth of shares than managers in good mergers whereas the difference in

purchases is insignificant. The observed pattern of trading is once again consistent with

opportunistic trading behavior.

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Running for the exits

Next, I examine whether these trades represent significant changes in insider holdings or just

marginal portfolio readjustments. If managers are really trading opportunistically, then their sales

prior to bad mergers should constitute a big proportion of their stock holdings. In order to see

whether this is indeed the case, I repeat the analysis in Table 6 using the percentage of common

stock holdings purchased or sold rather than absolute dollar values. Percentage of common stock

holdings purchased or sold is equal to total value of purchases or sales in the period divided by

the value of common share holdings at the beginning of the period. Columns (3) and (6) of Table

6 present the results using size and industry matched control firms9. Results show that managers

liquidate significant portions of their holdings -not just marginal amounts- prior to bad mergers

by increasing their sales and reducing their purchases. In the twelve months prior to the merger

announcement month, managers in bad mergers sell 50.3 % of their holdings compared to an

expected value of 20.3 % and the difference is significant at 1% level. At the same time they

limit their purchases to only 4.1% of holdings, down from an expected value of 9.4 %. A closer

look at these trading patterns using Figure 3 reveals that the biggest percentage sales occur in the

six months before the merger announcement month whereas the abnormal selling activity

completely disappears after the announcement. Overall, managers in bad mergers sell a

significant 14.5 % more of their holdings than managers in good mergers during the twelve

months prior to the merger announcement month. On the surface this resembles a “running-for-

the exits” behavior where managers are desperately trying to get out of company stock.

However the analysis so far focused only on average trading activity; which tells only one part of

the story. Therefore it is crucial to examine the distribution to see how wide-spread this

opportunistic trading behavior is among different insiders and firms. Looking at medians is not

very informative; due to the infrequency of insider trading, they are in almost all cases equal to

zero. Instead I look at the percentage of acquirers having net purchases less than -30%, between -

9 Results using size-industry-prior return matched sample are qualitatively similar, hence not reported for brevity.

21

30% and 10%, between 10% and 0, equal to 0, between 0 and 10%, between 10% and 30% and

higher than 30% relative to the beginning-of-the-period common share holdings.10

Figure 4a shows the distribution of acquirer firms across different levels of net purchases for

good and bad mergers during one-year and six-month periods prior to the merger. In both

figures, the distribution of net purchases is more skewed to the left for bad mergers indicating a

greater propensity for managers to abandon company stock prior to bad mergers.11 During the

one-year period prior to the merger, 23.2% of acquirers in bad mergers sold more than 30% of

their holdings compared to just 15.6% in good mergers, suggesting that my results are not driven

by a small number of highly net-selling firms.

To get a better idea about how insiders in a typical firm are behaving prior good and bad

mergers, Figures 1b shows the distribution of insiders across different net purchasing levels for

the average firm. Interestingly, prior to both good and bad mergers, more than half of all

managerial insiders do not trade at all.12 Those who do trade however mainly sell more than 10%

of their holdings. Once again the distribution is more skewed to left for bad mergers than for

good mergers; during the one-year prior to bad mergers, 26% of all insiders sell more than 30%

of their holdings compared to 19.4% prior to good mergers.

The distribution of insider trading does not show a collective “running-for-the-exits” behavior

among the insiders prior to bad mergers; rather it points to the existence of a group of insiders

who systematically engage in informed trading. More than half of the insiders do not trade at all,

however the majority of those who do trade prefer to do so in the extremes; 55% of those who

trade on the open market prior to bad mergers have net purchases lower than -30% and 10% have

net purchases higher than 30%, compared to 46% and 16% for good mergers. These extreme

trading levels seem too high to be explained only by portfolio rebalancing or liquidity motives; if

informed insider trading exists at all in the sample, it will most likely be found in these extreme 10 The categorization of net purchases as <-30%, between -30% and -10%, -10% and 0, 0 and 10%, 10% and 30% and >30% is somewhat arbitrary but provides a more economically interesting picture than using a histogram with equally spaced bins none of which has any apparent economic meaning. 11 A Wilcoxon rank sum test formally confirms this casual observation: the distributions of net purchases prior to good and bad mergers are significantly different from each other at better than 1 % level. 12 Nevertheless, these insiders are reported in the insiders database because they engage in non-open market transactions.

22

trading groups. Indeed the difference between good and bad mergers is mostly due to different

trading patterns in these extreme groups which confirms the existence of information-motivated

managerial trading in these groups. Since a significant number of insiders trade in these extreme

groups, I can conclude that the average trading figures are not driven by just a handful of insiders

who are heavily selling company stock.

My results point to the existence of opportunistic trading among the managers prior to the

mergers. This stands in sharp contrast to Seyhun (1990b) who finds no evidence of managerial

opportunism prior to 23 “good” mergers and 42 “bad” mergers as defined by 2-day acquisition

announcement abnormal returns. Since my methodology improves upon Seyhun (1990b), the

difference is attributable to the completely different sample period (1975-1986 vs. 1984-2000)

and sample size (393 vs. 2,136 total acquisitions; 23 vs. 251 good acquisitions, 42 vs. 491 bad

acquisitions) studied.

These results are not consistent with theories of managerial overoptimism, overconfidence or

hubris as a primary motivation to engage in acquisitions. Roll’s (1986) hubris hypothesis predicts

that managers will overestimate their abilities to generate returns, both in their current firms and

potential takeover targets. Malmendier and Tate (2003) argue that overconfident CEOs are more

likely to conduct mergers when they have sufficient internal resources, and they conduct more

“bad” mergers on average. However, if managerial optimism or overconfidence were indeed a

primary motive in mergers, then this optimism and overconfidence should also be reflected in

managerial trades prior to the merger. In other words, there is no reason for managers to trade

differently prior to “good” and “bad” mergers; in both cases one should observe increases in

purchases and no increase in sales. This is clearly not the case for the sample studied. Not only

managers in bad mergers increase their sales above normal levels prior to the announcement,

they also sell more than managers in good mergers.

One important difference between my approach and Malmendier and Tate’s (2003) is that my

measure of “bad” mergers is different. Malmendier and Tate (2003) use diversifying acquisitions

to proxy for “bad” mergers. However recent research provides no justification for singling out

diversifying acquisitions as value destroying. For example, Akbulut and Matsusaka (2003) show

23

that during Malmendier and Tate’s sample period from 1980 to 1994 there are no significant

differences in acquirer returns in diversifying and related acquisitions. Moreover, the market

does not view diversifying acquisitions as value destroying on average; the combined

announcement returns are 0.30% (insignificant) for 1980-83, 1.67% (significant at 1% level) for

1984-89 and 0.44% (insignificant) for 1990-93 periods. My method of looking at market’s

reaction to the merger is a more objective way of classifying mergers as “bad”, or “value-

destroying”. Moreover, Malmendier and Tate (2003) completely rule out the insider information

motive for prolonged holding of stock options. In their story, a CEO holding his stock options

too long is doing so because he is “overconfident”, and their entire analysis is based on this

overconfidence measure. An alternative explanation for prolonged holdings might be that the

CEO has private information that the stock prices will rise in the future due to an upcoming

merger, so he will wait until the merger announcement to exercise his options. If this is the case,

one will find a higher propensity to acquire among the managers who hold options until

expiration. My results for good mergers support this alternative explanation. Figure 2 shows that

managers do not increase their sales prior to the merger but once the merger is announced, they

increase their sales significantly; $1.1 million in month 0, $5.6 million in 3 months after the

merger and $8.8 million in 6 months after the merger. Considering that much of this managerial

selling activity is due to selling of exercised stock options in the open market13, one would find it

difficult to argue that managers do not take advantage of their insider information by postponing

their option exercise until after the announcement of the “good” merger. Malmendier and Tate

(2003) argue that “..This (insider information) explanation, however suggest that we would

observe insider trades right around the merger. This does not seem to be the case empirically

(Boehmer and Netter, 1997)”. Indeed, Boehmer and Netter (1997), following a similar method

with us, find no significant changes in managerial trading patterns for 371 acquisitions between

1975 and 1987. Their sample consists of 184 good and 187 bad acquisitions as defined based on

the 6-day announcement abnormal return being positive or negative. However their sample does

not include the stock-merger boom of the late 1990s where, ex-post many mergers are believed

to be undertaken by opportunistic managers trying to take advantage of high stock market

13 Managers purchase a total of $1.6 million in month 0, $7.1 million in three months after the merger and $11.2 million in six months after the merger through the exercise of stock options (not shown in tables). These figures represent a significant increase above the expected values of $0.2 million, $0.9 million and $1.6 million respectively.

24

valuations. My results are derived from a more recent period (1984-2000) and my total sample

size is considerably larger (2,136 vs. 371). Therefore it is likely that some of the “overconfident”

CEOs as defined by Malmendier and Tate (2003) are in fact behaving “opportunistically” with

respect to their private trades before the mergers in general.

Another important difference between my approach and that of Malmendier and Tate (2003) is

that I look at the behavior of all top management whereas Malmendier and Tate (2003)

concentrate on the CEOs only. One might argue that it might be possible to have an

overconfident CEO with opportunistic managers under him. Or it might be the case that the CEO

is trading strategically; he is not selling right after the merger to signal the market his confidence

in his firm. Both these points deserve further empirical investigation. However considering the

relatively high dollar volume of trading for the CEO and CEO-CB groups compared to other

groups (Table 2b), and the fact that they earn the highest abnormal returns to their sell

transactions, (23.37% for CEO and 9.75% for CEO-CB) in 15 trading days (Table 4), it is

difficult to see why their trading patterns around the mergers should be very different from the

rest of the management.

In the light of this evidence for managerial opportunism, it seems overconfidence, overoptimism

or hubris are not the primary motives behind the sample of mergers studied.

2.3.3 Stock Mergers versus Cash Mergers

An alternative way to see whether the managers trade opportunistically prior to mergers is to

compare the trading behavior before stock and cash mergers. The asymmetric information story

tells us that the method of payment chosen may signal new information about the true value of

the firm to the market. For example when stock is used as a method of payment, the market’s

reaction will compound both a reaction to the merger itself and the increase in the outstanding

equity. If managers have more information about the true value of the firm than the market, they

will want to issue new equity when they think that their stock is overvalued (Myers and Majluf,

1984). As a result, the market will react negatively to the issuance of new stock. An extensive

empirical literature shows that seasoned equity issues are associated with negative announcement

25

returns of about -3 percent on average (Smith,1986), and the returns from merger announcements

are about 3 percent lower when stock is used instead of cash (Andrade et al.,2001). These

findings seem to suggest that overvalued firms prefer stock as a method of payment whereas

undervalued firms prefer cash. Therefore, opportunistic managers will increase their sales and

decrease their purchases prior to stock mergers and decrease their sales and increase their

purchases prior to cash mergers.

Columns (1) and (4) of Table 7 show the average dollar value of shares purchased and sold by

the managers between months -12 to -1 relative to the merger announcement month (month 0)

followed by the expected values computed using size and industry matched control firms.

Results show a dramatic increase in sales prior to stock mergers: managers sell a staggering

$31.6 million worth of shares on average while the expected sales are just $3.7 million and the

difference is significant at better than the 1% level. Similar findings are reported by studies

examining managerial trading around seasoned equity issues. Karpoff and Lee (1991), Lee

(1997) and Kahle (2000) all find that insider sales increase relative to insider purchases before

seasoned equity offerings. More recently, Jenter (2003) finds increased managerial selling in

years when there is a seasoned equity offering, after controlling for managerial ownership levels.

For cash mergers, there is a smaller but a significant increase in managerial selling and a

significant increase in managerial buying, but overall, managers in stock mergers sell a total of

$22.5 million more and purchase $0.2 million less than managers in cash mergers, and these

differences are significant at 1% level. These results are robust to controlling for prior stock

returns; Columns (2) and (5) of Table 7 show that managers in stock mergers sell $17.1 million

worth of shares on average compared to an expected value of $5.7 million and they sell $9.2

million more and purchase $0.9 million less than managers in cash mergers. Moreover, these

trades represent significant portfolio adjustments: Columns (3) and (6) of Table 7 show that

managers sell 36.6% of their holdings prior to stock mergers compared to an expected value of

18.9% and the difference is significant at 1% level. Finally, the distribution of net purchases

shown in Figures 7a and 7b is more skewed to the left for stock mergers, indicating that

26

managers have a greater propensity to sell more and buy less prior to stock mergers and a greater

propensity to buy more and sell less prior to cash mergers14.

The evidence reported for stock and cash mergers lends itself to multiple interpretations. One

possible interpretation is a pure asymmetric information story. Assume that the only inside

information the managers possess is that they will conduct a merger soon and that the managers

do not possess any insider information about the value of the firm. In this case, managerial

trades will only be affected by the anticipation of the merger and not due to any perceived

misvaluation. If the managers can correctly anticipate the mergers as value-destroying or value-

creating, then prior to bad mergers there will be increased sales and reduced purchases whereas

prior to good mergers there will be increased purchases and reduced sales. Similarly if the

managers can anticipate the method of payment that will be used, they will sell more and

purchase less prior to stock mergers. This is because of the well-known fact that announcement

returns in stock mergers are on average 3% lower than those in cash mergers. If this is the case,

looking at managerial trades prior to the mergers will not help in measuring market misvaluation;

it will only reflect manager’s anticipation of the upcoming merger event.

There are a number of concerns with this interpretation. First, managers do not seem to anticipate

the merger itself or its value consequences long before the merger, but they somehow act as if

they correctly anticipate the method of payment as early as 10 to 12 months prior to the merger.

There are no significant differences in sales prior to bad mergers and good mergers as early as 10

and 12 months prior to the merger ((-10, -12) period, shown in Figure 2). On the other hand,

managers in stock mergers sell $4.6 million more worth of shares in the (-10,-12) period than

managers in cash mergers and the difference is significant at 1% level (Figure 5). It would be a

bit forced to interpret these findings as managers correctly anticipating the method of payment

that will be used as early as 10 to 12 months prior to merger when they cannot correctly

anticipate the valuation consequences that early. Instead it might be more plausible to think that

managers in stock and cash mergers are simply reacting to information they have at that time,

and that information is different for future cash-acquirers and future stock-acquirers. One such

14 A Wilcoxon rank sum test confirms that the distributions of net purchases prior to stock and cash mergers are significantly different from each other at 1% level.

27

information is the managers’ perceptions about the value of their firms. The observed managerial

trading behavior prior to stock and cash mergers is consistent with the interpretation that

managers in future stock-acquirers are more likely to believe that their firm is overvalued and

therefore increase their selling more than the managers in future cash-acquirers. If the managers

believe that their firm is overvalued, they will be more likely to acquire for stock than for cash

since they expect negative future returns on their stock. On the other hand, if they believe that

their firm is undervalued, they will be more likely to acquire for cash than stock since the

expected future returns on stock is higher than the expected future returns on cash. This is not to

say that managers do not anticipate the value consequences at all, my results from bad mergers

show that they do, at least starting from month -6. However given the prevalence of the different

trading patterns for cash and stock mergers starting as early as month -12, I believe that trading

based on the value consequences of the merger is a secondary motive in managerial trading prior

to the merger.

A second concern with the merger anticipation idea is that, if the managerial trades are only

reflecting the managers’ anticipation of the merger then there is no reason for the pre-merger

stock returns to be different in firms whose managers are net sellers and firms whose managers

are net buyers. However, as will be shown in the next section, this is exactly the case; acquirers

with high net-seller managers have 33.1% (Table 10a) higher pre-merger one-year returns than

acquirers with high net-buyer managers. Once again the observed pattern is more consistent with

a misvaluation story where managers in firms with high stock returns in the past are selling their

overvalued shares prior to the merger whereas managers in firms with low past returns believe

their firm is undervalued and are increasing their purchases.

Taken together, the evidence from good versus bad and stock versus cash acquisitions suggest

that the managers trade opportunistically in their personal portfolios around the mergers.

Moreover, the evidence from stock versus cash acquisitions seems to be consistent with a

misvaluation story where the managers take advantage of their private knowledge about the

value of the firm in their personal trades. Therefore, examining the managerial trades prior to the

mergers gives us a good indication of the degree of misvaluation of the firm.

28

3. Market Misvaluation and Merger Characteristics

3.1 Acquirer Misvaluation - Univariate Tests

Table 8 lists the acquisition characteristics sorted by acquirer valuation levels as measured by

managerial trading activity. Panel A presents the results for all acquisitions, Panel B for stock

acquisitions and Panel C for cash acquisitions.

Table 8 points to several important differences in the merger characteristics of net-seller and net-

buyer acquirers. First, the likelihood of stock payment increases and the likelihood of cash

payment decreases as acquirer becomes a higher net-seller. High net-seller (HS) acquirers use

stock as the method of payment 56.9% of the time whereas high net-buyer (HB) acquirers use

stock 36.4% of the time. The difference is 20.5% and significant at the 1% level. Moreover, high

net-seller acquirers use cash only 22.3 % of the time whereas high net-buyer acquirers use it for

31.4% of the time, the difference is -9.1% and significant at the 5% level. These results are

consistent with the market misvaluation theory of mergers, which predicts that overvalued firms

will prefer to use stock and undervalued firms will use cash as the method of payment in

acquisitions.

Second, acquirer announcement abnormal returns are inversely related with the degree of insider

net-selling. The 4-day announcement cumulative abnormal return (CAR) is -2.5% for high net-

seller acquirers compared to a mere -0.2% for high net-buyer acquirers. Moreover, this

difference mostly comes from stock acquisitions where acquirer announcement CAR is -4.0%

for high net-seller acquirers and -1.4% for high net-buyer acquirers and the difference is highly

significant. This suggests that the merger announcement itself causes a partial correction of

preexisting acquirer overvaluation.

Third, in cash acquisitions, target announcement CAR is higher if the acquirer is a net-buyer

rather than a net-seller. The targets of net-buyer (TB) acquirers have an announcement CAR of

27.3% compared to 23.1% for the targets of net-seller (TS) acquirers which reflects the higher

29

bid premia paid by net-buyer acquirers. The average bid premium offered by net-buyer acquirers

is 49.2% which is significantly higher than that offered by net-seller acquirers; 37.3%. This

suggests that targets are more reluctant to accept bids from undervalued acquirers and require

higher bid premia, even if the method of payment is cash. As a result, target shareholders get

higher announcement CARs. This is consistent with Shleifer and Vishny (2003)’ prediction that

management resistance to some cash tender offers is in the interest of target shareholders.

Fourth, consistent with the prediction of the Shleifer and Vishny (2003) model, net-seller

acquirers are more likely to make diversifying acquisitions than net-buyer acquirers. Shleifer and

Vishny (2003) argue that a high valuation firm might find acquisition targets in the same

industry to be very expensive, especially when the whole industry is overvalued. In this case,

making a diversifying acquisition for stock might be cheaper for the overvalued firm. Table 8

shows the percentages of diversifying acquisitions completed for each trading group.

Diversification is defined as acquirer and target not having a common 2-digit SIC code in their

first 6 SIC codes. 13.4% of the acquisitions of net-seller acquirers are diversifying whereas net-

buyer acquirers make diversifying acquisitions 9.7% of the time. The difference is higher in

stock mergers; 11% for net-seller acquirers and 4.6% for net-buyer acquirers. On the other hand

there are no significant differences in the percentage of diversifying acquisitions across different

trading levels for cash mergers.

The evidence presented so far shows how acquirer insider trading levels are correlated with

acquisition characteristics. Next I present evidence about the relationship between target trading

levels and acquisition characteristics.

3.2 Target Misvaluation - Univariate Tests

Sorting acquisition characteristics by target insider trading levels in Table 9 reveals significant

differences across different target trading categories. Perhaps the most important difference is

observed in the method of payment used; high net-seller targets are much more likely to be

acquired for stock than high net-buyer targets. 55.8% of the high net-seller targets are acquired

for stock, compared to 41.1% for high net-buyer targets. Conversely, high net-seller targets are

30

less likely to be acquired for cash than high net-buyer targets, the difference is -9.5% and

significant at 5% level. These results are consistent with the prediction of the misvaluation

theory that acquirers in general prefer to use stock to buy overvalued targets because this way

they can make the target shareholders shoulder some of the possible decrease in acquirer stock

price resulting from paying too much for the target. Two possible explanations were offered to

explain why target managers should agree to be acquired for overvalued stock. Shleifer and

Vishny (2003) assume that the target managers have a relatively short horizon than acquirer

managers and they want to sell out for reasons of retirement or ownership of illiquid stock

options. In this case they will accept the offer, exchange their overvalued target shares for

overvalued acquirer shares and sell out. The market is assumed to be irrational and therefore

does not react to this exploitation. Rhodes-Kropf and Vishwanathan (2004) suggest that the

rational target correctly adjusts bids for overvaluation, but when the market-wide overvaluation

is high, the target overestimates the synergies from the merger because it underestimates the

market-wide misvaluation and hence accepts the bid.

Acquirer announcement CAR decreases monotonically as the target firm becomes a higher net-

seller. Acquirers of high net-seller targets earn an announcement CAR of -2.9% compared to -

0.2% for the acquirers of high net-buyer targets. Once again, this difference is more pronounced

in stock mergers; -5.0% for acquirers of high net-seller targets compared to -0.7% for acquirers

of high net-buyer targets. A possible interpretation is that market realizes the overvaluation of

the target at the merger announcement and conjectures that the acquirer is likely to be

overvalued. Therefore it partially corrects the overvaluation of the acquirer upon merger

announcement.

There is also an indication of greater bid premia for high net-buyer targets. This is consistent

with the predictions of the Shleifer and Vishny (2003) model, which argues that undervalued

targets will resist the bid until it reflects the true value of the firm, thereby driving the bid

premium up. The average bid premium for high net-buyer targets is a significant 17% higher

than that for high net-seller targets. This high premium is reflected in target announcement CAR

too; high net-buyer targets have a significant 4.7% higher average announcement CAR than high

31

net-seller targets. However I do not find any evidence of increased probability of hostile bids for

the undervalued targets.

3.3 Long-Run Returns

3.3.1 Raw Returns

The evidence presented so far seems to be consistent with the market misvaluation story. Sorting

the mergers based on prior managerial trading activity in acquirers and targets yields significant

differences in merger characteristics among firms in which managers are net-sellers and those in

which managers are net-buyers. However central to my interpretation is the correlation between

managerial trading patterns and misvaluation. One way of understanding whether managerial

trades are motivated by misvaluation is to look at pre-merger and post-merger long-run returns.

Market misvaluation theory predicts that overvalued firms will have high pre-merger returns and

abnormally high insider sales prior to the merger and negative long-run returns after the merger

as the market gradually corrects the overvaluation. On the other hand undervalued firms will

have low pre-merger returns but high post-merger returns. To explore this prediction, pre-merger

and post-merger total and excess returns are calculated for acquirers and targets. Total returns are

calculated for two different periods: the one-year period between two years prior to the merger

and one year prior before the merger (t-2, t-1) and the one-year period prior to the merger (t-1, t).

Table 10a presents the results.

Table 10a reveals a clear difference in the pre-merger and post-merger returns of net-seller and

net-buyer acquirers, consistent with the predictions of market misvaluation theory. Acquirers

with the highest pre-merger net-selling also have the highest pre-merger stock returns; 41.2% for

high net-seller firms compared to just 8.1% for high net-buyer firms, yielding a significant

difference of 33.1% during the one-year prior to the merger. This difference in past returns is

even more dramatic for stock mergers; high net-seller acquirers in stock mergers have on average

40.1% higher past stock returns. This pattern is completely reversed after the merger, with high

net-seller firms earning a significant 18.2% lower average post-merger return than high net-

buyer firms in the year following the merger.

32

There is also evidence suggesting that overvalued targets are acquired mostly by overvalued

acquirers. The targets of high net-seller acquirers earn 19.6% higher past stock returns in the year

before the merger than the targets of high net-buyer acquirers. Moreover, the results from Table

10b -which presents acquirer and target raw returns sorted by target insider trading levels- shows

that the acquirers of high net-seller targets have 20.1% higher pre-merger one-year returns than

the acquirers of high net-buyer targets, suggesting once again that overvalued targets are

acquired mostly by overvalued acquirers. Before drawing any further conclusions however, I

need to make sure that the observed differences in raw returns of net-seller and net-buyer firms

are not just due to risk factors that happen to be correlated with managerial trading activity. In

order to control for such risk factors, I calculate excess returns next.

3.3.2 Excess Returns

There is a lot of controversy surrounding the calculation of long-run excess returns in the

literature. Ritter (1991), Barber and Lyon (1997) and Loughran and Vijh (1997) advocate the use

of Buy-and Hold Returns (BHARs) whereas Fama (1998), and Mitchell and Stafford criticize the

BHAR approach and advocate Calendar-Time Portfolio Regressions approach (CTPR). Since

there is no single perfect measure, I employ both methods when calculating long-run excess

returns.

Buy-and-Hold Abnormal Returns (BHAR)

First I calculate buy-and-hold abnormal returns for the one-year period prior to the merger and

the one-year period after the merger. To calculate excess returns, each firm is matched to its

corresponding 10x10 Fama-French size and book-to-market portfolio. The matching portfolios,

which are constructed at the end of each June, are the intersections of 10 portfolios formed on

size and 10 portfolios formed on the ratio of book equity to market equity. The size breakpoints

for year t are the NYSE market equity deciles at the end of June of t. BE/ME for June of year t is

the book equity in December t-1 divided by ME for December of t-1. The BE/ME breakpoints

are NYSE deciles. The portfolios for July of year t to June of t+1 include all NYSE, AMEX, and

33

NASDAQ stocks with market equity data for December of t-1 and June of t, and positive book

equity data for t-1. The breakpoints and portfolio return data was made available by Kenneth

French. Buy-and-hold returns are measured over months -12,-1 for pre-merger returns and

months +1, +12 for post-merger returns for both the sample firm and the control portfolios, and

the difference is recorded as the abnormal buy-and-hold return to the sample firm. Table 11a

shows the results sorted by acquirer’s managerial trading activity. Once again, there is a

pronounced difference between the pre-merger and post-merger return patterns of net-buyer and

net-seller acquirers, and this difference mainly comes from stock mergers. For example, in stock

mergers, acquirers with the highest insider net-selling earn a significant 40.3% more pre-merger

average BHAR than acquirers with the highest insider net-buying. This result is completely

reversed in post-merger returns, this time high net-seller acquirers underperform high net-buyer

acquirers by as much as 27.1%.15 Sorting BHARs based on target’s insider trading levels yields

similar results. Table 11b shows that a high level of insider net-selling in the target firm is

associated with high pre-merger returns for both the target and the acquirer, but low post-merger

returns for the acquirer. In stock mergers, acquirers which buy high net-seller targets outperform

those which buy high net-buyer targets by 17.2% prior to the merger, and underperform them by

10.5% after the merger. Managerial insider trading activity prior to the merger seems to be

closely related to pre-merger and post-merger excess stock return performance.

Calendar Time Portfolio Regressions Method (CTPR)

As a next step I calculate the long-run excess returns using the calendar-time portfolio regression

(CTPR) method. At every month from January 1984 to December 2000, calendar time portfolios

are formed by including all the acquiring firms in a given trading activity category (HS, MS, LS,

NO, LB, MB and HB) which announced a merger with another company within the previous 12,

24 and 36 months. The portfolio is rebalanced every month to include all the new event

companies and to drop off the old ones whose announcement date falls outside of the holding

horizon. Both value-weighted and equally weighted monthly returns are calculated for each

portfolio and monthly abnormal returns are calculated as the intercepts estimated using both the

Fama-French 3-factor model and the Fama-French-Carhart 4-factor model (Carhart 1997).

15 These results are robust to using value weighted returns instead of equal weighted returns.

34

Heteroskedasticity is an important concern in calendar-time portfolio regressions; since the

composition of the calendar-time portfolio changes every month, results from a simple OLS

specification will be plagued with heteroskedasticity. In order to correct this problem, Hou et al.

(2001) suggest the following GARCH (1, 1) specification:

where εt is an independently and identically distributed Gaussian random error, Rpt is the

portfolio return, Rft is the one-month Treasury bill rate, Rmt is the value-weighted monthly

return of CRSP index, SMBt is the difference in the returns of a value weighted portfolio of

small stocks and big stocks, HMLt is the difference in the returns of a value-weighted portfolio

of high book-to-market stocks and low book-to-market stocks, and the intercept αp represents the

average monthly abnormal return of the event portfolio. In the second equation, Nt is the number

of stocks included in portfolio at month t and σ2t is the conditional volatility of εt. By making the

conditional volatility depend on the number of stocks in the calendar time portfolio each month

heteroskedasticity problem is alleviated. I follow the approach of Hou et al. (2001) and estimate

the Fama-French 3-factor model using the above specification. Fama-French-Carhart 4-factor

model is estimated by adding in the momentum-factor, UMD, to the Fama-French 3-factor

model as follows:

UMD measures the difference in the returns of a value weighted portfolio of two high prior

return portfolios minus and the two low prior return portfolios among the six value-weight

portfolios formed on size and prior returns and was made available by Kenneth French.

Tables 12a and 12b present the estimates of the αp’s which represent the average monthly

abnormal return of the event portfolio using Fama-French 3-factor and Fama-French-Carhart 4-

tttt

ttptpftmtppftpt

N

HMLhSMBsRRRR

32

122

112

)(

γεγσγωσ

εβα

+++=

+++−+=−

−−

tttt

ttptptpftmtppftpt

N

UMDuHMLhSMBsRRRR

32

122

112

)(

γεγσγωσ

εβα

+++=

++++−+=−

−−

tttt

ttptpftmtppftpt

N

HMLhSMBsRRRR

32

122

112

)(

γεγσγωσ

εβα

+++=

+++−+=−

−−

35

factor models respectively. I present the results for 12-month, 24-month and 36-month horizons

using both equally weighted and value-weighted returns sorted by acquirer’s trading activity.

Table 12a shows that high net-seller acquirers tend to underperform high-net buyer acquirers

after the merger and this effect is stronger in stock mergers. For example for the 12-month

holding horizon and using equally weighted returns, high net-seller stock-acquirers under-

perform high net-buyer stock-acquirers by a significant 1.58% which translates into a 19%

(1.58*12) underperformance in one year after the merger. Using value-weighted returns, the

underperformance drops to 1.23% per month, but is still significant. For longer holding horizons,

the underperformance weakens because the impact of the event will gradually decline through

time and there will be more firms in the portfolio that had the event long ago. Nevertheless, the

underperformance of high net-seller acquirers relative to high-net buyer acquirers is still a

significant 1.15% per month for the 24-month holding horizon using equally-weighted returns.

These results are robust to using the Fama-French-Carhart 4-factor model instead of the Fama-

French 3-factor model; high net-seller acquirers in stock mergers significantly underperform

high-net buyer acquirers by 1.47% per month using equally-weighted returns and 1.45% using

value-weighted returns for the 12-month holding horizon (Table 12b).

There is also a negative correlation between acquirer long-run abnormal returns and the degree

of net selling in the target. Table 12c shows that in stock mergers, acquirers that buy net-seller

targets tend to underperform those that buy net-buyer targets. The underperformance is 0.6% per

month (which corresponds to 7.2% per year) using the Fama-French 3-factor model with equal

weights and a 12-month holding horizon. Similar results obtain using the Fama-French-Carhart

4-factor model, which are not presented here for brevity.

The main conclusion from examining acquirer’s calendar-time portfolio abnormal returns is that

there is a negative correlation between acquirer’s post-merger long-run abnormal returns and the

degree of insider net-selling in the acquirer and the target prior to the merger, and this correlation

is stronger in stock mergers. This confirms the earlier findings obtained from raw returns and

buy-and-hold abnormal returns.

36

Overall, the evidence from raw and excess long-run returns presented so far is in line with the

predictions of the misvaluation theory. The most overvalued acquirers have the highest pre-

merger raw and excess returns whereas the most undervalued ones have the lowest. Consistent

with the eventual correction of misvaluation, post-merger raw and excess returns are lower for

overvalued acquirers and higher for undervalued acquirers. These differences are more

pronounced in stock mergers, where the overvaluation is expected to be more severe. Overvalued

targets are more likely to be bought by acquirers with high past excess returns, possibly

overvalued acquirers: acquirers buying net-seller targets have a pre-merger BHAR of -10.3%

compared to -0.6% for acquirers buying net-buyer targets (Table 11b). These findings all point to

the preexisting misvaluation of the acquirers and the eventual correction of it in the long run.

Moreover these results are robust to alternative methods of calculating excess returns.

The univariate evidence presented in this section seems to be consistent with the predictions of

the market misvaluation theory. Next I provide evidence from multivariate tests.

3.4. Multivariate Tests

3.4.1 Method of payment

The most important predictions of market misvaluation theory involve the method of payment

decision in mergers. Therefore I devote this section to analyze whether market misvaluation is a

determinant of the method of payment decision.

Martin (1996) extensively analyzes the determinants of the method of payment decision in

mergers. He finds that the higher the acquirer’s growth opportunities, as measured by Tobin’s Q,

the more likely the acquirer is to use stock to finance an acquisition. He also finds that the

likelihood of stock financing increases with higher pre-acquisition market and acquiring firm

stock returns and decreases with higher cash availability, higher institutional shareholdings and

blockholdings and in tender offers.

37

In order to see the effect of misvaluation on the method of payment decision, I perform

multivariate tests using binary and multinomial logistic regression16 models using a variable

measuring misvaluation and variables that Martin (1996) showed to be important determinants of

the method of payment decision. There are four dependent variables. The first dependent

variable represents the decision between the stock method of payment and cash method of

payment, it is equal to 1 if the merger is financed fully with stock, and equal to 0 if it is financed

fully with cash. The second dependent variable is equal to 1 if the method of payment is mixed

(a combination of cash and stock) and equal to 0 if the method of payment is cash. The third

dependent variable is equal to 1 if the method of payment is stock and 0 if the method of

payment is mixed. The fourth dependent variable takes on 3 possible values; 3 if the method of

payment is stock, 2 if mixed and 1 if cash.

I measure misvaluation both using the continuous NET variable (which is equal to the value of

net purchases during the one-year period prior to the merger divided by the value of the

common-share holdings one year prior to the merger) and using a discrete rank variable called

NETRANK, which takes on 7 values: 1 if the firm is a high net-seller (HS) firm, 2 if the firm is a

medium net-seller (MS) firm, 3 if the firm is a light net-seller (LS) firm, 4 if the firm is a no net

insider activity (NO) firm, 5 if the firm is a light net-buyer (LB) firm, 6 if the firm is a medium

net-buyer (NB) firm , and 7 if the firm is a high net-buyer (HB) firm. By construction, for both

NET and NETRANK variables, lower values represent higher levels of overvaluation. I create

this rank variable for both acquirer and the target. Dong et al. (2003) use book to price ratio

(B/P) of acquirer and target along with the ratio of residual income model value to price (V/P) as

a proxy for measuring overvaluation. Lower values of B/P represent higher levels of

overvaluation. They find support for the market misvaluation theory using B/P, namely they find

that the probability of stock payment increases as B/P decreases. I include B/P in an alternative

specification to see whether the overvaluation rank variables still have explanatory power once

an alternative measure of overvaluation is included.

16 In the multinomial logistic regression, the intercepts are necessary to determining the probability an acquisition will be stock-, mixed-, or cash-financed. For example, to determine the probability of mixed financing as opposed to stock financing, one would subtract the cumulative distribution probability of stock financing (computed using the first intercept) from that of mixed financing (computed using the second intercept).

38

For each dependent variable, there are two model specifications. The first model is the simplest

form, it only includes the acquirer and target misvaluation variables, diversification dummy, log

of target size and log of relative size and some of the variables that Martin (1996) showed to be

important for the method of payment decision, namely acquirer cash holdings, acquirer cash flow

and acquirer leverage. In the second model, book to price ratios (B/P) and pre-merger one-year

returns and volatilities for the acquirer and the target are added to the model to make sure that the

misvaluation variables are not simply proxying for these variables. Since B/P and Tobin’s Q are

very closely related, I do not estimate a separate model using Tobin’s Q instead of B/P as was

done by Martin (1996). All regressions include year dummies as well as 2-digit SIC industry

dummies for both the acquirer and the target based on the classification used by Grinblatt and

Moskowitz (1999). Table 13a presents the results using the continuous NET variable and Table

13b presents the results using the discrete NETRANK variable.

The most important result from Table 13a is that the probability of using stock as a method of

payment increases as target increases its net-selling. Furthermore, this result is robust to the

inclusion of B/P and pre-merger one year return and volatility of the acquirer, all of which are

significant predictors of the stock payment decision. The coefficient for the Target NET variable

is always negative and significant in models 1-3 and 7-8. On the other hand acquirer NET

variable has the wrong sign and does not seem to be related with the method of payment choice.

Other variables have the expected signs, consistent with Martin (1996), the probability of stock

payment increases with acquirer’s past returns. Consistent with the findings of Dong et al. (2003)

the coefficients of acquirer and target B/P variables have negative signs and significantly predict

the likelihood of stock method of payment.

When NETRANK variable is used in the regressions instead of NET, results in Table 13b show

that the likelihood of using stock rather than cash as the method of payment increases with both

acquirer and target overvaluation. The coefficients of acquirer and target NETRANK variables

are negative and significant in model 7. Once other potential measures of misvaluation like B/P

and past returns are included, the explanatory power of acquirer and target NETRANK variables

diminish, but they nevertheless have the correct signs. Target NETRANK variable does a good

39

job in explaining the decision between stock and mixed financing in models 5 and 6, the

coefficients are negative, significant and robust to the inclusion of B/P and past returns.

The evidence in Tables 13a and 13b documents a positive relation between the level of net-

selling in acquirer and target firms and the likelihood of using stock as the method of payment.

This relation is stronger for target net-selling; target NET variable retains its explanatory power

even after the inclusion of alternative measures of misvaluation like B/P and past returns. To the

extent that managerial trades reflect the managers’ inside knowledge about the true value of the

firm, these results can be interpreted as being consistent with the key prediction of market

misvaluation theory of mergers that the likelihood of using stock as a method of payment

increases in acquirer and target overvaluation.

3.4.2 Acquisition Characteristics

Tables 14a and 14b explore the effects of misvaluation on acquirer and target announcement

CARs and bid premium using ordinary least squares regressions. Table 14a presents the results

using the NET variable and Table 14b presents the results using the NETRANK variable. There

are two model specifications. In the first specification, the independent variables are acquirer and

target NET or NETRANK variables, dummy variables for hostile offer, tender offer, cash

payment and stock payment, log of relative size and log of target size. In the second specification

I add B/P ratios of the acquirer and the target to see whether acquirer and target NET and

NETRANK variables contain additional information about misvaluation not captured by the B/P

ratios. As before, all regressions control for year and acquirer and target industry fixed-effects.

Table 14a shows that acquirer announcement CAR decreases with increased insider net-selling in

both acquirer and target firms. The coefficients of acquirer and target NET variables are 0.17 and

0.48 respectively and they are both significant. Moreover, this result is robust to the inclusion of

B/P in the model. To the extent that managerial trading is motivated by inside information about

the value of the firm, these results can be interpreted as evidence that overvalued acquirers and

acquirers that buy overvalued targets get lower announcement period CAR. This interpretation is

consistent with the misvaluation theory and suggests that the market at least partially corrects

40

acquirer overvaluation with the announcement of the acquisition. Acquirer and target net-selling

activity does not explain target announcement CAR when NET variable is used. When

NETRANK variable is used instead in Table 14b, the results show that acquirer insider net-

selling is negatively related to target announcement CAR.

Finally, there seems to be a negative relation between acquirer and target insider net-selling as

measured by NETRANK and bid premium suggesting that highest net-buyer acquirers on

average pay 6.93% (1.16*6) more bid premium than the highest net-seller acquirers and the

highest net-buyer targets receive 6.16% (1.03*6) more bid premium than the highest net-seller

targets. The fact that net-buyer targets receive higher bid premium is in line with the

misvaluation story of Shleifer and Vishny (2003) which suggests that undervalued targets will

resist to bids demanding higher prices thus driving up the bid premium.

To summarize, both univariate and multivariate tests repeatedly point out to differences in the

merger characteristics between the firms grouped by their pre-merger managerial insider trading

patterns. While it is possible that managerial trading does not reflect any inside information, it

seems unlikely that complete non-informed trading could produce the observed differences in the

acquisition characteristics of net-seller and net-buyer firms. Both the magnitude and direction of

the differences are consistent with the view that managerial trading is motivated by inside

information about the value of the firm. Using managerial trading activity as a proxy for

misvaluation, the results are best interpreted as supporting the predictions of market misvaluation

theory as advanced by Rhodes-Kropf and Vishwanathan (2004) and Shleifer and Vishny (2003).

4. Conclusion

This paper examined the effects of market misvaluation on merger activity by measuring firm-

level market misvaluation as the discrepancy between the valuations of the managers and the

market. Data on the personal trades of the managers in their own company stock are used to infer

whether the managers regarded their firms as “overvalued” or “undervalued” prior to the

acquisitions. I first establish that the managers on average trade opportunistically rather than

optimistically prior to the mergers. I then group the firms as overvalued and undervalued

41

according to the net trading activity of the managers during the one year period prior to the

merger. Using my measure of misvaluation, I find consistent differences in the acquisition

characteristics and acquisition decisions of overvalued and undervalued firms. Market

misvaluation explains the method of payment choice, variations in acquirer and target

announcement returns, acquirer pre-merger and post-merger long run returns, and bid premium.

Consistent with the market misvaluation theory of Rhodes-Kropf and Vishwanathan (2004) and

Shleifer and Vishny (2003), the acquirer prefers stock as the method of payment when it is

overvalued and cash when it is undervalued. Acquirers are more likely to buy overvalued targets

for stock. There is an abnormal increase in insider selling by the managers prior to the stock

mergers, further suggesting that acquirers in stock mergers are more likely to be overvalued.

Overvalued acquirers make more diversifying acquisitions, because when they are overvalued,

there is a good chance that their industry is also overvalued. Overvalued acquirers have higher

pre-merger returns but lower post-merger excess returns than undervalued acquirers. They also

have lower announcement returns, because the market corrects for some of the overvaluation at

the announcement of the acquisition. Targets require higher bid premiums when they are

undervalued and are more likely to resist offers from undervalued acquirers. Target

overvaluation results in lower announcement returns for the acquirer, since the market punishes

the acquirer for paying too much for the target. In addition, my results show that target

misvaluation seems to affects the method of payment decision more than acquirer misvaluation.

My results support the theory that market misvaluation affects merger activity.

42

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Roll, Richard, 1986, “The hubris hypothesis of corporate takeovers”, Journal of Business

59, 197-216. Rozeff, Michael S; Zaman, Mir A., 1998, “Overreaction and Insider Trading: Evidence

from Growth and Value Portfolios”, Journal of Finance. Vol. 53 (2), 701-716. Seyhun, H. Nejat, 1986, “Insiders’ Profits, Costs of Trading, and Market Efficiency.”,

Journal of Financial Economics, Vol. 16 (2), 189-212.

Seyhun, H. Nejat, 1988, The information content of aggregate insider trading, Journal of Business 61, 1-24.

Seyhun, H. Nejat. 1990. “Do Bidder Managers Knowingly Pay Too Much for Target

Firms?” Journal of Business, Vol. 63 (4), 439-464. Shleifer, Andrei and Robert W. Vishny, 2003, “Stock Market Driven Acquisitions,” Journal of

Financial Economics, Vol. 70(3), 1-29 Smith, Clifford W., 1986, “Investment Banking and the Capital Acquisition Process,” Journal of

Financial Economics, Vol. 15(1/2), 3-29. Washington Post, Friday, July 19, 2002 , A17

ObservationsAll public mergers in SDC between 1984-2000 where: 2,314 -Both acquirer and target have CRSP data -The acquirer owns less than 5% of the target before the merger -There are no other announcements during the 4-day event window

Acquirer is not in the insiders database -178Target is not in the insider database -132Compustat data is not available -34

Final sample 1,970

This table describes the data sources and catalogs the reasons why observations were deleted. Datasources are abbreviated as follows: CRSP is the stock database at the Center for Research in SecurityPrices at the University of Chicago, SDC is the SDC Platinum Mergers and Acquisitions Database.

Table 1aSample Construction

Mean Median

Acquirer Market Value (million $) 11.46 1.61Target Market Value (million $) 0.82 0.13Relative Size (%) 27.5 11.8

Annoucement Abnormal Returns [-2,+1] (%) Acquirer -1.35 -0.91 Target 18.38 16.48 Combined 1.24 0.83

Method of Payment (%) Stock Only 50.4 Mixed 25.2 Cash Only 24.4

Diversifying Acquisition (%) 12.5

Tender Offers (%) 18.0Hostile Bids (%) 2.0Multiple Bidders (%) 4.2

N 2,136

Table 2aDescriptive Statistics - Merger Data

Sample includes completed merger bids and tender offers where both acquirer and targetwere listed on the NYSE, AMEX, or NASDAQ during 1984-2000.Acquirer and target market values are measured at day -3 relative to the acquisitionannouncement date (day 0). Relative size is the ratio of target market value to acquirermarket value. Acquirer and target cumulative abnormal returns (CAR) are measured overthe four days (-2, 1) around the announcement (day 0) of the acquisition. Combined CARis calculated as the value weighted average of acquirer and target CARs weighted usingday -3 market values of the acquirer and the target. Diversification is defined as acquirerand target not having a common 2-digit SIC code in their first 6 SIC codes.

Figure 1bPercentage of CRSP Market Capitalization Acquired

Figure 1aAnnual Frequency of Mergers by Method of Payment

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

CASH

MIXEDSTOCK

0

20

40

60

80

100

120

140

160

Num

ber o

f Mer

gers

Year

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

CASH

MIXED

STOCK

0

0.005

0.01

0.015

0.02

0.025

0.03

% o

f CR

SP M

arke

t Cap

italiz

atio

n A

cqui

red

Year

PANEL A: Insider Trading by Firm Size

Size DecilesPercentage of Net

BuyersPercentage of Net

Sellers

Average / Median Size

of Net Purchase ($)

Average / Median Size of Purchases through Option Exercises ($)

Average Sizeof Net Purchase (% of common stock holdings)

Average Sizeof Purchases

through Option Exercises

(% of common stock holdings)

1 71% 29% 15,612 2,008 2.69% 0.35%5,489 0

2 66% 34% -5,889 4,501 -0.50% 0.38%5,451 0

3 62% 38% -34,495 12,202 -1.90% 0.67%4,778 0

4 57% 43% -99,546 22,422 -3.68% 0.83%1,617 0

5 52% 48% -204,202 45,602 -5.36% 1.20%-5,450 0

6 46% 54% -421,333 91,050 -7.35% 1.59%-31,710 0

7 41% 59% -814,858 196,424 -9.50% 2.29%-68,187 0

8 35% 65% -1,670,604 416,374 -11.60% 2.89%-174,102 0

9 30% 70% -3,310,664 1,199,913 -13.54% 4.91%-421,398 0

10 21% 79% -14,339,381 8,020,994 -12.31% 6.88%-1,446,919 0

PANEL B: Insider Trading by Managerial Position

Management GroupPercentage of Net

BuyersPercentage of Net

Sellers

Average / Median Size

of Net Purchase ($)

Average / Median Size of Purchases through Option Exercises ($)

Average Sizeof Net Purchase (% of common stock holdings)

Average Sizeof Purchases

through Option Exercises

(% of common stock holdings)

Chairman of the Board 41% 59% -2,491,378 794,056 -5.78% 1.84%-102,660 0

CEO 52% 48% -2,187,363 1,234,598 -7.95% 4.49%2,277 0

Both CEO and CB 43% 57% -6,633,689 3,671,934 -4.09% 2.26%-160,370 0

Directors 61% 39% -1,125,190 289,771 -12.59% 3.24%5,813 0

Officer-Directors 31% 69% -1,293,483 348,725 -9.36% 2.52%-100,144 0

High Level Officers 46% 54% -1,094,414 648,351 -13.93% 8.25%-19,031 0

Mid-Level Officers 25% 75% -1,422,447 1,056,632 -28.66% 21.29%-74,281 0

This table describes the average trading activity of different management groups for the entire 1984-2000 period. The management groups are constructed from the titledescriptions in the IDF database. I exclude the trades of large institutional shareholders and insiders who are not active in the management.

Table 2bDescriptive Statistics - Insider Trading Data

Insider TradingStatus N

Average(%)

Median(%)

Std. Deviation(%)

Minimum(%)

Maximum(%) N

Average(%)

Median(%)

Std. Deviation(%)

Minimum(%)

Maximum(%)

HS 408 -143.7 -64.7 249.9 -2000 -27.8 294 -114.6 -53.1 235.2 -2000 -20.0MS 409 -15.0 -13.9 6.3 -27.7 -6.2 295 -10.9 -10.1 4.1 -19.7 -5.0LS 409 -2.5 -2.3 1.8 -6.1 0 295 -2.0 -1.9 1.4 -5.0 0NO 384 0 0 0.0 0 0 584 0 0 0.0 0 0LB 119 0.5 0.5 0.3 0 1.1 167 0.8 0.8 0.6 0 2.1MB 120 3.0 2.7 1.4 1.1 5.7 167 6.2 5.7 3.1 2.2 13.1HB 121 82.8 17.9 270.5 5.7 2000.0 168 132.1 41.5 321.4 13.2 2000

0.0 0.0All Firms 1970 -28.1 -2.8 146.1 -2000 2000.0 1970 -7.2 0 142.9 -2000 2000

ACQUIRERS TARGETS

This table describes the summary statistics for the variable NET (ratio of dollar net purchases to the value of beginning-of-the-period common stock holdings) across different trading activity categories.Negative values for NET represent net-selling, positive values represent net-buying. Net selling firms (those with NET<0) are sorted into 3 groups by NET: Bottom one-third is named HS (high net-sellers), middle one-third is named MS (medium net-sellers) and top one-third is named LS (light net-sellers). Firms with no trading (NET=0) are named as NO. Net buying firms (those with NET>0) aresorted into 3 groups by NET: Bottom one-third is named LB (low net-buyers), middle one-third is named MB (medium net-buyers) and top one-third is named HB (high net-buyers).

Table 3aSummary statistics for the ratio of net purchases to common share holdings across different trading categories

NET SIZE B/P ROA CF CASH LEV RET1 RET2 VOL1

NET 1SIZE -0.06 1B/P 0.14 -0.18 1ROA -0.08 0.13 -0.17 1CF -0.01 0.02 0.05 0.01 1CASH -0.15 -0.03 -0.22 0.05 -0.21 1LEV 0.13 0.03 0.21 -0.39 0.10 -0.47 1RET1 -0.13 0.10 -0.21 -0.01 -0.04 0.16 -0.04 1RET2 -0.10 0.09 -0.28 -0.01 0.08 0.19 -0.06 0.06 1VOL1 -0.10 -0.03 -0.17 0.16 -0.17 0.49 -0.39 0.11 0.11 1

NET SIZE B/P ROA CF CASH LEV RET1 RET2 VOL1

NET 1SIZE -0.09 1B/P 0.08 -0.09 1ROA -0.03 0.04 -0.05 1CF 0.00 0.01 0.03 0.04 1CASH -0.08 -0.04 -0.23 -0.02 -0.09 1LEV 0.03 0.06 0.08 -0.09 0.04 -0.50 1RET1 -0.08 0.07 -0.08 -0.11 0.02 -0.02 0.03 1RET2 -0.08 0.06 -0.20 -0.06 0.00 -0.04 0.05 -0.02 1VOL1 -0.01 -0.10 0.02 0.06 -0.04 0.40 -0.25 -0.18 -0.26 1

This table describes the pairwise correlations between the variable NET (ratio of net purchases tobeginning-of-the-period common shareholdings) and firm characteristics for acquirer and target firms.SIZE is the market value 3 days prior to acquisition announcement. B/P is the book to price ratio. ROA(Return on assets) = EBIT/assets. CF (Cash flow) = EBITDA/Sales. CASH (Cash holdings) =(cash+marketable securities)/book value of assets. LEV (leverage) = (book value of debt)/(book value ofequity+book value of debt). All accounting variables are measured as of the fiscal year-end ending in thecalendar year preceding the acquisition year. RET1 is the raw stock return in the one year period prior tothe merger anouncement date. RET2 is the raw stock return between two years prior to the announcementdate and one year prior to the announcement date. VOL1 is the annualized volatility of the stock return inthe one year period prior to the merger anouncement date. The sample period is 1984-2000. Correlationcoefficients which are significant at 10% level are shown in italics.

Table 3bPairwise Correlations Between NET and Firm Characteristics

ACQUIRERS

TARGETS

N 1,3 1,5 1,10 1,15 N 1,3 1,5 1,10 1,15

Chairman of the Board 16,468 0.94 1.84 4.21 7.45 30,534 -0.33 -0.91 -3.32 -7.536.88 7.21 6.86 7.12 -3.76 -5.49 -7.94 -10.29

CEO 3,298 -0.33 -1.77 -7.31 -15.09 4,415 -1.52 -3.39 -10.78 -23.37-0.91 -2.62 -4.46 -5.29 -5.18 -5.90 -7.15 -8.71

Both CEO and CB 2,469 0.65 0.97 -0.20 -2.94 3,945 -0.35 -0.99 -4.55 -9.751.69 1.36 -0.11 -0.97 -1.30 -1.84 -3.21 -3.83

Directors 83,722 0.49 0.94 2.48 4.20 89,552 -0.36 -0.92 -3.18 -7.059.53 9.79 10.57 10.35 -6.92 -9.21 -12.64 -15.93

Officer-Directors 14,619 1.04 0.26 5.33 9.54 44,565 -0.12 -0.50 -2.28 -5.597.17 7.95 8.33 8.64 -1.74 -3.79 -6.71 -9.32

High Level Officers 15,989 1.09 2.30 6.07 10.53 27,516 -0.29 -0.77 -3.08 -6.907.61 8.62 9.36 9.44 -3.05 -4.22 -6.67 -8.44

Mid-Level Officers 36,744 1.04 2.16 6.44 12.37 178,541 -0.20 -0.55 -2.10 -4.9811.92 13.26 16.31 18.28 -6.54 -9.38 -14.10 -18.90

ALL INSIDERS 173,309 0.74 1.45 3.83 6.81 379,068 -0.26 -0.71 -2.67 -6.1518.86 20.00 21.63 22.31 -11.28 -16.00 -23.59 -30.75

Management Group Cumulative Abnormal Returns (%) Cumulative Abnormal Returns (%)

Table 4Abnormal Returns on Insider Transactions

This table describes the cumulative abnormal returns for 3, 5, 10 and 15 day event windows following the insider transaction date (day 0). The abnormal returns arecalculated by subtracting the return on the CRSP value weighted index from firm returns during the event window. For each transaction day for each insider, the netvalue of trades by the insider is calculated and the transaction day is labeled as a buy day or a sell day. Taking the transaction day as the event day (day 0), the abnormalreturns are calculated for both buy days and sell days. Average abnormal returns are calculated for each management group by averaging the abnormal returns followingthe buy and sell days of insiders in that group. t-statistics are shown in italics.

PURCHASES SALES

Months Relative Option Exerciseto Announcement Sales Purchases Purchases

(-12,-1) 19.6 0.5 13.33.7 0.2 3.60.00 1.00 1.00

(-9,-1) 15.7 0.5 10.63.0 0.2 2.90.00 1.00 1.00

(-6,-1) 12.1 0.3 7.82.3 0.1 2.00.00 1.00 1.00

(-3,-1) 6.9 0.1 4.31.2 0.1 1.10.00 0.56 1.00

(0) 0.8 0.0 1.00.5 0.0 0.40.01 0.68 1.00

(1,3) 3.1 0.1 3.71.5 0.1 1.10.00 1.00 1.00

(1,6) 7.4 0.3 7.23.0 0.1 2.30.00 1.00 1.00

Table 5Management Trading Activity for 1,279 Acquirer Firms around 2,136 Merger

Announcements(Size and Industry Matched Control Firms)

Open Market

This table describes the trading patterns of the acquirer firms' managers around merger announcements.All trading figures are in millions of 2002 dollars. The first entry is the average value of shares traded,followed by the expected value, computed as the median (in 1,000 replications) of the empiricaldistribution of the average number of shares traded for a random sample of matching control firms.Each acquirer firm is matched to control firms based on size and industry. Firm size is measured usingthe book value and firm industry is determined using the 2-digit SIC code. Each average value of sharestraded just exceeds the cpth (cumulative probability) fractile -shown below the expected value- of itsempirical distribution under the null hypothesis in 1,000 replications. High cp values indicate increasedpurchases or reduced sales, low cp values indicate reduced purchases or increased sales.

Dollar Volume (in millions)

All Mergers (N=2,136)

$ millions $ millions % $ millions $ millions %(1) (2) (3) (4) (5) (6)

Bad Mergers 0.4 0.3 4.1 22.3 9.7 50.30.2 0.2 9.4 3.5 4.1 20.30.99 0.98 0.03 0.00 0.00 0.00

N = 491 N = 252 N = 491 N = 491 N = 252 N = 491

Good Mergers 0.2 0.2 5.6 10.3 5.3 35.80.1 0.1 9.4 2.8 4.8 17.30.87 1.00 0.13 0.14 0.15 0.01

N = 251 N = 126 N = 251 N = 251 N = 126 N = 251

Bad minus Good 0.2 0.1 -1.5 12.0 4.4 14.50.1 0.0 0.0 0.7 -0.7 3.00.97 0.67 0.44 0.00 0.00 0.04

Table 6

Open Market Purchases Open Market Sales

Management Trading Activity prior to "Good" and "Bad" Mergers

This table describes the managerial insider trading patterns on the open market in the one-year period prior to "good"and "bad" mergers. A "bad" merger is one where the 4-day announcement excess return is lower than -5%, whereas a"good" merger is one where the 4-day announcement excess return is higher than 5%. Columns (1), (2), (4) and (5) showthe average dollar value of shares purchased and sold (in millions of 2002 dollars) whereas columns (3) and (6) show theaverage value of shares purchased and sold as a percentage of beginning-of-the-period managerial common stockholdings. In each column, the first number is the average value (or percentage) of shares purchased or sold by themanagers followed by the expected value, computed as the median (in 1,000 replications) of the empirical distributionof the average value (or percentage) of shares purchased or sold for a random sample of matching control firms.Columns (1),(3), (4) and (6) show results using size and industry matched control firms whereas columns (2) and (5) usesize, industry and prior stock return matched control firms. Firm size is measured using the book value, firm industry isdetermined using the 2-digit SIC code and the prior stock return is measured for the one year period prior to theannouncement date. Each average value (or percentage) of shares traded just exceeds the cpth (cumulative probability)fractile -shown below the expected value- of its empirical distribution under the null hypothesis in 1,000 replications.High cp values indicate increased purchases or reduced sales, low cp values indicate reduced purchases or increasedsales.

$ millions $ millions % $ millions $ millions %(1) (2) (3) (4) (5) (6)

Stock Mergers 0.5 0.2 6.3 31.6 17.1 36.60.2 0.3 9.4 3.7 5.7 18.91.00 0.36 0.06 0.00 0.00 0.00

N = 1,077 N = 689 N = 1,077 N = 1,077 N = 689 N = 1,077

Cash Mergers 0.7 1.1 5.3 9.1 8.0 36.30.2 0.3 11.7 3.3 3.0 16.21.00 1.00 0.02 0.03 0.00 0.00

N = 521 N = 302 N = 521 N = 521 N = 302 N = 521

Stock minus Cash -0.2 -0.9 1.0 22.5 9.2 0.30.0 0.0 -2.4 0.5 2.7 2.70.01 0.00 0.74 0.00 0.00 0.74

Table 7

Open Market Purchases Open Market Sales

Management Trading Activity prior to Stock and Cash Mergers

This table describes the managerial insider trading patterns on the open market in the one-year period prior to stock andcash mergers. Columns (1), (2), (4) and (5) show the average dollar value of shares purchased and sold (in millions of2002 dollars) whereas columns (3) and (6) show the average value of shares purchased and sold as a percentage ofbeginning-of-the-period managerial common stock holdings. In each column, the first number is the average value (orpercentage) of shares purchased or sold by the managers followed by the expected value, computed as the median (in1,000 replications) of the empirical distribution of the average value (or percentage) of shares purchased or sold for arandom sample of matching control firms. Columns (1), (3), (4) and (6) show results using size and industry matchedcontrol firms whereas columns (2) and (5) use size, industry and prior stock return matched control firms. Firm size ismeasured using the book value, firm industry is determined using the 2-digit SIC code and the prior stock return ismeasured for the one year period prior to the announcement date. Each average value (or percentage) of shares tradedjust exceeds the cpth (cumulative probability) fractile -shown below the expected value- of its empirical distributionunder the null hypothesis in 1,000 replications. High cp values indicate increased purchases or reduced sales, low cpvalues indicate reduced purchases or increased sales.

eeeeeeeeeeeeeeeeee

(Size and Industry Matched Control Firms).

Months Relative to Merger Announcement Month

Months Relative to Merger Announcement Month

Figure 2 Managerial Insider Purchases and Sales around "Good" and "Bad" Mergers.

0.000.100.200.300.400.500.600.700.800.901.00

Purc

hase

s ($

mill

ions

)

Purchases

Bad Mergers 0.09 0.15 0.12 0.05 0.02 0.14 0.28

Good Mergers 0.06 0.05 0.06 0.07 0.01 0.13 0.04

Diff.Bad-Good 0.03 0.10 0.06 -0.02 0.00 0.01 0.24

(-12,-10) (-9,-7) (-6,-4) (-3,-1) (0) (1,3) (4,6)*** ** *

**

*

** *****

*

***

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

Sale

s ($

mill

ions

)

Sales

Bad Mergers 3.8 3.8 6.6 8.1 0.4 3.0 4.1

Good Mergers 2.6 3.3 2.2 2.2 1.1 5.6 3.2

Diff.Bad-Good 1.2 0.5 4.4 5.9 -0.7 -2.7 0.9

(-12,-10) (-9,-7) (-6,-4) (-3,-1) (0) (1,3) (4,6)*** ****** ***

**

*****

**

*** << <<***

*****

This figure shows the average value of shares purchased and sold by the acquirer firms' managers during 3-month intervals around "good"and "bad" merger announcements. A "bad" merger is one where the 4-day announcement excess return is lower than -5%, whereas a "good"merger is one where the 4-day announcement excess return is higher than 5%. All trading figures are in millions of 2002 dollars. ***,**and *represent whether the observed purchase or sale activity is significantly higher than its expected value whereas <<<, << and < representwhether the observed purchase or sale activity is significantly lower than its expected value for 1%, 5% and 10% significance levels,respectively. The expected value is computed as the median (in 1,000 replications) of the empirical distribution of the average number ofshares traded for a random sample of matching control firms. Each acquirer firm is matched to control firms based on size and industry.Firm size is measured using the book value and firm industry is determined using the 2-digit SIC code. Significance levels are then computedby comparing the average value of shares traded to its empirical distribution under the null hypothesis in 1,000 replications.

***

eeeeeeeeeeeeeeeeee

(Size and Industry Matched Control Firms).

Months Relative to Merger Announcement Month

Months Relative to Merger Announcement Month

Figure 3Managerial Insider Purchases and Sales around "Good" and "Bad" Mergers as a % of Common Stock Holdings.

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

Purc

hase

s (%

of c

omm

on s

tock

hol

ding

s)

Purchases

Bad Mergers 6.6 4.9 4.2 3.4 3.0 12.5 5.4

Good Mergers 5.8 11.8 6.1 8.1 5.9 3.1 2.3

Diff.Bad-Good 0.8 -6.9 -1.8 -4.7 -2.9 9.4 3.1

(-12,-10) (-9,-7) (-6,-4) (-3,-1) (0) (1,3) (4,6)

*

***

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

Sale

s (%

of c

omm

on s

tock

ho

ldin

gs)

Sales

Bad Mergers 13.3 20.7 30.3 23.9 4.0 14.3 17.7

Good Mergers 19.4 24.9 16.7 24.9 17.6 10.7 27.4

Diff.Bad-Good -6.1 -4.2 13.6 -1.1 -13.6 3.6 -9.7

(-12,-10) (-9,-7) (-6,-4) (-3,-1) (0) (1,3) (4,6)***** **

** **

**

This figure shows the average percentage of beginning-of-the-period common stock holdings purchased and sold by the acquirer firms'managers during 3-month intervals around "good" and "bad" merger announcements. A "bad" merger is one where the 4-day announcementexcess return is lower than -5%, whereas a "good" merger is one where the 4-day announcement excess return is higher than 5%. ***,**and* represent whether the observed purchase or sale activity is significantly higher than its expected value whereas <<<, << and < representwhether the observed purchase or sale activity is significantly lower than its expected value for 1%, 5% and 10% significance levels,respectively. The expected value is computed as the median (in 1,000 replications) of the empirical distribution of the average number ofshares traded for a random sample of matching control firms. Each acquirer firm is matched to control firms based on size and industry.Firm size is measured using the book value and firm industry is determined using the 2-digit SIC code. Significance levels are then computedby comparing the average value of shares traded to its empirical distribution under the null hypothesis in 1,000 replications.

*

<

< <

<< <<

*** * **

Figure 4b

Distribution of Insider-Level Managerial Trading prior to Good and Bad Mergers

Figure 4a

Distribution of Firm-Level Managerial Trading prior to Good and Bad Mergers

12 Months Prior to Merger Announcement

23.2%

18.9%

22.7%

18.2%

13.3%

1.6% 2.0%

26.4%

4.4%

2.4%

15.6%

13.6%

22.4%

15.2%

0%

5%

10%

15%

20%

25%

30%

35%

40%

Heavy Selling <-30%

Moderate Selling-10%, -30%

Light Selling 0% , -10%

No trading 0

Light Buying 0% , 10%

Moderate Buying10%, 30%

Heavy Buying >30%

Net Purchase as a % of beginning common stock holdings

% o

f acq

uire

rs

Bad MergersGood Mergers

12 Months Prior to Merger Announcement

26.0%

6.8%4.5%

52.2%

4.0%1.8%

4.6%

57.7%

2.8%

6.7%

19.4%

5.9%3.7% 3.8%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Heavy Selling <-30%

Moderate Selling-10%, -30%

Light Selling 0% , -10%

No trading 0

Light Buying 0% , 10%

Moderate Buying10%, 30%

Heavy Buying >30%

Net Purchase as a % of beginning common stock holdings

% o

f all

insi

ders

in a

typi

cal a

cqui

rer

Bad MergersGood Mergers

6 Months Prior to Merger Announcement

17.5%16.4%

23.4%

26.1%

12.1%

2.1% 2.5%

39.7%

2.8%4.5%

11.7% 11.3%

19.8%

10.1%

0%

5%

10%

15%

20%

25%

30%

35%

40%

Heavy Selling <-30%

Moderate Selling-10%, -30%

Light Selling 0% , -10%

No trading 0

Light Buying 0% , 10%

Moderate Buying10%, 30%

Heavy Buying >30%

Net Purchase as a % of beginning common stock holdings

% o

f acq

uire

rs

Bad MergersGood Mergers

6 Months Prior to Merger Announcement

16.1%

5.2% 3.8%

69.1%

2.7%1.0% 2.0%

78.9%

1.1%2.7%

10.0%

2.6% 3.1%1.6%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Heavy Selling <-30%

Moderate Selling-10%, -30%

Light Selling 0% , -10%

No trading 0

Light Buying 0% , 10%

Moderate Buying10%, 30%

Heavy Buying >30%

Net Purchase as a % of beginning common stock holdings

% o

f all

insi

ders

in a

typi

cal a

cqui

rer

Bad MergersGood Mergers

eeeeeeeeeeeeeeeeee

(Size and Industry Matched Control Firms).

Months Relative to Merger Announcement Month

Months Relative to Merger Announcement Month

Figure 5Managerial Insider Purchases and Sales around Stock and Cash Mergers.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Purc

hase

s ($

mill

ions

)

Purchases

Stock Mergers 0.05 0.32 0.06 0.05 0.02 0.13 0.15

Cash Mergers 0.05 0.04 0.60 0.06 0.04 0.11 0.06

Diff.Stock-Cash 0.01 0.28 -0.54 -0.01 -0.02 0.02 0.09

(-12,-10) (-9,-7) (-6,-4) (-3,-1) (0) (1,3) (4,6)

**

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Sale

s ($

mill

ions

)

Sales

Stock Mergers 6.5 5.2 8.4 11.6 1.1 4.1 6.6

Cash Mergers 1.8 2.6 2.7 2.0 0.8 2.4 2.6

Diff.Stock-Cash 4.6 2.6 5.7 9.6 0.3 1.7 4.0

(-12,-10) (-9,-7) (-6,-4) (-3,-1) (0) (1,3) (4,6)****** ***

* *

***

This figure shows the average value of shares purchased and sold by the acquirer firms' managers during 3-month intervals around stock andcash merger announcements. All trading figures are in millions of 2002 dollars. ***,** and * represent whether the observed purchase orsale activity is significantly higher than its expected value whereas <<<, << and < represent whether the observed purchase or sale activity issignificantly lower than its expected value for 1%, 5% and 10% significance levels, respectively. The expected value is computed as themedian (in 1,000 replications) of the empirical distribution of the average number of shares traded for a random sample of matching controlfirms. Each acquirer firm is matched to control firms based on size and industry. Firm size is measured using the book value and firmindustry is determined using the 2-digit SIC code. Significance levels are then computed by comparing the average value of shares traded toits empirical distribution under the null hypothesis in 1,000 replications.

*

*** *** ***

<<<

** *

*** ***

***

***

*** ***

*

*** *** *** *** ***

eeeeeeeeeeeeeeeeee

(Size and Industry Matched Control Firms).

Months Relative to Merger Announcement Month

Months Relative to Merger Announcement Month

Figure 6Managerial Insider Purchases and Sales around Stock and Cash Mergers as a % of Common Stock Holdings.

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

Purc

hase

s(%

of c

omm

on s

tock

hol

ding

s)

Purchases

Stock Mergers 4.3 2.9 2.4 6.2 4.0 2.4 3.8

Cash Mergers 18.2 4.3 7.8 3.4 3.1 12.9 3.3

Diff.Stock-Cash -13.9 -1.4 -5.4 2.8 0.9 -10.6 0.5

(-12,-10) (-9,-7) (-6,-4) (-3,-1) (0) (1,3) (4,6)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

Sale

s (%

of c

omm

on s

tock

ho

ldin

gs)

Sales

Stock Mergers 12.8 19.3 20.5 13.5 10.2 14.7 20.1

Cash Mergers 20.3 23.0 19.4 30.4 9.6 23.1 16.9

Diff.Stock-Cash -7.5 -3.8 1.1 -16.9 0.6 -8.4 3.2

(-12,-10) (-9,-7) (-6,-4) (-3,-1) (0) (1,3) (4,6)******

*** **

<<<

This figure shows the average percentage of beginning-of-the-period common stock holdings purchased and sold by the acquirer firms'managers during 3-month intervals around stock and cash merger announcements. ***,** and * represent whether the observed purchase orsale activity is significantly different from its expected value for 1%, 5% and 10% significance levels, respectively. The expected value iscomputed as the median (in 1,000 replications) of the empirical distribution of the average number of shares traded for a random sample ofmatching control firms. Each acquirer firm is matched to control firms based on size and industry. Firm size is measured using the bookvalue and firm industry is determined using the 2-digit SIC code. Significance levels are then computed by comparing the average value ofshares traded to its empirical distribution under the null hypothesis in 1,000 replications.

<<< <

<<<

*** **

*

* <<

***

*

<< <<<

*** ***

<< <<< <<<

*** <

<<<

Figure 7b

Distribution of Insider-Level Managerial Trading prior to Stock and Cash Mergers

Figure 7a

Distribution of Firm-Level Managerial Trading prior to Stock and Cash Mergers

12 Months prior to the Merger Announcement

20.7%

17.4%

27.7%

16.3%

14.7%

1.8% 1.4%

22.4%

3.1% 2.7%

16.6% 16.0%

26.8%

12.5%

0%

5%

10%

15%

20%

25%

30%

35%

Heavy Selling <-30%

Moderate Selling -10% , -30%

Light Selling 0% , -10%

No trading 0

Light Buying 0% , 10%

Moderate Buying 10% , 30%

Heavy Buying >30%

Net Purchase as a % of beginning common stock holdings

% o

f acq

uire

rs

Stock MergersCash Mergers

12 Months prior to the Merger Announcement

23.2%

6.7%4.5%

54.8%

4.0%2.4%

4.5%7.4%

18.8%

5.9%3.3% 2.9% 2.9%

58.7%

0%

10%

20%

30%

40%

50%

60%

70%

Heavy Selling <-30%

Moderate Selling -10% , -30%

Light Selling 0% , -10%

No trading 0

Light Buying 0% , 10%

Moderate Buying 10% , 30%

Heavy Buying >30%

Net Purchase as a % of beginning common stock holdings

% o

f all

insi

ders

in a

typi

cal a

cqui

rer

Stock MergersCash Mergers

6 Months prior to the Merger Announcement

14.2% 14.0%

32.0%

24.0%

12.7%

1.5% 1.6%

30.8%

3.5% 2.9%

12.2%11.4%

25.0%

14.3%

0%

5%

10%

15%

20%

25%

30%

35%

Heavy Selling <-30%

Moderate Selling -10% , -30%

Light Selling 0% , -10%

No trading 0

Light Buying 0% , 10%

Moderate Buying 10% , 30%

Heavy Buying >30%

Net Purchase as a % of beginning common stock holdings

% o

f acq

uire

rs

Stock MergersCash Mergers

6 Months prior to the Merger Announcement

14.4%

5.4% 4.4%

69.1%

3.2%1.4% 2.1%

3.9%

11.5%

3.6% 2.7% 2.2% 1.8%

74.4%

0%

10%

20%

30%

40%

50%

60%

70%

Heavy Selling <-30%

Moderate Selling -10% , -30%

Light Selling 0% , -10%

No trading 0

Light Buying 0% , 10%

Moderate Buying 10% , 30%

Heavy Buying >30%

Net Purchase as a % of beginning common stock holdings

% o

f all

insi

ders

in a

typi

cal a

cqui

rer

Stock MergersCash Mergers

AcquirerInsider Trading

Status NCash

%Stock

%

TenderOffer

%

Hostile Offer(%)

MultipleBidders

(%)

Bid Premium

(%)

AcquirerMarketValue

($ millions)

TargetMarket Value

($ millions)

Relative Size(%)

DiversifyingAcquisition(2-digit SIC)

Acquirer Ann.

CAR (%)

TargetAnn.

CAR (%)

CombinedAnn.

CAR (%)

HS 408 22.3 56.9 17.4 2.0 2.9 34.7 25,388 1,209 17.2 12.5 -2.5 18.6 -0.2MS 409 26.2 52.6 19.3 1.7 5.6 37.1 16,478 769 20.6 14.2 -1.4 19.5 0.9LS 409 23.2 52.8 17.1 1.7 3.2 35.0 8,381 695 21.9 13.4 -1.3 18.7 1.0NO 384 26.8 42.4 22.7 2.1 6.0 36.1 4,435 788 42.7 13.0 -1.0 16.8 2.2LB 119 17.6 56.3 14.3 2.5 3.4 34.1 4,766 644 46.9 6.7 -1.0 17.4 1.8MB 120 19.2 52.5 10.0 0.8 5.0 42.7 3,231 231 24.7 11.7 -0.9 19.4 1.8HB 121 31.4 36.4 21.5 5.0 5.8 46.1 2,560 430 38.8 10.7 -0.2 21.6 3.8

HS-HB -9.1 20.5 -4.1 -3.0 -2.8 -11.5 22,828 779.0 -21.6 1.8 -2.3 -3.0 -4.0-2.05 -1.02 -1.43 -1.24 -2.2 6.01 2.95 -4.75 0.51 -2.96 -1.45 -4.67

TS 1,226 23.9 54.1 17.9 1.8 3.9 35.6 16,742 891 19.9 13.4 -1.7 18.9 0.6TB 360 22.8 48.3 15.3 2.8 4.7 41.0 3,513 434 36.8 9.7 -0.7 19.5 2.5

TS-TB 1.1 5.7 2.7 -1.0 -0.8 -5.4 13,229 456.2 -16.9 3.7 -1.1 -0.5 -1.90.44 1.92 1.17 -1.03 -0.64 -2.15 8.17 3.61 -2.47 1.98 -2.56 -0.46 -4.46

Table 8Acquisition Characteristics Grouped by Acquirer's Managerial Trading Activity

Panel A: All Acquisitions

This table describes mean acquisition characteristics sorted by acquirer's managerial trading activity. Acquirer managerial trading activity is measured by the variable NET, which is the ratio of dollar netpurchases of all management insiders to the value of beginning-of-the-period common stock holdings. Acquirer is a net seller if NET<0 , a net buyer if NET>0 and a non-trading firm if NET=0. Net selleracquirers are sorted by the variable NET and are categorized into 3 groups. The bottom one-third is called High Net-Sellers (HS), the middle group is called Medium Net-Sellers (MS) and the top one-third iscalled Light Net-Sellers (LS). Acquirers with no managerial trading are labeled as NO. Net buyer acquirers are also sorted by the variable NET and are categorized into 3 groups. The bottom one-third is calledLight Net-Buyers (LB), the middle group is called Medium Net-Buyers (MB) and the top one-third is called High Net-Buyers (HB). As a result we have the following ordering ranging from High Net-Sellers (mostovervalued) to High Net-Buyers (most undervalued): HS, MS, LS, NO, LB, MB and HB. Summary categories Total Net-Sellers (TS) and Total Net-Buyers (TB) include the firms which are in HS, MS or LSgroups and LB, MB or HB groups respectively. Acquirer and target market values are measured at day -3 relative to the acquisition announcement date (day 0). Relative size is the ratio of target market value toacquirer market value. Bid premium is the ratio of the bid price offered by the acquirer to the target stock price 3 days prior to the announcement of the takeover bid. Acquirer and target cumulative abnormalreturns (CAR) are measured over the four days (-2, 1) around the announcement (day 0) of the acquisition. Combined CAR is calculated as the value weighted average of acquirer and target CARs weighted usingday -3 market values of the acquirer and the target. Diversification is defined as acquirer and target not having a common 2-digit SIC code in their first 6 SIC codes. Sample includes completed merger bids andtender offers where both acquirer and target were listed on the NYSE, AMEX, or NASDAQ during 1984-2000. HS-HB and TS-TB denote difference in means between ranks HS and HB and TS and TB, and theirsignificance is tested using a two-sample t-test. Panel A reports results for all acquisitons, Panel B for cash acqusitions and Panel C for stock acqusitions. t-statistics are shown in italics.

AcquirerInsider Trading

Status NCash

%Stock

%

TenderOffer

%

Hostile Offer(%)

MultipleBidders

(%)

Bid Premium

(%)

AcquirerMarketValue

($ millions)

TargetMarket Value

($ millions)

Relative Size(%)

DiversifyingAcquisition(2-digit SIC)

Acquirer Ann.

CAR (%)

TargetAnn.

CAR (%)

CombinedAnn.

CAR (%)

HS 232 - - 0.4 0.4 1.3 34.4 23,793 1,266 18.7 11.2 -4.0 17.8 -1.5MS 215 - - 0.5 0.5 2.3 33.2 20,616 1,046 21.4 10.7 -2.1 17.9 0.1LS 216 - - 1.4 0.5 0.5 33.9 7,684 745 16.3 11.1 -1.3 16.9 0.5NO 163 - - 0.6 0.6 2.5 25.1 3,751 1,319 36.0 11.0 -2.6 11.3 -0.5LB 67 - - - - 3.0 33.8 4,345 679 57.2 4.5 -1.1 15.2 0.9MB 63 - - - - 3.2 37.9 2,835 242 25.1 6.3 -0.5 15.6 1.6HB 44 - - - 2.3 2.3 38.6 2,502 404 32.9 2.3 -1.4 12.6 0.8

HS-HB - - 0.4 -1.8 -1.0 -4.2 21,291 861.8 -14.2 8.9 -2.7 5.2 -2.3- - 0.99 -0.8 -0.41 -0.47 4.13 2.14 -2.62 2.89 -2.13 2.22 -2.13

TS 663 - - 0.8 0.5 1.4 33.8 17,514 1,025 18.8 11.0 -2.5 17.6 -0.3TB 174 - - - 0.6 2.9 36.5 3,332 451 39.4 4.6 -1.0 14.7 1.2

TS-TB - - 0.8 -0.1 -1.5 -2.7 14,182 573.6 -20.6 6.4 -1.6 2.9 -1.5- - 2.24 -0.18 -1.12 -0.79 6.07 2.73 -1.5 3.2 -2.66 2.28 -2.82

HS 91 - - 58.2 3.3 4.4 36.6 30,596 253 9.4 22.0 0.1 24.0 1.7MS 107 - - 57.0 3.7 9.3 39.6 18,068 311 11.1 22.4 0.1 24.0 2.0LS 95 - - 53.7 4.2 7.4 35.3 10,006 356 22.1 21.1 -0.5 21.3 1.7NO 103 - - 58.3 4.9 7.8 42.7 6,313 230 37.2 15.5 1.8 26.6 5.2LB 21 - - 52.4 4.8 4.8 32.0 8,865 548 28.6 14.3 -1.1 21.9 3.2MB 23 - - 34.8 - 4.3 51.5 952 76 26.1 21.7 -1.0 21.9 2.3HB 38 - - 50.0 5.3 10.5 57.3 3,653 529 36.7 23.7 0.8 33.5 6.2

HS-HB - - 8.2 -2.0 -6.1 -20.7 26,943 -275.9 -27.3 -1.7 -0.7 -9.5 -4.5- - 0.85 -0.48 -1.12 -2.44 3.18 -1.32 -3.54 -0.21 -0.58 -2.16 -3.15

TS 293 - - 56.3 3.8 7.2 37.3 19,345 308 14.1 21.8 -0.1 23.1 1.8TB 82 - - 46.3 3.7 7.3 49.2 4,230 407 31.6 20.7 -0.2 27.3 4.4

TS-TB - - 10.0 0.1 -0.1 -11.9 15,114 -99.2 -17.5 1.1 0.1 -4.2 -2.5- - 1.6 0.04 -0.04 -2.12 4.41 -0.73 -3.69 0.22 0.16 -1.55 -2.81

Table 8 (continued)Acquisition Characteristics Grouped by Acquirer's Managerial Trading Activity

Panel B: Stock Acquisitions

Panel C: Cash Acquisitions

TargetInsider Trading

Status NCash

%Stock

%

TenderOffer

%

Hostile Offer(%)

MultipleBidders

(%)

Bid Premium

(%)

AcquirerMarketValue

($ millions)

TargetMarket Value

($ millions)

Relative Size(%)

DiversifyingAcquisition(2-digit SIC)

Acquirer Ann.

CAR (%)

TargetAnn.

CAR (%)

CombinedAnn.

CAR (%)

HS 294 21.4 55.8 16.3 2.0 5.4 30.9 18,571 1,770 29.2 12.2 -2.9 16.9 0.2MS 295 22.4 52.5 19.0 3.1 4.7 34.2 10,088 1,253 27.6 12.9 -2.0 18.6 1.0LS 295 23.4 52.9 20.7 2.7 3.4 34.1 12,104 857 28.5 11.5 -1.2 19.1 1.7NO 584 26.4 49.0 17.5 1.2 3.8 39.1 11,865 399 21.9 15.2 -1.3 18.4 1.0LB 167 21.0 50.9 22.2 3.6 4.2 38.6 13,337 372 22.3 11.4 -0.7 21.7 2.3MB 167 23.4 50.9 12.6 1.2 3.6 34.2 9,761 394 27.1 7.2 -0.4 15.6 1.7HB 168 31.0 41.1 22.0 1.2 7.7 47.9 4,169 280 46.5 12.5 -0.2 21.5 2.2

HS-HB -9.5 14.7 -5.7 0.9 -2.3 -17.0 14,402 1490.7 -17.3 -0.3 -2.7 -4.7 -2.0-2.21 3.06 -1.47 0.71 -0.93 -4.07 4.75 3.85 0.97 -0.08 -3.58 -2.37 -2.72

TS 884 22.4 53.7 18.7 2.6 4.5 33.1 13,582 1,293 28.4 12.2 -2.1 18.2 1.0TB 502 25.1 47.6 18.9 2.0 5.2 40.2 9,079 348 32.0 10.4 -0.4 19.6 2.0

TS-TB -2.7 6.1 -0.3 0.6 -0.7 -7.1 4,503 944.8 -3.5 1.9 -1.6 -1.4 -1.1-1.13 2.19 -0.11 0.74 -0.53 -3.31 1.77 5.41 -0.56 1.06 -4.07 -1.36 -2.66

Table 9Acquisition Characteristics Grouped by Target's Managerial Trading Activity

Panel A: All Acquisitions

This table describes mean acquisition characteristics sorted by target’s managerial trading activity. Target managerial trading activity is measured by the variable NET, which is the ratio of dollar net purchases ofall management insiders to the value of beginning-of-the-period common stock holdings. Target is a net seller if NET<0, a net buyer if NET>0 and a non-trading firm if NET=0. Net seller targets are sorted by thevariable NET and are categorized into 3 groups. The bottom one-third is called High Net-Sellers (HS), the middle group is called Medium Net-Sellers (MS) and the top one-third is called Light Net-Sellers (LS).Targets with no managerial trading are labeled as NO. Net buyer targets are also sorted by the variable NET and are categorized into 3 groups. The bottom one-third is called Light Net-Buyers (LB), the middlegroup is called Medium Net-Buyers (MB) and the top one-third is called High Net-Buyers (HB). As a result we have the following ordering ranging from High Net-Sellers (most overvalued) to High Net-Buyers(most undervalued): HS, MS, LS, NO, LB, MB and HB. Summary categories Total Net-Sellers (TS) and Total Net-Buyers (TB) include the firms which are in HS, MS or LS groups and LB, MB or HB groupsrespectively. Acquirer and target market values are measured at day -3 relative to the acquisition announcement date (day 0). Relative size is the ratio of target market value to acquirer market value. Bid premiumis the ratio of the bid price offered by the acquirer to the target stock price 3 days prior to the announcement of the takeover bid. Acquirer and target cumulative abnormal returns (CAR) are measured over the fourdays (-2, 1) around the announcement (day 0) of the acquisition. Combined CAR is calculated as the value weighted average of acquirer and target CARs weighted using day -3 market values of the acquirer andthe target. Diversification is defined as acquirer and target not having a common 2-digit SIC code in their first 6 SIC codes. Sample includes completed merger bids and tender offers where both acquirer andtarget were listed on the NYSE, AMEX, or NASDAQ during 1984-2000. HS-HB and TS-TB denote difference in means between ranks HS and HB and TS and TB, and their significance is tested using a two-sample t-test. Panel A reports results for all acquisitons, Panel B for cash acqusitions and Panel C for stock acqusitions. t-statistics are shown in italics.

TargetInsider Trading

Status NCash

%Stock

%

TenderOffer

%

Hostile Offer(%)

MultipleBidders

(%)

Bid Premium

(%)

AcquirerMarketValue

($ millions)

TargetMarket Value

($ millions)

Relative Size(%)

DiversifyingAcquisition(2-digit SIC)

Acquirer Ann.

CAR (%)

TargetAnn.

CAR (%)

CombinedAnn.

CAR (%)

HS 164 - - 1.2 1.8 3.0 28.7 21,526 2,454 27.2 10.4 -5.0 14.4 -2.0MS 155 - - 0.6 - 0.6 29.9 10,582 1,615 25.3 11.0 -3.3 16.0 -0.6LS 156 - - 0.6 - 1.3 30.8 14,909 888 26.2 9.0 -1.9 16.3 0.3NO 286 - - 0.7 - 1.7 34.5 12,849 334 20.6 13.6 -1.7 16.0 -0.1LB 85 - - - 1.2 2.4 32.8 12,298 401 18.4 3.5 -0.4 19.5 1.7MB 85 - - - 1.2 2.4 30.1 2,541 346 23.2 3.5 -0.8 12.0 0.5HB 69 - - - - 1.4 51.0 5,376 326 48.1 8.7 -0.7 20.7 1.4

HS-HB - - 1.2 1.8 1.6 -22.3 16,150 2127.2 -20.9 1.7 -4.2 -6.3 -3.4- - 1.41 1.73 0.8 -3.03 3.39 3.11 0.61 0.38 -3.48 -2.05 -3.21

TS 475 - - 0.8 0.6 1.7 29.8 15,782 1,666 26.3 10.1 -3.4 15.5 -0.8TB 239 - - 0.8 2.1 37.1 6,830 360 28.7 5.0 -0.6 17.2 1.2

TS-TB - - 0.8 -0.2 -0.4 -7.3 8,952 1305.8 -2.4 5.1 -2.8 -1.6 -1.9- - 2.01 -0.29 -0.36 -2.4 2.67 4.25 -0.23 2.57 -4.78 -1.18 -3.7

HS 63 - - 54.0 1.6 7.9 33.1 20,829 479 16.0 20.6 0.5 21.4 2.6MS 66 - - 63.6 12.1 13.6 45.4 12,153 399 22.4 19.7 0.3 29.5 3.5LS 69 - - 62.3 2.9 5.8 38.8 12,829 428 25.3 21.7 0.5 25.5 3.4NO 154 - - 53.2 3.2 4.5 37.9 11,820 188 22.0 20.8 0.0 23.9 2.5LB 35 - - 71.4 5.7 8.6 38.7 29,678 275 28.9 28.6 -0.7 22.5 2.5MB 39 - - 41.0 2.6 5.1 46.8 15,541 349 28.3 15.4 0.9 21.5 3.4HB 52 - - 40.4 - 9.6 49.7 3,854 172 15.8 15.4 0.8 26.7 3.8

HS-HB - - 13.6 1.6 -1.7 -16.6 16,976 306.5 0.2 5.3 -0.2 -5.3 -1.2- - 1.44 0.99 -0.32 -2.43 2.56 2.23 -0.04 0.71 -0.23 -1.28 -1.02

TS 198 - - 60.1 5.6 9.1 39.2 15,149 434 21.4 20.7 0.4 25.5 3.2TB 126 - - 49.2 2.4 7.9 45.8 14,645 256 23.3 19.0 0.4 23.9 3.3

TS-TB - - 10.9 3.2 1.2 -6.6 505 178.6 -1.9 1.7 0.1 1.6 -0.1- - 1.93 1.49 0.36 -1.48 0.08 1.94 -0.35 0.36 0.09 0.64 -0.16

Panel C: Cash Acquisitions

Table 9 (continued)Acquisition Characteristics Grouped by Target's Managerial Trading Activity

Panel B: Stock Acquisitions

AcquirerInsider Trading

Status N

Acquirerpre-merger1-year total

return (t-2,t-1)

(%) N

Acquirerpre-merger1-year total

return (t-1,t) (%)

Acquirerpost-merger1-year total

return (t,t+1)

(%) N

Targetpre-merger1-year total

return (t-2,t-1)

(%) N

Targetpre-merger1-year total

return (t-1,t) (%)

HS 408 34.2 408 41.2 -5.3 370 5.1 408 19.4MS 409 25.3 409 24.5 2.3 392 7.5 409 16.8LS 409 20.9 409 18.3 8.9 386 11.6 409 10.6NO 284 16.1 384 18.2 -10.8 353 3.1 384 13.8LB 119 17.7 119 9.1 10.6 118 8.2 119 12.8MB 120 19.7 120 12.4 11.1 119 9.7 120 4.5HB 121 11.8 121 8.1 13.0 116 4.2 121 -0.2

HS-HB 22.5 33.1 -18.2 0.9 19.65.75 8.07 -3.57 0.17 3.87

TS 1,226 26.8 1,226 28.0 2.0 1,148 8.1 1,226 15.6TB 360 16.4 360 9.9 11.6 353 7.4 360 5.6

TS-TB 10.5 18.1 -9.6 0.7 10.04.62 7.96 -3.77 0.29 3.35

HS 232 40.1 232 53.6 -11.4 204 7.7 232 26.4MS 215 26.4 215 30.4 -1.0 203 11.2 215 23.3LS 216 22.5 216 23.0 5.1 202 17.5 216 13.7NO 110 19.8 163 26.9 -29.4 146 6.4 163 20.7LB 67 19.5 67 13.1 4.5 66 13.3 67 14.6MB 63 22.0 63 21.5 10.1 63 15.1 63 8.1HB 44 15.2 44 13.5 23.6 40 15.1 44 6.4

HS-HB 24.9 40.1 -35.0 -7.4 20.03.29 6.36 -5.12 -1.04 2.68

TS 663 29.9 663 36.1 -2.6 609 12.1 663 21.2TB 174 19.3 174 16.2 11.4 169 14.4 174 10.2

TS-TB 10.6 19.9 -14.0 -2.3 11.03.66 5.85 -4.06 -0.69 2.55

HS 91 28.7 91 18.9 7.0 88 1.3 91 10.7MS 107 28.3 107 14.0 6.9 103 -1.3 107 5.8LS 95 20.7 95 12.0 17.0 93 3.5 95 8.3NO 80 10.6 103 9.5 6.1 96 -4.6 103 15.9LB 21 10.9 21 1.3 25.3 21 0.7 21 18.1MB 23 2.6 23 7.9 24.5 22 -9.1 23 -2.8HB 38 3.6 38 0.9 8.1 38 -13.6 38 -7.2

HS-HB 25.1 18.0 -1.1 14.9 17.93.89 2.5 -0.11 1.6 1.69

TS 293 25.9 293 14.9 10.2 284 1.1 293 8.1TB 82 5.2 82 3.0 17.1 81 -8.6 82 0.5

TS-TB 20.8 11.9 -6.9 9.7 7.64.61 2.92 -1.35 1.73 1.25

Panel A: All Acquisitions

Panel B: Stock Acquisitions

Panel C: Cash Acquisitions

Table 10aAcquirer and Target Pre-Merger and Post-Merger Stock Returns

This table shows acquirer and target pre-merger and post-merger one-year stock returns sorted by acquirer's managerial trading activity. Acquirermanagerial trading activity is ranked from High Net-Sellers (most overvalued) to High Net-Buyers (most undervalued). Summary categories TotalNet-Sellers (TS) and Total Net-Buyers (TB) include the firms which are in HS, MS or LS groups and LB, MB or HB groups respectively. Returnsare calculated for the one year period between two years before the announcement date and one-year before the announcement date (t-2,t-1) andone-year before the announcement date and the announcement date(t-1,t). Panel A reports results for all acquisitons, Panel B for cash acqusitionsand Panel C for stock acqusitions. t-statistics are shown in italics.

TargetInsider Trading

Status N

Acquirerpre-merger1-year total

return (t-2,t-1)

(%) N

Acquirerpre-merger1-year total

return (t-1,t) (%)

Acquirerpost-merger1-year total

return (t,t+1)

(%) N

Targetpre-merger1-year total

return (t-2,t-1)

(%) N

Targetpre-merger1-year total

return (t-1,t) (%)

HS 282 29.2 294 38.3 -4.8 291 13.3 294 28.6MS 284 21.8 295 26.5 4.6 294 12.1 295 11.5LS 284 26.7 295 16.7 -1.8 295 15.2 295 9.4NO 526 23.6 584 22.0 -1.3 472 -0.2 584 9.4LB 164 20.1 167 18.3 6.2 167 7.7 167 8.9MB 165 17.2 167 11.2 9.2 167 5.2 167 13.2HB 165 16.9 168 18.2 7.2 168 -5.6 168 16.1

HS-HB 12.2 20.1 -12.0 18.9 12.53.05 4.84 -2.39 3.92 2.25

TS 884 25.9 850 27.2 -0.6 880 13.5 884 16.5TB 502 18.1 494 15.9 7.5 502 2.4 502 12.7

TS-TB 7.8 11.2 -8.2 11.1 3.83.88 5.07 -2.97 4.37 1.36

HS 154 36.4 164 50.3 -13.3 161 23.1 164 35.0MS 150 24.2 155 34.2 3.7 155 16.4 155 22.2LS 153 27.8 156 23.8 -5.3 156 17.6 156 16.2NO 254 29.6 286 31.3 -15.7 213 -1.3 286 11.0LB 83 22.0 85 20.6 4.2 85 12.0 85 13.4MB 85 14.3 85 16.9 16.4 85 7.1 85 19.4HB 68 19.6 69 25.6 9.0 69 5.4 69 22.6

HS-HB 16.8 24.7 -22.3 17.7 12.43.00 3.69 -2.76 2.52 1.44

TS 475 29.5 457 36.3 -5.1 472 19.1 475 24.7TB 239 18.5 236 20.7 9.9 239 8.4 239 18.2

TS-TB 11.0 15.6 -15.0 10.7 6.54.1 4.77 -3.73 3.03 1.64

HS 63 21.4 63 17.5 14.7 63 -5.2 63 19.0MS 63 20.5 66 16.1 8.8 66 2.1 66 -3.2LS 66 24.7 69 10.1 11.7 69 6.8 69 6.9NO 140 20.4 154 10.7 11.1 137 -2.6 154 9.5LB 34 16.9 35 15.0 7.4 35 6.1 35 5.4MB 39 14.8 39 0.1 11.0 39 -2.2 39 -1.0HB 50 12.1 52 10.6 6.0 52 -17.0 52 19.1

HS-HB 9.3 6.9 8.7 11.9 -0.11.45 1.14 1.17 1.33 -0.01

TS 198 22.2 192 14.4 11.7 198 1.4 198 7.4TB 126 14.3 123 8.6 7.9 126 -6.0 126 9.1

TS-TB 8.0 5.9 3.7 7.5 -1.71.87 1.43 0.76 1.43 -0.28

Panel A: All Acquisitions

Panel B: Stock Acquisitions

Panel C: Cash Acquisitions

Table 10bAcquirer and Target Pre-Merger and Post-Merger Stock Returns sorted by Target's Trading Activity

This table shows acquirer and target pre-merger and post-merger one-year stock returns sorted by target's managerial trading activity. Targetmanagerial trading activity is ranked from High Net-Sellers (most overvalued) to High Net-Buyers (most undervalued). Summary categories TotalNet-Sellers (TS) and Total Net-Buyers (TB) include the firms which are in HS, MS or LS groups and LB, MB or HB groups respectively. Returnsare calculated for the one year period between two years before the announcement date and one-year before the announcement date (t-2,t-1) andone-year before the announcement date and the announcement date(t-1,t). Panel A reports results for all acquisitons, Panel B for cash acqusitionsand Panel C for stock acqusitions. t-statistics are shown in italics.

AcquirerInsider Trading

Status N

Acquirerpre-merger

1-year excess return (t-1,t) (%) N

Acquirerpost-merger

1-year excess return (t,t+1)

(%) N

Targetpre-merger

1-year excess return (t,t+1)

(%) N

Acquirerpre-merger

1-year excess return (t-1,t) (%) N

Acquirerpost-merger

1-year excess return (t,t+1)

(%) N

Targetpre-merger

1-year excess return (t,t+1)

(%)

HS 362 24.3 376 -3.3 309 -0.8 362 23.6 376 -4.1 309 -1.0MS 366 6.8 373 -6.4 327 -2.8 366 6.0 373 -7.1 327 -2.6LS 371 2.9 384 -1.2 307 -7.1 371 2.4 384 -1.9 307 -6.8NO 241 20.2 292 -15.5 285 -7.7 241 20.1 292 -15.4 285 -7.0LB 108 -6.1 113 -1.1 90 -9.2 108 -5.1 113 -1.0 90 -8.8MB 106 -2.0 108 0.8 85 -15.7 106 -2.5 108 0.4 85 -15.2HB 99 -5.2 110 -2.9 96 -18.5 99 -4.5 110 -2.7 96 -16.8

HS-HB 29.5 -0.4 17.7 28.1 -1.4 15.86.70 -0.04 3.15 6.57 -0.13 2.81

TS 1,099 11.2 1,133 -3.6 943 -3.5 1,099 10.6 1133 -4.4 943 -3.5TB 313 -4.4 331 -1.1 271 -14.5 313 -4.0 331 -1.1 271 -13.6

TS-TB 15.7 -2.5 11.0 14.7 -3.2 10.27.00 -0.63 3.59 6.55 -0.80 3.32

HS 203 37.4 215 -17.8 174 4.8 203 36.5 215 -18.7 174 4.7MS 196 11.1 201 -7.0 171 2.8 196 10.6 201 -7.7 171 2.8LS 201 6.6 208 -3.1 158 -4.8 201 6.1 208 -3.6 158 -4.8NO 95 2.7 120 -29.0 118 -4.6 95 3.5 120 -28.8 118 -4.0LB 63 -2.1 65 -6.4 47 -9.4 63 -1.3 65 -6.3 47 -9.4MB 55 4.9 56 2.4 41 -12.3 55 4.7 56 1.5 41 -11.2HB 36 -2.9 37 9.3 31 -14.5 36 -2.0 37 8.5 31 -13.7

HS-HB 40.3 -27.1 19.3 38.5 -27.2 18.46.65 -4.01 1.92 6.54 -4.02 1.82

TS 600 18.5 624 -9.4 503 1.1 600 17.9 624 -10.1 503 1.1TB 154 0.2 158 0.4 119 -11.7 154 0.7 158 -0.1 119 -11.1

TS-TB 18.3 -9.8 12.9 17.2 -10.1 12.25.73 -3.24 2.70 5.35 -3.28 2.57

HS 84 -0.3 86 -4.4 70 -10.5 84 -0.2 86 -5.3 70 -10.5MS 95 -0.5 96 -7.0 91 -11.3 95 -1.7 96 -7.9 91 -10.6LS 85 -2.8 86 4.4 76 -12.9 85 -3.3 86 4.3 76 -12.5NO 68 60.3 85 -3.0 81 -4.8 68 61.0 85 -3.2 81 -3.6LB 20 -13.0 20 7.4 14 -3.0 20 -12.0 20 7.2 14 -2.0MB 22 -8.3 23 16.5 17 -31.5 22 -9.5 23 15.3 17 -32.1HB 30 -15.2 35 -8.1 33 -24.1 30 -14.7 35 -7.7 33 -22.1

HS-HB 14.9 3.6 13.6 14.5 2.4 11.61.79 0.43 1.30 1.79 0.28 1.10

TS 264 -1.2 268 -2.5 237 -11.6 264 -1.7 268 -3.1 237 -11.2TB 72 -12.5 78 3.1 64 -21.5 72 -12.3 78 2.9 64 -20.3

TS-TB 11.3 -5.7 9.9 10.6 -6.0 9.22.70 -1.03 1.62 2.55 -1.10 1.48

Table 11aAcquirer and Target Pre-Merger and Post-Merger Long-Run Excess Stock Returns

Buy and Hold Abnormal Returns Method (BHAR)

Panel C: Cash Acquisitions

EQUAL WEIGHTED VALUE WEIGHTED

Panel A: All Acquisitions

Panel B: Stock Acquisitions

This table shows pre-merger and post-merger one-year Buy-and-Hold Abnormal Returns (BHARs) to acquirers and targets grouped by Acquirer's Managerial Trading Activity. Acquirer managerial tradingactivity is ranked from High Net-Sellers (most overvalued) to High Net-Buyers (most undervalued). Summary categories Total Net-Sellers (TS) and Total Net-Buyers (TB) include the firms which are in HS,MS or LS groups and LB, MB or HB groups respectively. To calculate excess returns, each firm is matched to its corresponding 10x10 Fama-French size and book-to-market portfolio. The matchingportfolios, which are constructed at the end of each June, are the intersections of 10 portfolios formed on size and 10 portfolios formed on the ratio of book equity to market equity. The size breakpoints foryear t are the NYSE market equity deciles at the end of June of t. BE/ME for June of year t is the book equity in December t-1 divided by ME for December of t-1. The BE/ME breakpoints are NYSE deciles.The portfolios for July of year t to June of t+1 include all NYSE, AMEX, and NASDAQ stocks with market equity data for December of t-1 and June of t, and positive book equity data for t-1. Thebreakpoints and portfolio return data was made available by Kenneth French. Buy-and-hold returns are measured over months -12,-1 for pre-merger returns and months +1,+12 for post-merger returns forboth the sample firm and the control portfolios, and the difference is recorded as the abnormal buy-and-hold return to the sample firm. Panel A reports results for all acquisitons, Panel B for cash acqusitionsand Panel C for stock acqusitions. t-statistics are shown in italics.

TargetInsider Trading

Status N

Acquirerpre-merger

1-year excess return (t-1,t) (%) N

Acquirerpost-merger

1-year excess return (t,t+1)

(%) N

Targetpre-merger

1-year excess return (t,t+1)

(%) N

Acquirerpre-merger

1-year excess return (t-1,t) (%) N

Acquirerpost-merger

1-year excess return (t,t+1)

(%) N

Targetpre-merger

1-year excess return (t,t+1)

(%)

HS 251 17.1 274 -12.3 252 9.4 251 16.1 274 -13.2 252 9.4MS 253 10.9 266 12.5 252 -6.9 253 10.5 266 12.1 252 -7.2LS 254 2.9 267 -9.2 257 -6.6 254 2.6 267 -9.4 257 -6.7NO 454 17.1 493 -8.9 360 -12.8 454 16.8 493 -9.5 360 -12.0LB 149 1.8 156 -4.3 134 -11.3 149 1.6 156 -4.6 134 -11.1MB 145 -4.0 150 -2.2 118 -11.6 145 -3.7 150 -2.4 118 -10.0HB 147 3.9 150 -7.3 126 -7.2 147 3.8 150 -7.6 126 -6.3

HS-HB 13.2 -4.9 16.6 12.3 -5.5 15.73.20 -1.13 2.97 3.03 -1.26 2.79

TS 758 10.3 807 -3.1 761 -1.4 758 9.7 807 -3.6 761 -1.5TB 441 0.6 456 -4.6 378 -10.0 441 0.6 456 -4.9 378 -9.1

TS-TB 9.7 1.5 8.7 9.1 1.3 7.64.42 0.29 3.08 4.20 0.25 2.68

HS 141 25.4 155 -17.1 143 14.5 141 24.5 155 -18.1 143 14.2MS 134 16.9 140 1.3 129 2.7 134 16.7 140 0.9 129 2.4LS 141 8.2 143 -12.0 131 -1.5 141 7.9 143 -12.4 131 -1.3NO 219 17.3 244 -17.3 166 -13.8 219 17.0 244 -18.0 166 -13.1LB 77 0.7 79 -8.6 67 -10.3 77 0.6 79 -8.9 67 -10.1MB 75 0.4 79 2.8 57 -6.5 75 0.6 79 2.8 57 -6.0HB 62 8.2 62 -6.7 47 -5.5 62 8.5 62 -7.0 47 -4.9

HS-HB 17.2 -10.5 20.0 16.0 -11.1 19.12.43 -1.64 2.21 2.33 -1.73 2.12

TS 416 16.8 438 -9.6 403 5.5 416 16.4 438 -10.2 403 5.4TB 214 2.8 220 -4.0 171 -7.7 214 2.9 220 -4.2 171 -7.3

TS-TB 14.1 -5.6 13.2 13.5 -6.0 12.74.41 -1.65 3.20 4.24 -1.77 3.07

HS 53 0.2 58 -2.3 56 -2.3 53 -0.7 58 -2.9 56 -1.9MS 55 1.3 59 -2.3 60 -20.0 55 0.6 59 -2.3 60 -20.5LS 59 -4.0 64 -2.0 63 -7.2 59 -4.4 64 -2.5 63 -7.4NO 125 31.9 135 1.1 105 -13.1 125 32.1 135 0.8 105 -12.0LB 31 0.1 34 1.2 26 -12.3 31 -0.4 34 0.2 26 -12.2MB 37 -19.8 35 -2.4 27 -27.6 37 -19.2 35 -2.9 27 -24.2HB 44 -4.6 46 -8.6 45 -6.3 44 -5.1 46 -9.3 45 -4.6

HS-HB 4.7 6.3 4.0 4.3 6.4 2.70.74 0.88 0.35 0.69 0.87 0.23

TS 167 -0.9 181 -2.2 179 -10.0 167 -1.6 181 -2.6 179 -10.1TB 112 -8.3 115 -3.8 98 -13.8 112 -8.5 115 -4.6 98 -12.0

TS-TB 7.4 1.6 3.8 6.9 2.0 1.91.66 0.35 0.61 1.56 0.42 0.31

Table 11bAcquirer and Target Pre-Merger and Post-Merger Long-Run Excess Stock Returns Sorted by Target's Trading Activity

Buy and Hold Abnormal Returns Method (BHAR)

Panel C: Cash Acquisitions

EQUAL WEIGHTED VALUE WEIGHTED

Panel A: All Acquisitions

Panel B: Stock Acquisitions

This table shows pre-merger and post-merger one-year Buy-and-Hold Abnormal Returns (BHARs) to acquirers and targets grouped by Target's Managerial Trading Activity. Target managerial tradingactivity is ranked from High Net-Sellers (most overvalued) to High Net-Buyers (most undervalued). Summary categories Total Net-Sellers (TS) and Total Net-Buyers (TB) include the firms which are in HS,MS or LS groups and LB, MB or HB groups respectively. To calculate excess returns, each firm is matched to its corresponding 10x10 Fama-French size and book-to-market portfolio. The matchingportfolios, which are constructed at the end of each June, are the intersections of 10 portfolios formed on size and 10 portfolios formed on the ratio of book equity to market equity. The size breakpoints foryear t are the NYSE market equity deciles at the end of June of t. BE/ME for June of year t is the book equity in December t-1 divided by ME for December of t-1. The BE/ME breakpoints are NYSE deciles.The portfolios for July of year t to June of t+1 include all NYSE, AMEX, and NASDAQ stocks with market equity data for December of t-1 and June of t, and positive book equity data for t-1. Thebreakpoints and portfolio return data was made available by Kenneth French. Buy-and-hold returns are measured over months -12,-1 for pre-merger returns and months +1,+12 for post-merger returns forboth the sample firm and the control portfolios, and the difference is recorded as the abnormal buy-and-hold return to the sample firm. Panel A reports results for all acquisitons, Panel B for cash acqusitionsand Panel C for stock acqusitions. t-statistics are shown in italics.

AcquirerInsider Trading

Status12-MonthHorizon t

24-MonthHorizon t

36-MonthHorizon t

12-MonthHorizon t

24-MonthHorizon t

36-MonthHorizon t

HS -0.40 -1.70 -0.24 -1.09 -0.21 -1.15 0.09 0.29 -0.12 -0.44 -0.17 -0.78MS -0.24 -1.15 -0.21 -1.20 -0.29 -1.66 0.28 1.11 0.06 0.33 0.06 0.31LS -0.04 -0.20 -0.16 -1.12 0.00 0.02 0.09 0.38 0.20 1.34 0.04 0.27NO 0.12 0.49 -0.03 -0.13 -0.05 -0.28 0.02 0.06 -0.05 -0.19 0.04 0.20LB -0.07 -0.23 0.07 0.35 0.10 0.51 0.05 0.17 0.40 1.57 0.14 0.58MB 0.33 1.05 0.12 0.56 0.00 0.01 -0.10 -0.26 -0.01 -0.02 -0.14 -0.56HB 0.38 1.15 0.22 0.79 -0.02 -0.07 0.42 1.07 0.11 0.37 0.22 0.98

HS-HB -0.78 -1.92 -0.46 -1.30 -0.19 -0.57 -0.33 -0.65 -0.23 -0.57 -0.39 -1.25

TS -0.08 -0.55 -0.17 -1.39 -0.07 -0.57 0.12 0.83 0.03 0.22 -0.03 -0.26TB 0.29 1.46 0.10 0.59 -0.03 -0.19 -0.12 -0.52 0.15 0.90 0.12 0.74

TS-TB -0.37 -1.51 -0.28 -1.28 -0.04 -0.21 0.25 0.88 -0.12 -0.59 -0.15 -0.75

All Acquirers 0.12 1.16 -0.02 -0.21 0.01 0.09 0.07 0.56 0.03 0.31 -0.03 -0.26

HS -0.64 -2.11 -0.33 -1.21 -0.10 -0.38 -0.20 -0.50 -0.06 -0.18 0.01 0.05MS -0.48 -1.37 -0.45 -1.87 -0.35 -1.57 -0.05 -0.11 -0.38 -1.60 -0.28 -1.34LS 0.10 0.40 -0.04 -0.18 0.07 0.37 0.02 0.08 0.16 0.63 0.02 0.08NO -0.29 -0.77 0.02 0.07 -0.14 -0.52 -0.01 -0.02 0.06 0.21 0.04 0.12LB -0.51 -1.59 -0.16 -0.61 0.23 1.03 -0.18 -0.50 -0.10 -0.33 -0.08 -0.32MB 0.60 1.53 0.22 0.72 0.09 0.31 -0.24 -0.44 -0.01 -0.01 -0.15 -0.41HB 0.94 1.89 0.82 1.90 0.32 0.95 1.03 2.06 0.74 1.77 0.51 1.58

HS-HB -1.58 -2.71 -1.15 -3.11 -0.42 -0.98 -1.23 -1.92 -0.80 -1.48 -0.50 -1.15

TS -0.26 -1.16 -0.33 -1.52 -0.09 -0.50 0.04 0.20 -0.12 -0.66 -0.15 -0.94TB 0.19 0.65 0.16 0.72 0.09 0.52 -0.02 -0.07 0.03 0.16 0.07 0.42

TS-TB -0.45 -1.22 -0.49 -1.58 -0.18 -0.72 0.06 0.17 -0.15 -0.55 -0.23 -0.95

All Acquirers -0.13 -0.82 -0.13 -0.94 -0.01 -0.07 -0.01 -0.05 -0.10 -0.66 -0.11 -0.84

HS -0.11 -0.29 -0.20 -0.78 0.10 0.38 -0.12 -0.29 -0.30 -0.92 -0.17 -0.57MS -0.02 -0.05 0.18 0.66 0.11 0.48 0.27 0.73 0.56 1.71 0.34 1.25LS 0.75 2.10 0.46 2.50 0.37 2.13 0.34 1.10 0.38 1.47 -0.02 -0.08NO 0.55 1.69 0.33 1.27 0.12 0.46 0.06 0.15 0.34 1.06 0.39 1.48LB 0.48 0.70 0.16 0.30 0.00 0.00 -0.22 -0.30 0.36 0.75 0.37 0.84MB 1.11 1.34 0.98 1.56 0.68 1.25 0.55 0.68 0.72 1.39 0.46 0.84HB 0.94 1.57 0.00 0.00 -0.28 -0.73 1.09 2.05 0.41 0.78 0.35 0.83

HS-HB -1.04 -1.49 -0.20 0.38 0.82 -1.21 -1.81 -0.72 -1.15 -0.52 -1.01

TS 0.18 0.92 0.12 0.79 0.19 1.26 0.36 1.28 0.36 1.95 0.21 1.28TB 0.58 1.21 0.31 1.07 0.07 0.29 0.08 0.17 0.14 0.41 0.28 0.93

TS-TB -0.39 -0.76 -0.19 -0.58 0.12 0.42 0.29 0.54 0.22 0.55 -0.07 -0.20

All Acquirers 0.28 1.55 0.02 0.10 0.08 0.67 0.40 1.82 0.27 1.65 0.19 1.23

Table 12aAcquirer Post-Merger Long-Run Excess Stock Returns

Calendar Time Portfolio Regressions Method (CTPR) - Fama-French 3-Factor Model

Panel A: All Acquisitions

Panel B: Stock Acquisitions

Panel C: Cash Acquisitions

VALUE WEIGHTEDEQUAL WEIGHTED

This table shows the average monthly abnormal returns for the acquirer firm portfolios obtained from Calendar Time Portfolio Regressions, sorted by acquirer's managerial trading activity.Acquirer managerial trading activity is ranked from High Net-Sellers (most overvalued) to High Net-Buyers (most undervalued). Summary categories Total Net-Sellers (TS) and Total Net-Buyers (TB) include the firms which are in HS, MS or LS groups and LB, MB or HB groups respectively. At every month from January 1984 to December 2000, calendar time portfolios areformed by including all the acquiring firms in a given trading activity category which announced a merger with another company within the previous 12, 24 and 36 months. The portfolio isrebalanced every month to include all the new event companies and to drop off the old ones whose announcement date falls outside of the holding horizon. Both value-weighted and equallyweighted monthly returns are calculated for each portfolio and regressed on the three Fama-French risk factors using the following GARCH (1,1) specification:

tttt

ttptpftmtppftpt

N

HMLhSMBsRRRR

32

122

112

)(

γεγσγωσ

εβα

+++=

+++−+=−

−−

where εt is an independently and identically distributed Gaussian random error, Rpt is the portfolio return, Rft is the one-month Treasury bill rate, Rmt is the value-weighted monthly return of CRSP index, SMBt is the difference in the returns of a value weighted portfolio of small stocks and big stocks, HMLt is the difference in the returns of a value-weighted portfolio of high book-tmarket stocks and low book-to-market stocks, and the intercept αp represents the average monthly abnormal return of the event portfolio. In the second equation, Nt is the number of stocks included in portfolio at month t and σ2

t is the conditional volatility of εt. Panel A reports the results for all acquirers, Panel B for acquirers in stock acquisitions and Panel C for acquirers in cash acquisitions. t-statistics are shown next to αp estimates.

AcquirerInsider Trading

Status12-MonthHorizon t

24-MonthHorizon t

36-MonthHorizon t

12-MonthHorizon t

24-MonthHorizon t

36-MonthHorizon t

HS -0.40 -1.70 -0.24 -1.09 -0.21 -1.15 0.09 0.29 -0.12 -0.44 -0.17 -0.78MS -0.24 -1.15 -0.21 -1.20 -0.29 -1.66 0.28 1.11 0.06 0.33 0.06 0.31LS -0.04 -0.20 -0.16 -1.12 0.00 0.02 0.09 0.38 0.20 1.34 0.04 0.27NO 0.12 0.49 -0.03 -0.13 -0.05 -0.28 0.02 0.06 -0.05 -0.19 0.04 0.20LB -0.07 -0.23 0.07 0.35 0.10 0.51 0.05 0.17 0.40 1.57 0.14 0.58MB 0.33 1.05 0.12 0.56 0.00 0.01 -0.10 -0.26 -0.01 -0.02 -0.14 -0.56HB 0.38 1.15 0.22 0.79 -0.02 -0.07 0.42 1.07 0.11 0.37 0.22 0.98

HS-HB -0.78 -1.92 -0.46 -1.30 -0.19 -0.57 -0.33 -0.65 -0.23 -0.57 -0.39 -1.25

TS -0.08 -0.55 -0.17 -1.39 -0.07 -0.57 0.12 0.83 0.03 0.22 -0.03 -0.26TB 0.29 1.46 0.10 0.59 -0.03 -0.19 -0.12 -0.52 0.15 0.90 0.12 0.74

TS-TB -0.37 -1.51 -0.28 -1.28 -0.04 -0.21 0.25 0.88 -0.12 -0.59 -0.15 -0.75

All Acquirers 0.12 1.16 -0.02 -0.21 0.01 0.09 0.07 0.56 0.03 0.31 -0.03 -0.26

HS -0.45 -1.42 -0.05 -0.16 0.24 0.92 -0.13 -0.26 0.07 0.25 0.06 0.24MS -0.26 -0.70 -0.18 -0.72 -0.19 -0.72 0.13 0.32 -0.22 -0.87 -0.19 -0.87LS 0.20 0.79 0.04 0.20 0.23 1.18 -0.22 -0.75 0.03 0.14 -0.02 -0.10NO -0.48 -1.20 -0.06 -0.19 -0.07 -0.27 -0.03 -0.05 0.06 0.21 0.19 0.65LB -0.54 -1.59 -0.10 -0.39 0.28 1.37 -0.29 -0.84 -0.07 -0.22 -0.13 -0.51MB 0.65 1.57 0.27 0.87 0.17 0.57 -0.12 -0.21 0.10 0.24 -0.17 -0.48HB 1.02 2.01 0.75 1.63 0.27 0.79 1.33 2.86 0.56 1.26 0.20 0.65

HS-HB -1.47 -2.46 -0.80 -1.79 -0.03 -0.07 -1.45 -2.16 -0.49 -0.92 -0.14 -0.34

TS -0.05 -0.24 0.00 0.00 0.08 0.45 0.08 0.38 -0.03 -0.17 -0.08 -0.49TB 0.30 0.91 0.22 0.95 0.17 0.84 -0.09 -0.29 0.01 0.03 -0.04 -0.19

TS-TB -0.35 -0.88 -0.22 0.00 -0.08 -0.31 0.17 0.46 -0.04 -0.14 -0.04 -0.15

All Acquirers -0.07 -0.46 0.04 0.26 0.06 0.41 -0.03 -0.14 -0.04 -0.26 -0.04 -0.30

HS 0.15 0.42 -0.11 -0.42 0.26 1.04 -0.05 -0.13 -0.46 -1.39 -0.24 -0.81MS 0.19 0.64 0.42 1.60 0.40 1.61 0.18 0.47 0.52 1.71 0.34 1.20LS 1.05 3.04 0.56 2.80 0.48 2.66 0.45 1.36 0.39 1.47 -0.02 -0.06NO 0.55 1.64 0.56 2.02 0.26 1.20 -0.06 -0.14 0.22 0.64 0.09 0.35LB 0.55 0.70 0.18 0.33 0.07 0.16 -0.06 -0.07 0.27 0.60 0.27 0.63MB 0.95 1.11 0.93 1.41 0.61 1.08 0.43 0.52 0.71 1.31 0.37 0.66HB 0.88 1.15 0.10 0.25 -0.14 -0.36 1.25 1.37 0.44 0.81 0.45 1.03

HS-HB -0.73 -0.86 -0.21 0.40 0.86 -1.30 -1.30 -0.90 -1.42 -0.70 -1.31

TS 0.35 1.91 0.30 1.86 0.28 1.98 0.34 1.16 0.27 1.40 0.19 1.07TB 0.68 1.32 0.37 1.21 0.14 0.59 0.19 0.41 0.11 0.32 0.27 0.84

TS-TB -0.33 -0.60 -0.07 -0.21 0.14 0.49 0.15 0.28 0.15 0.38 -0.08 -0.21

All Acquirers 0.43 2.36 0.26 1.83 0.27 2.22 0.28 1.20 0.17 0.90 0.16 1.00

Table 12bAcquirer Post-Merger Long-Run Excess Stock Returns

Calendar Time Portfolio Regressions Method (CTPR) - Fama-French-Carhart 4-Factor Model

Panel A: All Acquisitions

Panel B: Stock Acquisitions

Panel C: Cash Acquisitions

VALUE WEIGHTEDEQUAL WEIGHTED

This table shows the average monthly abnormal returns for the acquirer firm portfolios obtained from Calendar Time Portfolio Regressions, sorted by acquirer's managerial tradingactivity. Acquirer managerial trading activity is ranked from High Net-Sellers (most overvalued) to High Net-Buyers (most undervalued). Summary categories Total Net-Sellers (TS) andTotal Net-Buyers (TB) include the firms which are in HS, MS or LS groups and LB, MB or HB groups respectively. At every month from January 1984 to December 2000, calendar timeportfolios are formed by including all the acquiring firms in a given trading activity category which announced a merger with another company within the previous 12, 24 and 36 months.The portfolio is rebalanced every month to include all the new event companies and to drop off the old ones whose announcement date falls outside of the holding horizon. Both value-weighted and equally weighted monthly returns are calculated for each portfolio and regressed on the three Fama-French risk factors using the following GARCH (1,1) specification:

where εt is an independently and identically distributed Gaussian random error, Rpt is the portfolio return, Rft is the one-month Treasury bill rate, Rmt is the value-weighted monthly return of CRSP index, SMBt is the difference in the returns of a value weighted portfolio of small stocks and big stocks, HMLt is the difference in the returns of a value-weighted portfolio of highbook-to-market stocks and low book-to-market stocks, UMD t measures the difference in the returns of a value weighted portfolio of two high prior return portfolios minus and the two low prior return portfolios among the six value-weight portfolios formed on size and prior returns and the intercept αp represents the average monthly abnormal return of the event portfolio. In the second equation, Nt is the number of stocks included in portfolio at month t and σ2

t is the conditional volatility of εt. Panel A reports the results for all acquirers, Panel B for acquirers in stock acquisitions and Panel C for acquirers in cash acquisitions. t-statistics are shown next to αp estimates.

tttt

ttptptpftmtppftpt

N

UMDuHMLhSMBsRRRR

32

122

112

)(

γεγσγωσ

εβα

+++=

++++−+=−

−−

TargetInsider Trading

Status12-MonthHorizon t

24-MonthHorizon t

36-MonthHorizon t

12-MonthHorizon t

24-MonthHorizon t

36-MonthHorizon t

HS -0.23 -1.11 -0.20 -1.17 -0.14 -0.82 0.33 1.45 0.05 0.23 0.17 1.02MS -0.09 -0.43 -0.17 -0.84 -0.27 -1.53 0.01 0.05 -0.12 -0.59 -0.24 -1.48LS -0.45 -1.69 -0.45 -2.23 -0.08 -0.39 -0.07 -0.30 0.05 0.27 0.06 0.38NO -0.01 -0.02 -0.01 -0.04 -0.01 -0.08 0.37 2.00 0.08 0.49 0.00 0.02LB -0.28 -0.98 0.02 0.12 -0.03 -0.18 0.01 0.05 0.09 0.44 0.00 0.00MB 0.06 0.17 0.15 0.51 0.10 0.47 -0.29 -0.99 0.09 0.34 -0.09 -0.38HB -0.13 -0.47 -0.04 -0.21 0.07 0.39 -0.25 -0.68 -0.17 -0.81 -0.16 -0.87

HS-HB -0.10 -0.29 -0.15 -0.58 -0.21 -0.84 0.57 1.34 0.22 0.72 0.33 1.33

TS -0.23 -1.39 -0.24 -1.73 -0.11 -0.98 0.04 0.21 0.04 0.25 -0.05 -0.38TB 0.00 -0.02 0.00 -0.01 0.10 0.75 -0.12 -0.59 0.12 0.74 -0.07 -0.47

TS-TB -0.23 -0.79 -0.24 -0.79 -0.21 -1.21 0.16 0.58 -0.08 -0.37 0.02 0.09

All Acquirers 0.12 1.16 -0.02 -0.21 0.01 0.09 0.07 0.56 0.03 0.31 -0.03 -0.26

HS -0.31 -0.95 -0.71 -3.54 -0.46 -2.02 -0.26 -0.58 -0.36 -1.09 -0.28 -1.02MS 0.03 0.09 -0.15 -0.54 -0.27 -1.05 -0.26 -0.63 -0.36 -1.43 -0.31 -1.17LS -0.59 -1.94 -0.54 -2.11 -0.24 -0.99 -0.18 -0.57 0.22 0.83 0.09 0.42NO -0.50 -1.95 -0.40 -2.39 -0.39 -1.84 -0.20 -0.48 -0.14 -0.58 -0.28 -1.21LB -0.21 -0.63 -0.10 -0.43 -0.01 -0.07 0.15 0.47 -0.24 -0.85 -0.19 -0.78MB 0.75 1.79 0.23 0.97 0.36 1.31 0.64 1.53 0.12 0.48 0.16 0.65HB -0.07 -0.19 -0.16 -0.61 0.02 0.09 -0.47 -0.90 -0.03 -0.10 0.27 1.05

HS-HB -0.24 -0.49 -0.54 -2.93 -0.48 -1.49 0.21 0.30 -0.33 -0.70 -0.55 -1.46

TS -0.29 -1.30 -0.27 -1.36 -0.13 -0.67 -0.10 -0.48 -0.22 -1.36 -0.19 -1.31TB 0.31 1.09 0.05 0.26 0.18 0.83 -0.49 -1.77 0.09 0.41 0.16 0.84

TS-TB -0.60 -1.66 -0.32 -1.18 -0.31 -1.07 0.39 1.10 -0.32 -1.13 -0.35 -1.46

All Acquirers -0.13 -0.82 -0.13 -0.94 -0.01 -0.07 -0.01 -0.05 -0.10 -0.66 -0.11 -0.84

HS 0.67 1.89 0.19 0.77 0.21 0.88 0.62 1.66 0.55 1.69 0.33 1.28MS 0.65 2.08 0.13 0.40 0.02 0.07 0.54 1.15 0.44 1.03 0.13 0.38LS 0.09 0.22 0.20 0.74 0.33 1.23 0.76 1.52 0.21 0.58 0.44 1.68NO 0.32 1.11 0.17 0.77 0.01 0.06 0.66 2.25 0.70 2.86 0.09 0.51LB -0.32 -0.61 0.22 0.55 -0.34 -1.04 0.34 0.69 0.47 1.31 -0.01 -0.01MB 0.41 0.80 0.95 2.59 0.93 4.30 -0.53 -1.14 -0.03 -0.08 -0.37 -1.19HB 0.10 0.30 -0.10 -0.24 0.12 0.39 -0.33 -0.97 -0.22 -0.43 -0.28 -0.75

HS-HB 0.56 1.14 0.29 0.09 0.23 0.95 1.88 0.76 1.27 0.61 1.35

TS 0.28 1.05 0.08 0.41 0.12 0.61 0.36 0.96 0.26 1.01 0.32 1.38TB 0.06 0.20 0.08 0.37 0.00 0.03 -0.03 -0.11 0.22 0.75 -0.10 -0.45

TS-TB 0.22 0.55 0.00 0.00 0.12 0.45 0.39 0.80 0.04 0.10 0.42 1.29

All Acquirers 0.28 1.55 0.02 0.10 0.08 0.67 0.40 1.82 0.27 1.65 0.19 1.23

Table 12cAcquirer Post-Merger Long-Run Excess Stock Returns sorted by Target's Trading Activity

Calendar Time Portfolio Regressions Method (CTPR)

Panel A: All Acquisitions

Panel B: Stock Acquisitions

Panel C: Cash Acquisitions

VALUE WEIGHTEDEQUAL WEIGHTED

This table shows the average monthlyabnormal returns for the acquirer firm portfoliosobtained from Calendar Time PortfolioRegressions, sorted by target's managerial tradingactivity. Target managerial trading activity is ranked from High Net-Sellers (most overvalued) to High Net-Buyers (most undervalued). Summary categories Total Net-Sellers(TS) and Total Net-Buyers (TB) include the firms which are in HS, MS or LS groups and LB, MB or HB groups respectively. At every month from January 1984 to December2000, calendar time portfolios are formed by including all the acquiring firms in a given trading activity category which announced a merger with another company within theprevious 12, 24 and 36 months. The portfolio is rebalanced every month to include all the new event companies and to drop off the old ones whose announcement date fallsoutside of the holding horizon. Both value-weighted and equally weighted monthly returns are calculated for each portfolio and regressed on the three Fama-French risk factors

i h f ll i GA C (1 1) ifi i

tttt

ttptpftmtppftpt

N

HMLhSMBsRRRR

32

122

112

)(

γεγσγωσ

εβα

+++=

+++−+=−

−−

where εt is an independently and identically distributed Gaussian random error, Rpt is the portfolio return, Rft is the one-month Treasury bill rate, Rmt is the value-weighted monthlyreturn of CRSP index, SMBt is the difference in the returns of a value weighted portfolio of small stocks and big stocks, HMLt is the difference in the returns of a value-weighted portfolio of high book-to-market stocks and low book-to-market stocks, and the intercept αp represents the average monthly abnormal return of the event portfolio. In the second equation, Nt is the number of stocks included in portfolio at month t and σ2

t is the conditional volatility of εt. Panel A reports the results for all acquirers, Panel B for acquirers in stock acquisitions and Panel C for acquirers in cash acquisitions. t-statistics are shown next to αp estimates.

(1) (2) (3) (4) (5) (6) (7) (8)Acquirer NET 0.053 0.079 0.126 0.200 -0.082 -0.025 0.035 0.089

0.25 0.20 0.11 0.08 0.13 0.68 0.34 0.04

Target NET -0.134 -0.121 -0.212 -0.192 -0.078 -0.083 -0.124 -0.1000.02 0.06 0.05 0.13 0.20 0.23 0.01 0.03

Acquirer B/P -1.456 -0.543 -0.872 -0.9990.00 0.09 0.01 0.00

Target B/P -0.210 -0.009 -0.572 -0.4970.27 0.97 0.00 0.00

Diversification Dummy -0.440 -0.650 -0.642 -0.835 0.158 0.243 -0.324 -0.4400.04 0.01 0.01 0.01 0.53 0.43 0.04 0.01

Acquirer Cash Holdings 2.428 0.356 0.567 -0.062 2.456 0.703 2.334 0.5980.00 0.60 0.40 0.94 0.00 0.32 0.00 0.21

Acquirer Cash Flow -0.204 -0.069 -0.126 -0.863 -0.127 0.032 -0.164 -0.0220.30 0.82 0.67 0.30 0.45 0.67 0.22 0.79

Acquirer Leverage 1.010 1.174 1.404 1.766 -0.561 -0.383 0.360 0.6350.00 0.01 0.00 0.00 0.13 0.40 0.15 0.03

Acquirer Pre-Merger 1 year return (t-1 , t) 1.013 0.676 0.589 0.8050.00 0.04 0.02 0.00

Acquirer Pre-Merger 1 year return (t-2, t-1) -0.483 -0.454 0.152 -0.1530.07 0.11 0.57 0.39

Acquirer Pre-Merger 1 year volatility (t-1 , t) 2.894 0.899 2.427 2.2200.00 0.20 0.00 0.00

Target Pre-Merger 1 year return (t-1 , t) -0.139 -0.302 -0.052 -0.1460.48 0.19 0.81 0.29

Target Pre-Merger 1 year return (t-2, t-1) 0.616 0.334 0.013 0.3130.00 0.20 0.95 0.04

Target Pre-Merger 1 year volatility (t-1 , t) 0.222 0.158 0.836 0.3930.52 0.73 0.05 0.13

Log of relative Size 0.117 0.124 0.245 0.332 -0.242 -0.300 0.023 0.0620.01 0.05 0.00 0.00 0.00 0.00 0.48 0.17

Log of Target Size 0.266 0.217 0.265 0.288 0.052 0.051 0.190 0.1360.00 0.00 0.00 0.00 0.28 0.48 0.00 0.01

Intercept -3.513 -2.562 -1.105 -1.022 -3.490 -3.596 -3.290 -2.4870.00 0.10 0.28 0.55 0.00 0.01 0.00 0.01

Intercept 2 -2.022 -1.1310.00 0.24

Sample Size 1,307 1,063 834 685 1,299 1,042 1,720 1,395

Psuedo R2 0.220 0.296 0.245 0.309 0.228 0.282 0.190 0.265

p-value of L.R 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3 if STOCK

Table 13aLogistic Regression Results

Continuous Managerial Trading Variable

Dependent Variable

1 if STOCK0 if MIXED

2 if MIXED1 if CASH

1 if STOCK0 if CASH

1 if MIXED0 if CASH

This table lists the results for binary (models 1-6) and multinomial (models 7-8) logistic regressions of method of payment variables on acquirer and target characteristics. For models 1 and 2, the dependent variable is 1 if the method of payment is all stock; 0 if the method of payment is all cash. For models 3 and 4, the dependent variable is 1 if method of payment is mixed; 0 if the method of payment is all cash. For models 5 and 6, the dependent variable is 1 if method of payment is all stock; 0 if the method of payment is mixed. For models 7 and 8, the dependent variable is 1 if the method of payment is cash; 2 if the method of payment is mixed and 3 if the method of payment is stock. B/P is the book to price ratio. Acquirer cash holdings = (cash+marketable securities)/book value of assets. Acquirer cash flow = EBITDA/Sales. Acquirer Leverage = (book value of debt)/(book value of equity+book value of debt). All variables are measured as of the fiscal year-end ending in the calendar year preceding the acquisition year. Acquirer Net Managerial Purchase as a % of holdings (NET) = Net Managerial Purchases in the one-year period prior to the merger announcement / total value of common share holdings at the beginning of the one-year period. Pre-merger 1-year return (t-1,t) is the raw return on acquirer and target stocks in the one year period prior to the merger anouncement date. Pre-merger 1-year return (t-2,t-1) is the raw return on acquirer and target stocks between two years prior to the announcement date and one year prior to the announcement date. The sample period is 1984-2000. P-values are shown in italics. All regressions include year and acquirer and target 2-digit SIC major industry dummies.

(1) (2) (3) (4) (5) (6) (7) (8)Acquirer NETRANK -0.094 -0.051 0.045 0.053 -0.126 -0.082 -0.073 -0.022

0.04 0.33 0.36 0.35 0.00 0.11 0.02 0.54

Target NETRANK -0.044 -0.004 -0.016 0.004 -0.099 -0.106 -0.054 -0.0170.28 0.93 0.73 0.94 0.02 0.03 0.06 0.62

Acquirer B/P -1.477 -0.585 -0.890 -0.9890.00 0.07 0.01 0.00

Target B/P -0.216 -0.021 -0.536 -0.4760.26 0.93 0.00 0.00

Diversification Dummy -0.432 -0.628 -0.658 -0.866 0.150 0.211 -0.318 -0.4370.04 0.01 0.01 0.00 0.55 0.50 0.04 0.01

Acquirer Cash Holdings 2.194 0.184 0.376 -0.350 2.256 0.558 2.167 0.4430.00 0.78 0.57 0.68 0.00 0.43 0.00 0.35

Acquirer Cash Flow -0.226 -0.107 -0.166 -1.103 -0.133 0.028 -0.180 -0.0300.25 0.82 0.58 0.19 0.42 0.71 0.18 0.74

Acquirer Leverage 0.959 1.148 1.336 1.701 -0.535 -0.368 0.364 0.6200.01 0.01 0.00 0.00 0.15 0.42 0.15 0.04

Acquirer Pre-Merger 1 year return (t-1 , t) 0.933 0.677 0.510 0.7440.00 0.04 0.05 0.00

Acquirer Pre-Merger 1 year return (t-2, t-1) -0.525 -0.436 0.144 -0.1840.04 0.12 0.59 0.30

Acquirer Pre-Merger 1 year volatility (t-1 , t) 2.880 0.819 2.390 2.1650.00 0.24 0.00 0.00

Target Pre-Merger 1 year return (t-1 , t) -0.130 -0.243 -0.093 -0.1410.51 0.27 0.68 0.31

Target Pre-Merger 1 year return (t-2, t-1) 0.630 0.346 -0.010 0.3420.00 0.18 0.96 0.02

Target Pre-Merger 1 year volatility (t-1 , t) 0.217 0.218 0.802 0.3930.53 0.63 0.06 0.13

Log of relative Size 0.149 0.142 0.245 0.346 -0.208 -0.280 0.054 0.0780.00 0.03 0.00 0.00 0.00 0.00 0.11 0.09

Log of Target Size 0.235 0.208 0.277 0.297 -0.001 0.007 0.158 0.1240.00 0.01 0.00 0.00 0.98 0.93 0.00 0.02

Intercept -2.652 -2.172 -1.246 -1.061 -1.947 -2.292 -2.409 -2.1530.03 0.18 0.27 0.55 0.06 0.12 0.00 0.03

Intercept 2 -1.140 -0.8040.10 0.43

Sample Size 1,307 1,063 834 685 1,299 1,042 1,720 1,395

Psuedo R2 0.219 0.293 0.238 0.302 0.234 0.286 0.190 0.261

p-value of L.R 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1 if STOCK0 if CASH

1 if MIXED0 if CASH

1 if STOCK0 if MIXED

2 if MIXED1 if CASH

3 if STOCK

Table 13bLogistic Regression Results

Discrete Managerial Trading Variable

Dependent Variable

This table lists the results for binary (models 1-6) and multinomial (models 7-8) logistic regressions of method of payment variables on acquirer and target characteristics.For models 1 and 2, the dependent variable is 1 if the method of payment is all stock; 0 if the method of payment is all cash. For models 3 and 4, the dependent variable is1 if method of payment is mixed; 0 if the method of payment is all cash. For models 5 and 6, the dependent variable is 1 if method of payment is all stock; 0 if themethod of payment is mixed. For models 7 and 8, the dependent variable is 1 if the method of payment is cash; 2 if the method of payment is mixed and 3 if the method ofpayment is stock. B/P is the book to price ratio. Acquirer cash holdings = (cash+marketable securities)/book value of assets. Acquirer cash flow = EBITDA/Sales.Acquirer Leverage = (book value of debt)/(book value of equity+book value of debt). All variables are measured as of the fiscal year-end ending in the calendar yearpreceding the acquisition year. Managerial trading activity in acquirers and targets is measured by a rank variable (NETRANK) which take on 7 values from 1 to 7 , with 1representing the Highest Selling (HS) firms and 7 representing the Highest Buying (HS) firms. Trading Activity rank variable is equal to 1 if HS=1, 2 if MS=1, 3 if LS=1,4 if NO=1, 5 if LB=1, 6 if MB=1 and 7 if HB=1. Pre-merger 1-year return (t-1,t) is the raw return on acquirer and target stocks in the one year period prior to the mergeranouncement date. Pre-merger 1-year volatility (t-1,t) is the annualized volatility of the return on acquirer and target stocks in the one year period prior to the mergeranouncement date. Pre-merger 1-year return (t-2,t-1) is the raw return on acquirer and target stocks between two years prior to the announcement date and one year prior tothe announcement date. The sample period is 1984-2000. P-values are shown in italics. All regressions include year and acquirer and target 2-digit SIC major industrydummies.

Acquirer NET 0.17 0.28 0.10 0.03 -0.01 -0.261.65 2.55 0.44 0.11 -0.02 -0.63

Target NET 0.48 0.36 -0.22 -0.24 -0.75 -0.682.42 1.78 -0.54 -0.52 -1.07 -0.93

Hostile 1.91 2.11 3.45 2.56 25.77 22.681.85 1.83 1.17 0.85 4.97 4.07

Tender Offer 0.60 0.49 6.26 6.10 1.55 4.251.15 0.80 3.98 3.60 0.54 1.47

Cash 0.84 1.29 1.83 0.01 -5.98 -2.831.86 2.36 1.30 0.00 -1.53 -0.92

Stock -0.70 -0.10 -1.40 -2.16 -7.27 -0.80-1.64 -0.20 -1.34 -1.77 -2.16 -0.31

Acquirer B/P 0.48 0.12 -0.790.84 0.11 -0.33

Target B/P 0.25 2.02 4.850.77 2.32 2.05

Log of relative Size -0.10 -0.24 -1.71 -2.02 -2.16 -1.45-0.92 -1.72 -5.42 -5.53 -2.79 -2.22

Log of Target Size -0.44 -0.41 -0.59 -0.17 -4.18 -3.07-3.48 -2.62 -2.04 -0.51 -5.38 -4.17

Intercept 2.41 0.30 16.48 0.17 89.62 72.031.42 0.12 3.00 2.76 7.37 5.84

Sample Size 1970 1497 1970 1497 1970 1497

R2 0.074 0.101 0.113 0.139 0.091 0.119

Table 14aLeast Squares Regression Results

Continuous Managerial Trading Variable

AcquirerAnnouncement

Period CAR

Target Announcement

Period CARBid Premium

This table lists the results for least squares regressions. Dependent variables are acquirer and target cumulative announcement abnormal returns andbid premium. Cumulative abnormal returns are calculated as the cumulative returns in excess of the return on the CRSP value-weighted index overa 4-day event window of [-2,+1] days around the merger announcement date. Bid premium is defined as the ratio of the difference between the bidprice and day -3 price of the target to day -3 price of the target. Acquirer Net Managerial Purchase as a % of holdings (NET) = Net ManagerialPurchases in the one-year period prior to the merger announcement / total value of common share holdings at the beginning of the one-year period.The dummy variables Hostile, Tender Offer, Cash and Stock take on the value 1 if the acquisition is hostile, mode of acquisition is tender offer, themethod of payment is all cash and the method of payment is all stock respectively. B/P is the book to price ratio. B/P for calendar year t iscalculated as the book value belonging to the fiscal year ending in calendar year t-1 divided by the stock price at the end of calendar year t-1. Logof Relative Size is the logarithm of TMV3/AMV3, where TMV3 is target's market value at day -3 and AMV3 is the acquirer's market value at day -3All regressions include year and acquirer and target 2-digit SIC major industry dummies.

Acquirer NETRANK 0.25 0.32 0.43 0.62 1.16 1.392.53 2.82 1.67 2.10 1.76 2.11

Target NETRANK 0.25 0.24 0.14 0.27 1.03 1.272.56 2.06 0.54 0.95 1.98 2.25

Hostile 1.85 2.08 3.41 2.53 25.75 22.671.80 1.84 1.17 0.85 5.05 4.11

Tender Offer 0.55 0.40 6.12 5.89 0.95 3.561.08 0.66 3.86 3.46 0.33 1.22

Cash 0.94 1.38 1.86 0.16 -5.76 -2.282.09 2.55 1.32 0.10 -1.48 -0.74

Stock -0.63 0.00 -1.23 -1.87 -6.57 0.07-1.49 0.00 -1.17 -1.54 -1.99 0.03

Acquirer B/P 0.43 -0.26 -1.870.74 -0.23 -0.82

Target B/P 0.19 1.87 4.350.61 2.18 2.00

Log of relative Size -0.19 -0.33 -1.83 -2.16 -2.50 -1.76-1.75 -2.30 -5.56 -5.80 -2.97 -2.68

Log of Target Size -0.32 -0.32 -0.42 0.07 -3.49 -2.32-2.54 -1.99 -1.41 0.21 -4.50 -3.05

Intercept -1.30 -2.88 12.43 11.29 73.60 55.20-0.65 -1.15 2.26 1.85 5.95 4.49

Sample Size 1970 1497 1970 1497 1970 1497

R2 0.082 0.101 0.115 0.142 0.093 0.126

Table 14bLeast Squares Regression Results

Discrete Managerial Trading Variable

AcquirerAnnouncement

Period CAR

Target Announcement

Period CARBid Premium

This table lists the results for least squares regressions. Dependent variables are acquirer and target cumulative announcement abnormal returns andbid premium. Cumulative abnormal returns are calculated as the cumulative returns in excess of the return on the CRSP value-weighted index overa 4-day event window of [-2,+1] days around the merger announcement date. Bid premium is defined as the ratio of the difference between the bidprice and day -3 price of the target to day -3 price of the target. Managerial trading activity in acquirers and targets is measured by a rank variable(NETRANK) which take on 7 values from 1 to 7 , with 1 representing the Highest Selling (HS) firms and 7 representing the Highest Buying (HB)firms. Trading Activity rank variable is equal to 1 if HS=1, 2 if MS=1, 3 if LS=1, 4 if NO=1, 5 if LB=1, 6 if MB=1 and 7 if HB=1. The dummyvariables Hostile, Tender Offer, Cash and Stock take on the value 1 if the acquisition is hostile, mode of acquisition is tender offer, the method ofpayment is all cash and the method of payment is all stock respectively. B/P is the book to price ratio. B/P for calendar year t is calculated as the boAll regressions include year and acquirer and target 2-digit SIC major industry dummies.