Bessembinder Zhang LRReturn2012 Apr 30

Firm Characteristics and Long-run Stock Returns after Corporate Events*

Hendrik Bessembinder

David Eccles School of Business

University of Utah

1655 E. Campus Center Drive

Salt Lake City, UT 84112

[email protected]

Tel: 801-581-8268

Feng Zhang

David Eccles School of Business

University of Utah

1655 E. Campus Center Drive

Salt Lake City, UT 84112

[email protected]

Tel: 801-587-9476

This Version: May 2012

Abstract

The frequently-documented negative abnormal long run buy-and-hold returns to bidding firms,

SEO firms, and IPO firms can be attributed to imperfect control-firm matching. Control firms

are most often selected on the basis of firm size and market-to-book ratios. However, event

firms differ from control firms in terms of idiosyncratic volatility, illiquidity, beta, and return

momentum, each of which is also known to be related to returns. We propose a simple

regression-based approach to control for differences between event and control firms in

characteristics other than those used to match, and show that long run abnormal returns do not

differ significantly from zero for bidding firms, SEO firms or IPO firms in the 1980 to 2005

period. Our method also reconciles results of studies relying on time series factor model

regressions with those focusing on abnormal buy-and-hold returns.

Keywords: firm characteristics, long-run stock returns, BHARs, calendar-time portfolio, wealth

relative, event study

JEL classification: G34

Firm Characteristics and Long-run Stock Returns after Corporate Events

Abstract

The frequently-documented negative abnormal long run buy-and-hold returns to bidding firms,

SEO firms, and IPO firms can be attributed to imperfect control-firm matching. Control firms

are most often selected on the basis of firm size and market-to-book ratios. However, event

firms differ from control firms in terms of idiosyncratic volatility, illiquidity, beta, and return

momentum, each of which is also known to be related to returns. We propose a simple

regression-based approach to control for differences between event and control firms in

characteristics other than those used to match, and show that long run abnormal returns do not

differ significantly from zero for bidding firms, SEO firms or IPO firms in the 1980 to 2005

period. Our method also reconciles results of studies relying on time series factor model

regressions with those focusing on abnormal buy-and-hold returns.

Keywords: idiosyncratic volatility, liquidity, long-run stock return, BHAR, wealth relative, event

study

JEL classification: G34

1

Firm Characteristics and Long-Run Stock Returns after Corporate Events

Two approaches have commonly been employed to measure long-run abnormal stock

returns after corporate events. The first approach is based on “buy-and-hold abnormal returns

(BHARs)”, which are assessed as the difference between long run buy-and-hold returns to event

firms compared to control firms that are matched to event firms based on firm characteristics

such as size and book-to-market ratio (B/M). The second, “calendar time portfolio,” approach

focuses on mean time series returns to a portfolio of event firms, after adjusting for portfolio

exposure to various risk factors.

The two approaches produce contrasting results in many studies. For example, Betton,

Eckbo, and Thorburn (2008) study bidding firms in mergers and acquisitions (M&A), and report

statistically significant five-year BHARs of -21.9% during the 1980 to2003 period, but also

report an economically small (0.08% per month) and statistically insignificant “alpha” (intercept

in the regression of calendar portfolio returns on risk factors) estimate for the same firms. Eckbo,

Masulis, and Norli (2008) study industrial firms that issued secondary (SEO) and initial (IPO)

offerings of common stocks during the 1980 to 2000 period and report significant five-year

BHARs of -29.7% and -18.0%, respectively. In contrast, the estimated alphas are for -0.18%

and -0.16%, respectively, and neither is statistically significant. Table 1 provides a summary of

results reported in several additional studies of returns to bidders and to firms issuing common

stock.1

There are at least three possible reasons for the discrepancy in results across the two

methods. First, the risk factors used to adjust calendar time portfolio returns may be imperfect,

1 See Fama (1998), Loughran and Ritter (2000), Betton, Eckbo, and Thorburn (2008), and Eckbo, Masulis, and Norli

(2008) for additional discussion of the contrasting results.

2

which is the “bad-model problem” identified by Fama (1998). Second, returns to equal-

weighted portfolios and coefficient estimated in OLS return regressions are biased due to noise

in security prices, while buy-and-hold returns are largely free of such bias.2 Third, while

matching firms are typically selected on the basis of firm characteristics known to be related to

average stock returns, it is typically not practical to match firms based on more than two or three

characteristics. The literature has identified numerous characteristics capable of explaining

variation in average stock returns, leaving open the possibility that the matched firms

systematically differ in non-matched characteristics that also affect returns. Further, firm

characteristics may change after corporate events, implying that pairs of firms that are well-

matched in terms of selected characteristics at a point in time may not remain so.

In this study, we assess whether the conflicting results obtained in studies of long run

abnormal returns obtained across the matched-firm versus time-series portfolio methods can be

explained on the basis of imperfect matching. We study long-run returns to bidding firms in

mergers and acquisitions (M&As), to firms that issue seasoned equity (SEOs), and to firms

making initial public offerings (IPOs) of common stock. The matched firm approach is most

often implemented on the basis of firm size and book-to-market ratio. However, we show that

event firms differ significantly from their size- and B/M-matched counterparts in terms of

idiosyncratic volatility, illiquidity, market beta, and return momentum.3 In particular, bidding

firms, SEO firms and IPO firms all typically have significantly greater idiosyncratic volatility

2 Lyon, Barber, and Tsai (1999) note that buy-and-hold returns avoid the upward bias that exists in rebalanced (e.g.

equal-weighted) portfolio returns. Such bias was first emphasized by Blume and Stambaugh (1983). Asparouhova,

Bessembinder, and Kalcheva (2012) show that the bias extends also to coefficients estimated in OLS regressions

with security or portfolio returns as the dependent variable. 3Idiosyncratic volatility is computed as the annualized standard deviation of the residual stock returns in the Fama-

French three factor regression, as in Ang, Hodrick, Xing, and Zhang (2006). Illiquidity is computed as the average

of the daily ratio of absolute stock return to dollar trading volume, as in Amihud (2002). Momentum is measured by

the cumulative return from 12 months to 2 months before the match date, as in Jegadeesh and Titman (1993). We

skip one month before the match date when calculating momentum to avoid the short-term reversal documented by

Jegadeesh (1990).

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than their matching firms. All three types of event firms (M&As, SEOs, and IPOs) are

significantly less illiquid than their matching firm, and all three types are characterized by higher

market betas than their matching firms, while both SEO and M&A merger firms have

significantly higher momentum than their size and B/M matched counterparts.

Differences in idiosyncratic volatility, illiquidity, market beta, and momentum across

event firms and their matched counterparts potentially affect BHARs. Ang, Hodrick, Xing, and

Zhang (2006) among others show that idiosyncratic volatility is negatively associated with

expected stock returns.4 Amihud and Mendelson (1986), Amihud (2002), and Pástor and

Stambaugh (2003), among others, find that both measures of illiquidity and systematic illiquidity

risk are priced in the cross-section of stock returns.5 Numerous studies, commencing with

Jegadeesh and Titman (1993), have documented that firms with relatively high recent returns

tend to continue earning high returns in the intermediate term.

These results imply that matching event and control firms on the basis of firm size and

B/M may not provide a complete control for differences in average returns that are unrelated to

the event being studied, i.e. that the matched firms may not provide an appropriate benchmark to

assess the event firms’ performance. Consistent with this reasoning, when we divide sample

event firms into quintiles based on differences in idiosyncratic volatility and illiquidity between

event firms and matching firms over the 12 months before M&As and SEOs or over the 12

months after IPOs, we observe that BHARs significantly decrease with the difference in

idiosyncratic volatility and increase with the difference in illiquidity.

4 Fu (2009), in contrast, finds that expected idiosyncratic volatility is positively associated with expected stock

returns. Fu (2009) and Ang, Hodrick, Xing, and Zhang (2006, 2009) imply return premia with opposite signs. See

the following research for more evidence on the relation between idiosyncratic volatility and stock returns: Bali and

Cakici (2008), Guo and Savickas (2008), Huang, Liu, Rhee, and Zhang (2010), and Jiang, Xu, and Yao (2009). 5 See Amihud, Mendelson, and Pedersen (2005) for summaries of the literature on illiquidity and stock returns.

4

In light of the evidence that firm size and B/M ratio matching is only partially successful

in allowing for differences in mean returns across event and matched firms, and since matching

on a large number of firm characteristics is likely to be impractical, we propose a simple new

approach to measuring abnormal returns after corporate events. In particular, we propose to

assess abnormal returns as the intercept obtained when regressing differences in monthly log

returns across matched and control stocks on standardized differences in relevant firm

characteristics, including idiosyncratic volatility, illiquidity, momentum, and market beta.

A substantial prior literature has focused on statistical issues that arise when testing

whether long run BHARs differ from zero.6 Lyon, Barber, and Tsai (1999) note that the matched

firm approach, where buy-and-hold returns to event firms are compared to returns on size and

B/M matched control firms, yields test statistics that are well-behaved in random samples.

These apparently well-behaved test statistics indicate significantly negative long run abnormal

returns to bidding firms, IPO firms, and SEO firms (though the latter only on a value-weighted

basis). We contribute to this literature from a new angle, by investigating whether additional

firm characteristics, including idiosyncratic volatility, illiquidity, and momentum, are able to

explain observed BHARs. We show that the proposed regression approach that allows for

differences in firm characteristics across event and control firms can fully explain observed

BHARs when studying M&A, IPO, and SEO events. The method therefore substantially

reconciles the conflicting evidence in the literature obtained from BHAR versus time series

portfolio return methods.7

6 See Fama (1998), Brav (2000), and Kothari and Warner (2007) for summaries of this literature. For details of the

statistical issues and suggested solutions, see Barber and Lyon (1997), Kothari and Warner (1997), Lyon, Barber,

and Tsai (1999), Mitchell and Stafford (2000), and Jegadeesh and Karceski (2004). 7 Further, since the method is implemented in continuously compounded returns, it is free of biases attributable to

noisy prices identified in Asparouhova, Bessembinder, and Kalcheva (2012), while the calendar time portfolio

method is not.

5

The extant study that is closest to our own in terms of the research question posed is

Eckbo, Masulis, and Norli (2000). They note that the negative BHARs observed for SEO firms

in earlier studies potentially arise because event firms differ from matched firms in terms of

sensitivity to an array of macroeconomic risks. Consistent with this interpretation, they estimate

time series regressions of “zero-investment” portfolio (long event firms and short control firms)

returns on macroeconomic factors and report insignificant intercepts and some significant slope

coefficients. We note, though, that they estimate insignificant intercepts for portfolios of event

firms and portfolios of control firms as well, implying that abnormal returns are zero for both

SEO and control firms. Their study therefore highlights the tension between results obtained by

studying calendar time portfolio alphas versus studying BHARs relative to matched firms. We

show how this tension can be resolved by considering additional firm characteristics.

I. Data

I.A. Our Samples of Event Firms

This paper focuses in particular on the impacts of imperfect matching on long-run

abnormal stock returns after three types of corporate events: mergers and acquisitions, seasoned

equity offerings, and initial public offerings. However, the insights obtained here potentially

apply to studies of other corporate events such as stock splits, dividend initiations, etc.

To form the merger and acquisition sample, we identify completed mergers and

acquisitions by US public companies over the period 1980 to 2005 from Thomson Financial’s

SDC database. The sample ends with deals completed in 2005, to allow a five-year period to

measure bidder firms’ long-run stock returns. We impose the following filters. First, the

acquisition must take the following forms: merger (SDC deal form M), acquisition of majority

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interest (AM), acquisition of remaining interest (AR), acquisition of partial interest (AP), or

acquisition of assets (AA). Second, the acquisition must be a control bid where the acquirer

owns less than fifty percent of the target and intends to hold more than fifty percent of the target

after the acquisition. These two filters follow Betton, Eckbo, and Thorburn (2008). In addition,

we require the relative size of the deal (transaction size divided by the market value of the bidder

firm before deal completion) to be greater than five percent, and the transaction value to be more

than one million dollars. The last filter excludes small deals that are less likely to have material

impacts on the long-run performance of the acquirer. We are able to identify 5,148 such

transactions.

We identify matching firms for the mergers and acquisitions sample using a procedure

similar to that of Lyon, Barber, and Tsai (1999) and Eckbo, Masulis, and Norli (2000). Each

matched firm is selected, on the basis of data from the end of December preceding the deal, as

the firm with the closest book-to-market ratio among firms with market capitalization between

70% and 130% of the bidder firm. To be included, the matching firm must not have itself

acquired other firms during the ten years around the matching date. If a matching firm delists,

then the candidate matching firm with the second closest book-to-market ratio is added for the

remainder of the five-year period. If the second matching firm delists, the candidate matching

firm with the third closest book-to-market ratio is added, etc. We are able to identify matching

firms for 4,579 of the bidder firms in our sample.

To form the SEO sample we first identify all completed SEOs contained in the SDC

database during the 1980 to 2005 period. Following Eckbo, Masulis, and Norli (2000, 2008) we

exclude ADRs, GDRs, unit offerings, financial companies, and public utilities. There are 7,204

such deals. For each, we select as a match the firm with the closest book-to-market ratio among

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firms whose market capitalization is between 70% and 130% of the SEO firm at the end of

December preceding the SEO. To be included, the matching firm must not have conducted any

SEO during the ten years around the matching date. If a matching firm delists, then the

candidate matching firm with the second closest book-to-market ratio is added for the remainder

of the five-year period. We are able to identify matching firms for 5,573 of the SEO offerings in

our sample.

To form the IPO sample we first identify all completed IPOs in the SDC database over

the period 1980-2005, excluding REITs, closed-end funds, and ADRs, of which there are 9,035.

To form the matched firm sample we follow Loughran and Ritter (1995). Each IPO firm is

matched, based on data at the end of December after the IPO, with the firm with the closest but

greater market capitalization.8 To be included, the matching firm must have been publicly traded

for more than five years. If a matching firm delists, then the candidate matching firm with the

second closest market capitalization is substituted for the remainder of the five-year period We

are able to identify matching firms for 8,987 of the IPO firms in our sample.

Table 2 reports the number of event firms in each sample on an annual basis. The

number of M&A deals is small before 1984 due to the limited coverage of the SDC database.

The number of event firms also shows substantial variation across sample years. In particular,

the number of IPOs jumps substantially in the 1990s to a peak of 797 in 1996, before declining

to less than 100 per year in years 2001 to 2003.

I.B. Characteristics of Event and Control Firms

The matching of event with control firms on the basis of firm size and B/M ratio is

standard in the literature on long run returns. However, as we demonstrate, firms matched on

8 Loughran and Ritter did not match on B/M ratio.

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these characteristics are not necessarily well-matched on other characteristics, including

idiosyncratic volatility, illiquidity, return momentum, and market beta. In addition, firms that are

well-matched in terms of size and market capitalization as of a given date do not typically remain

well-matched in these dimensions as time passes.

We construct a measure of idiosyncratic volatility following Ang, Hodrick, Xing, and

Zhang (2006). In particular, for each event and control stock, idiosyncratic volatility is

computed as the annualized standard deviation of the residuals in a regression of daily stock

returns on the three Fama-French (1993) factors. We compute separate estimates for each of the

60 months before and after the corporate event. We measure illiquidity using the metric

introduced by Amihud (2002). In particular, for each stock and for each of the 60 months before

and after the corporate event, we compute the Amihud illiquidity measure as the average of the

daily ratio of absolute stock return to dollar trading volume. For each of the 120 months around

corporate events, we calculate return momentum for event and matching firms as their respective

cumulative returns from the 12th

month to the 2nd

month prior to that month. Market beta is

estimated for each firm in each of the 120 months around the corporate events by implementing

the market model in daily stock returns.

Figure 1 displays cross-sectional median idiosyncratic volatility, illiquidity, return

momentum, market beta, firms size, and B/M ratio for bidding firms and for matched firms, on a

monthly basis from 60 months before to 60 months after the bid. Notably, sample bidding firms

have greater median idiosyncratic volatility than their matching firms. Bidding firms also differ

from control firms in terms of illiquidity, as the median illiquidity of bidding firms is always

smaller than that of their matching firms over the 120 months around the M&A. Though not

displayed, bidding firms also have greater mean idiosyncratic volatility and smaller mean

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illiquidity than their matching firms. We also observe that bidding firms have larger market

betas throughout the 120 month interval, and higher return momentum during approximately the

24 months surrounding the bid.

Figure 2 plots the same information for SEO sample firms and their size-and-book-to-

market-matched comparable firms. The SEO firms have greater median idiosyncratic volatility

than their matching firms over the 120 months around SEO. SEO firms have much lower levels

of illiquidity and larger betas as compared to their matching firms, particularly in the months

after the SEO. Further, SEO firms have greater return momentum in approximately the twenty

four months around the issue.

Figure 3 displays monthly medians on the same variables for the sample IPO firms and

their size-matched comparable firms over the 60 months after going public. The IPO firms are

characterized by substantially greater median idiosyncratic volatility and market beta, and

moderately less median illiquidity as compared to their matching firms.

Figures 1 to 3 also provide evidence regarding the extent to which event and control

firms are well-matched on the basis of firm size and book-to-market through time. While each

sample is indeed well matched on average at a point in time (the December prior to the event for

the merger and SEO samples and the December after the event for the IPO sample), the

closeness of the match degrades as time passes. The median size of bidding firms exceeds that

of control firms by the event date, and thereafter. The median size of SEO firms increases

substantially in the months before the SEO, and substantially exceeds that of the matched sample

throughout the post event period. In contrast the median size (as well as the median book-to-

market ratio) of IPO firms is considerably less than that of control firms during most of the post-

event period.

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To summarize, Figures 1-3 show that the event firms differ on average from their size-

and-book-to-market-matched comparable firms in terms of idiosyncratic volatility, illiquidity,

market beta, and return momentum. All three types of event firms have greater idiosyncratic

volatility, smaller median illiquidity, and larger market betas than their matching firms. Since a

number of studies have shown mean security returns to be related to idiosyncratic volatility,

momentum, beta, and illiquidity, these results imply that divergences in buy-and-hold abnormal

returns (BHARs) after the corporate events returns across event and control firms in the months

following the events may be attributable in whole or part to differences in idiosyncratic volatility

and illiquidity. In addition, while event and control firms are well matched in terms of size and

book-to-market ratio at a point in time, the closeness of the match degrades in subsequent

months.

We assess in Section II whether BHARs are indeed related to divergences in firm

characteristics across event and control firms. In Section III we assess whether differences in

idiosyncratic volatility, illiquidity, momentum, and market beta, in combination with “drift” in

size and book-to-market ratios, can fully explain average BHARs for sample firms.

II. Firm Characteristics and BHARs: Univariate Analysis

To assess the potential effects of idiosyncratic volatility, illiquidity, return momentum,

and beta on computed BHARs, we divide the sample firms into quintiles based on the differences

in these characteristics between the event firms and their matching firms. For the M&A and

SEO samples we compute idiosyncratic volatility, illiquidity, return momentum, and beta over

the 12 months before the event, while for the IPO sample, these four firm characteristics are

computed over the 12 months after the IPO. We then average these estimates across the 12

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months, and compute the difference in the average across event and matching firms. The

difference in return momentum between an event firm and its matching firm is calculated using

the cumulative returns over months -12 to -2 before the event (for M&As and SEOs) or over

months 1 to 11 after the event (for IPOs).

Table 3 reports the 10th

through the 90th

percentiles of the distribution of differences in

idiosyncratic volatility, illiquidity, return momentum, and beta between the event firms and their

matching firms. The results indicate substantial variation across firms in the extent of the

mismatch between event and control firm idiosyncratic volatility and illiquidity. For example,

more than forty percent of the SEO sample have idiosyncratic volatility that differs (in absolute

value) by more than 12% per year from that of their matching firms. The idiosyncratic volatility

of the 10th

percentile IPO firm differs from that of the 90th

percentile IPO firm by over 75% per

year. Sixty percent of bidding firms have estimated market betas that differ by more than 0.8, etc.

In Table 4 we report equal- and value-weighted BHARs for event firms over the 60

months after the corporate events. Following Eckbo, Masulis, and Norli (2008), the buy-and-

hold return of each event firm is calculated as its cumulative stock return from the first month

after the event to the earliest of the 60th

month after the event, the delisting date of the event firm,

or the next corporate event of the same type. Each BHAR is computed as the buy-and-hold

return for the event firm less the buy-and-hold return for the matching control firm. We divide

event firms into quintiles based on differences in idiosyncratic volatility, illiquidity, return

momentum, and beta across event and control firms, and report average BHARs by quintile in

Table 4.

Panel A presents the BHARs of the bidding firms. Consistent with Betton, Eckbo, and

Thorburn (2008) we find that, for the full sample (reported on the bottom row), bidding firms

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suffer negative BHARs of -12.04% on an equal- weighted basis and -17.16% when value-

weighted. However, BHARs differ substantially across subsamples with differing firm

characteristics. The BHARs of bidding firms significantly decrease as the difference in

idiosyncratic volatility increases. Bidding firms in the first two quintiles have modestly negative

or positive BHARs while the bidding firms in the last two quintiles have large negative BHARs.

For example, the bidding firms in the first idiosyncratic volatility quintile have a 60-month

equal-weighted BHAR of -2.60%, while those in the fifth quintile have a BHAR of -46.49%.

The BHARs of bidding firms significantly increase as the difference in illiquidity

increases. For example, the bidding firms in the first illiquidity difference quintile have a 60-

month equal-weighted BHAR of -29.49%, while those in the fifth quintile have a BHAR of

+13.09%.

In addition, we find that the 60-month BHARs weighted by market capitalization of the

bidding firms significantly decrease as the differences in beta and return momentum increase.

For example, the bidding firms in the first momentum difference quintile have 60-month value-

weighted BHARs of -9.61%, while those in the fifth quintile have a 60-month value-weighted

BHARs of -50.05%. Figure 4 depicts the BHARs of the bidding firms across the quintiles based

on idiosyncratic volatility, illiquidity, return momentum, and beta.

Panel B of Table 4 reports on BHARs for sample SEO firms. Consistent with Eckbo,

Masulis, and Norli (2000), Loughran and Ritter (1995), and Spiess and Affleck-Graves (1995),

we find that SEO firms earn negative BHARs: the equal-weighted 60-month BHARs are -8.65%,

while the value-weighted 60-month BHARs are -19.55%. However, we again observe cross-

sectional variation in BHARs related to firm characteristics. The BHARs for SEO firms

significantly decrease as the difference in idiosyncratic volatility increases, a similar pattern as

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the bidding firms. The SEO firms in the first quintile based on difference in idiosyncratic

volatility have positive five-year BHARs, while those in the last quintiles have negative BHARs.

For example, the SEO firms in the first idiosyncratic volatility difference quintile have a 60-

month equal-weighted BHAR of +18.75%, while those in the fifth quintile have a BHAR of -

26.68%.

The BHARs of the SEO firms also significantly increase as the difference in illiquidity

increases. The pattern, again, is similar to that of the bidding firms. For example, the SEO firms

in the first quintile based on difference in illiquidity have a 60-month equal-weighted BHAR of -

13.07%, while those in the fifth quintile have a BHAR of +13.08%.

The value-weighted BHARs of the SEO firms also significantly decrease as the

differences in beta and return momentum increase. For example, the SEO firms in the first

momentum difference quintile have a60-month value-weighted BHARs of -6.74%, while those

in the fifth quintile have 60-month value-weighted BHARs of -35.19%. Figure 5 depicts the

BHARs of the SEO firms across the quintiles based on idiosyncratic volatility, illiquidity, return

momentum, and beta.

Panel C of Table 4 presents BHARs for IPO firms. Consistent with Loughran and Ritter

(1995) and Eckbo, Masulis, and Norli (2008), IPO firms suffer significant negative BHARs for

the full sample, of -47.90% on an equal-weighted basis and -55.06% on a value-weighted basis.

The BHARs of the IPO firms significantly decrease as the difference in idiosyncratic volatility

increases, a similar pattern as the bidder firms and the SEO firms. The IPO firms in the first two

quintiles based on difference in idiosyncratic volatility have positive BHARs, while those in the

last two quintiles have significantly negative BHARs. For example, the IPO firms in the first

idiosyncratic volatility difference quintile have a 60-month equal-weighted BHAR of -8.59%,

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while those in the fifth quintile have a BHAR of -113.44%. In contrast, the BHARs of the IPO

firms are not monotone across the quintiles based on difference in illiquidity. The value-

weighted BHARs of the IPO firms decrease significantly as the beta difference increases. The

IPO firms in the first beta difference quintile have value-weighted BHARs of -24.49%, much

greater than the value-weighted BHARs of the IPO firms in the fifth beta difference quintile

which are -104.81%. Figure 6 depicts the BHARs of the IPO firms across the quintiles based on

idiosyncratic volatility and illiquidity.

In summary, we find that long-run BHARs for M&A bidder firms and firms issuing

initial and secondary equity offerings are systematically related to differences in idiosyncratic

volatility and illiquidity between the event firms and their matching control firms. In general,

the effects on BHARs are as would be anticipated based on the extant literature. In particular,

Ang, Hodrick, Xing, and Zhang (2006) document lower average returns for firms with greater

idiosyncratic volatility. Consistent with this insight, we document larger BHARs for firms with

lower idiosyncratic volatility as compared to their matched counterparts, and vice versa, for the

bidder, SEO, and IPO samples. Amihud (2002) and others document higher average returns for

firms with greater illiquidity. Consistent with this finding, we document smaller BHARs for

firms with smaller illiquidity, as compared to their matched counterparts, and vice-versa, for the

bidder and SEO samples. For the IPO sample, in contrast, the relation between BHARs and the

extent of the illiquidity mismatch is not monotone. We also find weaker evidence that the

BHARs of the event firms are associated the differences in beta and return momentum between

the event firms and their matching firms.

The analysis in this section was based on univariate comparisons. Of course, illiquidity,

idiosyncratic volatility, return momentum, and beta may be correlated, and may all matter for

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BHARs. We next turn to a multivariate analysis of BHARs that simultaneously considers

illiquidity, idiosyncratic volatility, firm size, market to book ratios, return momentum, and

market beta.

III. Firm Characteristics and BHARs

In Section II, we show that firm characteristics, and in particular illiquidity and

idiosyncratic volatility, are significantly related to the BHARs of the event firms. In this section,

we propose a general framework to investigate the effects of time series and cross-sectional

variation in firm characteristics on long-run abnormal stock returns after corporate events.

III.A Model for Long-run Stock Returns after Corporate Events

The BHAR of event firm e over T months after a corporate event at date 0 is:

})1ln(exp{})1ln(exp{ )1()1(1111

T

t

mt

T

t

et

T

t

mt

T

t

eteT rrrrBHAR , (1)

where ret and rmt are the monthly stock returns of the event firm and its firm-characteristics-

matched comparable firm, respectively. Consider also the wealth relative (WR) as defined by

Ritter (1991) and Loughran and Ritter (1995):

.

)1(

)1(

)}1ln()1{ln(exp{

1

1

1

T

t

mt

T

t

et

mt

T

t

eteT

r

r

rrWR

(2)

Wealth relative measures the T-period gross return to a $1 investment in the event firm relative

to the T-period gross return to the same investment in the matching firm. Testing whether

0eTBHAR is equivalent to testing whether 1eTWR , as both equations hold if the time series

mean log return is equal across event and control firms.

16

Guided by the evidence reported in Section II and the equivalence between testing

expressions (1) and (2), we propose to explain the differences in monthly stock returns between

the event firm and its matching firm based on differences in firm characteristics. In light of the

evidence displayed on Figures 1 through 3, we measure firm characteristics on a monthly basis,

allowing time variation in the closeness of the match between event and control firms to

contribute to a potential explanation of abnormal returns.

We consider six characteristics that have been shown to be associated with expected

stock returns: market beta, firm size, B/M, momentum, illiquidity, and idiosyncratic volatility.

Market beta for July of year t to June of year t+1 is estimated using the market model in monthly

stock returns during years t-5 to t-1. Firm size is measured as market capitalization at the end of

the latest June. B/M is defined as the ratio of the book value of common equity at the end of

fiscal year t-1 to the market value of common equity at the end of the latest June. Momentum is

the cumulative return over months -12 to -2. Idiosyncratic risk is the annualized standard

deviation of the residuals obtained in a Fama-French three factor regression implemented in

daily returns during month -2. Illiquidity for July of year t to June of year t+1 is computed as

the average ratio of daily absolute stock return to dollar trading volume from July of year t-1 to

June of year t, relative to market average illiquidity during the same period, as in Amihud

(2002).

Specifically, we employ the following regression model to investigate the effects of firm

characteristics on long-run stock returns after a corporate event:

, ..., 3, 2, ,1 ; ..., 3, 2, ,1

/)1ln()1ln(

654

321

TtEe

IdioVolyIlliquiditMom

MBSizeBetarr

etetetet

etetetmtet

(3)

17

where Δ denotes a normalized difference in the associated firm characteristic across the event

firm and the matching firm.

We normalize the firm-characteristic variables to make comparable coefficients across

characteristics, as follows. For each of the six firm characteristics (beta, size, B/M, momentum,

illiquidity, and idiosyncratic volatility), we compute the difference between the event firm and its

matching firm on a monthly basis. Then, for each characteristic, the positive differences are

sorted from smallest to largest and converted to percentile rankings. Negative differences are

separately sorted from least to most negative and converted to minus the percentile ranking.

The normalized differences therefore range from -1 to 1 for each of the firm characteristics.

Note that each of the normalized characteristics is measured on the basis of information available

prior to month t.

The key to assessing abnormal returns in this specification is the estimated intercept. As

in any regression specification, the intercept measures the mean of the dependent variable,

conditional on outcomes of zero for each independent variable. When (2) is estimated without

any explanatory variables the estimated intercept measures the differential in the average

continuously compounded return across event and control firms. As noted above, testing

whether this differential is zero is equivalent to testing whether the BHAR is zero. When

standardized explanatory variables are included in the regression the intercept estimates the mean

abnormal log return to event firms, conditional on no difference in firm characteristics across

event and control firms.

This regression-based method offers four advantages over the BHAR method

implemented in the previous literature. First, it accommodates variation in firm characteristics

18

other than those used to select the matched firms.9 Second, it accommodates variation across

time in firm characteristics. Third, it addresses the compounding problem of BHARs noted by

Fama (1998) and Mitchell and Stafford (2000), whereby BHARs grow as stock returns are

cumulated over a long period even if the abnormal return is confined to a few periods.

Fourth, as Barber and Lyon (1997) and Lyon, Barber, and Tsai (1999) observe, long-run

BHARs are skewed and have fat tails, rendering statistical inferences difficult. In contrast, the

difference in monthly log returns, which is used as the dependent variable in our proposed

specification, has better statistical properties. In Table 5, we report the standardized (scale

invariant) skewness and kurtosis of BHARs for our samples. The skewness of the BHARs

ranges from -2.24 (SEOs) to 27.24 (M&As), and the kurtosis of the BHARs ranges from 69.22

(SEOs) to 1365.07 (M&As). The skewness and kurtosis of the differences in log returns are

much smaller, with the skewness ranging from -0.23 to -0.15 and the kurtosis ranging from 9.36

to 11.68.

III.B. Firm Characteristics and Abnormal Returns after M&As

Table 6 reports results of estimating equation (3) for returns to bidders and matched firms

in the sixty months following mergers and acquisitions completed from 1980-2005. Panel A1

reports results obtained from estimating equation (3) in pooled time series and cross sectional

data, while Panel A2 reports results obtained by the Fama-MacBeth procedure, where a cross-

sectional regression is estimated for each sample month and final estimates are obtained as the

time-series mean of the monthly estimates.

9 In principle the matching algorithm can consider additional characteristics as well, but the quality of matches will

degrade as the number of matching characteristics increases.

19

Column (1) of Table 6 Panel A1 reports results obtained by pooled OLS estimation, with

no controls for firm characteristics. The estimated constant, which is the mean continuously

compounded differential in returns for bidding vs. control firms, is -0.42%. Stated alternately,

the wealth relative is given by expression (2) as 0.78 (= exp(-0.0042*60)), even though bidders

are matched to control firms on the basis of size and B/M ratio.

The residuals in a pooled time series cross sectional analysis may be correlated within

firms or over time, leading to biases in estimates of the coefficient standard errors, as

emphasized by Petersen (2009). To allow for the possible correlations, we compute standard

errors while allowing for residual clustering by M&A deal and by date, and report results in

columns (2) and (3), respectively. Clustering by deal barely changes the t-statistic for the

intercept, indicating relatively small correlation of residuals across time within deals. On the

other hand, clustering by date significantly decreases the magnitudes of the t-statistic from -7.92

to -4.21. The decrease in the absolute t-statistic indicates significant correlations in residuals

across firms on given dates. This correlation, in turn, likely reflects that a given calendar date

will typically be contained in the sixty month interval used to assess BHAR for numerous events.

The remainder of the results we report allow for clustering by date.

The regression results reported in column (4) are based on the same specification as used

for column (3), but we exclude firm-months where any firm characteristic datum is unavailable.

The estimated constant in column (4) is -0.42%, the same as column (3) results, indicating that

the restriction on availability of firm characteristics does not materially affect inference

regarding BHARs.

Column (5) reports results when illiquidity and idiosyncratic volatility are included in the

pooled OLS regression with standard errors clustered by date. We observe that illiquidity is

20

positively associated with abnormal returns, while idiosyncratic volatility is negatively

associated with abnormal returns. Both effects are statistically significant at the one percent level.

The estimated intercept is reduced from -0.42% in column (2) to -0.26% (corresponding to a

wealth relative of 0.86) in column (5). That is, variation across firms and over time in illiquidity

and idiosyncratic volatility explains about 35% (= (0.86-0.78)/(1-0.78)) of the observed BHARs

for bidding firms. Column (6) reports results when market beta is added as an explanatory

variable. Market beta is negatively and statistically insignificantly associated with abnormal

returns, but has no material effect on the estimated intercept.

Column (7) reports results when all six firm characteristics are included in the regression.

We observe that B/M, momentum, and illiquidity are positively associated with abnormal returns,

while idiosyncratic volatility is negatively associated with returns. The estimated slope

coefficients on B/M, momentum, illiquidity, and idiosyncratic volatility are all statistically

significant in column (7), underscoring the importance of allowing for variation in these

characteristics. That the coefficient on B/M is significant even though firms are matched on

this characteristic reflects both imperfect matching and that the closeness of the match degrades

over time.

However, the inclusion of size, B/M, and momentum has no material impact on the

estimated constant, which decreases from -0.25% from in column (6) to -0.27% (corresponding

to a wealth relative of 0.85) in column (7). We conclude that inclusion of firm characteristics in

the pooled regression explains about a third of the bidder abnormal returns.

Petersen (2009) demonstrates that, in those cases where residuals are correlated across

firms at a given date but not over time, the Fama-MacBeth (1973) procedure provides unbiased

estimates of standard errors. In addition, the Fama-MacBeth procedure has been widely used in

21

the literature to assess relations between stock returns and firm characteristics (e.g. Fama and

French, 1992, 1993).

We present results of estimating expression (3) by the Fama-Macbeth method for the

bidding firm sample in Panel A2 of Table 6. Column (1) reports the results obtained by

implementing the Fama-MacBeth procedure, with no controls for firm characteristics. The

estimated intercept term decreases in absolute magnitude to -0.29% (corresponding to a wealth

relative of 0.84) from -0.42% (corresponding to a wealth relative of 0.78) in column (1) of Panel

A1. The constant term in the pooled OLS regression reveals the mean abnormal return across

the firm-months in our sample, while the constant term estimated from the Fama-MacBeth

procedure reveals the time series mean of monthly average abnormal returns. Stated alternately,

the pooled OLS regression assigns equal weight to each event firm, while the Fama-MacBeth

procedure assigns equal weight to each event month. The latter procedure alone reduces the

estimated abnormal returns by about 28%.10

Column (2) of Panel A2 reports results obtained when illiquidity and idiosyncratic

volatility are included in the Fama-MacBeth regression. Both idiosyncratic volatility and

illiquidity are statistically significant. The estimated intercept decreases further, to -0.22%

(corresponding to a wealth relative of 0.88), with an associated t-statistic of 2.47.

Column (3) reports results when market beta is also included in the Fama-MacBeth

regression. Although the estimated coefficient on market beta is not significant (t-statistic = -

1.42), its inclusion alters inference: the estimated constant term increases to -0.15%,

(corresponding to a wealth relative of 0.91), which is statistically insignificant (t-statistic = -1.26).

10

We do not take a position that either method is inherently superior, but simply observe that the weighting method

is relevant to inference. See Fama (1998), Loughran and Ritter (2000), and Mitchell and Stafford (2000) for

additional discussion of the event firm vs. event time weighting issue.

22

Thus, this specification supports the conclusion that there is no long run abnormal return

performance for bidding firms.

Finally, column (4) of Panel A2 reports results when all six characteristics are included in

the Fama-MacBeth regression. Idiosyncratic volatility remains negative and significant (t-

statistic = 2.11) in this specification, verifying that BHARs computed without controlling for

differentials in volatility will be misleading. Most important, the estimated intercept becomes

positive in this specification to +0.54%, which is statistically insignificant with an associated t-

statistics of 0.95. That is, the Fama-MacBeth specification, implemented while allowing for time

varying differences in firm characteristics across event and control firms, provides no evidence

of statistically significant long run abnormal returns for the sample of bidder firms. This result

stands in contrast to Loughran and Vijh (1997), but is consistent with the conclusions of Moeller,

Schlingemann, and Stulz (2004), Harford (2005), and Betton, Eckbo, and Thorburn (2008), and

Rau and Vermaelen (1998).

III.C. Firm Characteristics and Abnormal Returns after SEOs

We next assess results of estimating expression (3) for sample of SEO firms and their

matched counterparts. Panel B1 of Table 6 reports results of pooled time-series cross-sectional

estimation, while Panel B2 reports results of implementing the Fama-MacBeth procedure.

Consistent with prior studies, including Loughran and Ritter (1995), Spiess and Affleck-

Graves (1995), Jegadeesh (2000) and Eckbo, Masulis, and Norli (2008), the estimated intercept

in each of the first four regressions of Table 6 Panel B1 is negative and statistically significant,

23

indicating that abnormal returns are significant negative. In terms of economic significance, the

estimated intercept is -0.25% for the full sample, corresponding to a wealth relative of 0.86.11

Column (5) reports results when illiquidity and idiosyncratic volatility are included in the

pooled OLS regression. We observe that illiquidity is positively associated with abnormal return,

while idiosyncratic volatility is negatively associated with abnormal return. Both effects are

statistically significant. More important, the intercept becomes both statistically and

economically insignificant with only these two firm characteristics included. The magnitude of

the intercept shrinks from -0.25% (corresponding to a wealth relative of 0.86) to -0.07%

(corresponding to a wealth relative of 0.96). The associated t-statistics shrinks from -2.14 in

column (3) to -0.71 in column (5).

In column (6), market beta is also included as a control variable in addition to illiquidity

and idiosyncratic volatility. Adding market beta to the regression does not materially change the

regression results. For example, the intercept becomes -0.06% and remains statistically

insignificant with an associated t-statistic of -0.68. Column (7) reports results when all six firm

characteristics are included in the regression. We observe that B/M, momentum, and illiquidity

are positively associated with abnormal returns, while idiosyncratic volatility is negatively

associated with returns. Notably, the inclusion of size, B/M, and momentum has no material

impact on the estimated constant, which decreases from -0.06% from in column (6) to -0.08%

(corresponding to a wealth relative of 0.95) in column (7) and remains statistically insignificant.

We conclude that variation in firm characteristics across event and control firms fully explain

abnormal returns to firms that completed SEOs during our 1980 to 2005 sample period.

11

Comparing estimated t-statistics across column 1 (estimated by OLS), column 2 (allowing for clustering by deal)

and column 3 (allowing for clustering by date), we again conclude that clustering by date is empirically important,

and implement such for the remainder of our SEO results.

24

For completeness, we present results of estimating expression (3) by the Fama-Macbeth

method for the SEO firm sample in Panel B2 of Table 6. Column (1) reports the results

obtained by implementing the Fama-MacBeth procedure, with no controls for firm

characteristics. The estimated intercept term decreases in absolute magnitude to -0.16%

(corresponding to a wealth relative of 0.91) from -0.25% (corresponding to a wealth relative of

0.86) in column (1) of Panel A1, and becomes statistically insignificant with an associated t-

statistic of -1.34. The results indicate that the Fama-MacBeth estimation procedure, which

equal weights each event month, is alone is able to partially explain the BHARs of the SEO firms.

A subset or all of the six firm characteristics are included as controls in the Fama-

MacBeth regressions in columns (2)-(4) of Table 6 Panel B2. Across these three columns, we

observe that B/M, momentum, and illiquidity are positively and significantly associated with

abnormal returns, while idiosyncratic volatility is negatively and significantly associated with

abnormal returns. More important, the estimated intercepts in the three specifications are all

statistically insignificant. Thus, both pooled and Fama-MacBeth regression analyses support the

conclusion that, after allowing for variation in firm characteristics, there is no evidence of

significant abnormal long run returns for SEO firms in the 1980 to 2005 period.

III.D. Firm Characteristics and Abnormal Returns after IPOs

We now turn to results of estimating expression (3) for firms conducting IPOs between

1980 and 2005. Panel C1 of Table 6 reports results of pooled time-series cross-sectional

estimation, while Panel C2 reports results of implementing the Fama-MacBeth procedure.

Consistent with Loughran and Ritter (1995), the estimated intercept from the pooled

sample without controls for firm characteristics (column 1 of Panel C1) indicates very significant

25

underperformance of IPO firms versus matching firms. The point estimate of -1.16% implies a

wealth relative of 0.50. Clustering standard errors by date dramatically reduces the magnitude

of the t-statistic of the estimated intercept from -31.58 to -4.97 in column (3). Restricting the

sample to IPOs for which all firm characteristic data is available reduces the estimated intercept

(column 4) slightly to -0.97%.

Column (5) reports results when idiosyncratic volatility and illiquidity are included in the

regression. As anticipated, the estimated coefficient on the former is negative and significant,

while that on the latter is positive and significant, and their inclusion further reduces the absolute

magnitude of the estimated intercept, to -0.74%. Inclusion of all six firm characteristics

(column 7) reduces the estimated intercept further, to -0.44% with a corresponding wealth

relative of 0.77. The combined effect of allowing for differences in firm characteristics across

IPO and control firms is to reduce the estimated intercept by more than half.

As in the cases of M&As and SEOs, we find that B/M, momentum, and illiquidity are

positively and significantly associated with abnormal returns, while idiosyncratic volatility is

negatively and significantly associated with abnormal returns. These results reinforce the

desirability of controlling for variation in firm characteristics in studies of long run abnormal

returns.

As noted, Petersen (2009) recommends the Fama-MacBeth procedure when residuals are

clustered within a date but not across dates. Table 6 Panel C2 reports results of estimating

expression (3) for the IPO sample by the Fama-MacBeth method. Column (1) reports the

estimated constant when no firm characteristics are included. The point estimate is -0.90%

(corresponding to a wealth relative of 0.68) indicating that equally weighting each time period

(rather than equally-weighting each firm-month, as in Panel C1) reduces the sixty month only

26

modestly. Column (2) reports results when idiosyncratic volatility and illiquidity are included as

regressors. Here also, the estimated coefficient on illiquidity is positive and that on volatility is

negative, and each is statistically significant. The accompanying intercept estimate is reduced to

-0.77% per month.

Column (3) reports results when market beta is included as a regressor. Similar to results

obtained for M/A bidder firms, the estimated coefficient on market beta is not significant (t-

statistic = -0.39), but its inclusion alters inference. In particular, the estimated intercept declines

in absolute magnitude to -0.38% per month, with a t-statistic of -1.84.

Column (4) reports results obtained when all six firm characteristics are included in the

Fama-MacBeth regression. Here, the difference in B/M ratio, difference in momentum, and

difference in idiosyncratic volatility continue to have significant explanatory power for abnormal

returns. Most importantly, the estimated intercept in this specification is reduced in absolute

magnitude to -0.24%, which corresponds to a wealth relative of 0.87, and which does not differ

significantly from zero (t-statistic = -1.15).

To summarize, this analysis studies mean long run abnormal returns after three important

corporate events, including returns to bidder firms in merger and acquisition deals, to issuing

firms making secondary public offerings, and to firms making initial public offerings. We

document that abnormal returns to event firms do not differ significantly from zero after

controlling for differences between event firms and control firms in observable firm

characteristics.

III.D Discussion

27

Numerous studies document significant BHARs after corporate events, and ascribe the

returns to the events themselves. However, we document that event and control firms are

imperfectly matched. The characteristics we consider, i.e. firm size, B/M ratio, illiquidity, return

momentum, market beta, and idiosyncratic volatility, are known from the prior literature to have

explanatory power for stock returns. Observing significant BHARs for event firms therefore has

at least two possible interpretations. The abnormal returns may be directly associated with the

event being studied, or may reflect, in full or part, differences in firm characteristics across event

and control firms. The results in this section show that the latter interpretation is appropriate.

When results are weighted equally across firms and events (as in OLS estimation of a pooled

time-series cross-sectional version of expression (3) we find that variation in characteristics

explains a substantial portion or even all of observed BHARs. When results are weighted

equally across time (when the Fama-MacBeth procedure is implemented to estimate expression

(3) we find that variation in firm characteristics completely explains BHARs for M&A bidding

firms, IPO firms, and SEO firms.

IV. Robustness

We perform a series of robustness tests regarding the key results reported in Section III.

First, we assess the potential effect of non-linear relations between abnormal returns and firm

characteristics. Figure 6 indicates that the relation between BHARs and illiquidity for IPO firms,

in particular, may be non-linear. To do so, we estimate equation (3) by OLS and by the Fama-

MacBeth method, while including both the level and the square of each normalized difference in

firm characteristics.

28

Results are reported in Table 7; Columns (1) and (2) pertain to the bidder firm sample,

columns (3) and (4) to the SEO sample, and columns (5) and (6) to the IPO sample. In general,

the results provide little evidence of significant non-linear effects. While OLS estimation in the

pooled sample yields some apparently significant t-statistics (e.g. for the squared effect of

idiosyncratic volatility and market beta when explaining abnormal returns to bidders), these are

generally no longer significant when the Fama-MacBeth procedure is used. More important,

estimated intercepts obtained when using both pooled OLS and the Fama-MacBeth method are

statistically insignificant for all three samples. We conclude that our key empirical finding, that

differences in firm characteristics between event and control firms can fully explain the apparent

long term abnormal returns to bidder, IPO, and SEO firms, is robust to possible non-linear

relations. Indeed, when non-linear effects are included, firm characteristics fully explain

abnormal returns after these corporate events, whether weighting each deal equally (as in pooled

OLS estimation) or weighting each time period equally (as in the Fama-MacBeth estimation).

As noted in Section II, we follow the existing literature in matching event and control

firms for the bidder and SEO samples on the basis of market capitalization and M/B ratio as of

the end of the December prior to the event. However, as Figures 1 and 2 show, this matching

method does not ensure that firms remain well-matched thereafter. In particular, Figure 2 shows

that SEO event firms are considerably larger and have lower B/M ratios as of the event date.

We therefore assess outcomes when matching firms are paired with event firms on the

basis of firm size and B/M ratios at the month end prior to the event date, rather than the prior

December. Results of estimating expression (3) for returns to event firms and the revised set of

control firms are reported in Table 8. Panel A pertains to the bidder firm sample, while Panel B

pertains to the SEO sample.

29

In general, results are very similar to those reported on Table 6. Results of implementing

the Fama-MacBeth procedure continue to indicate insignificant intercepts for both the bidder

firm (t-statistic = -0.89) and the SEO (t-statistic = 0.01) samples. We conclude that the key

empirical result that differences in firm characteristics between event and control firms explain

apparent long term abnormal returns is robust to choosing matching firms for the bidder and

SEO firms on the basis of event date size and M/B as well.

V. Conclusions

Numerous studies document significant abnormal returns after corporate events, and

ascribe the returns to the events themselves. However, conclusions often differ depending on

the method used to measure abnormal returns. We focus on the “buy-and-hold abnormal return”

(BHAR) method, where accumulated returns to event firms are compared to accumulated returns

on control firms, typically matched on the basis of size and B/M ratio.

We document that typical matching algorithms for event and control firms are imperfect,

in that matched firms differ significantly in terms of illiquidity, idiosyncratic volatility, return

momentum, and market beta. Further, though typical matching procedures are successful in

matching event and control firms on the basis of size and B/M ratio at a point in time, the quality

of these matches degrade over time. These mismatches in firm characteristics are potentially

relevant since the extant literature shows that returns are systematically related to these firm

characteristics. Observing significant BHARs for event firms therefore has at least two possible

interpretations. The abnormal returns may be directly associated with the event being studied,

or may reflect, in full or part, differences in firm characteristics across event and control firms.

30

We introduce a simple regression-based method to distinguish between these

explanations, and show that the latter interpretation is appropriate. When results are weighted

equally across firms and events (by OLS estimation of a pooled time-series cross-sectional

regression), we find that variation in characteristics explains a substantial portion of observed

BHARs. When results are weighted equally across time (by implementation of the Fama-

MacBeth method), we find that variation in firm characteristics completely explains BHARs for

M/A bidding firms, IPO firms, and SEO firms. Our results therefore substantially reconcile the

diverging results obtained in the literature in studies that assess long run abnormal returns by

measuring BHARs versus those that study “alphas” to calendar-time portfolios formed from

event firms.

We focus in particular on firms undergoing three specific corporate events, including

bidding firms engaged in mergers and acquisitions, and firms issuing equity in initial or

secondary public offerings. However, the issues that we address and the regression based

estimation procedure we implement, are applicable to a wide variety of corporate events where it

may be of interest to measure abnormal stock returns after the event, including dividend

initiations and omissions, management turnover, stock splits, etc.

31

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Returns Following Seasoned Equity Offerings,” Journal of Financial Economics 38, 243-267.

34

Figure 1 Characteristics of the Bidding Firms and Their Size- and B/M-Matched Comparable Firms

This figure plots the time series of the median size, B/M, momentum, idiosyncratic volatility, and illiquidity of the

bidding firms and their size- and B/M-matched comparable firms, for the 60 months before and after the deal

effective date. Month 0 corresponds to the month in which the deal is completed. At the end of the latest December

before the deal effective date, each bidder is matched with a firm whose market capitalization is between 70% and

130% of the bidder and has the closest book-to-market ratio. The sample has 4,579 bidders over the period 1980-

2005. Beta is the monthly market beta estimated using daily stock returns in each month. Size is the market

capitalization at the end of each month. B/M is defined as the ratio of the book value of common equity at the end of

fiscal year t-1 to the market value of common equity at the end of June of year t. Momentum is the cumulative return

over months -12 to -2. Idiosyncratic risk is the annualized standard deviation of the residual daily stock returns in

the Fama-French three factor regression. Illiquidity is the average daily ratio of absolute stock return to dollar

trading volume.

.5.6

.7.8

.9

Be

ta

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

Bidder Match

Median Beta of Bidder and Matching Firm

100

00

02

00

00

03

00

00

04

00

00

05

00

00

0S

ize

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

Bidder Match

Median Size of Bidder and Matching Firm

.35

.4.4

5.5

.55

BM

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

Bidder Match

Median BM of Bidder and Matching Firm

0

.05

.1.1

5.2

Mo

men

tum

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

Bidder Match

Median Momentum of Bidder and Matching Firm

.32

.34

.36

.38

.4

Idio

syn

cra

tic V

ola

tilit

y

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

Bidder Match

Median Idiosyncratic Volatility of Bidder and Matching Firm

0

.05

.1.1

5

Illiq

uid

ity

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

Bidder Match

Median Illiquidity of Bidder and Matching Firm

35

Figure 2

Characteristics of the SEO Firms and Their Size- and B/M-Matched Firms

This figure plots the time series of the median size, B/M, momentum, idiosyncratic volatility, and illiquidity of the

SEO firms and their size- and B/M-matched comparable firms, for the 60 months before and after the equity offering.

Month 0 corresponds to the month of equity offering. At the end of the latest December before the offering, each

SEO firm is matched with a firm whose market capitalization is between 70% and 130% of the SEO firm and has

the closest book-to-market ratio. The sample has 5,573 SEO firms over the period 1980-2005. Beta is the monthly

market beta estimated using daily stock returns in each month. Size is the market capitalization at the end of each

month. B/M is defined as the ratio of the book value of common equity at the end of fiscal year t-1 to the market

value of common equity at the end of June of year t. Momentum is the cumulative return over months -12 to -2.

Idiosyncratic risk is the annualized standard deviation of the residual daily stock returns in the Fama-French three

factor regression. Illiquidity is the average daily ratio of absolute stock return to dollar trading volume.

.6.8

1

Be

ta

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

SEO Match

Median Beta of SEO and Matching Firm

100

00

01

50

00

02

00

00

02

50

00

03

00

00

03

50

00

0S

ize

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

SEO Match

Median Size of SEO and Matching Firm

.3.4

.5.6

BM

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

SEO Match

Median BM of SEO and Matching Firm

-.2

0.2

.4.6

Mo

men

tum

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

SEO Match

Median Momentum of SEO and Matching Firm

.32

.34

.36

.38

.4

Idio

syn

cra

tic V

ola

tilit

y

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

SEO Match

Median Idiosyncratic Volatility of SEO and Matching Firm

0

.05

.1.1

5

Illiq

uid

ity

-60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0 6 12 18 24 30 36 42 48 54 60Month

SEO Match

Median Illiquidity of SEO and Matching Firm

36

Figure 3

Characteristics of the IPO Firms and Their Size-Matched Comparable

This figure plots the time series of the median size, B/M, momentum, idiosyncratic volatility, and illiquidity of the

IPO firms and their size-matched comparable firms, for the 60 months after the public offering. Month 0

corresponds to the month of initial public offering. At the end of December after the IPO, each IPO firm is matched

with a firm with the closest but greater market capitalization. The sample has 8,987 IPOs over the period 1980-2005.

Beta is the monthly market beta estimated using daily stock returns in each month. Size is the market capitalization

at the end of each month. B/M is defined as the ratio of the book value of common equity at the end of fiscal year t-1

to the market value of common equity at the end of June of year t. Momentum is the cumulative return over months

-12 to -2. Idiosyncratic risk is the annualized standard deviation of the residual daily stock returns in the Fama-

French three factor regression. Illiquidity is the average daily ratio of absolute stock return to dollar trading volume.

.4.4

5.5

.55

.6.6

5

Be

ta

0 6 12 18 24 30 36 42 48 54 60Month

IPO Match

Median Beta of IPO and Matching Firm

700

00

800

00

900

00

100

00

0S

ize

0 6 12 18 24 30 36 42 48 54 60Month

IPO Match

Median Size of IPO and Matching Firm

.1.2

.3.4

.5.6

BM

0 6 12 18 24 30 36 42 48 54 60Month

IPO Match

Median BM of IPO and Matching Firm

0.2

.4.6

.8

Mo

men

tum

0 6 12 18 24 30 36 42 48 54 60Month

IPO Match

Median Momentum of IPO and Matching Firm

.3.3

5.4

.45

.5

Idio

syn

cra

tic V

ola

tilit

y

0 6 12 18 24 30 36 42 48 54 60Month

IPO Match

Median Idiosyncratic Volatility of IPO and Matching Firm

0

.05

.1.1

5.2

.25

Illiq

uid

ity

0 6 12 18 24 30 36 42 48 54 60Month

IPO Match

Median Illiquidity of IPO and Matching Firm

37

Figure 4

BHARs of the Bidding Firms, by Differences in Firm Characteristics between the Bidder Firms and Their

Size- and B/M-Matched Comparable Firms

This figure plots the equal- and value-weighted BHARs of the bidding firms. The bidders are grouped into quintiles

based on the differences in the average beta over the 12 months before deal completion, cumulative return over

months -12 to -2 before deal completion, average idiosyncratic volatility over the 12 months before deal completion,

and average illiquidity over the 12 months before deal completion, respectively, of the bidder and its size- and book-

to-market-ratio-matched comparable firm. At the end of the latest December before the deal effective date, each

bidder is matched with a firm whose market capitalization is between 70% and 130 percent of the bidder and has the

closest book-to-market ratio. The sample has 4,579 bidders over the period 1980-2005. Beta is the monthly market

beta estimated using daily stock returns in each month. Momentum is the cumulative return over months -12 to -2

before deal completion. Idiosyncratic risk is the annualized standard deviation of the residual daily stock returns in

the Fama-French three factor regression. Illiquidity is the average daily ratio of absolute stock return to dollar

trading volume.

-30

-20

-10

0

10

1 2 3 4 5

5-y

ear

EW

BH

AR

(%)

Quintiles based on difference in beta 1 year before M&A

-40

-30

-20

-10

0

1 2 3 4 5

5-y

ear

VW

BH

AR

(%)

Quintiles based on difference in beta 1 year before M&A

-25

-20

-15

-10

-5

0

5

1 2 3 4 5

5-y

ear

EW

BH

AR

(%)

Quintiles based on difference in momentum before M&A

-60

-50

-40

-30

-20

-10

0

1 2 3 4 5

5-y

ear

VW

BH

AR

(%)

Quintiles based on difference in momentum before M&A

-60

-40

-20

0

20

1 2 3 4 5

5-y

ear

EW

BH

AR

(%)

Quintiles based on difference in idiosyncratic volatility 1 year

before M&A

-60

-40

-20

0

20

1 2 3 4 5

5-y

ear

VW

BH

AR

(%)

Quintiles based on difference in idiosyncratic volatility 1 year

before M&A

-40

-30

-20

-10

0

10

20

1 2 3 4 5

5-y

ear

EW

BH

AR

(%

)

Quintiles based on difference in illiquidity 1 year before M&A

-40

-30

-20

-10

0

10

1 2 3 4 5

5-y

ear

VW

BH

AR

(%

)

Quintiles based on difference in illiquidity 1 year before M&A

38

Figure 5

BHARs of the SEO Firms by Differences in Firm Characteristics between the SEO Firms and Their Size- and

B/M-Matched Comparable Firms

This figure plots the equal- and value-weighted BHARs of the SEO firms. The SEO firms are grouped into quintiles

based on the differences in the average beta over the 12 months before the offering, cumulative return over months -

12 to -2 before the offering, average idiosyncratic volatility over the 12 months before the offering, and average

illiquidity over the 12 months before the offering, respectively, of the SEO firm and its size- and book-to-market-

ratio-matched comparable firm. At the end of the latest December before the offering, each bidder is matched with a

firm whose market capitalization is between 70% and 130 percent of the SEO firm and has the closest book-to-

market ratio. The sample has 5,573 bidders over the period 1980-2005. Beta is the monthly market beta estimated

using daily stock returns in each month. Momentum is the cumulative return over months -12 to -2 before the

offering. Idiosyncratic risk is the annualized standard deviation of the residual daily stock returns in the Fama-

French three factor regression. Illiquidity is the average daily ratio of absolute stock return to dollar trading volume.

-30

-20

-10

0

10

20

1 2 3 4 5

5-y

ear

EW

BH

AR

(%)

Quintiles based on difference in beta 1 year before SEO

-60

-50

-40

-30

-20

-10

0

10

1 2 3 4 5

5-y

ear

VW

BH

AR

(%)

Quintiles based on difference in beta 1 year before SEO

-25

-20

-15

-10

-5

0

5

1 2 3 4 5

5-y

ear

EW

BH

AR

(%)

Quintiles based on difference in momentum before SEO

-40

-30

-20

-10

0

1 2 3 4 5

5-y

ear

VW

BH

AR

(%)

Quintiles based on difference in momentum before SEO

-20.00

-10.00

0.00

10.00

20.00

1 2 3 4 5

5-y

ear

EW

BH

AR

(%

)

Quintiles based on difference in idiosyncratic volatility 1 year

before SEO

-40.00

-30.00

-20.00

-10.00

0.00

10.00

20.00

1 2 3 4 5

5-y

ear

VW

BH

AR

(%

)

Quintiles based on difference in idiosyncratic volatility 1 year

before SEO

-30.00

-20.00

-10.00

0.00

10.00

20.00

30.00

1 2 3 4 5

5-y

ear

EW

BH

AR

(%

)

Quintiles based on difference in illiquidity 1 year before SEO

-30.00

-20.00

-10.00

0.00

10.00

20.00

30.00

1 2 3 4 5

5-y

ear

VW

BH

AR

(%

)

Quintiles based on difference in illiquidity 1 year before SEO

39

Figure 6

BHARs of the IPO Firms by Differences in Firm Characteristics between the IPO Firms and Their Size-

Matched Comparable Firms

This figure plots the equal- and value-weighted BHARs of the IPO firms. The IPOs are grouped into quintiles based

on the differences in the average beta over the 12 months after the offering, cumulative return over months 1 to 11

after the offering, average idiosyncratic volatility over the 12 months after the offering, and average illiquidity over

the 12 months after the offering, respectively, of the IPO firm and its size- and book-to-market-ratio-matched

comparable firm. At the end of December after IPO, each IPO firm is matched with a firm with the closest but

greater market capitalization. The sample has 8,987 IPOs over the period 1980-2005. Beta is the monthly market

beta estimated using daily stock returns in each month. Momentum is the cumulative return over months 1 to 11

after the offering. Idiosyncratic risk is the annualized standard deviation of the residual daily stock returns in the

Fama-French three factor regression. Illiquidity is the average daily ratio of absolute stock return to dollar trading

volume.

-60

-40

-20

0

1 2 3 4 5

5-y

ear

EW

BH

AR

(%)

Quintiles based on difference in beta 1 year after IPO -150

-100

-50

0

1 2 3 4 5

5-y

ear

VW

BH

AR

(%)

Quintiles based on difference in beta 1 year after IPO

-300

-200

-100

0

100

200

1 2 3 4 5

5-y

ear

EW

BH

AR

(%)

Quintiles based on difference in momentum after IPO

-300

-200

-100

0

100

1 2 3 4 5

5-y

ear

VW

BH

AR

(%)

Quintiles based on difference in momentum after IPO

-120

-100

-80

-60

-40

-20

0

1 2 3 4 5

5-y

ear

EW

BH

AR

(%)

Quintiles based on difference in idiosyncratic volatility 1 year

after IPO

-150

-100

-50

0

50

1 2 3 4 5

5-y

ear

VW

BH

AR

(%)

Quintiles based on difference in idiosyncratic volatility 1

year after IPO

-100

-80

-60

-40

-20

0

1 2 3 4 5

5-y

ear

EW

BH

AR

(%)

Quintiles based on difference in illiquidity 1 year after IPO

-150

-100

-50

0

1 2 3 4 5

5-y

ear

VW

BH

AR

(%)

Quintiles based on difference in illiquidity 1 year after IPO

40

Figure 7

Distributions of BHARs and Difference in Monthly Log Return

This figure plots the distributions of the 60-month equal-weighted BHARs and the difference in monthly log returns

between the event firms and their matching firms. The corporate events include mergers and acquisitions, seasoned

equity offerings, and initial public offerings. Each bidding firm and SEO firm is matched with a control firm based

on size and B/M at the end of the December prior to deal completion. Each IPO firm is matched with a control firm

based on size at the end of the December after going public. BHARs are the differences in the cumulative buy-and-

hold returns over the 60-month period between the event firms and their matching firms. Difference in monthly log

return is the differences in monthly log returns between the event firms and their matching firms.

Bidding Firms

SEO Firms

IPO Firms

0

.02

.04

.06

.08

.1

Den

sity

-50 0 50 100 150 20060-month BHARs

0.5

11

.52

2.5

Den

sity

-4 -2 0 2 4Difference in Log Return

Distribution of Difference in Log Return

0.1

.2.3

Den

sity

-60 -40 -20 0 2060-month BHARs

0.5

11

.52

2.5

Den

sity

-4 -2 0 2 4Difference in Log Return

Distribution of Difference in Log Return

0

.05

.1

Den

sity

-100 0 100 20060-month BHARs

0.5

11

.52

2.5

Den

sity

-4 -2 0 2 4Difference in Log Return

Distribution of Difference in Log Return

41

Table 1: Summary of Findings in the Related Literature

This table summarizes the empirical findings of selected previous studies on the long-run stock performance of

event firms after M&As, SEOs, and IPOs. These studies report the three- or five-year BHARs of the event firms

and/or the Jensen’s Alphas of monthly portfolios of the event firms. Superscript *** indicates that the BHAR or the

Alpha is statistically significant at the 1% level.

aThe authors did not report statistical significance level.

Author(s) Sample Sample Firm/deal Holding EW Alpha - Calendar

Period Size Type Period BHARs Time Portfolio

Panel A: M&As

Loughran and Vijh (1997) 1970-1989 385 Stock M&As 5 years -24.2%***

Loughran and Vijh (1997) 1970-1989 196 Cash M&As 5 years 18.5%

Loughran and Vijh (1997) 1970-1989 207 Mixed M&As 5 years -9.6%

Loughran and Vijh (1997) 1970-1989 788 All 5 years -6.5%

Rau and Vermaelen (1998) 1980-1991 2823 Mergers 3 years -4.04%

Rau and Vermaelen (1998) 1980-1991 316 Tender offers 3 years 8.85%

Moeller, Schlingemann, and Stulz

(2004) 1980-2001 12023 All 3 years

0.18%

Harford (2005) 1981-2000

All

0.25%

Betton, Eckbo, and Thorburn

(2008) 1980-2003 11483 All 5 years -21.9%*** 0.08%

Panel B: SEOs

Loughran and Ritter (1995) 1970-1990 3702 All 5 years -59.4%a

Spiess and Affleck-Graves (1995) 1975-1989 1247 All 3 years -22.8%***

Jegadeesh (2000) 1970-1993 2992 All 5 years -34.3%*** -0.31%***

Brav, Geczy, and Gompers (2000) 1975-1992 3775 All 5 years -26.3%a -0.19%

Eckbo, Masulis, and Norli (2008) 1980-2000 4971 Industrial 5 years -29.7%*** -0.18%

Fu, Huang, and Lin (2012) 1980-2002 5062 All 3 years -12.67%*** -0.40%***

Fu, Huang, and Lin (2012) 2003-2010 1583 All 3 years -4.65% -0.19%

Panel C: IPOs

Loughran and Ritter (1995) 1970-1990 4753 All 5 years -50.7%a

Brav, Geczy, and Gompers (2000) 1975-1992 3501 All 5 years 6.6%a -0.19%

Eckbo, Masulis, and Norli (2008) 1980-2000 5018 Industrial 5 years -18.0%*** -0.16%

42

Table 2: Number of Event Firms

This table presents the number of M&As, SEOs, and IPOs in our sample by year. Our samples of event firms are

retrieved from the SDC database.

Year M&A SEO IPO

1980 1 188 99

1981 9 197 262

1982 1 224 93

1983 0 518 589

1984 6 109 274

1985 76 172 265

1986 103 200 571

1987 101 134 444

1988 103 60 222

1989 116 112 195

1990 82 86 178

1991 117 263 366

1992 149 210 536

1993 190 308 694

1994 257 193 509

1995 343 293 513

1996 396 367 797

1997 391 296 536

1998 467 213 346

1999 379 228 503

2000 332 221 364

2001 233 175 94

2002 160 176 79

2003 177 204 70

2004 202 247 199

2005 188 179 189

Total 4579 5573 8987

43

Table 3: Percentiles of Differences in Firm Characteristics

This table presents the 10th

– 90th

percentiles of the differences in beta, momentum, idiosyncratic volatility and

illiquidity between the event firms and their size- and book-to-market-ratio-matched comparable firms. At the end of

the latest December before M&A (SEO), each bidder (SEO firm) is matched with a firm whose market

capitalization is between 70% and 130 percent of the bidder and has the closest book-to-market ratio. For each

bidder (SEO firms) and its matching firm, we calculate their market beta, illiquidity, and idiosyncratic volatility

using daily stock returns over the 12 months before the event. Beta is the monthly market beta estimated using daily

stock returns in each month. Idiosyncratic risk is the annualized standard deviation of the residual daily stock returns

in the Fama-French three factor regression. Illiquidity is the average daily ratio of absolute stock return to dollar

trading volume. We then compute the average beta, illiquidity, and idiosyncratic volatility over the 12 months before

M&A (SEO) for the bidders (SEO firms) and their matching firms. Momentum is the cumulative return over months

-12 to -2 before M&A (SEO). At the end of December after IPO, each IPO firm is matched with a firm with the

closest but greater market capitalization. The 12-month average beta, illiquidity, and idiosyncratic volatility after

IPO are computed using the same method. Momentum for IPOs and their matching firms is the cumulative returns

over months 1 to 11 after the offering.

Percentiles Difference in

Beta Momentum Idio. volatility Illiquidity

Mergers and acquisitions

10th percentile -0.852 -0.675 -0.277 -1.923

20th percentile -0.491 -0.364 -0.139 -0.290

30th percentile -0.254 -0.179 -0.076 -0.050

40th percentile -0.096 -0.036 -0.024 -0.009

50th percentile 0.068 0.097 0.015 -0.001

60th percentile 0.239 0.238 0.059 0.000

70th percentile 0.422 0.402 0.111 0.003

80th percentile 0.670 0.631 0.187 0.047

90th percentile 1.088 1.191 0.318 0.677

SEOs

10th percentile -0.762 -0.490 -0.217 -1.091

20th percentile -0.429 -0.172 -0.118 -0.285

30th percentile -0.185 0.021 -0.059 -0.070

40th percentile 0.013 0.200 -0.011 -0.017

50th percentile 0.186 0.361 0.027 -0.003

60th percentile 0.353 0.529 0.071 0.000

70th percentile 0.537 0.764 0.117 0.006

80th percentile 0.791 1.070 0.174 0.042

90th percentile 1.159 1.762 0.273 0.275

IPOs

10th percentile -0.845 -0.913 -0.301 -4.763

20th percentile -0.478 -0.590 -0.148 -1.422

30th percentile -0.242 -0.385 -0.054 -0.461

40th percentile -0.050 -0.225 0.019 -0.109

50th percentile 0.139 -0.082 0.086 -0.009

60th percentile 0.328 0.068 0.151 0.006

70th percentile 0.544 0.237 0.231 0.051

80th percentile 0.833 0.444 0.330 0.250

90th percentile 1.259 0.798 0.481 1.212

44

Table 4: Impacts of Firm Characteristics on Long-run BHARs

This table presents the equal- and value-weighted BHARs of the event firms over the 60-months after the event.

Each event firm is matched with a comparable firm based on size and B/M. At the end of the latest December before

M&A (SEO), each bidder (SEO firm) is matched with a firm whose market capitalization is between 70% and 130

percent of the bidder and has the closest book-to-market ratio. For each bidder (SEO firms) and its matching firm,

we calculate their market beta, illiquidity, and idiosyncratic volatility using daily stock returns over the 12 months

before the event. Beta is the monthly market beta estimated using daily stock returns in each month. Idiosyncratic

risk is the annualized standard deviation of the residual daily stock returns in the Fama-French three factor

regression. Illiquidity is the average daily ratio of absolute stock return to dollar trading volume. We then compute

the average beta, illiquidity, and idiosyncratic volatility over the 12 months before M&A (SEO) for the bidders

(SEO firms) and their matching firms. Momentum is the cumulative return over months -12 to -2 before M&A

(SEO). At the end of December after IPO, each IPO firm is matched with a firm with the closest but greater market

capitalization. The 12-month average beta, illiquidity, and idiosyncratic volatility after IPO are computed using the

same method. Momentum for IPOs and their matching firms is the cumulative returns over months 1 to 11 after the

offering. Superscripts ***, **, and * correspond to statistical significance at the one, five, and ten percent levels,

respectively, for the t-test of the null hypothesis that BHAR = 0.

Panel A: BHARs of Bidding Firms

Mean Buy-and-Hold

Returns (%) Mean BHARs

Quintile N Bidders Matches EW VW

Based on difference in market beta 1 year before M&A

1 916 38.68 50.05 -11.37 -0.14

2 916 42.43 56.18 -13.75 -11.26***

3 915 47.71 61.85 -14.14 -15.82***

4 916 36.69 64.45 -27.76*** -21.29***

5 915 60.88 54.09 6.80 -36.21***

Based on difference in momentum 1 year before M&A

1 913 45.21 53.95 -8.74 -9.61***

2 912 40.14 62.79 -22.65*** -11.6***

3 912 45 60.17 -15.17* -7.07*

4 912 58.34 57.92 0.42 -14.60**

5 912 38.84 52.69 -13.85 -50.05***

Based on difference in idiosyncratic volatility 1 year before M&A

1 916 43.67 46.27 -2.60 -8.24

2 916 61.73 49.58 12.15 14.09**

3 915 71.48 61.81 9.67 -23.63***

4 916 32.92 65.92 -32.99*** -28.70***

5 915 16.58 63.07 -46.49*** -53.51***

Based on difference in illiquidity 1 year before M&A

1 916 32.42 61.91 -29.49*** -33.85***

2 915 42.55 60.62 -18.07 -4.17

3 916 30.5 48.81 -18.31** -20.93***

4 915 45.05 52.2 -7.14 -15.35***

5 915 76.01 62.91 13.09 4.74

Total 4579 45.28 57.31 -12.04* -17.16***

45

Panel B: BHARs of SEO Firms

Mean Buy-and-Hold

Returns (%) Mean BHARs

Quintile N SEOs Matches EW VW

Based on difference in market beta 1 year before SEO

1 1114 47.38 33.03 14.35** 5.67

2 1114 46.22 53.06 -6.84 -18.36***

3 1113 51.15 58.55 -7.40 -31.32***

4 1114 40.73 61.91 -21.17** -24.18***

5 1113 36.65 59.05 -22.39*** -47.50***

Based on difference in momentum 1 year before SEO

1 1112 39.43 38.02 1.41 -6.74

2 1112 48.53 68.68 -20.15*** -7.38*

3 1111 51.08 59.70 -8.63 -36.65***

4 1112 48.72 52.20 -3.48 -20.08***

5 1111 34.15 47.60 -13.45 -35.19***

Based on difference in idiosyncratic volatility 1 year before SEO

1 1114 46.62 27.88 18.75 16.81***

2 1114 45.09 52.47 -7.38 -24.49***

3 1113 49.84 63.56 -13.72** -12.07**

4 1114 46.27 60.70 -14.44** -31.97***

5 1113 34.31 60.99 -26.68*** -40.93***

Based on difference in illiquidity 1 year before SEO

1 1053 43.29 56.35 -13.07 -15.06*

2 1053 45.91 61.60 -15.69* -11.96

3 1053 43.67 57.90 -14.23** -25.01***

4 1053 40.32 50.77 -10.45* -12.07***

5 1053 49.26 36.17 13.08* -9.22

Total 5573 44.42 53.07 -8.65*** -19.55***

46

Panel C: BHARs of IPO Firms

Mean Buy-and-Hold

Returns (%) Mean BHARs

Quintile N IPOs Matches EW VW

Based on difference in market beta 1 year after IPO

1 1798 34.25 76.01 -41.75*** -24.49***

2 1797 32.67 87.75 -55.08*** -12.03**

3 1797 36.67 82.96 -46.28*** -37.16***

4 1797 44.32 88.05 -43.73*** -45.97***

5 1797 46.47 99.06 -52.59*** -104.81***

Based on difference in momentum 1 year after IPO

1 1762 -21.6 185.06 -206.66*** -189.92***

2 1761 -3.38 110.01 -113.39*** -99.82***

3 1762 17.92 65.21 -47.29*** -35.26***

4 1761 62.82 40.02 22.79*** 41.05***

5 1761 140.74 35.98 104.76*** 69.76***

Based on difference in idiosyncratic volatility 1 year after IPO

1 1798 45.43 54.02 -8.59 3.59

2 1797 62.26 83.52 -21.26* 10.02

3 1798 43.3 89.56 -46.25*** -13.83***

4 1797 51.68 101.65 -49.96*** -51.49***

5 1797 -8.37 105.07 -113.44*** -134.24***

Based on difference in illiquidity 1 year after IPO

1 1737 17.08 93.79 -76.71*** -127.24***

2 1736 47.33 95.15 -47.82*** -72.21***

3 1736 67.46 81.35 -13.89 -37.43***

4 1736 43.55 82.8 -39.25*** -76.94***

5 1736 21.55 69.77 -48.23*** -88.78***

Total 8987 38.86 86.76 -47.90*** -55.06***

47

Table 5: Summary Statistics of BHARs and Difference in Monthly Log Return

This table reports the summary statistics of the 60-month equal-weighted BHARs and the difference in log monthly

returns between the event firms and their matching firms. The corporate events include mergers and acquisitions,

seasoned equity offerings, and initial public offerings. Each bidding firm and SEO firm is matched with a control

firm based on size and B/M at the end of the December prior to deal completion. Each IPO firm is matched with a

control firm based on size at the end of the December after going public. BHARs are the differences in the

cumulative buy-and-hold returns over the 60-month period between the event firms and their matching firms.

Difference in monthly log return is the differences in monthly log returns between the event firms and their

matching firms.

M&As SEOs IPOs

Variable 60-month Difference in 60-month Difference in 60-month Difference in

BHARs log return BHARs log return BHARs log return

Mean -0.120 -0.004 -0.086 -0.003 -0.479 -0.012

Std. Dev. 4.452 0.227 2.414 0.212 4.402 0.246

Skewness 27.243 -0.151 -2.244 -0.162 5.206 -0.225

Kurtosis 1365.070 11.684 69.223 11.179 489.452 9.361

Minimum -62.446 -4.057 -49.73 -4.014 -123.778 -3.739

p5 -3.058 -0.352 -2.903 -0.327 -4.616 -0.396

p25 -0.815 -0.107 -0.802 -0.105 -1.431 -0.129

Median -0.050 -0.002 -0.005 0.000 -0.323 -0.007

p75 0.675 0.100 0.698 0.102 0.510 0.111

p95 2.545 0.336 2.46 0.317 3.252 0.359

Maximum 220.954 2.933 26.073 3.292 188.708 2.708

48

Table 6: Explaining Differences in Stock Returns between Event Firms and Their Matching Firms

This table presents the OLS/Fama-MacBeth regression results for the difference in the log monthly returns of the event firm and its size- and B/M-matched

comparable firms. Each event firm is matched with a comparable firm based on size and B/M. For mergers and acquisitions and SEOs, each event firm is

matched with a firm whose market capitalization is between 70% and 130% of the event firm and has the closest book-to-market ratio at the end of the latest

December before the event. At the end of December after IPO, each IPO firm is matched with a firm with the closest but greater market capitalization. Market

beta for July of year t to June of year t+1 is estimated with the monthly stock returns in years t-5 to t-1 using the market model. Size is the market capitalization

at the end of the latest June. B/M is defined as the ratio of the book value of common equity at the end of fiscal year t-1 to the market value of common equity at

the end of the latest June. Momentum is the cumulative return over months -12 to -2. Idiosyncratic risk is the annualized standard deviation of the residual daily

stock returns in the Fama-French three factor regression in month -2. Illiquidity for July of year t to June of year t+1 is computed as the average daily ratio of

absolute stock return to dollar trading volume from July of year t-1 to June of year t divided by the market average illiquidity over the same period, as defined by

Amihud (2002). For each of the six firm characteristics (beta, size, B/M, momentum, illiquidity, and idiosyncratic volatility), we compute the difference between

the event firm and its size-and B/M-matched comparable firm. In every month over our sample period, the positive differences in each firm characteristic are

ranked and normalized to be its percentile ranking. All negative differences are ranked and normalized to be one minus its percentile ranking. Consequently, the

normalized differences in each firm characteristics take a value from -1 to 1, with 0 corresponds to the difference in firm characteristic that is the closest to 0.

Panels A1, B1, and C1 report the pooled-OLS regression results for our M&A, SEO, and IPO samples respectively; Panels A2, B2, and C2 report the Fama-

MacBeth regression results. All model specifications employ robust standard errors. The associated t-statistics are reported in the parentheses below each

coefficient. Superscripts ***, **, and * correspond to statistical significance at the one, five, and ten percent levels, respectively.

49

Panel A1: Mergers and Acquisitions – Pooled OLS Regressions

(1) (2) (3) (4) (5) (6) (7)

Dependent variable Difference in Log Return

Normalized difference in market beta

-0.0023 -0.0025

(-0.806) (-0.885)

Normalized difference in size

-0.0011

(-0.637)

Normalized difference in B/M

0.0046**

(2.276)

Normalized difference in momentum

0.0125***

(3.017)

Normalized difference in illiquidity

0.0085*** 0.0080*** 0.0064***

(3.664) (3.647) (3.080)

Normalized difference in idiosyncratic

volatility

-0.0210*** -0.0206*** -0.0176***

(-4.629) (-5.179) (-4.541)

Constant -0.0042*** -0.0042*** -0.0042*** -0.0042*** -0.0026*** -0.0025*** -0.0027***

(-7.924) (-7.213) (-4.206) (-4.234) (-3.260) (-3.278) (-3.356)

Cluster by M&A deal No Yes No No No No No

Cluster by date No No Yes Yes Yes Yes Yes

Observations 179,799 179,799 179,799 166,579 179,633 175,175 166,579

Adjusted R-squared 0.000 0.000 0.000 0.000 0.003 0.003 0.004

50

Panel A2: Mergers and Acquisitions – Fama-MacBeth Regressions

(1) (2) (3) (4)

Dependent variable Difference in Log Return

Normalized difference in market beta

-0.0046 0.0052

(-1.421) (0.556)

Normalized difference in size

-0.0007

(-0.186)

Normalized difference in B/M

-0.0091

(-1.017)

Normalized difference in momentum

0.0095

(1.197)

Normalized difference in illiquidity

0.0057* 0.0038 0.0146

(1.697) (1.092) (1.544)

Normalized difference in idiosyncratic

volatility

-0.0221*** -0.0211*** -0.0225**

(-6.836) (-6.257) (-2.113)

Constant -0.0029*** -0.0022** -0.0015 0.0054

(-3.116) (-2.471) (-1.255) (0.947)

Observations 179,799 179,633 175,175 166,579

Adjusted R-squared 0.000 0.061 0.087 0.149

51

Panel B1: SEOs – Pooled OLS Regressions

(1) (2) (3) (4) (5) (6) (7)

Dependent variable Difference in Log Return

Normalized difference in market beta

-0.0040 -0.0036

(-1.564) (-1.426)

Normalized difference in size

-0.0015

(-0.791)

Normalized difference in B/M

0.0032*

(1.928)

Normalized difference in momentum

0.0129***

(3.851)

Normalized difference in illiquidity

0.0044** 0.0036** 0.0031*

(2.330) (2.047) (1.768)

Normalized difference in idiosyncratic

volatility

-0.0206*** -0.0195*** -0.0170***

(-5.896) (-6.350) (-5.563)

Constant -0.0025*** -0.0025*** -0.0025** -0.0034*** -0.0007 -0.0006 -0.0008

(-5.802) (-5.123) (-2.104) (-2.717) (-0.707) (-0.682) (-0.943)

Cluster by M&A deal No Yes No No No No No

Cluster by date No No Yes Yes Yes Yes Yes

Observations 233,942 233,942 233,942 208,474 226,896 219,902 208,474

Adjusted R-squared 0.000 0.000 0.000 0.000 0.003 0.003 0.004

52

Panel B2: SEOs – Fama-MacBeth Regressions

(1) (2) (3) (4)

Dependent variable Difference in Log Return

Normalized difference in market beta

-0.0013 -0.0009

(-0.478) (-0.406)

Normalized difference in size

-0.0002

(-0.135)

Normalized difference in B/M

0.0043**

(2.331)

Normalized difference in momentum

0.0124***

(4.902)

Normalized difference in illiquidity

0.0048*** 0.0071** 0.0045**

(2.638) (1.997) (1.979)

Normalized difference in idiosyncratic

volatility

-0.0204*** -0.0226*** -0.0191***

(-7.999) (-5.867) (-9.164)

Constant -0.0016 -0.0005 -0.0018 -0.0010

(-1.339) (-0.490) (-0.891) (-0.954)

Observations 233,942 226,896 219,902 208,474

Adjusted R-squared 0.000 0.026 0.040 0.069

53

Panel C1: IPOs – Pooled OLS Regressions

(1) (2) (3) (4) (5) (6) (7)

Dependent variable Difference in Log Return

Normalized difference in market beta

-0.0011 -0.0015

(-0.392) (-0.480)

Normalized difference in size

-0.0011

(-0.413)

Normalized difference in B/M

0.0079***

(4.018)

Normalized difference in momentum

0.0196***

(4.344)

Normalized difference in illiquidity

0.0133*** 0.0106*** 0.0087***

(5.787) (4.000) (3.331)

Normalized difference in idiosyncratic

volatility

-0.0321*** -0.0291*** -0.0232***

(-6.087) (-6.190) (-4.605)

Constant -0.0116*** -0.0116*** -0.0116*** -0.0097*** -0.0074*** -0.0055*** -0.0044***

(-31.580) (-27.033) (-4.973) (-3.903) (-4.565) (-3.868) (-2.991)

Cluster by M&A deal No Yes No No No No No

Cluster by date No No Yes Yes Yes Yes Yes

Observations 447,839 447,839 447,839 193,868 387,874 246,087 193,868

Adjusted R-squared 0.000 0.000 0.000 0.000 0.005 0.004 0.005

54

Panel C2: IPOs – Fama-MacBeth Regressions

(1) (2) (3) (4)

Dependent variable Difference in Log Return

Normalized difference in market beta

-0.0021 -0.0059

(-0.386) (-1.033)

Normalized difference in size

-0.0010

(-0.297)

Normalized difference in B/M

0.0072***

(3.280)

Normalized difference in momentum

0.0208***

(5.996)

Normalized difference in illiquidity

0.0090*** 0.0068 0.0067

(3.143) (1.362) (1.634)

Normalized difference in idiosyncratic

volatility

-0.0301*** -0.0285*** -0.0190***

(-4.508) (-5.977) (-4.768)

Constant -0.0090*** -0.0077*** -0.0038* -0.0024

(-4.698) (-3.166) (-1.835) (-1.152)

Observations 447,839 387,874 246,087 193,868

Adjusted R-squared 0.000 0.042 0.055 0.089

55

Table 7: Explaining Differences in Stock Returns between Event Firms and Their Matching Firms: Nonlinear Relations

This table presents the OLS/Fama-MacBeth regression results for the difference in the log monthly returns of the event firm and its size- and B/M-matched

comparable firms. Each event firm is matched with a comparable firm based on size and B/M. For mergers and acquisitions and SEOs, each event firm is

matched with a firm whose market capitalization is between 70% and 130% of the event firm and has the closest book-to-market ratio at the end of the latest

December before the event. At the end of December after IPO, each IPO firm is matched with a firm with the closest but greater market capitalization. Market

beta for July of year t to June of year t+1 is estimated with the monthly stock returns in years t-5 to t-1 using the market model. Size is the market capitalization

at the end of the latest June. B/M is defined as the ratio of the book value of common equity at the end of fiscal year t-1 to the market value of common equity at

the end of the latest June. Momentum is the cumulative return over months -12 to -2. Idiosyncratic risk is the annualized standard deviation of the residual daily

stock returns in the Fama-French three factor regression in month -2. Illiquidity for July of year t to June of year t+1 is computed as the average daily ratio of

absolute stock return to dollar trading volume from July of year t-1 to June of year t divided by the market average illiquidity over the same period, as defined by

Amihud (2002). For each of the six firm characteristics (beta, size, B/M, momentum, illiquidity, and idiosyncratic volatility), we compute the difference between

the event firm and its size-and B/M-matched comparable firm. In every month over our sample period, the positive differences in each firm characteristic are

ranked and normalized to be its percentile ranking. All negative differences are ranked and normalized to be one minus its percentile ranking. Consequently, the

normalized differences in each firm characteristics take a value from -1 to 1, with 0 corresponds to the difference in firm characteristic that is the closest to 0. All

model specifications employ robust standard errors. The associated t-statistics are reported in the parentheses below each coefficient. Superscripts ***, **, and *

correspond to statistical significance at the one, five, and ten percent levels, respectively.

56

(1) (2) (3) (4) (5) (6)

Event M&A SEO IPO

Method OLS

Fama-

MacBeth OLS

Fama-

MacBeth OLS

Fama-

MacBeth

Dependent variable Difference in Log Return

Normalized difference in market beta -0.0024 -0.0121 -0.0036*** -0.0019 -0.0012 -0.0011

(-0.831) (-1.344) (-3.899) (-0.715) (-0.432) (-0.544)

Normalized difference in market beta^2 -0.0046** 0.0023 0.0004 -0.0016 -0.0003 0.0004

(-2.155) (0.286) (0.227) (-0.602) (-0.094) (0.152)

Normalized difference in size -0.0015 0.0013 -0.0011 0.0001 -0.0011 -0.0005

(-0.869) (0.310) (-1.198) (0.071) (-0.417) (-0.159)

Normalized difference in size^2 -0.0034 -0.0286 -0.0062*** -0.0020 -0.0014 -0.0033

(-1.221) (-1.239) (-3.404) (-0.609) (-0.310) (-1.205)

Normalized difference in B/M 0.0042** 0.0059 0.0030*** 0.0038** 0.0079*** 0.0074***

(2.137) (0.770) (3.000) (1.990) (4.077) (3.042)

Normalized difference in B/M ^2 -0.0000 0.0154** -0.0009 0.0059 -0.0058* -0.0007

(-0.005) (2.186) (-0.502) (0.696) (-1.719) (-0.206)

Normalized difference in momentum 0.0124*** 0.0147* 0.0129*** 0.0111*** 0.0195*** 0.0186***

(3.003) (1.844) (13.696) (3.734) (4.310) (6.257)

Normalized difference in momentum^2 -0.0011 -0.0134 0.0026 0.0068** -0.0061* 0.0042

(-0.433) (-1.315) (1.461) (2.335) (-1.667) (1.478)

Normalized difference in illiquidity 0.0063*** 0.0120** 0.0035*** 0.0045* 0.0087*** 0.0064*

(3.016) (2.096) (3.414) (1.705) (3.331) (1.891)

Normalized difference in illiquidity^2 -0.0028 -0.0134 -0.0017 -0.0037 0.0020 -0.0040

(-0.901) (-0.968) (-0.781) (-0.986) (0.580) (-1.178)

Normalized difference in idiosyncratic volatility -0.0176*** 0.0032 -0.0169*** -0.0179*** -0.0226*** -0.0210***

(-4.555) (0.146) (-16.810) (-7.676) (-4.804) (-6.898)

Normalized difference in idiosyncratic

volatility^2 -0.0054* -0.0040 -0.0008 -0.0005 -0.0040 -0.0050*

(-1.840) (-0.602) (-0.385) (-0.150) (-0.868) (-1.813)

Constant 0.0030 0.0144 0.0014 -0.0018 0.0007 -0.0022

(1.342) (1.278) (1.092) (-0.917) (0.192) (-0.705)

Observations 166,579 166,579 208,474 208,474 193,868 193,868

Cluster by date Yes No Yes No Yes No

Adjusted R-squared 0.004 0.212 0.004 0.095 0.006 0.109

57

Table 8: Explaining Differences in Stock Returns between Event Firms and Their Matching Firms: Match at

the Event Time

This table presents the OLS/Fama-MacBeth regression results for the difference in the log monthly returns of the

event firm and its size- and B/M-matched comparable firms. Each event firm is matched with a comparable firm

based on size and B/M. Each M&A and SEO firm is matched with a firm whose market capitalization is between 70%

and 130% of the event firm and has the closest book-to-market ratio at the end of the month prior to the completion

of the acquisition or equity offering. Market beta for July of year t to June of year t+1 is estimated with the monthly

stock returns in years t-5 to t-1 using the market model. Size is the market capitalization at the end of the latest June.

B/M is defined as the ratio of the book value of common equity at the end of fiscal year t-1 to the market value of

common equity at the end of the latest June. Momentum is the cumulative return over months -12 to -2.

Idiosyncratic risk is the annualized standard deviation of the residual daily stock returns in the Fama-French three

factor regression in month -2. Illiquidity for July of year t to June of year t+1 is computed as the average daily ratio

of absolute stock return to dollar trading volume from July of year t-1 to June of year t divided by the market

average illiquidity over the same period, as defined by Amihud (2002). For each of the six firm characteristics (beta,

size, B/M, momentum, illiquidity, and idiosyncratic volatility), we compute the difference between the event firm

and its size-and B/M-matched comparable firm. In every month over our sample period, the positive differences in

each firm characteristic are ranked and normalized to be its percentile ranking. All negative differences are ranked

and normalized to be one minus its percentile ranking. Consequently, the normalized differences in each firm

characteristics take a value from -1 to 1, with 0 corresponds to the difference in firm characteristic that is the closest

to 0. All model specifications employ robust standard errors. The associated t-statistics are reported in the

parentheses below each coefficient. Superscripts ***, **, and * correspond to statistical significance at the one, five,

and ten percent levels, respectively.

Panel A: Mergers and Acquisitions

(1) (2) (3) (4)

Method OLS OLS OLS

Fama-

MacBeth

Dependent variable Difference in Log Return

Normalized difference in market beta

-0.0024 -0.0035

(-0.839) (-1.223)

Normalized difference in size

-0.0015 -0.0028

(-0.825) (-0.948)

Normalized difference in B/M

0.0043** 0.0033

(2.409) (0.938)

Normalized difference in momentum

0.0126*** 0.0109***

(2.992) (3.082)

Normalized difference in illiquidity

0.0034* 0.0029

(1.685) (0.594)

Normalized difference in idiosyncratic

volatility

-0.0165*** -0.0176***

(-4.063) (-3.855)

Constant -0.0045*** -0.0045*** -0.0034*** -0.0011

(-8.429) (-4.406) (-4.017) (-0.885)

Cluster by date No Yes Yes No

Observations 187,652 187,652 170,659 170,659

Adjusted R-squared 0.000 0.000 0.003 0.147

58

Panel B: SEOs

(1) (2) (3) (4)

Method OLS OLS OLS

Fama-

MacBeth

Dependent variable Difference in Log Return

Normalized difference in market beta

-0.0026 -0.0025

(-1.146) (-1.165)

Normalized difference in size

-0.0013 -0.0016

(-0.716) (-0.913)

Normalized difference in B/M

0.0028* 0.0030**

(1.918) (2.253)

Normalized difference in momentum

0.0126*** 0.0125***

(3.662) (4.954)

Normalized difference in illiquidity

0.0042*** 0.0043***

(2.599) (2.613)

Normalized difference in idiosyncratic

volatility

-0.0192*** -0.0189***

(-6.131) (-9.827)

Constant -0.0017*** -0.0017 -0.0004 0.0000

(-4.037) (-1.531) (-0.421) (0.014)

Cluster by date No Yes Yes No

Observations 251,245 251,245 218,776 218,776

Adjusted R-squared 0.000 0.000 0.004 0.062