Download The Transition in Private / Public Ownership:

A New Measure for Shareholder Value Creation and the Performance
of Mergers and Acquisitions
Last Revised: November 2011
Julie Lei Zhu
Boston University School of Management
Email: [email protected]
Abstract
This paper develops a new measure for shareholder value creation to assess the efficiency of
acquiring firms in utilizing capital before mergers and acquisitions (M&As) and links this
measure to acquirers’ post-acquisition performance. Based on the concept of residual
earnings, I define the measure as the residual from regressions of firms’ excess earnings—
earnings in excess of cost of capital—on invested capital and other firm characteristics for
each industry and year. A positive (negative) residual indicates a firm is able to generate
excess return on invested capital at a higher (lower) rate than its industry peers in a given
year. The announcement returns for acquirers with low residuals are lower than those for
acquirers with high residuals. Moreover, this measure, constructed before the M&A
transaction, (a) predicts both the operating and long-run abnormal stock performance of
merged firms after the acquisitions and (b) hedge portfolios based on the measure generate
substantial abnormal returns. Overall, the results indicate that investors do not fully
recognize how efficient acquirers have been in utilizing capital before M&As and that
incorporating the new value creation measure into the decision process of large-scale M&As
can help protect shareholder wealth.
Keywords: Merger and acquisition, earnings, residual earnings, invested capital, stock
returns.
JEL Classifications: G14, G34, M41.

I appreciate useful comments from Bjorn Jorgensen, Sanjai Bhagat (WFA discussant), Brian Bolton (WFA codiscussant), Patricia Dechow, Kenneth French, Amy Hutton, Wei Jiang, Sharon Katz, Duane Kennedy (AAA
discussant), Partha Mohanram, Doron Nissim, Derek Oler, Stephen Penman, Lakshmanan Shivakumar, Doug
Skinner, Philip Strahan, Kent Womack, and seminar/session participants at Boston College, Boston University,
Columbia Business School, Dartmouth College, New York University, Western Finance Association (WFA)
Meetings in Hawaii and AAA meetings in San Francisco. Financial support from Boston University is
gratefully acknowledged. All errors are my own.
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1. Introduction
Large-scale mergers and acquisitions (M&As) require substantial investment capital.
While good M&As can lead to significant growth and value creation, bad M&As can
generate massive losses for acquiring firms’ shareholders (e.g., Moeller, Schlingemann, and
Stulz, 2005). Before approving an M&A transaction—that is, before allowing managers to
deploy resources and capital to a large acquisition—what should the board of directors and
shareholders of the acquiring firm be concerned about? In this paper, I propose that one
factor should be the firm’s efficiency in utilizing capital. If capital has been directed to
productive projects and has created positive (net) returns for shareholders in the past—a
strong indicator of overall effective management—then the board and investors should have
confidence in management to continue the process of value creation. If, however, capital was
misallocated to negative-NPV projects, then shareholders should be cautious in approving the
new M&A deal to avoid further losses.
What should be the proper measure(s) to evaluate shareholder value creation, given
invested capital? Both anecdotal and large-sample evidence suggest that investors and the
market can be fixated on earnings when valuing firms (e.g., Sloan and Skinner, 2002).1
However, accounting scholars have long criticized earnings as an imperfect measure for a
firm’s economic profitability because earnings do not reflect the cost of equity capital raised
(e.g., Solomons, 1965; Morse and Zimmerman, 1997). Hence, a firm can grow earnings, at
least temporarily, by investing in projects that generate sufficient profits to cover the cost of
debt but not the cost of equity capital (i.e., negative-NPV projects). If these firms are
allowed to launch large-scale acquisitions, it is possible that these new M&A deals can lead
to more wealth destruction.
1
Also see, for example, La Porta, Lakonishok, Shleifer, and Vishny (1997), Bernard, Thomas, and Wahlen
(1997), La Porta (1996), and Dechow and Sloan (1997).
1
In this regard, I develop a new measure of value creation from investment capital,
based on the concept of residual earnings, which takes into account the cost of capital.
Specifically, I compare the rate at which an acquiring firm’s invested capital generates excess
earnings—earnings in excess of cost of capital—in comparison to industry peers in a given
year. Acquirers that generate higher net returns than their peers are expected to continue to
deliver superior returns to their shareholders in the new M&A transaction, whereas acquirers
that underperform their peers are likely to repeat the subpar performance. I test this
hypothesis by examining whether my value added measure for an acquiring firm, constructed
before the M&A deal announcement date, can predict the firm’s post-acquisition operating
and stock performance. If better ex-ante value creation is associated with better postacquisition performance, then this positive link would validate both my measure and the
hypothesis of the persistence of management effectiveness in value creation. This link would
also imply that investors and the market do not fully recognize how efficiently acquirers have
been utilizing capital before the acquisition.
My M&A sample includes more than 1,900 completed deals over the period 19802005. I define the measure of shareholder value creation as the residual from regressing
excess earnings—earnings in excess of the cost of capital—on current and lagged total
invested capital, controlling for firm size, financing constraints, and other factors. Cost of
capital is calculated as weighted average of cost of debt and equity. I first run the regressions
by industry and year, using all Compustat firms, and then I obtain residual estimates for each
of the acquirers and their matching non-acquirers as of the fiscal year-end before the M&A
deal announcement date. A positive (negative) residual indicates that a firm is able to
generate excess return on invested capital at a higher (lower) rate than do its industry peers in
a given year. I include matching non-acquirers in my analyses so as to rule out mean
reversion as the reason for the possible reversal in acquirers’ operating performance.
2
With the value added measures for each acquirer and its matching non-acquirer in
hand, I examine whether, at the time of the M&A deal announcement, investors and the
market differentiate acquirers with high levels of shareholder value creation from those with
low value creation. I also test whether the value creation measure can predict postacquisition performance of the merged firms. Specifically, I run regressions with the postacquisition return on assets (defined as net operating profits, or NOPAT, scaled by average
assets), and with the merged firm’s short-run and long-run abnormal stock returns as the
dependent variable, while controlling for firm and M&A deal characteristics and other factors
that have been shown to affect performance. To measure long-run abnormal stock returns, I
use the “Buy-and-Hold Abnormal Return,” or BHAR, commonly used in the long-run event
study literature, and subtract a benchmark return from the raw buy-and-hold returns. The
benchmark return is the return on the characteristic-based benchmark portfolios constructed
by Daniel, Grinblatt, Titman, and Wermers (1997) and Wermers (2004) (hereafter DGTW).2
The main explanatory variable in these regressions is the firm-specific residual estimates
from the excess earnings-invested capital regressions, constructed at the fiscal year-end
before the M&A deal announcement date.
I find that acquirers with low residuals have lower announcement period returns than
acquirers with high residuals. This result suggests that, triggered by the M&A deal
announcement, the market adjusts its valuation of acquirers with different levels of
shareholder value creation. While the direction of the adjustment is correct, the magnitude is
too small, in that the residuals also strongly predict both the operating performance and the
long-run abnormal stock returns of post-merger firms. For example, during the first year
after acquisition, the return on assets (ROA) for merged firms with residuals in the lowest
20% of the sample is 6.7% lower than the ROA for those in the top 20%. The comparative
2
The DGTW benchmarks are available via
http://www.smith.umd.edu/faculty/rwermers/ftpsite/Dgtw/coverpage.htm.
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underperformance of ROA persists in two-year and three-year post-acquisition windows.
Moreover, in the first year post-acquisition, the abnormal stock returns for merged firms with
their residuals in the lowest 20% of the sample is 10.8% lower than those in the top 20%.
The underperformance of the abnormal returns for the low residual firms is 29.2% during the
first two years and 25.7% during the first three years post acquisition.
Taken together, the results confirm the validity of my ex-ante value creation measure
in assessing management effectiveness prior to M&A deals and support my hypothesis that
such management effectiveness persists and is manifested in the quality of the new M&A
transactions. The results also imply that investors and the market do not fully understand
how acquirers have differed in their capacity for value creation before M&As. To the best of
my knowledge, this paper is the first to document a link between the ex-ante value creation
measure, based on the concept of residual earnings, and the ex-post performance of acquiring
firms.3 For practical purposes, this measure can be used by the board of directors and/or
shareholders of acquiring firms to make prudent decisions before approving M&A deals. This
measure can also be used to investigate management effectiveness in other significant
corporate events.
In addition, my measure sheds new light on the underperformance “anomaly” of highM/B “glamour” acquirers relative to low-M/B “value” acquirers post acquisition.
Specifically, Loughran and Vijh (1997) and Rau and Vermaelen (1998) first document that
glamour acquirers earn negative abnormal stock returns after M&As and postulate that this
underperformance is due to naïve investors over-extrapolating glamour acquirers’ past
earnings performance in assessing the value of the acquisition.4 Consistent with this
3
Balachandran and Mohanram (2010) decompose the earnings growth of a general firm sample into
components including growth in residual income and growth in invested capital, and then use this
decomposition to explain the cross-sectional relation between earnings and stock returns.
4
Motivated by this finding, Shleifer and Vishny (2003) argue that firms’ managers take advantage of market
misvaluations when making M&A decisions and that such decisions in turn affect post-acquisition performance.
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interpretation, I find that glamour acquirers, defined as acquirers with M/B in the top 20%
during the fiscal year-end before the M&A deal announcement, show much higher earnings
than value acquirers (M/B in the lowest 20%) and matching non-acquirers during the threeyear period before an acquisition. However, the positive earnings momentum of glamour
acquirers reverses sharply after the acquisition, relative to that of value acquirers and
matching glamour non-acquirers, suggesting that the decline cannot be explained by mean
reversion alone. Moreover, the mean residual (value creation) estimate of glamour acquirers
is −1.9% (below the predicted value/rate), significantly lower than that of the medium-M/B
group (3.2%) and matching glamour non-acquirers (5.0%); and not statistically different from
that of the value acquirer group (−0.2%). Finally, the positive link between the value
creation measure and ex-post performance is robust to controlling for acquirers’ M/B ratios
before M&As. These results indicate a new interpretation of the glamour-versus-value
phenomenon: Investors may have been focusing on bottom-line earnings, rather than on the
acquirers’ true economic profitability, in assessing the value of an acquisition. The reversal
in earnings experienced by glamour acquirers is not due to mean reversion of earnings after
acquisitions, but possibly to these acquirers’ inferior ability to create value, which was not
recognized by investors until after the M&As.
To demonstrate the economic significance of the measure, I use it to calculate hedge
returns and compare them to returns from a strategy based on the M/B of acquirers before
M&As. A strategy of shorting low-residual acquirers and going long on high-residual
acquirers yields abnormal returns of 8.0% in the first year, 28.3% in two years, and 20.1% in
three years post acquisition. These returns are higher than those from hedging portfolios of
long low-M/B acquirers and short high-M/B acquirers, a strategy that prior literature has
shown to consistently generate abnormal returns. A combined strategy of shorting lowresidual and high-M/B acquirers and going long on high-residual and low-M/B acquirers
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yields significant abnormal returns that are greater than the sum of those yielded by the two
strategies that are based on M/B or residuals alone. This suggests that the strategy based on
my value creation measure adds to the returns of the previously known glamour-versus-value
strategy and that the abnormal returns are economically significant.
A few papers link cross-sectional variations in post-merger performance to certain
firm characteristics. For example, Erickson and Wang (1999) and Louis (2004) find that
stock acquirers underperform cash acquirers because stock acquirers inflate accruals in the
quarter immediately prior to the acquisition. Gong, Louis, and Sun (2008) show that the
post-merger underperformance can be explained by lawsuits aimed at pre-acquisition
earnings management. Harford (1999) and Oler (2008) find that higher levels of acquirers’
cash holdings before acquisitions are associated with worse announcement returns and postmerger performance. The main result of my paper, the impact of ex-ante shareholder value
creation on post-merger performance, is robust to the inclusion of acquirers’ accruals and
cash holdings and of other factors that have been shown to influence performance. There is
also a strand of literature examining firms’ operating performance around corporate events.
For example, Bouwman, Fuller, and Nain (2009) find that post-merger operating
performance is negatively related to the valuation level of the entire market, so that merged
firms perform worse during high-valuation periods.5 My paper extends this literature by
developing a new measure of shareholder value creation and linking it to post-merger stock
and operating performance and by showing the persistence of management effectiveness in
value creation after M&A transactions.
Section 2 of the paper defines the measure of value creation for shareholders and
describes the empirical methodologies. Section 3 describes the M&A sample and presents
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In addition, Loughran and Ritter (1997) find, using a sample of seasoned equity offerings (SEOs) in the 1980s,
that the operating performance of issuing firms peaks around the time of the offering but deteriorates afterwards.
They conjecture that the issuers are investing in what the market views as positive-NPV projects, but in fact
these projects have negative NPVs. The authors do not, however, provide evidence to support this conjecture.
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results on the impact of the ex-ante value creation measure on the announcement period
returns and post-acquisition performance of merged firms. Section 4 presents robustness
tests. Section 5 concludes.
2. Empirical Methodologies
I first define a new measure of shareholder value creation for acquiring firms. I then
describe the empirical procedures in examining the relation between the pre-merger value
creation measure and post-acquisitions performance. I also provide explanations of the key
variables measuring the short-run and long-run stock performance and long-run operating
performance of acquiring firms.
2.1 Measure of Shareholder Value Creation
I construct a value added measure for acquiring firms based on the concept of residual
earnings. As discussed earlier, it has been established in prior literature that residual earnings
is a better measure of a firm’s economic profitability than earnings, because residual earnings
includes a charge for capital employed. A firm can temporarily grow earnings by investing
in projects that generate enough profits to cover the cost of debt, but not the cost of equity
capital. The resulting earnings growth does not create values for shareholders. By contrast,
residual earnings growth can be generated only if the invested capital earns a return above the
return required by investors, thereby creating shareholder value. Theory work has used
residual earnings to develop structural models to value firms (Ohlson, 1995; Feltham and
Ohlson, 1995). The residual-income valuation models have also been used empirically to
estimate the intrinsic value of the firm (Frankel and Lee, 1997). Residual earnings is
therefore a natural starting point for measuring value added to shareholders.
Following prior literature, I define a firm’s excess earnings or abnormal return on
assets as a firm’s gross return on assets less its weighted average cost of capital (WACC). To
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assess how much value has been created from the firm’s available resources, I estimate a
firm-specific model of abnormal return on assets as a linear function of the firm’s invested
capital over the previous three years and the firm’s size, age, and financing constraint
(leverage), as follows:
is the abnormal returns on assets for firm i in year t. I define “normal”
where
return on assets (ROA) as NOPAT (net operating profits) scaled by the average assets of the
current and previous years. NOPAT is defined as earnings adding back net financing
expenses, where earnings is equal to net income minus preferred dividends and after-tax
special items and net financing expense is equal to after-tax net interest expense plus
preferred dividends. Abnormal ROA (
) is then calculated as ROA minus
WACC.6
The main independent variables in Eq. (1) are one-, two-, and three-year lagged
invested capital (
). Invested capital is defined
as the sum of long-term debt, short-term debt, minority interests, and common equity, scaled
by average assets. I also control for firm size (
, firm age
, and leverage
at the beginning of the year, all of which can affect firms’ ROA. I estimate
the model for each industry-year, based on 2-digit SIC codes for all industries with at least 20
observations in a given year. The predicted value from this regression is the estimate of the
average industry-year abnormal rate of return on capital; the residual estimate from the
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WACC is calculated by (1) estimating a CAPM cost of equity using the past 60 monthly returns, (2) inferring
after-tax cost of debt from interest expenses, total interest-bearing debt, and the tax rate, and (3) using the
market value of equity and book value of total debt as weights in the WACC formula. I estimate stock βs using
at least 24 months and up to 60 months of lagged returns; βs below 0.4 are set to 0.4 and those above 3 are set to
3.
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regression is the firm-specific measure of value added in a given year. As discussed earlier,
the interpretation of the residual is intuitive: A positive residual indicates that a firm earns a
higher abnormal return on all the capital used than do its industry peers in a given year, while
a negative residual indicates that the firm earns a lower abnormal return from its capital than
do its industry peers in a given year.
2.2 Performance Measures of Merged Firms
Announcement Period Returns and Long-run Abnormal Returns
Following Brown and Warner (1985), I use the modified market model to estimate
abnormal announcement period returns. I calculate daily abnormal returns for an acquirer by
deducting the equally-weighted index return from the acquirer’s raw return (results are
similar when using value-weighted index return):
where
is firm i’s daily stock return on date t and
is the return for the equally-
weighted CRSP index on date t. I calculate cumulative abnormal returns (CARs) by
summing the abnormal daily returns over a three-day event window around the M&A deal
announcement date.
To measure the long-run stock performance of merged firms, I follow the literature on
long-run event studies and use the “buy-and-hold” returns of a sample firm less the “buy-andhold” return of a properly chosen benchmark portfolio. The buy and hold abnormal return, or
BHAR, is calculated as:
∏
where
is the month t return for firm i,
is the benchmark portfolio return, and T is the
time horizon over which returns are calculated. I use the characteristic-based portfolio
constructed in Daniel, Grinblatt, Titman, and Wermers (1997) and Wermers (2004) as my
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benchmark portfolio. The DGTW benchmark portfolio for a given stock during a given
month is constructed to directly match that stock’s three main characteristics: size, (industryadjusted) book-to-market (B/M) ratio, and past momentum. Therefore, DGTW form
benchmarks that directly match the characteristics of the stocks being evaluated. This
approach can be contrasted with the alternative “factor-based” approach that forms factor
portfolios based on characteristic-sorted stocks; returns on these factor portfolios are then
used as regressors in a traditional three- or four-factor model.
There are several advantages of using the DGTW benchmark portfolios. First,
empirical evidence suggests that the characteristics of stocks provide better ex-ante forecasts
of the cross-sectional patterns of future stock returns (see, for example, Daniel and Titman,
1997). Second, characteristic matching also does a better job of matching future realized
returns; that is, the average fraction of the variance of the stock returns explained by the
benchmark is higher and the standard error of the estimates of the stock’s abnormal
performance is lower. Therefore, characteristic matching should have more statistical power
than factor-based models do to detect abnormal performance (Wermers (2004)).
Post-acquisition Operating Performance
As discussed above, in constructing the measure of shareholder value creation, I use
NOPAT scaled by average assets minus WACC as the abnormal returns ROA, then regress
this variable on invested capital and firm controls. To be consistent, I also use NOPAT
scaled by average assets as ROA to measure the post-acquisition operating performance. It is
useful to discuss the implications that the differences in accounting and payment methods in
M&As have for using NOPAT as a measure of post-acquisition operating performance.
Since NOPAT is defined as earnings adding back net financing expenses, it is not affected by
the methods of payment in acquisitions. As discussed in Healy, Palepu, and Ruback (1992),
if an acquisition is financed by debt and cash, its post-acquisition income will be lower than
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if it is financed by stock, because income is computed after deducting interest expenses. I
also exclude the period between the M&A deal announcement and the completion date from
the post-acquisition period to account for the differences in the timing of consolidating
targets under the purchase or pooling method. Under purchase accounting, earnings are
usually lower in the year of M&A deal completion because the target’s financial statements
are consolidated with those of the acquirer from the date of deal completion. Under pooling
accounting, however, financial statements are consolidated from the beginning of the year,
which can be much earlier than the M&A deal completion date.
NOPAT, in contrast, is not immune to the differences in depreciation and
amortization expenses, which are generally higher under purchase accounting than under the
pooling method. The purchase method restates the assets and liabilities of the target firm at
their market values and records any difference between the purchase price and the market
value of the target’s identifiable assets and liabilities as goodwill. No such re-valuation (and
no goodwill) is recorded under the pooling method. To ensure that my results are not driven
by differences in accounting and payment methods, I include, as controls in regressions, an
indicator for the pooling method and another indicator that equals one if more than 50% of
the consideration for acquisition is paid for with the acquirer’s stock.
2.3 Regression Framework
To examine the link between the ex-ante measure of value creation and the ex-post
performance of the merged firms, I run multivariate regressions to control for factors that
may impact a firm’s (abnormal) performance. The dependent variables are the three-day
CARs, the one-, two-, and three-year post-acquisition BHARs, and ROA. For stock
performance, I estimate the following model:
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(2)
In Eq. (2), AR is the three-day CARs or the one-, two-, and three-year post-acquisition
BHARs of acquirers.
(
) is an indicator that equals one if an
acquirer’s residual falls in the bottom quintile (middle three quintiles) of all the acquirers’
residual estimates from Eq. (1) and equals zero otherwise.
(
) is an
indicator that equals one if the acquirer’s M/B ratio, calculated one quarter before the M&A
announcement date, is in the top quintile (middle three quintiles) of the M/B ratios of all the
acquirers that announced acquisitions in the same year and equals zero otherwise. These
indicators are included because prior literature (e.g., Rau and Vermaelen, 1998) find that
high-M/B glamour acquirers tend to underperform low-M/B value acquirers post acquisition.
Moeller, Schlingemann, and Stulz (2004) show that smaller acquirers tend to have higher
announcement period returns. Therefore, I include
as a control variable,
defined as an acquiring firm with market capitalization below the 25th percentile of NYSE
firms as of the fiscal year-end immediately before the M&A announcement date. Prior
literature finds that acquirers in stock acquisitions show higher abnormal accruals before the
acquisition announcement relative to cash acquirers; and that the high abnormal accruals are
related to lower post-acquisition returns (Erickson and Wang (1999) and Louis (2004)).
Accordingly, I control for
and net operating assets (
, which are the balance
sheet representation of the cumulative accruals. I also control for acquirers’ preannouncement cash levels (
), since Harford (1999) finds that cash-rich
acquirers have lower announcement returns and Oler (2008) finds that an acquirer’s cash
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level is associated with worse long-term post-acquisition stock returns.
Previous research has demonstrated that the relative size of an acquisition to the size
of the acquirer affects the acquirer’s post-acquisition returns (Asquith, Bruner, and Mullins,
1983). Therefore, I include
, defined as the transaction value divided by the
acquirer’s market capitalization at the end of fiscal year immediately before the deal
announcement date.7 I also include an indicator,
, which equals one if the target
and acquirer have different two-digit SIC codes and zero otherwise. As discussed above, I
include a
indicator that equals one if the acquirer uses the pooling method.
is
an indicator that equals one if more than 50% of the deal is paid for with the acquirer’s stock.
This is included because Bhagat et al. (2005) find that stock-based deals experience a
negative announcement period return; and, Loughran and Vijh (1997) show that stock deals
have worse long run post-acquisition returns.
is a dummy variable that equals one if
the acquisition is a tender offer and zero otherwise. Jagadeesh and Titman (1993) documents
that the pre M&A announcement price run-up leads to short-term price momentum and
DeBondt and Thaler (1985) documents that price reverses in the long run. Therefore, I
include
, the mean pre-announcement return of acquirers from 200 days to 31
days prior to the deal announcement date to account for short-run price momentum.
In the regression model for long-run operating performance, in addition to including
all the variables in Eq. (2), I also control for matching non-acquirers’ post-acquisition
operating performance to ensure that the results are not driven by the (possible) mean
reversion properties of long-run operating performance. Freeman, Ohlson, and Penman
(1982) and Nissim and Penman (2001) both show that extreme values of operating
performance, such as sales growth or return on assets, are strongly mean-reverting in
subsequent periods. To find matching non-acquirers, I follow the procedure used in the long-
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I also use the sizes of the acquirer and target separately, as in Schwert (2000); results are very similar.
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run stock performance literature and match each acquirer with a non-acquirer, chosen on the
basis of firm (asset) size, industry, and M/B (Barber and Lyon, 1997; Kothari and Warner,
1997). Specifically, the candidate matching firms for an acquirer are those listed on the
AMEX, NYSE, or Nasdaq with the same 2-digit SIC codes and with asset size at the end of
fiscal year before the deal announcement date that is 50% to 200% of the asset size of the
acquirer. From this set of firms, those that have not made an acquisition during the three
years prior to and three years after the deal announcement year are ranked based on their
M/B. The firm with the closest M/B is chosen as the matching non-acquirer.
2.4 Hedge Returns
I also conduct a non-parametric test to gauge the magnitude of the long-run postacquisition abnormal returns in economic terms. Specifically, I calculate the return on an
implementable trading strategy that takes a long position on acquirers with high residuals
from the excess earnings-invested capital regressions and a short position on acquirers with
low residuals. An acquirer falls into the low-residual (high-residual) group if its residual
from the regression in Equation (1) is in the bottom (top) quintile of all acquirer residuals one
year before the acquisition announcement. The positions are taken on the day of M&A deal
completion and closed out 12 months, 24 months, and 36 months after that. As discussed
above, I calculate abnormal returns using the DGTW benchmark portfolio that matches
acquirers on size, M/B, and momentum. The combined returns are the total returns on both
long and short positions for one year, two years, and three years post acquisition.
3. Data and Empirical Results
From the Securities Data Company’s (SDC) U.S. M&A database, I identify all
completed corporate acquisitions during the period of 1980-2005, based on standard sample
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selection criteria.8 As is common practice, I exclude financial institutions and regulated
utility firms. In addition, if an acquirer announces multiple M&A deals in the same year, I
only keep the deal with the largest transaction value. Finally, I only include deals in which
sufficient Compustat data is available to calculate the summary statistics shown in Table 1.9
This procedure generates a sample of 1,938 acquisitions, for which I analyze the value added
measure, operating performance, and stock returns.
[Insert Table 1 here.]
Table 1 reports summary statistics of all the acquirers during the sample period of
1980-2005 and in three sub-periods. Panel A reports deal characteristics and Panel B reports
firm characteristics. Panel A shows that there were significantly more acquisitions during the
1990s than in the other two periods (1,012 deals in the 1990s, 376 in the 1980s, and 637 in
the 2000s). The period from 2000 to 2005 had the highest average deal value and the 1980s
had the lowest. About 50% of the deals in the 1980s were tender offers, while 60% of the
deals in the 1990s were paid for with the acquirer’s stock. About 37% of the deals in the
1990s used the pooling method to account for the acquisitions, compared to only 2% in the
1980s and 6% in the 2000s.10 Panel B shows that the average M/B ratio of acquirers was
highest in the 2000s (3.38) and lowest in the 1980s (1.59). Most of the acquirers’ accounting
ratios increased over time (e.g., accrual, NOA, acquirer cash, invested capital, and size), but
leverage decreased over time, from 0.24 in the 1980s to 0.18 in the 2000s, and average
acquirers’ age also decreased from 24.9 years in the 1980s to 18.2 in the 2000s.
8
My sample selection criteria include the following: The deal value is at least $10 million; both acquirer and
target are public firms and the acquirer is listed on NYSE, AMEX or Nasdaq; the acquisition is announced
during the sample period of 1980-2005; all partial acquisitions (i.e., acquiring less than 100% of the target
assets) are excluded.
9
These include NOA, accruals, and the acquirer’s cash, invested capital, size, leverage, M/B, and age at the
fiscal year-end prior to the M&A deal announcement date.
10
SFAS 141 requires all firms to use the purchase method for acquisitions initiated after June 30, 2001.
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3.1 Univariate Analyses
As indicated in Panel A of Table 2, the average announcement period return for all
acquisitions is negative (-1.0%), as is the post-acquisition long-run abnormal return, with the
average one-year return being -1.0%, the two-year return -2.8%, and the three-year return 2.7%. These patterns are consistent with the findings in prior work (e.g., Bouwman, Fuller,
and Nain, 2007; Oler, 2008). 11
[Insert Table 2 here.]
I next examine the announcement and long-run abnormal returns for the low-residual
and high-residual groups versus the other acquirers. Once again, an acquirer falls into the
low-residual (high-residual) group if its residual from the excess earnings-invested capital
regression (in Eq. 1) is in the bottom (top) quintile of all acquirer residuals at the fiscal yearend before the deal announcement date. The low-residual group has a lower average
announcement return than do the rest of the acquirers (non-low-residual), but the difference is
not statistically significant. By contrast, the low-residual group has significantly lower postacquisition returns than the other acquirers in all three years. The average return for the lowresidual group is 4.8% lower in the first year after acquisition, 11.5% lower in the first two
years, and 9.8% lower in the first three years after acquisition than that of other acquirers; all
the differences are statistically significant. Not surprisingly, when I compare the highresidual group to all the other acquirers, I obtain the opposite results, which suggest that the
low-residual (high-residual) group underperforms (outperforms) relative to the other
acquirers in terms of abnormal stock returns post acquisition. Panel B of Table 2 provides
the same comparisons for operating performance (ROA). Again, the low-residual group has
significantly worse post-acquisition operating performance than the others. The gaps in ROA
during the one-year, two-year, and three-year post-acquisition periods are 6.1%, 4.8%, and
11
See Brunner (2002) for a comprehensive survey of the studies examining shareholder returns for M&A.
16
4.0%, respectively; all the differences are highly significant. By contrast, the high-residual
group has significantly higher post-acquisition ROAs in the same periods after acquisition.
In summary, while there is no difference in the announcement period return between
the low-residual groups and other acquirers, the low-residual acquirers significantly
underperform other acquirers in both operating and stock performance post acquisition.
These results support the notion that, prior to the acquisitions, the market and investors do not
fully differentiate the acquirers’ ability to create value for their shareholders and are
subsequently disappointed by the poor post-acquisition performance of low-residual
acquirers.
3.2. Multivariate Analyses
In this subsection, I present the multivariate results from the announcement period
(event) study and long-run stock and operating performance analyses.
Announcement Period Returns and Long-run Abnormal Returns
I test whether univariate evidence of the underperformance of low-residual acquirers
in the post-acquisition period holds in a multivariate setting. Table 3 shows that the
announcement period return is 1.4% lower for low-residual acquirers than for high-residual
acquirers (the default group in the regressions) after controlling for firm and deal
characteristics; the result is significant at 5% (Column 1). As has been found in prior
research (e.g., Servaes, 1991; Rau and Vermaelen, 1998), the market responds negatively to
stock acquisitions and when the target is large relative to the acquirer. The announcement
period return is higher if the acquirer experiences larger pre-announcement stock returns,
consistent with the short-term stock price persistence documented in Jagadeesh and Titman
(1993). On the other hand, the announcement returns are lower in a tender offer or when the
acquirer uses pooling accounting, but these results are not statistically significant.
Announcement returns do not seem to be affected by the acquirer’s M/B ratio at the time of
17
the acquisition announcement. I also find that the announcement returns decrease in the level
of the acquirer’s cash holdings, consistent with Harford (1999), who interprets this result as
the market taking cash-rich acquirers to have more severe agency problems, as indicated by
Jensen (1986). Finally, the announcement returns also decrease in the acquirers’ NOA
(scaled by lagged total assets), which Hirshleifer, Hou, Teoh, and Zhang (2004) propose as
an indicator of weak earnings quality.
The next three columns of Table 3 show the regression results for the buy-and-hold
abnormal returns (BHAR) calculated over the one-year, two-year, and three-year windows
post acquisition. The long-run abnormal returns are lower for acquirers with high M/B ratios
(low-M/B acquirers are the default group) and for acquirers with greater pre-announcement
price run-ups, both findings are consistent with prior studies (Rau and Vamaelen, 1998;
Jagadeesh and Titman, 1993). Controlling for firm and deal characteristics, I find that lowresidual acquirers have significantly lower post-acquisition returns over all three windows
after the deal completion date; the return gap between low-residual acquirers and highresidual acquirers (the default group) over the one-, two-, and three-year windows is 10.8%,
29.2%, and 25.7%, respectively; all the results are significant at the 5% level. The size and
significance of the coefficients of the Low Residual indicator suggest that the pre-acquisition
value creation measure is an important determinant for long-run post-acquisition stock
performance.
Combining the announcement period results with the long-run abnormal returns, I
conclude that the market, triggered by an M&A deal announcement, partially recognizes
low-residual acquirers’ poor performance in creating value for shareholders. While the
adjustment’s direction (during the announcement period) is correct, its magnitude is too
small, in that low-residual acquirers continue to underperform the high-residual acquirers
during the first three years after an acquisition.
18
[Insert Table 3 here.]
Post-acquisition Operating Performance
Table 4 reports results from regressions for long-run operating performance (ROA).
Operating performance is worse if the acquirer is small, has large (pre-announcement) cash
holdings, and uses stock as the main method of payment. The relation between cash level
and long-run post-acquisition operating performance is consistent with the findings in Oler
(2008). The ROA of matching non-acquirers in the post-acquisition period is positively and
significantly related to the acquirers’ post-acquisition ROA, which illustrates the success of
the matching procedure. Operating performance is also better if the acquirer has high preacquisition M/B and uses the pooling method. The result for the pooling method is consistent
with earlier discussions of the differences between the pooling and purchase accounting
methods; in particular, acquirers generally have higher post-acquisition earnings if they use
the pooling rather than the purchase method. More importantly, as in the stock return
regressions (Table 3), operating performance is significantly worse if the acquirer belongs to
the low-residual group: The gap in ROA between the high- and low-residual groups is 6.7%
for the first year, 2.7% for the first two years, and 3.4% for the first three years post
acquisition; all the results are significant at 5%.
In summary, the results from Tables 3 and 4 show that the ex-ante value creation
measure strongly predicts the post-acquisition stock and operating performance of acquiring
firms, validating both the measure itself—as an assessment of management effectiveness—
and the hypothesis on which the measure is based—the persistence of management’s
effectiveness (or lack of it) in creating value. Acquirers that generated higher net returns than
their peers before acquisitions are expected to continue to deliver superior returns to their
shareholders after the M&A transaction, while acquirers that underperformed their peers are
likely to repeat the subpar performance.
19
[Insert Table 4 here.]
3.3. Hedge Returns
To further gauge the economic significance of the positive link between the ex-ante
shareholder value creation and the ex-post performance of merged firms, I calculate the
returns to an implementable trading strategy and report the results in Table 5. The strategy
calls for taking a long position on high-residual acquirers and a short position on low-residual
acquirers. The positions are formed on the day of M&A deal completion and closed 12
months (Column 1), 24 months (Column 2), and 36 months (Column 3) after the completion
date. Panel A shows that this investment strategy produces significant abnormal returns in all
three post-acquisition windows, with an abnormal return of 8.0% during the first year, 28.3%
in the first two years, and 20.1% in the first three years after M&A deal completion.
I also compare the hedge returns based on the ex-ante value creation measure to the
returns on a previous known investment strategy based on acquirers’ M/B ratios. This
strategy involves going short on high-M/B acquirers and long on low-M/B acquirers. An
acquirer falls in the high-M/B (low-M/B) group if its M/B ratio at the time of the acquisition
announcement is in the top (bottom) quintile of M/B ratios of all the acquirers that announced
acquisitions in the same year. Panel B shows that the mean abnormal return from this
strategy is 10.1% in the first year, 21.9% in the first two years, and 7.3% in the first three
years post acquisition. These results are consistent with the findings in Rau and Vermaelen
(1998), which show that the high-M/B glamour acquirers tend to underperform the low-M/B
value acquirers post acquisitions. Comparing the results from Panels A and B, it is clear that
the strategy of going long on high-residual and short on low-residual acquirers generates
significantly higher returns than the M/B-based strategy over the two- and three-year postacquisition windows.
While strategies based on the value added residual generate higher hedge returns than
20
the M/B-based strategies, it is unclear whether the hedge returns based on the residuals
subsume or add to the returns based on M/B. To test this, I examine the hedge returns for a
combined strategy, in which I go long on high-residual and low-M/B acquirers and short on
low-residual and high-M/B acquirers. To have a reasonable number of stocks in each
portfolio, I group acquirers’ value added residuals and M/B ratios into terciles (rather than
quintiles); thus the long (short) portfolio contains acquirers with residuals in the top (bottom)
tercile and M/B in the lowest (highest) tercile. Panel C of Table 5 shows that this combined
strategy generates substantial abnormal returns, which are greater than the sum of the
abnormal returns generated by the two strategies separately in all three post-acquisition
windows. Interestingly, the larger abnormal returns seem mainly to come from the stocks in
the long portfolio. The results suggest that the hedge returns based on acquirers’ ex-ante
ability to create shareholder value add to the previously known glamour-versus-value strategy
and are both economically and statistically significant.
[Insert Table 5 here.]
3.4. Implications of the Comparison of Glamour versus Value Acquirers
The new shareholder value added measure also provides new insight on the postacquisition underperformance of high-M/B glamour acquirers relative to low-M/B value
acquirers. Loughran and Vijh (1997) and Rau and Vermaelen (1998) first document this
underperformance “anomaly” and Rau and Vermaelen interpret the result as naïve investors
over-extrapolating glamour acquirers’ past earnings performance in assessing the value of the
acquisition. In this section, I provide a new interpretation of the finding based on the
shareholder value added measure: Investors and the market may be focusing on the acquirer’s
bottom-line earnings, rather than on its true economic profitability before acquisitions. Thus,
the glamour acquirer’s reversal in earnings is not due to mean reversion of earnings, but
rather to the glamour acquirer’s inferior ability to create value for shareholders, which is not
21
recognized by the market until after the M&A.
Operating Performance of Glamour versus Value Acquirers
I first examine the time-series patterns of operating performance for the high- and
low-M/B acquirers and their matching non-acquirers before and after the M&As. As
discussed above, in defining high- and low-M/B acquirers, I sort the acquirers, for each
announcement year, into quintiles by their M/B ratios at the fiscal quarter-end immediately
preceding the acquisition announcement date. As has been done in prior work, I define the
quintile with the lowest M/B as “value” acquirers, the quintile with the highest M/B as
“glamour” acquirers, and the rest (60% of the sample) as “medium-M/B” acquirers.
[Insert Table 6 and Figure 1 here.]
Table 6 presents the mean ROAs of acquirers and matching non-acquirers for the
three fiscal years before the M&A deal announcement date and the three fiscal years after the
deal completion date. Figure 1 plots the mean ROA of glamour and value acquirers and their
matching non-acquirers during the same period around M&As. Both the table and figure
show that the operating performance of glamour acquirers evolves quite differently around
M&As than does that of other groups. For glamour acquirers, ROA rises during the three
years prior to the acquisition date, peaking at 26.7% of assets in the year before deal
announcement (Year −1), only to fall sharply to 17.2% one year after deal completion (Year
+1) and 13.7% two years later. Other measures of operating performance (sales growth and
EBITDA) all go through the same rise-and-fall pattern (results not reported in tables). In
fact, almost all the accounting ratios of glamour acquirers peak one year before acquisitions,
only to rapidly decline one year after deal completion and continue their slide in the
following years. While ROAs (and other operating ratios) for matching high-M/B nonacquirers also show an overall downward trend from Year −3 to Year+3, the drop is not as
pronounced as that of glamour acquirers. Finally, the operating performance of value
22
acquirers is much more stable during the same period and not much different from that of
their matching non-acquirers.
The observed time-series patterns of operating performance suggest that glamour
acquirers launch acquisitions at the peak of their performance. This finding is consistent with
the stock-market-driven acquisition theory of Shleifer and Vishny (2003), who posit that fully
rational managers can take advantage of market misvaluations by timing acquisitions and
choosing the means of payment.12 The comparisons with matching non-acquirers rule out
mean reversion in operating performance as the only reason for the sharp decline in the ROA
of glamour acquirers. I next examine whether the inferior ability of glamour acquirers to
create value for shareholders before acquisitions is a possible source of these acquirers’
subsequent drop in ROA.
Value Added Residuals of Glamour and Value Acquirers
Table 7 reports the univariate comparisons of the value added residuals across
different M/B groups and Figure 2 plots the residuals for different groups. I find that both
glamour acquirers and value acquirers have negative average residuals (-1.9% and -0.2%,
respectively), indicating that both groups earn returns on invested capital at a lower rate than
their industry peers. Both groups’ residuals are also lower than that of their matching nonacquirer group, with the residuals of glamour acquirers considerably lower than that of their
matching firms (+5.0%). On the other hand, the medium-M/B acquirers earn a rate on
invested capital that is 3.2% higher than that of their industry peers and their matching firms
and much higher than that of the glamour acquirers. Overall, glamour acquirers have the
lowest average residuals among all the acquirer groups and matching groups (the difference
between glamour and value acquirers is not statistically significant), yet their average ROA
and M/B are the highest among all the groups. In addition, the positive relationship between
12
Market timing is also documented in other corporate events, such as initial public offerings (IPOs) and
seasoned equity offerings (SEOs); see, for example, Graham and Harvey (2001), Baker and Wurgler (2002), and
Loughran and Ritter (1997).
23
the ex-ante value added measure and ex-post performance of merged firms is robust to
including acquirer M/B ratios (recall Tables 3 and 4).
[Insert Table 7 and Figure 2 here.]
These results suggest that investors and the market may be fixated on bottom-line
earnings rather than on the true ability to create value for shareholders before acquisitions.
This fixation provides a possible source of misvaluation of some acquirers at the time of
acquisition. Investors and the market did not realize the inferior ability of glamour acquirers
to create value for shareholders until after observing the post-acquisition reversal of the
acquirers’ operating performance.
4. Robustness Tests
In this section, I briefly discuss results from a number of robustness checks on the
methodology of calculating hedge returns, sample period, and different specifications of
constructing the ex-ante value creation measure.
4.1. Calendar-time Results
The hedge return results in Table 5 are based on an event-time approach. That is,
abnormal returns are calculated across M&A transactions for one- to three-year windows
after the completion of these transactions, even though the acquisitions occur at different
(calendar) times. This approach weights different acquisitions equally and implicitly tests a
strategy of investing equal amounts in each acquisition. One problem with this approach is
that the significance of long-run returns can be overstated because of cross-correlations
among returns (Bernard, 1987; Mitchell and Stafford, 2000; Kothari and Warner, 2004). An
alternative approach is to use calendar-time returns. In other words, one tracks the
performance of an event portfolio in calendar time. This technique weighs each month
24
equally and tests a strategy of investing equal amounts in acquisitions each month. Thus, this
approach is immune to the potential cross-correlation problem. I recalculate the abnormal
returns for the strategy of going short on low-residual acquirers and long on high-residual
acquirers using the calendar-time approach.
For each month during my sample period, I create high- and low-residual event
portfolios as follows: The high-residual (low-residual) portfolio consists of all the acquirers
that completed an acquisition within the previous one, two, or three years. Portfolios are
rebalanced monthly to drop all the acquirers that reach the end of their one-, two-, or threeyear period and add all the acquirers that have just completed an M&A transaction. Table 8
presents the mean abnormal monthly returns from long and short portfolios consisting of
acquirers that completed acquisitions within the previous one-, two-, and three-year windows.
The short-position portfolios (of low-residual acquirers) now generate negative but
statistically insignificant (from zero) returns. On the other side, the strategy of going long on
high-residual acquirers continues to produce substantial abnormal returns for each of the
three portfolios and the returns are all statistically significant. Overall, the results using the
calendar-time approach corroborate the results of using the event-portfolio approach and
confirm that acquirers’ ability to generate positive returns from invested capital is an
important predictor of post-acquisition returns.
[Insert Table 8 here.]
4.2 Sub-sample Periods and Alternative Specifications of the Value Creation Measure
I have shown in different tests with different dependent variables that the value added
measure is an important predictor for acquirers’ post-acquisition performance. All the results
presented so far are based on the sample period of 1980-2005. It is well known, however,
that acquisitions tend to cluster in time (see, for example, Holmstrom and Kaplan, 2001). As
25
shown in Table 1, the number of deals was much greater in the 1990s than in other periods.
Moreover, Moeller, Schlingemann, and Stulz (2005) show that shareholders of acquiring
firms experienced much greater losses in the 1990s than in other periods. To rule out the
possibility that my findings are driven by the deals made in the 1990s, I split the sample
period into three sub-periods: acquisitions announced from 1980 to 1990, from 1991 to 2000,
and from 2001 to 2005. I rerun stock return and operating performance regressions for each
of these sub-periods. Panel A of Table 9 reproduces the main results from Tables 3 and 4.
Panels B, C, and D show that there are some differences among these sub-periods. However,
the main results on the positive relation between the ex-ante value creation measure and expost acquirer performance are not driven by any particular sample period. In fact, the value
creation measure remains a strong predictor for post-acquisition performance in each subperiod.
Since NOPAT is not immune to the differences in depreciation expenses due to the
use of pooling or purchase accounting, I rerun the regression of post-acquisition operating
performance using an alternative measure: earnings before interest, taxes, depreciation, and
amortizations (EBITDA). The advantage of using EBITDA is that it excludes the effects of
interest expenses and taxes, goodwill, and depreciation and is therefore unaffected by the
accounting method and the method of financing (cash, debt, or equity). Panel E of Table 9
shows that the results are very similar.
Finally, in the regression to obtain the residual estimates (Equation (1)), I used one-,
two-, and three-year lagged invested capital as the main independent variables. One can
argue that using only one- and two-year lagged invested capital is more appropriate. I rerun
the regressions using only one- and two-year lagged invested capital (dropping the three-year
lag) to obtain residual estimates. Panel F of Table 9 shows that the results remain very
similar to those reported in Tables 3 and 4.
26
[Insert Table 9 here.]
5. Summary and Concluding Remarks
Prior literature has shown that large-scale mergers and acquisitions (M&As) can lead
to enormous losses for acquiring firms’ shareholders. Generally, a significant amount of
“due diligence” effort is expended before approving an M&A transaction. This paper offers
another factor that the acquiring firm’s shareholders and board of directors should consider—
the firm’s efficiency in utilizing capital leading up to the proposed M&A transaction. If
management has allocated capital to productive projects and created positive returns for
shareholders in the past, then shareholders and the board should have confidence in
management to deploy capital to the acquisition project. However, if management has
misallocated capital to negative-NPV projects in the past, then shareholders should be
cautious in approving the new M&A deal to avoid more losses.
In order to assess management’s ability to create value for shareholders, I develop a
new measure based on the concept of residual earnings. Accounting research has long argued
and shown that residual income is a better measure of a firm’s economic profitability than
earnings. This is because earnings do not reflect the cost of equity capital raised, whereas
residual income includes a charge against cost of capital. If investors are fixated on bottomline earnings in assessing management effectiveness, they may well allow a firm that has
shown strong earnings and earnings growth to pursue a large-scale M&A deal, even though
that firm has actually invested in negative-NPV projects.
I define the measure of shareholder value creation as the residual from regressions of
firms’ excess earnings—earnings in excess of cost of capital—on invested capital and other
firm characteristics for each industry and year. I first run the regressions using all Compustat
firms, then obtain residual estimates for each of the acquirers and their matching non-
27
acquirers as of the fiscal year-end before the M&A deal announcement date. A positive
(negative) residual indicates that a firm is able to generate excess return on invested capital at
a higher (lower) rate than its industry peers do in a given year. I include matching nonacquirers in my analyses so as to rule out mean reversion as the reason for the possible
reversal in acquirers’ operating performance.
I then link my value added measure for an acquiring firm, constructed before the
M&A deal announcement date, to the firm’s post-acquisition operating and stock
performance. I find that announcement period returns are lower for acquirers with low
residuals from the excess earnings-invested capital regressions than for acquirers with high
residuals. This result suggests that the market, triggered by the M&A deal announcement,
adjusts its valuation of acquirers with different levels of shareholder value creation. While
the direction of the adjustment is correct, the magnitude is too small, in that the residuals also
strongly predict both the operating performance and the long-run abnormal stock returns of
post-merger firms. Hedge portfolios based on shorting the low-residual acquirers and going
long on the high-residual acquirers generate abnormal returns that are substantially higher
than returns on the alternative strategy of going long on value acquirers (with low M/B ratios
before the M&A deal announcement) and going short on glamour acquirers (with high M/B
ratios).
The fact that better ex-ante value creation is associated with better post-acquisition
performance validates both my measure and the hypothesis of the persistence of management
effectiveness in value creation. That is, acquirers that generate higher net returns than their
peers are expected to continue to deliver superior returns to their shareholders while acquirers
that underperform their peers are likely to repeat the subpar performance in the new M&A
transaction. This positive link also indicates that investors and the market do not fully
recognize the cross-sectional differences in shareholder value added (based on my measure)
28
before the acquisition. In practice, this measure can be used by an acquiring firm’s board of
directors and/or shareholders to make more prudent decisions in assessing the new M&A.
Overall, this paper contributes to the literature on market efficiency around corporate
events by introducing a new measure of shareholder value added based on the concept of
residual earnings and by documenting a positive link between this ex-ante value added
measure and ex-post acquisition performance. This measure and similar methodologies can
be used to investigate management effectiveness in other significant corporate events.
29
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Solomons, D. 1965. Divisional Performance Measurement and Control. Richard D. Irwin,
Homewood, IL.
31
Travlos, N. G. 1987. Corporate takeover bids, methods of payment, and bidding firms’ stock returns. Journal of
Finance, 42(4), 943–963.
Wermers, Russ R., Is Money Really 'Smart'? New Evidence on the Relation Between Mutual Fund Flows,
Manager Behavior, and Performance Persistence (May 2003). Available at SSRN:
http://ssrn.com/abstract=414420 or doi:10.2139/ssrn.414420
32
Table 1 Summary Statistics
This table shows deal characteristics (Panel A) and acquirer’s firm characteristics (Panel B) during the
full sample period of 1980 to 2005 and by sub-periods. The summary statistics in Panel A are based on
a sample of 2,025 acquisitions with non-missing deal characteristics, out of which 1,938 acquisitions
have sufficient Compustat data to calculate the summary statistics in Panel B. All the accounting
variables in Panel B are measured at the fiscal year-end before the deal announcement date.
Acquisitions are included in this sample if (a) the acquirer is a U.S. firm listed on NYSE, AMEX, or
Nasdaq, (b) both acquirer and target are public firms, (c) deal value is at least $10 million, (d) the
acquirer obtains 100% of the target assets, (e) the method of payment is cash, stock, or a mixture of the
two, and (f) the deal is announced during 1980-2005. If an acquirer announces multiple deals in the
same year, the deal with the largest transaction value is retained. See Appendix A for all variable
definitions.
Panel A: Deal Characteristics
N
Relative
Size
Diversify
Pool
Stock
Tender
Small
Acquirer
Deal Value
All
2025
0.57
42%
21%
53%
28%
17%
1443.2
1980-1990
376
0.88
59%
2%
31%
50%
20%
438.6
1991-1999
1012
0.54
40%
37%
60%
23%
18%
1457.4
2000-2005
637
0.42
35%
6%
53%
22%
13%
2015.4
Panel B: Firm Characteristics
N
M/B Accruals NOA
Acquirer
Cash
Invest
Cap
Size
Leverage
Age
All
1938
2.74
0.03
1.02
0.17
0.81
5872.7
0.21
19.9
1980-1990
357
1.59
0.03
0.65
0.13
0.77
3400.1
0.24
24.9
1991-1999
982
2.76
0.03
1.01
0.16
0.81
4592.5
0.22
18.9
2000-2005
599
3.38
0.05
1.25
0.22
0.82
9422.3
0.18
18.2
Table 2 Univariate Analysis
This table reports univariate relations between residual estimates from the following regression at the
fiscal year-end prior to acquisition announcement, three-day abnormal announcement returns, and
long-run buy and hold abnormal returns (BHARs) for the three years post acquisition (Panel A) and
returns on assets (ROA) for the three years post acquisition (Panel B). Low- (high-) residual firms are
identified using cutoffs set at the 20th (80th) percentile of acquirers’ residual estimates at the fiscal
year-end prior to acquisition announcement. N in Panel A is the number of acquisitions with nonmissing residual estimates and three-day announcement returns. N in Panel B is the number of
acquisitions with non-missing residual estimates and ROA at Year +1. See Appendix A for variable
definitions. t-statistics for means reflect a two-sided t-test for means. Bold font indicates significance at
least at the 10% level.
Panel A: Univariate Post-acquisition Returns
All
Low-residuals
Non-low-residuals
Low – Non-low
t-stat
Announcement
Returns
-1.0%
-1.9%
-0.9%
-1.0%
1.26
BHAR
Year +1
-1.0%
-4.8%
0.0%
-4.8%
-2.49
BHAR
Year 1& 2
-2.8%
-6.8%
4.7%
-11.5%
-2.12
BHAR
Year 1, 2, & 3
-2.7%
-11.3%
-1.5%
-9.8%
-1.96
High-residuals
Non-high-residuals
High – Non-high
t-stat
Announcement
Returns
-1.1%
-1.0%
-0.1%
-0.09
BHAR
Year +1
3.2%
-0.4%
3.6%
3.47
BHAR
Year 1& 2
21.5%
3.0%
18.5%
3.48
BHAR
Year 1, 2, & 3
8.8%
1.2%
7.6%
1.98
N
1406
Panel B: Univariate Post-acquisition Operating Performance
All
Low-residuals
Non-low-residuals
Low – Non-low
t-stat
ROA
Year +1
14.4%
9.5%
15.6%
-6.1%
-7.93
ROA
Year 1& 2
13.6%
9.8%
14.6%
-4.8%
-5.89
ROA
Year 1, 2, & 3
13.1%
9.9%
13.9%
-4.0%
-4.93
High-residuals
Non-high-residuals
High – Non-high
t-stat
ROA
Year +1
19.6%
13.7%
5.9%
3.95
ROA
Year 1& 2
15.4%
13.2%
2.2%
2.26
ROA
Year 1, 2, & 3
15.1%
12.7%
2.4%
1.96
N
1318
Table 3 Regression Analysis of Short-run and Long-run Abnormal Returns
This table reports results on regressions of acquirers’ three-day announcement returns (calculated using
the CRSP equally weighted index) and BHARs for the three years post acquisitions on the variables.
See Appendix A for variable definitions. Industry and year dummies are not shown. t-statistics are
provided in parentheses. *, **, and *** indicate significance at the 1%, 5%, and 10% levels,
respectively.
VARIABLES
LowResidual
MediumResidual
SmallAcquirer
RelativeSize
Diversify
Pooling
Stock
Tender
PreAnnReturn
HighMB
MediumMB
Accruals
NOA
AcquirerCash
Constant
Observations
Adjusted R-squared
(1)
Announcement
Returns
(2)
BHAR
Year +1
(3)
BHAR
Year 1& 2
(4)
BHAR
Year 1, 2, & 3
-0.014**
(-2.39)
-0.004
(-0.73)
0.010*
(1.82)
-0.004***
(-3.56)
-0.002
(-0.39)
-0.002
(-0.31)
-0.023***
(-4.69)
-0.002
(-0.39)
0.005**
(2.19)
-0.006
(-0.83)
-0.005
(-0.91)
0.026
(1.09)
-0.007**
(-2.27)
-0.056***
(-4.29)
0.048
(1.18)
-0.108**
(-2.13)
-0.109**
(-2.52)
-0.028
(-0.60)
-0.017
(-1.54)
-0.010
(-0.30)
0.007
(0.14)
0.033
(0.79)
0.008
(0.19)
-0.037**
(-2.04)
-0.094
(-1.40)
-0.132***
(-2.87)
-0.245
(-1.23)
0.005
(0.19)
-0.072
(-0.64)
-0.188
(-0.95)
-0.292**
(-2.07)
-0.260**
(-2.17)
0.132
(0.99)
-0.008
(-0.25)
-0.078
(-0.86)
-0.251*
(-1.82)
0.049
(0.42)
-0.068
(-0.59)
-0.090*
(-1.79)
-0.147
(-0.79)
-0.229*
(-1.77)
-0.423
(-0.76)
0.022
(0.32)
-0.173
(-0.55)
-0.448
(-0.82)
-0.257**
(-2.39)
-0.218**
(-2.41)
-0.085
(-0.82)
-0.003
(-0.12)
0.027
(0.40)
-0.028
(-0.26)
-0.113
(-1.28)
-0.110
(-1.25)
-0.096**
(-2.57)
-0.080
(-0.57)
-0.226**
(-2.31)
-0.387
(-0.93)
0.072
(1.36)
-0.518
(-1.16)
-0.690
(-0.87)
1,218
0.08
1,106
0.02
1,083
0.03
1,023
0.03
Table 4 Regression Analysis of Post-acquisition Operating Performance
This table reports regressions of acquirers’ ROA for the three years post acquisitions on the variables.
Match_ROA is ROA for matching non-acquirers. For each acquirer, I find a matching non-acquirer
using the following procedure: Candidate matching firms for an acquirer are those listed on the AMEX,
NYSE, or Nasdaq with the same 2-digit SIC codes and with asset size at the end of fiscal year before
the deal announcement date that is 50% to 200% of the asset size of the acquirer. From this set of
firms, those that have not made an acquisition during the three years prior to and three years after the
deal announcement year are ranked based on their M/B. The firm with the closest M/B is chosen as the
matching non-acquirer. See Appendix A for all the other variable definitions. Industry and year
dummies are not shown. t-statistics are provided in parentheses. *, **, and *** indicate significance at
the 1%, 5%, and 10% levels, respectively.
VARIABLES
LowResidual
MediumResidual
SmallAcquirer
RelativeSize
Diversify
Pooling
Stock
Tender
PreAnnReturn
HighMB
MediumMB
Accruals
NOA
AcquirerCash
Match_ROAYear+1
Match_ROAYear+2
Match_ROAYear+3
Constant
Observations
Adjusted R-squared
(1)
ROA
Year +1
-0.067***
(-6.87)
-0.033***
(-3.91)
-0.034***
(-3.58)
-0.001
(-0.52)
-0.007
(-1.05)
0.056***
(5.66)
-0.044***
(-5.24)
-0.010
(-1.26)
-0.004
(-1.10)
0.087***
(6.92)
0.021**
(2.36)
-0.095**
(-2.37)
0.008
(1.59)
-0.153***
(-7.02)
0.202***
(8.40)
----0.099
(1.55)
(2)
ROA
Years 1& 2
-0.027**
(-2.30)
-0.006
(-0.64)
-0.058***
(-5.02)
0.002
(1.28)
-0.007
(-0.95)
0.031***
(2.69)
-0.046***
(-4.63)
-0.016
(-1.61)
-0.001
(-0.34)
0.089***
(6.07)
0.024**
(2.28)
-0.068
(-1.37)
-0.003
(-0.49)
-0.132***
(-5.04)
--0.085***
(2.78)
--0.159***
(3.39)
(3)
ROA
Years 1, 2, & 3
-0.034***
(-3.18)
-0.023**
(-2.54)
-0.047***
(-4.55)
0.004***
(2.90)
-0.008
(-1.15)
0.034***
(3.28)
-0.045***
(-5.05)
-0.010
(-1.17)
-0.008**
(-2.14)
0.080***
(6.15)
0.027***
(2.88)
-0.059
(-1.32)
0.008*
(1.69)
-0.149***
(-6.15)
----0.081***
(2.87)
0.156***
(3.81)
1,304
0.31
1,265
0.17
1,193
0.21
Table 5 Returns to Investment Strategies
This table presents mean abnormal returns on various investment strategies. Panel A shows returns on
a strategy of shorting all low-residual acquirers and going long on all high-residual acquirers. Panel B
shows returns on a strategy of shorting all high-M/B acquirers and going long on all low-M/B
acquirers. Panel C shows returns on a strategy of shorting all high-M/B and low-residual acquirers and
going long on all low-M/B and high-residual acquirers. Abnormal returns are calculated using the
DGTW benchmark portfolio matched to each acquirer on size, industry-normalized M/B, and
momentum. Positions are taken on the day following the target delisting and are closed out one year,
two years, and three years later. For each announcement year, acquirers are sorted into quintiles by
their M/B at the fiscal quarter-end preceding the acquisition announcement date. The quintile with the
lowest (highest) M/B is defined as “low-MB” (“high-MB”) acquirers. Low- (high-)residual firms are
identified as using cutoffs set at the 20th (80th) percentile of acquirers’ residual estimates in the fiscal
year-end prior to acquisition announcement. See Appendix A for variable definitions. t-statistics for
means reflect a two-sided t-test for means. Bold font indicates significance at least at the 10% level.
Panel B
Long
t-stats
Short
t-stats
Long-Short
t-stats
Panel A: High- vs. Low-Residual
Year 1
Year 1& 2
N
Return
N
Return
246
3.2%
239
21.5%
1.64
2.21
256
-4.8%
250
-6.8%
-1.68
-1.65
8.0%
28.3%
1.74
2.99
Year 1,2, & 3
N
Return
229
8.8%
1.81
232
-11.3%
-2.33
20.1%
2.01
Panel A
Long
t-stats
Short
t-stats
Long-Short
t-stats
Panel B: High-M/B vs. Low-M/B
Year 1
Year 1& 2
N
Return
N
Return
365
8.0%
351
16.8%
0.96
2.07
367
-2.1%
348
-5.1%
-1.45
-2.13
10.1%
21.9%
1.67
1.98
Year 1,2, & 3
N
Return
324
13.2%
1.86
326
5.9%
-0.30
7.3%
2.77
Panel C: High-M/B & Low-residuals vs. Low-M/B & High-residuals
Panel C
Long
t-stats
Short
t-stats
Long-Short
t-stats
Year 1
Return
13.7%
2.510
118
-6.4%
-2.55
20.1%
2.85
N
104
Year 1 & 2
N
Return
100
70.4%
2.730
118
-10.1%
-2.72
80.5%
3.14
Year 1, 2, & 3
N
Return
99
27.2%
2.413
107
-9.0%
-1.93
36.2%
2.78
Table 6 Operating Performance by Acquirers’ M/B around M&As
This table shows acquirers’ ROA during the three years prior to and post acquisitions by M/B groups.
For each acquirer, I find a matching non-acquirer using the following procedure: Candidate matching
firms for an acquirer are those listed on the AMEX, NYSE, or Nasdaq with the same 2-digit SIC codes
and with asset size at the end of fiscal year before the deal announcement date that is 50% to 200% of
the asset size of the acquirer. From this set of firms, those that have not made an acquisition during the
three years prior to and three years after the deal announcement year are ranked based on their M/B.
The firm with the closest M/B is chosen as the matching non-acquirer. For each announcement year,
acquirers and matching non-acquirers are sorted into quintiles by their M/B at the fiscal quarter-end
preceding the acquisition announcement date. The quintile with the lowest (highest) M/B is defined as
“low-M/B” (“high-M/B”) acquirers or matching non-acquirers and the rest of are “medium-M/B”
acquirers or matching non-acquirers. See Appendix A for other variable definitions. t-statistics for
means reflect a two-sided t-test for means. Bold font indicates significance at least at the 10% level.
All Acquirers
Yr -3
16.8%
Yr -2
16.7%
Yr -1
17.4%
Yr +1
13.1%
Yr +2
12.2%
Yr +3
11.7%
Low-M/B Acquirers
Match Low-M/B Non-acquirers
12.7%
11.5%
12.0%
10.5%
11.0%
10.6%
10.3%
10.1%
9.7%
10.3%
10.2%
10.0%
Medium-M/B Acquirers
16.2%
Match Medium-M/B Non-acquirers 16.3%
16.7%
15.7%
16.8%
15.2%
12.6%
13.3%
12.2%
13.4%
11.8%
13.3%
High-M/B Acquirers
Match High-M/B Non-acquirers
22.0%
21.0%
26.7%
20.9%
17.2%
17.2%
15.2%
17.3%
13.7%
17.5%
23.8%
21.0%
Drop in ROA from Year -1 to Year +3
Low-M/B
0.8%
Match Low-M/B
0.6%
High – Low
12.2%
5.45
Medium MB
5.0%
1.9%
High – Match High
9.6%
2.89
High-M/B
13.0%
Match MediumM/B
Match-High M/B
3.4%
Low – Match Low
0.2%
0.58
Table 7 Univariate Analysis of Residuals by Acquirer’s M/B
This table reports univariate relation between residual estimates from the following regression and
different M/B groups, both measured at the fiscal year-end prior to acquisition announcement. For
each acquirer, I find a matching non-acquirer using the following procedure: Candidate matching firms
for an acquirer are those listed on the AMEX, NYSE, or Nasdaq with the same 2-digit SIC codes and
with asset size at the end of fiscal year before the deal announcement date that is 50% to 200% of the
asset size of the acquirer. From this set of firms, those that have not made an acquisition during the
three years prior to and three years after the deal announcement year are ranked based on their M/B.
The firm with the closest M/B is chosen as the matching non-acquirer. For each announcement year,
acquirers and matching non-acquirers are sorted into quintiles by their M/B at the fiscal quarter-end
preceding the acquisition announcement date. The quintile with the lowest (highest) M/B is defined as
“low-M/B” (“high-M/B”) acquirers or matching non-acquirers and the rest are “medium-M/B”
acquirers or matching non-acquirers. Low- (high-) residual firms are identified as using cutoffs set at
the 20th (80th) percentile of acquirers’ residual estimates in the fiscal year-end prior to acquisition
announcement. See Appendix A for variable definitions. t-statistics for means reflect a two-sided ttest for means. Bold font indicates significance at least at the 10% level.
All
N
Residuals
Year -1
1421
1.4%
Low-M/B
Match Low-M/B
-0.2%
-0.7%
Medium-M/B
Match Medium-M/B
3.2%
0.3%
High-M/B
Match High-M/B
-1.9%
5.0%
High – Low
Low – Match Low
High – Match High
Difference
-1.7%
0.5%
-6.9%
t-stat
-0.49
0.08
-2.76
Table 8 Calendar-Time Returns on Investment Strategies
This table presents calendar-time abnormal returns on the investment strategy of shorting all lowresidual acquirers and going long on all high-residual acquirers. Low- (high-) residual firms are
identified by using cutoffs set at the 20th (80th) percentile of all acquirers’ residual estimates in the
fiscal year-end prior to acquisition announcement. Each month from 1980-2005, a portfolio is formed
based on acquirers’ residual estimates and from all sample firms that announced an acquisition in the
previous one year (1st two columns), in the previous two years (3rd and 4th columns), and the previous
three years (5th and 6th columns). Abnormal returns are calculated using the DGTW benchmark
portfolio matched each acquirer on size, industry-normalized M/B, and momentum. t-statistics for
means reflect a two-sided t-test for means. Bold font indicates significance at least at the 10% level.
High- vs. Low-residuals
long
t-stats
short
t-stats
Long-Short
t-stats
Prior 12 months
N
Monthly Return
169
0.9%
2.88
135
-0.1%
-0.58
1.0%
2.78
Prior 24 months
N
Monthly Return
186
0.6%
1.88
139
-0.2%
-0.70
0.8%
1.98
Prior 36 months
N
Monthly Return
172
0.9%
2.08
136
0.1%
-0.45
0.8%
2.21
Table 9 Regressions Relating Residuals to Post-acquisition Performance: Robustness
Tests
This table reports the coefficients on the low-residual variable for various regression specifications and
using subsamples. Panel A reproduces the benchmark results from Tables 3 and 4. In the other panels,
I vary either the sample period or the specification. All regressions include the same set of explanatory
variables as those reported in Tables 3 and 4.
Explanatory Variables
BHAR
BHAR
Year +1
BHAR
Year1&2 Year1,2,&3
ROA
ROA
ROA
Year +1
Year1&2
Year1,2,&3
Panel A: Full sample (Replication of Tables 3 and 4)
LowResidual
-0.108**
-0.292**
-0.257**
-0.067***
-0.027**
-0.034***
(-2.13)
(-2.07)
(-2.39)
(-6.87)
(-2.30)
(-3.18)
Panel B: Sub-period 1980-1990
LowResidual
-0.171*
-0.393**
-0.420*
-0.044**
-0.033*
-0.065***
(-1.73)
(-2.46)
(-1.93)
(-2.18)
(-1.86)
(-2.72)
Panel C: Sub-period 1991-2000
LowResidual
-0.119*
-0.459*
-0.348*
-0.082***
-0.016*
-0.030*
(-1.68)
(-1.69)
(-1.85)
(-5.13)
(-1.84)
(-1.90)
Panel D: Sub-period 2000-2005
LowResidual
-0.010
-0.110
-0.073*
-0.044***
-0.024*
-0.032*
(-0.02)
(-1.14)
(-1.69)
(-3.07)
(-1.87)
(-1.72)
-0.061***
-0.039***
-0.035***
(-6.71)
(-3.71)
(-3.52)
Panel E: Use EBITDA/Avg Assets as ROA
LowResidual
Panel E: Use 1-year and 2-year lags of InvestCap in the regression of obtaining residual estimates
LowResidual
-0.106**
-0.275**
-0.263*
-0.063***
-0.036***
-0.029***
(-2.09)
(-2.05)
(-1.69)
(-6.51)
(-3.06)
(-2.71)
Figure 1
This figure shows mean ROA for the high-M/B and low-M/B acquirers and their corresponding
matching non-acquirers for three years prior to and post acquisitions. For each acquirer, I find a
matching non-acquirer using the following procedure: Candidate matching firms for an acquirer are
those listed on the AMEX, NYSE, or Nasdaq with the same 2-digit SIC codes and with asset size at the
end of fiscal year before the deal announcement date that is 50% to 200% of the asset size of the
acquirer. From this set of firms, those that have not made an acquisition during the three years prior to
and three years after the deal announcement year are ranked based on their M/B. The firm with the
closest M/B is chosen as the matching non-acquirer. For each announcement year, acquirers and
matching non-acquirers are sorted into quintiles by their M/B at the fiscal quarter-end preceding the
acquisition announcement date. The quintile with the lowest (highest) M/B is defined as “low-M/B”
(“high-M/B”) acquirers or matching non-acquirers. See Appendix A for other variable definitions.
ROA around M&A
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
Yr-3
Yr-2
Yr-1
Yr +1
Yr+2
Low MB Acquirers
Match Low MB
High MB Acquirers
Match High MB
Yr+3
Figure 2
This figure shows mean residual estimates from Equation (1) for the high-M/B and low-M/B acquirers
and their corresponding matching non-acquirers for the year prior to the acquisition announcement
year. For each acquirer, I find a matching non-acquirer using the following procedure: Candidate
matching firms for an acquirer are those listed on the AMEX, NYSE, or Nasdaq with the same 2-digit
SIC code and with asset size at the end of fiscal year before the deal announcement date that is 50% to
200% of the asset size of the acquirer. From this set of firms, those that have not made an acquisition
during the three years prior to and three years after the deal announcement year are ranked based on
their M/B. The firm with the closest M/B is chosen as the matching non-acquirer. For each
announcement year, acquirers and matching non-acquirers are sorted into quintiles by their M/B at the
fiscal quarter-end preceding the acquisition announcement date. The quintile with the lowest (highest)
MB is defined as “low-M/B” (“high-M/B”) acquirers or matching non-acquirers. See Appendix A for
other variable definitions.
Residuals Year -1
6.0%
5.0%
4.0%
3.0%
2.0%
1.0%
0.0%
-1.0%
-2.0%
-3.0%
Low MB
Match Low MB
High MB
Match High MB
APPENDIX A
Definitions of Variables
NOPAT
The net operating profit is defined as earnings adding back net financing
expense. Earnings is equal to net income minus preferred dividend and after-tax
special item; net financing expense is equal to after-tax net interest expense,
plus preferred dividends.
ROA
The return on assets is defined as NOPAT scaled by average assets for the last
two years.
Abn_ROA
The abnormal return on assets is defined as ROA minus WACC.
InvestCap
The invested capital is defined as the sum of long-term debt, short-term debt,
minority interest, and book value of common equity, scaled by average assets.
AcquirerCash
The acquirer’s cash and short-term investments at the end of fiscal year
immediately prior to the acquisition announcement divided by average assets
for the last two years.
Age
The number of years a firm has been in Compustat until the end of the year
prior to acquisition announcement.
Leverage
The sum of long-term and short-term debt divided by total assets.
LnSize
The logarithm of total book assets as of the end of fiscal year immediately prior
to the acquisition announcement.
LowResidual
An indicator variable equal to one if an acquirer’s regression residual from
Equation (1) is in the bottom quintile of all the acquirers’ residual estimates
from Equation (1) and equal to zero otherwise.
MediumResidual
An indicator variable equal to one if an acquirer’s regression residual from
Equation (1) is in the middle three quintiles of all the acquirers’ residual
estimates from Equation (1) and equal to zero otherwise.
HighResidual
An indicator variable equal to one if an acquirer’s regression residual from
Equation (1) is in the top quintile of all the acquirers’ residual estimates from
Equation (1) and equal to zero otherwise.
HighMB
An indicator variable equal to one if an acquirer’s market-to-book (M/B) ratio at
one quarter prior to acquisition announcement is in the top quintile of M/B
ratios for all acquirers that announced acquisitions in the same year and equal
to zero otherwise.
MediumMB
An indicator variable equal to one if an acquirer’s M/B ratio at one quarter prior
to acquisition announcement is in the middle three quintiles of M/B ratios for all
acquirers that announced acquisitions in the same year and equal to zero
otherwise.
LowMB
An indicator variable equal to one if an acquirer’s M/B ratio at one quarter prior
to acquisition announcement is in the bottom quintile of M/B ratios for all
acquirers that announced acquisitions in the same year and equal to zero
otherwise
SmallAcquirer
An indicator variable equal to one if an acquirer’s market capitalization is below
the 25th percentile of NYSE firms and zero otherwise.
Accruals
Total accruals are defined, following Fairfield, Whisenant, and Yohn (2003), as:
ACC = ∆WC – DEP, where:
∆WC = change in working capital = change in accounts receivable + change in
inventories + change in other current assets – change in accounts payables –
change in other current liabilities; and DEP is depreciation and amortization.
NOA
The net operating assets are calculated, following Fairfield, Whisenant, and
Yohn (2003), as:
NOA = AR + INV + OTHERCA + PPE + INTANG + OTHERLTA –AP – OTHERCL –
OTHERLTL, where:
AR is accounts receivables, INV is inventory, OTHERCA is other current assets,
PPE is net property, plant, and equipment, INTANG is intangibles, OTHERLTA is
other long-term assets, AP is accounts payable, OTHERCL is other current
liabilities, and OTHERLTL is other long-term liabilities.
Stock
An indicator variable equal to one if more than 50% of the consideration is paid
using the acquirer’s own stock and zero otherwise.
Diversify
An indicator variable equal to one if the acquirer and target are not in the same
primary industry, defined as 2-digit SIC code, and zero otherwise.
RelativeSize
The transaction value divided by the acquirer’s market capitalization at the end
of fiscal year prior to acquisition announcement.
WACC
Weighted average cost of capital is calculated by (1) estimating a CAPM cost of
equity using the past 60 monthly returns, (2) inferring after-tax cost of debt
from interest expense, total interest-bearing debt, and the tax rate, and (3) using
market value of equity and book value of total debt for their relative weights. I
estimate β using at least 24 months and up to 60 months of lagged returns. β
below 0.4 are set to 0.4 and above 3 are set to 3.
Pooling
An indicator variable equal to one if an acquisition is accounted for under
pooling and zero otherwise.
PreAnnReturn
Acquirer’s average stock return measured over 200 days to 31 days before the
announcement date.