Market Valuation and Target Horizon in Mergers & Acquisitions
Tao Lin
University of Hong Kong [email protected]
Liyan Miao University of Hong Kong
First draft: March, 2006 This draft: May, 2006
We are also grateful to Prof. Eric Chang, Qiao Liu, Xianming Zhou and participants at the seminar of the
University of Hong Kong. All errors are ours.
Market Valuation and Target Horizon in Mergers & Acquisitions
Abstract
Under the market-driven acquisition theory, targets tend to be underpaid by rational
acquirers. One of the reasons targets agree to the transaction is that they are short-horizon
oriented. Using a sample of 770 mergers and acquisitions announced between 1993 and
2002, this study empirically shows that targets are more likely to be underpaid when they
have shorter horizon-oriented shareholders and CEOs. Shareholder turnover ratio, CEO
age, tenure, equity-based wealth and incentive to cash out are proxied for horizon.
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1. Introduction
Much of the research in corporate finance has focused on the underlying motivation for
mergers and acquisition. Jensen (1988) summarizes that in the long-run, takeovers
generate substantial gains, which result from easier access to resources, transfer to more
highly-valued use of assets, etc. The managerial hubris hypothesis posited by Roll (1986)
suggests a psychological interpretation and implies that managers in bidding firms tend to
overpay for the target. Agency theory focuses on the interest disparity between
shareholders and executives. Grinstein and Hribar (2004) investigate the CEO
compensation package, the alignment of interest between shareholders and executives and
managerial incentives in mergers and acquisitions.
Recent research work tries to explain the observed merger waves and the positive
correlation between market valuation and merger waves. Shleifer and Vishny (2003)
establish a model of mergers and acquisitions which argues that stock market
misvaluation drives acquisitions. According to this model, they posit that the long-run
reason for bidders to takeover the target is the undervaluation of the target. Shareholders
of the acquiring firm will gain from the acquisition if the target is undervalued to
fundamentals in cash acquisitions and relatively undervalued to the bidder in stock
acquisitions. Overvalued firms are more likely to be bidders and undervalued firms are
more likely to be targets. Target shareholder will gain in the short-run. However,
shareholders holding onto shares in an overvalued market will lose in the long-run.
Therefore, target managers will protect long-horizon shareholders’ interest if they resist
the offer. The model predicts that bidders in stock acquisitions either have longer horizon
than targets or pay personal deals to target managers to agree to the acquisition.
Rhodes-Kropf and Viswanathan (2004) propose a different model, which separates
misvaluation into firm-specific component and market-wide component. The model
shows that the potential market valuation deviation from fundamental values impacts
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merger activities. Their theory predicts that acquisitions are more likely to occur in
overvalued markets. Because in the more overvalued market, the rational target managers
with limited information will overestimate the firm-specific misvaluation, and thus
overvalue the synergies from the acquisition. Method of payment will contain a higher
fraction of stock in overvalued markets and higher fraction of cash in undervalued
markets.
The follow-up empirical research provides strong support to the theory of Shleifer and
Vishny (2003) and Rhodes-Kropf and Viswanathan (2004). Dong et al. (2003) tests the
hypotheses of misvaluation driven acquisitions. Their study employs two
contemporaneous measures of misvaluation: pre-takeover ratio of book value of equity to
price and pre-takeover ratio of residual income value to price. They find evidence that
bidders are relatively overvalued to targets, bidders are more overvalued in stock
acquisitions than in cash acquisitions, undervalued targets receive higher premium and
are more likely to resist the offer, which is consistent with the predictions by Shleifer and
Vishny (2003). Rhodes-Kropf, Robinson and Viswanathan (2005), based on the theory of
Rhodes-Kropf and Viswanathan (2004), decompose M/B into three components:
firm-specific error, time-series sector error and long-run value to book to measure
misvaluation. The empirical results show that acquirers and targets appear to share the
common misvaluation component of time-series sector. The difference in M/B between
acquirers and targets is mainly attributed to the firm-specific error. High firm-specific
error is positively correlated to the possibility of being involved in a merger as an acquirer
and using stock as method of payment.
Similar to Dong et al. (2003) and Rhodes-Kropf, Robinson, and Viswanathan (2004),
Ang and Cheng (2005) find evidence on the market-driven acquisitions theory. In addition,
they also find some new results which are consistent with the critical assumption made by
Shleifer and Vishny (2003). Shareholders of acquiring firms do not suffer loss in the
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long-run compared to those of matching firms. However, former shareholders of target
firms are the losers if they hold shares in a long-term post-merger period.
Although previous empirical research has found evidence on the misvaluation of firms
and the long-run return for the market-driven acquisitions, we still lack evidence on the
reason of targets to accept the acquisition offer which hurts the value of shareholders in
the long-run. Shleifer and Vishny (2003) conjecture that target agrees to the acquisition
because the targets have shorter horizons in the firm or the self-benefited target managers
get private payment from the acquirer.
This study intends to test several empirical predictions under the market-driven
acquisition theory inspired by Shleifer and Vishny (2003). Previous literature shows that
targets are undervalued compared to non-target match firms in cash acquisition and
relatively undervalued compared to bidder in stock acquisition. Although bidders usually
offer premium price, the premium may be not enough to compensate the undervaluation
of targets in cash acquisitions and the overvaluation of bidders in stock acquisitions. It is
expected that underpaid targets shareholders will lose money in the long run. Therefore,
either the shareholders or the managers have short horizons in the firm. Targets with
long-horizon investors and managers will be more likely to resist the offer and thus less
likely to complete the acquisition.
2. Research Design
2.1 Main Hypotheses
Although bidders are blamed for overpayment in mergers and acquisitions, the
overvaluation of bidders and undervaluation of targets is ignored. If taking the
misvaluation into consideration, we may conclude that the premium provided by
acquirers is not huge enough to compensate the undervaluation of target market value in a
cash acquisition and the overvaluation of bidder market value in a stock acquisition.
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Based on the theory of Shleifer and Vishny (2003), I develop several hypotheses for
empirical test.
First, acquirers will tend to underpay targets in market-driven acquisitions, which
should be found empirically. Second, if targets have shorter horizon-oriented shareholders
and CEOs, they are more likely to be underpaid. Last, it is expected that firm horizon is
also related to the magnitude of underpayment.
2.2 Variable Construction
2.2.1 Measurement of underpayment
Target value should be measured to see whether the deal value offered by acquirer is
appropriate. Two principle valuation approaches are the Price to book (P/B) method and
the Residual Income Model (RIM). P/B is the market-to-book ratio of the stock.1 In the
RIM based on Ohlson (1995), firm value is current period book value of equity plus all
expected future discounted earnings in excess of required return on the equity capital.
Empirically, future earnings are proxied by analyst earnings forecasts (e.g., Lee et al.,
1999). Misvaluation, either overvalued or undervalued, is measured by the difference
between market price and equity book value or fundamental value resulted from RIM.2
The definition of underpayment is based on the theory of Shleifer and Vishny (2003).
Targets are underpaid if the price is less than target’s long-run stand-alone value in cash
acquisitions. In stock acquisitions, targets are underpaid if the price is less than targets’
proportionate value of the short-term post-merger combined firm.
To make it more empirically practical, a slightly different measure of underpayment is
employed. According to the assumption of Shleifer and Vishny (2003), the misvalued
market will return to efficient in the long run. Therefore, the intrinsic value of the target
1 A large amount of literature has demonstrated that P/B ratio is a good proxy to measure mispricing (e.g., Barberis and Huang (2001), Daniel et al. (2001)). 2 Dong et al. (2003) and Ang and Cheng (2005) employ both of RIM and P/B method to measure bidder and target valuation.
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can be measured using the long run market to book ratio. In cash acquisitions,
underpayment is the difference between offer price and long-run stand-alone value of the
target. In stock acquisitions, underpayment is measured by the difference between
original target shareholders’ value in the combined firm and the stand-alone value in the
long run. Here three-year term is used for the long-run base.
Since target’s stand alone market performance can hardly be traced once merged,
target’s stand-alone value is measured by the average value of the matching firm without
merger announcement. The definition of matching firms is that firms, within the same
industry according to Fama-French 38 industry classification, have the same quartile of
market-to-book ratio with the target in the quarter prior to the announcement date.
In cash acquisitions, it is straight forward that the price offered by acquirers is the real
value the targets can get, therefore, underpayment is measured by
Underpayment = (P-q)*K, (1)
where P is offering price per unit of capital, q is the average price per unit of capital of
target’s matching firms three years after the acquisition, K is target book equity.
In stock acquisitions, however, acquirers pay targets using their overvalued stocks, the
offered price is even more misleading especially when the target stocks are undervalued,
therefore, the offer price should be adjusted to reflect the real value,
Underpayment = (Pq1/Q1-q)*K, (2)
where P is offering price per unit of capital, q1 is the average price per unit of capital of
combined firm three years after merger completion, Q1 is the market price per unit of
capital of acquirers at the end of quarter prior the announcement. Still, q is the average
price per unit of capital of target’s matching firms three years after the acquisition, K is
target book equity.
If the value of the equation is less than 0, then the target is underpaid, the more
negative the value is, the more underpayment of the target. Otherwise, the target is
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overpaid.
2.2.2 Proxies for Shareholder Horizon
Shareholder daily turnover ratio is used to proxy for shareholder horizon. Turnover is
measured as daily trading volume divided by total shares outstanding, multiplying 1000.
It is commonly stated that the larger the turnover ratio is, the shorter horizon shareholders
hold in the firms (e.g. Ang and Cheng (2005), Gaspar et al. (2005)).
Turnover may contain too much information in addition to shareholder horizon. For
example, firms with larger analyst coverage will have a relatively more active trading
pattern. Firms with different size are more likely to attract different groups of investors.
To subtract the noise information from turnover, we use the residual mean of target firms’
turnover ratio after the regression on logarithm of market value of equity,
jijiji MELgTurnover εββ +∗+= )(10 , (3)
where is the turnover ratio of firm j on i days before the announcement,
is the logarithm form of market value of firm j on i days before the
announcement. The shareholder horizon variable mreone for firm j is constructed as
jiTurnover
)( jiMELg
∑=
=395
313651
ijijmreone ε , (4)
where jiε is the residual of the regression (3), the period for the mean of residual is
chosen as one year before one month prior the announcement of the mergers and
acquisitions. We choose the period starting from one month prior the announcement to
exclude the abnormal volatility and trading since the rumors of mergers and acquisitions
of firms usually occur before the official announcement.
2.2.3 Proxies for CEO Horizon
Age: This study employs two methods to proxy age for CEO horizon. The first method
follows Dikolli et al. (2003), which assume that CEO’s horizon is a decreasing and
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concave function of the CEO age. The variable Horizon is defined as ( )[ ]210/CEOage− .
In addition, to study the specific impact of different age on CEO horizon in the firm, we
also use several dummy variables to separate age to different groups.3
Tenure: We classify tenure into three groups, no more than 3 years, 3 to 5 years and 5
years more. CEOs with different tenure in their position tend to have different level of
career concerns and equity-based wealth in the firms. We use the cutting points of 3 and 5
year based on the study of Gibbons and Murphy (1992), which states that the CEO tenure
with the highest possibility of stepping down from the position clusters in 4 and 5 years.
It is expected that CEOs with the tenure between 3 to 5 years have the shortest horizons
compared to other two groups. One reason is given out by Gibbons and Murphy (1992),
CEOs with the tenure between 3 to 5 years are most likely to step down. In addition,
CEOs with longer tenure will have more equity-based wealth in their firms. The large
stake in their firm can align the interests of CEOs and shareholders, therefore partly solve
the horizon problem of CEOs with longer tenure.
Equity-based wealth: CEOs’ equity-based wealth in their firms include value of stock
holding, restricted grants money value, in the money unexercised exercisable option
dollar value and in the money unexercised unexercisable option dollar value. The wealth
is held and valued at the end of last fiscal year prior the announcement of the mergers and
acquisitions. Generally, the equity-based wealth, used as a forward-looking compensation
contract, will lead CEOs to have a longer horizon.4
To pair different horizons of CEOs in the acquirer and target, we construct a variable
welredif to measure the difference of horizons between acquirer and target CEOs.
Welredif is the difference of residuals of CEO equity-based wealth after regression wealth 3 Cheng (2001) uses the age of 63 to distinguish different horizons held by CEO. Survey by Murphy (1999) shows that the typical age of retirement is 64 around. Therefore the age of 64 can also be used as a flag to differentiate horizon. 4 Lambert and Larcker (1991) suggest that contract tying managers’ compensation to firm’s market performance can partly solve the problem of managerial myopia.
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on size of acquirers and targets. The larger the welredif, the acquirer CEO horizon is
longer than target CEO.
Incentive to cash out: CEOs trapped in the firm by a large holding of restricted stocks
have strong incentive to cash out. Since restrictions on stocks and options are usually
exempted upon a deal of merger and acquisition (Moeller, 2005), we use the ratio of
restricted stock grants value over total equity-based wealth or market value to measure
the incentive to cash out. It is expected that CEOs with larger ratio will have shorter
horizons in their firms.
2.2.4 Control Variables
Several other variables that may affect the transaction are controlled in the tests.
Targets firm size and the relative size to acquirers will affect the deal value.5
To control the industry factor, we include a dummy variable to indicate whether the
merger and acquisition is undertaken by firms in the same industry. The impact of
diversification is ambiguous. It is possible that acquirers will pay higher price to targets
because they lack perfect information and knowledge to value targets. However, we can
also expect that the level of misvaluation is different among industries. Diversified
acquisition occurs between more overvalued industry as acquirer and relatively
undervalued industry as target. Targets are actually receiving lower price and therefore
more likely to be underpaid.
Prior literature addresses that firms with higher valuation are associated with greater
use of stock as a method of payment (Dong et al., 2003). We add an indicator variable of
stock to control the impact of method of payment.
Finally, we also include several market return indicators to control the influence of
broad market environment.
5 Moeller (2005) finds that the larger the target relative to the acquirer, the lower the takeover premium the target receives.
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2.3 Sample Selection
The samples are selected from the mergers and acquisitions database of Securities Data
Company (SDC). Transactions with an announcement date between 1993 and 2002 are
included in the sample. We choose the period from 1993 to 2002 because CEO
information from ExecuComp is available from 1992 and complete three year
post-merger market performance from CRSP ends in 2005. In addition, Andrade et al.
(2001) find that merger waves in different decades have different characteristics. The
merger wave in 1990s has notable features distinguished from others. Therefore, here we
only focus on the 1990s merger wave. Both the acquirers and targets are U.S. public firms
listed in NYSE, Amex or Nasdaq. The sample is limited to listed firms so that the market
valuation can be studied. The method of payment is either all cash or all stock. The status
of transaction is either completed or withdrawn. Buyback, exchange offer and
recapitalization are excluded from the sample. SDC database provides data on the value
of transaction, the announcement date, acquirer and target ticker and Standard industrial
classification (SIC) codes and status of transaction.
Daily stock price, trading volume, and number of shares outstanding are obtained from
Center for Research in Securities Prices (CRSP) database. Financial statement
information about acquirers and targets is obtained from Compustat Industrial Quarterly.
Financial information, including stockholders’ equity and number of common shares
outstanding at the end of each quarter and closing price of stock at the 3rd month of each
quarter, is used to calculate market-to-book ratio of firms at the end of quarter. In addition,
we also use all firms registered in Compustat as the base to constructing matching groups.
Information of CEO characteristics and compensation is extracted from ExecuComp
database, which reports CEO name, age, annual compensation and equity-based wealth
including share ownership, restricted grants and options. I also check the proxy
statements and other SEC filings to fill the missing values in ExecuComp database and
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collect information on CEO tenure, outside blockholding, etc.
Finally, observations in which targets have more than one transactions recorded in SDC
are excluded. After all the data collection, the final number of observations in the base
sample is 770.
2.4 Descriptive Statistics
Table 1 presents summary statistics for the sample. Of all the observations during 1993
and 2002, most of the transactions cluster in 1996 to 2001, partly subject to data
availability. Generally, in a merger and acquisition, acquirer size is much larger than
target, with an average acquirer market value of 24,287.91 million dollars and target
market value of 1,091.95 million dollars. Mean deal value of transaction is slightly larger
than average target size.
In the sample, 58% of all target firms are underpaid by an average value of 529.41
million dollars. Under the assumption of Shleifer and Vishny (2003), the motivation for
rational acquirer managers to undertake mergers and acquisitions in the misvalued stock
market is to arbitrage by underpaying targets. Therefore, it is expected that empirically
most firms should be underpaid. Result of binomial test shows that percentage of
underpayment is larger than 0.5 at the significance level less than 0.001.
Prior literature has stated a greater use of stock as a method of payment in the 1990s
merger wave (e.g., Dong et al. (2003) and Holmstrom and Kaplan (2001)), the summary
statistics shows the increase of stock payment with a large fall in the 2002. The
percentage of diversification is 32, which means that over two-thirds of transactions in
our sample occur between acquirers and targets in the same industry. This result is
consistent with recent studies (e.g., Andrade et al. (2001) and Gaspar et al. (2005)) which
report an increase of related acquisition in the 1990s.
Finally, table 1 shows that most of the observations in our sample ended with complete.
Although we include both completed and withdrawn transactions, only 7% of the sample
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firms are withdrawn acquisitions.
<Insert Table 1 here>
Further study is focused on firm characteristics related to firm horizon. Table 2
summarizes acquirers and targets characteristics and relative difference between them in
different groups classified by underpayment. Panel A and Panel B reports the means of
various variables of targets and acquirers respectively. Our hypotheses predict that targets
with CEOs who have larger equity-based wealth in the firms will be less likely underpaid,
while acquirers with CEOs who have larger equity-based wealth will more likely
underpay targets. Therefore, target CEOs in Underpayment are assumed to have smaller
equity-based compensation, while acquirer CEOs in Underpayment are assumed to have
larger equity-based compensation. The descriptive statistics in Panel A and B show that
most variables on CEO compensation and equity-related variables are larger in group of
Underpayment than in group of Overpayment for both acquirers and targets. It is mainly
due to the reason that firm size has not been controlled in the comparison. Although the
dollar value and number of securities holding are ambiguous, statistics on share
ownership percentage is consistent with hypothesis. Table 2 shows that CEOs’ stock
ownership percentage is lower for targets and higher for acquirers in the group of
Underpayment than Overpayment. The difference for acquirers is significant at 5% level.
Statistics on age and tenure reported in Panel A and B shows involvement of CEOs with
older age and longer tenure in the overpayment. Since age and tenure are expected to
have nonlinear relation with CEO horizon, the implication of group mean difference is
unclear.
To control the firm size effect and impact of acquirer CEO horizon on target decision,
Panel C describes the relative size, age difference and the variable welredif, which is the
difference of residuals after regressing CEOs’ equity-based wealth on firm size between
acquirers and targets. Welredif is constructed to measure difference of CEO horizons
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between acquirers and targets. As a result, welredif is positive in Underpayment and
negative in Overpayment, with significant difference between two groups. The result
indicates that targets will accept underpaid offer when their CEOs have relatively shorter
horizon than those of acquirers. However, when targets’ CEOs have longer horizon than
those of acquirers, they will strive for a higher price even resulting in acquirers’
overpayment.
Table 2 reports a summary statistics on all observations in the sample, including both
completed and withdrawn transactions. To check the influence of different status of the
deal, same test is done for the sample with only completed transactions and the results are
similar to Table 2.6
<Insert Table 2 here>
In Table 2, the shareholder horizon variable mreone is significantly higher in targets
underpaid then overpaid, which means that shareholders in underpaid firms have shorter
horizon. This is consistent with the hypothesis.
Table 3 presents a further study on mreone of targets in different classifications. Panel
A categorizes all targets into four groups based on the status and whether they are
underpaid or overpaid. Shareholder horizon is reasonably expected to affect final status of
the transaction. Generally, if an investor has long horizon in the firm and intends to hold
onto shares in a long-term period, he will be less likely to accept a merger offer. Taking
underpayment and overpayment into consideration, long-horizon oriented shareholders of
targets will benefit either by rejecting an underpaid offer or accepting an overpaid offer.
Shareholders are expected short-horizon oriented to accept an underpaid offer, with an
intention to profit from short-term stock price boost around the announcement. For targets
which finally withdraw from transactions with overpaid offer price, shareholders’
horizons are ambiguous. The order of mreone magnitude in four groups reported in Panel 6 Results are available upon request.
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A is consistent with expectation. Targets underpaid with status of competed have largest
group mean of mreone, which indicates that their shareholders have shortest horizon.
Targets overpaid and with status of completed and targets which are underpaid but
withdraw from the deal have relatively lower group mean of mreone than the other two
groups. Group mean of targets which withdraw from an overpaid deal is in between.
However, F-statistics for mean difference among the four groups is not significant.
Since the major difference is between Group 1 with underpaid and completed targets
and other groups, Panel B and Panel C report T-test statistics of the difference between
Group 1 and other two groups. It shows that shareholders in targets which competed
underpaid deals have shorter horizon than those in targets which competed overpaid deals
and which withdraw from underpaid deals, both significant at less than 5% level.
<Insert Table 3 here>
Shareholder horizon variable mreone in Table 2 and Table 3 is constructed as mean of
target’s turnover residuals over a period from 31 to 395 days before announcement. To
exclude an impact of extreme values during a specific period, Fig. 1 and Fig. 2 describe
daily residual means of targets’ turnover ratio after the regression on logarithm of firms’
market value of equity over the period of 31 to 395 days prior announcement. By
comparing daily mean of underpaid targets and overpaid targets, Figure 1 presents
consistently higher daily means of shareholder turnover residuals in underpaid targets
than those in overpaid targets in that one year period. It is consistent with our prediction
that shareholders in underpaid targets have shorter horizon than in overpaid targets.
<Insert Figure 1 here>
Figure 2 presents lines of daily mean of shareholder turnover residuals in targets with
different status. With only 54 withdrawn observations in the sample, the difference is not
so obvious as in Fig. 2. Even though mreone of targets with status of withdrawn have
several extreme values in specific days, targets with status of completed still consistently
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present higher daily mreone than targets with status of withdrawn.
<Insert Figure 2 here>
3. Regression Analysis
3.1 Target Shareholder horizon and Underpayment
Table 4 presents the relation between shareholder horizon and underpayment using
different regression models. ModelⅠreports logistic regression results of the likelihood
of a target being underpaid based on following model:
iiii ControlsrHorizonShareholdeupob εβββ +++== 210)1(Pr , (5)
where variable up equals 1 if underpayment is less than 0, which denotes that targets
are underpaid. UP equals 0 if underpayment is larger than or equal to 0, which denotes
that targets are overpaid. is the variable measuring target
shareholder horizon of firm i, proxied by mreone. is a group of control
variables including relative size of targets to acquirers, diversification, target equity value,
method of payment and the market environment before announcement. In Model Ⅰ, the
coefficient of mreone is significantly positive, which indicates that the higher the
shareholder turnover, i.e. the shorter the shareholder horizon, the more likely the target is
underpaid. In regard of control variables, it shows that targets are more likely to be
underpaid when acquirers are in different industries. It confirms previous prediction that
diversified acquisitions occur between more overvalued industry as acquirer and
relatively undervalued industry as target. I use logarithm of equity value of target to
control target firm size effect and find significant positive relation with the likelihood of
underpayment. This is consistent with Schwert (2000) and Gaspar et al. (2005) which find
that target premium is negatively related to firm size. Relative size, method of payment,
and market return are not statistically significant.
irHorizonShareholde
iControls
Model Ⅱ in Table 4 uses OLS regression to examine the relation between shareholder
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horizon and the magnitude of underpayment based on:
iiii ControlsrHorizonShareholdeUnderpay εβββ +++= 210 . (6)
Dependent variable in Model Ⅱ is underpay, measured by the defined methods. Since
underpay is calculated as multiple of target book value of equity, equity value is
employed as control variable instead of the logarithm form. Although coefficient for
shareholder horizon variable mreone is not significant at 10% level, the sign is as
expected. The negative coefficient between mreone and underpay implies that targets with
shorter-horizon shareholders are more underpaid. Two statistically significant control
variables are rsize and equityvalue. The larger the target size and size relative to acquirer,
the more the target is underpaid. Moeller (2005) reports a negative relation between target
relative size and takeover premium. Measurement of underpayment is partly determined
by takeover premium. Therefore, statistic results of rsize and equityvalue are consistent
with prior empirical research.
Figure 3 plots the magnitude of underpayment and the quantile position in the overall
dataset. Most of the observations gradually increase with the fraction of data and cluster
closely around 0. Extreme values exist and drive the scale of underpayment. To reduce
the impact of the outliers on the estimation, I transform the value of underpay into
cumulative distribution function.7 By using the percentile of underpay instead of its
original value, the scale of underpay is adjusted and more consistent with the independent
variables.
<Insert Figure 3 here>
Model Ⅲ in Table 4 reports estimation of the relation between shareholder horizon
and underpayment. Generalized linear model (GLM) is used to control for potential
econometric problems such as heteroskedasticity:
7 Aggarwal and Samwick (1999) transform variance of returns into cumulative distribution function to measure CEOs’ pay-performance sensitivity.
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iii ControlsrHorizonShareholdeunderpayF εβββ +++= 210)( . (7)
Dependent variable F(underpay) is the cumulative distribution function (CDF) of
underpay. The CDF values of zero and one correspond to the minimum and maximum of
underpay in the sample. Mreone is negatively related to the CDF of underpay at 5%
significant level. It shows that in the whole sample, targets with shorter horizon
shareholders are more underpaid compared to other observations. Firm size and market
performance before announcement also impact the level of underpayment. Smaller target
size and higher market return increase the percentile of CDF, which means less
underpayment. Relative size, diversification and method of payment do not have
statistically significant impact on level of underpayment in Model Ⅲ.
<Insert Table 4 here>
3.2 Target CEO horizon and Underpayment
3.2.1 CEO horizon and Probability of Underpayment
To study the relation between CEO horizon and probability of underpayment, similar
logistic regression as model (6) is employed:
iiii ControlsCEOhorizonupob εβββ +++== 210)1(Pr , (8)
where dependent variable is the probability of underpayment, denoted by the dummy
variable of up. is a vector of target CEO horizon in firm i, is a
group of control variables same as those used in estimating the impact of shareholder
horizons.
iCEOhorizon iControls
Different sets of variables are included in Table 5 to measure CEO horizon. Model Ⅰ
employs two age variables of target CEO to indicate CEO horizons. Following the
method of Dikolli et al. (2003) which assume that CEO’s horizon is a decreasing and
concave function of the CEO age, I expect that CEO horizon has nonlinear relation with
age. Therefore, I construct the variable horizon as ( )[ ]210/CEOage− . Since horizon is a
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square term of age, variable of age is also added to depict the concave function. In Model
Ⅰ, the coefficients for horizon and age are negative, which demonstrates that there is a
positive quadratic relation between CEO age and probability of underpayment. Either
elder or younger CEOs have shorter horizon and therefore are more likely to accept
underpaid offers. CEOs with age in between have the longest horizon.
Following the results of Model Ⅰ, Model Ⅱ employs two dummy variables of age. It
is expected that the bottom of the curve can be depicted by two cutting points. Consistent
with the results reported in Model Ⅰ, results in Model Ⅱ show that target CEOs with
age in between are least likely to make their firms underpaid. Two cutting points for age
are 65 and 70. The dummy variables age65less and age70up classify CEO age into three
groups and make targets with CEO age between 65 and 70 as a base group. The
coefficients for two dummy variable age65less and age70up are significantly positive,
which denotes that CEOs whose ages are smaller than 65 or elder than 70 are more likely
to agree with underpaid deals.
Cheng (2001) uses age of 63 to distinguish different horizons, CEOs younger than 63
are classified as holding longer horizon. Dikolli et al. (2003) assume decreasing relation
between age and horizon. Model Ⅱ reports different results with prior literature. It is
found that CEO horizon increases with age at first stage, and starts to decrease with age
after reaching a specific range. Empirical results in Model Ⅱ show that CEOs with age
between 65 and 70 have longest horizon in the firm. It is reasoned that CEOs who are
elder than usual retirement age but still hold their positions in the firm may have large
interests or special power in the firms, and therefore may have longer horizon.8 However,
when CEOs are too old, they may concern more about their current financial gains instead
of future careers and long-term interests in the firms.
Model Ⅲ of Table 5 examines tenure as proxy for CEO horizon for the probability of 8 Murphy (1999) shows that the typical age for CEOs retirement is 64 around.
18
target underpayment. It shows that CEOs with tenure between 3 and 5 years have
significantly higher chance of accepting underpaid offers. This is consistent with the
survey of Gibbons and Murphy (1992). Since CEOs with tenure between 3 and 5 years
are most likely to step down, their career concern is lower. Therefore, they may care more
about current financial gains. CEOs with tenure shorter than 3 years usually lack strong
control of firm decisions. In addition, they are less likely to agree with the deal for their
own career concern9, which forces acquirers to provide a higher offer price. On the other
hand, CEOs that stay in the top position for a very long period either have large stake of
interests in the firm or strong control for career security. Both the large amount of
equity-based wealth and the career concern lead CEOs to make long-horizon oriented
decisions for their firms.
Last column in Table 5 employs both age and tenure variable to measure CEO horizon.
The results of Model Ⅳ are similar to those of models that only include either age or
tenure. Regression results on control variables are consistent across all the models in
Table 6 and also consistent with the logistic regression of shareholder horizons.
<Insert Table 5 here>
Table 6 adds several CEO compensation variables in the logistic regression models. It
is expected that the larger CEOs’ equity-based wealth in the firms, the less likely they will
have their firms underpaid in mergers and acquisitions. However, when CEOs hold a
large percentage of restricted stock grants, they will have strong incentives to cash out
and therefore less reluctant to agree an underpaid offer. Model Ⅰ in Table 6 adds
logarithm of target CEO wealth and ratio of restricted stock grants over target size. The
negative coefficient of target CEO wealth and positive coefficient of restricted ratio
9 According to the survey of Gibbons and Murphy (1992), it is reasonable to expect that CEOs with shorter tenure than typical retiring tenure have longer period left to stay in their position. However, once the firms are acquired, most of target CEOs can not maintain their position in the combined firms. Hartzell et al. (2004) finds that target CEOs experience high turnover rates both at the announcement of and years after the acquisitions.
19
proves the expectation, even though the coefficients are not statistically significant.
In the negotiation process of merger and acquisition, both the horizons of target and
acquirer CEOs will have impact on the bid price. Long-horizon oriented acquirers and
short-horizon oriented targets are more likely to involve in a transaction underpaying
targets. To control the impact of acquirer CEOs’ horizon, Model Ⅱ employs two variables.
Welredif in Model Ⅱ is the difference between residuals of CEO equity-based wealth
after regressing on firm size of acquirers and targets. Larger welredif indicates that
acquirer CEO has longer horizon than target CEO. As shown in Model Ⅱ, the coefficient
of welredif is significantly positive, which means that the longer of acquirer CEO horizon
than target CEO, the more likely targets are underpaid. Model Ⅲ includes both target
CEO equity-based wealth and acquirer CEO equity-based wealth. It presents that larger
acquirer CEO’s wealth and smaller target CEO’s wealth will increase the probability of
underpayment.
The potential problem of using market value of CEO equity-based wealth is the bias of
misvalution. Under the market driven acquisition, targets tend to be relatively
undervalued while acquirers tend to be overvalued. Therefore, CEO value of equity-based
wealth in the firm based on market price will also be affected. To control the impact of
market misvalution on CEO wealth measurement, Model Ⅳ of Table 6 uses ratio of
wealth to firm size. Variable weltosize is ratio of target CEO equity-based wealth over
target firm size at the end of fiscal year prior the announcement. After dividing wealth by
firm size, market value does not impact the measurement of CEO wealth, only the relative
stake in the firm matters.
The incentive to cash out is proxied by variable of tgresratio, which is measured by
restricted stock grants over target size. Guay (1999) states the difference between stock
options and stocks. Unexercisable stock options with restrictions on selling are expected
to have same effect as restricted grants. The different impact of different kinds of
20
equity-based wealth on CEO horizon will probably contradict and mitigate each other.
Model Ⅳ adds a new variable of shownpc to separate different effect of stocks from
other equity-based wealth. Shownpc is the percentage of common shares held by target
CEO.
Model Ⅳ reports a significantly positive coefficient for weltosize and negative
coefficient for shownpc. After adding a variable of share percentage owned by CEO, the
relation between target CEO wealth and probability of underpayment is positive, this is
contradictory with previous results on CEO wealth variables. A further study is needed to
investigate the impact of stock ownership and other equity-based wealth. Since wealth is
the sum of all kinds of equity-based equity value, variable weltosize can be regarded as
the sum of share ownership percentage, i.e. shownpc, and stocks underlying options and
restricted stocks as a percentage of total shares outstanding. The impact of common
stocks owned by CEO should be determined by two coefficients for both weltosize and
shownpc. Shownpc is the number of percents. To make the scale of shownpc consistent
with weltosize, the coefficients should be multiplied by 100. Therefore, the total impact
coefficient in the logistic regression for shownpc is (-0.0695*100+6.2937), which is
negatively related to the probability of underpayment. Controlling shares ownership of
CEO, the coefficient for other equity-based wealth including restricted stock grants and
stocks underlying both exercisable and unexercisable options over total shares
outstanding is positive. It shows that CEOs with larger stock ownership are less likely to
undersell their firms, however, CEOs will be more likely to agree with underpaid deals
when they have large stake in other equity-based wealth. The results in Model Ⅳ
denotes that stock ownership can align the interests of shareholders and CEOs and lead
CEOs to make long-term oriented decisions. However, the increasing use of stock options
and restricted stock grants has the opposite effect, which lends CEOs incentive to cash out
and therefore makes them more short-horizon oriented.
21
Variables on CEO age, tenure and control variables present almost same effect in the
logistic regression model as in Table 5.
<Insert Table 6 here>
3.2.2 CEO horizon and Level of Underpayment
Same as for shareholder horizon, two regression models are employed to examine the
relation between CEO horizon and the level of underpayment. Panel A. in Table 7 uses
OLS regression to examine the relation between CEO horizon and the magnitude of
underpayment based on:
iiii ControlsCEOhorizonUnderpay εβββ +++= 210 . (9)
Different sets of CEO horizon variables are included in Panel A. The positive
coefficients of variables horizon and age depict a first increasing and then decreasing
relation between CEO age and underpayment. The larger the value of variable underpay,
the more the target is overpaid. Therefore, targets with CEOs aged in between are most
overpaid; targets with CEOs either elder or younger are more underpaid. CEOs with
tenure between 3 and 5 years make their firms most underpaid. The results are consistent
with those reported in logistic regression. However, CEO age and tenure variables are not
statistically significant in both models of Panel A.
Statistical results on logarithm of target CEO equity-based wealth, acquirer CEO
equity-based wealth and welredif are consistent with those in the logistic regression.
Variable of tgresratio in Panel A reports contradictory sign to expectation. None of these
variables are significant except welredif.
Results of control variables show that relative size of targets to acquirers and target size
will significantly increase the level of underpayment. In addition, targets involved in
diversified transactions, paid by stock and in relatively bearish market are more
underpaid.
22
Panel B. in Table 7 reports regression results of generalized linear model (GLM).
Dependent variable F(underpay) is the cumulative distribution function (CDF) of
underpay.
iii ControlsCEOhorizonunderpayF εβββ +++= 210)( , (10)
Panel B presents consistent results with Panel A and the logistic regression. Using
cumulative distribution function of underpay as dependent variable, Panel B reduces the
impact of outliers and makes the dependent and independent variables more comparable
in scale. Model Ⅲ to Model Ⅴ report significant statistical results on age and tenure
variables.
Target CEO ratio of restricted stock grants in Model Ⅲ and Ⅳ presents expected
sign. CEOs with larger ratio of restricted stock grants will make their firms more
underpaid. Variables on CEO wealth in both targets and acquirers present that larger
acquirer CEO’s wealth and smaller target CEO’s wealth will result in a more underpaid
deal for targets. The larger the difference between acquirer and target CEO’s wealth, the
more the targets are underpaid.
Model Ⅴ employs two variables weltosize and shownpc, same as Model Ⅳ in Table
6. The negative coefficient for weltosize is mainly driven by the impact of stock options
and restricted stock grants, since the coefficient for stock ownership is positive.
<Insert Table 7 here>
3.2.3 Correlation between CEO characteristic variables
When studying CEO horizons, I employ several CEO characteristic variables such as
age, tenure and compensation structures. Since previous literature states that CEO age and
tenure are highly correlated with equity-based compensation,10 Table 8 reports the
correlation between different CEO characteristic variables. Panel A of Table 8 presents
10 Lewellen et al. (1987) find that CEOs who are near retirement have a larger fraction of stock-based compensation. Gibbons and Murphy (1992) present that CEOs with longer tenure are paid more by equity-based compensation.
23
the correlation matrix among CEO characteristic variables. The magnitudes of
correlations do not suggest the problem of multicollinearity. Statistical results of tenure in
previous regressions present a curved relation with CEO horizon. CEOs with tenure
between 3 and 5 years have the shortest horizons in their firms. Panel B reports mean and
median of CEO compensation variables in different tenure groups. It shows that target
CEO equity-based wealth, either in logarithm form or as relative ratio to firm size,
increases with tenure. Share ownership percentage also increases with tenure. The only
variable which presents different trend across tenure groups is tgresratio, calculated by
target CEO’s value of restricted stock grants over target size. Tgresratio is constructed to
measure level of CEO’s wealth trapped in the firm by restrictions and incentive to cash
out. Group of tenure between 3 and 5 years presents highest mean of tgresratio. The
median of tgresratio is same across all three groups and the group difference is not
significant. Mean and median difference among different tenure groups demonstrates that
the impact of tenure on CEO decision horizons is not driven by compensation structure.
The major determinant for CEOs’ decision horizons in different tenure groups is their
career concern.
<Insert Table 8 here>
3.3 Target horizon and Underpayment
Previous regression models focus either on CEO horizon or on shareholder horizon
alone. Table 9 includes both sets of variables on CEO horizons and shareholder horizons
to examine the relation between target horizon and underpayment. Panel A. reports
logistic regression results of the likelihood of a target being underpaid. Panel B. uses OLS
regression to examine the relation between target horizon and the magnitude of
underpayment. Panel C. reports regression results of generalized linear model (GLM).
Dependent variable F(underpay) is the cumulative distribution function (CDF) of
underpay.
24
Shareholder horizon variable mreone presents expected sign in all the models. However,
none of the coefficients are statistically significant. Statistical results on CEO horizon
variables in Table 9 are quite similar to those in Table 6 and Table 7 that only include
CEO horizon variables. It posits that the relation between target horizon and
underpayment is mainly determined by CEO horizons.
<Insert Table 9 here>
4. Conclusion
This study examines the hypotheses inspired from Shleifer and Vishny (2003). Using a
sample of 770 mergers and acquisitions announced between 1993 and 2002, I find that the
number of targets underpaid in the deals is significantly larger than the number of targets
overpaid.
Target shareholder horizon, denoted by mean of daily turnovers over one year period
before the month of announcement after controlling firm size effect, has negative relation
with probability of underpayment.
CEO horizon is measured by age, tenure in that position, equity-based wealth in the
firm, and incentives to cash out. It is found that CEOs have short horizon when they are
either younger than 65 or older than 70, or when their tenure is between 3 to 5 years.
Equity-based wealth can align the interests of shareholders and CEOs and reduce the
probability of underpayment. However, the overuse of restricted stock grants will induce
CEOs to seek cashing out opportunities and agree with an underpaid offer.
This study mainly focuses on target side horizon. It is based on the assumption that
mergers and acquisitions are a form of arbitrage by rational acquirers’ managers. Since
the benefit of acquirers for the transaction is in long-term, it is reasonable to expect that
acquirers’ horizon also plays an important role in the deal negotiation. Here I only employ
acquirer CEOs’ equity-based wealth to denote acquirer horizon. Further study can be done
25
by using more proxies for shareholder and CEO horizons in acquiring firms.
26
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28
Figure 1
Shareholder Turnover Residuals over 31 to 395 days prior Announcement: Comparison between
Underpaid and Overpaid Targets
LOS
392
373
354
335
316
297
278
259
240
221
202
183
164
145
126
107
88
69
50
31
Mea
n
12
10
8
6
4
2
0
-2
-4
mreup
mreop
Figure 1 describes daily residual means of target firms’ turnover ratio after the regression on logarithm of
market value of equity in two groups: underpaid and overpaid. Mreup is daily mean of shareholder turnover
residuals in underpaid targets. Mreop is daily mean of shareholder turnover residuals in overpaid targets.
Residuals are calculated using equation (3):
( ) jijiji MELgTurnover εββ ++= *10 ,
where is the turnover ratio of firm j on i days before the announcement, jiTurnover ( )jiMELg is the
logarithm form of market value of firm j on i days before the announcement. The mean of targets
shareholder turnover residuals on i days before the announcement is constructed as:
∑=
=m
jjii m
mreup1
1 ε ; ∑=
=n
jjii n
mreop1
1 ε ;
where m is the number of underpaid targets on i days before the announcement, n is the number of overpaid
targets on i days before the announcement. In figure 1, the MEAN on Y-axis measures the value of mreup
and mreop. LOS on X-axis is the number of days before announcement.
29
Figure 2
Shareholder Turnover Residuals over 31 to 395 days prior Announcement: Comparison between
Targets with Status of Completed and Withdrawn
LOS
392
373
354
335
316
297
278
259
240
221
202
183
164
145
126
107
88
69
50
31
Mea
n
30
20
10
0
-10
mrecom
mreuncom
Figure 2 describes daily residual means of target firms’ turnover ratio after the regression on logarithm of
market value of equity in two groups with different status: completed and withdrawn. Mrecom is daily
mean of shareholder turnover residuals in targets with status of completed. Mreuncom is daily mean of
shareholder turnover residuals in targets with status of withdrawn. Residuals are calculated using equation
(3):
( ) jijiji MELgTurnover εββ ++= *10 ,
where is the turnover ratio of firm j on i days before the announcement, jiTurnover ( )jiMELg is the
logarithm form of market value of firm j on i days before the announcement. The mean of targets
shareholder turnover residuals on i days before the announcement is constructed as:
∑=
=m
jjii m
mrecom1
1 ε ; ∑=
=n
jjii n
mreuncom1
1 ε ;
where m is the number of targets with completed status on i days before the announcement, n is the number
of targets with withdrawn status on i days before the announcement. In figure 2, the MEAN on Y-axis
measures the value of mrecom and mreuncom. LOS on X-axis is the number of days before announcement.
30
0 .25 .5 .75 1Fraction of the data
Figure 3 plots the quantiles of underpayment. X-axis is the fraction of the data. Y-axis is the magnitude of
underpayment. Underpayment is measured by the defined equations. Positive value of underpay means
overpayment, negative value of underpay means underpayment. The larger the value of underpay, the more
the targets are overpaid; the smaller the value of underpay, the more the targets are underpaid. The dots in
Fig. 4 describe the magnitude of underpayment and the quantile position in the overall dataset.
31
-150
000
-100
000
-500
000
5000
0Q
uant
iles
of U
ND
ER
PA
Y
Quantiles of Underpayment
Figure 3
The Sample of mergers and acquisitions comes from SDC database and has required information from CRSP, Compustat, ExecuComp and SEC Proxy Statement.
Transactions with announcement date between 1993 and 2002 are included. This table reports year of announcement, number of transactions, mean of target and acquirer size,
mean deal value of transaction, mean value of underpayment and percentage of transactions that are underpaid, using all stock as a method of payment, acquirers and targets
from different industries and finally completed. Target and acquirer size is the market value of firm at the end of year prior the announcement, calculated as the multiplication
of stock price and shares outstanding obtained from CRSP. Deal value is obtained from SDC. Underpayment is measured using the defined method. Target size, acquirer size,
deal value and underpay are in millions of dollars.
Year N Target Size Acquirer Size Deal Value Underpay % Underpay % Stock % Diversified % Complete
1993 1 68.26 674.40 30.00 -194.41 1.00 0.00 1.00 1.00
1994
16 462.13 29427.58 612.99 -55.59 0.63 0.25 0.25 0.94
1995 43 1072.62 11504.24 753.37 -1036.09 0.56 0.37 0.28 0.84
1996 59 594.22 16808.68 721.45 404.27 0.44 0.41 0.31 0.97
1997 96 620.58 20317.53 825.36 -844.52 0.58 0.46 0.38 0.93
1998 110 765.46 19654.37 809.39 -236.46 0.60 0.51 0.33 0.95
1999 154 1861.48 40373.40 1367.01 -820.03 0.51 0.49 0.38 0.94
2000 137 1379.76 20314.24 1928.17 -327.10 0.58 0.49 0.36 0.91
2001 103 607.29 23790.16 648.85 -801.87 0.72 0.49 0.23 0.92
2002 51 1375.16 23144.62 1443.91 -491.98 0.69 0.27 0.20 0.94
Total 770 1091.95 24287.91 1127.55 -529.41 0.58 0.46 0.32 0.93
Summary Statistics for Mergers and Acquisitions
Table 1
32
Table 2
Firm Characteristics classified by Underpayment
In table 2 are means of target and acquirer firms’ characteristics and paired variables related to both
acquirers and targets. The statistics are classified by variable of up. If up is 1 then the observation is
classified as underpayment, else is classified as overpayment. Underpayment is measured by the defined
method. Difference in the last column is calculated by the mean of Overpayment group minus mean of
Underpayment group. P-value of standard t-test is given in the parentheses to compare the means of two
groups. Panel A. reports target firms’ characteristics. Mreone is the mean residual of target firms’ turnover
ratio 395 to 31 days before the announcement of mergers and acquisitions after the regression on logarithm
of market value of equity. Tcc is the CEO total current compensation in the last fiscal year. Rstkgrnt is the
value of restricted stock grants in the last fiscal year owned by CEO. Inmonex is value of in the money
unexercised exercisable options held in the last fiscal year. Inmonun is value of in the money unexercised
unexercisable options held by CEO in the last fiscal year. Shrown is the number of shares owned by CEO at
the end of last fiscal year. Shrownpc is the percentage of common stocks to the shares outstanding held by
CEO at the end of last fiscal year. Shrval is the value of common stock holdings at the end of last fiscal year.
Wealth is the sum of value of restricted stock grants, value of common stock holdings, value of in the
money unexercised exercisable and unexercisale options. Exsecop is the number of securities underlying
unexercised exercisable options granted to CEO at the end of last fiscal year. Unsecop is the number of
securities underlying unexercised unexercisable options. Age is the CEO age at the year of mergers and
acquisitions announcement. Tenure is the number of years CEO has stayed in the position till announcement.
Panel B reports acquirer firms’ characteristics. Variables in Panel B are all about CEO characteristics and
have the same meaning with variables in Panel A. Panel C reports relative difference between acquirers and
targets. Rsize equals target size divided by acquirer size. Both target and acquirer size are measured by
multiplication of stock price and shares outstanding at the end of year prior the announcement. Agedif is the
difference of CEO age between acquirers and targets, measured by the mean of difference between acquirer
CEO age and target CEO age in each observation. Welredif is the difference of residuals between acquirers
and targets after regressing CEO wealth on firm size.
33
Table 2 (cont.)
Total Underpayment Overpayment Difference (p
1.64 0.1830
tcc 499416.8 4.9
rnt
07 07 07 12)
Pane quirer Characteristics
51925 471519.6 -47735.28 (0.26)
rstkg 115357.7 148390 68905.98 -79484.06 (0.27)
inmonex 2428763 2669387 2090384 -579003 (0.43)
inmonun 1580944 1468193 1739501 271307.2 (0.63)
shrown 955796 1073696 789998.5 -283698 (0.19)
shrownpc 6.41 5.87 7.14 1.27 (0.16)
shrval 2.60e+ 3.25e+ 1.69e+ -1.56e+07 (0.
wealth 3.01e+07 3.68e+07 2.08e+07 -1.60e+07 (0.13)
exsecop 224211.8 249514 188957.2 -60556.8 (0.098)
unsecop 212709.6 241288.1 172794.8 -68493.34 (0.07)
age 52.79 51.94 54 2.06 (<0.001)
tenure 7.50 7.06 8.12 1.06 (0.05)
l B. Ac
1400689 1310677
aqrsgrnt
08 08 08 2)
Pa rget and Acq
1445713 579152.3 2664313 2085161 (0.31)
aqinex 1.55e+07 1.74e+07 1.28e+07 -4542501 (0.15)
aqinun 9986936 1.02e+07 9662899 -554463 (0.80)
aqshrown 5656307 8211621 2062898 -6148723 (0.02)
aqshownpc 4.57 5.48 3.28 -2.20 (0.05)
aqshrval 5.80e+ 9.18e+ 1.06e+ -8.12e+08 (0.0
aqwealth 6.07e+08 9.46e+08 1.31e+08 -8.15e+08 (0.01)
aquexex 574065.8 554627.2 601401.3 46774.13 (0.72)
aquexun 500584.5 535526.5 451447.4 -84079.14 (0.34)
aqage 54.68 54.19 55.37 1.18 (0.04)
nel C. Ta uirer
0.71 0.67
agedif 1.99 2.25 1.61 -0.64 (0.43)
welredif 08 +08 5) 5816.43 2.46e+ -3.46e -5.93e+08 (0.0
-value)
Panel A. Target Characteristics
mreone 1.04 -1.4592 (0.05)
aqtcc 1363282 -90011.69 (0.54)
rsize 0.69 -.04 (0.92)
34
Table 3
Mreone of Targets ent Classification
able 3 reports mreone of targets in different classifications. Mreone is residual means of target firm’s
Panel A. All Targets classified into 4 groups
in Differ
T
turnover ratio after regressing on logarithm of market value of equity, calculated by equation (3) and (4). N
is the number of observations in that group. Panel A categorizes all targets into four groups based on the
status and whether they are underpayment or overpayment. Group 1 includes underpaid targets with status
of completed. Group 2 includes overpaid targets with status of completed. Group 3 includes underpaid
targets with status of withdrawn. Group 4 includes overpaid targets with status of withdrawn. F-statistics for
the mean difference among the four groups is reported in the last column. Panel B compares underpaid
targets and overpaid targets in all completed transactions. Difference is measured by mreone in group of
Underpayment minus than in group of Overpayment. Panel C compares all underpaid targets with status of
completed and with status of withdrawn. Difference is measured by mreone in group of Completed minus
that in group of Withdrawn. P-value in Panel B and Panel C is obtained from T-test.
Group 1 F-statistics Group 2 Group 3 Group 4
(p-value)
1.74 (0.16)
N 417 299 33 21
Panel l Targets wit tus of Compl B. Al h sta eted
ment Overpayment
(p-value)
1.64 (0.04)
N 417 299
Panel C. All Und Targets erpaid
Withdrawn
(p-value)
2.30 (0.02)
N 417 33
mreone 1.81 0.17 -0.49 0.36
Underpay Difference
mreone 1.81 0.17
Completed Difference
mreone 1.81 -0.49
35
Table 4
Shareholder Hor Underpayment
able 4 reports the regression results for the entire sample. ModelⅠpresents a logistic regression of the
Model Ⅰ Model Ⅱ Model Ⅲ
izon and
T
likelihood of a target being underpaid. The dependent variable in Model Ⅰ is the probability of
underpayment, measured by variable UP. If UP is 1 then the observation is classified as underpayment, else
is classified as overpayment. ModelⅡuses OLS regression to examine the relation between shareholder
horizon and the magnitude of underpayment. Dependent variable in ModelⅡis underpay, measured by the
defined methods. ModelⅢ uses generalized linear model (GLM) to estimate the relation between
shareholder horizon and underpayment. Dependent variable F(underpay) is the cumulative distribution
function of underpay. Mreone is the mean residual of target firms’ turnover ratio 395 to 31 days before the
announcement of mergers and acquisitions after the regression on logarithm of market value of equity. Rsize
equals target size divided by acquirer size. Both target and acquirer size are measured by multiplication of
stock price and shares outstanding at the end of year prior the announcement. Diver is to measure whether
the transaction occurs between different industries. Diver equals 1 if targets and acquirers have different
industry code. Logev is the logarithm of equity value of target at the announcement day. Equityvalue is
targets’ book value of equity at the announcement day. Stock is a dummy variable to measure the method of
payment. Stock equals 1 when targets are paid by all stock, and 0 when by all cash Mvwretd is the average
monthly value-weighted return (including distributions) in three months prior the announcement of a
transaction. * denotes 10% significant level, ** denotes 5% significant level, *** denotes 1% significant
level.
(n=770) (n=770) (n=770)
P(Underp Underpay F(Underp
0.0149* -14.3156 -0.0021**
rsize -0.0043 -104.1902***
**
ue .5447***
.0464 .0197
td *
.00004
diver 0.3276** -139.4161 -0.0299
logev 0.0924* -0.0341*
equityval -1
stock 0 15.6567 0
mvwre -2.8641 1715.42 0.7594*
constant -1.4384 179.1935 1.1146***
Dependent Variable
ay) ay)
mreone
36
Tab 5
CEO characteristics an ility of Underpayment
able 5 reports logistic regression of underpayment and CEO characteristic variables. Probability of
underpayment, else is classified as overpayment. Horizon is measured by – ( ). Age is the CEO
age70up
Dependent Variable = Probability of Underpayment
le
d Probab
T
underpayment is denoted by dummy variable up. If up is 1 then the observation is classified as
2age
age at the year of mergers and acquisitions announcement. Age65less and are two dummy
variables. Age65less equals 1 if target CEO’s age is smaller than 65, 0 otherwise. Age70up equals 1 if target
CEO’s age is larger than or equal to 70, 0 otherwise. Ten3to5 is 1 if target CEO’s tenure in current position
is longer than 3 years and shorter than 5 years. Rsize, diver, logev, stock and mvwretd are defined same as in
Table 5. * denotes 10% significant level, ** denotes 5% significant level, *** denotes 1% significant level.
100/
Mod Model Ⅳ el Ⅰ Model Ⅱ Model Ⅲ
(n=770) (n=770) (n=770) (n=770)
-0.1481** -0.1572**
age -0.1888** -0.1973**
age65less .0299***
.4801** .4547**
.0025 .0044
td
1
age70up 1.1132**
ten3to5 0 0
rsize -0 -0 -0.0034 -0.0021
diver 0.3258** 0.3427** 0.3694** 0.3436**
logev 0.1109** 0.0855* 0.0869* 0.1062**
stock 0.0792 0.0541 0.0938 0.0909
mvwre -2.5983 -2.5767 -2.5728 -2.4844
constant 3.9591* -2.301** -1.4291 4.1584*
horizon
37
Table 6
CEO compensation and ility of Underpayment
Horizon, age, ten3to5, rsize, diver, logev, stock an mvwretd are defined as in Table 5. Logtgwel and
Dependent Variable = Probability of Underpayment
Probab
d
logaqwel are logarithm form of CEO wealth in targets and acquirers. Wealth is the sum of value of
restricted stock grants, value of common stock holdings, value of in the money unexercised exercisable and
unexercisale options at the end of fiscal year prior the announcement. Welredif is the difference of residuals
between acquirers and targets after regressing CEO wealth on firm size. Tgresratio is measured by target
CEO’s value of restricted stock grants over target size. Weltosize is ratio of target CEO equity-based wealth
over target firm size at the end of fiscal year prior the announcement. Shownpc is percentage of common
shares held by target CEO. * denotes 10% significant level, ** denotes 5% significant level, *** denotes
1% significant level.
Mod Model Ⅳ el Ⅰ Model Ⅱ Model Ⅲ
(n=770) (n=770) (n=770) (n=705)
-0.1662** -0.1662** -0.1442* -0.1667**
age -0.2057***
o5
.69e-11*
121.1903 29.699 12.0751
.0016 .0028 .0041
*
td
-0.2078*** -0.1778** -0.2042**
ten3t 0.4354* 0.4538** 0.6737*** 0.4090*
logtgwel -0.0545 -0.1197***
logaqwel 0.1141***
welredif 4
tgresratio 110.7376 1 1
weltosize 6.2937**
shownpc -0.0695***
rsize -0 -0 0 -0.0026
diver 0.3812** 0.3737** 0.2787 0.3103*
logev 0.1419*** 0.1124** 0.1462* 0.1070**
stock 0.1449 0.1045 0.1227 0.1352
mvwre -2.6584 -2.6247 -0.7396 -2.6832
constant 4.4610* 4.3213* 2.5731 4.1859*
horizon
38
Table 7
CEO Horizon derpayment
able 7 examines the relation between CEO horizon variables and level of underpayment. Panel A. uses
Panel A. Underpay Panel B. F(Underpay)
and Un
T
OLS regression to examine the relation between shareholder horizon and the magnitude of underpayment.
Dependent variable in Panel A. is underpay, measured by the defined methods. Panel B. uses generalized
linear model (GLM) to estimate the relation between shareholder horizon and underpayment. Dependent
variable F(underpay) is the cumulative distribution function of underpay. Dependent variables are same as
defined in Table 6.
Model
Model Ⅲ Ⅴ Ⅰ Model Ⅱ
(n=770) (n=770) (n=770)
Model Ⅳ Model
(n=705) (n=705)
53.4331 84.5958 0.0122 0.0166* 0.0153
age 60.3952 101.8556
o5 *
*
.27e-08** .46e-12***
8726.86 .2594
.2736*
15.9819*** .3955*** .0013 .0002
** ** **
alue .5320*** .5046***
.0128 .0118 .0133
*
*
0.0160 0.0213** 0.0199*
ten3t -604.4351 -417.2946 -0.0732* -0.0467 -0.0430
logtgwel 83.5425 0.0040
logaqwel -68.6217 -0.0134*
welredif -6 -5
tgresratio 1 43133.03 -8 -6.0905
weltosize -0
shownpc 0.0025
rsize -1 -107 -0 -0 -0.0002
diver -169.137 -179.3366 -0.0190 -0.0326 -0.0266
logev -0.0340* -0.0351* -0.0361*
equityv -1 -1
stock -171.9826 -46.6475 0 0 0
mvwretd 1536.951 1594.538 0.6015 0.7183* 0.7062*
constant -1391.014 -2713.068 0.7995* 0.4925* 0.5503*
horizon
39
Table 8
Panel A. Correlation Matri Characteristic Variables
Variable age tenure Exec_dir CEO_block shownpc incent tgresratio tgweltosize logtgwel logtcc
x for CEO
age 1 .0000
tenure 1.0 0
ir 1.0 0
k 1.0 0
1.0 0
1.0 0
io 1.0 0
1.0 00
1.0 0
1.0 0
0.3930 00
Exec_d 0.0395 0.0465 00
CEO_bloc 0.0164 0.2796 0.0586 00
shownpc 0.0225 0.2861 0.0434 0.6156 00
incent -0.0781 -0.2306 0.0031 -0.3131 -0.2502 00
tgresrat 0.1038 -0.0069 0.0151 -0.0697 -0.0491 0.1880 00
tgweltosize -0.0050 0.2478 0.0444 0.5556 0.9112 -0.2008 -0.0351 0
logtgwel 0.0287 0.1758 0.1017 0.3146 0.3554 -0.0069 -0.0039 0.3948 00
logtcc 0.1302 0.0510 0.0572 -0.1568 -0.1193 0.1445 0.0425 -0.0553 0.4084 00
Panel B. Mean and Median in Different Tenure Groups
Shorter than 3 yrs 3 to 5 yrs Longer than 5 yrs
Mean Median Mean ian Mean Median Med
logtgwel 5 9 6 14.624 14.8196 15.365 15.4297 15.571 15.6010
tgweltosize
0 59 56
0.041 0.013 0.059 0.032 0.077 0.041
tgresratio 0.0001 0 0.0001 0 0.0001 0
shownpc 3.740 1 5.224 2.74 8.049 3.7
Table 8 reports the correlation between CEO characteristic variables and the mean and median in different
tenure groups. Age is the CEO age at the year of mergers and acquisitions announcement. Tenure is the
number of years CEO has stayed in the position till announcement. Exec_dir is the dummy variable to
denote whether CEO is also director in the board. CEO_block is the dummy variable to denote whether
CEO is a blockholder. Blockholder is defined as shareholder holding no less than 5% shares of total shares
outstanding. Shownpc is percentage of common shares held by target CEO. Incent is measured by (value of
restricted stock grants + value of in the money unexercised unexercisable options )/ wealth. Tgresratio is
measured by target CEO’s value of restricted stock grants over target size. Weltosize is ratio of target CEO
equity-based wealth over target firm size at the end of fiscal year prior the announcement. Logtgwel is
logarithm form of CEO wealth in targets. Logtcc is the logarithm form of target CEO total current
compensation.
40
Table 9
Target Horizon and Underpayment
Panel A. P(Underpay) Panel B. Underpay Panel C. F(Underpay)
Model Ⅰ
(n=770)
Model Ⅱ
(n=770)
Model Ⅲ
(n=705)
Model Ⅳ
(n=770)
Model Ⅴ
(n=770)
Model Ⅵ
(n=705)
Model Ⅶ
(n=770)
Model Ⅷ
(n=770)
Model Ⅸ
(n=705)
mreone 0.0094 0.0096 0.0070 -11.9840 -11.0774 -12.7634 -0.0015 -0.0014 -0.0014
horizon -0.1521** -0.1614** -0.1619** 69.6642 76.7105 61.7793 0.0146 0.0156 0.0143
age -0.1901** -0.2010*** -0.1967** 84.9798 91.2734 77.1717 0.0188* 0.0199* 0.0185*
ten3to5 0.4516** 0.4503** 0.4077* -405.6874 -410.3706 -449.2893 -0.0452 -0.0458 -0.0423
welredif 4.68e-11* -6.19e-08** -5.40e-12***
tgresratio 113.41 40326.34 -6.4494
weltosize 6.2211** 93.9147 -0.2621*
shownpc -0.0690*** -8.4379 0.0023
rsize -0.0024 -0.0031 -0.0031 -105.3318*** -106.8945*** -105.719*** -0.0002 -0.0001 -0.0001
diver 0.3309** 0.3611** 0.2947* -145.6569 -164.2534 -143.4404 -0.0293 -0.0307 -0.0249
logev 0.1061** 0.1122** 0.1089** -0.0355*** -0.0349*** -0.0361***
equityvalue -1.5460*** -1.5064*** -1.5482***
stock 0.0679 0.0816 0.1081 1.9578 -14.6487 -9.5105 0.0182 0.0160 0.0174
mvwretd -2.6283 -2.7753 -2.8327 1517.563 1793.814 1358.622 0.7226** 0.7445** 0.7281*
constant 3.9373* 4.1124* 3.9255* -2249.287 -2390.144 -2000.822 0.5707* 0.5330* 0.5912*
41
