Prior target valuations and acquirer returns: risk or perception?
*
Thomas Moeller
Neeley School of Business
Texas Christian University
Abstract
In a large sample of public-public acquisitions, target valuation changes between their 52-week highs and
just prior to the acquisition announcements are positively related to acquirer announcement returns.
Behavioral biases based on prospect theory potentially explain this relation. Yet, the target valuation
change variables are highly correlated with target valuation uncertainty proxies that also affect acquirer
announcement returns. These findings suggest that rational explanations based on target valuation
uncertainty are at least as relevant as behavioral stories for explaining the significant empirical relation of
prior target valuation changes and acquirer announcement returns.
JEL Classification: G24, G34
Keywords: Mergers, acquisitions, valuation uncertainty, prospect theory.
January 2010
*
I thank Jack Cooney, Jan Jindra, and Mike Stegemoller for helpful comments and the Luther King Capital
Management Center for Financial Studies at the Neeley School of Business at TCU for its financial support for this
research. All errors are my own.
Contact information: Thomas Moeller, Neeley School of Business, TCU Box 298530, Fort Worth, TX 76129; email:
t.moeller@tcu.edu; phone: 817.760.0050
Prior target valuations and acquirer returns: risk or perception?
In a large sample of public-public acquisitions, target valuation changes between their 52-week highs and
just prior to the acquisition announcements are positively related to acquirer announcement returns.
Behavioral biases based on prospect theory potentially explain this relation. Yet, the target valuation
change variables are highly correlated with target valuation uncertainty proxies that also affect acquirer
announcement returns. These findings suggest that rational explanations based on target valuation
uncertainty are at least as relevant as behavioral stories for explaining the significant empirical relation of
prior target valuation changes and acquirer announcement returns.
1
1. Introduction
Recent empirical studies show that prior target valuation changes affect acquirer
announcement returns. For private targets, Cooney, Moeller, and Stegemoller (2009) find that
target valuation changes between a withdrawn initial public offering (IPO) and a subsequent
acquisition are positively related to acquirer announcement returns. For public targets, Baker,
Pan, and Wurgler (2009) report that targets’ 52-week high share prices inflate takeover offer
premiums. Pre-offer target prices below their 52-week highs are associated with more negative
acquirer announcement returns, presumably because the acquirers offer excessive takeover
premiums. The more the offer premiums are driven by the targets’ 52-week highs, the more
negative is the effect of offer premiums on acquirer announcement returns. Thus, target valuation
changes between a 52-week high and just prior to a subsequent acquisition offer are positively
related to acquirer announcement returns.
It is not obvious why prior target valuations affect acquirer announcement returns, although
the empirical findings are similar to the partial adjustment effect in initial public offerings
(Hanley, 1993, Loughran and Ritter, 2002). In fact, it is easier to argue that prior valuations
should be irrelevant. In this spirit, Baker et al. (2009) attribute the effect of prior target valuation
changes to irrational behavioral biases of acquirers and targets. In contrast, Cooney et al. (2009)
favor a rational explanation in which target valuation changes proxy for target valuation
uncertainty. This valuation uncertainty fundamentally affects the acquirer announcement returns.
Unfortunately, the sample in Cooney et al. (2009) is small and quite distinct. To generalize their
results, similar tests with a larger sample of public acquisitions would be necessary.
Any test of the relevance of prior valuation changes relies on defining an anchor valuation. In
Cooney et al. (2009), the only feasible target valuation comes from the target’s anticipated
valuation at the time of its failed IPO. In acquisitions of public targets, choosing the anchor
valuation is largely arbitrary. Fortunately, Baker et al. (2009) make a strong case that, of all prior
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target valuations, the 52-week high has the most empirical relevance. Part of their reasoning is
based on the fact that takeover offers cluster heavily around the targets’ 52-week high prices. In
research with a sample of acquisitions of recent IPOs, Jindra and Moeller (2009) introduce two
other choices by selecting the target’s IPO price and end of first trading day price as anchors.
In this paper, I examine whether the effect of prior target valuation changes on acquirer
announcement returns is driven by behavioral biases or by rational considerations. As in Baker et
al. (2009), I use a large sample of public-public acquisitions and base my main target valuation
change measure on the target’s 52-week high. Similar to Cooney et al. (2009), by focusing on
target valuation risk, I expand the search of explanations beyond behavioral biases.
Overall, I find strong support that valuation changes from a target’s 52-week high affect
acquirer announcement returns. My main measure of target valuation changes is Target
Δ
high,
the target’s share price one week prior to the acquisition announcement divided by the target’s 52-
week high share price (also for the period ending one week prior to the acquisition
announcement) minus one. Acquirers of targets in the bottom tercile of Target
Δ
high have
average announcement returns of -2.8% compared to -1.2% in acquisition of targets in the top
tercile. Regression results further show that the farther the target price just prior to the acquisition
is below its 52-week high, the more negative is the acquirer announcement return. This result is
consistent with behavioral biases based on prospect theory (Kahneman and Tversky, 1979). It also
parallels the finding in Cooney et al. (2009) that acquirer announcement returns are positively
related to target valuation changes. Yet, I also find that measures of target valuation uncertainty
are strongly related to target valuation changes and that they affect acquirer announcement
returns.
My main proxies for target valuation uncertainty are Target price range, the 52-week high
minus the 52-week low, standardized by the mid-point of the 52-week high and low, Industry M/B
stdev, the standard deviation of the market-to-book ratios of firms in the target industry with
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assets between half and twice the target’s assets, and Target price stdev, the standard deviation of
the target’s share prices, measured from 370 to 15 days before the acquisition announcement.
Higher target valuation uncertainty is related to lower acquirer announcement returns. Acquirers
of targets in the bottom tercile of Target price range have average announcement returns of -1.3%
compared to -3.3% in acquisition of targets in the top tercile. The average acquirer announcement
returns for the bottom (top) terciles of Industry M/B stdev and Target price stdev are -1.6%
(-3.1%) and -1.3% (-3.2%), respectively.
Why do investors react negatively to acquisitions of risky targets? My risk proxies measure
idiosyncratic and industry-specific target valuation uncertainty. In Cooney et al. (2009), acquirer
announcement returns are positively related to target valuation risk. They explain the positive
relation with risk-averse acquirer managers requiring compensation for the assumption of
valuation risk in form of lower acquisition prices. Acquirer shareholders who are less risk-averse
than managers focus more on the lower acquisition price than the added idiosyncratic risk (that
they can largely diversify away). Since the targets in Cooney et al. (2009) are private, their
owners are likely undiversified and benefit from offloading valuation risk to acquirers. Therefore,
the acquirer’s need for compensation for assuming valuation risk is matched by the target’s
willingness to provide it. Consequently, higher target valuation risk is associated with higher
acquirer announcement returns in private acquisitions.
Shareholders of public targets are likely more diversified than the owners of private firms.
Therefore, they have no incentive to provide compensation for offloading idiosyncratic risk.
Without compensation in the form of lower acquisition prices, acquirer managers would have to
be compelled by other, likely private, benefits to undertake acquisitions of risky targets.
1
Examples of these costly benefits are higher compensation from running a larger firm or a better
ability to hide poor performance in a more complex firm. If the takeover market is competitive,
1
An acquisition, even of a risky target, can be attractive because of synergies. However, there is no reason
why synergies and idiosyncratic risk should be correlated, and empirical evidence of synergies is sparse.
4
the costs of the managerial benefits are borne by the acquiring firm. Therefore, the more risky the
public target, the more negative is the investors’ reaction.
Private targets in Cooney et al. (2009) and public targets here are likely the reason for the
opposite effects of target valuation risk on acquirer announcement returns. Target valuation risk
seems to affect public and private takeovers in fundamentally different ways, similar to the
unconditional differences in acquirer announcement returns that are on average positive for
private targets (Fuller, Netter, and Stegemoller, 2002) and negative for public targets (e.g.,
Moeller, 2005).
In contrast to Cooney et al. (2009), the target valuation change and target valuation
uncertainty variables are negatively correlated in my study. The farther the target’s stock price
one week before the acquisition announcement is below its 52-week high, the higher is the target
valuation uncertainty. This relation is intuitive because a larger absolute distance from a prior
value suggests that investors are uncertain about the appropriate target valuation. Consequently,
Target
Δ
high can be interpreted as a risk proxy. Higher valuation risk is associated with lower
Target
Δ
high, which in turn is associated with lower acquirer announcement returns. The
difference to Cooney et al. (2009) can be due to their target valuation change variable having
positive and negative values (Target
Δ
high is smaller than or equal to zero in my sample). Their
positive correlation of the target valuation risk proxy with positive target valuation changes is
intuitive because larger absolute valuation changes suggest higher risk. For negative target
valuation changes, it is unintuitive. An alternative interpretation would be that negative target
valuation changes are a measure of overvaluation instead of valuation risk.
One difficulty in interpreting the empirical results is that target valuation change variables
and target valuation uncertainty proxies are highly correlated. In addition, these variables have
substantial correlation with acquirer and target market-to-book ratios. Absent a convincing
empirical identification, only qualitative arguments can favor some explanations over others.
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Considering the evidence, there is no reason to attribute the effects of target valuation changes on
acquirer announcement returns solely to behavioral biases. There is significant evidence that
target valuation uncertainty drives at least part of the effect. Furthermore, the target valuation
uncertainty rationale seems to be more appealing than the irrational behavioral bias explanation.
Since rational and irrational explanations are not mutually exclusive, both types of theories can
cause the empirical relevance of prior target valuation changes for acquirer announcement
returns.
The rest of the paper is organized as follows: Section 2 describes the sample and section 3
presents the empirical results. Section 4 addresses robustness issues and section 6 concludes.
2. Data
I start with 6,142 completed takeovers, announced between 1982 and 2008, from Thomson
Reuters’ SDC Mergers & Acquisitions database where the target and the acquirer are public U.S.
firms and the acquirer holds no more than 10% of the target’s shares before the acquisition
announcement and no less than 90% afterwards. CRSP and Compustat matches are available for
targets and acquirers in 3,702 takeovers. I further require that the deal value is at least $30 million
(in year 2000 dollars) and that the market value of the target’s equity represents at least 1% of the
acquirer’s equity value (both measured at the last fiscal year-end before the acquisition
announcement).
2
Together with some missing data items, these requirements reduce the main
sample to 2,550 observations.
Table 1 presents summary statistics. Acquirer CAR is the three-day return of the acquirer in
excess of the CRSP equal-weighted index centered on the acquisition announcement. The mean
of -1.9% is statistically significant. Target
Δ
high is the target’s share price one week prior to the
acquisition announcement divided by the target’s 52-week high share price (also for the period
2
Jarrell and Poulsen (1989) report that acquisitions of relatively small targets have little impact on the
value of the acquirer. I remove those acquisitions to reduce noise. The results are similar with 2%, 5%, and
10% relative size cutoffs.
6
ending one week prior to the acquisition announcement) minus one. One week before the
acquisition announcement, the mean (median) target share price change from the 52-week high is
a decline of 22% (14.5%).
To test whether the 52-week high is a unique anchor, I analyze two similar anchors. Target
Δ
low is the target’s share price one week prior to the acquisition announcement divided by the
target’s 52-week low share price minus one. The target share price one week before the
announcement increased by a mean (median) of 61.3% (40.9%) from the 52-week low. Target
Δ
six months arbitrarily selects the share price 180 calendar days before the announcement as the
anchor and is otherwise calculated as the two prior variables. The average (median) share price
change over the roughly six months is 12.5% (8.2%).
I consider several target valuation risk measures. Target price range is the 52-week high
minus the 52-week low, standardized by the mid-point of the 52-week high and low. The mean
(median) Target price range is 65.1% (57.3%). Industry M/B stdev is the standard deviation of the
market-to-book ratios of firms in the target industry with assets between half and twice the
target’s assets. I define industry using the four-digit standard industrial classification (SIC) code
and require at least ten matching firms. If there are fewer matches, I use the first three digits of
the SIC code, then the first two, and if there are still fewer than ten matches only the first digit.
Industry M/B stdev has a mean and median of 1.3 and 0.7, respectively. Target price skew is the
skewness of the target’s share prices, measured from 370 to 15 calendar days before the
acquisition announcement. The average and median skewness is positive with values of 0.274 and
0.225, respectively.
The market value of equity is calculated from Compustat data as of the last fiscal year-end
before the acquisition announcement. Median Acquirer market value is $1.4 billion while median
Target market value is $189 million. Relative size is the ratio of target to acquirer market value of
equity. The median target has approximately one sixth of the market value of the acquirer. The
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market-to-book ratios are calculated as (market value of equity + book value of assets – book
value of equity) divided by book value of assets. Acquirer (Target) M/B has a median of 1.4 (1.3).
The average fraction of the acquisition price that is paid with acquirer stock (Stock pct) is 60.1%.
Target cash flow/ cash, the net cash flow from operating activities divided by cash and short-term
investments, has a mean of 631.8% and a median of 56.5% while Target net income/ assets, the
target’s net income divided by its total assets, has a mean and median of -1% and 1.5%,
respectively. Target runup is the return of the target from 60 calendar days before to the beginning
of the announcement return window. Its mean is 10% and its median is 7.2%.
Panel B shows that the SDC Mergers & Acquisitions database classifies only 1.7% of the
sample takeovers as hostile, while 14.9% involve tender offers. The acquisition is paid with at
least 90% stock (Stock) in 70.8% of the observations. I describe the variable Risk index below.
Panel C shows the distribution of the sample takeovers over time. The highest activity is
between 1995 and 2000, accounting for slightly more than half of the sample.
3. Results
I test the effect of prior target valuation changes and target valuation uncertainty proxies on
acquirer announcement returns.
3.1. Univariate results
In Table 2, I create subsamples by splitting the observations into terciles based on target
valuation change, target valuation uncertainty proxies, and various control variables. Then I
compare the mean and median Acquirer CAR of the bottom and top terciles.
Target
Δ
high has a strong positive relation with Acquirer CAR. Acquirers of targets in the top
tercile of Target
Δ
high have mean and median announcement returns of -1.2% and -1.3% while
the mean and median are -2.8% and -1.8% for the bottom tercile, respectively. Both means and
medians are statistically different at the 0.01 level. The means of Target
Δ
low and Target
Δ
six
8
months are also positively related to acquirer announcement returns, but the medians have a
negative relation. Both differences are insignificant, indicating that the 52-week high is a stronger
anchor than the 52-week low or an arbitrarily selected price.
The target valuation uncertainty proxies Target price range, Industry M/B stdev, and Target
price stdev have strong negative relations with Acquirer CAR. The mean (median) acquirer
announcement returns for the bottom and top terciles of Target price range are -1.3% and -3.3%
(-1.1% and -2.6%), respectively. Both differences are significant at the 0.01 level. The differences
for Industry M/B stdev and Target price stdev are of similar magnitudes and also significant at the
0.01 level, except for the virtually identical medians of Industry M/B stdev. Despite the similar
medians, a Wilcoxon signed-rank test shows Acquirer CAR for both terciles of Industry M/B stdev
to be significantly different at the 0.05 level.
Acquirer size (Acquirer market value) seems to have no impact on acquirer announcement
returns, but both Target market value and Relative size show that acquisitions of (relatively) larger
targets are associated with significantly lower acquirer announcement returns.
Acquirer and target market-to-book ratios are significantly negatively related to Acquirer
CAR and so is Stock pct. When I split the sample using the dummy variable Stock instead of the
terciles based on Stock pct, the results are similar. If acquirer market-to-book is a measure of
overvaluation, paying with stock can signal the overvaluation and cause the negative relations of
Acquirer M/B and Stock pct with Acquirer CAR. The significance of Target M/B can be due to
Target M/B being a proxy for overvaluation and overpaying by the acquirer, but its significance
can also be spurious because acquirer and target market-to-book ratios are highly correlated.
Finally, I combine the three measures of target valuation uncertainty (Target price range,
Industry M/B stdev, and Target price stdev) into a summary risk variable. Risk index ranges from
zero to three and adds one point for each target valuation risk variable that ranks in the top tercile.
Consistent with the results for the individual risk variables, acquirer announcement returns are
significantly lower when Risk index equals two or three, indicating high risk, than when it equals
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zero. Mean and median differences are significant at the 0.01 level. This summary variable
alleviates concerns that individual target valuation uncertainty proxies mismeasure risk. While it
discards valuable information, it is useful in regressions to address concerns about nonlinearities
and outliers.
Overall, Table 2 shows that the target’s price change from its 52-week high to just prior to the
acquisition announcement is negatively related to acquirer announcement returns. The more the
target’s price declines prior to the acquisition, the lower are the acquirer announcement returns.
This result is consistent with behavioral biases based on prospect theory. If the 52-week high
serves as an anchor valuation for the target,
3
target management should negotiate harder, the
further the target’s current price is from this anchor. A tougher negotiation stance of the target
should lead to a worse deal for the acquirer, resulting in lower acquirer announcement returns.
The behavioral bias can also occur on the acquirer side. If the acquirer anchors on the target’s 52-
week high, the further the target’s price is below that level, the better the perceived deal for the
acquirer and presumably the more lax its negotiation approach.
The relations of the three target valuation uncertainty proxies and the combination measure
Risk index to acquirer announcement returns are of similar magnitude and significance as the
relation of Target
Δ
high and Acquirer CAR. These relations suggest that target valuation risk has
a significantly negative effect on acquirer announcement returns. The interesting question is
whether the target valuation uncertainty proxies measure essentially the same underlying effect as
Target
Δ
high. I contend that Target
Δ
high can be related to both behavioral biases and target
valuation risk while it is difficult to interpret the target valuation uncertainty proxies as measures
related to behavioral biases. Therefore, the relation of Target
Δ
high and the target valuation
uncertainty proxies and their joint effect on Acquirer CAR should help determine the underlying
force behind the relation of target valuation changes and acquirer announcement returns.
3
For ease of exposition, I frequently use only” acquirer” or “target” to refer to the respective decision
makers instead of mentioning the acquirer’s and target’s management or shareholders explicitly.
10
3.2. Regression results
To confirm the univariate results in the presence of control variables, I regress Acquirer CAR
separately on Target
Δ
high and the target valuation risk proxies. All regressions have acquisition
year dummy variables (not reported in tables) and heteroskedasticity-adjusted standard errors
following White (1980) and MacKinnon and White (1985). “Log” in front of the variable name
indicates the natural logarithm of the variable, or one plus the variable where needed to avoid
logarithms of negative numbers. The reason for using logarithms is to reduce the impact of
outliers.
In column 1 of Table 3, Log target
Δ
high is positive and significant at the 0.01 level. The
closer the target trades to its 52-week high one week before the acquisition announcement, the
higher is the acquirer announcement return. This result is consistent with the partial adjustment
effect for private targets found in Cooney et al. (2009). In columns 2 to 4, Target price range, Log
industry M/B stdev, and Log target price stdev have negative coefficients and are significant at the
0.01, 0.01, and 0.05 levels, respectively. The combination target valuation uncertainty measure
Risk index is also significantly negative at the 0.01 level in column 5. Therefore, Table 3 confirms
the significant relations of acquirer announcement returns with Target
Δ
high and the target
valuation risk proxies. It is noteworthy that the regression with Risk index has the highest adjusted
R
2
.
Among the control variables, Log relative size, Log acquirer M/B x Stock, and Stock pct are
consistently negative and significant at the 0.01 level. I include Log relative size as a control
variable because Faccio, McConnell, and Stolin (2006) and Asquith, Bruner, and Mullins (1983)
find a positive relation between acquirer announcement returns and relative size in private and
public acquisitions, respectively. The negative coefficient on Log relative size in Table 3 is
inconsistent with these earlier studies.
11
Moeller, Schlingemann, and Stulz (2004) find that larger acquirers earn approximately 2%
lower announcement returns than do smaller acquirers. They interpret this finding as evidence of
hubris (Roll, 1986). Therefore, I include Log acquirer market value. The results are mixed with
Log acquirer market value being significantly negative in columns 1 to 3, but insignificant when
Log target price stdev or Risk index are the target uncertainty proxy.
For acquisitions of private firms, Fuller et al. (2002) and Faccio et al. (2006) find higher
acquirer returns when the acquirer pays with stock. Officer, Poulsen, and Stegemoller (2009)
show that using stock as a method of payment mitigates asymmetric information about the target
and leads to more positive acquirer returns. In univariate tests of acquisitions of public targets,
Moeller et al. (2004) find lower acquirer announcement returns when the method of payment is
stock. In my sample, Stock pct is significantly negatively related to Acquirer CAR. Lang, Stulz,
and Walkling (1989) show that acquirers with high Tobin’s Q gain more than acquirers with low
Tobin’s Q. In my sample, Log acquirer M/B negatively affects Log acquirer CAR, but only if the
method of payment is stock (Stock equals one if at least 90% of the purchase price is paid with
acquirer stock). While these results differ from those in Lang et al. (1989) and the findings in
research focusing on private targets, they are consistent with investors realizing that overvalued
acquirers have incentives to make stock acquisitions. Furthermore, when I control for selection
effects in section 4, paying with stock has a positive effect on acquirer announcement returns.
Consider a one standard deviation drop in Log target
Δ
high, an about 21% drop in the target
price from the 52-week high, to assess the economic significance of the change from the average
Acquirer CAR. The coefficient of 0.021 on Log target
Δ
high in column 1 means that Acquirer
CAR decreases by about 0.8 percentage points. For the median acquirer market value of equity of
$1,439 million, the 0.8% represents $12 million. With a median deal value of $304 million, the
$12 million account for approximately 4% of that value. For a one standard deviation increase in
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Target price range and Log industry M/B stdev, Acquirer CAR decreases by 0.7 and 0.6
percentage points, respectively.
Cooney et al. (2009) hypothesize that the skewness of the distribution of possible target
values affects takeover prices and acquirer announcement returns. Target price skew is potentially
a proxy for this skewness. However, it is not significantly related to acquirer announcement
returns in column 6 of Table 3.
My goal is to determine to what extend the target valuation change from its 52-week high and
the three proxies for target valuation uncertainty measure the same underlying effect on acquirer
announcement returns. In Table 4, I include Log target
Δ
high and one of the target valuation risk
proxies at a time as explanatory variables. In column 1, Target price range still has a negative
point estimate but is insignificant. The coefficient of Log target
Δ
high declines from 0.021 to
0.017 and its significance declines from the 0.01 to the 0.05 level. In column 2, Log industry M/B
stdev is less negative and less significant (at the 0.05 level versus the 0.01) than in Table 3. The
point estimate and significance of Log target
Δ
high are also slightly reduced. In column 3, Log
target price stdev is insignificant and the point estimate and significance of Log target
Δ
high are
slightly reduced compared to Table 3. Finally, the combination target valuation uncertainty
measure Risk index remains negative and significant at the 0.01 level in column 4. Here, Log
target
Δ
high’s significance is reduced to the 0.05 level.
Overall, the first four columns of Table 4 indicate that there is some overlap in the effects of
Log target
Δ
high and the target valuation uncertainty measures on acquirer announcement
returns. This overlap suggests that Log target
Δ
high is at least partially a measure of target
valuation uncertainty. With the exception of Risk index, Log target
Δ
high dominates the target
valuation risk measures in terms of significance. However, in light of substantial multicolinearity,
this dominance is not particularly meaningful. For example, it is possible that Log target
Δ
high is
simply a more precise measure of target valuation uncertainty than the other proxies.
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Column 5 adds High risk, a dummy variable that equals one when Risk index has values of
two or three and zero otherwise. I create this dummy variable to test how the interaction of target
valuation risk and prior target valuation changes affects acquirer announcement returns. Log
target
Δ
high x High risk is significant at the 0.05 level with a point estimate of 0.028. Log target
Δ
high and High risk are insignificant. This regression shows that Log target
Δ
high only affects
acquirer announcement returns when there is substantial uncertainty in valuing the target. It
supports the claim that Log target
Δ
high is largely a proxy of target valuation risk. At least, it
demonstrates that the effects of targets’ prior valuation changes and targets’ valuation uncertainty
are tightly intertwined.
Column 6 tests the effect of Target price skew in the presence of Log target
Δ
high. Again,
Target price skew is insignificant.
3.3 Correlation of target valuation change and target valuation risk measures
Because the results in Table 4 suggest substantial overlap of the target valuation uncertainty
measures and Log target
Δ
high, I examine the correlation between these variables in Table 5.
Target price range has correlations with Log industry M/B stdev and Log target price stdev of
0.44 and 0.35, respectively. By design, all three variables are highly correlated with Risk index,
with correlations between 0.54 and 0.73. Among these four target risk variables, Target price
range has the highest correlation with Log target
Δ
high (-0.7), followed by Risk index (-0.47),
Log industry M/B stdev (-0.27), and Log target price stdev (-0.23). Overall, these correlations are
moderate to high and further support the contention that Log target
Δ
high at least partially
measures target valuation uncertainty.
The correlation of Target price skew with Log target
Δ
high and the target valuation risk
measures is low to moderate and ranges from -0.13 to 0.21. Target price skew appears to measure
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a different aspect of target valuation uncertainty than the other risk variables and has a low
correlation with Log target
Δ
high.
Many factors can affect target valuation changes. Therefore, I repeat the correlation analysis
with control variables in a regression framework in Table 6. I add acquisition year dummy
variables to control for time effects and Target market value to address differences due to size. I
also control for fundamental drivers of value with Log target cash flow/ cash and Log target net
income/ assets, both of which have a significant positive effect on Log target
Δ
high. Target
market value is generally significant, but its sign changes depending on the target valuation
uncertainty proxy in the regression.
Confirming the correlation analysis, each risk variable has a highly significant negative
coefficient in columns 1 through 5 of Table 6. In column 6, I include all target valuation risk
variables together with the exception of Target price range. Target price range dominates the
other risk measures when it comes to their correlations with Log target
Δ
high, as shown by the
correlation coefficient of -0.7 and the adjusted R
2
of 0.51 in column 1. Three of the remaining
four risk measures are significantly negative in column 6. Log industry M/B stdev has a negative
point estimate but a p-value of 0.267. Overall, the correlation analysis shows a tight relation
between my target valuation uncertainty proxies and Log target
Δ
high.
4. Method of payment
The method of payment is an important determinant of acquirer announcement returns. In
both the univariate tests and the regressions, the use of stock as payment reduces acquirer
announcement returns, in particular when the acquirer’s market-to-book ratio is high. These
results suggest that the method of payment reveals information about the acquirer that affects
acquirer announcement returns, e.g., that the acquirer management believes the acquirer is
overvalued or that the acquirer does not have sufficient cash to make a cash acquisition. However,
the method of payment can also be affected by the type of target. Officer et al. (2009) show that
15
using stock is beneficial for acquirers when targets are difficult to value. Because the method of
payment is likely correlated with the target valuation uncertainty that I focus on here, the
regression results can be inconsistent and biased when this relation is not adequately addressed.
In Table 7, I use a treatment model to explicitly account for the correlation of method of
payment and the error term in the acquirer announcement return regressions. The treatment model
uses a two-step process to address the effects of endogeneity and selection. I use the maximum
likelihood approach suggested by Maddala (1983) as implemented in Stata to estimate the model.
In the first step, I estimate the probability of a stock acquisition, i.e., the likelihood that at least
90% of the deal value is paid with stock. Size should affect the method of payment because it is
likely difficult to raise sufficient cash for very large acquisitions. While Log acquirer market
value is insignificant, Log relative size is positive and significant at the 0.01 level. Acquirers with
high current market valuations have an incentive to make stock acquisitions. Consistent with this
rationale, Log acquirer M/B is positive and significant at the 0.01 level.
In column 1 of Table 7, I add Log target
Δ
high. It has a significantly negative coefficient,
meaning that the further the target price is below the 52-week high, the higher the probability of a
stock offer. This result is consistent with Log target
Δ
high being a measure of target valuation
uncertainty because acquirers seem to prefer stock offers when they have difficulty valuing the
target.
4
Next, I add my target valuation uncertainty proxies one at a time. The results for Target
price range, Log target price stdev, and Risk index are similar. All three have significantly
positive coefficients, indicating that acquirers pay for harder-to-value targets with stock.
Inconsistent with the other target risk variables, Log industry M/B stdev has a negative coefficient
in column 3. However, this coefficient becomes insignificant in column 4 when I remove the
significantly positive Log acquirer M/B from the control variables. The surprising estimate for
4
Note that all observations of Log target
Δ
high are negative or zero. Therefore, the negative coefficient
implies a higher probability of a stock offer the larger the absolute value of Log target
Δ
high.
16
Log industry M/B stdev is potentially due to the estimation method being sensitive to the high
correlation of 0.55 between Log industry M/B stdev and Log acquirer M/B.
The second step of the treatment model estimates regressions similar to those in Table 3. For
consistency with the first step, I use the dummy variable Stock instead of Stock pct. I also omit
Log acquirer M/B x Stock.
5
Most important, the treatment model accounts for the correlation
between the method of payment (Stock) and the estimation error in the acquirer announcement
return regression. It should therefore produce consistent and unbiased estimates.
The coefficients on Log target
Δ
high, Target price range, Log target price stdev, and Risk
index are slightly larger (in absolute terms) then in Table 3 and at least as significant.
Interestingly, Stock is now positive and significant at the 0.01 level, consistent with the findings
in Officer et al. (2009) who find evidence that acquirers benefit when they acquire hard-to-value
targets with equity. While the treatment model reverses the sign on the method of payment
variable Stock, the other variables are qualitatively unchanged. The correlation between Stock and
the error in the acquirer announcement return regression, as measured by ρ, is about -0.8. A Wald
test of ρ being equal to zero is strongly rejected. So it is important to control for endogeneity and
selection. When using the treatment model, only the estimate of the method of payment variable
changes. This fact enhances the confidence in the robustness of my main results.
Again, I estimate the regressions with Log industry M/B stdev with and without Log acquirer
M/B. With Log acquirer M/B in column 3, the coefficient is insignificant. Without Log acquirer
M/B in column 4, the coefficient on Log industry M/B stdev is significantly negative, consistent
with the results in the previous tables.
In column 7, I examine the effects of Target price skew. It now has a significant negative
relation with acquirer announcement return. Acquirers fare better when they acquire targets with
large negative outliers in their pricing than large positive outliers. Given that Target price skew
5
When I add Log acquirer M/B x Stock, it is only significant in column 4, and the effect on the other
explanatory variables is negligible.
17
and Log target
Δ
high are negatively correlated, large high price outliers should be associated
with larger declines from the target’s 52-week high, and vice versa. This result is the opposite of
what Cooney et al. (2009) predict for their sample of private acquisitions. However, the way I
measure target price skewness here is not necessarily consistent with what they call skewness in
their discussion.
5. Robustness and alternative explanations
I examine alternative anchors for the target valuation change variable and add a target runup
variable.
5.1. Alternative target valuation anchors
Column 1 of Table 8 is the same regression as column 1 of Table 3, except I replace Log
target
Δ
high with Log target
Δ
low, i.e., instead of the 52-week high the 52-week low is the
supposed anchor of the target valuation. Log target
Δ
low has an insignificant coefficient. Baker
et al. (2009) argue that the 52-week high is a unique psychological anchor. The significance of
Log target
Δ
high and the insignificance of Log target
Δ
low support their claim. If only the
valuation change from some arbitrary base mattered, Log target
Δ
low should be as significant as
Log target
Δ
high. Therefore, this result weakens the target valuation risk explanation. However,
Target
Δ
low has much more variability than Target
Δ
high, with more than twice the range
between its 10
th
and 90
th
percentile and more than four times the standard deviation. These
measurement issues can favor the significance of Log target
Δ
high over Log target
Δ
low.
Next, I replace Log target
Δ
high with Log target
Δ
six months. For Log target
Δ
six months I
arbitrarily selected the target’s stock price 180 calendar days before the acquisition announcement
window as the anchor. In column 2, the coefficient is positive and significant at the 0.1 level. In
contrast to Log target
Δ
high and Log target
Δ
low, Log target
Δ
six months can have positive and
18
negative values because the stock price can increase or decrease over the six month period (it can
only decrease from the 52-week high and increase from the 52-week low). If the target valuation
change is a proxy for target valuation risk, the absolute change should be more important than the
direction of the change. Both large positive and large negative changes should affect acquirer
announcement returns in the same way. To test the effect of the absolute changes, I split Log
target
Δ
six months into two variables representing negative and positive valuation changes. In
column 3, the coefficient on the negative observations of Log target
Δ
six months is positive and
significant at the 0.01 level while the coefficient on the positive changes of Log target
Δ
six
months is negative and less significant at the 0.1 level. These estimates are consistent with the
positive coefficients on Log target
Δ
high observed in the earlier analyses and the negative
coefficients on the other target valuation uncertainty proxies. They also show that target valuation
changes have a larger affect on acquirer announcement returns after bad things happened to the
target, i.e., after the target stock price declined.
5.2. Target price runup
Another potential explanation of the relation between prior target valuation changes and
acquirer announcement returns is based on markup pricing (Schwert, 1996). Under markup
pricing, the target’s pre-announcement runup is unrelated to post-announcement increases in the
target’s stock price. Therefore, the pre-announcement runup constitutes an additional cost to
acquirers. This explanation implies that acquirers do not take into account targets’ recent stock
price runups when they determine what premium to offer. Consequently, they overpay for targets
with positive price runups. Applied to my study, markup pricing implies that the acquirers'
announcement returns should be lower when the targets experience higher prior valuation
changes. However, I find the opposite. Regardless, I add Log target runup as a control variable.
In column 4 of Table 8, the point estimate and significance of Log target
Δ
high slightly decline
compared to the same regression without Log target runup in Table 3. Log target runup is
19
positive and significant at the 0.1 level. In columns 5 and 6, the results for Target price range and
Log industry M/B stdev in the presence of Log target runup are largely identical to the estimates
without the target runup variable in Table 3. Surprisingly, Log target runup has a significantly
positive coefficient. I conclude that the target runup effect differs from my results for target
valuation changes and target valuation risk.
6. Conclusions
Using a broad sample of public-public acquisitions, I explore why prior target valuation
changes affect acquirer announcement returns. This initially surprising phenomenon that also
occurs in IPOs, the so called partial adjustment effect, is frequently attributed to behavioral biases
of managers or investors. However, similar to Cooney et al. (2009) for private acquisitions, I find
that rational explanations based on target valuation uncertainty are at least equally likely causes in
my sample.
It is difficult to disentangle behavioral from rational effects. Yet, prior target valuation
changes are natural measures of target valuation uncertainty. Large valuation changes essentially
show that investors are uncertain about the value of a firm. The high correlations of target
valuation changes with target valuation uncertainty measures further support the hypothesis that
target valuation change variables at least partially measure valuation uncertainty. While
behavioral biases can explain the empirical findings regarding the effects of prior valuation
changes, they do not explain why the valuation uncertainty variables matter. In my opinion, the
rational explanations based on valuation uncertainty provide more comprehensive and appealing
justifications for the observed effects than behavioral stories. This paper develops some potential
rational explanations and provides evidence that is consistent with those explanations.
20
References
Asquith, P., Bruner, R., Mullins, D., 1983. The gains to bidding firms from merger. Journal of
Financial Economics 11, 121-139.
Baker, M., Pan, X., Wurgler, J., 2009. The psychology of pricing in mergers and acquisitions.
Unpublished working paper. Harvard Business School.
Cooney, J., Moeller, T., Stegemoller, M., 2009. The underpricing of private targets. Journal of
Financial Economics 93, 55-66.
Faccio, M., McConnell, J., Stolin, D., 2006. Returns to acquirers of listed and unlisted targets.
Journal of Financial and Quantitative Analysis 41, 197-220.
Fuller, K., Netter, J., Stegemoller, M., 2002. What do returns to acquiring firms tell us? Evidence
from firms that make many acquisitions. Journal of Finance 57, 1763-1793.
Hanley, K., 1993. The underpricing of initial public offerings and the partial adjustment
phenomenon. Journal of Financial Economics 34, 231-250.
Jarrell, G., Poulsen, A., 1989. The returns to acquiring firms in tender offers: evidence from three
decades. Financial Management 18, 12–19.
Jindra, J., Moeller, T., 2009. Prior target valuation changes and acquirer announcement returns in
acquisitions of recently listed targets. Unpublished working paper, Texas Christian
University.
Kahneman, D., Tversky, A., 1979. Prospect theory: An analysis of decision under risk.
Econometrica 47, 263-292.
Loughran, T., Ritter, J., 2002. Why don’t issuers get upset about leaving money on the table in
IPOs? Review of Financial Studies 15, 413-443.
Lang, L., Stulz, R., Walkling, R., 1989. Managerial performance, Tobin’s q, and the gains from
successful tender offers. Journal of Financial Economics 24, 137-154.
MacKinnon, J., White, H., 1985. Some heteroskedasticity consistent covariance matrix estimators
with improved finite sample properties. Journal of Econometrics 29, 305-325.
Maddala, G.,1983. Limited-Dependent and Qualitative Variables in Econometrics. Cambridge
University Press.
Moeller, S., Schlingemann, F., Stulz. R., 2004. Firm size and the gains from acquisitions. Journal
of Financial Economics 73, 201-228.
Moeller, T., 2005. Let’s make a deal! How shareholder control impacts merger payoffs. Journal of
Financial Economics 76, 167-190.
Officer, M., Poulsen, A., Stegemoller, M., 2009. Target-firm information asymmetry and acquirer
returns. Review of Finance 13, 467-493.
21
Roll, R., 1986. The hubris hypothesis of corporate takeovers. Journal of Business 59, 197-216.
Schwert, W., 1996. Markup pricing in mergers and acquisitions. Journal of Financial Economics
41, 153-192.
White, H., 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for
heteroskedasticity. Econometrica 48, 817-838.
22
Table 1
Descriptive statistics
Panel A contains the mean, median, standard deviation, 10%, and 90% percentiles of the main
variables. Acquirer CAR is the three-day return of the acquirer in excess of the CRSP equal-
weighted index centered on the acquisition announcement. Target
Δ
high is the target’s share
price one week prior to the acquisition announcement divided by the target’s 52-week high share
price (for the period ending one week prior to the acquisition announcement) minus one. Target
Δ
low is the target’s share price one week prior to the acquisition announcement divided by the
target’s 52-week low share price minus one. Target
Δ
six months uses the share price 180 days
before the announcement as the anchor and is otherwise calculated as the two prior variables.
Target price range is the 52-week high minus the 52-week low, standardized by the mid-point of
the 52-week high and low. Industry M/B stdev is the standard deviation of the market-to-book
ratios of firms in the target industry with assets between half and twice the target’s assets. Target
price stdev and Target price skew are the standard deviation and skewness of the target’s share
prices, measured from 370 to 15 days before the acquisition announcement. Acquirer market
value and Target market value are the market value of equity as of the prior fiscal year-end.
Relative size is the ratio of Target to Acquirer market value. Acquirer M/B and Target M/B are
calculated as (market value of equity + book value of assets – book value of equity) divided by
book value of assets. Stock pct is the fraction of the acquisition price that is paid with acquirer
stock. Target cash flow/ cash is the target’s net cash flow from operating activities divided by
cash and short-term investments. Target net income/ assets is the target’s net income divided by
its total assets. Target runup is the return of the target from 60 calendar days before to the
beginning of the announcement return window. Panel B shows the proportions with which the
dummy variables equal one and with which the categorical variable Risk index takes on its
possible values. Risk index ranges from zero to three and adds one point for each target valuation
risk variable (Target price range, Industry M/B stdev, and Target price stdev) that ranks in the top
tercile. Hostile and Tender are from the SDC Mergers & Acquisitions database. Stock is a dummy
variable that equals one when Stock pct is at least 90%, and zero otherwise. Panel C shows the
distribution of the acquisition announcements over time.
23
Panel A
Variable Mean Median St. Dev. 10% 90%
Acquirer CAR -0.019 -0.014 0.076 -0.099 0.053
Target Δ high -0.220 -0.145 0.218 -0.560 -0.009
Target Δ low 0.613 0.409 0.989 0.084 1.204
Target Δ six months 0.125 0.082 0.529 -0.301 0.510
Target price range 0.651 0.573 0.347 0.282 1.159
Industry M/B stdev 1.318 0.749 2.588 0.062 2.617
Target price stdev 3.246 2.108 4.841 0.691 6.242
Target price skew 0.274 0.225 0.677 -0.483 1.088
Acquirer market value ($ million) 7,028 1,439 19,535 158 15,169
Target market value ($ million) 1,156 189 4,497 32 1,988
Relative size 0.319 0.162 0.463 0.024 0.780
Acquirer M/B 2.266 1.413 3.936 1.025 3.731
Target M/B 1.888 1.257 2.158 0.986 3.172
Stock pct 0.601 0.780 0.435 0.000 1.000
Target cash flow/ cash 6.318 0.565 215.449 -0.284 8.646
Target net income/ assets -0.010 0.015 0.201 -0.134 0.109
Target runup 0.100 0.072 0.234 -0.123 0.363
24
Panel B
Variable Proportion
Hostile = 1 0.0165
Tender = 1 0.1486
Stock = 1 0.7078
Risk index = 0 0.3882
Risk index = 1 0.3231
Risk index = 2 0.1984
Risk index = 3 0.0902
Panel C
Acquisition
year Observations
Acquisition
year Observations
1983 4 1996 194
1984 3 1997 266
1985 11 1998 254
1986 13 1999 222
1987 17 2000 187
1988 44 2001 134
1989 46 2002 76
1990 25 2003 107
1991 38 2004 127
1992 38 2005 109
1993 49 2006 114
1994 133 2007 110
1995 176 2008 53
Total 2,550
25
Table 2
Univariate tests for acquirer announcement returns
The sample is split into terciles based on the variables in column 1, except for Stock (split into 0
versus 1) and Risk index (split into 0 versus 2 or 3). Columns 2 and 3 first show the tercile means
and then the tercile medians of Acquirer CAR. The first (second) value in column 4 is the p-value
from a t-test (Wilcoxon signed-rank test) of the difference in means (medians) between columns 2
and 3. All variables are defined in prior tables.
***
,
**
,
*
denote significance at the 0.01, 0.05, and 0.10 level, respectively.
Terciles based on Bottom tercile Top tercile p-value
Target
Δ
high
-0.0281 -0.0123 0.000***
-0.0180 -0.0128 0.000***
Target
Δ
low
-0.0235 -0.0219 0.663
-0.0137 -0.0178 0.961
Target
Δ
six months
-0.0244 -0.0184 0.134
-0.0151 -0.0173 0.531
Target price range
-0.0125 -0.0333 0.000***
-0.0115 -0.0258 0.000***
Industry M/B stdev
-0.0156 -0.0312 0.000***
-0.0166 -0.0167 0.044**
Target price stdev
-0.0132 -0.0315 0.000***
-0.0103 -0.0238 0.000***
Target price skew
-0.0185 -0.0220 0.329
-0.0152 -0.0128 0.973
Acquirer market value
-0.0201 -0.0199 0.968
-0.0170 -0.0138 0.290
Target market value
-0.0107 -0.0266 0.000***
-0.0096 -0.0224 0.000***
Relative size
-0.0078 -0.0305 0.000***
-0.0065 -0.0282 0.000***
Acquirer M/B
-0.0141 -0.0293 0.000***
-0.0144 -0.0176 0.030**
Target M/B
-0.0138 -0.0269 0.000***
-0.0141 -0.0152 0.081*
Stock pct
0.0043 -0.0313 0.000***
-0.0010 -0.0242 0.000***
Stock (0 , 1)
0.0062 -0.0295 0.000***
-0.0001 -0.0237 0.000***
Risk index (0, 2 or 3)
-0.0107 -0.0377 0.000***
-0.0102 -0.0269 0.000***
26
Table 3
Regression results for acquirer announcement returns
Log acquirer CAR is the dependent variable. All regressions include acquisition year dummy
variables. “Log” in front of the variable name indicates the natural logarithm of the variable, or of
one plus the variable where needed. All variables are defined in prior tables. p-Values, based on
heteroskedasticity-adjusted standard errors, are in brackets.
***
,
**
,
*
denote significance at the 0.01, 0.05, and 0.10 level, respectively.
(1) (2) (3) (4) (5) (6)
Log target
Δ
high
0.0209***
[0.000]
Target price range -0.0224***
[0.000]
Log industry M/B stdev -0.0047***
[0.000]
Log target price stdev -0.0051**
[0.025]
Risk index -0.0107***
[0.000]
Target price skew -0.0025
[0.258]
Log acquirer market value -0.0018* -0.0022** -0.0021** 0.0004 -0.0011 -0.0012
[0.082] [0.039] [0.045] [0.752] [0.307] [0.270]
Log relative size -0.0076*** -0.0084*** -0.0083*** -0.0064*** -0.0075*** -0.0077***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Log acquirer M/B -0.0023 -0.0023 -0.0013 -0.0053 -0.0000 -0.0049
[0.705] [0.705] [0.826] [0.378] [0.998] [0.415]
Log acquirer M/B x Stock -0.0203*** -0.0188*** -0.0197*** -0.0186*** -0.0199*** -0.0195***
[0.002] [0.004] [0.002] [0.004] [0.002] [0.003]
Log target M/B 0.0053 0.0066 0.0085 0.0052 0.0098* 0.0040
[0.313] [0.213] [0.121] [0.319] [0.069] [0.451]
Stock pct -0.0238*** -0.0249*** -0.0265*** -0.0253*** -0.0241*** -0.0251***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Tender 0.0035 0.0040 0.0044 0.0027 0.0044 0.0030
[0.470] [0.414] [0.374] [0.579] [0.366] [0.540]
Hostile 0.0173 0.0150 0.0168 0.0163 0.0162 0.0163
[0.133] [0.187] [0.139] [0.150] [0.146] [0.150]
Adjusted R
2
0.1133 0.1096 0.1062 0.1046 0.1144 0.1029
Observations 2,550 2,550 2,550 2,550 2,550 2,550
27
Table 4
Relation of target valuation change and target valuation uncertainty with acquirer announcement
returns
Log acquirer CAR is the dependent variable. All regressions include acquisition year dummy
variables. “Log” in front of the variable name indicates the natural logarithm of the variable, or of
one plus the variable where needed. All variables are defined in prior tables. p-Values, based on
heteroskedasticity-adjusted standard errors, are in brackets.
***
,
**
,
*
denote significance at the 0.01, 0.05, and 0.10 level, respectively.
(1) (2) (3) (4) (5) (6)
Log target Δ high
0.0173** 0.0194*** 0.0198*** 0.0142** -0.0047 0.0207***
[0.018] [0.001] [0.001] [0.022] [0.649] [0.000]
Log target Δ high x High risk
0.0281**
[0.030]
Target price range -0.0070
[0.342]
Log industry M/B stdev -0.0034**
[0.010]
Log target price stdev -0.0022
[0.331]
Risk index -0.0078***
[0.000]
High risk -0.0042
[0.467]
Target price skew -0.0008
[0.713]
Log acquirer market value -0.0020* -0.0025** -0.0012 -0.0016 -0.0015 -0.0019*
[0.051] [0.017] [0.337] [0.133] [0.156] [0.079]
Log relative size -0.0079*** -0.0081*** -0.0071*** -0.0076*** -0.0075*** -0.0077***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Log acquirer M/B -0.0019 0.0001 -0.0025 0.0005 -0.0002 -0.0023
[0.754] [0.980] [0.672] [0.939] [0.969] [0.706]
Log acquirer M/B x Stock -0.0200*** -0.0203*** -0.0199*** -0.0203*** -0.0210*** -0.0203***
[0.002] [0.002] [0.002] [0.002] [0.001] [0.002]
Log target M/B 0.0058 0.0083 0.0057 0.0090* 0.0073 0.0052
[0.257] [0.119] [0.272] [0.087] [0.165] [0.319]
Stock pct -0.0239*** -0.0248*** -0.0239*** -0.0234*** -0.0224*** -0.0238***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Tender 0.0038 0.0046 0.0035 0.0045 0.0043 0.0036
[0.440] [0.344] [0.477] [0.357] [0.380] [0.461]
Hostile 0.0168 0.0177 0.0173 0.0170 0.0143 0.0173
[0.146] [0.123] [0.132] [0.133] [0.208] [0.131]
Adjusted R
2
0.1133 0.1148 0.1133 0.1182 0.1196 0.1130
Observations 2,550 2,550 2,550 2,550 2,550 2,550
28
Table 5
Correlations of target valuation change and target valuation uncertainty variables
The table presents the correlations among the target valuation change and target valuation
uncertainty variables. All variables are defined in prior tables.
Log target Δ
high
Target price
range
Log industry
M/B stdev
Log target
price stdev Risk index
Target price
skew
Log target Δ high
1.00
Target price range -0.70 1.00
Log industry M/B stdev -0.27 0.44 1.00
Log target price stdev -0.23 0.35 0.07 1.00
Risk index -0.47 0.73 0.55 0.54 1.00
Target price skew -0.13 0.21 0.08 0.08 0.15 1.00
29
Table 6
Relation of target valuation change with target valuation uncertainty variables
The dependent variable is Log target
Δ
high. All regressions include acquisition year dummy
variables. “Log” in front of the variable name indicates the natural logarithm of the variable, or of
one plus the variable where needed. All variables are defined in prior tables. p-Values, based on
heteroskedasticity-adjusted standard errors, are in brackets.
***
,
**
,
*
denote significance at the 0.01, 0.05, and 0.10 level, respectively.
(1) (2) (3) (4) (5) (6)
Target price range -0.7731***
[0.000]
Log industry M/B stdev -0.0624*** -0.0070
[0.000] [0.267]
Log target price stdev -0.1489*** -0.0507***
[0.000] [0.000]
Risk index -0.1642*** -0.1331***
[0.000] [0.000]
Target price skew -0.0653*** -0.0310**
[0.000] [0.012]
Target market value -0.0124*** -0.0091* 0.0401*** 0.0136*** -0.0072 0.0244***
[0.001] [0.051] [0.000] [0.002] [0.127] [0.000]
Log target cash flow/ cash 0.0160*** 0.0542*** 0.0452*** 0.0294*** 0.0568*** 0.0277***
[0.002] [0.000] [0.000] [0.000] [0.000] [0.000]
Log target net income/ assets 0.1971*** 0.5027*** 0.6157*** 0.4516*** 0.5368*** 0.4855***
[0.006] [0.000] [0.000] [0.000] [0.000] [0.000]
Adjusted R
2
0.5108 0.2131 0.2427 0.3092 0.1825 0.3169
Observations 2,416 2,416 2,416 2,416 2,416 2,416
30
Table 7
Regression results for acquirer announcement returns with treatment model
Stock is the dependent variable in the first step and Log acquirer CAR is the dependent variable in
the second step. The second step regressions include acquisition year dummy variables. “Log” in
front of the variable name indicates the natural logarithm of the variable, or of one plus the
variable where needed. The correlation between Stock and the error in the acquirer announcement
return regression is measured by ρ. The Wald test of ρ being equal to zero is rejected in all
columns. All variables are defined in prior tables. p-Values, based on heteroskedasticity-adjusted
standard errors, are in brackets.
31
Table 7 (continued)
***
,
**
,
*
denote significance at the 0.01, 0.05, and 0.10 level, respectively.
(1) (2) (3) (4) (5) (6) (7)
Dependent variable: Log acquirer CAR
Log target Δ high
0.0250***
[0.000]
Target price range -0.0274***
[0.000]
Log industry M/B stdev 0.0010 -0.0054***
[0.536] [0.000]
Log target price stdev -0.0101***
[0.000]
Risk index -0.0119***
[0.000]
Target price skew -0.0055**
[0.035]
Log acquirer market value -0.0030** -0.0035*** -0.0022* -0.0042*** 0.0006 -0.0021* -0.0024**
[0.014] [0.004] [0.069] [0.001] [0.660] [0.083] [0.049]
Log relative size -0.0174*** -0.0184*** -0.0174*** -0.0175*** -0.0151*** -0.0171*** -0.0178***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Log acquirer M/B -0.0248*** -0.0229*** -0.0315*** -0.0262*** -0.0218*** -0.0276***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Log target M/B 0.0048 0.0056 0.0075 0.0016 0.0051 0.0089* 0.0039
[0.361] [0.284] [0.166] [0.734] [0.329] [0.093] [0.459]
Stock 0.0847*** 0.0854*** 0.0832*** 0.0812*** 0.0852*** 0.0840*** 0.0855***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Tender 0.0041 0.0042 0.0055 0.0061 0.0035 0.0049 0.0039
[0.373] [0.367] [0.231] [0.186] [0.449] [0.286] [0.393]
Hostile 0.0099 0.0087 0.0104 0.0101 0.0092 0.0095 0.0091
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Correlation
ρ
-0.790 -0.794 -0.786 -0.776 -0.792 -0.788 -0.793
Wald test ρ = 0 174.056 174.520 164.165 140.929 184.060 172.292 181.581
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Dependent variable: Stock
Log target Δ high
-0.2639***
[0.002]
Target price range 0.3202***
[0.001]
Log industry M/B stdev -0.1207*** -0.0023
[0.000] [0.911]
Log target price stdev 0.1306***
[0.000]
Risk index 0.0652**
[0.049]
Target price skew 0.0860**
[0.033]
Log acquirer market value 0.0172 0.0238 -0.0076 0.0376** -0.0259 0.0094 0.0138
[0.294] [0.148] [0.655] [0.022] [0.180] [0.572] [0.405]
Log relative size 0.2725*** 0.2831*** 0.2614*** 0.2687*** 0.2407*** 0.2719*** 0.2779***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Log acquirer M/B 0.3188*** 0.2890*** 0.5212*** 0.3309*** 0.3054*** 0.3532***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Observations 2,550 2,550 2,550 2,550 2,550 2,550 2,550
32
Table 8
Robustness tests
Log acquirer CAR is the dependent variable. All regressions include acquisition year dummy
variables. “Log” in front of the variable name indicates the natural logarithm of the variable, or of
one plus the variable where needed. All variables are defined in prior tables. p-Values, based on
heteroskedasticity-adjusted standard errors, are in brackets.
***
,
**
,
*
denote significance at the 0.01, 0.05, and 0.10 level, respectively.
(1) (2) (3) (4) (5) (6)
Log target
Δ
low
0.0021
[0.715]
Log target
Δ
six months
0.0106*
[0.076]
Log target
Δ
six months (if <0)
0.0321***
[0.004]
Log target
Δ
six months (if ≥0)
-0.0143*
[0.078]
Log target Δ high
0.0169***
[0.006]
Target price range -0.0214***
[0.000]
Log industry M/B stdev -0.0046***
[0.000]
Log target runup 0.0195* 0.0304*** 0.0315***
[0.063] [0.002] [0.001]
Log acquirer market value -0.0010 -0.0013 -0.0017 -0.0016 -0.0020* -0.0020*
[0.339] [0.235] [0.113] [0.125] [0.055] [0.059]
Log relative size -0.0075*** -0.0073*** -0.0077*** -0.0073*** -0.0078*** -0.0077***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Log acquirer M/B -0.0050 -0.0054 -0.0047 -0.0035 -0.0036 -0.0026
[0.400] [0.370] [0.430] [0.559] [0.555] [0.667]
Log acquirer M/B x Stock -0.0198*** -0.0198*** -0.0181*** -0.0197*** -0.0180*** -0.0188***
[0.002] [0.002] [0.004] [0.002] [0.005] [0.003]
Log target M/B 0.0039 0.0051 0.0059 0.0052 0.0067 0.0086
[0.449] [0.340] [0.266] [0.320] [0.200] [0.109]
Stock pct -0.0252*** -0.0242*** -0.0246*** -0.0241*** -0.0249*** -0.0265***
[0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
Tender 0.0027 0.0046 0.0053 0.0031 0.0035 0.0038
[0.586] [0.326] [0.262] [0.530] [0.478] [0.432]
Hostile 0.0164 0.0162 0.0151 0.0181 0.0167 0.0185
[0.153] [0.160] [0.188] [0.115] [0.143] [0.103]
Adjusted R
2
0.1026 0.1037 0.1094 0.1152 0.1157 0.1128
Observations 2,550 2,533 2,533 2,550 2,550 2,550
