Learning about target firms and pricing of acquisitions*
Jan Jindraa and Thomas Moellerb
October 2013
Abstract
We analyze the effects of learning about target firms on acquisition pricing. Newly public firms
should be more opaque than established firms with long track records. An acquirer with superior
information about a newly public firm can negotiate a favorable takeover price because lessinformed potential acquirers have a bidding handicap. Consistent with the effects of learning,
acquirer announcement returns decrease and takeover premiums increase with the length of time
since the targets’ initial public offerings. Our results provide new insights into the determinants
of acquirer announcement returns and the effects of learning on acquisition pricing. Moreover,
the target’s listing status alone does not seem to fully explain why acquirer announcement returns
in acquisitions of public targets are significantly lower than in acquisitions of private targets.
JEL Classification: G24, G34
Keywords: Mergers and acquisitions, initial public offerings, learning, valuation uncertainty.
*
We thank Jack Bao, Chris Barry, Steve Mann, Oguz Ozbas, René Stulz, Matthew Wynters, and seminar participants
at Texas Christian University and the 2013 China International Conference in Finance for helpful comments and
discussions. Thomas Moeller wishes to thank the Luther King Capital Management Center for Financial Studies in
the Neeley School of Business at TCU for its financial support for this research. Jan Jindra thanks the Charles A.
Dice Center for Research in Financial Economics at the Ohio State University for financial support for this research.
All errors are our own.
a
Menlo College, 1000 El Camino Real, Atherton, CA 94026; [email protected]; phone: 650.489.6807.
b
Neeley School of Business, TCU Box 298530, Fort Worth, TX 76129; [email protected]; phone: 817.760.0050.
Learning about target firms and pricing of acquisitions
Abstract
We analyze the effects of learning about target firms on acquisition pricing. Newly public firms
should be more opaque than established firms with long track records. An acquirer with superior
information about a newly public firm can negotiate a favorable takeover price because lessinformed potential acquirers have a bidding handicap. Consistent with the effects of learning,
acquirer announcement returns decrease and takeover premiums increase with the length of time
since the targets’ initial public offerings. Our results provide new insights into the determinants
of acquirer announcement returns and the effects of learning on acquisition pricing. Moreover,
the target’s listing status alone does not seem to fully explain why acquirer announcement returns
in acquisitions of public targets are significantly lower than in acquisitions of private targets.
1. Introduction
Pastor and Veronesi (2003) document that uncertainty about a firm’s valuation
parameters decreases over time since its initial public offering (IPO). Does the declining
uncertainty about firm’s valuation parameters play a role in acquisition pricing? We rely
on a recently developed theory of learning and examine the role uncertainty plays on
acquisition pricing. We also consider an alternative framework under which an acquirer’s
desire to gain exposure to the option value of a target’s valuation uncertainty affects
acquisition pricing.
Pastor and Veronesi (2003) develop a learning model and show that as market
participants accumulate more information and learn about a firm, the uncertainty
regarding the firm’s valuation parameters declines over time. In the context of
acquisitions, since learning is costly, in terms of either actual out-of-pocket or
opportunity costs, a potential bidder with the lowest cost of learning self-selects into
learning about the target and obtains information about the target firm’s valuation
parameters. This competitive advantage in the cost of learning gives the potential bidder
superior information that allows the bidder to extract rents in the form of lower takeover
premiums that should also be reflected in higher acquirer announcement returns. As
public information about the target accumulates over time, target valuation uncertainty
declines, the associated cost of learning differential declines, and, consequently, these
information rents decline as well.
An alternative effect of target uncertainty on acquisition pricing is related to agency
issues and the option value that such uncertainty offers to managers of the acquiring firm.
Uncertainty about valuation parameters makes valuations attractive to, for example,
1
managers of firms whose rewards for good performance and punishment for poor
performance are asymmetric. If managers’ exceptionally good performance gets highly
rewarded while exceptionally poor performance results in comparatively little
punishment, then as the uncertainty about the target firm declines, the target becomes less
valuable to the acquiring managers. From the acquirer’s shareholders perspective,
acquirers overpay for targets with high option value, leading to low acquirer
announcement returns and high takeover premiums for young firms. Over time, as the
uncertainty
about
targets
declines,
takeover
premiums
decline
and
acquirer
announcement returns become less negative. We examine whether the learning
framework or the acquirer’s desire to gain exposure to the option value of a target’s
valuation uncertainty explain acquisition pricing better.
In a sample of targets acquired within ten years after their IPOs, we find that proxies
for target valuation uncertainty are related to both the time since the targets’ IPOs and the
acquisition pricing. Furthermore, acquirer announcement returns decrease and takeover
premiums increase with the time since the targets’ IPOs. These results are inconsistent
with the option value of uncertainty explanation. Instead, they support the learning
hypothesis and suggest that learning plays a significant role in explaining acquirer
announcement returns.
Note that in this paper we examine the effects of learning about a potential target over
time by all potential acquirers. In contrast, Aktas, de Bodt, and Roll (2013) focus on
serial acquirers’ learning from each acquisition and whether the acquirers make better
(value-increasing) acquisitions as their experience accumulates over time. While both our
and Aktas, de Bodt, and Roll’s (2013) research study the effects of learning by acquirers
2
in mergers and acquisitions, each type of learning is sufficiently different from each
other, so that their effects should be distinct as well. In related research, Hsieh, Lyandres,
and Zhdanov (2011) propose that a potential private acquirer first completes an initial
public offering to learn about its own value before attempting an acquisition of another
firm. Similar to Aktas, de Bodt, and Roll (2013), the learning in Hsieh et al. (2011) is
focused on the acquirer learning about itself instead of learning about a potential target.
Our results also provide new insights into the well-documented “target listing effect”
in acquisitions. The target listing effect refers to acquirers realizing positive average
announcement returns when acquiring private targets and negative or zero average
announcement returns when acquiring public targets (for example, Chang, 1998; Fuller,
Netter, and Stegemoller, 2002; Faccio, McConnell, and Stolin, 2006; and Moeller,
Schlingemann, and Stulz, 2004). Our results show that a portion of the target listing effect
is likely not due to the private versus public status of the target, but rather due to learning
and the resulting resolution of target valuation uncertainty.
2. Analytical framework
Market participants face uncertainty about parameters affecting the valuation of firms,
such as growth in future profitability. We analyze how such uncertainty affects
acquisition pricing. We first review the learning theory and summarize its predictions for
acquisition pricing. We then review the alternative theory of the effect of option value of
uncertainty on acquisition pricing.
3
2.1. Learning
Under the learning framework, market participants learn about the valuation
parameters by observing and collecting new information. Pastor and Veronesi (2009)
argue that this learning process explains many financial market phenomena, such as stock
return volatility, high levels of trading by investors, high valuations and volatility of
young firms, and stock price “bubbles.”
Pastor and Veronesi (2006) show that an increase in the uncertainty about the growth
rate in the Gordon growth formula increases stock prices. Specifically, they argue that the
market-to-book ratio of a firm follows:
exp
exp
̅
2
,
where M/B is the market-to-book value of equity, g is the firm’s normally distributed
constant growth rate, ̅ and σ2 are the mean and variance of the firm’s growth rate, and T
is the time by which the firm’s abnormal earnings are eliminated by competition. From
expression (1), it follows that the market-to-book is increasing in the uncertainty of the
growth (σ2). Therefore, as uncertainty about the growth of the firm’s declines due to
learning, the market-to-book ratio declines as well.
Pastor and Veronesi (2003) use mean reverting accounting profitability with unknown
mean and allow investors to learn about its value over time by (Bayesian) updating their
beliefs. In effect, investors learn about the distribution of g by updating information about
its mean and variance. As time passes and investors learn about the mean profitability, the
variance (uncertainty) declines, which leads to a gradual decline in the market-to-book
ratio (Pastor and Veronesi, 2003). Pan, Wang, and Weisbach (2013) study the effect of
learning on stock price volatility following a CEO turnover, adopt the learning model,
4
and propose that uncertainty declines following a CEO turnover. In sum, an existing
implication of the learning framework, that serves as the beginning point of our analysis,
is that the uncertainty about a firm’s valuation parameters should be negatively related to
the length of time of its public status.1
In the presence of uncertainty, an acquirer that is interested in learning about a
particular target firm can expend its limited resources to learn about the target and obtain
superior information relative to other market participants. Market participants self-select
into learning about particular assets. Since they have limited resources, they are unable to
learn about every firm and therefore self-select into learning about firms where they have
a competitive advantage as reflected by their lower cost of learning.2 The limited
competition for the target allows the acquirer to extract rents from its informational
advantage by offering a low takeover premium and capturing more of the synergies when
acquiring a newly listed firm characterized by high valuation uncertainty.
We distinguish between two types of learning about the target: passive learning and
active learning. In the context of the learning model of Pastor and Veronesi (2003), the
uncertainty about the valuation parameters decline over time, however, an individual
market participant i can expend its own resources to arrive at a more precise (less
uncertain) private estimate of the valuation parameter
. As new information about the
target becomes available over time, all market participants are exposed to such
1
The theoretical predictions of the learning models are supported by the empirical result that market-tobook ratios decline with firms’ lengths of public listing (Pastor and Veronesi, 2003) and an increase and
subsequent decline in stock price volatility around CEO turnover (Pan, Wang, Weisbach, 2013). Both
models imply convex effects of time.
2
The assumption that acquirers self-select targets for which they have a competitive bidding advantage is
supported by Ragozzino and Reuer (2011) who find that targets that give credible valuation signals are
acquired by more geographically distant acquirers than targets that do not give such signals. The signals
(venture capital backing, reputation of target’s lead underwriter, and IPO underpricing) reduce asymmetric
information that is likely greater the farther away target and acquirer are located. Without target signals,
only local acquirers compete for the target.
5
information, passively learn about the firm, and their private estimates of the valuation
parameters converge. As a result of such passive learning, the amount of uncertainty that
can be resolved by active learning decreases over time. Correspondingly, the cost of
active learning for all potential bidders declines over time as well. As the cost differential
of active learning about the target declines over time, the amount of rents the acquirer can
extract from learning about the more seasoned targets shrinks. Therefore, under the
learning framework of acquisition pricing, the target’s length of listing affects acquirer
announcement returns negatively and takeover premiums positively.
Learning hypothesis: If learning affects acquisition pricing, the length of a target’s
public listing affects acquirer announcement returns negatively and takeover
premiums positively.
An example can illustrate the learning hypothesis of acquisition pricing. Suppose one
potential acquirer has a competitive advantage in learning about a highly uncertain
target’s value, i.e., this acquirer can learn at a lower cost than any other potential acquirer.
The competitive advantage can come from a number of sources, such as the acquirer
being in the same industry as the target, being skilled in analyzing firms like the target, or
having a business connection with the target. Under the learning framework, the target
and all potential acquirers know the distribution of the target’s value, but only costly
learning can determine the target’s value. In this situation, the acquirer with the
competitive advantage (lowest learning cost) self-selects into learning about the target’s
value. If the target value plus any synergy is less than the unconditional expected value of
the target, i.e., the target’s market value before any acquisition news, the acquirer walks
away. If the acquirer learns that the target’s value plus synergy is higher than the current
6
market value, it can set the takeover bid price below the conditional expected value of the
target (conditional on being higher than the current market value) by the amount
sufficient to keep the next best acquirer from making a bid. This amount is slightly lower
than the learning cost of the next best acquirer. As the second best acquirer anticipates
such strategic bidding, it knows that it is guaranteed a negative payoff. Hence, it
rationally chooses not to enter the bidding. Therefore, the competitive advantage in
learning allows the acquirer to take over the target at an advantageous price.
When the target’s valuation uncertainty declines, the first acquirer’s learning
advantage becomes less profitable as the cost of learning of the next best acquirer
declines and, in the limit, approaches the cost of the first acquirer. Therefore, the first
acquirer’s competitive advantage declines as both the uncertainty and the learning costs
decline. With fewer benefits from learning, the takeover price ends up being closer to the
target’s conditional value plus synergy, resulting in lower acquirer announcement returns
and higher takeover premiums. Therefore, under the learning hypothesis, the length of a
target’s public listing affects acquirer announcement returns negatively and takeover
premiums positively. An extended example of the effects of learning on acquisition
pricing is in the appendix.
2.2. Option value of uncertainty
An alternative view of the effect of uncertainty on acquisition pricing derives from
the option value that such uncertainty can offer. Under this option value of uncertainty
framework, risky targets with high valuation uncertainty can provide benefits to acquiring
managers and cause the acquirer managers to pay higher premiums for targets with such
higher valuation uncertainty.
7
If managerial compensation is asymmetric, such that exceptionally good performance
gets highly rewarded while exceptionally poor performance results in comparatively little
punishment, then acquiring managers may develop a preference for acquiring risky
targets. The presence of managerial stock options is an example of how such asymmetric
performance rewards may arise; another mechanism is the accrual of private benefits. For
example, high-risk targets tend to be in “sexy” industries, e.g., dot-com firms during the
1997 to 2000 period or the social networking industry recently. Acquiring managers’
associations with such industries via acquiring highly visible targets provides them with
personal benefits, without corresponding benefits necessarily accruing to the acquirers’
shareholders. Therefore, if acquirer managers overpay for risky targets due to their
private benefits, acquirer announcement returns should be low while takeover premiums
should be high.
As uncertainty about the target’s valuation parameters dissipates with the length of
listing, a result documented by Pastor and Veronesi (2003), the private benefits to
acquiring managers decline. Correspondingly, acquiring managers should offer lower
premiums for more seasoned targets. Therefore, under the option view of uncertainty, the
length of a target’s public listing affects acquirer announcement returns positively and
takeover premiums negatively.
Option value of uncertainty hypothesis: If the option value of valuation uncertainty
affects acquisition pricing, the length of a target’s public listing affects acquirer
announcement returns positively and takeover premiums negatively.
8
3. Data
We start with all completed U.S. IPOs that took place between 1979 and 2008 in the
SDC New Issues database. Using the SDC Mergers & Acquisitions database, we identify
firms that are acquired within ten years of their IPOs. We only consider completed U.S.
domestic acquisitions by public acquirers that seek to own at least 90% of the target’s
equity and do not own more than 10% before the acquisition announcement. We further
require acquirer and target data in the Center for Research in Securities Prices (CRSP)
and Compustat databases. Finally, we only use observations for which the ratio of target
to acquirer market value of equity, measured at the last fiscal year-end before the
acquisition announcement, exceeds 0.02.3 These requirements reduce our sample to 810
observations. Our sample accounts for approximately 30% of all similar acquisitions in
the SDC Mergers & Acquisitions database, i.e., same selection criteria except for the
proximity of the acquisition to the target’s IPO date.
Descriptive statistics are in Table 1. Unless otherwise noted, all variables are
measured at the last fiscal year-end prior to the acquisition. We contend that the market
learns about a firm over time, in particular since it became public, and that such learning
reduces the firm’s valuation uncertainty. Therefore, our proxy for learning, consistent
with Pastor and Veronesi (2003), is Time to acquisition, the number of calendar days from
the target’s IPO to the acquisition announcement scaled by the number of calendar days
in a ten-year period. Time to acquisition has a median of 0.35, indicating that a typical
firm in our sample gets acquired 3.5 years after its IPO.
3
Jarrell and Poulsen (1989) show that acquisitions of relatively small targets have little impact on the
values of acquirers. The inclusion or exclusion of the relatively small targets does not substantially affect
our results or conclusions.
9
With respect to acquisition pricing, our focus variables are the acquirer announcement
return and target premium. Acquirer CAR is calculated as the return in excess of the
CRSP equal-weighted index for the three days centered on the acquisition announcement
date. The average and median Acquirer CAR for our sample are statistically significant
-1.9% and -1.5%, respectively. These acquirer announcement returns are similar to the
-1% reported for acquisitions of public targets in Moeller et al. (2004). Although our
targets only recently became public, in terms of Acquirer CAR, our sample deals are more
similar to acquisitions of public than private targets. We calculate Target premium as the
target return in excess of the CRSP equal-weighted index, starting ten trading days prior
to the announcement date and ending on the earlier of 180 calendar days (roughly 6
months) after the announcement or on the delisting date (Schwert, 1996). The average
Target premium in our sample is 30.1%.
Since we specifically focus on the learning over time, we assess the extent of
valuation uncertainty. IPO underpricing has been shown to be related to various
uncertainty proxies. Rock (1986) constructs an adverse-selection model and proposes that
the presence of asymmetric information explains the systematic underpricing of IPOs.
Michaely and Shaw (1994) find support for the predictions of the adverse-selection
model. Finally, Lowry, Officer, and Schwert (2010) report that “IPO initial return
variability is considerably higher when the fraction of difficult-to-value companies going
public (young, small, and technology firms) is higher.”4 Target IPO underpricing,
measured as the first day return, averages 23.2%, slightly higher than reported in Ritter
and Welch (2002).
4
IPO underpricing can be also correlated with perceived overvaluation of the target’s shares (Celikyurt,
Sevilir and Shivdasani, 2010). Hence, the interpretation of the results based on Target IPO underpricing
may reflect two factors: valuation uncertainty and overvaluation.
10
The target’s underpricing in its IPO is a proxy for the target’s initial valuation
uncertainty at the time of the offering. Our additional measures of industry-wide and
target-specific valuation uncertainty are Target industry M/B stdev and Target return
stdev, respectively. Similar to prior literature, Target industry M/B stdev is calculated as
the standard deviation of market-to-book ratios of firms in the same industry with assets
between half and twice the target size (Cooney, Moeller, and Stegemoller, 2009). We
define industry using the four-digit standard industrial classification (SIC) code and
require at least ten matching firms in each industry. If there are fewer matches, we 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. Target return stdev is the standard deviation of the daily
target returns from the IPO date to two weeks before the acquisition announcement. Both
measures of target valuation uncertainty show considerable cross-sectional variation.
The time between the target’s IPO and its acquisition is likely influenced by its ability
to raise outside capital. We control for this ability by assessing the target’s (potential)
seasoned equity offerings (SEOs). Arguably, it is easier and less costly to issue equity
when the target’s stock is highly valued, in absolute or in relative (to book equity) terms.
The average Target market value is $463 million with a median of $136 million. The
average and median Target market-to-book ratio, calculated as (market value of equity +
total assets – book value of equity) divided by total assets, are 3.3 and 1.5, respectively.
While the average appears high, the median Target market-to-book ratio is consistent with
Pastor and Veronesi (2003). Finally, we control for actual SEOs with an indicator
variable. Approximately 46% of the target firms issued primary shares in a seasoned
equity offering (Target SEO), as indicated in the SDC New Issues database.
11
We measure targets’ pre-acquisition performance with their stock returns and
accounting-based variables. The target return from the first day (after the IPO) close until
two weeks before the acquisition (Target prior return) averages 52.5% with a median of
-8.7%. Prior market return, a control variable used in our regression analysis, is the
return of the CRSP equal-weighted index from the IPO date to two weeks before the
acquisition announcement and averages 221.7% with a median of 117.7%. While these
index returns may seem high, we note that they are measured over periods of up to ten
years. Target cash flow/ cash is the ratio of the target’s operating cash flow to cash
holdings, and Target net income/ assets is the ratio of the target’s net income to total
assets. We use net income to account for the effects of one-time items on a firm’s cash
position. Both variables are measured over the most recent fiscal year prior to the
acquisition and show wide dispersions. Target cash burn is an indicator variable set equal
to one if the ratio of the target’s cash flow from operations to cash in the prior fiscal year
falls into the bottom 20% of the in-sample distribution and to zero otherwise.5
We use typical mergers and acquisitions (M&A) control variables in our analysis.
Relative size has a mean and median of 0.31 and 0.17, similar to the mean and median of
0.28 and 0.33 reported by Moeller et al. (2004). Average Acquirer market value is $3.8
billion, larger than the $1.7 billion reported in Moeller et al. (2004), likely because all of
our targets are public and acquirers of public firms tend to be larger than acquirers of
private firms that are part of the Moeller et al. (2004) sample. The average and median
We also estimate an alternate definition of Target cash burn by setting it equal to one when the target’s
cash flow from operations (negative in about 42% of our sample) used up more than one third of the firm’s
cash in the prior fiscal year. The alternative definition does not affect the significance of our results.
5
12
Acquirer market-to-book ratio, calculated as (market value of equity + total assets – book
value of equity) divided by total assets, are 4.2 and 1.8, respectively.6
Panel B of Table 1 shows that 70% of acquisitions are paid with at least some
acquirer stock (Stock). The SDC Mergers & Acquisitions database classifies only 1% of
our acquisitions as hostile (Hostile)7 and 16.9% as tender offers (Tender). About 50% of
the targets in our sample were backed by venture capitalists (VCs) at the time of the
IPOs. Approximately 56% (8%) of the targets went public during hot (cold) IPO periods.
We define hot and cold IPO periods based on the monthly volume of IPO issuance as in
Helwege and Lian (2004). Specifically, we calculate three-month centered moving
averages of the number of IPOs for each month in the sample using data reported in
Ibbotson, Sindelar, and Ritter (1994) and updated through 2008 on Jay Ritter’s website.
Following Helwege and Lian (2004), hot periods are defined as months for which the
number of IPOs exceeds the top quartile of the moving average. Cold periods are defined
as months for which the number of IPOs is less than the bottom third of the moving
average.
4. Results
We first analyze the effects of Time to acquisition on target market-to-book ratios,
acquirer announcement returns, target premiums, and target as well as acquisition
6
The average acquirer market-to-book value is high because of several outliers: Razorfish, Inc., Kana
Software Inc., Akamai Technologies Inc., VA Linux, and Sage Inc. All of these outliers occur in either 1999
or 2000. Our main results are largely unchanged when we exclude these outliers.
7
In addition to the SDC classification, Schwert (2000) uses characterizations of hostility in the Wall Street
Journal and the Dow Jones News Retrieval, unnegotiated tender offers, “bear hugs,” pre-takeover 13D
filings, merger rumors about the target, and principal component analysis to identify hostile deals. Overall,
he concludes that “most deals described as hostile in the press are not distinguishable from friendly deals in
economic terms, except that hostile transactions involve publicity as part of the bargaining process.” (p.
2599)
13
characteristics in a univariate setting. We then perform regression analyses of the
determinants of Time to acquisition, acquirer announcement returns, and target premiums.
4.1. Univariate results
Pastor and Veronesi (2003) show that market-to-book ratios of firms decline with firm
age. In Table 2, we report the average and median ratios for subsamples based on the time
from the IPO. We focus on two-year windows in our univariate analysis to ensure
sufficient numbers of observations in each group. For our sample targets, the market-tobook ratios decline over time. The median market-to-book ratio of targets acquired in the
first two years after the IPO is 2.2 which is consistent with the market-to-book ratios of
2.3 and 1.8 for firms one and two years after the listing, respectively, in Pastor and
Veronesi (2003).
In Table 2, we also report the average and median Acquirer CAR for acquisitions of
targets in five subsamples based on the time between IPO and acquisition. The average
acquirer announcement returns are generally highest in the first window, i.e., with targets
that are acquired within two years after their IPOs. However, the decline in Acquirer CAR
is not monotonic. Furthermore, none of the differences in average Acquirer CAR between
the most recently listed targets and the other groups are statistically significant.
In Table 2, the average Target premium is the lowest for firms acquired shortly after
the IPO. However, the increase in Target premium is also not monotonic. Only the
differences in average and median measures of Target premium between acquisitions
taking place in the first versus the second window are statistically different from zero.
The univariate results provide only weak evidence, at best, of a relation between the
time since the target’s IPO and acquisition pricing. However, the univariate results are
14
potentially confounded by other effects that can cause the decline in acquirer
announcement returns and increase in the target premium over time to be understated. For
example, the proportion of acquisitions paid with stock also decreases with the time since
the target’s IPO and use of stock is associated with more negative acquirer announcement
returns and lower target premiums (Wansley, Lane, and Yang, 1983; Huang and Walkling,
1987; and Travlos, 1987).
Other target characteristics likely affect acquisition pricing. We specifically examine
the role of target size because firm size should be positively correlated with the amount of
information about the firm that is available to the market. The average Target market
value shows a non-linear decline over time as well. The non-linear decline in target size
should therefore work against finding support for the learning hypothesis since
information availability is positively correlated with firm size.
To assess the role of uncertainty about the target, we examine proxies for target
valuation uncertainty over the time from the target’s IPO. The measures of valuation
uncertainty decline significantly over time, although the decline is not monotonic. For
example, the averages of Target industry M/B stdev for firms acquired in the first and
second versus third and fourth years after their IPOs are 3.7 and 1.8, respectively. This
decline suggests that targets acquired shortly after their IPOs are from industries with
higher dispersions of valuation multiples than firms acquired later on. It is also consistent
with the notion that a firm’s valuation uncertainty declines over time. However, for years
five and six after the IPO, the Target industry M/B stdev increases to 2.2. Therefore,
based on the univariate analysis, most of the decline in uncertainty occurs during the first
couple years.
15
Since acquisition pricing may be affected by targets’ financial characteristics, we also
examine the role of the pre-acquisition target performance. We do not observe a
monotonic trend in any of the three measures. Only Target prior return indicates that
firms acquired later after their IPOs perform, on average, better than firms acquired
quickly after their public listing.
Next, we analyze the relation of acquisition pricing with measures of uncertainty. In
Table 3, we divide our sample into terciles based on the measures of the target valuation
uncertainty. Then we calculate the average and median Acquirer CAR (Panel A) and
Target premium (Panel B) for the bottom and top terciles of each variable of interest.
According to the learning hypothesis, we should observe higher Acquirer CAR and lower
Target premium for targets with higher valuation uncertainty. For the bottom and top
terciles of Industry M/B stdev, Acquirer CAR averages -1.2% and -3.3%, respectively, and
the Target premium averages 22.8% and 31.3%, respectively. The differences for both
variables are statistically significant at 0.05 level. Therefore, higher target valuation
uncertainty is associated with lower Acquirer CAR and higher Target premium. For
Acquirer CAR (Target premium) we obtain consistent, significant results with Target IPO
underpricing (Target return stdev). Overall, the univariate results in Table 3 results are
consistent with target valuation uncertainty playing an important role in acquisition
pricing. Prima facie, the results appear to be inconsistent with the learning hypothesis
and are instead suggestive of the effect of option value of uncertainty on acquisition
pricing. However, these conclusions ignore the interplay between target valuation
uncertainty and the time between IPO and acquisition.
16
4.2. Analysis of Time to acquisition
To gain an understanding of the relation between the absolute level of target valuation
uncertainty and the time since the target’s IPO, we first examine univariate correlations,
followed by regressions analyzing the determinants of Time to acquisition.
In Table 4, Time to acquisition has significant negative correlations with Target
Industry M/B stdev, Target return stdev, and Target IPO underpricing. These negative
correlations are consistent with valuation uncertainty decreasing with the length of the
target’s public listing. We also find significant positive correlations of Time to acquisition
with Target Prior return and Prior market return. However, the accounting-based Target
cash burn and Target net income/ assets have insignificant correlations with Time to
acquisition. These results suggest that the newly listed targets become easier to value
over time and stay independent longer when they perform better.
Table 5 reports the regression analysis of Time to acquisition. In column 1 we include
control variables related to target pre-acquisition performance and other characteristics as
well as the target’s IPO market conditions:8 Log Target prior return, Log Prior market
return, Target cash burn, Target net income/ assets, Log Target market-to-book, Target
SEO, VC, Hot IPO market, Cold IPO market, and Bubble IPO. Consistent with prior
research, we define the IPO bubble from October 1998 through the end of 2000.9 In
regression 1, consistent with Pastor and Veronesi (2003), we find that target market-to-
8
The results are not affected by inclusion of target industry fixed effects based on one digit SIC codes.
Loughran and Ritter (2004) in their analysis of IPO underpricing define the “internet bubble” period from
January 1999 to December 2000. Bradley, Jordan, and Ritter (2008) use an identical period in their analysis
of analyst following of IPOs during the “bubble period.” Lowry et al. (2009) find that firms going public
during a period starting in October 1998 and ending in August 2000 experienced unusually high first day
returns and refer to this period as “IPO bubble period.” Our conclusions are not sensitive to a broad (i.e.,
October 1998 to December 2000) or a narrow (i.e., January 1999 to August 2000) definition of the IPO
bubble period.
9
17
book ratios are negatively associated with the time between the target’s IPO and the
acquisition announcement. We also find that Log Prior market return and SEO issuance
affects the Time to acquisition positively while Log Target prior return, Cold IPO market,
and Bubble IPO affect the Time to acquisition negatively.
In regression 2 of Table 5, we include proxies for target valuation uncertainty. With
respect to valuation uncertainty, Target return stdev and Target IPO underpricing have
significantly negative coefficients. The negative coefficients indicate that firms acquired
later after their IPOs tend to be easier to value.
In regressions 3 and 4, we analyze a subsample of firms acquired within a three-year
period. Given the empirical predictions of the learning theory, we expect the effects of
learning to be more pronounced for acquisitions that take place closer to the IPO. The
results of Time to acquisition for this subsample are generally consistent with the overall
sample, but with higher p-values, potentially due to the smaller sample size.
Overall, we find that Time to acquisition is significantly negatively related with target
valuation uncertainty and significantly positively related with pre-acquisition
performance variables, although not all relevant proxies are significant. Therefore, we
include the target valuation and pre-acquisition performance variables as controls in the
acquirer announcement return and target premium regressions.
4.3. Regression results: Acquirer CAR
We test the learning and option value of uncertainty hypotheses, starting with
regression analyses of Acquirer CAR. To reduce the impact of outliers, we use the natural
logarithm of (1 + continuous variable) in the regressions, indicated by “Log” in front of
the variable name. Our dependent variable is Log Acquirer CAR, the natural logarithm of
18
(1 + Acquirer CAR). In regression 1 of Table 6, we regress Log acquirer CAR on Time to
acquisition and typical M&A control variables as well as on target financial performance
controls. In regression 2, we also include target valuation uncertainty characteristics. In
regressions 3 and 4, we analyze the subsample of acquisitions taking place within three
years after the IPO.
We control for previously documented effects on acquirer announcement returns with
the natural logarithms of Acquirer market-to-book, Acquirer market value, and Relative
size and several indicator variables. We include Log acquirer market-to-book because
Lang, Stulz, and Walkling (1989) show that acquirers with high Tobin’s Q gain more than
acquirers with low Tobin’s Q and Rhodes-Kropf, Robinson, and Viswanathan (2005) find
significant effects of market-to-book ratios on takeover activity. Moeller et al. (2004) find
that larger acquirers earn lower announcement returns than do smaller acquirers. Faccio,
McConnell, and Stolin (2006) and Asquith, Bruner, and Mullins (1983) find positive
relations between acquirer announcement returns and relative size in private and public
acquisitions, respectively. Indicator variables control for the method of payment (Stock),
venture capital presence at the target’s IPO (VC), whether the deal is characterized as
Hostile, and whether the merger is classified as a Tender. We include acquisition year
indicator variables in all regressions as is customary (for example, Moeller et al., 2004).
Finally, we use heteroskedasticity consistent standard errors clustered at the acquirer
level.
In regression 1, the coefficient on Time to acquisition is negative and significant at
the 0.05 level. The size of the coefficient implies that one additional year of target listing
results in a 0.9% incremental decline in Acquirer CAR. In regression 2 we include target
19
valuation uncertainty characteristics. The results are consistent with regression 1 and the
coefficient on Time to acquisition remains largely unchanged and significant at the 0.05
level. In regressions 3 and 4, for the subsample of quick acquisitions, the effect appears to
be more negative, indicating that the effects of learning are stronger in the first three
years after the target’s IPO.
With respect to the proxies for target valuation uncertainty and pre-acquisition
performance, we observe negative and significant effects of Log Target IPO underpricing
and Target net income/ assets on Acquirer CAR and a positive and significant effect of
Target cash burn on Acquirer CAR. The coefficients on Target net income/ assets and
Target cash burn indicate that acquisitions of targets experiencing poor operating
performance are more profitable for acquirers. The other measures of target valuation
uncertainty (Log Target Industry M/B stdev and Log Target return stdev) and
pre-acquisition stock returns have insignificant coefficients. Among the control variables,
Log relative size has a significantly negative coefficient. If acquirers on average overpay
for targets, relatively larger targets appear to magnify the effect. Acquirer market-to-book
and stock payment are negatively related to Acquirer CAR for the entire sample.10
Overall, supporting our learning hypothesis, the regression results show that Time to
acquisition is negatively related to acquirer announcement returns.
4.4. Regression results: Target premium
We examine whether the length of the target’s public listing also affects takeover
premiums. If the negative effect of Time to acquisition on Acquirer CAR reflects the
10
In untabulated results, we also control for the runup in the target’s stock price starting one month prior to
the announcement date. The coefficient of the target runup is insignificant and its inclusion in the
regression does not affect our conclusions about the significance of Time to acquisition.
20
acquirer’s ability to capture a higher proportion of synergies, or to buy firms shortly after
the IPO at a discount relative to firms that are listed for a longer period of time, we
should observe a positive relation between Time to acquisition and Target premium.
To mitigate the effects of outliers, we use the natural logarithm of (1 + Target
premium) as the dependent variable in Table 7. The regressions follow the specifications
in Table 6. In regression 1, Time to acquisition has a positive coefficient that is significant
at the 0.1 level. Targets acquired shortly after their IPOs are purchased at a discount
compared to targets acquired at a later time. This discount is the counterpart to the higher
Acquirer CAR in acquisitions of recently listed targets reported in Table 6. The results are
even stronger in regression 3 that uses only targets acquired within three years after their
IPOs.
Regression 2 indicates that the effect of Time to acquisition is robust to the inclusion
of target valuation uncertainty characteristics. Consistent with prior results for Acquirer
CAR, we observe more pronounced effects of Time to acquisition for the subsample of
firms acquired within three years after their IPOs (regression 4). We conclude that the
relation between the target’s length of listing and Target premium is positive and
therefore supports the learning hypothesis.
With respect to the proxies for target valuation uncertainty and pre-acquisition
performance, we note negative and significant effects of Log Target IPO underpricing,
Log Target prior return, and Log Prior market return on Target premium and a positive
and significant effect of Log Target return stdev on Target premium. The finding that the
coefficients on Log Target IPO underpricing and Log Target return stdev have opposite
signs is likely due to the fact that they capture uncertainty measured at different points in
21
time. Among the control variables, the hostile indicator and the tender offer indicator
have significantly positive coefficients, suggesting that acquirers pursuing hostile tender
offers tend to pay richer premiums.11
Overall, we find that the target’s length of listing affects acquirer announcement
returns negatively and target premiums positively. These results support the learning
hypothesis that posits that acquisition pricing reflects the costs and benefits of learning.
They contradict our option value of uncertainty hypothesis.
5. Robustness and alternative explanations
We examine the robustness of the negative relation between the time since the target’s
IPO and acquirer announcement returns and the positive relation between the time since
the target’s IPO and target premiums.
5.1. Size of target
The size of firms should be related to the available public information about them.
For example, larger firms are more likely to be followed by multiple analysts while
smaller firms may not be covered at all. Hence, size should be negatively related to
valuation uncertainty. While we control for the relative size of the target and the acquirer
in our regressions, we do not specifically control for the target’s absolute size. In
untabulated results, we include Target market value in all regressions. The coefficients on
Time to acquisition retain their signs and significance. Therefore, controlling for target
size does not affect our conclusions.
11
In untabulated results, we also control for the runup in the target’s stock price starting one month prior to
the announcement date. The coefficient of target runup is negative and significant at the 0.05 level.
However, the inclusion of the target runup in the regression does not affect our conclusions about the
significance of Time to acquisition.
22
5.2. Alternative measures of time between IPO and acquisition
We redefine the variable that measure the time between the target’s IPO and
acquisition announcement and examine the robustness of our results. First, we define
Year of acquisition as equal to one through ten for acquisitions taking place in the first
through the tenth year after the IPO. Second, we define Log Time to acquisition as the
natural logarithm of the number of days between the IPO and the acquisition
announcement.
Results with these alternative specifications are reported in Table 8. In regressions 1
and 2 Log Acquirer CAR is the dependent variable, and Log Target premium is the
dependent variable in regressions 3 and 4. All regressions follow specifications in prior
tables. In regression 1, the coefficient on Year of acquisition is negative and significant at
the 0.1 level. In regression 2, Log Time to acquisition has a negative coefficient,
significant at 0.05 level. Overall, the results for Acquirer CAR are not affected by the way
we define time since the IPO. In regressions 3 and 4 of Table 8, Target premium is the
dependent variable. The alternate measures of time since the target’s IPO variables have
all positive signs that are significant. For example, Log Time to acquisition has a positive
coefficient that is significant at the 0.05 level in regression 4. In untabulated analyses, we
discretize Time to acquisition into Month of acquisition, the number of months (instead of
days) between the target’s IPO and its acquisition scaled by 120, the number of months in
a ten year period. Overall, our results are largely unaffected by how precisely, i.e.,
rounded to days, months, or years, we measure the time between the target’s IPO and its
acquisition.
23
5.3. Acquirer CAR and Target premium
Table 9 presents regressions using Acquirer CAR and Target premium instead of Log
Acquirer CAR and Log Target premium to assess whether the log transformation
influences our results. Again, in regressions 1 and 2 analyzing Acquirer CAR, all
coefficients related to the time between the target’s IPO and the acquisition
announcement are negative and significant at the 0.05 level. Furthermore, the coefficient
estimates and their statistical significance are comparable to the results reported in prior
tables. In regressions 3 and 4 analyzing Target premium, the coefficients on Time to
acquisition are both positive and significant at the 0.05 level. Overall, the log
specification of our dependent variables does not affect our conclusions about the effects
of the target’s length of listing on acquirer announcement returns and takeover premiums.
5.4. IPO market conditions
We next examine the effects of particular IPO time periods on our results. We conduct
this analysis for two reasons. First, we want to assess whether unusual IPO markets
unduly affect our results. Second, and potentially more interesting, time period effects
can provide further insights into fundamental drivers of the learning effect. For example,
the type of IPO market affects the characteristics of the firms going public, e.g., their
average age. These characteristics can be related to valuation uncertainty and can affect
learning. We focus on IPO time effects here because our acquisition year indicator
variables should already capture most acquisition time effects.
First, we focus on the IPO bubble period. We analyze Acquirer CAR in regression 1 of
Table 10. We add IPO bubble indicator and interact it with Time to acquisition. We note
that the Time to acquisition continues to have a significantly negative coefficient. IPO
24
bubble indicator has an insignificantly positive coefficient. The interaction term is
insignificantly negative.
Regression 3 reports results for Target premium. The coefficient on Time to
acquisition is positive and significant at the 0.1 level. Neither the IPO bubble indicator,
nor the interaction term is significant. The results in regressions 1 and 3 show that our
conclusions regarding the effects of target’s length of listing on acquisition pricing are not
driven by an unusual IPO period.
Second, IPO activity exhibits substantial monthly variation. To assess whether
unusual IPO periods unduly affect our conclusions, we group each target based on
whether its IPO occurred during a hot, neutral, or cold IPO market and report the results
in regressions 2 and 4. Again, in the regression analyzing acquirer announcement returns,
the coefficient on Time to acquisition continues to be negative and significant at the 0.05
level. None of the interaction terms are significant. Only Cold IPO indicator has a
negative coefficient, significant at the 0.1 level. For Target premium, in regression 4, the
coefficient on Time to acquisition is positive and significant at the 0.05 level. The
coefficients on Time to acquisition interacted with the hot and cold IPO period indicators
are negative and significant, suggesting that the effect of time on premium is lower
relative to more average IPO periods. Since the absolute level of the coefficients on the
interaction terms is lower than the coefficient on Time to acquisition, the overall effect of
Time to acquisition on target premium is still positive even during the hot and cold IPO
periods. In summary, we conclude that the differences between hot, neutral, and cold IPO
periods do not affect our conclusions.
25
5.5. Endogeneity considerations
Heckman (1979) shows that statistical analyses based on non-random samples can
lead to erroneous conclusions. The endogeneity of the acquirer’s choice to acquire a
particular target and the target’s choice to get acquired can affect our conclusions. For
example, targets may choose between a seasoned equity offering and being acquired.
Acquirers may try to acquire attractive targets before other potential bidders appear.
A potential endogeneity concern may arise if acquirers and their shareholders have a
preference for relatively “safe” targets, i.e., targets with relatively low valuation
uncertainty, and such “safe” targets get acquired shortly after listing. In this case, our
results would obtain because targets that are acquired early are fundamentally different
from targets that are acquired later, not because acquirers learn about targets over time.
We implement Heckman’s (1979) estimation method to address these potential
endogeneity concerns. In the first stage, we estimate the probability of an acquisition
taking place within three years of the target’s IPO and calculate the Heckman , also
known as the inverse Mills ratio, for each observation in our sample. Heckman (1979)
shows that including  in the second stage model controls for the selection bias. In the
first stage, we follow the specification of regression 2 in Table 5. In particular, we include
target valuation uncertainty measures, target pre-acquisition performance measures, and
whether the target firm has VC backing. To satisfy the identification requirement of the
two-stage estimation, we also include several target-specific variables in the first stage
that are not included in the second stage: Log Target market-to-book ratio, Target SEO
indicator, Hot IPO indicator, Cold IPO indicator, and IPO bubble indicator.
26
We contend that a potential target’s seasoned equity offering should extend the time it
stays independent, e.g., because the cash raised in the seasoned equity offering makes it
unlikely that the target seeks an acquirer in order to get access to capital anytime soon.
The effect of a potential target’s seasoned equity offering on acquirer announcement
returns and the takeover premium is less obvious, making the indicator Target SEO a
good identification variable. Similarly, when a firm is highly valued by the market, it
should be a good time to get acquired. Therefore, a high target valuation, as measured by
Log Target market-to-book, should lead to the target getting acquired sooner, but again
there is no obvious relation between target market-to-book ratios and acquirer
announcement returns or takeover premiums.
The type of IPO market, i.e., cold, hot, or bubble, can also affect the time to
acquisition. For example, hot IPO markets likely come with high firm valuations that
make takeovers attractive for both targets and acquirers. However, it is not obvious how
these IPO market conditions should be reflected in acquirer announcement returns or
takeover premiums.
Columns 1 and 2 in Table 11 report the results of the Heckman estimation with Log
Acquirer CAR. The first stage indicates that firms with higher firm-specific valuation
uncertainty, pre-acquisition stock returns, and market-to-book ratios as well as firms with
lower industry-specific valuation uncertainty and pre-acquisition market returns that did
not issued seasoned equity are more likely to be acquired within three years of their IPOs.
Most important, in column 2, the coefficient on Time to acquisition continues to be
negative and significant at the 0.05 level. Furthermore, the absolute size of the coefficient
increases relative to the coefficients reported in Table 6. Therefore, while potential
27
endogeneity issues may affect analyses of Acquirer CAR, our conclusions are unchanged
when we control for them.
Columns 3 and 4 in Table 11 report the results of the Heckman estimation with Log
Target premium. The coefficient on Time to acquisition continues to be positive, increases
in size, and is significant at the 0.01 level. Overall, potential endogeneity issues do not
affect our conclusions.
6. Conclusions
We find that target valuation uncertainty and pre-acquisition performance are related
to the length of the target’s listing. The length of time a target is publicly listed
significantly decreases acquirer announcement returns and increases takeover premiums,
even when we control for target valuation uncertainty and pre-acquisition performance of
the targets. Learning and accumulation of information about newly listed target firms can
explain these results and seems to at least partly explain the target listing effect in
acquisition pricing. Public targets are not a homogenous group in terms of valuation
uncertainty and the differences among targets affect the acquisition pricing.
28
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Appendix A: Effect of learning on acquisition pricing
We provide an illustrative example of how learning can affect acquisition payoffs. We
have one target and two potential acquirers. The target’s stand-alone value is uncertain,
however, it is common knowledge that the target’s value is uniformly distributed between
$50 and $150. Absent any takeover offer, the target’s market value is its expected value of
$100. It is also common knowledge that the acquisition of the target generates synergies
of $30.
The potential acquirers can learn about the value of the target at a cost. Assume that
potential acquirer A can learn at a lower cost, e.g., because it operates in the same
industry, has relationships with the target, or has better analysts. In this learning
framework, it is always the potential bidder with the lowest learning cost who chooses to
learn. We normalize firm A’s cost of learning to zero and set potential bidder B’s cost of
learning to $20.13
A’s goal is to take advantage of its knowledge of the target value that is unknown to
both B and the target. To preserve its informational advantage, A has to select an offer
price that keeps B from engaging in costly learning. A always makes the first bid because
it is beneficial for B to wait and extract information from A’s offer.
We assume that the target always rejects offers with negative premiums, i.e., offers
below $100. Therefore, A does not make a takeover offer when it learns that the target
value plus synergies is below $100. For target values plus synergies above $100, A’s goal
is to find the lowest offer price that keeps B from engaging in learning. If A always
offered $100, B would learn that the true target value (including synergy) is between
13
Note that with zero cost, firm A always chooses to learn. Increasing the learning cost of firm A does not
affect the implications of the learning example as long as its cost is below the learning cost of firm B.
32
$100 and $180 with an expected value of $140. After B spends $20 on learning, it’s
expected gain from learning and making an offer would be $20. The reason is that B
would get by with offering slightly more than $100 for a target that is worth $140 on
average. Therefore, A needs an offer schedule that varies with the target’s value to
prevent firm B from engaging in learning.
The key to the offer schedule is that the difference between expected target value and
the offer price is exactly $20, i.e., A’s learning cost advantage over firm B. So, if the
target value plus synergy is above $140, A offers $140, and B infers that the true target
value is between $140 and $180 with an expected value of $160. B rationally does not
expend resources on learning because, on average, it would acquire a target worth $160
for an amount slightly exceeding $140 plus the $20 learning cost. Following the same
rationale, for target values between $100 and $140, A offers $100.
With this offer schedule, B never chooses to learn, and A is the only actual bidder. A’s
average payoff is $20 because it acquires the target on average at a $20 discount to the
expected target value plus synergies. The target’s average payoff in excess of its pre-bid
value of $100 is $20. That is, the target payoff is with equal probability either zero (when
A offers $100) or $40 (when A offers $140).
We reflect lower valuation uncertainty in lower learning costs. The rationale is that it
should be less costly for B to learn the true value of a low valuation uncertainty target
than for a high valuation uncertainty target. With low valuation uncertainty, we set B’s
cost of earning to 10 and apply the same logic to analyze the acquisition pricing. A sets its
offer prices to $10 below the average target value, its learning cost advantage over B, to
prevent B from engaging in learning. Therefore, A offers $160 when the true target value
33
is between $160 and $180, $140 when the true target value is between $140 and $160,
etc. On average, relative to the high uncertainty scenario, A’s payoff decreases by $10,
equal to the reduction in its learning cost advantage over firm B. Since A has to share
more of the synergies with the target by offering higher takeover premiums, the target’s
average payoff increases to $30.
To summarize, higher target valuation uncertainty leads to higher payoffs for bidder
A. Conversely, higher target valuation uncertainty leads to lower payoffs for the target.
Therefore, as target valuation uncertainty declines, that is in terms of our empirical proxy,
as time since a target’s IPO increases, the acquirer announcement return should decline
and the takeover premium should increase on average.
In our example, we prohibit B from making “blind” offers, i.e., offers without prior
learning of the target value. If we allowed “blind” offers, A would be forced to always
offer the target’s true value. The acquirer payoffs would be zero and the target would
capture all acquisition gains. We believe prohibiting “blind” offers is reasonable because
it is unlikely that bidders would make takeover offers without thoroughly investigating
and analyzing the target, and without performing due diligence. Alternatively, we can
assume that the synergies are acquirer-specific. A potential acquirer would have to invest
in (costly) learning to determine its specific synergies with the target because it could not
learn about them from the other bidder’s offer. Not knowing its synergies with the target
would make blind offers unattractive and would have largely the same effects on the
example as prohibiting blind offers outright.14
14
We also consider an alternative strategic bid schedule that effectively keeps B from learning and bidding.
Under this alternative, continuous bid schedule, A learns about the target and offers an amount slightly
above the target value plus synergy less A’s learning cost advantage over B. Because A’s offer price is set to
guarantee that B always realizes a loss when it expends resources on learning and submits a bid above the
34
In our parsimonious example, both the target and acquirer payoffs are always
nonnegative. Adding features, for example, acquirer agency issues such as empire
building involving the pursuit of acquisitions with synergies below the other potential
bidder’s cost of learning, can make the acquirer payoffs negative while keeping the target
payoffs positive. Yet, even with such additional features, the effects on target and acquirer
payoffs of resolving target valuation uncertainty through learning should remain the
same. If target valuation uncertainty declines over time, acquirer announcement returns
should also decline and takeover premiums should increase.
current bid of A, it is never optimal for B to engage in learning and to make a bid. This form of strategic
bidding has the same implications as our main setup.
35
Table 1
Descriptive statistics
This table contains descriptive statistics for our sample of firms that went public between 1979
and 2008 and were acquired within ten years of their IPOs by public acquirers. Panel A presents
means, medians, standard deviations, and the 10% and 90% percentile values for continuous
variables. Time to acquisition is the number of calendar days from the target’s IPO to the
acquisition announcement scaled by the number of calendar days in a ten-year period. Acquirer
CAR is the acquirer’s three-day cumulative abnormal return around the acquisition
announcement. Target premium is the target’s cumulative abnormal return from 10 trading days
prior to the acquisition announcement through the earlier of 180 days after announcement date or
delisting. Expected returns are measured with the CRSP equal-weighted index. Target IPO
underpricing is the target’s first trading day return. Target industry M/B stdev is the standard
deviation of the market-to-book ratios of firms in the same industry with assets between half and
twice the target assets. Target return stdev is the standard deviation of the daily target returns
from the IPO date to two weeks before the acquisition announcement. Acquirer and Target market
value represent the market values of equity. Target and Acquirer market-to-book is calculated as
(market value of equity + total assets – book value of equity) divided by total assets. Target prior
return is the target’s return from the first trading day closing price to two weeks prior to the
acquisition announcement. Prior market return is the return of the equal-weighted CRSP index
from the IPO date to two weeks before the acquisition announcement. Target cash flow/ cash is
the ratio of the target’s cash flow from operating activities to cash and short-term investments.
Target net income/ assets is the ratio of the target’s net income to total assets. Relative size is the
ratio of target to acquirer market value of equity. Panel B shows the proportion of each indicator
variable that equals one. Target SEO equals 1 if, based on the SDC New Issues database, the
target raised primary seasoned equity between its IPO and the acquisition announcement date.
Target cash burn equals 1 if Target cash flow/ cash is in the bottom 20% of all sample firms.
Stock equals one when the acquisition price is paid at least partly in acquirer’s stock. Hostile
captures the deal attitude and Tender indicates the use of a tender offer. VC denotes presence of a
venture capital firm at the IPO. These three variables are from the SDC Mergers & Acquisitions
database. Hot and Cold IPO markets are based on the monthly volume of IPO issuance as in
Helwege and Lian (2004). All variables are measured at the fiscal year-end immediately prior to
the acquisition announcement, except for the market value of equity that uses the earliest
available date when it is unavailable at the end of the prior fiscal year.
36
Table 1 (continued)
Panel A
Mean
Median
St. Dev.
10%
90%
Time to acquisition
0.394
0.347
0.249
0.105
0.788
Acquirer CAR
-0.019
-0.015
0.091
-0.118
0.074
Target premium
0.301
0.243
0.466
-0.149
0.782
Target IPO underpricing
0.232
0.094
0.471
-0.025
0.621
Target industry M/B stdev
2.345
1.417
4.951
0.456
4.132
Target return stdev
0.046
0.042
0.021
0.023
0.074
463
136
1,066
19
1,086
Target market-to-book
3.260
1.533
15.359
0.977
4.876
Target prior return
0.525
-0.087
2.936
-0.899
1.887
Prior market return
2.217
1.177
2.904
0.173
5.657
Target cash flow/ cash
4.465
0.264
27.837
-1.002
7.352
Target net income/ assets
-0.109
0.013
0.388
-0.446
0.116
Relative size
0.313
0.170
0.403
0.039
0.747
Acquirer market value ($ million)
3,775
837
10,871
83
8,812
Acquirer market-to-book
4.203
1.805
22.011
1.025
5.504
Target market value ($ million)
37
Table 1 (continued)
Panel B
Proportion variable = 1
Target SEO
0.456
Target cash burn
0.195
Stock
0.700
Hostile
0.010
Tender
0.169
VC
0.501
Hot IPO market
0.556
Cold IPO market
0.079
38
Table 2
Univariate tests for acquirer announcement returns and target premiums by time from IPO
We split our sample by the number of years between the target’s IPO and acquisition. All
variables are defined in Table 1. ***, **, * indicate that the mean/median is significantly different
from zero using two-tailed t-test or sign test (reported only for Acquirer CAR and Target
premium). a, b, c indicate that the mean or median is significantly different from the mean/median
of acquisitions taking place in the first two years using two-tailed t-test/sign test.
Acquisition during a window (year after IPO):
(1, 2)
(3, 4)
(5, 6)
(7, 10)
N
206
267
161
176
Target market-to-book
Ave
Med
6.821
2.167
2.267
a
1.470
Acquirer CAR
Ave
Med
Target premium
Ave
Med
Stock
Ave
0.791
0.693
Target market value
($ million)
Ave
Med
516.0
175.1
402.4
118.1
Target industry M/B stdev Ave
Med
3.696
1.556
1.835
1.243
a
Target return stdev
Ave
Med
0.050
0.046
0.045
0.039
a
Target IPO underpricing
Ave
Med
0.297
0.106
0.180
0.086
a
Target prior return
Ave
Med
0.015
-0.141
0.432
0.015
a
Target net income/ assets
Ave
Med
-0.116
0.008
-0.134
0.015
-0.113
a
0.006
a
-0.059
0.021
Target cash burn
Ave
0.218
0.194
0.208
a
0.156
b
*
***
***
***, b
-0.0142
-0.0217
***
***
-0.0150
-0.0179
a
2.023
a
1.479
***
-0.0189
**
-0.0092
***
0.2491
0.3422
0.3204
***
***, c
***
0.2188
0.2980
0.2718
b
b
c
b
c
c
b
1.731
a
1.338
***
-0.0218
**
-0.0079
***
0.2816
***
0.2152
a
0.636
508.6
a
110.3
a
449.5
169.1
2.189
a
1.501
a
1.681
1.295
a
0.045
a
0.044
a
0.043
0.041
a
a
0.187
0.060
b
0.665
0.284
a
0.111
a
0.884
a
-0.262
a
b
b
a
0.934
a
0.129
39
Table 3
Univariate tests for Acquirer CAR and Target premium by target valuation uncertainty proxies
We split our sample into bottom and top tercile according to various target valuation uncertainty
proxies. Columns 1 and 2 show the mean and median values of Acquirer CAR (Panel A) and
Target premium (Panel B) for each tercile. Column 3 shows the difference in means and medians
between the bottom and top terciles. All variables are defined in prior tables. ***, **, * indicate
that the mean/median is significantly different from zero using two-tailed t-test or sign test.
Panel A
(1)
(2)
Acquirer CAR
(3)
Difference
Bottom tercile
Top tercile
(2) minus (1)
Target industry M/B stdev Ave
Med
-0.0122
-0.0159
-0.0326
-0.0256
-0.020 **
-0.010 **
Target return stdev
Ave
Med
-0.0163
-0.0133
-0.0179
-0.0184
-0.002
-0.005
Target IPO underpricing
Ave
Med
-0.0073
-0.0094
-0.0272
-0.0252
-0.020 **
-0.016 ***
Terciles based on
Panel B
Target premium
Difference
Bottom tercile
Top tercile
(2) minus (1)
Target industry M/B stdev Ave
Med
0.2283
0.1993
0.3127
0.2600
0.084 **
0.061 *
Target return stdev
Ave
Med
0.2181
0.1999
0.3636
0.2997
0.145 ***
0.100 ***
Target IPO underpricing
Ave
Med
0.3254
0.2673
0.2687
0.2511
Terciles based on
-0.057
-0.016
40
Table 4
Correlations
This table presents Pearson’s correlation coefficients for our sample of firms that went public
between 1979 and 2008 and were acquired within ten years of their IPOs by public acquirers. The
variables are defined in prior tables. p-values are in brackets.
Target
Time to industry
acquisition M/B stdev
Target industry M/B stdev
Target
return
stdev
Target IPO
underpricing
Target
prior
return
Prior
market
return
Target
cash
burn
-0.147
[0.000]
Target return stdev
Target IPO underpricing
Target prior return
Prior market return
Target cash burn
Target net income/ assets
-0.127
0.248
[0.000]
[0.000]
-0.045
0.097
0.316
[0.000]
[0.000]
[0.000]
0.124
-0.001
-0.169
-0.060
[0.000]
[0.000]
[0.000]
[0.000]
0.754
-0.071
-0.106
-0.065
0.162
[0.000]
[0.000]
[0.000]
[0.000]
[0.000]
-0.052
0.059
0.430
-0.028
-0.166
-0.070
[0.144]
[0.098]
[0.000]
[0.428]
[0.000]
[0.050]
0.070
-0.152
-0.514
-0.082
0.151
0.115
-0.551
[0.048]
[0.000]
[0.000]
[0.020]
[0.000]
[0.001]
[0.000]
41
Table 5
Regression results for time to acquisition
This table presents regression results for our sample of firms that went public between 1979 and
2008 and were acquired within ten years of their IPOs by public acquirers. The dependent
variable is Time to acquisition. “Log” in front of the variable name indicates the natural logarithm
of the variable or, if appropriate, of (1 + the variable). Bubble IPO indicates a target IPO that took
place between October 1998 and the end of 2000. All other variables are defined in prior tables.
All regressions have intercepts. p-values, based on heteroskedasticity adjusted standard errors, are
in brackets. ***, **, * denote significance at the 0.01, 0.05, and 0.10 level, respectively.
42
Table 5 (continued)
Time to acquisition
Entire sample
(1)
Target valuation uncertainty
Log Target industry M/B stdev
Log Target return stdev
Log Target IPO underpricing
Target pre-acquisition performance
Log Target prior return
Log Prior market return
Target cash burn
Target net income/ assets
VC
Hot IPO market
Cold IPO market
Bubble IPO
2
Adjusted R
Observations
(3)
(4)
-0.0004
-0.002
[0.930]
[0.440]
-0.048***
-0.007
[0.002]
[0.410]
-0.039*
-0.001
[0.051]
[0.958]
-0.010**
-0.021***
-0.011***
-0.012***
[0.037]
[0.000]
[0.003]
[0.003]
0.307***
0.313***
0.166***
0.168***
[0.000]
[0.000]
[0.000]
[0.000]
-0.006
-0.002
-0.012
-0.011
[0.703]
[0.880]
[0.129]
[0.182]
-0.011
-0.010
-0.009
-0.010
[0.544]
[0.572]
[0.288]
[0.299]
Target funding and other characteristics
Log Target market-to-book
-0.039***
Target SEO
(2)
Acquired within 3 years
-0.028***
-0.018***
-0.015***
[0.000]
[0.000]
[0.000]
[0.000]
0.033***
0.033***
0.021***
0.020***
[0.001]
[0.001]
[0.001]
[0.002]
0.012
0.021**
-0.005
-0.003
[0.207]
[0.034]
[0.427]
[0.640]
0.031***
0.033***
0.007
0.008
[0.002]
[0.001]
[0.190]
[0.138]
0.043**
0.036*
0.011
0.010
[0.024]
[0.056]
[0.342]
[0.391]
0.037**
0.054***
-0.014*
-0.010
[0.012]
[0.000]
[0.095]
[0.223]
0.7490
781
0.7540
781
0.5498
354
0.5520
354
43
Table 6
Regression results for log acquirer announcement returns
This table presents regression results for our full sample. The dependent variable is Log Acquirer
CAR. “Log” in front of the variable name indicates the natural logarithm of the variable or, if
appropriate, of (1 + the variable). The variables are defined in prior tables. All regressions contain
intercepts and acquisition year indicator variables. p-values, based on heteroskedasticity adjusted
standard errors clustered at the acquirer level, are in brackets. ***, **, * denote significance at the
0.01, 0.05, and 0.10 level, respectively.
44
Table 6 (continued)
Log Acquirer CAR
Entire sample
Time to acquisition
Acquired within 3 years
(1)
(2)
(3)
(4)
-0.092**
-0.095**
-0.988*
-1.135**
[0.042]
[0.038]
[0.068]
[0.035]
Target valuation uncertainty
Log Target industry M/B stdev
Log Target return stdev
Log Target IPO underpricing
-0.001
-0.003
[0.846]
[0.616]
0.011
0.003
[0.423]
[0.890]
-0.031*
-0.058*
[0.072]
[0.058]
Target pre-acquisition performance
Log Target prior return
-0.001
-0.003
-0.002
-0.009
[0.816]
[0.543]
[0.861]
[0.385]
Log Prior market return
Target cash burn
Target net income/ assets
M&A characteristics
Log Acquirer market-to-book
Log Acquirer market value
Log Relative size
Stock
VC
Hostile
Tender
2
Adjusted R
Observations
0.016
0.019
0.055
0.069
[0.310]
[0.246]
[0.255]
[0.140]
0.033***
0.029***
0.034**
0.029*
[0.004]
[0.009]
[0.029]
[0.065]
-0.025**
-0.024*
-0.039*
-0.035
[0.045]
[0.059]
[0.086]
[0.126]
-0.019***
-0.019**
-0.019**
-0.014
[0.004]
[0.013]
[0.023]
[0.164]
0.002
0.004
-0.003
0.001
[0.476]
[0.169]
[0.473]
[0.762]
-0.008**
-0.007**
-0.072**
-0.058*
[0.014]
[0.037]
[0.016]
[0.065]
-0.026***
-0.025***
-0.010
-0.007
[0.001]
[0.001]
[0.477]
[0.626]
-0.012*
-0.012
-0.011
-0.004
[0.094]
[0.124]
[0.352]
[0.743]
0.040
0.042*
-0.011
-0.011
[0.111]
[0.097]
[0.646]
[0.626]
0.005
0.005
0.004
0.004
[0.564]
[0.623]
[0.816]
[0.832]
0.1286
781
0.1341
781
0.1861
354
0.2010
354
45
Table 7
Regression results for log target premium
This table presents regression results for our sample of firms that went public between 1979 and
2008 and were acquired within ten years of their IPOs by public acquirers. The dependent
variable is Log Target premium. All variables are defined in prior tables. All regressions contain
intercepts and acquisition year indicator variables. p-values, based on heteroskedasticity adjusted
standard errors, clustered at the acquirer level, are in brackets. ***, **, * denote significance at the
0.01, 0.05, and 0.10 level, respectively.
46
Table 7 (continued)
Log Target premium
Entire sample
Time to acquisition
(1)
(2)
(3)
(4)
0.358*
0.320*
7.336***
7.074***
[0.060]
[0.082]
[0.008]
[0.007]
Target valuation uncertainty
Log Target industry M/B stdev
Log Target return stdev
Log Target IPO underpricing
Target pre-acquisition performance
Log Target prior return
-0.026
Log Prior market return
Target cash burn
Target net income/ assets
M&A characteristics
Log Acquirer market-to-book
Log Acquirer market value
Log Relative size
Stock
VC
Hostile
Tender
2
Adjusted R
Observations
Acquired within 3 years
0.006
0.034
[0.770]
[0.257]
0.116**
0.069
[0.023]
[0.445]
-0.309***
-0.269
[0.001]
[0.102]
-0.044**
0.003
-0.019
[0.158]
[0.039]
[0.937]
[0.696]
-0.139*
-0.107
-0.483**
-0.427**
[0.051]
[0.116]
[0.031]
[0.043]
-0.082
-0.116*
0.039
0.018
[0.188]
[0.064]
[0.664]
[0.852]
-0.053
-0.024
-0.110
-0.073
[0.401]
[0.702]
[0.248]
[0.448]
-0.014
-0.022
-0.015
-0.023
[0.534]
[0.401]
[0.649]
[0.561]
0.018
0.039***
-0.017
0.006
[0.110]
[0.005]
[0.364]
[0.814]
-0.024*
-0.010
-0.413***
-0.347**
[0.080]
[0.469]
[0.007]
[0.015]
-0.001
0.002
-0.064
-0.049
[0.965]
[0.962]
[0.246]
[0.413]
-0.038
-0.041
-0.074
-0.075
[0.225]
[0.208]
[0.141]
[0.151]
0.187**
0.208**
0.033
0.045
[0.033]
[0.016]
[0.834]
[0.805]
0.103***
0.093***
0.111**
0.104*
[0.002]
[0.006]
[0.038]
[0.065]
0.0846
764
0.1116
764
0.1689
354
0.1855
354
47
Table 8
Regression results for log acquirer announcement returns and log target premium using
alternative definitions of targets’ lengths of listing
This table presents regression results for our sample of firms that went public between 1979 and
2008 and were acquired within ten years of their IPOs by public acquirers. The dependent
variables are Log Acquirer CAR in regressions 1 and 2 and Log Target premium in regressions 3
and 4. Year of acquisition is the rounded number of years between the target’s IPO and
acquisition plus one. All other variables are defined in prior tables. All regressions contain
intercepts and acquisition year indicator variables. p-values, based on heteroskedasticity adjusted
standard errors clustered at the acquirer level, are in brackets. ***, **, * denote significance at the
0.01, 0.05, and 0.10 level, respectively.
Log Acquirer CAR
(1)
Year of acquisition
2
Adjusted R
Observations
(3)
-0.007*
0.030*
[0.082]
[0.063]
Log Time to acquisition
Target valuation uncertainty
Target pre-acquisition performance
M&A characteristics
(2)
Log Target premium
(4)
-0.029**
0.112**
[0.012]
[0.046]
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.1318
781
0.1386
781
0.1115
764
0.1161
764
48
Table 9
Regression results for acquirer announcement returns and target premiums
This table presents regression results for our sample of firms that went public between 1979 and
2008 and were acquired within ten years of their IPOs by public acquirers. The dependent
variables are Acquirer CAR and Target premium. All variables are defined in prior tables. All
regressions contain intercepts and acquisition year indicator variables. p-values are in brackets.
*** ** *
, , denote significance at the 0.01, 0.05, and 0.10 level, respectively.
49
Table 9 (continued)
Acquirer CAR
(1)
(2)
Time to acquisition
Target premium
(3)
(4)
-0.089**
-0.091**
0.496**
0.463**
[0.042]
[0.040]
[0.027]
[0.034]
Target valuation uncertainty
Log Target industry M/B stdev
Log Target return stdev
Log Target IPO underpricing
-0.0011
0.017
[0.836]
[0.479]
0.013
0.146***
[0.337]
[0.006]
-0.027*
-0.321***
[0.099]
[0.000]
Target pre-acquisition performance
Log Target prior return
-0.001
-0.002
[0.809]
[0.620]
[0.003]
[0.003]
Log Prior market return
Target cash burn
Target net income/ assets
M&A characteristics
Log Acquirer market-to-book
0.017
0.019
-0.208**
-0.177**
[0.282]
[0.233]
[0.019]
[0.037]
0.031***
0.027**
-0.049
-0.085
[0.006]
[0.016]
[0.433]
[0.163]
-0.027**
-0.025*
-0.061
-0.027
[0.047]
[0.060]
[0.367]
[0.692]
-0.009
-0.026
[0.743]
[0.376]
-0.018*** -0.018**
[0.004]
Log Acquirer market value
Log Relative size
Stock
[0.012]
0.001
0.003
0.014
0.038**
[0.704]
[0.310]
[0.316]
[0.015]
-0.007**
-0.006*
-0.030*
-0.015
[0.028]
[0.065]
[0.078]
[0.388]
-0.024*** -0.024***
VC
Hostile
Tender
2
Adjusted R
Observations
-0.061*** -0.076***
0.028
0.032
[0.001]
[0.001]
[0.498]
[0.437]
-0.010
-0.010
-0.019
-0.030
[0.130]
[0.143]
[0.592]
[0.432]
0.039
0.040
0.306**
0.332***
[0.135]
[0.121]
[0.019]
[0.009]
0.005
0.005
0.072*
0.062
[0.529]
[0.568]
[0.079]
[0.151]
0.1271
781
0.1321
781
0.1076
764
0.1346
764
50
Table 10
Regression results for log acquirer announcement returns and log target premiums
This table adds IPO bubble indicator, Hot IPO indicator, Cold IPO indicator, and their
interactions with Time to acquisition to the estimations. The dependent variables are Acquirer
CAR and Target premium. All variables are defined in prior tables. All regressions contain
intercepts and acquisition year indicator variables. p-values, based on heteroskedasticity adjusted
standard errors clustered at the acquirer level, are in brackets. ***, **, * denote significance at the
0.01, 0.05, and 0.10 level, respectively.
Log Acquirer CAR
Time to acquisition
IPO bubble indicator
(1)
(2)
(3)
(4)
-0.082*
-0.111**
0.327*
0.528**
[0.090]
[0.032]
[0.086]
[0.014]
0.029
-0.025
[0.223]
[0.835]
Hot IPO indicator
Cold IPO indicator
IPO bubble indicator X
Time to acquisition
2
0.167*
[0.538]
[0.057]
-0.038*
0.141
[0.077]
[0.103]
-0.015
[0.296]
[0.944]
Cold IPO indicator X
Time to acquisition
Adjusted R
Observations
0.010
-0.049
Hot IPO indicator X
Time to acquisition
Target valuation uncertainty
Target pre-acquisition performance
M&A characteristics
Log Target premium
-0.020
-0.359**
[0.495]
[0.021]
0.027
-0.365**
[0.587]
[0.031]
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.1367
781
0.1401
781
0.1120
764
0.1213
764
51
Table 11
Regression results for log acquirer announcement returns and log target premiums using
two-stage Heckman
This table presents regression results of a two-stage regression following Heckman (1979). The
first-stage probit dependent variable is an indicator for acquisitions occurring within three years
of the target’s IPO. The second-stage dependent variables are Log Acquirer CAR and Log Target
premium. All variables are defined in prior tables. All regressions contain intercepts and
acquisition year indicator variables. p-values, based on heteroskedasticity adjusted standard errors
clustered at the acquirer level, are in brackets. ***, **, * denote significance at the 0.01, 0.05, and
0.10 level, respectively.
52
Table 11 (continued)
Log Acquirer CAR
1st stage
(1)
2nd stage
(2)
Time to acquisition
Heckmann 
Target valuation uncertainty
Log Target industry M/B stdev
Log Target return stdev
Log Target IPO underpricing
Target cash burn
Target net income/ assets
1st stage
(3)
2nd stage
(4)
-0.344**
1.988***
[0.015]
[0.004]
-0.055***
0.053
[0.007]
[0.251]
-0.292***
-0.001
-0.277***
0.031
[0.001]
[0.881]
[0.001]
[0.276]
0.815***
-0.017
0.839***
0.087
[0.001]
[0.426]
[0.001]
[0.329]
0.751***
-0.072**
0.730***
-0.255*
[0.003]
[0.014]
[0.005]
[0.092]
Target pre-acquisition performance
Log Target prior return
0.264***
Log Prior market return
Log Target premium
-0.017*
0.245***
-0.011
[0.002]
[0.080]
[0.003]
[0.806]
-2.781***
0.148***
-2.796***
-0.500**
[0.000]
[0.007]
[0.000]
[0.026]
-0.027
0.033**
-0.088
0.015
[0.896]
[0.037]
[0.660]
[0.867]
0.057
-0.039*
0.134
-0.070
[0.784]
[0.085]
[0.489]
[0.438]
M&A characteristics
Log Acquirer market-to-book
Log Acquirer market value
Log Relative size
Stock
Hostile
Tender
-0.015
-0.022
[0.114]
[0.560]
0.002
0.006
[0.700]
[0.813]
-0.054*
-0.347**
[0.069]
[0.010]
-0.005
-0.050
[0.701]
[0.373]
-0.015
0.044
[0.463]
[0.787]
0.004
0.105**
[0.774]
VC
Log Target market-to-book
Target SEO
Hot IPO indicator
Cold IPO indicator
IPO bubble indicator
Wald 
P >
N
2
2
-0.210
0.0005
[0.134]
[0.968]
[0.048]
-0.230
-0.0783
[0.109]
[0.115]
0.338***
0.364***
[0.005]
[0.002]
-0.279**
-0.264**
[0.025]
[0.046]
-0.053
-0.095
[0.700]
[0.532]
-0.199
-0.079
[0.393]
[0.737]
-0.197
-0.247
[0.243]
[0.148]
5.84
1.50
0.016
781
0.220
764
53