Managerial Finance

How does information asymmetry affect the division of gains in mergers?

Mehmet Sinan Goktan
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MF
39,1 How does information asymmetry
affect the division of gains
in mergers?
60
Mehmet Sinan Goktan
Accounting and Finance, California State University,
Received May 2012
Revised September 2012 East Bay, Hayward, California, USA
Accepted September 2012
Abstract
Purpose – The purpose of this paper is to analyze the implications of the target valuation uncertainty
on the wealth distribution between the target and acquirer firms in successful mergers. The paper
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specifically analyzes the division of the total dollar gains between the two parties and also whether the
target and/or the acquirer experience a positive/negative gain in mergers when valuation of the target
company is more uncertain.
Design/methodology/approach – The analyses contrast the implications of the uncertainty in
three well-known merger hypotheses; the market-for-corporate-control, hubris and synergy.
Findings – The results are supportive of the implications of the synergy hypothesis. As target
valuation uncertainty decreases, it is more likely that both parties experience positive gains from the
transaction although more of the gains from the merger significantly shift towards the target company.
Originality/value – Results suggest that both parties are bargaining on the synergy gains and the
target is able to negotiate a greater portion of the synergy gains when the value of the target becomes
more predictable.
Keywords Mergers, Information asymmetry, Division of gains, Acquisitions and mergers,
Distribution of wealth
Paper type Research paper

1. Introduction
The division of total gains between the target and acquirer companies in mergers has
been examined from different perspectives and literature suggests that the division of
gains is mainly a function of the bargaining process between the two parties in the
transaction. The bargaining power of the target company is mainly shaped by its
governance structure, its industry, market conditions and various company and deal
characteristics (Hogfeldt and Hogholm, 2000; Stulz et al., 1990; Kale et al., 2003). What
I analyze in this paper is that once we control for the known bargaining factors that
influence the division of total gains in mergers, do information asymmetries about the
target valuation play a significant role in the division of gains between the target and
the acquirer? In other words, as the valuation of the target becomes more uncertain,
do the gains from the merger significantly shift to any of the two parties and if so, who
typically gains more from such uncertainty. This distinction is important since it gives
a unique opportunity to differentiate amongst the well-known hypotheses in mergers.
There are four frequently cited hypotheses as to why mergers occur; the market for
Managerial Finance corporate control, hubris, synergy and the managerial discretion hypotheses. In each
Vol. 39 No. 1, 2013
pp. 60-85 of these different hypotheses, the emphasis on information asymmetry and company
q Emerald Group Publishing Limited
0307-4358
DOI 10.1108/03074351311283577 JEL classification – G14, G34
 valuation differs. I differentiate between the market for corporate control, hubris and Division of gains
the synergy hypotheses by contrasting the effect of information asymmetry about the in mergers
target value on the division of gains between the two parties.
Overall, results are supportive of the synergy hypothesis. Results suggest that when
the information asymmetry about target value is low, the gains do shift significantly to
the target company. However, the results do not suggest that the acquirers simply
overpay for the targets at the expense of their own wealth when faced with uncertainty, 61
which would be consistent with the hubris hypothesis. Instead, results suggest that
when target uncertainty goes down, it is more likely that both parties experience
positive gains or for the target company to experience positive gains while the acquirer
loses from the transaction. As the information asymmetry about target valuation
decreases, both parties are more likely to walk away from the merger with positive
gains. Also, results suggest that both parties are bargaining on the possible synergy
gains from the transaction and the target is able to negotiate a greater portion of the
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synergy gains when the value of the target is less uncertain.

The paper is organized as follows. Section 2 presents the related prior literature
and the hypothesis development; Section 3 presents the data and methodology;
Section 4 discusses the empirical results and robustness checks. Section 5 concludes.

2. Prior literature and hypothesis development

2.1 Prior literature on information asymmetry and valuation in mergers
The impact of information asymmetries on firm value has been analyzed extensively
in the finance literature. Leland and Pyle (1977), Grossman and Hart (1981) and Myers
and Majiluf (1985) show theoretically that these informational asymmetries can have
significant effects on a firm’s financing and investment decisions.
A merger between two companies is a complex and dynamic task that requires both
long and short term strategic planning for the successful integration of the businesses.
Such plans need frequent updating to incorporate the new information gathered along
the merger process and to adapt to unexpected changes in the dynamic corporate
environment (Burgelman and McKinney, 2006). The difficulty in carrying out such
a difficult task is exacerbated by the information asymmetries between the parties
involved in the process. Information asymmetries between parties can make long and
short term planning even more uncertain and might significantly affect the likelihood
of success of the acquisition.
As explained in Parvinen and Tikkanen (2007), information asymmetry in the
merger process results from the fact that there are multiple stakeholders in the process
with different motivations and information sets. Target company managers probably
know more about their own corporate value compared to all other participants in
the merger deal. However, they have a difficult time in revealing their true value to
outside participants. This problem is similar in nature to the lemmons’ problem of
Akerlof (1970). Since signaling is costly, high quality companies with high information
asymmetries, such as those with more intangible assets, choose to go for IPOs rather
than mergers to reveal information to the markets about their quality and reduce
information asymmetries (Spence, 1974) about their true values. This leaves the M&A
market to potential targets that are of lower quality. Thus, as Reuer and Shen (2004)
suggests, the existence of IPO markets exacerbates the lemmons’ problem in the M&A
markets.
 MF Bidders, through the help of their financial advisors who probably have more
39,1 information about the sector in general, try to gather information to value the target
company and the potential synergy gains that they can achieve. Other than their
informational disadvantage to target managers, another disadvantage for bidders is the
fact that they are the initiators of the deal and they face an escalation of commitment
problem throughout the bargaining process (Staw, 1976; Whyte, 1986). Managers of
62 the bidder firms are also likely to have empire building motives (Hietala et al., 2003) and
these factors can cause the mangers of the bidder firms to be over optimistic about the
future prospects of the deal.
In addition, the financial advisors involved in the deal are highly motivated to close
the deal since their compensation is mainly contingent upon successfully completed
deals. This might also lead to more optimistic valuations about the target company
which might result in the acquirer overpaying for the target.
As one can see from these interactions, information asymmetry between the target
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and the acquirer can have a significant impact on the merger process and might
eventually cause parties to overestimate or underestimate the gains from merger.
This uncertainty might eventually cause the gains from the merger to shift from one
party to the other.
Other than the existence of the information asymmetries, the degree of information
asymmetry between parties might also affect the division of gains in mergers.
As Fulghieri and Hodrick (2006) and Morck and Yeung (2002) point out, when asset
specificity (intangible assets such as R&D expenditures) for the target is high, the
positive gain from the merger is more likely to be observed. According to Fulghieri and
Hodrick (2006), this is due to the joint effect of expected internal agency conflicts
of managers and synergy gains between parties in mergers. Managers tend to entrench
themselves in their companies when the stand alone value of their firm is relatively
high and a possible merger might be costly since the manager might be replaced after
the merger. However, the entrenchment incentive is low for company managers with
high asset specificity since the stand alone value is low relative to possible synergy
gains from mergers. As a result, mergers involving companies with greater intangible
assets are expected to generate greater value and thus might be valued more.
This might cause the target companies with greater intangible assets to be valued
more and might cause the gains to shift toward the target companies.
Since the degree of information asymmetry is not directly observable, researchers
must rely on proxy variables to test arguments on this topic. Empirical studies in
general argue that the asymmetric information problem is most severe for firms with
significant growth opportunities, and consequently, have used proxies for a firm’s
investment opportunity set as measures of information asymmetry. For example,
McLaughlin et al. (1998) use a firm’s market to book ratio as a measure of information
asymmetry and relate long run performance following a seasoned equity offering to
this variable. They find that firms with greater information asymmetry have more
negative abnormal performance following a seasoned equity offering. Following
a similar argument, in this paper, I argue that target companies with greater market
to book ratios will cause a greater information asymmetry between the target and the
acquirer firm and so there will be a greater valuation uncertainty for the target.
In addition, intangible assets such as patents are related to the growth opportunities
of the company and valuation of companies with high asset intangibility would
 be more difficult. Supporting such evidence, Barth et al. (2001) results suggests that Division of gains
more analyst coverage is needed to produce more informative stock prices for companies in mergers
with higher asset intangibility. Absent, the analyst coverage, such companies’ stock
prices become uninformative.
Thus, I use two alternative measures to market to book ratio as measures of
information asymmetry and valuation uncertainty. Following Clarke and Shastri (2000)
I use target asset tangibility ratio (property plant and equipment/total assets) and target 63
R&D ratio (Research & Development/total assets).
Given the above evidence from prior literature, I expect the degree of information
asymmetry on target value to play a significant role in the way parties involved in the
process incentivize the merger outcome. There is already an inherent lemons problem
in the merger process and the degree of information asymmetry can exacerbate the
agency conflict. This might lead to a different selection process and/or bargaining
outcome and might significant change the division of gains in mergers.
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When faced with such information asymmetry, different theories of mergers imply
different bargaining outcomes and I will explain these possible different outcomes
in Section 2.3 and form the hypotheses accordingly.

2.2 Prior literature on division of gains in takeovers

As Jensen and Ruback (1983) and Jarrell et al. (1988) make clear, there are numerous
papers that examine the stock price reaction of either the target firm or the acquiring
firm to some identified influence. Jensen and Ruback (1983), however, point out that
when bargaining issues are of concern, then the analysis needs to focus on the division
of gains rather than focusing only on the actual gains. Since this study focuses on the
division of gains between parties, I will review studies that use such a framework to
examine related issues.
To address the need for the division of gains analysis, Stulz et al. (1990) uses a
method in which dollar gains of the target, calculated from the cumulative abnormal
returns, is regressed on total dollar gains calculated from both the target and acquirer
dollar gains to examine the division of gains more directly. Using this approach, they
find that a target’s share of the gains is negatively related to bidder toeholds and
institutional ownership in the target. They further investigate the sample for single
and multiple contests and show that in multiple bidder contests, targets’ share of the
gains increase with target managerial share ownership and decrease with increases in
target institutional share ownership. Consistent with the bargaining hypothesis,
targets’ share of the gains decrease when the bargaining position of the target is
reduced (through larger acquirer toeholds) and increased when the bargaining position
of the target is increased (through more bidder competition). Institutional ownership
seems to reduce the bargaining position of target management.
Using the same division of gains approach, Hogfeldt and Hogholm (2000) examine the
effect of target blockholders, who have enough ownership to block a takeover attempt,
on the division of takeover gains between the target and the acquirer. They develop
a model that gives testable implications for the blocking effect. Examining data on
185 Swedish takeovers between 1980 and 1992, they find evidence that key parameters
associated with the blocking effect are positive and significant. Thus, they conclude that
as the blocking effect of target ownership increases, the target shareholders receive
more of the total gains.
 MF Turning to a different aspect of the bargaining process, Kale et al. (2003) and
39,1 Ma (2006) examine the effect of the quality of financial advisors for the target and
acquirer companies on the division of gains in a merger. The higher the ranking of the
financial advisor the better the party that they are advising is able to bargain in the
takeover. While Kale et al. (2003) use a different methodology to compute the division
of takeover gains than in prior studies, they find that the absolute wealth gain as well
64 as the share of the total takeover wealth gain accruing to the bidder increases as the
reputation of the bidder’s advisor increases relative to that of the target’s advisor.
Ma (2006) concentrates on the level of target advisor’s reputation instead of their
relative reputation and finds that target companies receive a 3 percent premium when
represented by a top-tier financial advisor.
Rosenkranz (2005) uses the division of gains methodology to examine the effects of
outside options and the lockup and termination provisions on the gains of the target
company in a merger. Rosenkranz argues that, in equilibrium, net termination fees are
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offered by firms with a superior bargaining position in exchange for a greater share of
merger synergies. Net termination fees and premiums are positively correlated, while
net fees decrease in targets’ bargaining power, proxied by market capitalization, and
increase in targets’ outside options, proxied by market to book ratios.
All of these studies appear to find evidence consistent with the notion that when the
target’s bargaining position is improved, the target achieves a greater share of the total
merger gains. Thus, as I analyze the division of gains in mergers under information
asymmetry of the target value, I will control for the factors that have an influence on
the bargaining position of each party in the merger. I further explain the details of the
methodology to analyze the division of gains in mergers in Section 4.

2.3 Prior literature on mergers and hypothesis development

There are four frequently cited hypotheses as to why mergers occur; the market for
corporate control, hubris, synergy and the managerial discretion hypotheses. In each of
these different hypotheses, the emphasis on company valuation differs (Mueller and
Sirower, 2003).
The market for corporate control hypothesis (Manne, 1965) assumes that the bid of
the acquirer raises the target’s share price to reflect the gains that would be realized
after replacing the incompetent target management. The model assumes that the
market for corporate control is competitive and so the premium that the successful
acquirer pays fully reflects the expected gains of the acquirer from replacing the
incumbent target management. In this hypothesis, the assumption is that the potential
acquirers can correctly value the target firm under new management and the premium
offered for the target is a result of the valuation difference between its current market
value and the expected value after the change in control.
According to the hubris hypothesis, bidders can make mistakes in their valuations
and the successful acquirers tend to overestimate the gains from the acquisition. The
bidders for the target are those potential acquirers whose target valuations exceed the
target’s current market valuation. Since the acquirer with the highest bid successfully
completes the takeover, the successful acquirer faces the winners’ curse and ends up
paying more that the fair price (Roll, 1986). In this hypothesis, the offer price of the
acquirer is a function of its target valuation and the loss the acquirer experiences is due
to its overestimation of the target’s fair value.
 The synergy hypothesis argues that once the merger is complete, new gains will Division of gains
be realized that otherwise would not be possible if the two companies operated in mergers
independently. The premium that the acquirer offers to the target is a function of the
possible synergy gains that would be achieved once the merger goes through
(Bradley et al., 1988). The synergy gains result from the marginal cash flows that will be
generated from the joint operation of the two companies. If the acquirer overestimates
(underestimates) the possible synergies from the acquisition, they may end up paying 65
more (less) for the target.
Finally, according to the managerial discretion hypothesis, the acquirer’s bid for the
target is independent of its valuation of the target but instead it merely represents
the price to close the deal. The acquirers’ motivation is to grow and the acquisition of
the target is a price that needs to be paid to achieve the goal (Mueller, 1969).
Market for corporate control, hubris and the synergy hypotheses rely on the valuation
of the target company by the acquirer whereas managerial discretion hypothesis does
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not explicitly rely on such assumption. So the hypotheses in this paper will be related
to the first three hypotheses. I differentiate between the market for corporate control,
hubris and the synergy hypotheses by analyzing the effect of information asymmetry
on the division of gains between the two parties.
If the market for corporate control hypothesis holds, in a competitive bidding
environment, the acquirer bid and its premium price should fully reflect the increased
value of the target firm under a competent management. Under this hypothesis,
a systematic misvaluation of the target by the acquirer is not implied so it should not
be a significant factor in the division of the gains between the two parties.
On the other hand, according to the synergy and hubris hypotheses, it is assumed
that the bidders can and do make mistakes in their valuations of the targets. Hubris
hypothesis suggests that on average, the successful acquirer who makes the highest
bid will tend to be overoptimistic about the target valuation and the possible synergy
gains and as a result will end up overpaying. In such a situation, the gain from the
merger will shift from the acquirer to the target.
According to the synergy hypothesis, the acquirer can overestimate (underestimate)
the synergy gains from the merger and can end up leaving more (less) of the expected
synergy gains to the target. Unlike the hubris hypothesis, when faced with uncertainty,
there is no assumption on whether the acquirer will tend to overpay or underpay for
the target. This is due to the fact that there is no bidding assumed in the synergy
hypothesis, whereas in the hubris hypothesis, the result is driven by the existence
of competing bids.
If information asymmetry does not cause any misvaluation during mergers, then
the premium paid to the target company should fully reflect the possible gains
and information asymmetry should not significantly affect the division of gains
between two parties. This would be consistent with the market for corporate control
hypothesis:
H1. Information asymmetry between the target and acquirer does not
significantly change the division of gains between two parties in mergers.
If target companies that are more difficult to value experience a significant gain in the
division of gains, then such evidence is supportive of the hubris hypothesis and
suggests that information asymmetry is a factor that changes the division of gains for
 MF the benefit of the target company. This hypothesis also suggests that on average,
39,1 target companies experience a positive gain whereas acquirer companies experience
a negative gain because it is assumed that the acquirer is paying more than its fair
price for the target and wealth is transferred from one party to the other:
H2. When information asymmetry between the target and the acquirer is high, the
gains from the merger will significantly shift towards the target and overall,
66 target companies will experience a positive total gain whereas acquirers will
experience a negative total gain from the merger.
On the other hand, if target companies that are more difficult to value experience a
significant loss in the division of gains and both the target and the acquirer experience
a positive gain from the merger, the results would be consistent with the synergy
hypothesis and would suggest that when faced with target valuation uncertainty,
acquirers tend to underestimate the actual synergy gains on average and as a result the
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premium they offer for the target is less than the fair amount. However, according to the
synergy hypothesis, both parties should still experience a positive overall gain from
the merger since the merger is expected to create additional positive value for both
parties involved. If target companies that are more difficult to value experience a
significant gain in the division of gains and both the target and the acquirer experience
positive gains from the merger, the result would again be supportive of the synergy
hypothesis but would suggest that acquirers tend to overestimate the synergy gains
from the merger and on average, the premium they offer for the target is more than the
fair amount:
H3. When information asymmetry between the target and the acquirer is high, the
gains from the merger can either shift to the acquirer or the target company
but in both cases the target and the acquirer company will experience a
positive total gain from the merger.

3. Sample and methodology

3.1 Sample
Since governance plays a critical role in the bargaining process, I identify my initial
sample as all firms with governance data in the Risk Metrics governance database
from 1990 through 2004. From this initial sample, I then focus on firms that are
dropped from the database to identify potential successful takeovers. I only consider
successful takeovers in this study.
From the Risk Metrics database dropouts, I identify 1,617 companies that were
dropped because they were acquired. For these firms to be included in the final sample,
I impose the following filters:
.
the announcement date of the takeover must be present in Thomson’s Merger
and Acquisition database;
.
both the target and the acquirer must be present in CRSP for the calculation of
abnormal returns;
.
there must be at least 100 days of trading in the six month window ending two
months prior to the first announcement date;
.
spin-offs and acquisitions out of bankruptcy are excluded;
 .
institutional stock ownership data for the target company must be available in Division of gains
Thompson’s 13f database; and in mergers
.
officers and directors stock ownership data must be available prior to the
announcement date on Compact Disclosure.

As a result of these filters, the final sample includes 627 transactions.

According to the bargaining argument in prior literature and following Goktan and 67
Kieschnick (2012), I identify the following control variables as significant factors that
play a role in the bargaining process. I report summary statistics for these variables in
Table I.
Corporate bylaws/charter provisions. To capture the antitakeover provisions in place
at a target firm, I use the Risk Metrics data, which are available for 1990, 1993, 1995, 1998,

Variable Mean Median SD

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M/B ratio 4.38 1.95 25.96

Tangibility ratio 0.31 0.25 0.24
R&D ratio 0.26 0.02 5.06
Target size 2,814.10 791.73 7,198.76
Acquirer size 18,121.30 4,185.28 49,692.59
Toehold 2.27 0.00 11.32
Insider ownership 10.55 3.16 16.41
Institution ownership 53.23 54.75 20.63
Target return 0.13 0.81 0.47
Acquirer return 0.28 0.23 0.52
Toehold 2.28 0 11.34
Dual class 0.08 0 0.27
Acquirer options 23.94 11 37.08
Top advisor 0.60 1 0.49
Multiple bidders 0.07 0 0.25
Hostile dummy 0.03 0 0.16
Unsolicited dummy 0.03 0 0.16
Collar dummy 0.13 0 0.34
Cash dummy 0.40 0 0.49
Acquirer options 24.32 11.00 38.34
Gindex 9.08 9.00 2.78
Notes: Tangible asset ratio is the value of the PP&E (property, plant and equipment)/total assets ratio
for the target company; Target return(Acquirer return) is the value of the annual return for the
target(acquirer) company in the year prior to merger; R&D ratio is the Research & Development
expenses/total assets for the target; Target size (Acquirer size) is the market value of target (acquirer)
equity calculated as of two months prior to initial offer; Toehold is the percentage of ownership of the
target firm held by the acquirer; Insider ownership is the percentage of stock held by officers and
directors in the target firm prior to the first announced takeover offer; Institutional ownership is the
percentage of stock held by institutional investors prior to the first announced takeover offer Acquirer
options represents the number of firms that received an offer that has the same four digit SIC code
within six months prior to announcement date of the target company; Gindex represents
Gompers et al.’s (2003) governance index; Multiple bidders is one if more than one bidders for the firm;
Top advisor is one if target’s financial advisor is one of the top five financial advisors; Hostile bid is one Table I.
if unsolicited bid was resisted by management; Unsolicited bid is one if the unsolicited bid was not Descriptive statistics for
resisted by management; Collar is one if a collar agreement was effected; Lockup is one if a lockup selected explanatory
agreement was effected variables
 MF 2000, 2002 and 2004. Following Gompers et al. (2003), I use the governance information
39,1 from the Risk Metrics publication prior to the first takeover announcement date for
a sample firm. Also, following Gompers et al. (2003) I compute their Gindex, which is
based upon an aggregation of the provisions identified in the Risk Metrics database.
Toehold. A toehold is the percentage of target company stock held by the acquirer
prior to the merger. Stulz et al. (1990) and Hogfeldt and Hogholm (2000) report evidence
68 that having a toehold is negatively associated with the target’s gain.
Outside options of acquirer. As modeled and tested in Rosenkranz (2005), outside
options of an acquirer is an important bargaining variable. During negotiations, the
availability of alternatives to the target company for the bidder may limit the extent to
which the target management can increase the target’s share of gains. I use the logarithm
of the number of firms that received an offer within the same four digit SIC code as the
target within six months prior to announcement date for the target company as a control
for this consideration. The number of companies receiving an offer includes both
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successful and unsuccessful offers.

Target and Acquirer size. I include proxies for the size of merger partners since the
size of a firm might affect its bargaining power. Consistent with this conjecture, Schwert
(2000) reports evidence of a negative relationship between target size and target takeover
premia. The size proxy is measured as the logarithm of the market capitalization of
each firm two months prior to the announcement date of the acquisition bid.
Institutional ownership. Stulz et al. (1990) find that higher institutional stock
ownership in the target decreases the gains to target in a merger. To measure this
ownership stake, I use Thomson’s 13f data to measure total institutional shareholdings
relative to the number of shares issued and outstanding at least six months prior the
first announcement date.
Insider ownership. According to the evidence in Stulz et al. (1990), insider ownership,
or the stock ownership of officers and directors, influences the bargaining position of
the target firm. I measure insider ownership as the percentage of target firm stock held
by officers and directors as reported on Compact Disclosure using SEC filings prior to
the announcement date.
Multiple bidder dummy. Bradley et al. (1988) and Stulz et al. (1990) find that having
multiple bidders for a target increases the gains of the target. Such evidence is
consistent with the notion that an auction serves the interests of target shareholders.
I account for this consideration by using a dummy variable that takes on the value 1 if
there is more than one offer for the target.
Dual class firms. Daines and Klausner (2001) suggest that dual class stock is a
substitute for most antitakeover provisions and so should be identified separately.
Consequently, I create a dummy variable that takes on the value of 1 if a firm has dual
class common stock.
Top-tier financial advisor. Top-tier financial advisors may increase the target gain
by matching the target company with an acquirer that can create more synergy or they
may be able to use their wider network to create an auction among possible acquirers
and increase the gains for the target. Ma (2006) show that targets represented by one of
the top five financial advisors gain 3 percent more premium on average. Thus, I include
a dummy that equals one if the target’s financial advisor is one of the top five advisors
(using Rau’s (2000) procedure): Credit Suisse First Boston, Goldman Sachs,
Lazard Freres, Morgan Stanley and Salomon Brothers.
 Hostile dummy. Hostile transactions are typically considered as unsolicited Division of gains
transactions in which the target resisted but at the end, the merger was completed. in mergers
Schwert (2000) argues that hostile acquisitions are actually an outcome of strategic
bargaining process in which target firms strategically resist the offer to order increase
their gain from the merger. Consequently, I create a dummy variable that takes on the
value 1 if the target resisted the offer.
Unsolicited dummy. These are transactions in which the offer made by the acquirer 69
to the target was unsolicited but the target did not resist the offer and the offer was
completed. The difference between the hostile and unsolicited is that in the unsolicited
case the target did not resist and maybe did not negotiate as aggressive as those firms
that are classified as a hostile transaction. Schwert (2000) shows that target premia are
negatively related to these types of offers. Thus, I create a dummy variable that takes
on the value one if the acquirer made an unsolicited but unresisted offer for the firm.
Collar dummy. A collar provision adjusts the exchange ratio or the price received
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by target shareholders in the event that the acquirer’s stock price goes outside specified
boundaries during the period between announcement and closing. According to
Subramanian (2003), to the extent that these provisions are traded off against the deal
price, one would expect to see a negative correlation between a collar agreement and
deal premiums (because the target should pay to receive this insurance). I control for
the existence of a collar provision with a dummy variable that takes on the value one
if it exists.
Lockup agreements. Lockup agreements grant the incumbent bidder a call option on
the target’s shares or assets, exercisable in the event that the target cancels the
agreement to accept a competing bid. Rosenkranz (2005) shows that in equilibrium, net
termination fees and lockup arrangements are offered by firms with a superior
bargaining position in exchange for a greater share of merger synergies. I include a
dummy for the existence of lockup agreement between the parties.
Tender offer dummy. Subramanian (2003) shows that friendly deals executed
through a first stage tender offer are far more likely to close than deals that are
executed through merger agreement, perhaps due to the faster execution of a tender
offer which might reduce the possibility of other bidders. The acquirer thus needs to
pay for this additional insurance that a tender offer provides. Consistent with the point,
prior evidence suggests that targets receive higher premiums in tender offer takeovers.
Consequently, I create a dummy variable that takes on the value one if the acquirer
makes a tender offer for the target firm.
White knight dummy. A white knight is a friendly party in a takeover and generally
purchases a stake in the takeover target in an attempt to block a planned takeover. The
existence of a white knight may increase the bargaining power of a target company,
resulting in a greater portion of the gain. A dummy variable is set to one if the
transaction involved a white knight.
Financial characteristic variables for the target and the acquirer. For each financial
characteristic below, I create two measures. The first measure represents the median
value of that financial characteristic for the firm’s industry, using Fama and French’s
48 industry delineations[1]. The second measure represents the firm value less its
industry’s median value. The separation of a firm’s financial measure into its industry
median and its deviation from its industry’s median allows me to identify the extent
to which industry conditions versus firm-specific conditions might account for a state
 MF that the company is in. I prefer to use deviation from the median instead of a ratio of
39,1 firm value to industry value because Astebro and Winter (2002) point out the ratio of
firm value to industry value can lead to economically meaningless interpretations
because they show that industry average financial ratios vary considerably over time
which might add noise to the estimation rather than having the industry adjustment
reflect some target level.
70 Second, and more importantly, the ratio of a firm’s value to its industry’s value as a
measure fails to make clear the extent to which it is acquired as a result of conditions in
their industry. Thus, I prefer to separate a firm’s measure into its industry and
firm-specific components.
R&D ratio. Previous studies suggest that information asymmetry increases with
the fraction of a company’s intangible assets, such as technology development (Clarke
and Shastri, 2000). Companies become more difficult to value when they have a greater
fraction of such assets. This variable is defined as Research & Development
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expenses/total assets.
Tangible asset ratio. Previous studies suggest that information asymmetry
decreases with the fraction of a company’s tangible assets, such as property, plant and
equipment (Clarke and Shastri, 2000). Company valuation uncertainty decreases when
they have a greater fraction of such assets. This variable is defined as PPE (property,
plant and equipment)/total assets.
Market to book value of equity. Many papers suggest that greater investment
opportunities are associated with greater information asymmetry since such
information is more available to insiders compared to outside investors.
McLaughlin et al. (1998) uses the ratio to examine the relation between information
asymmetry and the long run performance following information asymmetry.
Annual return. Previous studies suggest that performance of the target and the
acquirer can be a function of the gains. To control for the performance of the target and
the acquirer, I use the annual return of the companies in the year prior to the merger.

3.2 Methodology for the examination of the target, bidder and total gains by the division
of gains approach
Since I focus on the division of takeover gains between target and acquirer, I need to
compute measures of target, acquirer, and total gains for each sample transaction. For
these measures, I follow the method initially proposed in Bradley et al. (1988) and used in
Stulz et al. (1990), Hogfeldt and Hogholm (2000), Rosenkranz (2005) and Goktan and
Kieschnick (2012). Schwert (1996) and Goktan and Kieschnick (2012) uses a different
interval for estimation and computation than did Bradley et al. (1988) because of
differences in samples. Bradley, Desai and Kim restrict their data to successful tender
offers, which typically uses a much smaller event window (from the announcement date
until the deal closure). Schwert (1996) and Goktan and Kieschnick (2012), on the other
hand, use all successful takeovers, which includes tender offers and negotiated mergers.
I do so as well because as Schwert (2000) points out, both of these methods are
the outcome of a bargaining process between two parties. The primary difficulty that
this creates is that the inclusion of negotiated mergers requires a longer window after the
announcement period since negotiated deals usually take much longer to complete.
Another difference between Schwert (1996) and Bradley et al. (1988) is the
consideration given to the possible information leakage prior to announcement date.
 Schwert (1996) finds that there is an increasing positive cumulative abnormal return Division of gains
starting two months prior to announcement date. Thus, starting to calculate cumulative in mergers
abnormal returns starting two months prior to announcement date also helps to capture
the gains to both parties in a more complete way.
Like the above research, my estimates of the gains are based on market model
prediction errors. Specifically, I define the abnormal return for firm i on day t to be
ARit ¼ Rit 2 ai 2 BiRm. For each firm, ai and Bi are calculated with six months 71
(127 trading days) of daily returns (not less than 100 daily returns) ending two months
prior to the first bid announcement date. Rit is the realized return to firm i on day t and
Rmt is the return to the CRSP value-weighted portfolio of listed stocks for day t.
After the market model parameters are obtained, the cumulative abnormal returns
for both targets (T_CAR) and acquirers (A_CAR) are calculated for the period two
months (42 trading days) before the first offer announcement date till six months
(127 trading days) after the announcement date or till target is delisted[2]. Thus,
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I restrict the sample to firms that successfully completed the merger within six months
after the first bid.
Following Bradley et al. (1988), I compute the dollar gain to the target and acquiring
firms in each tender offer contest i as:

T_WLTHi ¼ WTi *Ti _CAR ð1Þ

A_WLTHi ¼ WAi *Ai _CAR ð2Þ

where WTi is the market value of the target’s common stock as of two months prior to
the first bid announcement for the target minus the market value of the shares held by
acquirer, and WAi is the market value of the acquirer’s common stock two months
prior to the first announced bid for the target firm. Double counting of the gains that
accrue to the bidder through its holdings of target shares is avoided by using the
number of shares not held by the bidder. Total gain from the transaction is the gain to
a weighted average portfolio of the target and the acquirer (C_WLTH). The descriptive
statistics for the variable mentioned above are presented in Table I.
The average cumulative abnormal return graphs for target and acquirer returns
are shown in Figures 1 and 2, respectively. The statistics for T_CAR and T_CAR are
also given in Panel A of Table I. The results are very similar to those in Schwert (1996).
The target cumulative average abnormal return starts to increase gradually from two
months prior and the average cumulative abnormal return as of six months after the
announcement date is 30.97 percent which is very close to the 30.1 percent reported in
Schwert (1996). With regard to returns to acquirers, Schwert (1996) found, as did earlier
studies, that in estimating parameters to measure CARs, the market model had a
positive slope during the preannouncement period. Asquith (1983) also found that
bidder firms had unusual stock price increases prior to their decisions to make
takeover bids. This fact potentially inflates the market model intercept.
Since I face the same concern, I follow Schwert (1996) and constrain each bidder’s
market model intercept to zero as a crude correction for this problem. Figure 2 shows the
average cumulative abnormal returns for both the constrained (meancar_int) and
unconstrained (meancar_noint) model. The correction seems to eliminate the negative
trend in the CARs for bidder. The result posted for cumulative average abnormal
return in Schwert (1996) for successful bidders is 1.4 percent whereas these results
 MF Cumulative Average Abnormal Returns for Target Companies
39,1

Cumulative Average Abnormal Returns

0.3

0.2
72
0.1

0
–50 0 50 100 150
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Event Date Relative to First BId

Figure 1.
Cumulative average Notes: Cumulative average abnormal returns to target firms’
abnormal returns for stocks from trading day –42 to 127 relative to the first bid for
target companies in 690 target firms from 1990-2004; market model parameters
successful mergers around used to define abnormal returns are estimated using the CRSP
the announcement period
value-weighted portfolio for days –294 to –42

Cumulative Average Abnormal Returns for Target Companies

Cumulative Average Abnormal Returns

0.04

0.02

–0.02

–0.04

–0.06

–50 0 50 100 150

Event Date Relative to First BId
meancar_noint meancar_int

Notes: Cumulative average abnormal returns to acquirer firms’

stocks from trading day –42 to 127 relative to the first bid for
Figure 2. 690 target firms from 1990-2004; market model parameters
Cumulative average used to define abnormal returns are estimated using the CRSP
abnormal returns for value-weighted portfolio for days –294 to –42; the upper line
acquirer companies in (meancar_noint) hows the effect of setting the intercepts to
successful mergers around zero; this adjustment removes the downward drift that is caused
the announcement period
by the abnormall high stock returns during estimation period
 give 3.04 percent. Although there is a slight difference in my results compared to Division of gains
Schwert’s (1996) result, when compared to the acquirer returns summarized in Jensen in mergers
and Ruback (1983) for successful acquisitions, my results are reasonable. The results are
also based on IRRC data which consists of S&P 1,500 companies that are relatively
larger compared to the average company used in other studies and that might also create
difference in the results. Nevertheless, results I have are in line with previous research.
When I compare the average cumulative abnormal return of the target to the acquirer 73
in Panel A of Table II, I see that the target has almost ten times larger CAR compared to
the acquirer (30.97 percent vs 3.04 percent). On the other hand, the average wealth gain of
the target is around three times greater than the acquirer’s ($620 million vs $196 million).
Malatesta (1983) argues that it is not possible to construct a meaningful measure of
the proportional wealth gain using percentage returns. The reason is that the acquiring
firms are generally much larger than target firms, so the same dollar wealth gain
results in a disproportionately large CAR for the average target firm when compared to
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bidder’s CAR. The descriptive statistics in Panel A of Table II support this argument.
To test the effect of the target value uncertainty on the division of gains, I regress the
target’s gain (T_WLTH, net of bidder’s toehold) on the share of the total gain (C_WLTH),
the target uncertainty variable, and selected control variables. Like Stulz et al. (1990), the
gain variables are normalized by the size of the target equity to avoid heteroscedasticity.
The dependent variable – normalized target gain (Ti ¼ T_WLTH/target size) – thus
becomes the target’s abnormal return. Nevertheless, the normalized total gain is
in the same units as the normalized target gain, so that, if the target shareholders

Panel A: descriptive statistics of abnormal wealth gains for targets and acquirers in sample takeovers
Mean Median SD Min. Max.
Target abnormal return,
T_CAR (%) 30.97 26.62 42.06 289.54 263.43
Target abnormal wealth
gain, T_WLTH ($million) 632.42 147.39 2,397.87 2 2,933.92 37,747.38
Acquirer abnormal return,
A_CAR (%) 3.42 3.76 28.57 279.21 182.31
Acquirer abnormal wealth
gain, A_WLTH ($million) 196.16 41.34 7,716.31 2 87,807.67 63,183.45
Combined abnormal return,
C_CAR (%) 8.27 7.91 27.60 2105.60 152.53
Combined abnormal wealth
gain, C_WLTH ($million) 812.60 195.46 8,261.05 2 87,300.00 63,200.00
Panel B: distribution of positive and negative wealth gains
Acquirer
Negative Positive Total
Target
Negative 77 55 132 (19.10%)
Positive 219 340 559 (80.90%)
Total 296 (42.84%) 395 (57.16%) 691 (100%)
Notes: The sample consists of 691 successful merges from 1990-2004; T_CAR, B_CAR and C_CAR
represent the target abnormal returns, bidder abnormal returns and combined abnormal returns to the
target and the bidder, respectively; T_WLTH and B_WLTH denote the abnormal wealth effects to the Table II.
target and the bidder, respectively, and C_WLTH is the return to the weighted average portfolio of Target and
the target and the acquirer acquirer gains
 MF got all of the total gain irrespective of the other explanatory variables, the regression
coefficient on the normalized total gain would be one. I allow for a different effect of
39,1 positive and negative total gains on the target’s gain. The target gain does not have to
be positively related to the takeover gain when the total gain is negative. I therefore
estimate the following regression:
0 þ
74 Ti ¼ c þ a0 xi þ b0 yi þ h2 g2 þ 0
i þ d *i gi þ ui ð3Þ
where c is a constant, gi is the normalized total gain (C_WLTH/target size); d is a dummy
that equals 1 if the normalized total gain is positive; xi is the target valuation uncertainty
variable, and y is a vector of control variables that influence the target’s share of the total
takeover gain. The estimates of the regression coefficients on the target uncertainty
variable can be interpreted as the effect of this variable on the target’s abnormal return
conditional on the total takeover gain.
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4. Empirical results and robustness

4.1 Empirical results
To be able to test the hypotheses, the analyses are going to be on the division of gains and
whether the gains for the target and acquirer are positive or negative. In Table II, Panel B
I display the joint frequency distribution of positive and negative abnormal wealth gains
for the target and the acquirer in successful mergers. Note that the target’s abnormal
wealth gain is positive in 80 percent of the sample, which is consistent with Asquith
(1983), and the acquirer’s wealth gain is positive in 57 percent of the sample. Though
not shown, the total wealth gain is positive in 65 percent of the sample.
These figures are interesting because they imply that the target’s abnormal wealth
gains are negative in 20 percent of the sample; the acquirer’s wealth gains are negative
in 43 percent of the sample; and total wealth gains are negative in 35 percent of the
sample. It important to focus on these estimates because prior research has focused its
attention on the averages of total gains and ignored the heterogeneity in these results
and their implications.
Specifically, if researchers are willing to interpret positive abnormal returns as
evidence of the market’s assessment that a transaction creates shareholder value, then
they must be willing to recognize that negative abnormal returns represent evidence of
the market’s assessment that a transaction destroys shareholder value. Put more
directly, some successfully completed merger transactions do not increase either target
or acquirer shareholder wealth. One may question whether the market’s assessment is
an unbiased assessment, but the evidence in Kaplan and Weisbach (1992) suggests that
it is. Further, the evidence in both Kaplan and Weisbach (1992) and Ravenscraft and
Scherer (1987) suggest that there are a number of successful takeovers that probably
should not have occurred.
To test H1, I run the division of gains regression model that I define in Section 5. The
control variables I use have the expected sign and significance and are consistent with
prior research. The variable of interest here is the market to book value of the target firm,
which is the proxy for uncertainty in target valuation as I define and provide motivation
for the choice of the variables in Section 3. As I explain further in Section 3, following
Astebro and Winter (2002), for every financial variable in the analyses, I create two
measures, the industry median of the variable and the deviation of the company value
from its industry median. According to the results in Table III, as the median market to
 Division of gains
Coefficient p-value
in mergers
Target M/B_D 20.001 0.00
Target M/B_M 0.005 0.09
Target return_D 20.170 0.01
Target return_M 20.351 0.02
Acquirer return_D 0.030 0.46
Acquirer return_M 0.099 0.40
75
Target size 20.062 0.00
Acquirer size 0.015 0.27
Toehold 0.001 0.48
Multiple bidders 0.102 0.16
Negaitve Normalized Total Gain 0.000 0.86
Positive Normalized Total Gain 0.301 0.00
Institutional ownership 0.002 0.04
Insider ownership 20.000 0.89
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Top advisor 2 0.013 0.74

Target Gindex 0.013 0.04
Acquirer Gindex 20.006 0.32
Dual class 0.304 0.05
Unsolicited offer 20.156 0.06
Cash offer 0.077 0.09
Hostile 0.107 0.14
Lockup 20.03 0.47
Constant 0.117 0.53
Two digit SIC Industry Dummies Yes
Observations 390
R2 0.46
Notes: The OLS regression is based on a sample of 390 completed mergers from 1990 to 2004; the
dependant variable is the abnormal wealth gain (T_WLTH) of the target company normalized by
target size; Target M/B_M is the median value of the market value of equity/book value of equity ratio
for the target company’s industry; industries are defined as Fama and French’s 48 industry
delineations; Target M/B_D is the deviation of the market to book ratio for the target company from its
industry median (Target M/B_M); Target return_M(Acquirer return_M) is the median value of
the annual return for the target(acquirer) company’s industry; Target return_D is the deviation of the
target return for the target company from its industry median (Tangible asset ratio_M); Target size
(Acquirer size) is the log of market value of target (acquirer) equity calculated as of two months prior to
initial offer; Toehold is the percentage of ownership of the target firm held by the acquirer; Insider
ownership is the percentage of stock held by officers and directors in the target firm prior to the first
announced takeover offer; Institutional ownership is the percentage of stock held by institutional
investors prior to the first announced takeover offer Acquirer options represents the number of firms
that received an offer that has the same four digit SIC code within six months prior to announcement
date of the target company; Dual class is one if the firm has dual class common stock; Gindex Table III.
represents Gompers et al.’s (2003) governance index. Multiple bidders is one if more than one bidders Analysis of the effect of
for the firm. Top advisor is one if target’s financial advisor is one of the top five financial advisors; target value uncertainty
Negative Normalized Total Gain is the negative total gain (C_WLTH , 0) normalized by target size; on the division of gains in
Positive Normalized Total Gain is the positive total gain (C_WLTH . 0) normalized by target size; successful corporate
standard errors are estimated using White’s (1980) heteroskedasticity-consistent standard errors takeovers

book value of the industry (M/B_M) goes up so does the portion of the total gains that the
target receives from the merger. This is intuitive since on average, companies in
industries with high market to book ratios can bargain more for the greater share of the
takeover gains since they have greater growth potential then other industries and during
 MF due diligence process for target valuation, industry median can be accepted as the norm.
39,1 However, as the deviation of a company’s market to book ratio from its industry median
(M/B_D) goes up, it has a negative and significant effect on the division of gains in
mergers. This result suggests that as a company increases in M/B ratio beyond its
industry average, they receive a smaller portion of the total gains. As a result, H1, which
argues that targets are always priced correctly at fair value and so uncertainty on target
76 valuation should not have an effect on how the gains in mergers are distributed between
two parties is not supported. The results suggest that as the deviation of a target
company’s market to book value from its industry median goes down, the gains
significantly shift towards the target firm.
This result is also contrary to H2 because H2 suggests that target companies should
gain more when the target valuation is more uncertain since uncertainty should lead
toan overpayment for the target company in a competitive bid market. To further test
the second argument in H2, I examine the effect of target value uncertainty on the odds
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of target shareholders receiving a positive takeover gain whereas acquirer shareholders

experiencing positive abnormal returns as a result of the takeover. The idea here is that if
acquirers consistently overpay for the target, targets are more likely to experience a
positive gain overall whereas acquirers are more likely to experience a negative gain.
I estimate a probit regression model with a dependent variable that takes on the
value one if the target receives positive gain whereas the acquirer experiences a negative
abnormal wealth gain. I report the results of this estimation in Table IV. According to
the results, deviation of the target value uncertainty from its industry median (M/B_D)
has a significant and negative effect on the dependent variable. In other words, as target
value uncertainty is resolved, we are less likely to observe a gain for the target at the
expense of the acquirer. However, H2 suggests a positive relation between this variable
of interest and the dependent variable so this piece of evidence also rejects H2.
The result in Table III where I find significant shift in gains to the target company is
not contrary to H3 since according to the synergy hypothesis, the gains can shift
significantly to either party under target valuation uncertainty. However, for H3 to be
fully supported, in addition to the above piece of evidence, the gains to both parties have
to be positive because the total synergy created from the merger should benefit both
parties. Thus, I also need to analyze the effect of target value uncertainty on the gains of
the two parties as I did for H2. To examine the effect of target value uncertainty on the
odds of target and acquirer shareholders experiencing positive abnormal returns as a
result of the takeover, I estimate a probit regression model with a dependent variable
that takes on the value one if both the target and acquirer abnormal wealth gains are
positive. I report the results of this estimation in Table V. According to the results,
deviation of the target value uncertainty from its industry median (M/B_D) does have a
significant and negative effect on the likelihood of observing positive gains for both
parties. In other words, as target value uncertainty is resolved, both parties are more
likely to have positive gains from the merger. This result is supportive of H3 and
suggests that as target company valuation uncertainty decreases, the odds of both
parties receiving a positive gain from the transaction goes up.
Overall, results are supportive of H3. Results suggest that acquirers do make
mistakes in determining the fair value of the target company (contrary to H1) and
as a result the gains do shift significantly to the target company. However, the results do
not suggest that the acquirers simply overpay for the targets at the expense of its own
 Division of gains
Coefficient p-value
in mergers
Target M/B_D 2 0.003 0.07
Target M/B_M 2 0.0905 0.55
Target return_D 2 0.226 0.20
Target return_M 0.02 0.95
Target size 2 0.045 0.429 77
Acquirer size 0.055 0.191
Insider ownership 0.001 0.77
Institutional ownership 2 0.003 0.44
Toehold 2 0.004 0.57
Acquirer options 2 0.084 0.24
Dual class 0.54 0.05
Gindex 0.039 0.12
Multiple bidders 2 0.186 0.50
Top advisor 2 0.026 0.85
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Hostile bid 0.178 0.65

Unsolicited bid 0.494 0.24
Collar 0.102 0.61
Constant 2 0.002 0.99
Two digit SIC Industry Dummies Yes
Observation 472
Notes: The dependent variable is a dummy variable that takes on the value 1 if the abnormal return
for the target is positive and the acquirer target return is negative, where those abnormal returns are
computed from xx days prior to the announcement date until completion of the transaction; Target M/
B_M is the median value of the market value of equity/book value of equity ratio for the target
company’s industry; industries are defined as Fama and French’s 48 industry delineations; Target M/
B_D is the deviation of the market to book ratio for the target company from its industry median
(Target M/B_M); Target return_M is the median value of the annual return for the target company’s
industry; Target return_D is the deviation of the target return for the target company from its industry
median (Tangible asset ratio_M); Target size (Acquirer size) is the log of market value of target Table IV.
(acquirer) equity calculated as of two months prior to initial offer; Toehold is the percentage of Analysis of the influence
ownership of the target firm held by the acquirer; Insider ownership is the percentage of stock held by of target valuation
officers and directors in the target firm prior to the first announced takeover offer; Institutional uncertainty on the
ownership is the percentage of stock held by institutional investors prior to the first announced likelihood of target
takeover offer Acquirer options represents the number of firms that received an offer that has the same shareholders receiving
four digit SIC code within six months prior to announcement date of the target company; Gindex positive abnormal returns
represents Gompers et al.’s (2003) governance index; Multiple bidders is one if more than one bidders and acquirer
for the firm; Top advisor is one if target’s financial advisor is one of the top five financial advisors; shareholders receiving
Hostile bid is one if unsolicited bid was resisted by management; Unsolicited bid is one if the unsolicited negative abnormal
bid was not resisted by management; Collar is one if a collar agreement was effected; Lockup is one if returns from a successful
a lockup agreement was effected takeover

wealth, which would be consistent with the hubris H2. Instead, results suggest that
when target uncertainty goes down, it is more likely that both parties experience positive
gains but it is more likely for the target company to receive more of the potential gains.
These results are supportive of the synergy hypothesis. As the target value
becomes predictable, both parties are more likely to walk away from the merger with
positive gains. Also, both parties are bargaining on the possible synergy gains from the
transaction and the target is able to negotiate a greater portion of the synergy gains
when the value of the target becomes more predictable. During the bargaining for the
 MF
Coefficient p-value
39,1
Target M/B_D 2 0.024 0.09
Target M/B_M 0.225 0.128
Target return_D 2 0.946 0.00
Target return_M 0.076 0.654
78 Target size 2 0.085 0.142
Acquirer size 2 0.028 0.50
Insider ownership 0.007 0.13
Institutional ownership 0.005 0.17
Toehold 2 0.013 0.85
Acquirer options 0.008 0.26
Dual class 2 0.426 0.15
Gindex 0.019 0.43
Multiple bidders 0.432 0.12
Top advisor 0.218 0.11
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Hostile bid 2 0.099 0.80

Unsolicited bid 2 0.786 0.09
Collar 0.064 0.74
Constant 2 1.473 0.04
Two digit SIC Industry Dummies Yes
Observations 484
Notes: The dependent variable is a dummy variable that takes on the value 1 if the target abnormal
returns for both the target and the acquirer are positive, where those abnormal returns are computed
from xx days prior to the announcement date until completion of the transaction; Target M/B_M is the
median value of the market value of equity/book value of equity ratio for the target company’s
industry; industries are defined as Fama and French’s 48 industry delineations; Target M/B_D is the
deviation of the market to book ratio for the target company from its industry median (Target M/B_
M); Target return_M is the median value of the annual return for the target company’s industry;
Target return_D is the deviation of the target return for the target company from its industry median
(Tangible asset ratio_M); Target size (Acquirer size) is the log of market value of target (acquirer)
equity calculated as of two months prior to initial offer; Toehold is the percentage of ownership of the
Table V. target firm held by the acquirer; Insider ownership is the percentage of stock held by officers and
Analysis of the influence directors in the target firm prior to the first announced takeover offer; Institutional ownership is the
of target valuation percentage of stock held by institutional investors prior to the first announced takeover offer Acquirer
uncertainty on the options represents the number of firms that received an offer that has the same four digit SIC code
likelihood of both within six months prior to announcement date of the target company; Gindex represents Gompers
target and acquirer et al.’s (2003) governance index; Multiple bidders is one if more than one bidders for the firm; Top
shareholders receiving advisor is one if target’s financial advisor is one of the top five financial advisors; Hostile bid is one if
positive abnormal returns unsolicited bid was resisted by management; Unsolicited bid is one if the unsolicited bid was not
from a successful resisted by management; Collar is one if a collar agreement was effected; Lockup is one if a lockup
takeover agreement was effected

synergy gains, acquirers end up leaving more of the potential gains for the target and in
some cases may even experience a negative overall return if the bargaining power of the
target goes up due to having more certain valuation. Results suggest that the bargaining
position of the target firm is improving when their valuation becomes more certain and
they get a greater portion of the possible synergy gains.

4.2 Robustness
The main target valuation uncertainty measure is the market to book ratio. This variable
is used extensively in literature to proxy for different factors, such as growth potential
 and investor optimism. In all of the different contexts in which market to book value Division of gains
is used, company valuation is becoming a greater challenge but one might also argue in mergers
that the variable might also pick up other factors which might be leading the results.
For that reason, I introduce two alternative measures – tangible asset ratio and
Research & Development ratio – that would also serve as proxy for valuation
uncertainty. I provide the definition and the rationale for the variables in Section 3. In
Table VI, I re-run the test in Table III but I replace market to book ratio with tangible 79
asset ratio. The results are consistent with the previous results. Results suggest that as
the deviation of the tangibility ratio from the industry median increases for the target
company, the gains shift more towards the target company. The same is true for the
industry median of the variable. This result confirms the previous finding which
suggests that target valuation certainty increase the bargaining power of the target
company in mergers.
In Table VI, Panel B, I report the results for the model using the R&D ratio. Since R&D
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ratio variable is missing for a large portion of the dataset, I do not break down each
variable into its industry median and its deviation to gain power on the regressions. These
results are also consistent with prior findings. Companies with greater R&D ratio receive
less of the total gains from the merger. Since R&D measures the intangibility portion of
the company assets, the negative relations suggests that as uncertainty about the
company valuation increases, the bargaining position of the target company decreases.

5. Conclusion
I analyze the implications of the target valuation uncertainty on the wealth distribution
between the target and acquirer firms in successful mergers. I specifically analyze the
division of the total dollar gains between the target and the acquirer and also whether
the target and/or the acquirer experience a positive/negative gain in mergers when
valuation of the target company is more uncertain. In other words, as the valuation of
the target becomes more uncertain, do the gains from the merger significantly shift to
any of the two parties and if so, who typically gains more from such uncertainty? This
distinction is important since it gives a unique opportunity to differentiate amongst the
well-known hypotheses in mergers.
The analyses contrast the implications of the information asymmetry in three
well-known merger hypotheses; the market for corporate control, hubris and synergy.
I do not find support for the market for corporate control hypothesis because results
suggest that acquirers cannot be precise in determining the fair value of the target
company after the merger and as a result, the gains do shift under different levels of
information asymmetry. The results do not suggest that the acquirers simply overpay for
the targets at the expense of their own wealth when faced with information asymmetry,
which would be consistent with the hubris hypothesis. Overall, results are supportive of
the synergy hypothesis. Results suggest that when the information asymmetry about
target value is low, the gains do shift significantly to the target company. Both parties are
bargaining on the possible synergy gains from the transaction and the target is able to
negotiate a greater portion of the synergy gains when the value of the target is less
uncertain.
Also, when target uncertainty goes down, it is more likely that both parties experience
positive gains or for the target company to experience positive gains while the acquirer
loses from the transaction. These results favor synergy hypothesis over hubris
 MF
39,1 Coefficient p-value

Panel A
Tangible asset ratio_D 0.363 0.10
Tangible asset ratio_M 0.323 0.07
Target return_D 2 0.156 0.02
80 Target Return_M 2 0.368 0.01
Acquirer return_D 0.048 0.22
Acquirer return_M 0.09 0.48
Target size 2 0.054 0.01
Acquirer size 0.004 0.81
Toehold 0.002 0.26
Multiple bidders 0.099 0.22
Negaitve Normalized Total Gain 0.000 0.97
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Positive Normalized Total Gain 0.295 0.00

Institutional ownership 0.002 0.03
Insider ownership 0.000 0.63
Top advisor 2 0.040 0.38
Target Gindex 0.013 0.05
Acquirer Gindex 2 0.009 0.183
Dual class 0.317 0.04
Unsolicited offer 2 0.210 0.04
Cash offer 0.093 0.05
Hostile 0.063 0.44
Lockup 2 0.041 0.39
Constant 2 0.684 0.03
Two digit SIC Industry Dummies Yes
Observations 367
R2 0.463
Panel B
R&D ratio 2 0.066 0.05
Target size 2 0.062 0.05
Acquirer size 0.02 0.26
Toehold 2 0.001 0.70
Multiple bidders 0.073 0.56
Negaitve Normalized Total Gain 0.000 0.97
Positive Normalized Total Gain 0.375 0.00
Institutional ownership 0.003 0.08
Insider ownership 2 0.000 0.91
Top advisor 2 0.028 0.68
Target Gindex 2 0.005 0.59
Dual class 0.766 0.00
Table VI. Unsolicited offer 2 0.191 0.11
Robustness checks on the Cash offer 0.126 0.11
effect of target value
Hostile 2 0.01 0.57
uncertainty on the
division of gains in Lockup 2 0.031 0.63
successful corporate Constant 2 0.985 0.01
takeovers (continued)
 Division of gains
Coefficient p-value
in mergers
Two digit SIC Industry Dummies Yes
Observations 206
R2 0.443
Notes: The probit regression is based on a sample of 367 completed mergers from 1990 to 2004; the 81
dependent variable is the abnormal wealth gain (T_WLTH) of the target company normalized by
target size; Tangible asset ratio_M is the median value of the PP&E (property, plant and equipment)/
total assets ratio for the target company’s industry; industries are defined as Fama and French’s 48
industry delineations; Tangible asset ratio_D is the deviation of the tangible asset ratio for the target
company from its industry median (Tangible asset ratio_M); Target return_M(Acquirer return_M) is
the median value of the annual return for the target(acquirer) company’s industry; Target return_D is
the deviation of the target return for the target company from its industry median (Tangible asset
ratio_M); R&D ratio is the Research & Development expenses/total assets; Target size (Acquirer size)
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is the log of market value of target (acquirer) equity calculated as of two months prior to initial offer;
Toehold is the percentage of ownership of the target firm held by the acquirer; Insider ownership is the
percentage of stock held by officers and directors in the target firm prior to the first announced
takeover offer; Institutional ownership is the percentage of stock held by institutional investors prior to
the first announced takeover offer Acquirer options represents the number of firms that received an
offer that has the same four digit SIC code within six months prior to announcement date of the target
company; Dual class is one if the firm has dual class common stock; Gindex represents Gompers et al.’s
(2003) governance index; Multiple bidders is one if more than one bidders for the firm; Top advisor is
one if target’s financial advisor is one of the top five financial advisors; Negative Normalized Total
Gain is the negative total gain (C_WLTH , 0) normalized by target size; Positive Normalized Total
Gain is the positive total gain (C_WLTH . 0) normalized by target size; standard errors are estimated
using White’s (1980) heteroskedasticity-consistent standard errors Table VI.

hypothesis since higher information asymmetry does not cause the acquirer to overpay
for the target.
The implication of the results in this paper is twofold. First, the results have important
implications for managers of the new start-up companies that are aiming to be acquired.
Focusing too much on intangible assets such as R&D and less on the operational aspects
of the business might not lead to optimal outcome in terms of the gains the target company
can achieve during acquisition negotiations. Such companies might consider more vertical
integration and/or enter into product development and sales before opening themselves
for acquisitions. Such action would reduce the uncertainty about the future prospects of
the business and can increase their bargaining position during negotiations.
Second, from the acquirer’s perspective, shareholders should not necessarily be
worried about a possible loss in wealth when the uncertainty about the target company
is high. Such mergers still create positive total synergy gain in mergers and the
managers seem to bargain for a greater share of the synergy gains when faced with
greater target valuation uncertainty.
To be able to analyze the division of gains in mergers, in this study, I only include
mergers that involve publicly traded targets and acquirers. An interesting extension of
this research would be to compare the abnormal returns for the acquirer shareholders
once they takeover firms that are private versus public. Since private firms are not
subject to strict disclosure standards as public firms, the effect of information
asymmetry in such mergers might be amplified.
 MF Notes
39,1 1. I use the median rather than the mean of industry values to mitigate the effect of outliers
in Compustat data on the industry measure.
2. I recognize that this creates a downward bias in the estimates of the target’s gain, but
changing this to an earlier date does not appear to change the conclusions.

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About the author

Mehmet Sinan Goktan is an Assistant Professor of Finance at the College of Business and
Economics at California State University East Bay. He received his PhD in Finance
from The University of Texas at Dallas in 2008. Mehmet Sinan Goktan can be contacted at:
sinan.goktan@csueastbay.edu

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