Information Asymmetry and the

Revaluation Effect after Failed Mergers

and AcquisitionsJuly 1, 2017

Abstract

Through the use of exogenously withdrawn mergers and acquisitions, this is the first

paper that investigates the effect of information asymmetry on failed mergers and acquisi-

tions. Based on the findings of Malmendier et al. (2016) and Liu (2017), a difference in

revaluation between opaque and transparent targets is expected. Two of the three measures

of information asymmetry used by this paper confirm this variation. While the difference

in revaluation between cash and stock bids is still present, the results show a lower revalua-

tion for opaque cash targets compared to transparent cash targets, and a higher revaluation

for opaque stock targets compared to transparent stock targets.

Master ThesisName Thijs DijkmanStudent nr. 10359524Supervisor prof. dr. T. CaskurluSpecialisation Corporate FinanceUniversity University of Amsterdam, Amsterdam Business SchoolFaculty Faculty of Economic and Business

Statement of Originality

This document is written by Thijs Dijkman who declares to take full responsibility for the

contents of this document. I declare that the text and the work presented in this document

is original and that no sources other than those mentioned in the text and its references have

been used in creating it. The Faculty of Economics and Business is responsible solely for the

supervision of completion of the work, not for the contents.

Contents

1 Introduction 3

2 Literature review 5

2.1 Drivers of mergers and acquisitions and method of payment . . 5

2.2 Information asymmetry in mergers and acquisitions . . . . . . 7

2.3 The revaluation effect . . . . . . . . . . . . . . . . . . . . . . 9

2.4 Hypotheses development . . . . . . . . . . . . . . . . . . . . 10

3 Data and Methodology 11

3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2.1 Model description . . . . . . . . . . . . . . . . . . . 13

3.2.2 Exogenously withdrawn sample . . . . . . . . . . . . 15

4 Descriptive statistics 17

4.1 Sample description . . . . . . . . . . . . . . . . . . . . . . . 18

4.2 Revaluation cash versus stock . . . . . . . . . . . . . . . . . 22

5 Results 23

5.1 Information asymmetry on the choice for cash or stock . . . . 24

5.2 Opaque versus transparent targets . . . . . . . . . . . . . . . 27

5.3 Information asymmetry on target revaluation . . . . . . . . . . 29

6 Conclusion 34

References 37

ii

1 Introduction

Mergers and acquisitions is one of the most impacting events during the lifetime of a company.

The value implications of mergers and acquisitions are under high scrutiny. While many studies

focus on the firms that successfully engage in a takeover transaction little is known about the

ones that fail in these endeavours. Still, a lot can be learned by investigating these types of

events. When a bid fails to succeed, there is a revaluation of the target’s stock price, meaning

the stock price before the announcement of the bid is different compared to the stock price

after the failure. Malmendier et al. (2016) investigate this revaluation and find a significant

difference in revaluation between cash and stock bids. They show that this revaluation effect

is larger for cash offers than for stock offers, implying that the market revalues a company at a

higher price when a bidder makes a cash offer compared to when a bidder makes a stock offer.

However, other characteristics of the deal could also affect this revaluation. In this paper, the

effect of information asymmetry on the revaluation effect is investigated.

One explanation for the difference in cash and stock revaluation relates to the valuation of

the acquirer and the target. Shleifer & Vishny (2003) find that firms are incentivised to get

their equity overvalued so that they can make “cheaper” acquisitions with stock. This is in

line with the market-timing theory, which predicts that a company will use stock instead of

cash when the managers believe their company is overvalued (Baker & Wurgler, 2002). For

stock bids this implies that investors receive a signal of general overvaluation (Rhodes-Kropf

et al., 2005). For cash bids this implies that investors receive a signal of general undervaluation

(Dong et al., 2006). Another related finding is that Liu (2017) shows that private equity is able

to spot undervalued firms and “cherry pick” their leveraged buyouts. He also finds that firms

that are considered to have higher information asymmetry have higher revaluation compared to

transparent firms.

These previous findings result in the question of whether or not information asymmetry of

the target firm affects the revaluation in failed cash and stock acquisitions. First, the choice

between using cash and stock itself could be affected by information asymmetry issues. Stock

can be used to align the target’s interest with the interest of the acquirer and yield the potential

synergies to both sides (Eckbo et al., 1990). If there is a high level of uncertainty about the

amount of synergies that can be obtained in the takeover, a stock acquisition can be used to

address the risk of overpayment. To further enhance this research, this paper first investigates

if information asymmetry drives the choice for cash or stock. Using a probit regression, I

3

first investigate whether opaque targets are more likely to receive a cash bid or more likely to

receive a stock bid. Then, I investigate the effect of information asymmetry on the revaluation

of the targets. To minimize endogeneity issues, an exogenously withdrawn sample is created

by extensive hand-collected data research with the use of Lexis-Nexis. Using this exogenously

withdrawn sample, I reconstruct the revaluation effect discovered by Malmendier et al. (2016).

Furthermore, the difference in opaqueness within both cash and stock are investigated. Finally,

a regression controlling for other factors is used to discover differences in revaluation between

cash and stock, and their level of information asymmetry.

The first part of my research shows that two of the three measures of information asym-

metry used for this research do affect the choice in method of payment. The results show that

opaque targets are more likely to be acquired with stock acquisitions. These results confirms

the hypotheses of Hansen (1987); Fishman (1989) and Eckbo et al. (1990).

The results provided by the research of the relationship between information asymmetry

and the revaluation of failed cash and stock acquisitions are harder to assess. While stock

targets still generally have a lower revaluation than cash targets, two of my three information

asymmetry measures imply a higher revaluation for opaque stock targets compared to transpar-

ent stock targets, and a higher revaluation for transparent cash targets compared to opaque cash

targets. This results in a converging difference in revaluation between cash and stock targets

when information asymmetry is high.

This paper contributes to the existing literature by elaborating on the revaluation effect

after failed mergers and acquisitions. It elaborates on the previously mentioned paper by Mal-

mendier et al. (2016) by incorporating the additional element of information asymmetry on the

revaluation effect. It also brings notice to the theory of misvaluation driving the mergers and

acquisition market, researched by Shleifer & Vishny (2003) and Rhodes-Kropf et al. (2005).

Furthermore, this paper brings notice to the concept of the choice for cash and stock being

dependent on the level of information asymmetry, and if stock is used to minimize cost of

information asymmetry (Hansen, 1987).

The paper is organised in the following manner. In Chapter 2, the related literature is

reviewed. I first review previous empirical studies which presented different underlying reasons

as to why mergers and acquisitions occur. The papers of Rhodes-Kropf et al. (2005); Dong et al.

(2006); Gorbenko & Malenko (2014) that cover the relationship between stock market valuation

and takeover activity are discussed. Next, literature about information asymmetry in mergers

4

and acquisitions is reviewed (Dionne et al., 2010; Andrade et al., 2001; Chang & Suk, 1998).

Subsequently, recent literature about the revaluation effect is examined (Malmendier et al.,

2016; Liu, 2017). Furthermore, the hypotheses of this research are formulated. In Chapter 3, the

data collection and the empirical strategy of the paper is presented. In order to compensate for

endogeneity issues, this paper makes use of an “exogenous” sample collection based on similar

empirical strategies used in related literature (Liu, 2017; Malmendier et al., 2016; Chang &

Suk, 1998). Chapter 4 provides the data description, and Chapter 5 defines the results. Finally

in Chapter 6, the limitations and final conclusion of the research are discussed.

2 Literature review

In this chapter the existing literature on this topic is reviewed. To understand where the revalu-

ation effect comes from the drivers of the mergers and acquisitions market are discussed. After

this examination, theories of the effect of information asymmetry on mergers and acquisitions

are discussed. Subsequently, studies investigating the revaluation effect are reviewed. Finally,

using the discussed theories the hypotheses for this empirical study are stated.

2.1 Drivers of mergers and acquisitions and method of payment

First of all, the neoclassical economical explanation of a merger or acquisition is that “the

whole has to be greater than the sum of the parts”. In other words, the reason for a takeover

are the potential synergies that can be obtained. In real life, the underlying reason to engage in

a takeover differs among the different situations. The company’s strategy, empire building or

financial opportunities are all reasons to do an acquisition (Humphery-Jenner, 2012; Gorbenko

& Malenko, 2014). The one thing that is certain, is that the acquiring company has to pay,

and most often a premium to the current share price. However, the value of a company can

be estimated in many different ways. In the case of public companies millions of investors

set the price of a company for you. If a company becomes relatively cheap it could become a

more attractive takeover target. This raises the question if the investor misvaluation in public

companies drives the takeover market. Different studies show evidence that this is indeed the

case (Rhodes-Kropf et al., 2005; Dong et al., 2006; Gorbenko & Malenko, 2014).

In the merger and acquisition market we observe observe so-called merger waves. Shleifer

& Vishny (2003) argue that the clustering in merger activity comes from the state of current

5

stock market valuation. They developed a model which explains why firms choose to pay

either in cash or in stock in their merger and acquisition transactions. Their model predicts

that in periods of overvaluation, bidders with overvalued stock use stock-for-stock acquisitions

to buy undervalued companies. The underlying reason behind this theory is that firms buy

(undervalued) real assets with their overvalued stock (Shleifer & Vishny, 2003). Therefore,

firms with overvalued equity are more likely to become an acquirer in takeovers and firms with

undervalued equity are more likely to become a target in takeovers.

Rhodes-Kropf & Viswanathan (2004) elaborate on the theory of Shleifer & Vishny (2003).

They argue that the simple explanation for overvalued bidders preferring to use stock is incom-

plete because targets can simply reject the offer. However, Rhodes-Kropf et al. (2005) do also

find that misvaluation, both under and over valuation, drives the takeover market. In their paper

they confirm that stock deals are more likely to occur when the acquirer is overvalued. They

also find that cash deals are more likely to be made in times of general undervaluation of the

market. Stock deals however are more likely to occur in times of general market overvaluation.

In general, this results in cash targets being more undervalued than stock targets. This is in sup-

port of the evidence of the paper by Malmendier et al. (2016), which shows the difference in

revaluation between stock and cash mergers. If the market is aware of these empirical findings,

the event that an acquirer uses cash to buy a company should result in a signal for the investor

that the target is undervalued.

In fact, other empirical evidence confirms there are some differences in types of acquisitions

which affect the stock market. Bhagat et al. (2005) investigated the stock returns for bidders

and targets in case of mergers and acquisitions. They find that combined bidder-target stock

returns are higher for hostile offers, lower for equity offers and lower for diversifying offers.

They argue that these effects reflect revelation about bidder stand-alone value, not differences

in gains from the potential synergies itself. They also find that bidders with low Tobin’s q

on average have more negative announcement period returns. Whereas, target announcement

period returns are negatively correlated to the targets’ Tobin’s q. Finally, the acquisition of

a smaller target by a large bidder on average creates a smaller value improvement measured

as a fraction of combined value, than combinations of similar-size firms. However, they do

argue that there could be a revelation bias in their research (Bhagat et al., 2005). This bias

shows that if the bid reveals favourable news to the market about stand-alone bidder value, the

cause of the combined bidder-target equity return will wrongly be attributed to the expected

6

value improvement. In this research the exogenously withdrawn sample is created to resolve

the revelation bias.

The key takeaway is that mergers and acquisitions can be driven by misvaluation in the

stock market. However, in previous literature the potential synergies always affected the ex-

pected returns of the mergers and acquisitions. In this research, withdrawn deals are used

which eliminates the noise of potential synergies. This revaluation effect is also investigated

by Malmendier et al. (2016). If firms pick undervalued companies we should observe a pos-

itive revaluation effect of the target. However, the choice for cash and stock may be affected

by asymmetric information about the target. Investors base their valuation on the information

provided. This effect on asymmetric information in mergers and acquisitions is explained in

the next section.

2.2 Information asymmetry in mergers and acquisitions

Asymmetric information is a well-known phenomenon that is present in almost every problem

in the world. In the world of finance most often asymmetric information affects the behaviour

of managers and investors, the price of an asset and premiums that are paid in mergers and

acquisitions. Hansen (1987) and Fishman (1989) studied the role of asymmetric information in

the choice for cash or stock in mergers and acquisitions. They assume two-sided information

asymmetry, where the acquirer and the target have their own private information about their

value. They show, that the division of the takeover gains becomes a function of the size of the

bid and the medium of exchange. The bidder that offers a high bid has relatively high expected

“overpayment cost”. On the other hand a low bid reduces the probability that the bid will be

successful. Unsuccessful bids have “lost synergy gain” costs. Eckbo et al. (1990) concludes

that in a cash offer, the acquirer bears the risk of overpayment. Whereas a stock offer transfers

some of the mispricing risk to the target. Therefore, a stock offer might be a safer opportunity

when there is a high level of information asymmetry.

Officer et al. (2009) investigate the relationship between target information asymmetry and

the acquirer returns in mergers and acquisitions. They document a significant and substantial

higher announcement return to stock acquirers when the target is difficult to value. They in-

terpret their results as an reward for using stock as payment method when target’s assets and

operations are difficult to value. The reward is higher for firms where operations are hard to

value, e.g. high R&D expenses or a lot of intangible assets. The reward may come from acquir-

7

ers who might be better to value the target’s assets. Opacity could first result in a target being

more misvalued and naturally attract acquirers. Subsequently, this misvaluation might trigger

the assumption that the target was undervalued in the first place and should recieve a positive

revaluation.

So do acquirers benefit from having better information than investors? In a study by Dionne

et al. (2010) mergers and acquisitions are compared to auctions. When an auction starts, the

more people that bid the more becomes clear about the value of the asset. Dionne et al. (2010)

find that informed buyers, buyers with at least 5% of the shares before the announcement, pay a

significantly lower premium compared to buyers that do not have privileged information. This

indicates that better informed buyers have the ability to better value their target, or at least have

better bargaining power.

Another explanation of information asymmetry affecting the choice of payment in mergers

and acquisitions could be related to the pecking-order theory and the market-timing theory.

Managers will try to “time the market” by issuing equity when the share price is overvalued

(Baker & Wurgler, 2002; Dong et al., 2006). However, due to information asymmetry, the

pecking order theory argues that an equity issue can be considered as a signal that the issuing

firm is overvalued (Myers & Majluf, 1984). This could explain the negative announcement

returns for acquirers that initiated a stock acquisitions (Andrade et al., 2001; Malmendier et

al., 2016). However, it is hard to implement the effect of the target revaluation using these

theories. Receiving a bid from an overvalued acquirer should not affect the targets revaluation

share price if the bid fails (Malmendier et al., 2016).

Andrade et al. (2001) provide extensive research on acquirer and target announcement re-

turns in mergers and acquisitions for the period in 1973 to 1998. They investigate announce-

ment returns for acquirer and targets and difference in stock and cash announcement returns.

They find a short window abnormal return for the target of 13% in stock acquisitions and 20.1%

announcement return in cash acquisitions. For acquirers the announcement returns are -1.5%

and 0.4%. In the long run acquirers financing with stock face a -9.0%*** in an equal-weighted

portfolio and a -4.3% in a value-weighted portfolio long run return and in cash acquisition a

not significant -1.4% compared to an equal-weighted portfolio, and 3.6% compared to a value-

weighted portfolio. This indicates a better performance (or price paid) by acquisitions financed

with cash. It also indicates that financing a merger or acquisition with stock should give a

negative signal of the value of the acquirer since the long performance will be negative.

8

These findings support the hypothesis that when mergers fail, bidding firms that offered

stock should experience a positive abnormal return. In contrast, firms that offered cash should

experience a negative abnormal return when the merger is withdrawn. Chang & Suk (1998)

find evidence for this theory. They observed the acquirer stock return upon the announcement

of withdrawn takeovers. On average they find a positive abnormal return when the acquirer

offered stock, and a negative abnormal return when the acquirer offered cash. Chang & Suk

(1998) argue that the causal relationship of this observation lies in the asymmetric information

hypothesis. The decision to not issue common stock conveys favourable information to the

market. If the management thinks their stock is undervalued they won’t go through with the

stock merger. The target firms suffer from a significant decline in stock price. Obviously, this

effect is due to the premium that shareholders would have earned if the acquisition would go

through. However, if we compare pre-announcement price versus the price after the failed

merger we observe a revaluation. Which implies that the market observes a signal about the

underlying value of the firm with the mergers and acquisition activity.

2.3 The revaluation effect

When the termination of a merger or acquisition is announced the market again perceives a

signal. If there are no rumours of a subsequent bid, the target will observe a negative shock

in its share price. Since no actual synergies can be obtained the expectation would be that the

price will return to its original pre-announcement value (Bradley et al., 1983). However, prices

heavily fluctuate after the withdrawal. Resulting in a far more negative returns relative to the

pre-announcement price but also far more positive returns relative to the pre-announcement

price. This “revaluation effect” has different underlying factors that might explain the direction

of the price adjustment.

Malmendier et al. (2016) investigate this revaluation effect. They observe a significant

difference between failed cash and stock bids. A failed cash offer has a revaluation effect of

15% while the stock offer returns to the pre-announcement value. So the signal of either a cash

or a stock offer are perceived as different. As is explained in the literature review, one of the

reasons could be that the choice for cash or stock might be affected by the valuation of acquirer

and target. A cash acquisition might be a signal for general undervaluation, resulting in a

positive revaluation. A stock acquisition might be a signal for acquirer overvaluation (Shleifer

& Vishny, 2003), which has nothing to do with the targets price. Malmendier et al. (2016) do

9

not find that this revaluation effect is caused by future takeovers nor by changes in operational

performances.

Ben-David et al. (2015) find that firms that are considered to be overvalued are more likely

to use stock-for-stock mergers. This could drive the underperformance of stock acquirers and

the over performance of cash acquirers. However, keep in mind that these results are exam-

ined by mergers that go through. Implying that this over- or underperformance is explained

by the valuation of the bidder instead of the target. Another view is considered by Gorbenko

& Malenko (2014). In their research they identify two types of bidders, strategic bidders and

financial bidders. They show a higher valuation of the target by strategic bidders compared to

financial bidders. Naturally, one could argue that this occurs because of synergies of strategic

bidders. However, in a large part of their subsample (22.4%), the financial bidder offers a higher

premium. This can be explained because financial bidders have access to debt at a lower cost

than strategic bidders (Gorbenko & Malenko, 2014). Implying that in worse economic condi-

tions financial bidders profit from the low cost of debt and low valuation of the companies. Liu

(2017) researches the revaluation effect of withdrawn LBO’s by private equity. The research

shows the same revaluation effect as the cash offer out of Malmendier et al. (2016). This con-

tributes to the theory that Private Equity can ‘cherry-pick’ their targets. Another interesting

finding is that the revaluation effect is stronger for firms with more information asymmetry.

This implies that due to information asymmetry a company can become more undervalued. Or

that the signal of receiving a takeover bid is stronger for firms with higher information asym-

metry. This also raises the question whether opaque targets are more likely to be taken over

with cash or stock.

2.4 Hypotheses development

So far the research to the revaluation effect has let to the discovery of the different revaluation

between cash and stock acquisitions. The effect of information asymmetry has yet to be investi-

gated. The first part of this empirical study investigates if the choice in cash or stock is affected

by the level of information asymmetry. Based on the theory of Hansen (1987), I expect that if

a firm has a lot of information asymmetry it will use stock for the acquisition because this will

reduce the “overpayment cost”. Therefore I state the following hypothesis:

Hypothesis 1 Firms with more information asymmetry have a higher chance to receive a stock

offer than a cash offer.

10

I also expect a negative relation between acquirer market-to-book ratio and cash as method

of payment. When an acquirer is overvalued he will be more tempted to do a stock-for-stock

acquisition in favour of the current shareholders (Shleifer & Vishny, 2003; Rhodes-Kropf et

al., 2005; Martin, 1996).

Subsequently, as is stated by Liu (2017), the revaluation effect for opaque targets is higher

than for transparent targets in leveraged buyouts. Opaque targets are naturally more vulnera-

ble to misvaluation and therefore have a higher chance to become undervalued or overvalued.

This could also be because of the information asymmetry the weight of the signal of being a

potential take-over target is stronger. I expect that this rationale will hold for other mergers and

acquisitions. So given that there could be more misvaluation due to the information symmetry,

I expect that the revaluation effect is stronger. This results in the following hypothesis:

Hypothesis 2 The revaluation effect of opaque targets is stronger than for transparent targets.

On the other hand, opaqueness could lead to uncertainty among investors. The best refer-

ence point to investors could be the price before the announcement. Therefore, it could also be

the case that the revaluation effect is weaker for opaque targets.

3 Data and Methodology

The aim of this paper is to investigate if the revaluation effect is larger for targets that have a

higher level of information asymmetry. First, this paper follows the paper of Malmendier et al.

(2016) by identifying the revaluation effect of failed cash and stock acquisitions. Secondly, this

paper investigates if the choice in cash or stock depends on the level of information asymmetry

of the target firm. Subsequently, the differences in revaluation between opaque and transparent

targets are investigated. Finally, while controlling for other variables, the effect of information

asymmetry on the revaluation effect is shown. In this section the data collection and empirical

strategy are discussed.

3.1 Data

I start the data collection by gathering information about successful and unsuccessful deals. In-

formation about mergers and acquisition bids is retrieved from the Thomson One SDC database.

I download all completed and withdrawn bids data from 1980 to 2015, and excluded bids with

11

a deal value smaller than $1 million. From the CRSP/Compustat merged database I retrieved

information about the fundamentals of the companies. Subsequently, the I/B/E/S database is

used to obtain information about forecasted earnings per share, actual earnings per share, fore-

cast dispersion and analyst coverage. For the withdrawn deals stock returns the CRSP event

study database is used. I calculate the abnormal returns (CAR) as

CAR =t∑

j=1

(rij − rmj) (1)

where rij and rmj denote firms i’s equity return and the CRSP value-weighted market return

at time j, respectively. The same methodology is used by Liu (2017) and Malmendier et al.

(2016). For each event the time frame consist of 25 days pre-announcement to 25 days after

the withdrawal. This timeframe comes from the run-up theory of Schwert (1996).

The databases use different company identifiers. To make the files ready to merge I retrieved

all CUSIP, NCUSIP, PERMCO and PERMNO numbers from the CRSP daily database. From

the sample of Thomson One I merged the Thomson One 6-digit CUSIP on the NCUSIP and

assign the PERMCO’s to the companies. From there the PERMCO and PERMNO numbers are

used to merge with the datasets from Compustat, CRSP and I/B/E/S. One of the problems is

that the databases do not cover the same firms. While the Thomson One SDC database covers

deals world wide, CRSP/Compustat only covers firms listed in the US and Canada. Especially

the I/B/E/S database misses a lot of information about the firms in the deals. This limits the

database to a small amount of deals as is shown in Chapter 4. Finally, for the full sample

selection I filter the dataset with the following rules. The deal value should be bigger than 1

million dollars, and percentage sought bigger than 50%. Furthermore, I require information

from I/B/E/S, CRSP and Compustat. I also remove targets that received subsequent bids that

occur in the next year after the withdrawal. Also, since the findings of Liu (2017) show a certain

revaluation effect of leveraged buyouts, I remove leveraged buyouts to exclude the effect of

private equity.

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3.2 Methodology

3.2.1 Model description

First, this paper examines the choice in method of payment for a M&A deal. Liu (2017)

find that private equity “cherry pick” their targets and that a higher revaluation occurs when

information asymmetry is high. This raises the question of other companies do also select

opaque targets for their takeovers. Another point of view could be that because the level of

information asymmetry is high, a firm will use stock to minimize the expected “overpayment

cost” (Eckbo et al., 1990). It is important to know whether the choice of cash and stock is

affected by the information asymmetry. If in fact the level of information asymmetry matters,

the findings of Malmendier et al. (2016) could suffer from omitted variable bias. This because

leaving the information asymmetry of the target out gives a bias to the magnitude of the method

of payment. For example, assuming that the revaluation is lower for opaque targets, if stock is

used as method of payment to acquire opaque targets the effect of using stock is biased. For

this research the following probit regression is used:

P (Cash) = β0 + β1InformationAsymmetry + β2Offerpremium

+ β3Hostile+ β4qtarget+ β5qacquirer (2)

The probit includes the three measures of information asymmetry and control variables.

As proxy’s for information asymmetry, I use the absolute difference between the forecasted

earnings per share and actual earnings per share divided by the share price (Forecast error),

the standard deviation of analyst forecasted earnings per share divided by share price (Analyst

dispersion) and the number of analyst that cover the target firm divided by total assets (Analyst

coverage). These measures are also used by Liu (2017); Duchin et al. (2010); Krishnaswami

& Subramaniam (1999) and He & Tian (2013). Note that the studies use different variables to

normalise the variables. For example, Liu (2017) does not normalise the number of analyst,

while Duchin et al. (2010) normalized the number of analyst by the book value of assets because

the size is correlated with their dependent variable. In my thesis, I choose to normalize the

difference in actual and forecasted EPS and analyst forecast dispersion by the share price, and

to normalise the number of analyst covering the firm by the book value of assets as is used in

Duchin et al. (2010). The proxy’s are measured one quarter before the announcement of the

deal. With these measures the opaque targets versus the transparent targets are divided into

13

two sub-samples. divided by stock price I included several control variables. The first one is

Offer Premium. The offer premium is on average higher for cash than stock deals as is shown

in Table III and therefore should be included as control variable. The second and third control

variables are q target and q acquirer, measured as market-to-book ratios. Rhodes-Kropf et al.

(2005); Dong et al. (2006) find that acquirers with a high market-to-book value are more likely

to use stock in the take-over. Firms that have lower valuation are more likely to receive cash

bids. Furthermore, the hostile deal dummy is included to see whether hostile deals occur more

often with cash or stock acquisitions.

Secondly, differences in revaluation between opaque firms and transparent firms are inves-

tigated. The suggested approach comes from a combination of Liu (2017) and Malmendier

et al. (2016). As revaluation measure, I use the cumulative abnormal return between 25 days

pre-announcement and 25 days after withdrawal, as is shown in Equation 1. This is used by

Liu (2017) and Malmendier et al. (2016) and comes from the run-up theory of Schwert (1996).

Since each number of days between announcement date and withdrawn date is different, I

normalised the time between these dates to a relative time-window, namely the time to com-

pletion in percentage. To construct this relative time-window I use a linear approximation. I

divide the days passed by the total of days in the window per company. I construct this relative

time window in percentages and round them of to 2 decimals. To make approximations of

the return between these relative time windows I make use of linear interpolation between the

actual trading days, i.e.,

CARi(tg) = (1− wi,tr)CARi(btRTic) + w(i,tr)CARi(btRTic+ 1) (3)

where bxc refers to the floor function andw(i,tr) = tRTi−btRTic . The cumulative abnormal

return (CAR) over time of stock and cash can be found in Figure I. Subsequently, I divide the

sample between cash and stock and search whether within cash and stock offers the level of

information asymmetry has an effect on the revaluation. Targets are considered to be opaque

if they are in the above the median of the |Consensus forecasted EPS − actual EPS| / Stock

price. For the Analyst dispersion targets are considered to be opaque if they are above the

sample median. For the Analyst coverage the targets are considered to be opaque if they are

below the sample median.

The method of Table V obviously suffers from omitted variable bias. As Rhodes-Kropf et

al. (2005); Dong et al. (2006); Malmendier et al. (2016) show the revaluation effect is affected

14

by the market-to-book ratios. In Equation 4, I control for these characteristics. I also control

for size and offer premium by using the Transaction Size and Offer premium.

Revaluation =β0 + β1InformationAsymmetry + β2Offerpremium+ β3qtarget

+ β4qacquirerβ5TransactionSize (4)

Here the sample is divided in a pure cash and stock sample. The results are shown in

Table VI. Finally, in Table VII the results are combined and added with a Cash dummy to see

what the difference between cash and stock opaque targets are. Here the following regression

is used:

Revaluation =β0 + β1InformationAsymmetry ∗ Cash+ β2InformationAsymmetry

+ β3Offerpremium+ β4qtarget+ β5qacquirer + β6TransactionSize

(5)

3.2.2 Exogenously withdrawn sample

The main issue by examining failed deals is that the reason for the withdrawal might be en-

dogenous. If, for example, information about the target comes out which affects the price of

the target the revaluation effect would be lower while it is not caused by the difference in cash

and stock. Nor is the amount of information asymmetry of importance.

To address this problem, exogenous reasons for withdrawal are used. This paper makes

use of exogenous withdrawal reasons which are used in previous papers (Chang & Suk, 1998;

Malmendier et al., 2016; Liu, 2017). The failure reasons are based on detailed news search

in LexisNexis and the deal synopsis of the Thomson One SDC database. In Table I the main

categories are displayed. The reasons for withdrawal are identified as “High Endogenous”,

“Semi Endogenous” and “Low Endogenous”.

The “High Endogenous” sample contains failed deals with reasons where a lot of infor-

mation about the target affects the price. “Target News” refers to failed deals associated with

public news about the target, for example when Cobra Electronics Corporation withdrew its

offer for Lowrance Electronics Inc..1 “Market Problems” refers to deals associated with prob-1http://academic.lexisnexis.eu/??lni=42X2-TJ60-0109-T2M8&csi=237924,3643&oc=00240&perma=true

15

lems in the industry. The downfall in the industry affects the price of both companies and

is therefore considered endogenous. “Outbid” refers to deals that are withdrawn because the

target is taken-over by someone else. “Withdrawn reason not specified” refers to deals where

there is no reliable information about the deal.

The “Semi Endogenous” reasons consist of reasons where endogeneity is less, but still of an

issue. “Alliance” refers to deals where the acquirer and target made a pact. For example when

Avalon Corporation withdrew its offer on Maynard Oil Company.2 “Shareholder rejection”

indicates rejection by shareholders by for example, an insufficient number of shares being

tendered or shareholders that vote against the offer in proxy filings.3 “Management rejection”

refers to deals where the board insists shareholders to block the deal or engage in anti-takeover

provisions.4 “Price too low” is when both parties could not agree on the price, or when I was

unable to identify if the targets shareholder or management rejected the offer.

The “Low Endogenous” sample consist of deals where endogeneity is considered as low or

non-existent. “Bidder Problems” refers to deals where the acquirer withdrew the deal because

of problems internally. “Management Terms” refers to negotiations where the board of the

acquirer and target could not come to an agreement about the management.5 “Lack of Finance”

refers to deals that couldn’t be completed because of the lack of financing. For example when

the Wickes Companies tried to acquire Lear Siegler and couldn’t obtain the required financing

from the bank,6 “Regulator” refers to deals where the government or regulators intervention

prevented the deal from going through. For example in a deal between Compuware and Viasoft7

and between Omnicare Inc. and PharMerica 8.

The number of cases and distribution looks familiar to Malmendier et al. (2016). Although,

I left out the category of Target News (private) since I was not able to find that information.

This explains why my “Withdrawn reason not specified” is somewhat bigger (39% instead of

36% in Malmendier et al. (2016). My subsample consist of 103 “Exogenous” withdrawn firms.

In comparison the sample of Malmendier et al. (2016) consist of 81.

2http://academic.lexisnexis.eu/??lni=3S8G-8XJ0-0007-J1GB&csi=237924&oc=00240&perma=true3http://academic.lexisnexis.eu/??lni=7W7T-N4X1-2PMW-W00Y&csi=237924&oc=00240&perma=true4http://academic.lexisnexis.eu/??lni=4KWY-BJS0-TXJ2-N2XM&csi=237924&oc=00240&perma=true5http://academic.lexisnexis.eu/?lni=3RH0-B6P0-0001-13K9&csi=237924&oc=00240&perma=true6http://academic.lexisnexis.eu/??lni=3S8G-8F30-0007-H2DD&csi=237924&oc=00240&perma=true7http://academic.lexisnexis.eu/??lni=45G0-VCX0-014S-Y3KJ&csi=237924&oc=00240&perma=true8http://academic.lexisnexis.eu/?lni=555Y-5TG1-DY7P-R1B7&csi=237924&oc=00240&perma=true

16

Table IReason for Withdrawal

Table I presents the reason for the withdrawal of the deal. Extensive news research has been done viaLexis-Nexis. The search includes news articles around the withdrawal date of the deal. The reasonsspecified in bold and italic are considered to be semi endogenous and low endogenous. Together theyrepresent the ’exogenous’ withdrawn sample. The methodology is consistent with the papers Chang &Suk (1998); Liu (2017) and Malmendier et al. (2016) who use identical reasons for similar identificationstrategies.

Failure reason Cases Percentage

High EndogenousWithdrawn reason not specified 115 39%Target News 18 6%Market Problems 8 3%Outbid 53 18%

Semi EndogenousAlliance 2 1%Shareholder rejection 17 6%Management rejection 20 7%Price too low 4 1%

Low EndogenousBidder Problems 2 1%Management Terms 7 2%Lack of Finance 7 2%Regulator 25 8%Other than bid price, target news, or target performance 19 6%

Bids in ”Exogenous” withdrawn sample 103 35%Bids considered as Endogenous 194 65%

4 Descriptive statistics

In this chapter the data is reviewed. Following the data gathering instructions, which can be

found in Chapter 3, the successful dataset consist of 2164 cash or stock deals. The unsuccessful

sample consist of 297 pure cash and stock deals. Of the withdrawn deals 222 are cash deals

and 75 are stock deals. One problem is that not all databases cover the same firms. Especially

the information of the I/B/E/S database is limited. Together with the criteria of the exogenous

deal selection this limits the data significantly. Krishnaswami & Subramaniam (1999) have

had the same problem. In comparison, Malmendier et al. (2016) pure cash and stock sample

17

consist of 236 deals, retrieved in the time frame of 1980 to 2008. Whereas the data set used

in this paper consist of 297 deals in the time frame 1980 to 2015. The exogenous withdrawn

sample in Malmendier et al. (2016) consist of 81 deals and the data set here consist of 103

deals. However, not all deals have information on the I/B/E/S database. This limits the number

of deals to 79 in Table V, and to 66 in Table VII.

4.1 Sample description

In Table II the summary statistics of the main dataset are displayed. The successful deals

are filtered to select deals with information out of all databases. The amount of observations

for the unsuccessful deals vary by each variable. This depends on which database is used to

retrieve the data. In the right column the p-value of the difference is given. The first three

variables contain the measures of information asymmetry. Related literature uses the same

measures of information asymmetry (Duchin et al., 2010; He & Tian, 2013; Krishnaswami &

Subramaniam, 1999). Furthermore, the percentage of cash and stock deals are displayed. There

are more stock acquisitions than cash acquisitions in the successful deal sample (30% and 60%

respectively) . However, in the withdrawn sample there are more cash offers (25% to 75%).

Also the percentage of hostile deals in the withdrawn sample is significantly higher than the

percentage of the successful deal sample. Malmendier et al. (2016) observe the same and this

could due the fact that hostile deals have a lower rate of completion than other bids (Ngo &

Susnjara, 2016). Another interesting fact, discovered both by this paper and Malmendier et

al. (2016) is that the offer premiums of successful deals are not necessarily higher than those

of unsuccessful deals. The mean and median of Transaction size and Target size are skewed

towards smaller enterprises in both successful deals as in unsuccessful deals. Also due to

the presence of large outliers, the mean of both q of target and q of acquirer are higher for

successful deals than for unsuccessful deals. However, the medians are comparable. To control

for the oultiers I winsorised from zero to the 95th percentile before running the regressions.

The median of q of the target is higher compared to the acquirer. This seems to support the

theory that low market to book acquirers acquire high market to book targets, as is shown by

Rhodes-Kropf et al. (2005).

The dataset of the unsuccessful deals is unbalanced. The observations of the measures of

information asymmetry are about two-thirds of the total observations. This significantly drops

the amount of deals which can be researched. This causes some problems with further analysis

18

since the low amount of observations drops significance and reliability of the data in Table V,

Table VI and Table VII.

In Table III the differences between cash and stock are displayed. The Offer premium for

cash offers is higher than for stock offers. The q of the target in cash offers is a little bit higher

than for stock offers. The q for acquirers for stock offers is bigger than cash offers. However,

these differences do not seem to be significant in the exogenous withdrawn sample. This is due

to the low amount of observations. For the larger sample the differences are significant.

19

Table IISummary statistics: Successful Deals and Unsuccesfull deals

Successful Deals Unsuccessful Deals

Variable Mean Median Std.dev Min Max Obs. Mean Median Std.dev Min Max Obs. p-value

Forecast error 0.03 0.00 0.17 0.00 3.92 2164 0.14 0.01 1.06 0.00 15.00 215 .00Analyst dispersion 0.00 0.00 0.00 0.00 0.16 2164 0.00 0.00 0.01 0.00 0.06 209 .091Analyst coverage 5.81 4.00 5.78 1.00 40.00 2164 6.44 4.00 6.29 1.00 39.00 216 .130Cash 0.40 0.00 0.49 0.00 1.00 2164 0.75 1.00 0.43 0.00 1.00 297 .000Stock 0.60 1.00 0.49 0.00 1.00 2164 0.25 0.00 0.43 0.00 1.00 297 .000Time to failure 133.50 113.50 90.38 11.00 906.00 2164 127 92 125 6 774 297 .238Offer premium 0.41 0.33 0.33 0.00 2.05 2164 0.38 0.27 0.42 0.00 3.27 295 .835Hostile 0.01 0.00 0.11 0.00 1.00 2164 0.34 0.00 0.47 0.00 1.00 297 .000Transaction size 3.10 0.31 15.35 0.00 520.39 2164 2.80 0.14 23.36 0.00 385.95 280 .777Target size 2.29 0.23 10.60 0.00 336.13 2164 2.57 0.11 23.80 0.00 405.54 295 .730q of target 3.49 1.23 6.04 0.07 36.43 2164 1.56 0.99 1.80 0.19 9.78 279 .000q of acquirer 1.30 0.89 1.89 0.04 14.16 2164 1.64 0.89 2.65 0.15 13.58 246 .000

Note: Table II summarizes the statistics for the successful deals and unsuccessful deals. The sample consist of 2164 successful pure cash and stock deals and 297withdrawn stock deals reported by the Thomson One SDC database in the time-frame of 1985 to 2015. The variables Cash, Stock, Offer premium and Hostile are measuredas percentages. Transaction size is measured in $billion and Time to failure is in days. CRSP/Compustat is used to compute the Target size (measured in $billion), the q oftarget and the q of acquirer which are market-to-book ratios for the target and the acquirer respectively. The I/B/E/S database is used to obtain the measures for informationasymmetry. Both Forecast error and Analyst Dispersion are normalized by the share price. Note that the Analyst Coverage measured as the number of analyst covering thefirm is not yet divided by the book value of assets. This is required and done for the further data analysis. In the last column p-values are given for the differences betweensuccessful and unsuccessful deal statistics.

20

Table IIISummary statistics: Exogenously withdrawn Cash and Stock bids

Cash Offers Stock Offers

Variable Mean Median Std.dev Min Max Obs. Mean Median Std.dev Min Max Obs.p-

value

Forecast error 0.32 0.00 2.12 0.00 15.00 50 0.16 0.00 0.65 0.00 3.69 32 .669Forecast Dispersion 0.00 0.00 0.01 0.00 0.05 49 0.00 0.00 0.01 0.00 0.06 31 .622Analyst Coverage 7.44 6.50 5.73 1.00 24.00 50 6.78 5.00 5.53 1.00 21.00 32 .608Time to failure 118.79 69.00 127.61 6.00 737.00 63 187.78 147.00 147.16 13.00 737.00 40 .013Offer premium 0.41 0.35 0.27 0.00 1.57 63 0.33 0.22 0.33 0.01 1.58 40 .185Hostile 0.68 1.00 0.47 0.00 1.00 63 0.63 1.00 0.49 0.00 1.00 40 .552Transaction size 1.94 0.16 3.93 0.00 23.08 63 3.03 0.21 8.02 0.00 44.30 38 .362Target size 1.41 0.11 2.78 0.00 14.66 63 2.75 0.16 7.09 0.00 39.02 40 .183q of target 2.04 1.14 2.26 0.29 9.58 62 1.52 1.02 1.73 0.23 9.78 38 .234q of acquirer 1.54 0.91 2.23 0.20 13.41 61 1.65 0.86 2.74 0.40 13.58 35 .836

Note: Table III summarizes the statistics for the Exogenous withdrawn sample. The sample consist of 63 withdrawn cash deals and 40 withdrawn stock dealsreported by the Thomson One SDC database in the time-frame of 1985 to 2015. The variables Cash, Stock, Offer premium and Hostile are measured as percentages.Transaction size is measured in $billion and Time to failure is in days. CRSP/Compustat is used to compute the Target size (measured in $billion), the q of target andthe q of acquirer which are market-to-book ratios for the target and the acquirer respectively. The I/B/E/S database is used to obtain the measures for informationasymmetry. Both Forecast error and Analyst Dispersion are normalized by the share price. Note that the Analyst Coverage measured as the number of analyst coveringthe firm is not divided by the book value of assets. This is required and done for the further data analysis. In the last column p-values are given for the differencesbetween cash and stock bids are displayed.

21

4.2 Revaluation cash versus stock

The effect of the announcement, negotiation and withdrawal of the pure cash and stock bids are

displayed in Figure I. The figure shows the cumulative abnormal return starting from 25 days

before the announcement of the bid to 25 days after the withdrawal of the bid. The cash targets

receive a higher announcement return than stock targets. This is mainly caused by a higher

offer premium. After the withdrawal the target has a revaluation of around 10%.

The stock targets have a lower announcement premium. This is mainly caused by a lower

premium offered by the acquirer. Between the announcement and withdrawal of the bid stock

targets already face a revaluation back to the original price. After the withdrawal the stock

targets face a negative return of around -4% The cash and stock acquirers face negative abnor-

mal returns during the whole period. This results are in line with Malmendier et al. (2016).

Although, the stock acquirers observed by Malmendier et al. (2016) have a revaluation of -20%

and in this paper around they observe a revaluation of -10%.

The figure shows that cash targets have a higher revaluation effect than stock targets. The

casual explanation lies in Rhodes-Kropf et al. (2005); Shleifer & Vishny (2003) and Dong et

al. (2006). Firms that have overvalued equity try to do acquisitions with stock. Therefore the

reason for the acquisition does not have to lie in an undervaluation of the target, but more in the

acquirer making an advantage of their overvalued stock (Rhodes-Kropf et al., 2005). Dong et

al. (2006) also argue that cash acquisitions are done when the market is undervalued. Therefore

a cash bid can be perceived as a signal that the target is undervalued, which could cause the

higher revaluation effect.

Note that in a non-controlled environment there could be a chance of future take-over that

could be reflected in the revaluation price. However, Malmendier et al. (2016) controlled for

this factor and showed that this is not the case. In this paper the sample selection consist

of withdrawn deals where the target did not received a takeover bid for the next two years.

This paper further investigates where the difference between cash and stock revaluation comes

from. It also studies if the revaluation whitin a certain method of payment differs by the level

of information asymmetry.

22

Figure ICAR over time

Figure I displays the cumulative abnormal return for the announcement of the bid and failure of the deal.The figure runs from 25 days before announcement of the bid (B) to 25 days after the failure of the bid(F). Between the bid and failure are the relative amount of trading days calculated as in Equation 3. Thesample consist of the exogenous withdrawn pure-cash and pure-stock deals.

5 Results

In this section the results of the research are displayed. First the choice for stock or cash is

examined. In Table IV the probit on the choice for cash and stock is displayed. After the

examination of the effect of information asymmetry on the method of payment, the difference

in revaluation between opaque and transparent targets are displayed, which can be found in

Table V. Finally, to control for other variables that may affect the revaluation, a regression

including the effect of information asymmetry on the revaluation is shown in Table VI and

Table VII.

23

5.1 Information asymmetry on the choice for cash or stock

First, the effect of information asymmetry on the choice for cash and stock is discussed. For

this analysis the probit regression, found in Equation 2, is used. The result is shown in Table IV.

In regression one to six the full sample, consisting of both successful as unsuccessful bids, is

used to examine the choice for cash and stock. In regression 7 to 12 the withdrawn sample is

used.

The full sample shows a negative significant effect for Analyst dispersion on the choice

for cash, implying a choice for stock when information asymmetry is high. This is in favour

of hypothesis 1. The Analyst coverage has a positive and significant effect on the choice for

cash, which implies a choice for cash when information asymmetry is low. This is also in

favour of the hypothesis of choosing stock over cash when the target information asymmetry

is high. Forecast error is not significant but by controlling for other factors in regression 4, a

correlation between Forecast error and the choice for cash that is in line with the hypothesis

is shown. Regarding the control variables, a higher Tobin’s q for the acquirer has a significant

negative effect on the probability to receive a cash bid. This is in line with the findings of

Martin (1996) which show that acquirers with a high market-to-book value are more likely to

use stock in the take-over.

Also the Offer premium is higher for cash offers. Therefore, for the revaluation effect there

should be controlled for the offer premium. Furthermore, q of the target is not significant.

Meaning that the market-to-book ratio of the target, a proxy for level of growth opportunities,

does not seem to affect the choice in cash or stock. Also, hostile deals are more likely to

be cash deals, consistent with findings of Malmendier et al. (2016) and Chang & Suk (1998).

The withdrawn sample shows that transparent targets are more likely to be taken over with cash.

However, only one of the three measures is significant. Which is tend to be too weak to confirm

hypothesis 1. Note that the amount of observations drops significantly in the withdrawn sample

and could be the cause of the insignificant results. Therefore the findings of the withdrawn

sample are less reliable than for the full sample. A causal relationship may be explained by

the failure of the bid. The failure might be caused by the firms not being able to overcome the

information asymmetry issues. However, this research does not provide clear evidence for this

matter.

Examining these results the evidence tends to confirm hypothesis 1. Two of the three

proxy’s for information asymmetry in the full sample show a significant effect of informa-

24

tion asymmetry affecting the choice for method of payment. Therefore it can be confirmed

that the effect of information asymmetry on choice of payment could be caused by the level of

information asymmetry which is in line with theories of Hansen (1987); Fishman (1989) and

Eckbo et al. (1990). Economically, the reason for these findings can be explained because due

to the higher level of information asymmetry it is harder to measure how the synergies can be

obtained. By using stock the acquirer reduces the risk of overpayment (Hansen, 1987).

25

Table IVInformation asymmetry and choice of payment

Dependent Variable = Revaluation CAR

Full sample Withdrawn sample

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Forecast error 0.01 -0.03 -0.09 -0.05(0.052) (0.056) (0.184) (0.269)

Analyst dispersion -0.21*** -0.16*** -0.09 -0.11(0.052) (0.055) (0.184) (0.252)

Analyst coverage 0.28*** 0.41*** 0.54*** 0.65**(0.052) (0.058) (0.192) (0.319)

Offer premium 0.21** 0.22*** 0.12 1.14** 1.11** 0.96**(0.083) (0.084) (0.085) (0.506) (0.495) (0.487)

Hostile 0.62*** 0.56*** 0.67*** 0.15 0.15 0.05(0.140) (0.141) (0.139) (0.270) (0.270) (0.284)

q target 0.00 0.00 0.00 0.07 0.06 0.01(0.001) (0.001) (0.002) (0.054) (0.054) (0.057)

q acquirer -0.02*** -0.02*** -0.02*** -0.07** -0.07** -0.08***(0.004) (0.004) (0.005) (0.030) (0.030) (0.031)

Transaction Size -0.01 0.00 0.01 -0.12* -0.12* -0.03(0.013) (0.013) (0.013) (0.069) (0.065) (0.072)

Intercept -0.17*** -0.06* -0.31*** -0.25*** -0.18*** -0.39*** 0.68*** 0.59*** 0.40*** -0.04 0.01 -0.06(0.037) (0.036) (0.037) (0.050) (0.053) (0.049) (0.131) (0.131) (0.126) (0.303) (0.316) (0.286)

Observations 2379 2372 2373 2277 2277 2277 217 217 210 118 118 118Pseudo R-squared 0.0000 0.0052 0.0093 0.0220 0.0249 0.0384 0.0010 0.0017 0.0342 0.0961 0.0960 0.1247

Note: The dependent variable is the Revaluation CAR. Forecast error is dummy, and proxy for information asymmetry, that is 1 if the firm is above sample median. Analystdispersion is a dummy, and proxy for information asymmetry that equals 1 if the firm is above the sample median. Analyst coverage is also a dummy, and proxy for informationasymmetry and equals 1 if the firm is above the sample median. A firm is considered opaque if EPS forecast-actual equals 1, if Analyst dispersion equals 1. and if Analystcoverage equals 0. The other variables are control variables. *, ** and *** indicate significance at 10%, 5% and 1%, respectively.

26

5.2 Opaque versus transparent targets

In Figure I it is shown that cash acquisition targets have a higher revaluation than stock acqui-

sition targets. This is in line with Malmendier et al. (2016) and confirms theories of Shleifer

& Vishny (2003); Rhodes-Kropf et al. (2005); Bhagat et al. (2005). However, the effect of

information asymmetry is yet to be investigated. As is stated in the hypothesis development,

opaque targets are expected to have a higher revaluation. In Table V the differences between

opaque and transparent targets are displayed.

As is explained in Chapter 3, the sample is divided into two subsamples by each measure

of information asymmetry. Then, the difference in cumulative abnormal return during the an-

nouncement and withdrawal of the bid between opaque and transparent targets is investigated.

The results show that cash targets have a higher revaluation effect than stock targets. The cash

targets’ revaluation is significant from zero. In two of the three measures transparent cash tar-

gets seem to have a higher revaluation in regard to opaque targets. However, none of these

results is significant and therefore the results are too weak to state a difference in revaluation

between opaque cash and transparent cash targets.

The stock targets revaluation is not significant from zero. In contrast to the cash bids, the

three measures of information asymmetry show a higher revaluation for opaque stock targets

but only one of the measures is significant. The results of the analyst dispersion show a signif-

icantly higher return for opaque stock targets than for transparent stock targets. This confirms

hypothesis 2, a higher level of information asymmetry results in a higher revaluation of the

stock.

However, overall it is hard to conclude any significant outcome. While one of the measures

for information asymmetry does show a significant higher revaluation effect for opaque stock

targets, the other ones do not show any significant difference. Therefore, it can be concluded

that the results do not confirm hypothesis 2. Keep in mind that there are no control variables

and this results could suffer from omitted variable bias. Also, the number of observations

becomes very low, 48 and 31 cases for cash and stock bids respectively. This might affect the

significance level of the outcomes.

27

Table VInformation asymmetry and target firms’ stock returns

Opaque targets Transparent targets Difference

Panel 5A: Analyst forecast error

Analyst forecast error >sample median

Analyst forecast <=sample median

Cash bids (48 cases)Announcement CAR .348*** .256*** .092Between CAR -.132*** -.042** -.090**Withdrawn CAR -.074*** -.039*** -.035Revaluation CAR .069* .153*** -.084

Stock bids (31 cases)Announcement CAR .207*** .107*** .101Between CAR -.085* -.076* -.009Withdrawn CAR -.051* -.072** .021Revaluation CAR -.017 -.059 .076

Panel 5B: Analyst forecast dispersion

Analyst dispersion >sample median

Analyst dispersion <=sample median

Cash bids (48 cases)Announcement CAR .332*** .271*** .061Between CAR -.072*** -.101*** .029Withdrawn CAR -.056*** -.057*** .001Revaluation CAR .155*** .068** .087

Stock bids (31 cases)Announcement CAR 0.136** .18*** -.044Between CAR -.02 -.137*** .118Withdrawn CAR -.011 -.108*** .097*Revaluation CAR .077* -.111** .189**

(continued)

28

Table V–Continued

Panel 5C: Analyst coverage

Analyst coverage < samplemedian

Analyst coverage >=sample median

Cash bids (48 cases)Announcement CAR .279*** .334*** -.055Between CAR -.084** -.097*** .013Withdrawn CAR -.05*** -.064*** .014Revaluation CAR .107*** .112*** -.005

Stock bids (31 cases)Announcement CAR .169*** .111** .059Between CAR -.097** -.048 -.049Withdrawn CAR -.004 -.067** .028Revaluation CAR .004 -.032 .036

Note: Table V reports the relationship between the level of information symmetry of the target and the abnor-mal stock returns running from 25 days before the announcement of the bid until 25 days after the withdrawalof the bid. The sample consist of 79 ”exogenously withdrawn” pure cash or stock bids. The table reports threemeasures for information asymmetry, obtained from I/B/E/S database. In Panel 5A, the |consensus forecastedEPS − actual EPS| / Stock price is used as measure of information asymmetry. In Panel 5B, the Standarddeviation of analyst forecasted EPS/ Stock price is used. And in Panel 5C the Number of analyst covering thetarget firms / Firm assets is used to divide the sample in opaque and transparent firms. All three informationasymmetry measures are measured one quarter before the announcement of the bid. Announcement CAR runsfrom 25 days prior to the announcement until one day after. Between CAR runs from one day after the biduntil one day before the withdrawal. Withdrawn CAR runs from one day prior to the withdrawal until 25 daysafter the withdrawal. Revaluation CAR runs from 25 days prior to the announcement until 25 days after thewithdrawal. The last column shows the difference in CAR between opaque and transparent firms. Statisticalsignificance at the 1%, 5%, and 10% level is indicated by ***, **, and *, respectively.

5.3 Information asymmetry on target revaluation

So far, only the difference in revaluation between opaque and transparent targets within cash

or stock deals is investigated. However, other factors could affect the revaluation. Therefore a

regression output is done to control for certain variables. In Table VI the regression output is

displayed.

The results for cash offers show no significant outcome for the variables of interest. Mean-

ing that the revaluation effect does not depend on whether the target is opaque or transparent.

It is also interesting to see that the offer premium does not have a significant effect on the

revaluation. The market-to-book ratio of the target has a negative effect on the revaluation.

Meaning that if the growth opportunities where low, the revaluation effect is lower. However,

if the acquirer has a high market-to-book value the revaluation effect is higher.

29

The results for stock offers are shown in regression 4, 5 and 6 in Table VI. The Forecast

error is positively significant at a 10% level. Implying that opaque stock targets have a higher

revaluation effect than transparent targets. The Analyst dispersion is positive and significant.

Again, this implies a higher revaluation for opaque stock targets. Analyst coverage is negatively

correlated however not significant at all.

These results confirm hypothesis 2 for the stock bids. However, due to the low amount of

observations the results have to be interpreted carefully. The significance and correlation of

control variables seem to be in line with previous research. In regression 5 the offer premium

becomes significant. This seems logical since a higher premium could, despite more synergies

between the acquirer and target, imply a bigger undervaluation pre-announcement. The q of

the target has a negative impact on the revaluation. Economically, this could be explained by

that the target was already valued at a high price and therefore had no need of revaluation. The

q of the acquirer has a positive impact on the revaluation. Implying that if the acquirer has

a high market-to-book ratio the target observes a higher revaluation. This could be explained

by that a high market-to-book ratio of a potential acquirer signals to the market that the target

company potentially could have a market-to-book ratio similar to the acquirer. Therefore in the

targets’ current state it could be undervalued and need a revaluation. Unfortunately the amount

of observations becomes very small which again limits the significance and certainty of the

results.

In Table VII the results of cash and stock offers combined are shown. The first proxy for

information asymmetry is positive and significant. Implying that for opaque targets that receive

stock offers the revaluation effect is higher than for transparent targets that receive stock offers.

The cash component also has a positive effect on the revaluation. However, the combination of

Cash and Forecast error is negative. This implies a lower revaluation for opaque cash targets

in regard to transparent cash targets. The same holds for the second proxy, Analyst Dispersion,

although, the differences in revaluation are considered to less apparent comparing the magni-

tude of the coefficients. The third proxy, Analyst coverage is not significant. The results seems

to partly confirm and partly contradict hypothesis 2. The level of information asymmetry does

affect the results whitin cash or stock targets differently. Opaque stock targets receive a higher

revaluation compared to transparent stock targets and opaque cash targets receive a lower reval-

uation compared to transparent cash targets. Overall, the results of Malmendier et al. (2016)

hold since cash targets have a significantly higher revaluation than stock targets. Regarding the

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control variables, the market-to-book ratio for the target has a negative effect on the revaluation,

and the market-to-book ratio of the acquirer a positive effect on the revaluation. This confirms

the theories and findings of Malmendier et al. (2016). The causal relationship for this findings

can be explained as follows. The theory by Hansen (1987) and Fishman (1989) predicts that

stock is used for opaque targets. So whitin the opaque stock targets it is possible that investors

see the signal of being target to a takeover as a positive signal of undervaluation of the target.

Transparent stock targets, however, are assumed to be fairly valued. Therefore, the signal of

being subject to a potential takeover does not affect the price.

For cash targets the transparent targets are perceived to be fairly valued, but because of the

bid, investors receive the signal of an undervaluation of the market (Dong et al., 2006). How-

ever, when the cash target is opaque the signal of being undervalued is not valued because the

firm remains hard to value. Therefore, the best reference point is the price pre-announcement.

By comparing the whole cash and stock sample, I find that information asymmetry con-

verges the revaluation effect. A causal explanation for this phenomenon is not yet investigated.

Reasoning from Baker et al. (2012) the best reference point after the revaluation is the pre-

announcement price. Therefore, for opaque targets the revaluation naturally is attracted to that

point. However, for transparent targets this might not hold since there is less information asym-

metry and therefore less magnitude of the reference point.

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Table VIInformation Asymmetry on Revaluation

Dependent Variable = Revaluation CAR

Cash Offers Stock Offers

(1) (2) (3) (4) (5) (6)

Forecast error -0.06 0.18*(0.101) (0.087)

Analyst dispersion 0.09 0.28***(0.076) (0.068)

Analyst coverage 0.06 -0.06(0.080) (0.139)

Offer premium 0.10 0.08 0.05 0.22 0.33*** 0.23(0.133) (0.138) (0.139) (0.126) (0.102) (0.163)

q of target -0.04* -0.04* -0.05*** -0.06*** -0.05*** -0.06***(0.019) (0.019) (0.016) (0.012) (0.009) (0.019)

q of acquirer 0.03*** 0.04*** 0.03** 0.06*** 0.06*** 0.06***(0.010) (0.010) (0.012) (0.010) (0.008) (0.019)

Transaction Size 0.02 0.03* 0.04** -0.03 -0.03 -0.05(0.023) (0.016) (0.016) (0.026) (0.026) (0.041)

Intercept 0.21* -0.01 0.09 -0.33 -0.56*** -0.01(0.125) (0.136) (0.124) (0.200) (0.133) (0.180)

Observations 45 45 45 20 20 20Adjusted R-squared 0.0863 0.1099 0.0854 0.4908 0.6595 0.3721

Note: Table VI shows the result of the proxy’s for information asymmetry on the revaluation of tar-get firms. The dependent variable is a the CAR of the stock price measured from 25 days before theannouncement until 25 days after the withdrawal. the sample consist 55 withdrawn deals pure cashand stock deals. The EPS forecast − actual, Analyst dispersion and Analyst coverage are measured inthe quarter before the deal announcement date. Standard errors are reported in parentheses. Statisticalsignificance at the 1%, 5% and 10% level is indicated by ***, **, and *

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Table VIIInformation Asymmetry on Revaluation of the target

(1) (2) (3)

Cash*Forecast error -0.29**(0.136)

Cash*Analyst dispersion -0.19*(0.114)

Cash*Analyst coverage -0.03(0.132)

EPS forecast-actual 0.19*(0.097)

Analyst dispersion 0.29***(0.087)

Analyst coverage -0.05(0.114)

Cash 0.52** 0.39** 0.17(0.199) (0.171) (0.213)

Offer premium 0.14 0.18 0.13(0.103) (0.118) (0.104)

q of target -0.03* -0.03* -0.03*(0.016) (0.016) (0.018)

q of acquirer 0.04*** 0.04*** 0.04***(0.006) (0.005) (0.007)

Transaction Size 0.01 0.02 0.01(0.017) (0.014) (0.019)

Intercept -0.29* -0.49*** 0.03(0.169) (0.169) (0.182)

Observations 66 66 66Adjusted R-squared 0.1957 0.2589 0.1414

Note:Table VII shows the result of the proxy’s for information asymmetry on the revaluation of targetfirms. The dependent variable is a the CAR of the stock price measured from 25 days before theannouncement until 25 days after the withdrawal. the sample consist 55 withdrawn deals pure cashand stock deals. The EPS forecast − actual, Analyst dispersion and Analyst coverage are measured inthe quarter before the deal announcement date. Standard errors are reported in parentheses. Statisticalsignificance at the 1%, 5% and 10% level is indicated by ***, **, and * respectively.

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6 Conclusion

In summary, this thesis elaborates on the revaluation effect that exists following the failure

of mergers and acquisitions. It is the first paper that investigates the relationship between

information asymmetry and the revaluation effect in a broader mergers and acquisitions context.

By using three measures of information asymmetry, I first test whether the choice of the method

of payment for acquisitions depends on the level of information asymmetry. Additionally, I

create an exogenous withdrawn sample to discover how information asymmetry affects the

revaluation effect.

The results show that two of the three measures of information asymmetry confirm the

relationship between information asymmetry and method of payment. While Forecast error

does not show a significant impact, following Analyst dispersion as a proxy for information

asymmetry, the results show that opaque targets are more likely to be acquired with stock as

method of payment. The proxy Analyst coverage also indicates that transparent targets are more

likely to be taken over with cash. This confirms theories presented by Eckbo et al. (1990);

Hansen (1987) and Fishman (1989) which assert that stock offers can be used to reduce the

problems arising by information asymmetry.

Furthermore, my paper confirms that the revaluation effect is higher for cash targets than for

stock targets, which is in line with findings by Malmendier et al. (2016). By dividing the sample

by the measures of information asymmetry into two subsamples, I investigate whether opaque

or transparent targets receive a higher revaluation. For the cash bids no statistical differences are

found. For the stock bids only the separation of the sample by analyst forecast dispersion shows

a positive significant effect for opaque targets. I find this evidence is too weak to support my

hypothesis of opaque targets receiving a higher revaluation. However, when controlled for other

variables, such as offer premium and the Tobin’s q for the target and the acquirer, there seem

to be a significant difference in revaluation. For opaque cash targets compared to transparent

cash targets, the results show a higher revaluation for transparent cash targets. Regarding stock

acquisitions, the revaluation is higher for opaque targets than for transparent targets. This shows

a difference in the effect of information asymmetry on the target’s revaluation. Viewed from a

broader perspective, opaqueness converges the difference between cash and stock revaluation.

This paper contributes to the existing literature by using information asymmetry in failed

mergers and acquisitions. My findings support the findings of Malmendier et al. (2016); Liu

(2017). The thesis also brings attention to the theories of the choice for cash or stock by Rhodes-

34

Kropf et al. (2005); Gorbenko & Malenko (2014). My thesis also contributes to the theory of

Hansen (1987) by confirming the hypothesis of stock being used as a method to reduce the cost

of information asymmetry between the target and acquirer.

The findings have to be interpreted carefully in broader context since this thesis suffers

from a number of limitations. One of the limitations is the relatively low amount of obser-

vations. While I reconstruct the sample used by Malmendier et al. (2016) and Liu (2017),

the I/B/E/S database does not cover all the firms from the previous data sets. Due to the low

amount of observations, many variables are not significant. Another limitation is the methodol-

ogy used to construct the exogenous withdrawn sample. While I make use of the same reasons

presented by related literature (Liu, 2017; Malmendier et al., 2016; Chang & Suk, 1998), the

Lexis-Nexis news research implies interpretation of news items which can be exposed to sub-

jectivity. As Malmendier et al. (2016) point out, the news has to be reliable. If the sources

hide certain reasons for withdrawal this research may be biased. Another limitation is that

information asymmetry is inherently difficult to measure. While my three measures of obser-

vations are commonly used in the literature, they may not fully capture the level of information

asymmetry. The used measures of information asymmetry capture the information asymmetry

between analyst and the management, they do not capture the information asymmetry between

investors and the management. While this does not delegitimize the findings of this paper,

future research could focus on the information asymmetry between investors and the manage-

ment. For example, stock return volatility, the spread, and volume can be used. Overall, this

paper provides another building block for future research on the value implications of mergers

and acquisitions.

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