Investment Bank Role in Corporate Restructuring

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INVESTMENT BANK ROLE IN CORPORATE RESTRUCTURING

Kien Cao

A Dissertation Submitted to the Faculty of

The College of Business

In Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Florida Atlantic University

Boca Raton, FL

August 2012

ACKNOWLEDGEMENTS

I would like to express my appreciation to my chair, Professor Jeff Madura, and my committee members, Professors Luis Garcia-Feijoo and William McDaniel, for their help and support. This dissertation would not have been possible without valuable insights from you. I also owe a great debt of gratitude to Professor Jeff Madura for his encouragement and friendship throughout my 4 years at Florida Atlantic University.

Without his encouragement, I would not have been able to finish the program.

ABSTRACT

Author: Kien Cao

Title: Investment Bank Role in Corporate Restructuring

Institution: Florida Atlantic University

Dissertation Advisor: Dr. Jeff Madura

Degree: Doctor of Philosophy

Year: 2012

Essay 1: Investment bank role in acquisitions of private targets

In essay 1, using a sample of acquisitions of private targets from January 1992 to

December 2010, I find that special information asymmetry when bidders pursue private targets alters the factors used by bidders and targets to decide whether to hire an investment bank. Regarding the effectiveness of the investment bank, I report several notable findings. First, there is no impact on the wealth effect of a bidder when it hires an investment bank. On the target side, the investment bank helps their client exploit increased benefits from the bidder. Second, the existence of an investment bank on the bidder side has a positively significant impact on the proportion of the equity payment.

Investment banks with a better reputation are associated with an increased use of equity as payment. Third, the risks of a bidder that hires an investment bank decrease

v significantly following the acquisition. Therefore, it appears that the investment bank has a significant impact on the outcome of the acquisition of a private target.

Essay 2: Investment bank role in asset sell-off transactions

In essay 2, I also find that special information asymmetry when a buyer pursues divested assets alters the factors used by the buyer and seller to decide whether to hire an investment bank. However, the country characteristics variables do not have any impact on the decision of either the buyer or the seller. I find that the existence of an investment bank does not have any impact on the method of payment of the asset sell-offs or the post-transaction operating performance of the buyer. However, the investment bank has a significant impact on the cumulative abnormal returns (CARs) as well as the risk-shifts of the buyer. I find evidence that the buyer realizes a significantly higher CAR when it employs an investment bank. On the other hand, the buyer has a significantly lower CAR when the seller uses an investment bank. Nevertheless, the benefits disappear when the buyer (seller) employs a top-tier investment bank. Moreover, I find that when the seller employs an investment bank, the increase in unsystematic and total risk of the buyer is greater than in cases when the seller does not use an investment bank.

DEDICATION to Cao Bá Cảnh and Đinh Thị Quy

My inspiring and loving parents.

INVESTMENT BANK ROLE IN CORPORATE RESTRUCTURING

List of Tables ...... xii General Introduction ...... 1

Literature Review...... 6

Essay 1: Investment Bank Role in Acquisitions of Private Targets ...... 13

Introduction ...... 13 Hypotheses ...... 18 Factors that Cause Public Bidders or Private Targets to Hire Financial Advisor ...... 18 Transaction Costs ...... 18 Information Asymmetry...... 20 Contracting Costs ...... 22 Country Characteristics ...... 24 Impact of Financial Advisors on Wealth Gains of Bidders Acquiring Private Targets ...... 28 Explanatory Variables ...... 29 Control Variables ...... 31 Impact of Financial Advisors on Acquisition Discounts of Private Targets ...... 32 Explanatory Variables ...... 34 Control Variables ...... 36 Impact of Financial Advisors on Method of Payment ...... 37 Explanatory Variables ...... 39 Control Variables ...... 41 Financial Constraints Variables ...... 41 vii

Asymmetric Information Variables ...... 44 Country’s Risk Variables ...... 46 Impact of Financial Advisors on Operating Performance of Bidders Acquiring Private Targets ...... 48 Explanatory Variables ...... 50 Control Variables ...... 50 Impact of Financial Advisors on Risk Shifts of Bidders Acquiring Private Targets ...... 50 Explanatory Variables ...... 52 Control Variables ...... 52 Methodology ...... 52 Detecting Factors that Cause Bidders and Private Targets to Hire Financial Advisors ...... 53 Testing the Impact of Financial Advisors on Wealth Gains of Bidders ...... 54 Testing the Impact of Financial Advisors on Valuation Multiples of Targets ...... 54 Testing the Impact of Financial Advisors on the Method of Payment in Acquisitions ...... 55 Testing the Impact of Financial Advisors on Operating Performance of Bidders ...... 56 Testing the Impact of Financial Advisors on Risk Shift of Bidders ...... 57 Sample...... 58 Data Description and Results of Univariate Analysis...... 59 Results of Multivariate Analysis ...... 60 Factors that Cause Public Bidders or Private Targets to Hire Financial Advisor ...... 60 Impact of Financial Advisors on Wealth Gains of Bidders Acquiring Private Targets ...... 63 viii

Impact of Financial Advisors on Acquisition Discounts of Private Targets ...... 65 Impact of Financial Advisors on Method of Payment ...... 67 Impact of Financial Advisors on Operating Performance of Bidders Acquiring Private Targets ...... 69 Impact Financial Advisors on Risk Shifts of Bidders Acquiring Private Targets ...... 70 Conclusion ...... 72 Essay 2: Investment Bank Role in Asset Sell-off Transactions ...... 75 Introduction ...... 75 Hypotheses ...... 80 Factors that Cause Buyers or Sellers to Hire Financial Advisors in Asset Sell-Offs ...... 80 Transaction Costs ...... 81 Information Asymmetry...... 82 Contracting Costs ...... 85 Country Characteristics ...... 86 Impact of Financial Advisors on Wealth Gains of Buyers in Asset Sell-offs ...... 89 Explanatory Variables ...... 90 Control Variables ...... 92 Impact of Financial Advisors on the Method of Payment in Asset Sell-offs ...... 93 Explanatory Variables ...... 95 Control Variables ...... 97 Financial Constraints Variables ...... 97 Asymmetric Information Variables ...... 100 Country’s Risk Variables ...... 102 Impact of Financial Advisors on Operating Performance Following Asset Sell-offs ...... 104 ix

Explanatory Variables ...... 106 Control Variables ...... 106 Impact of Financial Advisors on Risk Shifts Following Asset Sell-offs ...... 106 Explanatory Variables ...... 107 Control Variables ...... 107 Methodology ...... 107 Identifying Factors that Cause Buyers or Sellers to Hire Financial Advisors ...... 108 Testing the Impact of Financial Advisors on Wealth Gains in Asset Sell-offs ...... 109 Testing the Impact of Financial Advisors on the Method of Payment in Asset Sell-offs ...... 110 Testing the Impact of Financial Advisors on Operating Performance Following Asset Sell-offs ...... 111 Testing the Impact of Financial Advisors on Risk Shifts Following Asset Sell-offs ...... 111 Sample...... 113 Data Description and Results of Univariate Analysis...... 113 Results of Multivariate Analysis ...... 115 Factors that Cause Buyers or Sellers to Hire Financial Advisors in Asset Sell-Offs ...... 115 Impact of Financial Advisors on Wealth Gains of Buyers in Asset Sell-offs ...... 118 Impact of Financial Advisors on the Method of Payment in Asset Sell-offs ...... 120 Impact of Financial Advisors on Operating Performance Following Asset Sell offs ...... 122 Impact of Financial Advisors on Risk Shifts Following Asset Sell-offs ...... 122 x

Conclusion ...... 124 References ...... 154

LIST OF TABLES

Essay 1:

Table I - Definition of Variables...... 127

Table II - Descriptive Statistics...... 128

Table III - Correlation Matrix…………………………………………………………..130

Table IV - Logit and Tobit Regression Explaining the Decision to Hire

Investment Banks in Acquisition of Private Targets...... 131

Table V - OLS Regression Explaining the Wealth Effect of Bidders in

Acquisitions of Private Targets...... 132

Table VI - OLS Regression Explaining the Valuation of Private Targets...... 133

Table VII - Tobit Regression Explaining the Portion of Equity Financing

in Acquisitions of Private Targets...... 134

Table VIII - Tobit Regression Explaining the Portion of Equity Financing in

Acquisitions of Private Targets...... 135

Table IX - OLS Regression Explaining the Change in Operating

Performance of Bidders in Acquisitions of Private

Targets (-1 to +1)...... 136

Table X - OLS Regression Explaining the Change in Operating

Performance of Bidders in Acquisitions of Private

Targets (-1 to +2)...... 137

xii

Table XI - OLS Regression Explaining the Increase in Systematic Risk of

Bidders in Acquisitions of Private Targets……………………..138

Table XII - OLS Regression Explaining the Increase in Unsystematic Risk

of Bidders in Acquisitions of Private Targets…………………..139

Table XIII - OLS Regression Explaining the Increase in Total Risk of

Bidders in Acquisitions of Private Targets……………………..140

Essay 2:

Table XIV - Definition of Variables……………………………………………………141

Table XV - Descriptive Statistics……………………………………….……………...142

Table XVI - Correlation Matrix……………………………………………………...…144

Table XVII - Logit and Tobit Regression Explaining the Decision to Hire

Investment Banks in Assets Sell-off Transactions……………...145

Table XVIII - OLS Regression Explaining the Wealth Effect of Buyers in

Assets Sell-off Transactions……………………………………146

Table XIX - Tobit Regression Explaining the Portion of Cash Financing in

Assets Sell-off Transactions……………………………………147

Table XX - Tobit Regression Explaining the Portion of Cash Financing in

Assets Sell-off Transactions……………………………………148

Table XXI - OLS Regression Explaining the Change in Operating

Performance of Buyers in Assets Sell-off

Transactions (-1 to +1)…………...... 149

Table XXII - OLS Regression Explaining the Change in Operating

xiii

Performance of Buyers in Assets Sell-off

Transactions (-1 to +2)………………………………………….150

Table XXIII - OLS Regression Explaining the Increase in Systematic Risk

of Buyers in Assets Sell-off Transactions………………………151

Table XXIV - OLS Regression Explaining the Increase in Unsystematic

Risk of Buyers in Assets Sell-off Transactions………………...152

Table XXV - OLS Regression Explaining the Increase in Total Risk of

Buyers in Assets Sell-off Transactions…………………………153

xiv

GENERAL INTRODUCTION

In recent years, the acquisitions of private targets and divested assets have become an increasingly important method for corporate restructuring. Bates (2005) reports that two-thirds of the acquisitions in Thomson’s Securities Data Corporation

(SDC) database is acquisitions of non-publicly traded targets, including private targets and divested assets. This implies that the M&A market for private firms and divested assets, such as subsidiaries, should gain more attention from academic and practical studies.

The role of investment banks in corporate restructuring is very important. The influence of investment banks as financial advisors on the outcome of M&A transactions has been investigated in academic studies to some extent. However, while all of the studies about the role of investment banks in acquisitions investigate a sample of publicly traded targets, virtually no attention has been paid to the role of investment banks in acquisitions of non-publicly traded targets, such as private targets and divested assets.

The prior literature tends to use datasets that sample publicly traded targets and implicitly assume that conclusions drawn from those targets apply equally well to non-publicly traded ones. Yet, Berk (1983) argues that drawing conclusions from samples of publicly traded targets weakens the generalizability of the interpretations and undermines the

1 external validity of the evidence. Hence, given the substantial differences between publicly traded and non-publicly traded targets, this assumption is problematic.

By some accounts, the acquisitions of non-publicly traded targets require different skill sets than that of public targets. For example, the acquisitions of non-publicly traded targets usually belong to the mid-market. Moreover, some practical publications suggest that investment banks, normally viewed as top tier, focus their efforts on the largest mergers in order to maintain or increase their standings based on the market value of transactions. These investment banks may only pursue mid-market mergers when they still have the capacity to venture beyond the largest mergers. Thus, the main participants that serve as advisors might differ from those that serve mergers involving public targets.

Moreover, the acquisition of non-publicly traded targets can be considered as the best opportunity to investigate the certification impact of financial advisors. The good firms are more likely to hire investment banks as a signal of great firm value, while bad firms find it is not worthwhile to do so. The theory suggests that investment banks will certify the value and bring the most benefits to their clients and the market participants use the firms’ choice of investment banks to classify the two groups. The certification role is unclear and somewhat of a source of conflict in the current literature. For example,

Bowers and Miller (1990) Michel et al. (1991), and Golubov et al. (2011) find evidence that prestigious investment banks acting as financial advisors create value for firms in

M&A transactions. However, Servaes and Zenner (1996) and Da Silva Rosa et al. (2004) document that the prestige of the advisors does not have an impact on the wealth creation

2 of the transaction. I argue that the fact that the current literature considers only publicly traded targets may be one of the reasons for the conflicting results.

The information about non-publicly traded targets is often limited and the financial advisors should play a crucial role in these transactions. For example, while information asymmetry is crucial for all M&As, this issue is much more important for transactions of private targets in which standards for information disclosure are not as high as for publicly traded firms and for transactions of subsidiaries in which information might be manipulated by the selling firms. The difficulty about information asymmetry might hurt both the publicly traded bidder and the private target since it creates misunderstandings and conflicts during the negotiation process. It seems obvious that bidders will decrease the offer price to private targets to protect themselves from the possibility that the firms are less than fully informed about the business they are acquiring. On the other hand, quality targets might not be able to signal their true value to potential bidders.

Given the fundamental differences between the acquisitions of publicly traded and non-publicly traded targets, the role of investment banks may be different in the two situations. In fact, the role of investment banks may be much more important in cases of acquisitions of non-publicly traded targets. Therefore, I attempt to fill the gap in the literature by looking at the role of investment banks as financial advisors in acquisitions of non-publicly traded targets.

My goal is to empirically investigate the role of financial advisors by focusing on corporate restructuring that involves units that are not publicly traded. Specifically, I examine the influence of investment banks on the outcome of bidders in 1) acquisitions of private targets and 2) asset sell-off transactions.

The theme throughout the dissertation is to assess the decision to hire and the effectiveness of investment banks for both bidders and targets in acquisitions of non- publicly traded targets. In the M&A literature, transaction costs and information asymmetries are considered important factors that influence the outcomes of acquisition transactions. It is also well-understood that both bidders and sellers are hiring financial advisors to reduce the adverse influence of those factors.

There are several hypotheses regarding the role of investment banks as financial advisors. These hypotheses are in the area of transaction costs, asymmetric information, contracting, superior deal, deal completion, better deal, and strategic bargaining. All of these hypotheses suggest two roles of investment banks in M&A transactions.

First, they imply that financial advisors are hired to reduce the transaction costs for both bidder and seller. This argument claims that the parties who use financial advisors should have better outcomes. In addition, more experienced financial advisors should generate even better results for their clients.

The second role of financial advisors is in the area of certification. This argument suggests that the existence of financial advisors will lower the level of information

4 asymmetry, certify their client’s value, and certify the expected synergy which will be created.

While there are many reasons described above for bidders or targets to hire investment banks, many mergers are consummated without the help of investments banks. Perhaps the most prominent argument for not hiring an investment bank is that the costs are significant. For example, in the case of mergers, advisory fees for financial advisors have been estimated to represent 1.15% of the deal value1. Thus, the decision to hire an investment bank for any form of corporate restructuring must weigh potential benefits against the costs. However, when the participants decide to hire financial advisors, investment banks should have a positive impact on the outcome of the transaction and the better results should be associated with better financial advisors.

1 From Thomson Financial SDC database, M&A and Advisors’ Summary Report, Fourth Quarter 2005. 5

LITERATURE REVIEW

The foundation of literature for both essays is provided next. The literature regarding the role of investment banks in mergers and acquisitions focuses on the potential benefits and costs of hiring investment banks. Hunter and Walker (1987 and

1988) discuss the magnitude of fees charged by investment bankers for their services in acquisitions. Since then many authors have attempted to investigate the relationship between the use of investment banks as financial advisors and the outcome of acquisitions. There are two strands in the literature with respect to investment banks. The first is about the role of investment banks in the wealth creation of acquisitions. Bowers and Miller (1990) investigate the relationship between the value creation of acquisitions and the choice of an investment bank. They find that the value creation of acquisitions as total wealth gains for both bidder and target are larger when either the bidder or the target uses a top-tier investment bank. The result supports the hypothesis that more reputable investment banks have better expertise identifying firms and processing the transactions.

Michel et al. (1991) find that the group of top-tier investment banks outperforms the less prestigious one. By examining the abnormal returns of bidder and target around the acquisition announcement, they conclude that firms which are advised by top-tier investment banks generate superior abnormal returns in comparison to those who are advised by less prestigious investment banks. However, within the top-tier investment

6 banks group, the authors notice that the degree of prestige of an advisor does not vary directly with the abnormal returns earned as a result of the transaction.

Servaes and Zenner (1996) classify investment banks into top-tier and secondary- tier advisors. They find that there is no relation between the abnormal returns to the bidder in the announcement period and the tier of its advisor. Their results suggest that the presence of an investment bank as financial advisor does not seem to affect the bidder’s shareholder return at the announcement of the transaction. However, the choice to use an investment bank as financial advisor does depend on the complexity of the transaction, the type of transaction, the bidder’s prior acquisition experience, and the degree of diversification of the target firm.

Kale et al. (2003) study the role of financial advisors on wealth gains in tender offers. They document that when a firm (either bidder or target) employs a relatively more reputable financial advisor, it realizes a better wealth effect. The authors also report that the total dollar wealth gain is positively related to the reputation levels of both acquirer and target advisors. Moreover, the more prestigious advisors do a better job when they more frequently advise their clients to withdraw from value-destroying transactions.

More recently, Allen et al. (2004) investigate the role of commercial banks and investment banks as financial advisor to both bidders and targets in M&A transactions.

The authors argue that commercial banks have a comparative advantage in serving as

M&A advisors for their customers because they can provide a certification effect. This

7 comparative advantage is even stronger when they have a prior lending relationship with the customers. However, there might be a conflict of interest when a bank serves as both a merger advisor and a lender. Using the certification effect in terms of shareholder returns, the authors report evidence of a net certification effect for target firms only.

Moreover, they also show that the more intense the lending relationship with the bidder becomes, the greater is the likelihood that a commercial bank will be chosen as an advisor.

Using a sample of 6379 U.S. M&A transactions, Ismail (2010) finds that bidders advised by top-tier advisors realize a loss, whereas those advised by lower-tier advisors realize a gain during the merger announcement. However, the results are mainly driven by the huge loss of several bidders advised by the most prestigious advisors. The author concludes that choosing advisors based on the league tables could be misleading and firms should select financial advisors in M&A transactions according to the investment bank’s track record. Nevertheless, the existence of a top-tier advisor on at least one side of an M&A transaction has a positive impact on the total wealth gains of the deal.

The second strand of the literature focuses on the agreement contract between investment banks and their clients. Hunter and Walker (1990) examine the merger fee contracts and find that those contracts give incentive to the advisor as a payment contingent on the completion of the transaction. They model the investment bank as a searching agent who would perform better if it received a greater proportion of wealth created by the transaction. Hence, merger gains are positively related to the investment bank’s fees and the ease with which merger negotiations were conducted (as a proxy for 8 the investment bank’s efforts). McLaughlin (1990) also investigates the fee method of investment bank contracts in acquisition. Since more than 80 percent of the fee is paid only if the acquisition is complete, McLaughlin concludes that most fees are contingent on the transaction’s outcome, such as target firm fees contingent on transaction value and bidding firm fees contingent on the number of shares purchased. McLaughlin documents the potential conflict of interest between investment bank and client. However, he argues that even if investment banks were motivated by fee income, they might not want to increase the price of the acquisition because it would destroy the reputation of the investment bank.

Rau (2000) investigates the factors affecting the market share for investment banks as a financial advisor in mergers and tender offers. Investment bank market share is positively related to the contingent fee payments charged by the bank and to the percentage of deals completed in the past by the bank. Nevertheless, the author finds no relationship between advisor market share and the post-acquisition performance of an advisor’s clients. Rau also finds that more reputable investment banks charge higher portions of their fees contingent on the successful completion of the deal.

Rau and Rodgers (2002) look at the question of why top-tier investment banks are hired as a financial advisor in tender offers. They find that top-tier banks are not hired in complex transactions. On the other hand, they are hired by bidders with larger boards of directors, less concentrated equity ownership, and less insider ownership. Moreover, the announcement returns of bidders who use a top-tier investment bank are not higher compared to those of bidders who do not use a top-tier investment bank as financial 9 advisor. Additionally, the long-term returns are lower for bidders who use a top-tier investment bank.

Hunter and Jagtiani (2003) examine the factors affecting the probability and pace of a successful deal completion, the fees paid by both bidders and targets, and the post- merger gains of the bidders. They find that transactions advised by top-tier investment banks are more likely to be completed and have a shorter time to completion compared with transactions advised by a lower-tier investment bank. The authors also report that the proportion of fees contingent on the deal completion is negatively correlated with the time to completion and larger fees are associated with larger post-transaction gains. More importantly, the large advisor fees do not have any significant impact on the probability of deal completion.

Da Silva Rosa et al. (2004) investigate the decision to hire takeover advisors in the Australian market. Their results show that advisors are more likely to be hired when the transaction is large, hostile, and when it involves non-cash compensation. The authors document that deal completion is not correlated with the ranking of advisors and the prestige of advisors does not have an impact on the wealth creation of the transaction.

Chahine and Ismail (2009) investigate determinants of advising fees and the relation between advising fees and the premium of M&A transactions. Using a sample of

635 transactions, the authors report various significant factors that affect the level of advising fees, such as the reputation of investment banks and the complexity of the transactions. Furthermore, there is a positive (negative) relation between target’s

(bidder’s) advising fees and the premium. The study documents a trade-off between the advising fees and the premium that target (bidder) will receive (pay).

Bodnaruk et al. (2009) examine the holding status of financial conglomerates affiliated with investment banks which advise bidders in M&A transactions. They report evidence that those financial conglomerates take positions in the target prior to the transaction. There is a positive relationship between the stake of financial conglomerates in the target and the likelihood of deal completion, the termination fees, as well as the target premium. Despite the fact that these transactions are not wealth-creating, the authors claim that this is a high profit strategy for financial conglomerates.

Since only one-third of targets hire financial advisors, Forte et al. (2010) look at the factors influencing the choice of targets using financial advisors when they are being acquired. The authors show that the decision to hire an advisor depends on three main factors: (i) the intensity of the previous banking relationship, (ii) the reputation of the bidder company’s advisor, and (iii) the complexity of the deal. Moreover, the study suggests a “certification role” of investment banks since the wealth effect of target firms is higher when they have a closer prior banking relationship.

Most recently, Golubov et al. (2012) find that top-tier advisors are associated with higher bidder returns in public acquisitions only. They claim that top-tier investment banks have ability to identify and method mergers with higher synergy gains. As for deal completion, the authors find limited evidence that top-tier advisors are associated with higher deal completion rates. Moreover, deals advised by top-tier investment banks take

11 less time from announcement to completion. They also examine “in-house” acquisitions, where the bidding firm does not retain an investment bank for the transaction and find that firms with high “in-house” M&A expertise are less likely to use external advice.

Regarding unlisted firms (private or subsidiary firms), there is no effect of financial advisor reputation on bidder returns.

ESSAY 1

INVESTMENT BANKS AS FINANCIAL ADVISORS IN ACQUISITIONS OF

PRIVATE TARGETS

I. Introduction

Investment banks have a significant role as financial advisors in the market for corporate control. They play a very important role in reducing the information asymmetry between bidders and targets. Furthermore, investment banks can improve the quality of the matching of bidder and target as well as accelerate the matching process. They also provide valuable anonymity in the preliminary stages before the negotiation process begins.

However, their role in acquisitions of private targets has been neglected in the literature. With the existence of investment banks, the information asymmetry between bidders and targets is minimized, the quality of the matching of bidder and target is improved, the pace of the matching process is increased, and the negotiation process is more efficient. Nevertheless, the role of investment banks is even more important in cases of acquisitions of private targets. In the acquisitions of a private target, there is substantially more information asymmetry regarding the value of the target in comparison to that of a publicly traded target.

The high difficulty level of information asymmetry might be detrimental to both the bidder and the private target since it creates mistrust during the negotiation process.

For example, because of information asymmetry, private targets might have to accept a greater discount. According to Akerlof (1970), when the informed seller cannot credibly signal its value to potential bidders, it has to accept a price discount and lower the quality level in equilibrium. Bidders may decrease the offer price to a private target to protect themselves from the possibility that the firm is less than fully informed about the business it is acquiring. Makadok and Barney (2001) claim that the lack of information available on private firms provides more opportunities for acquirers to exploit private information situations and gain higher abnormal returns. Based on the same argument, bidders might mistakenly offer a price that is higher than the true value of the target when it cannot estimate the true value of the target. Hence, there is a need for a third party to minimize the information asymmetry between the bidder and target.

Different expectations about the target’s true value and the synergy gains are the major sources of disagreement between bidders and targets in M&As. When the divergence in expectations is large, both bidder and target can obtain opinions from investment banks. In this case, the bidders reduce the risk of over-paying while the targets can improve their bargaining power. Deeds et al. (1999) argue that acquirers are less aware of the existence of private targets because those targets are less visible and transparent to the investment community than are public targets. It is more difficult to locate and value such targets without the support of investment banks.

Furthermore, the negotiation between bidder and publicly held target is normally a non-public process. Acquisitions of publicly traded targets have greater publicity and visibility compared to acquisitions of private targets. In this non-public negotiation process, it is more difficult for outside investors, including individuals, stock analysts, and institutional investors, to obtain details regarding the negotiation process. Therefore, the existence of investment banks as financial advisors when considering private targets is very important.

Moreover, when participating in the takeover market, managers might have hubris-based or empire building motives. When managers are driven by such motives, they want to expand their firm beyond its optimal size by acquiring public firms, rather than acquiring typically smaller private firms. In acquisitions with these motives, managers have a strong opinion and the role of investment banks is limited. The motivations behind the acquisition of a private target are not likely to be hubris-based or empire building. In this situation, managers are more willing to listen to their financial advisors and investment banks have a more important role.

The literature widely supports the view that acquisitions of private targets create value for bidders and acquisitions of publicly traded targets destroy value. The positive wealth effect of bidders around the announcement period is persistent and robust. Chang

(1998) reports a positive announcement effects for bidders who acquire private targets. In his study, Chang attributes the positive impact to the creation of outside block holders when bidders use equity as currency. He shows that bidders experience no abnormal return in cash offers and a positive abnormal return in stock offers. According to Cooney 15 et al. (2009), bidders realize a positive announcement effect because of the underpricing of a private target. The authors claim that pricing effects associated with target valuation uncertainty is an important factor explaining the positive announcement returns for acquirers of private firms. With the same argument, Fuller et al. (2002) and Officer

(2007) also attribute a positive wealth effect to the discount factor of privately-held targets. They argue that the discount factor comes from the lack of liquidity of private targets and greater monitoring of the bidder’s management when a large block-holder is created. Furthermore, Officer et al. (2008) explain the positive announcement effect by the mitigation of the negative effect of information asymmetry when equity is used as currency. They document significantly higher announcement returns to stock-swap acquirers when the private target is difficult to value.

Despite the considerable effort to justify the gains in such transactions, there is little understanding of why acquisitions of private targets result in positive returns for bidders. Given the importance of investment banks in acquisitions of private targets, I attempt to investigate whether investment banks have a significant role in such situations.

I find that the high level of information asymmetry when bidders pursue private targets alters the factors used by bidders and targets to decide whether to hire an investment bank. Moreover, target country characteristics also affect the decision.

Regarding the method of the payment, the existence of an investment bank on the bidder side has a positively significant impact on the proportion of equity payment used.

Furthermore, a better reputation of the investment bank is associated with higher proportion of equity payment. On the other hand, the existence of an investment bank on 16 the target side has limited impact on the proportion of equity payment in the acquisition of a private target.

There is no evidence of wealth effects for the bidder, regardless of the tier of their investment bank. However, when the target hires an investment bank, the bidder will experience a significant reduction in its CAR. Similarly, the existence of the bidder’s investment bank does not have any impact on the valuation multiples of the target; whereas the target realizes significant valuation multiples when it employs an investment bank. Therefore, we can conclude that the target’s investment bank has some power to exploit the benefit from the bidder.

Interestingly, the results highlight the ineffectiveness of top-tier investment banks.

Regarding the short-term return (CAR) and valuation multiples, the benefits of hiring an investment bank disappear when a top-tier investment bank is employed. These results are consistent with Rau’s (2000) argument that top-tier investment banks focus more on the completion of the transaction, rather than on bringing the most benefits possible to their clients.

Considering the operating performance of the bidder, there is no evidence that the post-transaction operating performance of the bidder is affect by the employment of an investment bank on both the bidder and target side. On the other hand, there is some evidence that hiring an investment bank will reduce the bidder’s risk post-transaction.

II. Hypotheses

1. Factors that Cause Public Bidders or Private Targets to Hire Financial

Advisors?

The use of financial advisors is not an obligation for participants in the M&A market. Forte et al. (2010) report that one-third of their sample does not involve a financial advisor. Hence, the motivation for both bidder and target to hire investment banks as financial advisors is interesting to investigate. In this section, I will develop the hypotheses used to examine the incentives of both bidder and target to use financial advisors. Following Servaes and Zenner (1996), I classify my hypotheses into 3 categories: (1) transaction costs; (2) information asymmetry; and (3) contracting costs.

However, because of the qualitative differences between acquisitions of publicly traded and private targets, some characteristics that influence the incentives to use investment banks may differ in the two samples. In addition, I also investigate the impact of certain country characteristics on the decision to use investment banks. These include characteristics, such as whether the transaction is a cross-border deal, whether the target is from a country with good corporate governance, and the level of risk in the target country.

a. Transaction Costs

According to Servaes and Zenner (1996), the role of investment banks is more important to both bidder and target as the transaction costs increase. In this study, the following variables are used to proxy for the costs of the transaction:

Equity Payment. The form of payment is a major source of complexity in M&A transactions. If transactions are entirely financed by cash, it is easier for both bidder and target to evaluate. On the other hand, if equity is used as payment, the transaction becomes more complex. When bidders use equity to finance their investments, targets may be concerned about the true value of the equity and may be reluctant to accept that equity payment. Investment banks may use their expertise to value the bidder’s equity.

Bidders that wish to use equity as payment may need investment banks to prove the true value of their equity. I hypothesize that the likelihood of using investment banks will increase when equity is being used as payment. I use EQUITYPMT, which is a dummy variable equal to 1 if the buyer uses all or partial equity payment and 0 otherwise, to examine the impact of the form of payment on the decision to hire an investment bank.

Bidder’s prior takeover experience. Servaes and Zenner (1996) argue that since more experienced bidders are capable of spreading the fixed costs over multiple acquisitions, they are less likely to need investment banks to assist them in acquisitions. I expect that the likelihood of bidders using investment banks is negatively related to their prior experience. Following Kale et al. (2003), I measure bidders’ prior experience by the number of takeover related activities undertaken by the bidders in the preceding 10-year period (PRIOR). In addition to that, I also examine the prior experience of bidder in dealing with private targets (PRIORPRIVATE).

Transaction Size. Larger transactions are more complex and more difficult to handle. For example, it is more complicated for an acquisition of a large firm to be completed compared to that of a small firm. This argument is supported by the fact that it 19 takes longer to complete a large acquisition in comparison with the time needed to complete the acquisition of a small firm. This may not be surprising since regulatory and financial issues are typically more important for larger firms. Moreover, the larger the transaction, the more important is the deal for each party, and the potential adverse consequences of a bad decision are greater. Thus, with economically important transactions, both bidders and targets may be more willing to hire investment banks to justify takeover decisions. Hence, I expect that bidders and targets will use investment banks when the size of the transactions is larger. To investigate the effect of the transaction size, a variable SIZE which equals the natural logarithm of the transaction size is used.

b. Information Asymmetry

When information asymmetry between the bidder and target is large, investment banks have the economies of specialization and economies of scale concerning acquisition to help their clients. They accumulate information in the takeover market about previous deals that may not be publicly available and have a comparative advantage in finding the right match. The following proxies are used to investigate the impact of information asymmetry on the decision to use investment banks for both public bidders and private targets:

Industry relatedness. Chemmanur et al. (2009) show that relatedness between bidder and target can reduce information asymmetry between the two parties. Thus, the degree of asymmetric information surrounding the target's assets may be lower when

20 bidders are in the same industry as the targets. Servaes and Zenner (1996) argue that when a bidder acquires a target in the same industry, the bidder can rely on its capital budgeting expertise to value the target. Hence, the likelihood of using investment banks should decrease when bidder and target are in the same industry. The dummy variable

RELATED, which is equal to 1 if both parties have the same 4-digit SIC code, 0 otherwise, is used.

Bidder’s technological status. The information asymmetry surrounding the growth opportunities of hi-tech bidders is large. Moreover, the value of a high-tech bidder may be considered as less transparent due to the nature of its assets. Both parties should employ an investment bank to reevaluate the value and the potential of the combined firm. Hence, the likelihood of using an investment bank in transactions involving hi-tech bidders should be higher than that of transactions without the involvement of technology firms. TECHBIDDER is a dummy variable which equals 1 if the bidder (target) is categorized as belonging to primary SIC codes 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics), 3812

(navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379

(software) and 0 otherwise.

Target’s technological status. The information asymmetry surrounding the value of technology targets is high. Hence, the bidders might hire an investment bank when purchasing a hi-tech target to minimize the risk of misevaluation. The variable

TECHTARGET, which equals 1 if the target is categorized as belonging to primary SIC 21 codes 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669

(communications equipment), 3674 (electronics), 3812 (navigation equipment), 3823,

3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379 (software) and 0 otherwise, is included.

Bidder’s Growth Opportunities. The growth opportunity of the bidders also affects the decision to hire an investment bank. When the expected growth rate of a bidder is high, it might hire an investment bank to evaluate the fit of the purchased target.

In this section, I use Tobin’s Q, which equals market value divided by book value of the bidder, as a measure of bidder's growth opportunities to control for the impact of bidder’s growth opportunities on the method of payment (TOBINQ).

Relative Size. Faccio and Masulis (2005) argue that the exposure of bidders to an asymmetric information problem is more pronounced when the target value is relatively large. Since investment banks’ services are extremely valuable when the exposure of merging firms to information asymmetry is high, I expect the merging firms to use investment banks when the relative size of the transaction is high. In this section, I use a variable RELSIZE, which equals the transaction size divided by total market capitalization of the bidder, as of 4 weeks before the transaction.

c. Contracting Costs

Investment banks as financial advisors assist merging firms in identifying better mergers as well as structuring their transactions. In other words, investment banks may

22 find a better match and assess the value of the standalone as well as the combined values prior to the consummation of the transaction. In this case, the investment banks can produce information that makes hidden value more transparent. In other words, they certify the value for their clients. I also argue that merging firms use investment banks to monitor and provide a signal of the firms’ value to their opposite party. The following proxies are used to investigate the impact of contracting costs.

Insider ownership of bidder. If insiders own a large stake in the bidding firms, they are less likely to engage in value-destroying transactions. Thus, the monitoring purpose of investment banks is not as important when the stake of insiders is large.

However, in such a situation, the certifying purpose of investment banks is very important because they can certify that the true motivation of the transaction is the synergy that is being created rather than managerial self-motivations. I include the bidder’s insider ownership to investigate its impact on the decision to use investment banks (OWNERSHIP).

The bidder risk. When the likelihood of bankruptcy of the bidder is high, the bidder should be more careful when conducting an acquisition. On the other hand, the target should be cautious about its future when selling itself to a bidder that is more likely to default. I investigate the impact of the bankruptcy risk of the bidder on the decision to hire an investment bank by including, in the analysis, the variable ZSCORE which equals the Altman Z-score of the bidder. I also include the variable LEVERAGE, which equals the debt ratio of the bidder, in the regression models to examine whether the leverage of

23 the bidder has any impact on the decision to hire an investment bank for both the bidder and the target.

The credit crisis. Since employing an investment bank is expensive, the decision to hire an investment bank for the bidder and the target might be different during tight credit periods compared to that during good economic times. To investigate the effect of the credit crisis periods on the decision to hire an investment bank, a variable

CREDITCRISIS, which equals 1 during the crisis period and 0 otherwise, is used.

Hiring of an investment bank by the bidder (target). The fact that the bidder

(target) hires an investment bank might cause the opposite party to hire an investment bank as well. When the bidder (target) hires an investment bank, the target (bidder) should be concerned about the benefits that the investment bank can bring to the opposing party. I include the variable TARGETIB (BIDDERIB) to examine the influence of the target’s (bidder’s) decision to hire an investment bank on each other.

d. Country Characteristics

In the capital markets, frictions such as transaction costs, information asymmetry, and contracting costs prevent the efficient acquisition process. La Porta et al. (1997 and

1998) show that corporate governance, the quality of the legal system, and the regulatory environment within a country serve as proxies for some of these frictions. Hence, these proxies should affect the likelihood of using investment banks.

When a bidder decides to acquire a private target in a foreign country without the assistance of an investment bank, investors may question whether the motivation for the 24 acquisition is to maximize shareholders’ wealth or for management empire building. The literature shows that the quality of the firm’s managerial decisions is affected by its corporate governance. Hence, a firm with weak corporate governance must pay special attention to these concerns from investors. In this case, the merging firms may use investment banks as a signal to outside investors about the quality of the transaction.

Rossi and Volpin (2004) find that country characteristics are highly correlated with the corporate governance of firms that are located in that country. Therefore, I expect the likelihood of using investment banks to vary with several country characteristics.

Cross-border transactions. When U.S. bidders acquire cross-border targets, they encounter greater challenges due to institutional and cultural differences. Moreover, foreign private targets have much more hidden information, such as the ability of foreign employees to fit into the bidders’ organization, which can be explored only by experienced agents such as investment banks. Therefore, I expect merging firms are more likely to use investment banks when engaging in cross-border transactions.

CROSSBORDER is a dummy variable which equals 1 if the transaction is listed as a cross-border transaction, 0 otherwise.

Target country’s risk and governance characteristics. In addition to the general expectation regarding cross-border transactions, country risk and corporate governance characteristics are also important motivations for merging firms to employ investment banks. When the country’s standard of corporate governance is good and the country’s risk is low, managers are more disciplined and managerial agency problems are less serious. Moreover, if the merging firms have strong governance, they may have strong 25 control about the projects that they will engage in. Hence, shareholders have more confidence in managerial decisions, and those decisions are less likely to be value- destroying. As an example, Masulis et al. (2006) show that bidder firms with good governance lose less during merger announcements.

According to Shleifer and Vishny (1989), outside investors have the impression that the management of firms with poor governance may have more freedom to spend unnecessarily on fees without the concern of being punished. Therefore, poor governance firms might pursue help from investment banks to overcome this bad impression.

On the other hand, management in countries with good governance standards might employ investment banks to maximize shareholders’ wealth. Since investment banks have economies of specialization, economies of scale regarding acquisitions, and reduced search costs (Benston and Smith, 1976), management should exploit these advantages to identify the best match at a lower cost, determine the appropriate price, and smooth the negotiation process in order to serve the best interests of shareholders. In such a situation, bidding firms which acquire targets in countries with low risk and good corporate governance might be more inclined to use investment banks.

I consider the following measures of country risk and governance to examine the impact of country characteristics on the decision to use investment banks.

(i) Economic Freedom and Development. To control for target country risk, I

use the Economic Freedom Index. The Economic Freedom Index (see

Gwartney et al. 1996) assigns a rating to each country based on trade

policy, taxation, government intervention, foreign investment policy,

banking, pricing controls, property rights, and regulation. With the

Economic Freedom Index, a higher rating proxies for a less restrictive

environment. Based on the Heritage website, I collect the rating for each

asset seller's country in the sample and include these ratings in the

analysis. The variable FREEDOM is the natural logarithm of economic

freedom rating of the target country in the year prior to the transaction.

(ii) Shareholder Rights. The anti-director rights index that is introduced by La

Porta et al. (1997) is widely used to control for the corporate governance

at the country level. This is an index of the rights that shareholders have

with respect to the management team. The anti-director rights index is an

index that aggregates shareholder rights. The index ranges from 0 to 5, in

which a higher number reflects better shareholder protection. To control

for the corporate governance of the target country, I use the revised anti-

director rights index introduced by Spamann (2010). Following Moeller

and Schlingemann (2005), a dummy variable RIGHTS, which equals 1 if

the anti-director rights index of the seller country is three or above and 0

otherwise, is used in the analysis.

(iii) Legal System. To control for a broad indicator of investor protection, I use

the legal system of the target country. Common law systems are

considered to have the best shareholder rights. Civil law based systems are

considered to have the weakest shareholder rights. A dummy variable 27

COMMON, which equals 1 if the seller country is a common law country

and 0 otherwise, is used to control for the broad indicator of investor

protection.

2. Impact of Financial Advisors on Wealth Gains of Bidders Acquiring

Private Targets

The answer to the question of whether employing financial advisors affects the wealth creation in corporate takeovers gains much attention from the literature, but is not clear. Bowers and Miller (1990) and Michel et al. (1991) find evidence that investment banks as financial advisors create value for firms in M&A transactions. They show that investment banks have the power to identify better matches and to create value for merging firms. More recently, Forte et al. (2010) confirm the certification role of investment banks by reporting a positive relationship between the abnormal returns of target company shareholders and the intensity of their previous banking relationship.

However, Servaes and Zenner (1996) and Da Silva Rosa et al. (2004) report that financial advisors do not have an impact on the wealth creation of the transaction.

The wealth gains of a merging firm depend on the synergies that are created from the transaction and the portion of those synergies it will receive. Kale et al. (2003) argue that the strategic advisory activities of investment banks affect the portion of the total wealth gains that bidder and target will receive. Hence, the role of investment banks in

M&A transactions should be investigated using wealth gain measures that are not affected by the strategic actions of investment banks. The majority of the literature

28 examines the wealth effect of the bidders and targets separately, with the exception of

Kale et al. (2003). In their study, Kale et al. (2003) use the combined wealth gains to bidders and targets to investigate the role of investment banks as financial advisors in

M&A transactions. They find that the totals as well as the proportional wealth gains to a bidder or a target are positively correlated with the difference between the reputation of its advisors and that of its opponent’s advisors.

I argue that when a publicly traded bidder acquires a private target, the bidder will receive all the synergies that are created from the transaction2. Therefore, by investigating the wealth gains of bidders in these transactions, I can overcome the difficulty that is mentioned in Kale et al (2003)3. I use the following explanatory variables to examine the influence of investment banks on the total wealth gains from acquisitions of private targets.

Whether the bidder is advised by investment banks (BIDDERIB). To compare the difference in the wealth gains between the transactions in which the bidder uses and does not use an investment bank, I use a dummy variable that equals 1 if an investment bank is employed by the bidder, and 0 otherwise.

Whether the bidder is advised by top-tier investment banks (BIDTOPIB). To compare the difference in the wealth gains between the transactions in which the bidder

2 The bidder has to pay to get all the synergies that are created in the transaction. 3 There are studies that measure the wealth effect of bidder when it acquires a private target. However, none of those studies investigates the impact of investment banks on the wealth creation of acquisitions of private targets. 29 uses and does not use a top-tier investment bank, I use a dummy variable that equals 1 if there is at least one top-tier investment bank advising the bidder, and 0 otherwise.

Whether the target is advised by investment banks (TARGETIB). To compare the difference in the wealth gains between the transactions in which the target uses and does not use an investment bank, I use a dummy variable that equals 1 if an investment bank is employed by the target, and 0 otherwise.

Whether the target is advised by top-tier investment banks (TARTOPIB). To compare the difference in the wealth gains between the transactions in which the target uses and does not use a top-tier investment bank, I use a dummy variable that equals 1 if there is at least 1 top-tier investment bank advising the target, and 0 otherwise.

The reputation of the bidder’s investment bank (BIDIBSHARE). If the bidder employs one investment bank, this variable equals the market share of the given investment bank for its involvement in takeover activity. If the bidder uses more than one investment bank, following Kale et al. (2003), I define the variable as the highest market share of multiple investment banks. When the bidder does not employ any investment bank, the value of the variable equals 0.

The reputation of the target’s investment bank (TARIBSHARE). If the target employs one investment bank, this variable equals the market share of the given investment bank for its involvement in takeover activity. If the target uses more than one investment bank, following Kale et al. (2003), I define the variable as the highest market

30 share of multiple investment banks. When the target does not use any investment bank, the value of the variable equals 0

When bidder is advised by investment banks and target is not

(YESBIDNOTAR). To compare the difference in the wealth gains between the transactions in which the bidder uses and the target does not use an investment bank, I use a dummy variable that equals 1 if a bidder uses at least one investment bank and target does not use an investment bank, 0 otherwise.

When bidder is not advised by investment banks and target is

(NOBIDYESTAR). To compare the difference in the wealth gains between the transactions in which the bidder does not use and the target uses at least one investment bank, I use a dummy variable that equals 1 if the bidder does not use an investment bank and the target uses at least one investment bank, 0 otherwise.

Relative reputation of bidder and target’s investment bank (RELIBSHARE).

This variable is the difference between the reputation of the bidder’s investment bank and the reputation of the target’s investment bank.

In addition to these explanatory variables, I also use control variables that I use in the first section. Specifically, I use EQUITYPMT4, SIZE5, and PRIOR6 to control for the transaction costs of the deals. To control for the information asymmetry in the deals, I

4 EQUITYPMT is a dummy variable which equals 1 if the bidder uses equity in the payment method, and 0 otherwise. 5 SIZE is the natural logarithm of the value of the transaction. 6 PRIOR is the number of takeover-related activities undertaken by the bidders in the preceding 10-year period 31 use RELATED7, TECHBIDDER8, TECHTARGET9, TOBINQ10, and RELSIZE11. To control for contracting costs of the deal, I use ZSCORE12, LEVERAGE13,

INTCOVERAGE14, and CASHHOLDINGS15. To control for the country characteristics,

I use CROSSBORDER16, FREEDOM17, RIGHTS18, and COMMON19.

3. Impact of Financial Advisors on Acquisition Discounts of Private Targets

The valuation of a firm is one of the most important applications in corporate finance theory. One of the most popular techniques to evaluate firm value is the comparable valuation method which uses comparable situations to infer the value of a firm. The method estimates a firm’s value by multiplying a valuation multiple with the firm’s earnings before interest, taxes, depreciation, and amortization (EBITDA), earnings before interest and taxes (EBIT), sales, or some other performance measures.

7 RELATED equals 1 if both parties have the same 4-digit SIC code and 0 otherwise. 8 TECHBIDDER equals 1 if the bidder is categorized as belonging to primary SIC codes 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics), 3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379 (software) and 0 otherwise. 9 TECHTARGET equals 1 if the target is categorized as belonging to primary SIC codes 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics), 3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379 (software) and 0 otherwise. 10 TOBINQ is the Tobin’s Q ratio of the bidder. 11 RELSIZE equals the value of the private target divided by total market capitalization of the bidder. 12 ZSCORE is the Altman Z-score of the bidder. 13 LEVERAGE is the debt-ratio of the bidder. 14 INTCOVERAGE is the interest coverage ratio of the bidder. 15 CASHHOLDINGS is the cash holdings scaled by the total assets of the bidder. 16 CROSSBORDER is a dummy variable which equals 1 if the transaction is listed as a cross-border transaction and 0 otherwise. 17 FREEDOM is the natural logarithm of the Economic Freedom Index for the target country. 18 RIGHTS equals 1 if the antidirector rights index of the seller country is three or above, 0 otherwise. 19 COMMON is a dummy variable which equals 1 if the seller country is a common law country, and 0 otherwise. 32

Officer (2007) reports that private targets are selling at a discount compared to public targets since private targets are acquired at valuation multiples less than that offered to comparable publicly traded firms. According to Officer, one of the main reasons for the discount is information asymmetry. This problem is most severe in the case of acquisitions of private targets when standards for information disclosure are not high. He documents that bidders tend to lower the valuation multiples in acquisitions of private targets in order to protect themselves against the possibility that they are less than fully informed. Nevertheless, Officer does not pay attention to the existence of investment banks. Regarding the existence of investment banks, one interesting question arises whether the hiring of an investment bank affects the valuation, i.e., the multiples, of the private targets. In this section, I examine the impact of hiring investment banks on the valuation of private targets.

The impact of investment banks on valuation multiples in acquisitions of private targets has gained little attention in the literature. It is obvious that both bidders and targets hire financial advisors to reduce the amount of information asymmetry between the parties. Hence, I expect that the multiples are different for transactions in which bidders and/or targets hire investment banks in comparison to those in which bidders and/or targets do not hire investment banks. Specifically, I expect that financial advisors of bidders will advise the bidders to offer lower valuation multiples. On the other hand, financial advisors of targets will advise targets to demand higher valuation multiples.

In this section, I attempt to investigate the relationship between the existence of investment banks and these valuation multiples of acquisitions of private targets. The 33 reliability of the comparable technique depends on the similarity in characteristics of the firms that I am interested in comparing. These characteristics, such as risk, growth rate, and timing of cash flows, are different for private firms. Hence, it is very difficult to compare the valuation multiples of acquisitions of private firms. However, I attempt to minimize the above differences by employing many control variables in the analysis, such as time of acquisition, target’s industry, and target country’s characteristics.

Following Officer (2007), in order to adjust for the difference in valuation multiples of private targets in different industry, size, and time period, I collect the median valuation multiples of public firms of target's industry for the same size deciles at the time of the transaction. Then, I use the valuation multiples of private target that is adjusted by the median valuation multiples to test for the impact of the existence of investment banks.

I use the following explanatory variables to examine the influence of investment banks on the valuation multiples of private targets:

Whether the bidder is advised by investment banks (BIDDERIB). To compare the difference in the valuation multiples between the transactions in which the bidder uses and does not use an investment bank, I use a dummy variable that equals 1 if an investment bank is employed by the bidder, and 0 otherwise.

Whether the bidder is advised by top-tier investment banks (BIDTOPIB). To compare the difference in the valuation multiples between the transactions in which the bidder uses and does not use a top-tier investment bank, I use a dummy variable that

34 equals 1 if there is at least one top-tier investment bank advising the bidder, and 0 otherwise.

Whether the target is advised by investment banks (TARGETIB). To compare the difference in the valuation multiples between the transactions in which the target uses and does not use an investment bank, I use a dummy variable that equals 1 if an investment bank is employed by the target, and 0 otherwise.

Whether the target is advised by top-tier investment banks (TARTOPIB). To compare the difference in the valuation multiples between the transactions in which the target uses and does not use a top-tier investment bank, I use a dummy variable that equals 1 if there is at least 1 top-tier investment bank advising the target, and 0 otherwise.

35 share of multiple investment banks. When the target does not use any investment bank, the value of the variable equals 0

When bidder is advised by investment banks and target is not (YESBIDNOTAR).

To compare the difference in the valuation multiples between the transactions in which the bidder uses and the target does not use an investment bank, I use a dummy variable that equals 1 if a bidder uses at least one investment bank and target does not use an investment bank, 0 otherwise.

When bidder is not advised by investment banks and target is (NOBIDYESTAR).

To compare the difference in the valuation multiples between the transactions in which the bidder does not use and the target uses at least one investment bank, I use a dummy variable that equals 1 if the bidder does not use an investment bank and the target uses at least one investment bank, 0 otherwise.

Relative reputation of bidder and target’s investment bank (RELIBSHARE). This variable is the difference between the reputation of the bidder’s investment bank and the reputation of the target’s investment bank.

In addition to these explanatory variables, I also use control variables that I use in the first section. Specifically, I use EQUITYPMT20, SIZE21, and PRIOR22 to control for the transaction costs of the deals. To control for the information asymmetry in the deals, I

20 EQUITYPMT is a dummy variable which equals 1 if the buyer uses equity in the payment method, and 0 otherwise. 21 SIZE is the natural logarithm of the value of the transaction. 22 PRIOR is the number of takeover-related activities undertaken by the bidders in the preceding 10-year period 36 use RELATED23, TECHBIDDER24, TECHTARGET25, TOBINQ26, and RELSIZE27. To control for contracting costs of the deal, I use ZSCORE28, LEVERAGE29,

INTCOVERAGE30, and CASHHOLDINGS31. To control for the country characteristics,

I use CROSSBORDER32, FREEDOM33, RIGHTS34, and COMMON35.

4. Impact of Financial Advisors on Method of Payment

The literature reports that when bidders use equity to acquire other entities, they are more likely to use investment banks as financial advisors. For example, Servaes and

Zenner (1996) reports a higher probability of investment banks in transactions with equity payment compared to transactions without equity payment. However, our knowledge about the impact of investment banks on method of payment is limited since we only understand only the external matter of the issue, i.e., the involvement of equity or cash payment. Specifically, all of the existing studies use a dummy variable to demonstrate the involvement of equity payment in acquisitions. We do not understand the

23 RELATED equals 1 if both parties have the same 4-digit SIC code and 0 otherwise. 24 TECHBIDDER equals 1 if the bidder is categorized as belonging to primary SIC codes 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics), 3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379 (software) and 0 otherwise. 25 TECHTARGET equals 1 if the target is categorized as belonging to primary SIC codes 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics), 3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379 (software) and 0 otherwise. 26 TOBINQ is the Tobin’s Q ratio of the bidder. 27 RELSIZE equals the value of the private target divided by total market capitalization of the bidder. 28 ZSCORE is the Altman Z-score of the bidder. 29 LEVERAGE is the debt-ratio of the bidder. 30 INTCOVERAGE is the interest coverage ratio of the bidder. 31 CASHHOLDINGS is the cash holdings scaled by the total assets of the bidder. 32 CROSSBORDER is a dummy variable which equals 1 if the transaction is listed as a cross-border transaction and 0 otherwise. 33 FREEDOM is the natural logarithm of the Economic Freedom Index for the target country. 34 RIGHTS equals 1 if the antidirector rights index of the seller country is three or above, 0 otherwise. 35 COMMON is a dummy variable which equals 1 if the seller country is a common law country, and 0 otherwise. 37 continuous method of method of payment, i.e., percentage of cash and equity payment, and the influence of financial advisors on that method. In this study, I pursue a thorough investigation regarding the method of method of payment and its relationship to the use of investment banks as financial advisors. I use Tobit regressions to examine this relationship. One advantage of the Tobit regression is that it uses a continuous dependent variable to identify the impact of various explanatory variables on the method of the method of payment. By employing Tobit regressions, I can understand and compare the differences between the method of the payment of deals that are advised by top tier investment banks, second tier investment banks, and those with no support from an investment bank.

I hypothesize that different tier investment banks will advise bidders (private targets) to pay (receive) equity payment differently. Moreover, the method of method of payment is also different for transactions that have financial advisors and those that don’t.

From the bidders’ perspective, I expect that investment banks will advise bidders to use more equity to lower the probability for the misevaluation of the target by shifting some of the risk of misevaluation to the target. In addition, investment banks might suggest that bidders pay more in equity to retain talented employees and management. In the case of the acquisition of private targets, if talented employees and management were paid through equity and they believe their contributions can increase the combined value of the firm, they will stay and be a factor in the future success.

On the other hand, I expect that investment banks will advise targets to ask for more cash in the method of the payment. By doing so, targets will reduce the risk that 38 they have to share with the bidders about the misevaluation of themselves. Most of the time, the targets prefer getting as much of the portion of the deal in cash as they can, thereby guaranteeing the highest certain amount of payment possible. However, investment banks may suggest that targets accept more equity payment since it signals the high valuation of the targets. Therefore, the theoretical expectation about the role of investment banks in the method of the method of payment needs to be tested empirically.

I use various explanatory variables to test these expectations.

The reputation of the bidder’s investment bank (BIDIBSHARE). If the bidder has one investment bank, this variable equals the market share of the investment bank in the previous year of the takeover. If the bidder uses more than one investment bank, following Kale et al. (2003), I define the variable as the highest market share of the multiple investment banks. When the bidder does not use investment bank, the value of the variable equals 0.

The reputation of the target’s investment bank (TARIBSHARE). If the target has one investment bank, this variable equals the market share of the investment bank in the previous year of the takeover. If the target uses more than one investment bank, following

Kale et al. (2003), I define the variable as the highest market share of the multiple investment banks. When the target does not use investment bank, the value of the variable equals 0.

Whether the bidder is advised by investment banks (BIDDERIB). To compare the difference in the method of method of payment between the transactions in which the bidder uses and does not use an investment bank, I use a dummy variable that equals 1 if there is the use of an investment bank by the bidder and 0 otherwise.

Whether the bidder is advised by top tier investment banks (BIDTOPIB). To compare the difference in the method of method of payment between the transactions in which the bidder uses and does not use a top tier investment bank, I use a dummy variable that equals 1 if there is at least 1 top tier investment bank advising the bidder and

0 otherwise.

Whether the target is advised by investment banks (TARGETIB). To compare the difference in the method of method of payment between the transactions in which the target uses and does not use an investment bank, I use a dummy variable that equals 1 if there is the use of an investment bank by the target and 0 otherwise.

Whether the target is advised by top tier investment banks (TARTOPIB). To compare the difference in the method of method of payment between the transactions in which the target uses and does not use a top tier investment bank, I use a dummy variable that equals 1 if there is at least 1 top tier investment bank advising the target and 0 otherwise.

When bidder is advised by investment banks and target is not (YESBIDNOTAR).

To compare the difference in the method of method of payment between the transactions in which the bidder uses and the target does not use an investment bank, I use a dummy variable that equals 1 if the bidder uses at least one and the target does not use an investment bank, 0 otherwise.

When bidder is not advised by investment banks and target is (NOBIDYESTAR).

To compare the difference in the method of method of payment between the transactions in which the bidder does not use and the target uses an investment banks, I use a dummy variable that equals 1 if bidder does not use and target uses at least one investment bank,

0 otherwise.

In addition to those explanatory variables, I use control variables that can be categorized into three categories: financial constraints, asymmetric information, and country risk variables.

a. Financial constraints variables.

According to Myers (1984), bidders in healthy financial situations may use cash more in their investments since internal financing is preferred. Therefore, I expect that the financial constraints of bidders have some effect on the method of method of payment in acquisitions. I use the following control variables to control for the financial condition of bidders:

Bidder’s cash holding. Bidders with higher cash holdings are expected to use more cash to finance their investment because they prefer internal financing first. Hence, 41 when they have excessive cash available, they are more likely to use cash as payment. I use the variable CASHHOLDINGS, which is defined as cash holdings of the bidder as a percentage of total capitalization, to control for the effect of cash holdings on the method of payment.

Bidder’s Financial Leverage. The possibility for bidders who have high leverage to issue debt to finance their investment is low since it will substantially increase their bankruptcy costs. In such a situation, bidders are more likely to finance their investments with equity (Myers, 1977). I use the variable LEVERAGE, which equals total liabilities

(based on the financial statement) divided by bidder's market value of equity as of 4 weeks prior to the announcement, to control for the effect of bidder’s leverage on the method of payment.

Bidder’s Interest Coverage Ratio. The ability of a bidder to make interest payments on its existing debt can influence its willingness to issue more debt to finance the investment. Hence, I use the interest coverage ratio, INTCOVERAGE, which is the number of times a company could make the interest payments on its debt with its earnings before interest and taxes, to control for this aspect of financial strength on the method of payment.

Bidder’s Size. Larger firms usually have a better reputation and easier access to the debt market. Thus, they should have a lower cost of issuing debt. Facio and Masulis

(2005) claim that larger firms are more likely to issue debt to finance their capital expenditures and investments. In this section, I measure the variable BIDDERSIZE of the

42 bidder as the logarithm of bidder's pre-transaction total market capitalization and include this variable in the analysis to control for the size effect on the method of payment.

Bidder's Technology Status. Hi-tech firms are firms who have heavy initial investment in research and development (R&D). Thus, they might have limited access to cash and high growth opportunities. Jung et al. (1996) argue that managers of such growth opportunities prefer to finance their investments with equity rather than debt because equity financing gives them more discretion over the funds raised. I use a dummy variable to control for this influence. The dummy variable TECHBIDDER, is set equal to 1 if the bidder has their primary SIC codes as 3571, 3572, 3575, 3577, 3578

(computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics),

3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), and 7370, 7371, 7372, 7373, 7374, 7375, 7379

(software) and 0 otherwise.

Bidder’s Credit Constraints during Weak Economic Conditions. During weak economic conditions, bidders may be subject to credit constraints. In such a situation, the availability of credit is limited and the cost of getting more debt is high. Therefore, equity might be used more to finance the investments. On the other hand, equity values tend to be low in these periods, which might discourage bidders from using equity. I use

CREDITCRISIS, which is a dummy variable equaling 1 during recessionary periods and

0 otherwise, to control for the impact of the market condition on the method of payment.

The bidder risk. When the likelihood of bankruptcy of the bidder is high, the bidder should be more careful when using cash to finance its investments. I investigate the impact of the bankruptcy risk of the bidder on the method of payment by including in the analysis the variable ZSCORE, which equals the Altman Z-score of the bidder.

b. Asymmetric Information Variables.

In an acquisition, information asymmetry regarding the value of the bidder as well as the value of the target is common. According to Hansen (1987), the transaction process between the bidder and the target is a two-agent bargaining game under imperfect information and the bidder should use a higher proportion of equity payment since it has desirable contingent pricing characteristics. On the other hand, Hansen (1987) and

Travlos (1987) suggest that when a bidder finances an acquisition with equity, it emits a negative signal that it is capitalizing on the use of its overvalued stock. Thus, the target might prefer cash when the bidder's equity has a potential to be overvalued. I use the following control variables to control for the information asymmetry between the two parties:

Bidder’s Growth Opportunities. Martin (1996) shows that the bidders who have higher growth opportunities usually use more equity as currency when acquiring targets and the targets also accept their equity more frequently. In this section, I use Tobin’s Q as a measure of bidder's growth opportunities to control for the impact of those growth opportunities on the method of payment (TOBINQ).

Bidder’s Size of Purchase. There is an argument that bidders might be more exposed to an asymmetric information problem when the target value is relatively large

(Faccio and Masulis, 2005). Thus, bidders are more likely to use equity financing in large transactions. I control for this effect by including a variable RELSIZE, which equals the value of the private target divided by total market capitalization of the bidder, in the analysis.

Target’s technological status. The information asymmetry surrounding the value of technology firms is high. Hence, bidders might use a higher proportion of equity payment when purchasing a hi-tech target to induce risk sharing. The variable

TECHTARGET, which equals 1 if the target is categorized in primary SIC codes 3571,

3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics), 3812 (navigation equipment), 3823, 3825, 3826, 3827,

3829 (measuring and controlling devices), 4899 (communication services), or 7370,

7371, 7372, 7373, 7374, 7375, 7379 (software) and 0 otherwise, is included.

Relatedness between Bidder and Target. Chemmanur et al. (2009) claim that the information asymmetry between the bidder and target is lower when the degree of relatedness between the two parties is higher. Thus, the degree of asymmetric information may be lower when both parties are in the same industry. The dummy variable

RELATED, which equals 1if both parties have the same 4-digit SIC code, 0 otherwise, is used to control for the relatedness between the two parties.

Bidder’s prior takeover experience. If the bidder has had some experience in dealing with similar transactions, it should know better about the best combination between cash and equity payment. In this case, the bidder might prefer cash over equity as payment or vice versa in specific situations. To control for this factor, I include a variable regarding the experience of the bidder in the takeover market. Following Kale et al. (2003), I measure bidders’ prior experience by the number of takeover related activities undertaken by the bidders in the preceding 10-year period (PRIOR). In addition to that, I also examine the prior experience of bidders in dealing with private targets

(PRIORPRIVATE).

c. Country’s Risk Variables.

When bidders have the intention of acquiring targets located in foreign countries, they anticipate that there will be greater challenges because of institutional and cultural differences. Stiglitz (2000) argues that the value of assets being exchanged can be reduced by the greater level of uncertainty in cross-border transactions and thus, decrease the buyer’s value. Hence, the owners of a private target might be less willing to accept equity from a foreign bidder. Moreover, if they consider equity as payment, they will value that equity at a discount since they would incur monitoring costs to monitor the bidder firm in such a situation.

Furthermore, the method of payment among cross-border acquisitions may vary with country risk and governance characteristics. Henisz (2000) suggests that multinational firms that do business in risky foreign markets are more likely to share

46 ownership with local partners because such behavior will shift some of the risk to local investors who, in turn, can bear the risk in a less costly manner. If the foreign country has greater risk that has created more uncertainty surrounding the private target’s value, the bidder may ask the target to share in the risk by forcing the target to receive more equity.

I use the following control variables to control for the country risk characteristics:

Cross-border. To control for the cross-border effect, I use the variable

CROSSBORDER, which is a dummy variable equaling 1 if the transaction is listed as cross-border transaction and 0 otherwise.

Economic Freedom and Development. To control for the target country’s risk, I use the Economic Freedom Index. The Economic Freedom Index (see Gwartney et al.

1996) assigns a rating to each country based on trade policy, taxation, government intervention, foreign investment policy, banking, pricing controls, property rights, and regulation. With the Economic Freedom Index, a higher rating proxies for a less restrictive environment. Based on the Heritage website, I collect the rating for each asset seller's country in the sample and include those ratings into the analysis. The variable

FREEDOM is the natural logarithm of economic freedom rating of the target country in the year prior to the transaction.

Shareholder Rights. To control for the corporate governance of the target country,

I use the revised anti-director rights introduced by Spamann (2010). This is an index of the rights that shareholders have with respect to the management team. The anti-director rights index is an index that aggregates shareholder rights. The index ranges from 0 to 5,

47 in which a higher number reflects better shareholder protection. Following Moeller and

Schlingemann (2005),a dummy variable RIGHTS, which equals 1 if the anti-director rights index of the seller country is three or above and 0 otherwise, is used in the analysis.

Legal System. To control for a broad indicator of investor protection, I use the legal system of the target country. Common law systems are considered to have the best shareholder rights. Civil law based systems are considered to have the weakest shareholder rights. A dummy variable COMMON, which equals 1 if the seller country is a common law country and 0 otherwise, is used to control for the broad indicator of investor protection.

5. Impact of Financial Advisors on Operating Performance of Bidders

Acquiring Private Targets

Consistent with the mixed findings regarding initial market reactions to corporate mergers, the results regarding the relationship between changes in operating performance associated with mergers is not clear. Healy et al. (1992) show better long-run operating performance following acquisitions. However, Ghosh (2001) finds no evidence that operating performance improves following an acquisition. He argues that the better operating performance results are biased since acquirers who undertake acquisitions have experienced a period of superior performance.

More recent studies investigate the relationship between the change in bidder’s operating performance and several other characteristics of merger transactions. For example, Megginson et al. (2004) document a positive relationship between changes in

48 focus and long-term performance. They report that focus-decreasing transactions result in negative long-term performance with an average 18% loss in stockholder wealth, 9% loss in firm value, and significant declines in operating cash flows three years after merger.

Heron and Lie (2002) look at the relationship between the method of payment and changes in operation performance. The authors find a superior operating performance of bidders when compared with other firms. However, there is no evidence that the method of payment conveys information about changes in the bidder’s operation performance.

Moreover, Carline et al. (2009) attribute the improvement in operating performance of bidders to the corporate governance characteristics of bidders. They argue that changes in operating performance following mergers vary with different levels of corporate governance.

Even though changes in operating performance of the bidder are extensively investigated, little is known about the impact of investment banks as financial advisors on the operating performance following the transaction. Investment banks play a significant role in finding the best match for merging firms. Hence, they should have a significant impact on the subsequent operating performance of the merger.

In this section, I examine the impact of hiring investment banks on the operating performance of bidders in the acquisition of private targets. This study contributes to the literature in different ways. First, I examine the connection between the hiring of investment banks and operating performance effects. To my understanding, this is the first study that examines this connection. Second, this paper makes more of a generalization regarding the role of investment banks on the long-term performance of 49 the acquisition of private targets. Since it is difficult to evaluate the impact of investment banks on the wealth effect of this type of acquisition, the initial reaction of the market participants might be misleading. Hence, investigating the role of investment banks in long-term operating performance will shed light on this issue.

I expect that the hiring of investment banks will have a positive impact on the long-term performance of bidders in the acquisition of private targets. In order to evaluate my supposition, I use the same explanatory and control variables as in the previous section.

6. Impact of Financial Advisors on Risk Shifts of Bidders Acquiring Private

Targets.

Several studies have examined the post-merger risk shift of bidders. These studies report mixed results about the post-merger risk shift for both domestic and international mergers. However, the majority of the studies show a post-merger reduction in systematic risk for the bidder.

Relating to domestic mergers, Davidson et al. (1987) calculate the risk factor for pre-merger and post-merger portfolios and compare them with those of a matched control sample. They report evidence of risk reduction for a large number of merging firms and attribute this result to synergy. Lubatkin and O’Neill (1987) examine the changes in risk factors associated with a large sample of acquiring firms. The authors find that related mergers are associated with a significant decline in systematic and total risk.

On the other hand, Langetieg et al. (1980) report an opposite result for the risk shift following mergers. The authors show an increase in levels of systematic, total, and unsystematic risk for the consolidated firm. They suggest that the increasing risk level might reflect an aggressive attitude by the management of the bidder. In other words, an increase in risk is due to an increase in leverage following the transaction. Moreover, there are studies that report no risk shift following the merger. For example, Elgers and

Clark (1980) and Dodd (1980) find that the systematic risk of a portfolio of bidding firms is unchanged over the period examined.

Relating to international mergers, Fatemi (1984) finds that the systematic risk shift of cross-border bidders is negatively correlated with the degree of international involvement by bidders subsequent to the transactions. Gleason et al. (2005) investigate the cross-border acquisitions of financial services firms in the process of privatization.

They report an increase in total risk for both non-bank and bank bidders in the sample.

However, the authors also show that the systematic risk exposure with the privatization process for both bank and non-bank bidders decreases. By contrast, Amihud et al. (2002) report that there is no significant change in systematic or total risk within their sample of cross-border bank mergers.

The above literature considers risk-shifts of bidders in the takeover market without paying attention to the existence of investment banks as financial advisors. Since investment banks help merging firms to reduce information asymmetry and find a better match, risk-shifts following mergers might be different for bidders which hire and those which do not hire investment banks. Specifically, I expect that some bidders which hire 51 investment banks will experience much deeper shifts in risk in comparison with others following takeover transactions.

I will test my expectations in the context of acquisitions of private targets. Since there is great information asymmetry between bidders and private targets, such acquisitions might affect the total, systematic, and idiosyncratic risk of bidders. After transactions, the method of bidders is more complicated than it is prior to acquisitions.

This leads to more complexity in recognizing the post-acquisition value of bidders.

Moreover, if bidders use cash to acquire targets, they usually have higher leverage subsequent to the transactions (Harford et al., 2009). In the acquisition of private targets, the bidders (private targets) tend to use more cash payment. In such a situation, investors will perceive that the transactions have some effect on the post-transaction risks of bidders. However, the existence of investment banks might significantly reduce the anxiety of investors. Therefore, investment banks should have an important impact on risk shifts of bidders following acquisitions of private targets. In order to evaluate my supposition, I use the same explanatory and control variables as in the previous section.

III. Methodology

Following Rau (2000), I measure the average market share of each investment bank as the percentage of the total value of transactions advised by investment banks in any single year. In the spirit of Golubov et al. (2012), the top eight investment banks are classified as top-tier. The other investment banks are classified as non-top-tier. The rankings are stable across the sample period. In addition to the dummy variable that

52 represents the existence of an investment bank, I use a continuous variable, which equals the percentage of the market share of a particular investment bank, as a robustness check.

1. Detecting Factors that Cause Bidders and Private Targets to Hire Financial

Advisors

In order to find the significant determining factors in the decision to use investment banks in the acquisition of private targets, I employ logistic regression models. In each regression, the dependent variable is equal to one if the bidder (target) uses an advisor, and zero otherwise.

P(bidder uses investment bank) = f(transaction costs, information asymmetry, contracting costs, country characteristics)

P(bidder uses top-tier investment bank) = f(transaction costs, information asymmetry, contracting costs, country characteristics)

P(target uses investment bank) = f(transaction costs, information asymmetry, contracting costs, country characteristics)

P(target uses top-tier investment bank) = f(transaction costs, information asymmetry, contracting costs, country characteristics)

When applying these models to my sample, the quasi-maximum likelihood

(QML) White/Huber standard errors are used to correct for heteroscedasticity. For each hypothesis of a characteristic that I believe affects the decision to use an investment bank, an independent variable is used to proxy for that characteristic.

2. Testing the Impact of Financial Advisors on Wealth Gains of Bidders

The relationship between the choice of investment banks and shareholders’ wealth is examined by investigating the change in the market value of equity of bidders and targets around the announcement when they employ different investment banks. In this section, I use the standard event study methodology to compare the performance of investment banks. I use the market model for estimation, with an estimation period from t

=-300 to t=-46 days relative to the event day t = 0. Then I use the following model in the cross-sectional analysis with White correction for heteroscedasticity:

CARi = f(explanatory variables, control variables) where:

CARi is the cumulative abnormal returns of bidder i in the event window (-1, +1) surrounding the announcement day t = 0.

3. Testing the Impact of Financial Advisors on Valuation Multiples of

Targets

Following Officer (2007), I calculate the ratios of offer price to book value, offer price to earnings, transaction value to sales, or transaction value to EBIT. Moreover, I

54 collect the median valuation multiples of public firms of target's industry for the same size deciles at the time of the transaction. Then, I compare the valuation multiples of private targets adjusted for the median valuation multiples to test for the impact of the existence of investment banks. I use the following model in the cross-sectional analysis with White correction for heteroscedasticity:

MPi = f(explanatory variables, control variables) where:

MPi is the adjusted valuation multiples of target i

4. Testing the Impact of Financial Advisors on the Method of Payment in

Acquisitions

I apply instrumental variable Tobit multivariate models to investigate the weight of various explanatory and control variables on the method of payment decision in the acquisitions. In the Tobit regression models, the dependent variable is the equity proportion of the payment for the acquisition transaction, which must be in the interval

[0, 100]. In order to minimize the effect of endogeneity, all explanatory variables are instrumented by the SCOPE variable, which takes the value of one if, in the ten years prior to the transaction, the bidder employed an investment bank at least once for an acquisition of a private target and 0 otherwise. In my sample, all values of the dependent variable are within the [0, 100] interval. I apply the model:

* ’ yi = βXi + ui

* * where yi = yi if 0 < yi < 100,

’ Xi is the vector of explanatory and control variables

ui is an independently distributed error term assumed to be normal with zero mean and variance

When applying this model to the sample, the Newey’s two-step estimator is used to correct for heteroscedasticity. For each hypothesis of a characteristic that I believe affect the proportion of cash used versus stock used, an independent variable is used to proxy for that characteristic.

5. Testing the Impact of Financial Advisors on Operating Performance of

Bidders

I use operating income scaled by sales to investigate the impact of investment banks on operating performance. According to Heron and Lie (2002), the operating income scaled by sales is immune to the mechanical effects that the method of accounting for the transaction and the method of financing might have on financial statement items. I collect the industry-adjusted change in operating performance of the bidder and then I use the following model in the cross-sectional analysis with White’s correction for heteroscedasticity to investigate the impact of an investment bank on the operating performance:

ΔOPi,j = f(explanatory variables, control variables)

56 where:

ΔOPi,j is the change in operating performance of bidder i in j years after the transaction (j = 1 and 2).

6. Testing the Impact of Financial Advisors on Risk Shift of Bidders

In order to test the impact of the hiring of an investment bank on the risk shifts of a bidder when acquiring a private target, I define total risk as the variance of the bidder’s stock returns, idiosyncratic risk as variance of residuals of the market model of bidder’s stock, and systematic risk as the beta in the market model of bidder’s stock. Moreover, I also define the change in total risk of the bidder as total risk after the transaction divided by the total risk before the transaction and the change in idiosyncratic risk of the bidder as idiosyncratic risk after the transaction divided by the idiosyncratic risk before the transaction. I estimate the shift in systematic risk by using the market model with time dummies to determine the change in beta.

Rit= αi + βi * Rmt+ β΄i * Rmt* D1 + εit where

Rit is the return for bidder i on day t

βi is the systematic risk for bidder i

Rmt is the return on a market index on day t

β΄I is the shift in systematic risk for bidder i

D1 is the dummy variable equal to 1 on dates after the transaction, 0 otherwise.

To investigate the impact of investment banks on the risk shifts of bidders, I use the following model in the cross-sectional analysis with White’s correction for heteroscedasticity:

RISKi = f(explanatory variables, control variables) where:

RISKi is either the shift in total risk, idiosyncratic risk, or systematic risk of bidder i.

Following previous studies, I test for changes in total, idiosyncratic, and systematic risk for the event window of 180 days before and after the event. I also exclude returns for 30 days before and after the event dates from the return series.

IV. Sample

My initial sample consists of all acquisitions of private targets from January 1992 to December 2010. I obtain the observations from Thomson Financial Securities Data’s

SDC database that satisfy several screening criteria. First, the bidders must be U.S. publicly traded corporations. Second, targets must be private firms. However, I do not have any restriction on the target country. Third, only successful transactions that have value greater than $1 million and are worth more than 5 percent of the market value of equity of the bidders are investigated. Finally, I eliminate all transactions that belong to regulated industries.

I use the SDC database to collect various characteristics of the transactions. In addition, the Center for Research in Security Prices (CRSP) and COMPUSTAT are also used to collect other financial variables of the transactions. The final sample consists of

1122 acquisitions. Table I reports the variables that are being used in this paper.

Moreover, the information about the antidirector index comes from La Porta et al.

(1997) and Spamann (2010), while the economic freedom rating is obtained from the

Heritage website36. The origin of the legal system is collected from the Yale law school website37.

V. Data Description and Results of Univariate Analysis

Table II provides some useful information regarding the sample. The table indicates that bidder, target, and deal-specific characteristics are quite different across investment bank categories. Both the bidder and the target are more cautious when the transaction size is large. The mean and median of transaction value of transactions with the existence of an investment bank is much bigger than that of transactions without the existence of an investment bank. Moreover, both parties are more careful with relatively high value transactions since the mean and median for the relative size is larger for the sample with an investment bank.

Table II also reports that the mean proportion of equity payment in transactions in which the bidders (targets) employ an investment bank is higher than that in transactions in which the bidders (targets) do not employ an investment bank. The transactions with

36 http://www.heritage.org/index/default 37 http://library.law.yale.edu/foreign_resources?quicktabs_4=2 59 equity payment are more complicated and, therefore, both the bidder and the target seem concerned about more complicated transactions. The above results hold since the sample with an investment banks is the one that has a higher bidder’s Tobin’Q, and a higher likelihood of hi-tech bidders and targets. Interestingly, there is no difference between the country characteristics for the samples. The variables CROSSBORDER, FREEDOM, and

COMMON do not differ across the samples.

VI. Results of Multivariate Analysis

1. Factors that Cause Public Bidders or Private Targets to Hire Financial

Advisors

Table III is a correlation matrix of my independent variables. There is considerable correlation among my country characteristic variables. Thus, I will examine one variable at a time.

Table IV reports the results from applying the Logit and Tobit models to the sample to test variables that may influence the decision to hire an investment bank of the bidder and the target.

In the first two models, I present the findings on the bidder’s choice. Consistent with Servaes and Zenner (1996), the bidder is more likely to retain an investment bank when at least some equity payment is used. On the other hand, the bidder is less likely to use an investment bank when it has some prior experience. This finding is similar to Kale et al. (2003) who also report a negatively significant coefficient for a prior takeover

60 experience variable. Moreover, the decision to hire an investment bank by the bidder is strongly influenced by the decision to hire an investment bank by the target.

Regarding the decision to hire a top-tier investment bank by the bidder, equity payment and transaction size are positively significant. However, the prior experience and hi-tech status of the bidder are insignificant. In addition, the relatedness of the parties, the hi-tech status of the target, and the relative size of the transaction become positively significant. There is a higher chance that the bidder will hire a top-tier investment bank when it acquires a target that is in the same industry or is a high-tech target.

Moreover, the bidder will hire a top-tier investment bank when the relative size of the target is large. This result is consistent with the argument that bidders should spend money to hire an investment bank with a better reputation when they make a relatively large investment. Nevertheless, the likelihood of the bidder to hire a top-tier investment bank decreases during the credit crisis. Rau (2000) reports that the expense of hiring a top-tier investment bank is high. Thus, the bidder might want to avoid the excessively high investment bank fees during the tight credit periods. Moreover, the bidder has a stronger desire to hire a top-tier investment bank when the target hires an investment bank38.

The next two models document the findings on the target’s choice to hire an investment bank. The likelihood of the private target to hire an investment bank is higher

38 I only have information about insider ownership of the bidder for 300 observations. I run separate regression models with OWNERSHIP variable. The results show that insider ownership of the bidder does not have any impact on the decision to hire an investment bank of both the bidder and the target. 61 when some equity is used as payment and when the value of the target is high. The positive impact of equity payment and deal size support the argument that the client firm will hire an investment bank when the deal is complex. The positively significant coefficient for high-tech targets reinforces this argument. Interestingly, the Altman’s Z score of the bidder has a negative impact on the likelihood of employing an investment bank by the private target. The target pays attention to the financial strength of the bidder and it is more cautious when the bidder has a greater chance of going bankrupt. The fact that the bidder hires an investment bank strongly affects the decision to employ an investment bank by the target.

Regarding the decision to hire a top-tier investment bank by the target, the significant influence of equity payment, the size of the transaction, and the high-tech status of the target show the expected direction. In addition, the target is less likely to use a top-tier investment bank when the bidder has high growth opportunities and when the transaction occurs during the crisis period. The decision to hire a top-tier investment bank of the private target is also subject to credit availability. Again, the fact that the bidder hires an investment bank strongly affects the decision to employ a top-tier investment bank by the target.

Unlike the decision to hire a general investment bank, the decision to hire a top- tier investment bank by the target is affected by the country characteristic of the sample.

When the private target is being acquired by a foreign bidder, the likelihood that the target will hire a top-tier investment bank is higher. Furthermore, the better the

62 shareholder protection in the target country, the lower is the likelihood that the target will hire a top-tier investment bank39.

Regarding the power of the Logit regressions, the McFadden’s R-squares of the four models are 9.75%, 11.38%, 12.73%, and 15.88%, respectively. Moreover, the likelihood ratio indicates that all the models are significant at the 1 percent level. The above results indicate that the transaction costs, information asymmetry, contracting costs, and country characteristics are jointly have good explanatory power.

In the last two models, I run Tobit regressions with dependent variables as the market shares (reputation) of the bidder’s and the target’s investment banks. My results reinforce the findings in the first four models. As an additional robustness check, I run the ordered Probit regression for the decision to hire an investment bank of the bidder and the target. The results hold in the ordered Probit regressions40.

2. Impact of Financial Advisors on Wealth Gains of Bidders Acquiring

Private Targets

In this section, I use cross-sectional regression models to investigate the relationship between the decision to hire an investment bank as well as an investment

39 The results for FREEDOM and COMMON are the same with the result for RIGHTS. For expository reasons, I only report the result for RIGHTS. 40 In the ordered Probit regression for the bidder’s choice, the dependent variable equals 2 if the bidder hires a top-tier investment bank, equals 1 if the bidder hires a secondary-tier investment bank, and equals 0 if the bidder does not hire any investment bank. In the ordered Probit regression for the target’s choice, the dependent variable equals 2 if the target hires a top-tier investment bank, equals 1 if the target hires a secondary-tier investment bank, and equals 0 if the target does not hire any investment bank. 63 bank reputation and bidder’s CAR41. I control for various bidder-, target-, deal-, and country-specific characteristics that potentially affect bidder’s return. Table V reports the results in this section.

Consistent with the prior literature, when a bidder hires an investment bank, there is no impact on the wealth effect of the transaction. In all regressions, there is no evidence that the use of an investment bank will bring short-term benefits (CAR) to the bidder. On the other hand, when the private target hires an investment bank, there is evidence that the wealth gains of the bidder are reduced. We might argue that the bargaining power of the investment bank helps the private target to exploit higher benefits from the bidder.

However, the benefits of using investment banks disappear when the private target hires a top-tier investment bank. Rau (2000) argues that top-tier investment banks focus more on the completion of the transaction, rather than on bringing the most benefit possible for their clients. The results support Rau’s argument. Moreover, the coefficient for NOBIDYESTAR (for a transaction in which the bidder does not employ an investment bank and the target employs an investment) is insignificant. The target’s investment bank seems to have power only with the existence of the bidder’s investment bank42.

41 The bidders who hire an investment bank as well as the bidders who do not hire an investment bank realize positive and significant CARs. 42 The results for FREEDOM and COMMON are the same with the result for RIGHTS. For expository reasons, I only report the result for RIGHTS. 64

Among the control variables, I find that the higher the size of the transaction, in both a nominal and relative sense, the lower the CAR of the bidder. These results are consistent with the digestibility hypothesis, which states that when the assets needed by the bidder are held by another firm and are difficult to extricate from other unneeded assets, the bidder will have to acquire the whole target in order to get what it needs.

Moreover, the coefficients for bidder’s Tobin’s Q ratio and interest coverage ratio are positively significant, indicating a better wealth effect for the bidder with high growth opportunity and a strong financial situation.

Golubov et al. (2012) suggest that the decision regarding the hiring of an investment bank could be determined endogenously. Thus, the self-selection bias could emerge. Heckman (1979) argues that the self-selection bias is similar to the omitted variable bias and suggests a two-step procedure to control for the bias. I apply the

Heckman two-step procedure for my sample. In the first stage of the procedure, I run a

Logit regression to model the decision to hire an investment bank. In the second stage, I run an OLS regression with correction for selection bias. Similar to Golubov (2012), I find that the selection term in the second stage (the Inverse Mill’s ratio) is insignificant at any conventional level, indicating that the coefficient estimates in table V are reliable.

3. Impact of Financial Advisors on Acquisition Discounts of Private Targets

In this section, I investigate whether there is a difference in valuation of the private target in transactions with and without an investment bank. If the investment bank has some power, the multiples of the private target should be lower when the bidder hires

65 an investment bank. On the other hand, the multiples of the private target should be higher when the target hires an investment bank. Table VI contains the results from my analysis of the impact of an investment bank on the ratio of offer price to book value of the target43.

The results show that the ratio of offer price to book value of the target is not affected by the decision of the bidder to hire an investment bank. The coefficients for the variables of the existence of the bidder’s investment bank are not significant at any conventional level. On the other hand, the existence of the target’s investment bank has a positive effect on the valuation multiples of the private target. The coefficient for the existence of the target’s investment bank is positively significant at the 5 percent level and has a value of 4.12.

Nevertheless, the coefficient for the existence of the target’s top-tier investment bank is insignificant. Again, these results strengthen Rau’s (2000) argument that top-tier investment banks focus more on the completion of the transaction, rather than on bringing the most benefits possible for their clients. Interestingly, the coefficient for

NOBIDYESTAR (for transaction in which the bidder does not employ an investment bank and the target employs an investment bank) is insignificant. The target’s investment bank seems to have power only with the existence of the bidder’s investment bank.

Considering the control variables, the leverage of the bidder has a negative impact on the valuation multiples of the private target, indicating that the bidder with higher debt

43 The results for the ratio of offer price to earnings, the ratio of transaction value to sales, and the ratio of transaction value to EBIT are similar. 66 tends to pay lower multiples. Nevertheless, the bidder with higher growth opportunities

(Tobin’s Q) is willing to pay higher valuation multiples. Under perfect capital markets, the valuation multiples of firms should be the same regardless of the market of issue.

However, imperfections in the form of taxes, government imposed investment restrictions, transaction costs, and information costs should reduce the multiples that the bidder offers to a foreign target. The result supports this argument since the coefficient for CROSSBORDER is negatively significant, indicating lower valuation multiples for foreign targets. In addition, my findings show that the private target in a country with high investor protection has higher valuation multiples.

4. Impact of Financial Advisors on the Method of Payment

In this section, I investigate the impact of using an investment bank on the method of payment by employing Tobit regression models. It should be emphasized that the decision to hire an investment bank could be determined endogenously with the existence of equity payment. In fact, in the first section, I report that the existence of equity payment affects the decision to use an investment bank in the transactions. To correct for the endogeneity, I use an instrumental variable Tobit regression in the analysis. In the spirit of Golubov et al. (2011), I create the instrumental variable, SCOPE, which is a dummy variable, for this purpose. This variable should have an influence on the hiring of an investment bank, but not on the outcome of the transaction. SCOPE takes the value of one if in the ten years prior to the transaction the bidder employed an investment bank at least once in an acquisition of a private target and 0 otherwise.

Table VII shows the results from applying the instrumental variable Tobit model to the sample. The dependent variable is measured as the percentage of the transaction that is paid in equity. The results show that the decision to use an investment bank by the bidder has a significant impact on the proportion of equity payment in the acquisition of a private target. The coefficients for BIDDERIB and BIDTOPIB are significant and have the value of 0.83 and 1.32, respectively. These results indicate that the proportion of equity payment in the transactions with the existence of a top-tier investment bank is higher than that in the transactions with the existence of a second-tier investment bank.

The above results are strengthened by the significant coefficient of BIDIBSHARE. The positively significant coefficient of BIDIBSHARE implies that the better reputation of the bidder’s investment bank is associated with a higher proportion of equity payment.

On the other hand, the employment of an investment bank on the target side does not have any impact on the method of payment in an acquisition of a private target. The coefficients for TARGETIB, TARTOPIB, as well as for TARIBSHARE are not significant at any conventional level. These results suggest that the existence of an investment bank on the target side has limited impact on the proportion of equity payment in the acquisition of a private target.

As robustness tests, table VIII reports the impact of YESBIDNOTAR,

NOBIDYESTAR, and IBRELSHARE on the proportion of equity payment of the transactions. Consistent with the above results, the coefficient for YESBIDNOTAR is positively significant and the coefficient for NOBIDYESTAR is insignificant. The results in table V implies that the existence of an investment bank on the bidder side has an 68 important role, whereas the investment bank on the target side has no impact on the method of payment. These results are supported by the positively significant coefficient for IBRELSHARE.

Turning to the control variables in the regression models, I find that SIZE is negatively significant. Furthermore, I find that the coefficient for CREDITCRISIS is also negatively significant. During the credit crisis, the U.S. bidders spend a higher proportion of cash to acquire private target. On the other hand, the coefficients for TECHBIDDER,

TECHTARGET, and INTCOVERAGE are positively significant. The transactions with the involvement of high technology firms have higher proportion of equity payment.

Moreover, firms with better financial situation use more equity payment to finance their investments.

5. Impact of Financial Advisors on Operating Performance of Bidders

Acquiring Private Targets

Following Heron and Lie (2002), I measure the operating performance as operating income scaled by sales. Table IX contains six OLS regression models explaining the change in the ratio of the adjusted operating income to sales from the year before to the year after the acquisition. Table X reports six OLS regression models explaining the change in the ratio of the adjusted operating income to sales from the year before to two years after the acquisition.

The results show no evidence that the presence of an investment bank on either the bidder side or the target side has an impact on the operating performance of the

69 bidder. Hence, hiring an investment bank will not help improving the operating performance.

6. Impact of Financial Advisors on Risk Shifts of Bidders Acquiring Private

Targets.

I find that there are significant changes in risks of the bidders around the transactions. There is an increase in unsystematic risk, and total risk. However, there is a significant decrease in systematic risk of the bidder following the transaction. My main goal is to investigate the impact of financial advisor on the risk shifts of the bidder. Thus, table XI, XII, and XIII report the results of the OLS regression models explaining the impact of an investment bank advisor on the shifts in systematic, unsystematic, and total risk of the bidder after acquisitions, respectively. In these regression models, the dependent variables are the shifts in risks of the bidder.

The OLS regression models show that the hiring of an investment bank does not have any impact on the shift of the systematic risk of the bidder. After controlling for the involvement of investment banks, the coefficients for the relative size of the transaction and the Tobin’s Q of the bidder are positively significant, indicating that a higher relative size for the transaction and a higher the Tobin’s Q of the bidder will result in a greater increase in the systematic risk of the bidder. On the other hand, the higher the cash holdings of the bidder, the lower the increase in systematic risk of the bidder. The financial strength of the bidder might have some valuable impact on the shift in the systematic risk of the bidder.

Interestingly, the hiring of an investment bank does have some impact on the shift of the unsystematic risk of the bidder. While the fact that the bidder hires an investment bank does not have any influence on the shift of the systematic risk, the increase in unsystematic risk decreases when the bidder hires an investment bank. In other words, when the bidder employs an investment bank, there is a valuable effect on the shift of the unsystematic risk of the bidder. This result holds for both secondary-tier and top-tier investment banks.

Regarding the control variables, the involvement of equity payment and the

Altman Z- score of the bidder have a positively significant effect on the increase of the unsystematic risk of the bidder. The coefficients for CROSSBORDER and RIGHTS are significant at the 1 percent level and have a value of -0.02 and 0.02, respectively. When the bidder conducts a geographically diversified acquisition, there is a valuable effect on the shift in unsystematic risk. However, this valuable effect disappears when the bidder geographically diversifies to a country with high investors’ protection.

As far as the shift in total risk is concerned, there is no impact on the shift when either the bidder or the target employs an investment bank. Regarding the control variables, the involvement of equity payment, the cross-border status, and the investors’ protection in the target country have a significant impact on the shift in the total risk of the bidder.

VII. Conclusion

The role of investment banks in acquisitions of public targets has been investigated in prior literature. However, their importance in acquisitions of private targets has received limited attention. Due to the fundamental differences between acquisitions of private and public targets, such as the difference in information asymmetry between public and private targets, the influence of investment banks should be different in the two cases. This essay provides evidence regarding the impact of investment banks as financial advisors on the outcomes of acquisitions of private targets.

Regarding the decision to hire an investment bank in acquisitions of private targets, I find interesting results for the factors that affect the decision. On the bidder side, the hi-tech status of the bidder and the existence of the target’s investment bank have an impact on the decision of the bidders in acquisitions of a private target. The bidder is more likely to employ an investment bank when it knows that the private target is advised by one. Moreover, the likelihood of a hi-tech bidder to hire an investment bank is higher than that of a non-hi-tech bidder. This may due to the fact that the fit of a private target is less transparent for the hi-tech bidder and it needs the assistance from an investment bank. Similar to the results in acquisitions of public targets, I find that the decision to employ an investment bank by a bidder in the acquisition of a private target is also influenced by the involvement of equity in the payment, the previous experience of the bidder, and the size of the transaction.

On the target side, the prior literature shows that the involvement of equity in the payment and the existence of the bidder’s investment bank have a positive effect on the decision to hire an investment bank of the target. I also find that the size of the transaction, the hi-tech status of the target, and the bankruptcy risk of the bidder also significantly impact the decision to use an investment bank of the private target. It is more difficult for a hi-tech private target to evaluate itself; thus, it may need help from an investment bank. Moreover, the private target pays more attention to the prospects of the bidder when considering the employment of an investment bank. Specifically, managers of the private target may not want the combined firm go bankrupt because of the benefits of their current employees and the possibility that they will become a manager of the combined firm.

Moreover, there are fundamental differences between the services from top-tier investment banks and non-top-tier investment banks, such as fees and expected outcomes of the transactions. Prior literature does not pay attention to the difference in the decision of hiring a non-top-tier and a top-tier investment bank. I find that the access to the credit market is the main reason for the difference between the decision of hiring top-tier and non-top-tier investment banks for both the bidder and target. When the credit market is tight, both parties avoid the expensive services of a top-tier investment bank.

I also assess the wealth effect of the acquisition of private targets with the co- existence of investment banks on both the bidder and target sides. Unlike the results from the acquisitions of public targets, I find that the investment bank on the bidder side does not help its client in increasing CAR. On the other hand, the investment bank on the 73 target side significantly improves its client benefits, but at the expense of the bidder.

However, I find that the top-tier investment banks of both parties do not have any impact on the wealth effect in the transaction. Moreover, I investigate the question of whether the existence of investment banks affect the premium, i.e., the valuation multiples, of the private targets. I find that the bidder’s investment bank does not have any impact; whereas the target’s investment bank has a positive impact on the valuation multiples of the private target.

Prior literature does not consider the role of an investment bank on the method of payment, the operating performance, or the risk shifts post-transaction of the bidders. I find that the existence of an investment bank on the bidder side has a positive and significant impact on the proportion of equity as payment. Furthermore, the better is the reputation of the investment bank the higher is the proportion of equity as payment. On the other hand, the existence of an investment bank on the target side has limited impact on the proportion of equity as payment in the acquisition of a private target. Therefore, the investment banks may advise the bidder to use more equity as payment in order to protect the bidder from over-paying for the private target.

Moreover, I also find that while operating performance of the bidder following an acquisition is not affected by investment banks, the risk of bidders advised by investment banks is significant reduced following an acquisition. Overall, I find that special information asymmetry when bidders pursue private targets alters the factors used by bidders and targets to decide whether to hire an investment bank, and also alters the risk effect of the bidders following the acquisitions. 74

ESSAY 2

INVESTMENT BANK ROLE IN ASSET SELL-OFFS

I. Introduction

An asset sell-off is an important method of corporate restructuring that has gained much attention from the existing literature. Many studies find a positive wealth effect for both buyers and sellers in asset sell-offs44 and explain the sources of the wealth gains for both domestic and international transactions45. However, the role of financial advisors in the asset sell-off literature has been neglected. Therefore, this essay examines the extent to which investment banks as financial advisors affect the outcome of asset sell-offs.

Specifically, I argue that investment banks play an important role in the wealth creation and wealth transfers that occur as a result of these transactions.

Chemmanur and Yan (2004) suggest that a firm can maximize its equity value, calculated as the sum of the equity values of the parent and the divested unit, through an asset sell-off. In other words, with a good reason to divest, the sum of the equity values of the parent and the divested unit may be greater than the value of the joint organization in the absence of an asset sell-off. The finance literature has mentioned three main reasons

44 Some studies also show the positive but insignificant results for buyer of asset sell-offs. For example, Sicherman and Pettway(1987). 45For example, see Jain (1985), Hite et al. (1987), Sicherman and Pettway (1992), John and Ofek (1995), Datta et al. (2003), Afshar et al. (1992), Tsetsekos and Gombola (1992), Borde et al. (1998), Gleason et al. (2000), Clubb and Stouraitis (2002) and Slovin, et al. (2005). 75 for asset sell-offs: (i) the efficiency explanation, which is to shift specific assets to those who can operate them most efficiently; (ii) the focusing explanation, which is to intensify the corporation’s efficiency by reducing its degree of diversification; and (iii) the financing explanation, which is to relax credit constraints. However, asset sell-offs also can cause less positive results. They may represent a hindrance for both parties involved in the transaction, especially for the buyer. Johnson et al. (1990) state that asset sell-offs often cause an increase in uncertainty, a climate of ambiguity, and a lack of clarity regarding the future of the buyer. In order to get the positive effects from asset sell-offs, the buyers must overcome the negative effects of the transactions. Moreover, Sicherman and Pettway (1992) argue that the gains for buyers result from the benefits they can extract from the sellers in the negotiation process. Therefore, investment banks may efficiently help the buyers to get the best results.

The role of investment banks as financial advisors has also been investigated in the takeover literature46. However, there are fundamental differences between the process to acquire a public target and that of divested assets. Hence, the decision to hire and the impact of investment banks might be different in the case of asset sell-offs.

In an asset sell-off, the announcement of the prospective sale occurs months before the buyer is identified. After the announcement of the sale, the seller will look for the appropriate buyer through its business connections. On the other hand, in mergers, bidders will actively find and focus on the control of the targets. Moreover, when the seller hires an investment bank in an asset sell-off, the number of potential buyers will

46See Servaes and Zenner (1996), Rau (2000), and Kale et al. (2003) for example. 76 increase since the network of investment banks is much broader compared to that of an individual seller. Hence, through their expertise and connections, investment banks can improve the quality of the matching of buyer and seller as well as accelerate the matching process. Thus, the use of investment banks should bring benefits to both parties.

Additionally, at the early stage of the asset sell-off, when a seller persuades potential buyers to buy their assets, the seller must prepare a valuation, such as financial statements, of the unit being sold. Unlike the acquisitions of publicly traded targets, most corporations do not have the separate financial statements that are audited for each unit individually. Thus, they must prepare the new statements for the business unit being divested prior to searching for a buyer. When the seller provides such statements to the buyer, the buyer may ask the question of whether the information has been manipulated by the selling firm. Without the support of the investment bank, it is very difficult for the buyer who truly wants to purchase the divested unit to evaluate the correct value of the unit and the expected synergies from the transaction. In this case, the buyer may hire an investment bank to certify the transaction.

Furthermore, when the buyer engages in cross-border asset sell-off transactions, it faces greater challenges because of institutional and cultural differences. For example, the integration of assets is more difficult due to cultural differences that determine how business is conducted. Stiglitz (2000) argues that the greater lever of uncertainty in crossborder transactions reduces the value of assets being exchanged and is value destroying for the buyer. This increased level of uncertainty results from the information asymmetry

77 between the buyer and seller. In this case, both parties involved in the transaction may hire investment banks to reduce the information asymmetry that exists.

In addition, for both the buyer and the seller of an asset sell-off, the process of disentanglement of the unit being sold requires much more expertise when compared to that of a merger. According to Gole and Hilger (2008), while the integration of a merger occurs after the transaction is consummated, an asset sell-off requires intense preparation and rapid implementation of the separation of the unit being sold from the seller prior to the close of the transaction and the immediate reaction from the buyer. In other words, the parties who participate in an asset sell-off must execute under a much tighter time constraint. In this case, the buyer and seller may use the capabilities of investment banks to experience a more efficient and smoother disentanglement process. Hence the role of investment banks as financial advisors is more important in such transactions.

There also exists a fundamental difference regarding the level of communication and the challenges to management between asset sell-offs and mergers. Since the seller announces the prospective sale well in advance of finding out who the buyer is, an asset sell-off requires a much higher level of communication as well as increased challenges to management. For example, the attachment between employees of a unit being sold not as strong when compared to that of an entire firm being sold. Hence, the seller must inform and motivate these employees. If the employees of the unit being sold realize that sell-off is for the best, they will have more motivation to work and there is no substantial erosion of unit value. Moreover, when the buyer uses an investment bank, it shows the employees of the unit being purchased that it has serious intentions for the unit. In this case, 78 investment banks not only certify the value of both parties in the transaction but also certify the seriousness of both parties to the employees of the unit being sold who are worrying about their future. Thus, the value of investment banks as financial advisors in asset sell-offs should be different from that of investment banks in mergers.

Despite the uncertainty of asset sell-offs and the benefits that investment banks may bring to buyers in asset sell-offs, only one-third of buyers hire an investment bank as financial advisor47. Perhaps the most prominent argument for not hiring an investment bank is that the costs are significant. The decision to hire an investment bank for asset sell-offs must weigh potential benefits against the cost. Therefore, in this essay, I attempt to fill in the gaps in the existing literature by investigating the decision to hire and the value of hiring investment banks as financial advisors in asset sell-offs.

I find that special information asymmetry when buyer pursues divested assets alters the factors used by the buyer and seller to decide whether to hire an investment bank. However, the country characteristics variables do not have any impact on the decision of wither party. I also find that the existence of an investment bank on either the buyer side or the seller side does not have any impact on the method of payment of the transaction or the post-transaction operating performance of the buyer.

Nevertheless, the existence of an investment bank has some impact on the wealth effect of the buyer. I find evidence that the buyer realizes a significantly higher CAR when it employs an investment bank. On the other hand, the buyer has a significantly lower CAR when the seller uses an investment bank. We can conclude that investment

47 This information is extracted from my sample. 79 banks bring short-term benefits to their clients. However, these benefits disappear when they buyer (seller) employs a top-tier investment bank.

Furthermore, there is evidence that an investment bank has some impact on the risks of the buyer after the transaction. While the investment bank on the buyer side has no impact on the risk shifts, the investment bank on the seller side influences the risk shifts of the buyer. Specifically, when the seller employs an investment bank, the increases in unsystematic and total risks of the buyer are greater than in cases when the seller does not use an investment bank.

II. Hypotheses

1. Factors that Cause Buyers or Sellers to Hire Financial Advisors in Asset

Sell-Offs

The use of investment banks in asset sell-offs helps buyers find a better match, helps certify the value of the assets being sold, and enables management overcome numerous challenges in the disentanglement process. However, the cost of hiring investment banks is probably the most important reason why both parties do not use them. Hence, the question of which factor has the most influence on the decision to hire an investment bank is interesting to investigate. In this section, I will develop the hypotheses regarding the factors that affect the choice of management to hire an investment bank. I categorize these factors into four categories: transaction costs, asymmetric information, contracting costs, and country characteristics.

a. Transaction Costs Hypotheses.

Since the most important reason for a buyer to hire an investment bank is to reduce transaction costs, potential high transaction costs may have a major impact on the decision to hire an investment bank in asset sell-offs. I expect that the buyer is more likely to hire an investment bank when the costs are high. I use the following variables to proxy for transaction costs:

Cash payment. When a buyer intends to use equity to finance an asset sell-off, the negotiation process becomes more complex as the buyer and seller disagree on the value of the equity portion to be used. Specifically, the seller may be concerned about the true value of the equity and may be reluctant to accept that equity payment. In other words, the seller faces the risk of accepting over-valued equity. In these cases, investment banks may use their expertise to value the equity proportion. On the other hand, the negotiation process is less complicated when 100 percent cash is used to finance the transaction.

Therefore, we hypothesize that the buyer and seller might not hire an investment bank when cash is used to entirely finance the transaction. I define ALLCASH as a dummy variable, which equals 1 if the buyer uses 100 percent cash in the payment method and 0 otherwise.

Buyer’s prior asset sell-off experience. Servaes and Zenner (1996) argue that since more experienced bidders are capable of spreading the fixed costs of acquisitions, they are less likely to need investment banks to assist them in the acquisition. I hypothesize that the likelihood of buyers using investment banks is negatively related to

81 their prior experience. Following Kale et al. (2003), I measure buyers’ prior experience by a dummy variable that equals 1 if the buyers undertook at least 1 asset sell-off transaction in the preceding 10-year period (PRIOR).

Transaction Value. Larger transactions are more complex and more difficult to handle. Moreover, the larger the transaction, the more important is the deal for each party and the greater is the potential adverse consequences of a bad decision. Thus, with economically important transactions, the buyer may be more willing to hire an investment bank to justify the purchasing decisions. Hence, I expect that buyers will use investment banks when the size of the transaction is larger. To investigate the effect of the transaction size, a variable SIZE, which equals the natural logarithm of the transaction size, is used.

b. Information Asymmetry Hypotheses.

The value of investment banks is highest when there is much information asymmetry between buyer and seller. When the buyers do not have enough information regarding the assets being purchased or if the information is questionable, they may hire an investment bank to evaluate the situation and prepare the offer to the seller. Therefore,

I expect that buyers will use investment banks more when the information asymmetry is severe. I use the following variables to proxy for information asymmetry between buyers and sellers:

Buyer’s growth opportunities. When a buyer has high growth opportunities, the expected synergy from an asset sell-off is more difficult to estimate. Due to the

82 information asymmetry between the buyer and seller, the range of the expected synergy is large. Thus, it is more challenging for the buyer to make decision. Furthermore, the buyer also has more options to incorporate the assets being purchased into the existing operation. In order to realize the greatest benefit from the transaction, the buyer may consider hiring an investment bank. I hypothesize that the bidder may hire an investment bank when it has high growth opportunities. In this study, growth opportunities of the buyer are measured by the Tobin’s Q ratio (TOBINQ), which equals market value divided by book value of the buyer.

Buyer’s (Seller’s) technological status. The information asymmetry surrounding the value of technology firms is high. Moreover, the assets of high tech firms may be considered as less transparent. For the buyer, the true value of the assets being sold of the hi-tech seller is difficult to estimate. For the seller, the expected synergy of the hi-tech buyer and the assets being sold is hard to value. Thus, it is more challenging for the seller to decide on the price of the assets. Therefore, the likelihood of using an investment bank in transactions involving technology firms should be higher than that of transactions without. TECHBUYER (TECHSELLER) is a dummy variable which equals 1 if the buyer (seller) is categorized in primary SIC codes 3571, 3572, 3575, 3577, 3578

(computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics),

3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379

(software) and 0 otherwise.

Relatedness between buyer and seller. According to Chemmanur et al. (2009), the relatedness between bidder and target can proxy for information asymmetry between the two parties. Thus, the degree of asymmetric information may be lower when both parties are in the same industry. Moreover, Servaes and Zenner (1996) argue that when a bidder acquires a target in the same industry, the bidder can rely on its capital budgeting expertise to value the target. In the cases of asset sell-offs, I hypothesize that when buyers and sellers are in the same industry, the likelihood of hiring an investment bank decreases. The dummy variable RELATED, which equals 1 if both parties have the same

4-digit SIC code and 0 otherwise, is used.

Relative Size. Faccio and Masulis (2005) argue that the exposure of bidders to an asymmetric information problem is more pronounced when the target value is relatively large. When a buyer purchases large divested assets, the transaction has greater important economic meaning to itself since it spends a substantial amount of capital on the investment. In order to finish the transaction, the bidder might have to give up some other potentially profitable investments. Therefore, the bidder should consider such transactions carefully. I hypothesize that buyers will use investment banks more frequently when the size of the purchase is large. I use RELSIZE, which is the value of the divested assets being sold divided by total market capitalization of the buyer, to test this expectation as of 4 weeks prior to the transaction.

c. Contracting Costs Hypothesis.

Investment banks as financial advisors assist the buyer in identifying better assets being sold and in structuring their transactions. In other words, investment banks may find a better match for the buyer as well as evaluate the value of the standalone and the combined values prior to the consummation of the transaction. In this case, the investment bank can produce information that makes hidden value more transparent as well as certify this value for their clients. I also argue that buyers use investment banks to monitor and provide a signal of the firm’s value to the target. The following variables are used to investigate the impact of contracting costs.

Insider ownership of buyer. If insiders own a large stake in bidding firms, they are less likely to engage in value destroying transactions. Thus, the monitoring purpose of the investment bank is not as important when the stake of insiders is large. However, in such a situation, the certifying purpose of investment banks is very important since they can certify that the true motivation of the transaction is the synergy that is being created and not managerial self-motivations. I include bidder’s insider ownership to investigate its impact on the decision to use an investment bank (OWNERSHIP).

Buyer’s credit constraints during weak economic conditions. During weak economic conditions, a buyer may be subject to credit constraints. In this situation, the buyer can be reluctant to spend a large amount of cash on an investment bank. On the other hand, the costs of the transactions might be higher in weak economic conditions.

Hence, the buyer might need the investment bank to carefully evaluate the transaction. I

85 use CREDITCRISIS, which is a dummy variable equal to 1 during recessionary periods in which access to credit might be more limited and 0 otherwise, to examine the impact of market conditions on the likelihood of using an investment bank.

The bidder risk. When the likelihood of bankruptcy of the buyer is high, the buyer should be more careful when conducting an acquisition. I investigate the impact of the bankruptcy risk of the buyer on the decision to hire an investment bank by including in the analysis the variable ZSCORE, which equals the Altman Z-score of the buyer. I also include an alternative proxy LEVERAGE, which equals the debt ratio of the buyer, in the regression models to examine whether the leverage of the buyer has any impact on the decision to hire an investment bank for both the buyer and the seller.

Hiring of an investment bank by the buyer (seller). The fact that the buyer (seller) hires an investment bank might cause the opposing party to hire an investment bank as well. When the buyer (seller) hires an investment bank, the seller (buyer) should be concerned about the benefits that the investment bank can bring to the opposing party. I include the variable SELLERIB (BUYERIB) to examine the influence of the seller’s

(buyer’s) decision to hire an investment bank on each other.

d. Country Characteristics Hypotheses.

When buyers have the intention to purchase assets that are located in foreign countries, they anticipate greater challenges because of institutional and cultural differences. Stiglitz (2000) argues that the value of assets being exchanged can be reduced by a greater level of uncertainty in cross-border transactions and thus, decrease

86 the buyer’s value. In this case, the support from investment banks has a much higher value since they have expertise in dealing with such situations.

Moreover, when the risks in the target country are high, the buyers may also use an investment bank to minimize the risk of its exposure. For example, legal systems with different levels of protection for minority shareholders and enforceability of contracts increase the difficulty in valuing future cash flows. Hence, buyers may consider using investment banks when purchasing assets in countries with weak governance. Giroud and

Mueller (2011) show some evidence that firms who have weak governance also have lower value and worse operating performance. This means that the value or operating performance of these firms or their subsidiaries can be improved. However, the extent to which the value and the operating performance can be improved is very difficult to measure and buyers may want the support of investment banks. Therefore, I hypothesize that the likelihood that buyers hire investment banks is higher when they purchase assets in countries with a high level of country risk. In order to test my expectations regarding how country risk characteristics affect the decision to hire investment banks in crossborder asset sell-offs, I consider the following measures.

Cross-border. CROSSBORDER is a dummy variable which equals 1 if the transaction is listed as cross-border transaction, 0 otherwise.

Economic Freedom and Development. The Economic Freedom Index (see

Gwartney et al. 1996) assigns a rating to each country based on trade policy, taxation, government intervention, foreign investment policy, banking, pricing controls, property

87 rights, and regulation. With the Economic Freedom Index, a higher rating proxies for a less restrictive environment. I expect that this index has an impact on the decision to hire investment banks in asset sell-offs. Based on the Heritage website, I collect the rating for each asset seller's country in the sample and include those ratings into the analysis to investigate the effect of the seller country’s economic environment on the decision to use investment banks. The variable FREEDOM is the natural logarithm of economic freedom rating of the seller country in the year prior to the transaction.

Shareholder Rights. I use the revised index of the rights that shareholders have with respect to the management team (anti-director rights) introduced by Spamann (2010) to evaluate the impact of shareholder protection in the seller country on the decision to hire investment banks. The anti-director rights index is an index that aggregates shareholder rights. The index ranges from 0 to 5, in which a higher number reflects better shareholder protection. Following Moeller and Schlingemann (2005),a dummy variable

RIGHTS, which equals 1 if the anti-director rights index of the seller country is three or above, 0 otherwise, is used in the analysis.

Legal System. I also consider the legal system as a broad indicator of investor protection, as it is commonly used as a proxy for shareholder rights and degree of corporate governance. Common law systems are considered to have the best shareholder rights. Civil law based systems are considered to have the weakest shareholder rights. To examine the influence of the legal system, I use COMMON, which is a dummy variable which equals 1 if the seller country is a common law country, 0 otherwise.

2. Impact of Financial Advisors on Wealth Gains in Asset Sell-offs

The stock price reaction to the announcement of asset sell-offs has been investigated extensively. Most of the studies claim positive wealth gains for sellers in asset sell-offs. There are various explanations for the positive wealth gains of the sellers.

These hypotheses relate to reversing value-destroying diversification48, reducing financial constraints49, diminishing overinvestment50, decreasing agency costs of managerial discretion51, eliminating the costs of influence activities within the seller52, and maximizing the profitability of the sale (the difference between selling price and value- in-use of the assets)53. However, the literature reports a mixed result for buyers54. The more recent studies reveal the explanations for the mixed results for the buyers in asset sell-offs. For example, Slovin et al. (2005) find that asset sell-offs are deal-enhancing transactions for buyers who use equity to pay for the divested assets and neutral results for buyers who use cash to finance the asset sell-offs. They argue that the use of buyer equity conveys favorable information about the buyer.

Moreover, Sicherman and Pettway (1992) show positive wealth gains of buyers and argue that the gains come from the benefits they can attract from sellers in the negotiation process. Sicherman and Pettway’s conclusion implies that the negotiation process, as well as the bargaining power of both parties, is very important in the wealth

48 See Berger and Ofek (1995), John and Ofek (1995), and Berger and Ofek(1996) for example. 49 See Afshar et al. (1992) and Lasfer et al. (1996) for example. 50 See Sicherman and Pettway (1992) for example. 51 See Lang et al. (1995) for example. 52 See Meyer et al. (1992) for example. 53 See Clubb and Stouraitis (2002) for example. 54Hite et al. (1987) and Jain (1985) show significant positive abnormal returns, while Sicherman and Pettway (1987) reports positive and insignificant stock excess returns. 89 creation of the buyer in asset sell-offs. Investment banks who serve as financial advisors in asset sell-offs play a very important role in the negotiation process and contribute much to the bargaining power of both buyers and sellers in asset sell-offs. Nevertheless, the value of investment banks is neglected in the asset sell-off literature. Therefore, in this section, I attempt to fill in this gap by investigating the role of investment banks in the creation of wealth through asset sell-offs. I use the following explanatory variables for the empirical tests:

The reputation of the buyer’s investment bank (BUYIBSHARE). If the buyer has one investment bank, this variable equals the market share of the investment bank in the prior year of the transaction. If the buyer uses more than one investment bank, following

Kale et al. (2003), I define the variable as the highest market share of the multiple investment banks. When the buyer does not use investment bank, the value of the variable equals 0.

The reputation of the seller’s investment bank (SELIBSHARE). If the seller has one investment bank, this variable equals the market share of the investment bank in the prior year of the transaction. If the seller uses more than one investment bank, following

Kale et al. (2003), I define the variable as the highest market share of the multiple investment banks. When the seller does not use investment bank, the value of the variable equals 0.

Relative reputation of buyer and seller’s investment bank (RELIBSHARE). This variable is the difference between the reputation of the buyer’s investment bank and the reputation of the seller’s investment bank.

Whether the buyer is advised by investment banks (BUYERIB). To compare the difference in the wealth gains between the transactions in which the buyer uses and does not use an investment bank, I use a dummy variable that equals 1 if there is the use of an investment bank by the buyer and 0 otherwise.

Whether the buyer is advised by top tier investment banks (BUYTOPIB). To compare the difference in the wealth gains between the transactions in which the buyer uses and does not use a top tier investment bank, I use a dummy variable that equals 1 if there is at least 1 top tier investment bank advising the buyer and 0 otherwise.

Whether the seller is advised by investment banks (SELLERIB). To compare the difference in the wealth gains between the transactions in which the seller uses and does not use an investment bank, I use a dummy variable that equals 1 if there is the use of an investment bank by the seller and 0 otherwise.

Whether the seller is advised by top tier investment banks (SELTOPIB). To compare the difference in the wealth gains between the transactions in which the seller uses and does not use a top tier investment bank, I use a dummy variable that equals 1 if there is at least 1 top tier investment bank advising the seller and 0 otherwise.

When buyer is advised by investment banks and seller is not (YESBUYNOSEL).

To compare the difference in the wealth gains between the transactions in which the 91 buyer uses and the seller does not use an investment bank, I use a dummy variable that equals 1 if buyer uses at least one and seller does not use an investment bank, 0 otherwise.

When buyer is not advised by investment banks and seller is (NOBUYYESSEL).

To compare the difference in the wealth gains between the transactions in which the buyer does not use and the seller uses at least one investment bank, I use a dummy variable that equals 1 if buyer does not use and seller uses at least one investment bank, 0 otherwise.

In addition to those explanatory variables, I also use control variables that I were used in the first section. Specifically, to control for the transaction costs of the deals, I use

ALLCASH55, SIZE56, and PRIOR57. To control for the information asymmetry in the deals, I use RELATED58, TECHBUYER59, TECHSELLER60, RELSIZE61, and

TOBINQ62. To control for contracting costs of the deal, I use LEVERAGE63,

55 EQUITY is a dummy variable equals 1 if the buyer uses 100 percent cash in the payment method and 0 otherwise. 56 SIZE is the natural logarithm of the value of the transaction. 57 PRIOR is the number of takeover related activities undertaken by the bidders in the preceding 10-year period 58 RELATED equals 1if both parties have the same 4-digit SIC code, 0 otherwise. 59 TECHBUYER equals 1 if the bidder is categorized in primary SIC codes 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics), 3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379 (software) and 0 otherwise. 60 TECHSELLER equals 1 if the target is categorized in primary SIC codes 3571, 3572, 3575, 3577, 3578 (computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics), 3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379 (software) and 0 otherwise. 61 RELSIZE equals the value of the private target divided by total market capitalization of the bidder. 62 TOBINQ is the Tobin’s Q ratio of the buyer. 63 LEVERAGE is the debt ratio of the buyer. 92

CASHHOLDINGS64, INTCOVERAGE65, CREDITCRISIS66, and ZSCORE67. To control for the country characteristics, I use CROSSBORDER68, FREEDOM69, RIGHTS70, and

COMMON71.

3. Impact of Financial Advisors on the Method of Payment in Asset Sell-offs

The method of payment in takeovers has been investigated in the current literature showing that acquirers are more likely to use investment banks when they use equity as the transaction currency. With one exception (Cao and Madura, 2011), the current literature ignores the method of payment in asset sell-offs. In this study, the authors find the continuous method of payment, i.e., the percentage of equity in the payment, in asset sell-off transactions is impacted by financial constraints, information asymmetry, and country characteristics of buyers and sellers. They report that buyers facing more stringent cash constraints are more likely to use equity when purchasing assets, while sellers subjected to cash constraints prefer cash when selling assets. The proportion of equity as payment in an asset sell-off is positively correlated to asymmetric information.

Equity payment is more likely when sellers are based in countries that have relatively more government restrictions, weak shareholder rights, and a weak legal system.

64 CASHHOLDINGS is the cash holdings scaled by total assets of the buyer. 65 INTCOVERAGE is the interest coverage ratio of the buyer. 66 CREDITCRISIS is a dummy variable equals 1 if equals 1 if the transactions happen during 2001-2002 and 2007 crisis (from Q1/2001 to Q4/2002 and from Q3/2007 to Q4/2010). 67 ZSCORE is the Altman Z-Score of the buyer. 68 CROSSBORDER is a dummy variable which equals 1 if the transaction is listed as cross-border transaction, 0 otherwise. 69 FREEDOM is the natural logarithm of the Economic Freedom Index for the target country. 70 RIGHTS equals 1 if the antidirector rights index of the seller country is three or above, 0 otherwise. 71 COMMON is a dummy variable which equals 1 if the seller country is a common law country and 0 otherwise. 93

Moreover, our understanding regarding the extent to which the use of investment banks affects the method of payment in asset sell-offs is limited. In this section, I attempt to determine whether the use of investment banks has any impact on the method of payment in asset sell-offs. In addition, the question of whether the reputation of investment banks affect the method of payment in asset sell-offs is also addressed.

I hypothesize that the method of payment will be different for transactions that are advised by investment banks and for transactions that are not. Moreover, the prestige of investment banks also will affect the method of payment. Specifically, I expect that a more prestigious investment bank will advise the buyer to use more equity as the transaction currency in order to give the seller a greater risk portion of the transaction.

This argument is in line with Hansen (1987) who argues that bidders should use equity to induce risk sharing when a target's asymmetric information is high. Nevertheless, it is strongest in cases where the seller is located in a foreign country. Henisz (2000) suggests that multinational firms that do business in risky foreign markets are more likely to share ownership with local partners because such behavior will shift some of the risk to foreign investors who can bear the risk in a less costly manner.

Despite the willingness of buyers to use more equity in asset sell-offs, their power in determining the method of payment is limited since they have to convince sellers to accept their offers. Hege et al.(2009) show that a seller with unfavorable information about the assets will accept the highest cash offer while a seller with favorable information will accept the offer of buyer’s equity. However, when there is an equity

94 portion in the payment, it will take more time to negotiate and the chance of disagreement between both parties is substantially increased.

The literature on investment banks claims that finishing the transaction is one of the top priorities for banks since most merger fee contracts include a payment contingent on the completion of the merger (Hunter and Walker, 1990). Therefore, I expect that investment banks will advise sellers to ask for a higher portion of cash in the payment.

By doing so, sellers can finish the transactions and guarantee the highest certain amount of payment possible. I use the following explanatory variables to test my expectation about the impact of investment banks on the method of payment in asset sell-offs:

Kale et al. (2003), I define the variable as the highest market share of the multiple investment banks. When the buyer does not use investment bank, the value of the variable equals 0.

Kale et al. (2003), I define the variable as the highest market share of the multiple investment banks. When the seller does not use investment bank, the value of the variable equals 0.

Whether the buyer is advised by investment banks (BUYERIB). To compare the difference in the method of method of payment between the transactions in which the buyer uses and does not use an investment bank, I use a dummy variable that equals 1 if there is the use of an investment bank by the buyer and 0 otherwise.

Whether the buyer is advised by top tier investment banks (BUYTOPIB). To compare the difference in the method of method of payment between the transactions in which the buyer uses and does not use a top tier investment bank, I use a dummy variable that equals 1 if there is at least 1 top tier investment bank advising the buyer and 0 otherwise.

Whether the seller is advised by investment banks (SELLERIB). To compare the difference in the method of method of payment between the transactions in which the seller uses and does not use an investment bank, I use a dummy variable that equals 1 if there is the use of an investment bank by the seller and 0 otherwise.

Whether the seller is advised by top tier investment banks (SELTOPIB). To compare the difference in the method of method of payment between the transactions in which the seller uses and does not use a top tier investment bank, I use a dummy variable that equals 1 if there is at least 1 top tier investment bank advising the seller and 0 otherwise.

When buyer is advised by investment banks and seller is not (YESBUYNOSEL).

To compare the difference in the method of method of payment between the transactions in which the buyer uses and the seller does not use an investment bank, I use a dummy variable that equals 1 if the buyer uses at least one and the seller does not use an investment bank, 0 otherwise.

When buyer is not advised by investment banks and seller is (NOBUYYESSEL).

To compare the difference in the method of method of payment between the transactions in which the buyer does not use and the seller uses an investment bank, I use a dummy variable that equals 1 if the buyer does not use and the seller uses at least one investment bank, 0 otherwise.

In addition to these explanatory variables, I use control variables that were cited in Cao and Madura (2011). They attempted to explain the method of payment in asset sell-offs, but did not consider the influence of the financial advisor variables above, which are the key variables that I wish to assess. The control variables can be categorized into three categories: financial constraints, asymmetric information, and country risk variables.

a. Financial constraints variables.

According to Myers (1984), buyers in healthy financial situations might use cash more in their investments since internal financing is preferred for such bidders.

Therefore, I expect that the financial constraints of buyers have some effect on the

97 method of the payment in the acquisition. I use the following control variables to control for the financial condition of bidders:

Buyer’s cash holding. Buyers with higher cash holdings are expected to use more cash to finance their investment because they prefer internal financing first. Hence, when they have excessive cash available, they are more likely to use cash as payment. I use the variable CASHHOLDINGS, which is defined as the cash holdings of the bidder as a percentage of total capitalization, to control for the effect of cash holdings on the method of payment.

Buyer’s Financial Leverage. The possibility for buyers who have high leverage to issue debt to finance their investment is low since it will substantially increase their bankruptcy costs. In such a situation, buyers are more likely to finance their investments with equity (Myers, 1977). I use the variable LEVERAGE, which equals total liabilities

(based on the financial statement) divided by the market value of equity as of 4 weeks prior to the announcement, to control for the effect of bidder’s leverage on the method of payment.

Buyer’s Interest Coverage Ratio. The ability of a buyer to make interest payments on its existing debt can influence its willingness to issue more debt to finance the investment. Hence, I use the interest coverage ratio INTCOVERAGE, which is the number of times a company could make the interest payments on its debt with its earnings before interest and taxes, to control for this aspect of financial strength on the method of payment.

Buyer’s Size. Larger firms usually have a better reputation and easier access to the debt market. Thus, they should have a lower cost of issuing debt. Facio and Masulis

(2005) claim that larger firms are more likely to issue debt to finance their capital expenditures and investments. In this section, I measure the variable SIZE of the buyer as the logarithm of the buyer's pre-transaction total market capitalization and include this variable in the analysis to control for the size effect on the method of payment.

Buyer's Technology Status. Hi-tech firms are firms who have heavy initial investment in research and development (R&D). Thus, they might have limited access to cash while having high growth opportunities. Jung et al. (1996) argue that managers of such growth opportunities prefer to finance their investments with equity rather than debt because equity financing gives them more discretion over the funds raised. I use a dummy variable to control for this influence. The dummy variable TECHBUYER, is set equal to 1 if the buyer has their primary SIC codes as 3571, 3572, 3575, 3577, 3578

(computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics),

3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), and 7370, 7371, 7372, 7373, 7374, 7375, 7379

(software) and 0 otherwise.

Buyer’s Credit Constraints during Weak Economic Conditions. During weak economic conditions, buyers may be subject to credit constraints. In such a situation, the availability of credit is limited and the cost of increasing debt is high. Therefore, equity might be used more to finance the investments. On the other hand, equity values tend to be low in these periods, which might discourage buyers from using equity. I use 99

CREDITCRISIS, which is a dummy variable that equals 1 during recessionary periods and 0 otherwise, to control for the impact of the market condition on the method of payment.

The buyer risk. When the likelihood of bankruptcy of the buyer is high, the buyer should be more careful when using cash to finance its investment. I investigate the impact of the bankruptcy risk of the buyer on the method of payment by including in the analysis the variable ZSCORE, which equals the Altman Z-score of the buyer.

b. Asymmetric Information Variables.

In an acquisition, information asymmetry regarding the value of the buyer as well as the value of the assets being purchased is common. According to Hansen (1987), the transaction process between the buyer and the seller is a two-agent bargaining game under imperfect information and the buyer should use more equity payment since it has desirable contingent pricing characteristics. On the other hand, Hansen (1987) and

Travlos (1987) suggest that when a buyer finances an acquisition with equity, it emits a negative signal that it is capitalizing on the use of its overvalued stock. Thus, the seller might prefer cash when the bidder's equity has the potential to be overvalued. I use the following control variables to control for the information asymmetry between the two parties:

Buyer’s Growth Opportunities. Martin (1996) shows that the bidders who have higher growth opportunities usually use more equity as currency when acquiring targets and the targets also accept their equity more frequently. In this section, I use Tobin’s Q

100 ratio as a measure of buyer's growth opportunities to control for the impact of buyer’s growth opportunities on the method of payment (TOBINQ).

Buyer’s Size of Purchase. There is an argument that buyers might be exposed more to an asymmetric information problem when the divested assets’ value is relatively large (Faccio and Masulis, 2005). Thus, the buyers are more likely to use equity financing in large transactions. I control for this effect by including a variable RELSIZE, which equals the value of the assets being purchased divided by total market capitalization of the buyer, in the analysis.

Seller’s technological status. The information asymmetry surrounding the value of technology firms is high. Hence, the buyers might use more equity as payment when purchasing a hi-tech target to induce risk sharing. The variable TECHSELLER, which equals 1 if the seller is categorized in primary SIC codes 3571, 3572, 3575, 3577, 3578

(computer hardware), 3661, 3663, 3669 (communications equipment), 3674 (electronics),

3812 (navigation equipment), 3823, 3825, 3826, 3827, 3829 (measuring and controlling devices), 4899 (communication services), or 7370, 7371, 7372, 7373, 7374, 7375, 7379

(software) and 0 otherwise, is included.

Relatedness between Buyer and Seller. Chemmanur et al. (2009) claim that the information asymmetry between the bidder and target is lower when the degree of relatedness between the two parties is higher. Thus, the degree of asymmetric information may be lower when both parties are in the same industry. The dummy variable

101

RELATED, which equals 1if both parties have the same 4-digit SIC code, 0 otherwise, is used to control for the relatedness between the two parties.

Buyer’s prior takeover experience. If the buyer has some experience in dealing with these types of transactions, it should know better about the best combination between cash and equity payment. In this case, the buyer might prefer cash over equity payment or vice versa in specific situations. To control for this factor, I include a variable concerning the experience of the buyer on the market for assets sell-offs. I measure bidders’ prior experience by the number of takeover related activities undertaken in the preceding 10-year period (PRIOR).

c. Country’s Risk Variables.

When buyers have the intention of acquiring assets that are located in foreign countries, they anticipate that there will be greater challenges because of institutional and cultural differences. Stiglitz (2000) argues that the value of assets being exchanged can be reduced by the increased level of uncertainty in cross-border transactions and thus, decrease buyer value. Hence, the owners of the assets being sold might be less willing to accept equity from a foreign bidder. Moreover, if they consider equity as payment, they will value that equity at a discount price since they would need to monitor the buying firm in such a situation.

102 ownership with local partners because such behavior will shift some of the risk to local investors who can bear the risk in a less costly manner. If the foreign country has greater risk that has created more uncertainty surrounding the divested assets’ value, the buyer might ask the seller to share in the risk by forcing the target to accept more equity. I use the following control variables to control for the country risk characteristics:

Cross-border. To control for the cross-border effect, I use the variable

CROSSBORDER, which is a dummy variable equaling 1 if the transaction is listed as cross-border transaction and 0 otherwise.

Economic Freedom and Development. To control for the target country’s risk, I use the Economic Freedom Index. The Economic Freedom Index (see Gwartney et al.

FREEDOM is the natural logarithm of economic freedom rating of the target country in the year prior to the transaction.

Shareholder Rights. To control for the corporate governance of the target country,

I use the revised anti-director rights that introduced by Spamann (2010). This is an index of the rights that shareholders have with respect to the management team. The anti- director rights index is an index that aggregates shareholder rights. The index ranges from

103

0 to 5, in which a higher number reflects better shareholder protection. Following

Moeller and Schlingemann (2005), a dummy variable RIGHTS, which equals 1 if the anti-director rights index of the seller country is three or above and 0 otherwise, is used in the analysis.

4. Impact of Financial Advisors on Operating Performance Following Asset

Sell-offs

The wealth effect of asset sell-offs has been investigated extensively in the literature. However, most researchers focus on the short-term stock price reaction of the buyer and seller. The impact of asset sales on operating performance gains less attention from the literature. John and Ofek (2005) report an improvement in the operating performance of the seller following an asset sale. They attribute this improvement to the increase in the seller’s focus on the remaining assets. Their argument is that eliminating negative synergies between the divested and remaining assets will lead to better performance for the remaining assets after the transaction. On the other hand, Freund et al. (2003) find that the buyer experiences a decline in operating cash flow following the

104 purchase. The authors suggest that the buyer with fewer growth opportunities and large amounts of free cash flow might waste shareholder wealth by engaging in the asset sale transaction. Moreover, they also document a significant decrease in the return on assets and asset turnover ratios of the buyer during the three years after the transaction.

It seems that sellers generate greater long-term benefit as a result of the transaction, whereas buyers generate fewer long-term benefits. One of the main motivations for asset sell-off transactions is that the assets being purchased might be a better fit with the assets of buyers. Hence, buyers should not realize a decline in operating performance following the transaction. However, through time, investors realize that self- interested managers of buyers might overpay for these asset. This will lead to a decline in operating performance of the buyer.

In asset selloff transactions, buyers hire investment banks to help them correctly evaluate the fit and long-term synergies of the assets being purchased. The existence of investment banks as financial advisors in asset selloffs should have a positive impact on the operating performance of buyers. However, there is no study that assesses the influence of the hiring of investment banks on long-term operating performance.

Therefore, in this section, I attempt to empirically test this influence. Specifically, I investigate whether there is a difference in operating performance of buyers who hire investment banks and that of buyers who do not. By doing so, this study makes several contributions to the literature. First, it re-examines the operating performance of buyers in asset selloff transactions since this long-term performance gains little attention in asset selloff literature. Second, it examines the role of investment banks on the changes of 105 long-term operating performance of buyers in asset selloff transactions. I believe that this is the first study to investigate this aspect. In order to test my expectation, I use the same explanatory and control variables as in the second section.

5. Impact of Financial Advisors on Risk Shifts Following Asset Sell-offs

The risk shifts of bidders following acquisition transactions have been extensively investigated in the literature72. However, the role of investment banks has been neglected in the asset sell-off literature pertaining to risk shifts. In an asset sell-off, there is substantially more information asymmetry regarding the value of the assets being purchased in comparison to that of a merger target. Furthermore, the value of the divested assets is questionable since the valuation is based on the information provided by the seller. However, this information is easy to manipulate. Hence, the hiring of an investment bank as financial advisor in the asset sell-off should have a positive impact on the risk implication of the buyer subsequent to the transaction due to the certification role of investment banks.

In addition, Sicherman and Pettway (1992) claim that benefits for the buyer come from the benefits they can attract from the seller in the negotiation process. Therefore, because of the bargaining power of investment banks, I expect that the existence of investment banks in asset sell-offs will reduce the risk that buyers have to bear by asking the sellers to share the risk. In this section, I will test my expectation by focusing on the asset sell-off transactions.

72 See Davidson et al. (1987), Lubatkin and O’Neill (1987), Langetieg et al. (1980), Elgers and Clark (1980), Dodd (1980), Fatemi (1984), Amihud et al. (2002), and Gleason et al. (2005) for example. 106

We do not have a clear conclusion regarding the risk shifts of buyers after the asset sell-off transaction. One reason for this lack of attention might be the fact that, in asset sell-off transactions, the buyers focus on obtaining specific assets, not control of another firm. However, when asset buyers purchase large assets from another firm, such as subsidiaries, the risk shifts that are caused by these transactions should also be investigated since such transactions alter the corporate method of the buyer. For example, the post-transaction method of the buyer is more complicated than it was prior to the acquisition. Thus, this will create more opacity to understand the post-acquisition value of the buyer.

Moreover, if buyers use cash to acquire targets, they usually have higher leverage subsequent the transaction (Harford et al., 2009). In such a situation, investors will perceive that the transaction has some effects on the post-transaction risks of bidders.

Hence, investors might have a greater concern regarding the bankruptcy cost of buyers following asset sell-off transactions. In such situations, the existence of investment banks might significantly lower the concern of investors. Therefore, investment banks should have an important impact on the risk shifts of buyers following acquisitions of divested assets. . In order to test my expectation, I use the same explanatory and control variables as in the second section.

III. Methodology

Following Rau (2000), I measure the average market share of each investment bank as the percentage of the total value of transactions advised by investment banks in

107 any single year. In the spirit of Golubov et al. (2011), the top eight investment banks are classified as top-tier. The other investment banks are classified as non-top-tier. The rankings are stable across the sample period. In addition to the dummy variable that represents the existence of an investment bank, I use a continuous variable, which equals the percentage of the market share of a particular investment bank, as a robustness check.

1. Identifying Factors that Cause Buyers or Sellers to Hire Financial

Advisors

In order to find the significant determining factors in the decision to use investment banks in asset sell-offs, I employ logistic regression models. In each regression, the dependent variable is one if the buyer (seller) uses an advisor and zero otherwise.

P(buyer uses investment bank) = f(transaction costs, information asymmetry, contracting costs, country characteristics)

P(buyer uses top-tier investment bank) = f(transaction costs, information asymmetry, contracting costs, country characteristics)

P(seller uses investment bank) = f(transaction costs, information asymmetry, contracting costs, country characteristics)

108

P(seller uses top-tier investment bank) = f(transaction costs, information asymmetry, contracting costs, country characteristics)

When applying these models to my sample, the quasi-maximum likelihood

2. Testing Impact of Financial Advisors on Wealth Gains in Asset Sell-offs

The relationship between the choice of using an investment bank and the shareholder wealth is examined by investigating the change in the market value of equity of buyers around the announcement when they employ different investment banks. In this section, I use the standard event study methodology to compare the performance of investment banks. I use the market model for estimation, with an estimation period from t

=-300 to t=-46 days relative to the event day t = 0.

The following cross-sectional model with White’s correction for heteroscedasticity is used:

CARi = f(explanatory variables, control variables) where:

CARi is the cumulative abnormal returns for the buyer i in the event window (-1, +1) surrounding the announcement day t = 0.

109

3. Testing the Impact of Financial Advisors on the Method of Payment in

Asset Sell-offs

I apply instrumental variable Tobit multivariate models to investigate the weight of various explanatory and control variables on the method of payment decision in asset sell-offs. In the Tobit regression models, the dependent variable is the equity proportion of the payment for the sell-off transaction, which must be in the interval [0, 100]. In order to minimize the effect of endogeineity, all explanatory variables are instrumented by the

SCOPE variable, which takes the value of one if, in the ten years prior to the transaction, the bidder employed an investment bank at least once for an sell-off transaction and 0 otherwise. In my sample, all values of the dependent variable are within the [0, 100] interval. I apply the model:

* ’ yi = βXi + ui

* * where yi = yi if 0 < yi < 100,

’ Xi is the vector of explanatory and control variables

ui is an independently distributed error term assumed to be normal with zero mean and variance

When applying this model to the sample, Newey’s two-step estimator is used to correct for heteroscedasticity. For each hypothesis of a characteristic that I believe affects the proportion of cash used versus stock used, an independent variable is used to proxy for that characteristic.

110

4. Testing the Impact of Financial Advisors on Operating Performance

Following Asset Sell-offs

To investigate the impact of investment banks on the operating performance of buyers, I use the following cross-sectional model with White’s correction for heteroscedasticity:

ΔOPi,j = f(explanatory variables, control variables) where:

ΔOPi,j is the change in operating performance of buyer i in j years after the transaction (j = 1 and 2).

5. Testing the Impact of Financial Advisors on Risk Shifts Following Asset

Sell-offs

In order to test the impact of the hiring of an investment bank on the risk shifts of the buyer when purchasing divested assets, I define total risk as the variance of the buyer’s stock returns, idiosyncratic risk as the variance of residuals of the market model of the buyer’s stock, and systematic risk as the beta in the market model of the buyer’s stock. Moreover, I also define the change in total risk of the buyer as total risk after the

111 transaction subtracted by the total risk before the transaction and the change in idiosyncratic risk of the buyer as idiosyncratic risk after the transaction subtracted by the idiosyncratic risk before the transaction. I estimate the shift in systematic risk by using the market model with time dummies to determine the change in beta.

Rit= αi + βi * Rmt+ β΄i * Rmt* D1 + εit where

Rit is the return for buyer i on day t

βi is the systematic risk for buyer i

Rmt is the return on a market index on day t

β΄i is the shift in systematic risk for buyer i

D1 is the dummy variable equal to 1 on dates after the transaction, 0

otherwise.

To investigate the impact of investment banks on the risk shifts of buyers, I use the following model in the cross-sectional analysis with White’s correction for heteroscedasticity:

RISKi = f(explanatory variables, control variables) where:

RISKi is either the change in total risk, the change in idiosyncratic risk, or the shift in systematic risk of buyer i. 112

IV. Sample

My initial sample consists of all asset sell-offs from January 1992 to December

2010. I obtain the observations from Thomson Financial Securities Data’s SDC data base that satisfy several screening criteria. First, buyers must be U.S. publicly traded corporations. However, there is no restriction on the public status and located country for the seller. Second, only successful transactions that have value greater than $1 million and are worth more than 5 percent of the market value of equity of the bidders are investigated. Finally, I eliminate all transactions that belong to regulated industries.

I use the SDC database to collect the various characteristics of each transaction. In addition, the Center for Research in Security Prices (CRSP) and COMPUSTAT are also used to collect other financial variables for the transactions. The final sample consists of

809 transactions. Table XIV reports definitions of the variables that are being used in this paper.

The information regarding the anti-director index is from La Porta et al.(1997) and Spamann (2010), while the economic freedom rating is from the Heritage website73.

The origin of the legal system is from the Yale law school website74.

V. Data Description and Results of Univariate Analysis

Table XV provides some useful information regarding the sample. The transaction costs variables are similar when comparing the subsample with an investment

73 http://www.heritage.org/index/default 74 http://library.law.yale.edu/foreign_resources?quicktabs_4=2 113 bank to the subsample without an investment bank, with the exception of the transaction value variable. The mean transaction values in deals with the existence of an investment bank are higher compared to that in deals without the existence of an investment bank.

The mean (median) of the transaction value in the subsample of buyers with an investment bank is $324.95 mil ($117.6 mil); whereas the mean (median) of the transaction value in the subsample of buyers without an investment bank is $48.52 mil

($30.56 mil). On the seller side, the mean (median) of the transaction value in the subsample of sellers with an investment bank is $226.63 mil ($88.50 mil); whereas the mean (median) of the transaction value in the subsample of sellers without an investment bank is $53.82 mil ($28.27 mil). The results indicate that both buyers and sellers only hire an investment bank when they deal with large value transactions.

Regarding the information asymmetry variables, the subsample with an investment bank and the subsample without an investment bank have comparable buyer’s

Tobin Q ratios, similarity in SIC code between buyers and sellers, and the hi-tech status of buyers/sellers. However, the relative size of the transaction value to the buyer’s market value of the transactions with the existence of an investment bank is bigger than that of the transactions without the existence of an investment bank. Both buyers and sellers are more careful in cases of high economic implication transactions. Turning to the contracting costs and country characteristics variables, there is not a high discrepancy between the two subsamples. However, when the buyer (seller) hires an investment bank, there is a greater chance that the seller (buyer) will hire an investment bank.

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VI. Results of Multivariate Analysis

1. Factors that Cause Buyers or Sellers to Hire Financial Advisors in Asset

Sell-Offs

Table XVI is a correlation matrix of my independent variables. There is considerable correlation among my country characteristic variables. Thus, I will examine one variable at a time.

Table XVII reports the results of multivariate Logit and Tobit regression models testing the factors that affect the decision to hire an investment bank of buyers and sellers in assets sell-off transactions. The first two columns show the factors that influence the decision of the buyer. Among the transaction costs variables, ALLCASH and SIZE are statistically significant. The negative coefficient of ALLCASH indicates that when buyers use a 100 percent cash payment, they are less likely to use an investment bank; whereas the positive coefficient of SIZE suggests that buyers are more likely to use an investment bank when transactions are high in value. Moreover, the results imply that buyers have a tendency to use a top-tier investment bank when they buy larger assets.

Moving to the information asymmetry variables, hi-tech buyers are more likely to hire an investment bank when acquiring divested assets as evidenced by the positive coefficients for TECHBUYER, 0.46 and 0.76 in model 1 and model 2, respectively.

RELSIZE is positively significant in model 1. However, it is not significant in model 2.

The results imply that RELSIZE is not a significant factor in the decision to hire a top-tier investment bank by the buyers.

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Turning to the contracting costs variable, the variable SELLERIB is always positively significant, implying that the probability of hiring an investment bank on the buyer’s side is positively influenced by the decision of hiring an investment bank on the seller’s side. The variable LEVERAGE is insignificant in model 1 but positively significant in model 2. When buyers have high bankruptcy risk, they have a tendency to use a top-tier investment bank to advise them in the transaction. The results also show that the country characteristics variables have limited impact on the decision to use an investment bank by buyers. Only the variable CROSSBORDER is marginally significant in the first model75.

Model 3 and 4 report the factors that influence the decision of the sellers.

Regarding the seller’s decision to hire an investment bank, the variable SIZE is positively significant, indicating that sellers also value the assistance of investment banks when they sell large assets. Sellers are less likely to hire an investment bank when they sell assets to buyers who operate in the same industry as shown by the negatively significant variable

RELATED. Moreover, the variable BUYERIB is also positively significant. This indicates that, similar to the results from the first two models, the probability of hiring an investment bank on the seller’s side is positively influenced by the decision of hiring an investment bank on the buyer’s side. This finding supports Forte et al. (2010) who report that the decision to hire an advisor of the target depends on the reputation of the bidder

75 I only have information about insider ownership of the buyer for 200 observations. I run separate regression models with OWNERSHIP variable. The results show that insider ownership of the buyer does not have any impact on the decision to hire an investment bank of both the buyer and the seller. 116 company’s advisor. Moreover, the country characteristics variables have no impact on the decision to use an investment bank by sellers.

In model 4, I investigate the factors that influence the decision to hire a top-tier investment bank by sellers. SIZE and BUYERIB are still positively significant. However, unlike the results from model 3, the results from model 4 show that the variable

CREDITCRISIS has a significant impact on the decision to hire a top-tier investment bank by sellers. The results indicate that sellers are less likely to employ a top-tier investment bank during a credit constraint period. This is consistent with the argument from Rau (2000) that hiring a top-tier investment bank is very expensive and sellers might not want to spend that much during a credit constraint period.

Regarding the power of the Logit regressions, the McFadden’s R-squares of the four models are 27.58%, 34.13%, 18.21%, and 23.19%, respectively. Moreover, the likelihood ratio indicates that all the models are significant at the 1 percent level. The above results indicate that the transaction costs, information asymmetry, contracting costs, and country characteristics are jointly have good explanatory power.

In the last two models, I run Tobit regressions with dependent variables that represent the market shares (reputation) of the buyer’s and the seller’s investment banks.

My results reinforce the findings in the first four models. As an additional robustness check, I run the ordered Probit regression for the decision to hire an investment bank of

117 the bidder and the target. The results hold in the ordered Probit regressions76.

2. Impact of Financial Advisors on Wealth Gains in Asset Sell-offs

In this section, I use cross-sectional regression models to investigate the relationship between the decision to hire an investment bank as well as an investment bank reputation and buyers’ CAR77. I control for various bidder-, target-, deal-, and country-specific characteristics that potentially affect the buyer’s return. Table XVIII reports the results in this section.

Prior literature reports that an investment bank as financial advisor has limited impact on the wealth effect of an acquisition. In this section, I find that when a buyer hires an investment bank, there is a positive impact on the CAR of the buyer. The coefficient of BUYERIB is positively significant and has a value of 0.03. If we hold all other factors constant, the buyer who hires an investment bank realizes a 3 percent higher

CAR compared to the buyer who does not hire an investment bank.

On the other hand, when the seller hires an investment bank, there is evidence that the wealth gains of the buyer are reduced. The coefficient of SELLERIB is significant and has a value of -0.02. If we hold all other factors constant, the buyer’s CAR is reduced by 2 percent when the seller hires an investment bank. We might argue that the

76 In the ordered Probit regression for the buyer’s choice, the dependent variable equals 2 if the buyer hires a top-tier investment bank, equals 1 if the buyer hires a secondary-tier investment bank, and equals 0 if the buyer does not hire any investment bank. In the ordered Probit regression for the seller’s choice, the dependent variable equals 2 if the seller hires a top-tier investment bank, equals 1 if the seller hires a secondary-tier investment bank, and equals 0 if the seller does not hire any investment bank. 77 The buyers who hire an investment bank as well as the buyers who do not hire an investment bank realize positive and significant CARs. 118 bargaining power of the investment bank helps the seller in exploiting higher benefits from the buyer.

The above results are supported by the significant coefficients of

YESBUYNOSEL and NOBUYYESSEL. These coefficients have the value of 0.04 and -

0.02, respectively. If we hold all other factors constant, the buyer realizes a 4 percent higher CAR when the buyer uses an investment bank and the seller does not use an investment bank; whereas the buyer’s CAR is 2 percent lower when the buyer does not use an investment bank and the seller uses an investment bank. These results indicate that using an investment bank as financial advisor is an important source in creating value in asset sell-off transactions. Moreover, the R-squares of the regression models have values ranging from 3.82 percent to 4.51 percent, suggesting that the models have reasonable power to explain the wealth effect on the buyer.

Interestingly, the benefits of using investment banks disappear when the buyer and seller hire a top-tier investment bank. I find that the reputation of the investment bank does not have any impact on the wealth effect of the bidder. The coefficients of top-tier investment banks and market shares of investment banks are not significant. These results suggest that the top-tier investment banks might not bring benefits to their clients in the market for asset sell-offs. Rau (2000) argues that top-tier investment banks focus more on the completion of the transaction, rather than on bringing the most benefits possible for their clients. These results support Rau’s argument. A less prestigious but more focus investment bank might be the best choice for the buyer and seller who participate in the market for asset sell-offs. 119

Among the control variables, I find that the method of payment, hi-tech status, leverage, and the cash holdings of the buyer have a significant influence on the wealth effect. ALLCASH and LEVERAGE are negatively significant; while TECHBUYER and

CASHHOLDINGS are positively significant. The buyer realizes a lower CAR when using all cash as payment to finance their investment and when having a high level of outstanding debt. On the other hand, the buyer has a higher CAR if it belongs to a hi-tech industry and has an abundance of cash on hand. Moreover, the country characteristics variables do not have any impact on the wealth effect of the buyer78.

Golubov et al. (2012) suggest that the decision regarding investment banks could be determined endogenously. Thus, the self-selection bias could emerge. Heckman

(1979) argues that the self-selection bias is similar to the omitted variable bias and suggests a two-step procedure to control for this bias. I apply the Heckman two-step procedure on my sample. In the first stage of the procedure, I run a Logit regression to model the decision to hire an investment bank. In the second stage, I run an OLS regression with a correction for this selection bias. Similar to Golubov (2012), I find that the selection term in the second stage (the Inverse Mill’s ratio) is insignificant at any conventional level, indicating that the coefficient estimates in table XVIII are reliable.

3. Impact of Financial Advisors on the Method of Payment in Asset Sell-offs

In this section, I investigate the impact of using an investment bank on the method of payment by employing Tobit regression models. It should be emphasized that the

78 The results for FREEDOM and COMMON are the same with the result for RIGHTS. For expository reasons, I only report the result for RIGHTS. 120 decision to hire an investment bank could be determined endogenously with the existence of equity as payment. In fact, in the first section, I report that the existence of equity as payment affects the decision to use an investment bank in the transaction. To correct for the endogeneity, I use the instrumental variable Tobit regression in the analysis. In the spirit of Golubov et al. (2011), I create the instrumental dummy variable, SCOPE, for this purpose. This variable should have an influence on the hiring of an investment bank but not on the outcome of the transaction. SCOPE takes the value of one if in the ten years prior to the transaction the buyer employed an investment bank at least once in an asset sell-off transaction and 0 otherwise.

Table XIX shows the results from applying the instrumental variable Tobit model to the sample. The dependent variable is measured as the percentage of the transaction that is paid in cash. The results show that, after controlling for endogeneity, the decision to use an investment bank of the buyer and seller do not have any impact on the method of payment in an asset sell-off transaction. The results indicate that the method of payment in the assets sell-off transaction is not influenced by the existence of an investment bank. As robustness tests, table XX reports the impact of YESBUYNOSEL,

NOBUYYESSEL, and IBRELSHARE on the proportion of cash as payment in the transactions. Consistent with the above results, none of these variables is significant at a conventional level.

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4. Impact of Financial Advisors on Operating Performance Following Asset

Sell-offs

Following Heron and Lie (2002), I measure operating performance as operating income scaled by sales. Table XXI contains six OLS regression models explaining the change in the ratio of the adjusted operating income to sales from the year before to the year after the acquisition. Table XXII reports six OLS regression models explaining the change in the ratio of the adjusted operating income to sales from the year before to two years after the acquisition.

The results show no evidence that the presence of an investment bank on either the buyer side or the seller side has an impact on the operating performance of the buyer.

Hence, hiring an investment bank will not help improving the operating performance.

5. Impact of Financial Advisors on Risk Shifts Following Asset Sell-offs

I find that there are significant changes in risks of the bidders around the transactions. Specifically, there is a significant increase in unsystematic and total risks of the bidder post-transactions. My main goal is to investigate the impact of financial advisors on the risk shifts of the bidder. Thus, table XXIII, XXIV, and XXV reports the results of the OLS regression models explaining the impact of the existence of an investment bank on the shifts in systematic, unsystematic, and total risk of the buyer after the transactions, respectively. In these regression models, the dependent variables are the shifts in the risks of the buyer.

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The OLS regression models show that the hiring of an investment bank does not have any impact on the shift in the systematic risk of the buyer. After controlling for the involvement of investment banks, the hi-tech status of the seller, leverage, Tobin’s Q ratio, leverage, interest coverage ratio of the buyer, and the crisis period have a significant impact on the shift in the systematic risk of the buyer. The negative coefficients of TECHSELLER and INTCOVERAGE indicate that, when the buyer purchases assets from a hi-tech seller or when the buyer has good financial strength, the increase in systematic risk post transactions of the buyer is lower. On the other hand, the positive coefficients of LEVERAGE, TOBINQ, and CREDITCRISIS suggest that the increase in systematic risk post transaction of the buyer is higher when the buyer has a higher level of debt or higher growth opportunities. Furthermore, the level of increase in systematic risk of the buyer is even higher if the purchase takes place during a credit constraint period.

Interestingly, the hiring of an investment bank does have some impact on the shift in the unsystematic and total risks of the buyer. While the existence of an investment bank on the buyer side has no implication on the shift in risks, the existence of an investment bank on the seller side increases the risk of the buyer post transaction. Similar to the case of the shift in systematic risk, the interest coverage ratio of the buyer and the crisis period have a significant impact on the shift in the unsystematic and total risks of the buyer.

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VII. Conclusion

The role of investment bank in asset sell-off has been ignored in academic literature. Using a sample of asset sell-off transactions from January 1992 to December

2010, I investigate the factors that influence the decision to hire investment banks and the impact they might have on the outcome of these transactions.

Regarding the decision to hire an investment bank in asset sell-off transactions, I find unique results for the factors that affect the decision. On the buyer side, I find that the previous experience of the buyer does not have any impact on the decision to hire an investment bank. However, when the seller hires an investment bank, the buyer is more likely to as well. Moreover, I report that a hi-tech buyer is more likely to employ an investment bank. The information asymmetry issue is more severe for the hi-tech buyer; thus, it needs more assistance from investment banks to assess the fit of the assets being purchased to its current operation. Other than these results, I find similar results for asset sell-off transactions compared to acquisitions of public targets. The involvement of equity in the payment, size, and relative size of the transaction do have an impact on the decision to employ an investment bank of the buyer. On the seller side, the results show that the seller is more likely to hire an investment bank if the buyer has an investment bank advisor, when the size of the transaction is large, and when the transaction is between businesses in different industries.

There are fundamental differences between the services from top-tier investment banks and non-top-tier investment banks, such as fees and the expected outcome of the

124 transaction. Thus, the factors that influence the decision to hire a top-tier investment bank may be different than those that affect the decision to hire a non-top-tier investment bank in asset sell-off transactions. For the decision of the seller, I find that the access to the credit market is the main reason for the difference between the decision of hiring top-tier and non-top-tier investment banks. When the credit market is tight, the seller avoids the expensive services of a top-tier investment bank. Nevertheless, I find that the factors that affect the decision to hire a non-top-tier or a top-tier investment bank of the buyer are similar.

I also find that the existence of an investment bank on either the buyer or seller side has an impact on the wealth effect of the buyer. The buyer realizes a significantly higher CAR when it employs an investment bank. On the other hand, the buyer has a significantly lower CAR when the seller uses an investment bank. However, the benefits disappear when the buyer or seller employs a top-tier investment bank. Rau (2000) argues that top-tier investment banks involved in merger transactions focus more on the completion of the transaction, rather than on bringing the most benefit possible for their clients. My results support Rau’s argument.

I also find that the existence of an investment bank on either the buyer or seller side does not have any impact on the method of payment or the operating performance, post-transaction, of the buyer. However, I find some evidence that an investment bank has an impact on the risks of the buyer post- transaction. While the investment bank on the buyer side has no impact on risk shifts, the investment bank on the seller side significantly influences the risk shifts of the buyer. Specifically, when the seller employs 125 an investment bank, the increase in unsystematic and total risks of the buyer is greater than in cases when the seller does not use an investment bank.

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Table I

Definition of Variables

Summary of the variables that are used in the paper

Variable Name Variable Definition BIDDERIB equals 1 if there is the use of an investment bank by the buyer and 0 otherwise TARGETIB equals 1 if there is the use of an investment bank by the target and 0 otherwise BIDTOPIB equals 1 if there is at least 1 top tier investment bank advising the buyer and 0 otherwise TARTOPIB equals 1 if there is at least 1 top tier investment bank advising the target and 0 otherwise BIDIBSHARE the market share of the most reputable investment bank advising the buyer TARIBSHARE the market share of the most reputable investment bank advising the target YESBIDNOTAR equals 1 if the buyer uses at least one and the seller does not use an investment bank, 0 otherwise NOBIDYESTAR equals 1 if the buyer does not use and the seller uses at least one investment bank, 0 otherwise RELIBSHARE the difference between the market share of the most reputable investment bank advising the buyer and the most reputable investment bank advising the target CAR (-1, +1) cumulative abnormal return of the bidding firm’s stock in the three-day event window (-1, +1) where 0 is the announcement day. The returns are calculated using the market model with the market model parameters estimated over the period starting 180 days and ending 45 days prior to the announcement. MP the industry adjusted ratio of offer price to book value of the target the industry adjusted change in operating income scaled by sales of the bidder RISK the risk-shifts of the bidder following the transaction (shifts in either systematic risk, unsystematic risk, or total risk) EQUITYINV the percentage of equity proportion in the payment EQUITYPMT equals 1 if equity is used as a proportion of the method of payment SIZE the natural logarithm of the transaction value EXPERIENCE equals 1 if the bidder acquires a least a private target in the last 10 years, 0 otherwise RELATED equals 1 if the bidder and seller have the same four-digit SIC code, 0 otherwise TECHBIDDER equals 1 if the bidder is a high-tech firm, 0 otherwise TECHTARGET equals 1 if the target is a high-tech firm, 0 otherwise RELSIZE the relative size of the transaction to bidder's market value of equity, as of four weeks prior to the announcement LEVERAGE the bidder’s total liabilities divided by market value of equity, as of four weeks prior to the announcement TOBINQ Tobin Q’s ratio of the bidder. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis (from Q1/2001 to Q4/2002 and from Q3/2007 to Q4/2010) ZSCORE the Altman Z-Score of the bidder INTCOVERAGE interest coverage ratio of the bidder CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise FREEDOM natural logarithm of the economic freedom rating of the seller country RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise COMMON equals 1 if the seller is from a country with a common law system, 0 otherwise

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Table II

Descriptive Statistics

This table provides the descriptive statistics for the samples that are used in the paper. EQUITYPMT is a dummy variable equals 1 if equity is used as a proportion of the payment, 0 otherwise. SIZE is the total asset of the bidder. PRIOR equals 1 if the bidder acquires at least 1 private target in the previous 10-year, 0 otherwise. CASHHOLDINGS is the bidder’s cash holdings scaled by total assets. LEVERAGE is the debt ratio of the bidder. INTERESTRATIO is the interest coverage ratio of the bidder. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. RELATED equals 1 if the bidder and seller have the same four-digit SIC code, 0 otherwise. TECHBIDDER equals 1 if the acquirer is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to bidder’s market value of equity, as of four weeks prior to the announcement. TOBINQ is the Tobin Q’s ratio of the bidder. TECHTARGET equals 1 if the seller is a high-tech firm, 0 otherwise. OWNERSHIP is the percentage of the ownership of insiders. ZSCORE is the Altman Z score of the bidder. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. FREEDOM is the natural logarithm of the economic freedom rating of the target country. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. COMMON equals 1 if the seller is from a country with a British legal tradition, 0 otherwise. BIDDERIB equals 1 of the bidder hires an investment bank, 0 otherwise. TARGETIB equals 1 of the target hires an investment bank, 0 otherwise. BIDTOPIB equals 1 of the bidder hires a top-tier investment bank, 0 otherwise. TARTOPIB equals 1 of the target hires a top-tier investment bank, 0 otherwise. YESBIDNOTAR equals 1 of the bidder hires an investment bank and the target does not hire an investment bank, 0 otherwise. NOBIDYESTAR equals 1 of the bidder does not hire an investment bank and the target hires an investment bank, 0 otherwise. BIDIBSHARE is the market share of the bidder’s investment bank. TARIBSHARE is the market share of the target’s investment bank. RELIBSHARE is the difference between the market share of the bidder’s investment bank and the market share of the target’s investment bank.

Bidders with Investment Bidders without Investment Targets with Investment Targets without Investment All Sample Banks Bank Banks Bank Mean Median Mean Median Mean Median Mean Median Mean Median

128 EQUITYPMT 0.5 0.5 0.55 1 0.47 0 0.5 1 0.5 1 PRIOR 0.21 0 0.21 0 0.21 0 0.24 0 0.2 0

SIZE 144.69 40.03 261.19 93.82 66.09 21.79 220.24 118.81 70.75 22.54 RELATED 0.36 0 0.4 0 0.34 0 0.39 0 0.34 0 TECHBIDDER 0.45 0 0.51 0 0.41 0 0.48 0 0.44 0 TECHTARGET 0.41 0 0.46 0 0.37 0 0.46 0 0.38 0 TOBINQ 5.09 1.79 5.78 2.05 4.62 1.58 5.74 1.92 4.73 1.71 RELSIZE 0.28 0.15 0.34 0.19 0.24 0.13 0.3 0.17 0.27 0.14 OWNERSHIP 0.05 0.01 0.04 0.01 0.06 0.02 0.05 0.01 0.06 0.02 ZSCORE 1.85 2.95 2.77 3 1.22 2.91 1.96 2.86 1.79 2.97 CASHHOLDINGS 0.17 0.09 0.17 0.09 0.18 0.09 0.17 0.08 0.18 0.1 LEVERAGE 0.47 0.37 0.41 0.34 0.5 0.37 0.4 0.35 0.48 0.33 INTERESTRATIO -46.36 2.86 -40 2.78 -17.76 1.37 -7.28 1.57 -7.18 1.14 CREDITCRISIS 0.24 0 0.23 0 0.24 0 0.25 0 0.23 0 CROSSBORDER 0.11 0 0.12 0 0.11 0 0.12 0 0.11 0 FREEDOM 4.35 4.36 4.35 4.35 4.35 4.35 4.35 4.35 4.35 4.35 RIGHTS 0.77 1 0.77 1 0.76 1 0.77 1 0.77 1 COMMON 0.95 1 0.96 1 0.96 1 0.97 1 0.95 1 BIDDERIB 0.4 0 1 1 0 0 0.63 1 0.28 0 TARGETIB 0.35 0 0.55 1 0.22 0 1 1 0 0 BIDTOPIB 0.19 0 0.48 0 0 0 0.34 0 0.11 0 TARTOPIB 0.14 0 0.28 0 0.05 0 0.41 0 0 0 YESBIDNOTAR 0.18 0 0.45 0 0 0 0 0 0.28 0 NOBIDYESTAR 0.13 0 0 0 0.22 0 0.37 0 0 0 BIDIBSHARE 4.75 0 11.79 11.6 0 0 8.47 0.35 2.72 0

TARIBSHARE 3.3 0 6.22 0 1.38 0 9.42 1.7 0 0 RELIBSHARE 1.43 0 5.58 1.15 -1.38 0 -0.95 -0.1 2.72 0 NUMBER OF OBSERVATION 1122 1122 452 452 670 670 396 396 726 726

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Table III

Correlation Matrix

EQUITYPMT equals 1 if equity is used as a proportion of the method of payment. SIZE is the natural logarithm of total assets of the bidder. PRIOR equals 1 if the bidder acquires a least a private target in the last 10 years, 0 otherwise. RELATED equals 1 if the bidder and seller have the same four-digit SIC code, 0 otherwise. TECHBIDDER equals 1 if the acquirer is a high- tech firm, 0 otherwise. TECHTARGET equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to bidder's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the bidder’s debt ratio. TOBINQ is the Tobin Q’s ratio of the bidder. CASHHOLDINGS is the cash holdings of the bidder scaled by bidder's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the bidder. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the bidder. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. FREEDOM is the natural logarithm of the economic freedom rating of the target country in the year prior to the transaction. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. COMMON equals 1 if the target country is a common law country, 0 otherwise.

EQUITY PRIOR INT CASH TECH TECH CREDIT CROSS SIZE PMT LEVERAGE COVERAGE HOLDINGS RELATED BIDDER TARGET RELSIZE TOBINQ ZSCORE CRISIS BORDER FREEDOM RIGHTS COMMON SIZE 1 EQUITYPMT -0.28 1 PRIOR 0.11 -0.05 1 LEVERAGE -0.11 -0.03 0.05 1 INTCOVERAGE 0.03 -0.01 0.01 0.001 1 CASHHOLDINGS 0.01 0.04 0.001 -0.02 0.03 1 RELATED 0.08 -0.02 0.02 -0.02 0.02 -0.01 1

130 TECHBIDDER -0.09 0.20 0.17 -0.05 -0.03 0.06 0.08 1 TECHTARGET -0.10 0.19 0.16 -0.04 0.01 0.05 0.16 0.63 1

RELSIZE -0.26 0.14 -0.04 0.08 0.01 -0.06 -0.02 0.05 0.06 1 TOBINQ -0.20 0.13 -0.04 0.04 0.003 -0.06 -0.01 0.09 0.08 0.88 1 ZSCORE 0.23 0.08 -0.07 -0.76 0.01 -0.003 0.05 0.01 -0.03 -0.19 -0.14 1 CREDITCRISIS 0.16 -0.11 -0.01 -0.01 0.02 0.13 -0.03 -0.07 -0.07 -0.03 -0.06 -0.01 1 CROSSBORDER 0.04 0.03 -0.05 -0.01 -0.03 0.04 -0.05 0.04 -0.01 -0.04 -0.04 0.001 0.05 1 FREEDOM -0.11 -0.01 0.03 0.01 0.09 0.001 0.05 0.02 0.06 0.02 0.02 -0.004 -0.05 -0.38 1 RIGHTS 0.05 0.04 -0.03 -0.01 -0.06 0.02 -0.06 0.03 -0.01 -0.04 -0.04 -0.001 0.07 0.93 -0.48 1 COMMON -0.06 -0.05 0.06 0.01 -0.02 -0.03 0.05 -0.02 0.03 0.03 0.02 0.001 -0.04 -0.56 0.62 -0.56 1

Table IV

Logit and Tobit Regression Explaining the Decision to Hire Investment Banks in Acquisition of Private Targets

The estimation is based on a Logit and Tobit regression models. The z-stats are based on QML (Huber/White) heteroskedasticity-consistent standard errors. BIDDERIB equals 1 if the bidder uses an investment banks, 0 otherwise. TARGETIB equals 1 if the target uses an investment banks, 0 otherwise. BIDTOPIB equals 1 if the bidder uses a top tier investment banks, 0 otherwise. TARTOPIB equals 1 if the target uses a top tier investment banks, 0 otherwise. BIDIBSHARE is the market share of the bidder’s investment bank in the previous year. TARIBSHARE is the market share of the target’s investment bank in the previous year. YESBIDNOTAR equals 1 if the bidder uses an investment banks and the target does not use an investment banks, 0 otherwise. NOBIDYESTAR equals 1 if the bidder does not use an investment banks and the target uses an investment banks, 0 otherwise. EQUITYPMT equals 1 if equity is used as a proportion of the method of payment. SIZE is the natural logarithm of total assets of the bidder. PRIOR equals 1 if the bidder acquires a least a private target in the last 10 years, 0 otherwise. RELATED equals 1 if the bidder and seller have the same four-digit SIC code, 0 otherwise. TECHBIDDER equals 1 if the acquirer is a high-tech firm, 0 otherwise. TECHTARGET equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to bidder's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the bidder’s debt ratio. TOBINQ is the Tobin Q’s ratio of the bidder. CASHHOLDINGS is the cash holdings of the bidder scaled by bidder's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the bidder. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the bidder. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. ∗∗∗, ∗∗, and ∗ indicate statistical significance at 0.01, 0.05, and 0.10 level, respectively.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 (BIDDERIB) (BIBTOPIB) (TARGETIB) (TARTOPIB) (BIDIBSHARE) (TARIBSHARE) Variable Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Intercept -2.68 (-8.96)*** -4.43 (-12.05)*** -3.50 (-12.33)*** -4.98 (-12.73)*** -33.04 (-12.28)*** -34.77 (-13.57)*** 131 EQUITYPMT 0.01 (3.73)*** 0.01 (3.24)*** 0.004 (2.06)** 0.01 (3.66)*** 0.07 (4.18)*** 0.06 (3.27)*** PRIOR -0.33 (-1.96)** -0.22 (-1.04) -0.08 (-0.48) -0.38 (-1.61) -2.43 (-1.39) -2.13 (-1.32) SIZE 0.47 (8.83)*** 0.57 (9.52)*** 0.61 (11.23)*** 0.66 (10.01)*** 5.50 (12.93)*** 5.62 (12.49)*** RELATED 0.14 (0.98) 0.42 (2.43)** 0.10 (0.69) 0.17 (0.87) 3.04 (2.13)** 1.23 (0.88) TECHBIDDER 0.38 (2.22)** 0.31 (1.39) 0.12 (0.68) -0.11 (-0.41) 3.53 (2.07)** -1.10 (-0.68) TECHTARGET 0.15 (0.87) 0.45 (2.01)** 0.31 (1.76)* 0.67 (2.75)*** 2.49 (1.45) 4.71 (2.90)*** TOBINQ -0.01 (-0.32) 0.001 (0.16) -0.01 (-0.95) -0.01 (-1.77)* 0.04 (2.05)** 0.02 (0.75) RELSIZE 0.13 (0.92) 0.07 (2.36)** 0.10 (1.40) 0.10 (3.10)*** 0.15 (2.14)** 0.14 (1.94)** BIDDERIB 1.18 (7.92)*** 1.62 (7.31)*** 11.78 (9.00)*** TARGETIB 1.15 (7.63)*** 0.97 (5.32)*** 11.43 (8.04)*** ZSCORE 0.01 (0.87) -0.01 (-0.62) -0.02 (-2.45)** -0.001 (-0.07) 0.06 (0.65) -0.13 (-3.70)*** LEVERAGE -0.13 (-0.45) -0.25 (-0.89) -0.16 (-1.60) -0.15 (-0.38) -2.89 (-1.11) -0.83 (-1.17) CREDITCRISIS -0.21 (-1.32) -0.45 (-2.32)** -0.27 (-1.60) -0.56 (-2.29)** -4.29 (-2.71)*** -3.56 (-2.25)** CROSSBORDER 0.33 (0.80) -0.08 (-0.15) 0.62 (1.40) 1.12 (2.49)** 1.49 (0.42) 7.59 (2.10)** RIGHTS -0.21 (-0.46) -0.10 (-0.17) -0.83 (-1.64) -1.97 (-3.13)*** -1.49 (-0.36) -10.38 (-2.51)** No. Obs 1078 1078 1078 1078 1078 1078 McFadden's R2 9.75% 13.81% 12.73% 15.88% 12.25% 12.02%

Table V

OLS Regression Explaining the Wealth Effect of Bidders in Acquisitions of Private Targets

The estimation is based on a Least Square model. The t-stats are based on White heteroskedasticity-consistent standard errors & covariance. BIDDERIB equals 1 if the bidder uses an investment banks, 0 otherwise. TARGETIB equals 1 if the target uses an investment banks, 0 otherwise. BIDTOPIB equals 1 if the bidder uses a top tier investment banks, 0 otherwise. TARTOPIB equals 1 if the target uses a top tier investment banks, 0 otherwise. BIDIBSHARE is the market share of the bidder’s investment bank in the previous year. TARIBSHARE is the market share of the target’s investment bank in the previous year. YESBIDNOTAR equals 1 if the bidder uses an investment banks and the target does not use an investment banks, 0 otherwise. NOBIDYESTAR equals 1 if the bidder does not use an investment banks and the target uses an investment banks, 0 otherwise. EQUITYPMT equals 1 if equity is used as a proportion of the method of payment. SIZE is the natural logarithm of total assets of the bidder. PRIOR equals 1 if the bidder acquires a least a private target in the last 10 years, 0 otherwise. RELATED equals 1 if the bidder and seller have the same four-digit SIC code, 0 otherwise. TECHBIDDER equals 1 if the acquirer is a high-tech firm, 0 otherwise. TECHTARGET equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to bidder's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the bidder’s debt ratio. TOBINQ is the Tobin Q’s ratio of the bidder. CASHHOLDINGS is the cash holdings of the bidder scaled by bidder's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the bidder. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the bidder. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. ∗∗∗, ∗∗, and ∗ indicate statistical significance at 0.01, 0.05, and 0.10 level, respectively.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept 0.13 (0.42) 0.18 (0.58) 0.18 (0.60) 0.17 (0.54) 0.18 (0.59) 0.20 (0.65)

132 BIDDERIB -0.001 (-0.03) TARGETIB -0.02 (-1.96)**

BIDTOPIB -0.01 (-0.58) TARTOPIB -0.01 (-0.80) BIDIBSHARE -0.001 (-1.00) TARIBSHARE -0.001 (-0.33) YESBIDNOTAR 0.01 (1.01) NOBIDYESTAR -0.01 (-0.69) RELIBSHARE -0.001 (-0.52) SIZE -0.01 (-2.07)** -0.01 (-2.32)** -0.01 (-2.32)** -0.01 (-3.16)*** -0.01 (-3.05)*** -0.01 (-3.12)*** EQUITYPMT 0.001 (0.99) 0.001 (0.95) 0.001 (0.97) 0.001 (1.22) 0.001 (1.19) 0.001 (1.18) PRIOR 0.001 (0.10) 0.001 (0.05) 0.001 (0.07) 0.001 (0.14) 0.002 (0.18) 0.001 (0.15) RELATED -0.002 (-0.25) -0.002 (-0.25) -0.002 (-0.22) -0.003 (-0.33) -0.002 (-0.32) -0.002 (-0.30) TECHBIDDER -0.02 (-1.53) -0.02 (-1.55) -0.02 (-1.51) -0.02 (-1.63) -0.02 (-1.59) -0.02 (-1.54) TECHTARGET -0.003 (-0.30) -0.002 (-0.23) -0.003 (-0.25) -0.004 -0.39 -0.003 (-0.35) -0.004 (-0.36) RELSIZE -0.01 (-1.67)* -0.01 (-1.69)* -0.01 (-1.68)* -0.01 (-1.91)* -0.01 (-1.90)* -0.01 -1.88)* LEVERAGE 0.01 (0.51) 0.01 (0.56) 0.01 (0.55) 0.01 (0.60) 0.01 (0.52) 0.01 (0.54) TOBINQ 0.001 (1.51) 0.001 (1.53) 0.001 (1.53) 0.001 (1.72)* 0.001 (1.72)* 0.001 (1.70)* CASHHOLDINGS 0.003 (0.20) 0.003 (0.19) 0.003 (0.18) 0.003 (0.20) 0.004 (0.23) 0.003 (0.19) INTCOVERAGE 0.001 (2.38)** 0.001 (2.26)** 0.001 (2.27)** 0.001 (2.37)** 0.001 (2.42)** 0.001 (2.39)** CREDITCRISIS -0.01 (-1.54) -0.01 (-1.54) -0.01 (-1.53) -0.01 (-1.42) -0.01 (-1.46) -0.01 (-1.43) ZSCORE -0.001 (-1.58) -0.001 (-1.27) -0.001 (-1.28) -0.001 (-1.28) -0.001 (-1.36) -0.001 (-1.25) CROSSBORDER -0.002 (-0.16) -0.003 (-0.25) -0.003 (-0.25) -0.002 (-0.20) -0.003 (-0.23) -0.003 (-0.24) RIGHTS -0.02 (-0.22) -0.03 (-0.38) -0.03 (-0.39) -0.02 (-0.32) -0.03 (-0.37) -0.03 (-0.43) No. Obs 902 902 902 902 902 902 Pseudo R2 3.91% 3.61% 3.63% 3.61% 3.51% 3.50%

Table VI

OLS Regression Explaining the Valuation of Private Targets

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept 9.85 (2.70)*** 7.68 (2.03)** 7.92 (2.06)** 6.48 (1.94)* 6.72 (2.02)** 6.71 (2.01)** BIDDERIB 1.62 (0.81)

133 TARGETIB 4.12 (2.00)** BIDTOPIB 0.11 (0.04) TARTOPIB 1.72 (0.53) BIDIBSHARE 0.03 (0.24) TARIBSHARE 0.06 (0.46) YESBIDNOTAR -2.65 (-1.24) NOBIDYESTAR 0.23 (0.08) RELIBSHARE -0.01 (-0.09) SIZE 0.27 (0.41) 0.95 (1.39) 0.91 (1.37) 1.31 (2.28)** 1.15 (2.15)** 1.17 (2.13)** EQUITYPMT -0.79 (-0.54) -0.13 (-0.08) -0.20 (-0.14) 0.22 (0.15) 0.10 (0.07) 0.09 (0.06) PRIOR 1.03 (0.56) 1.43 (0.74) 1.37 (0.70) 1.20 (0.66) 1.29 (0.69) 1.33 (0.69) RELATED 0.79 (0.49) -1.29 (-0.38) 0.63 (0.40) 0.49 (0.30) 0.67 (0.42) 0.66 (0.41) TECHBIDDER 0.28 (0.12) 0.50 (0.21) 0.49 (0.20) 0.45 (0.19) 0.54 (0.23) 0.54 (0.23) TECHTARGET 0.41 (0.16) -0.01 (-0.01) -0.04 (-0.02) 0.35 (0.14) 0.01 (0.01) 0.02 (0.01) RELSIZE -1.79 (-2.18)** -1.16 (-1.37) -1.17 (-1.38) -0.96 (-1.32) -0.94 (-1.34) -0.95 (-1.34) LEVERAGE -5.53 (-1.87)* -4.86 (-1.66)* -4.81 (-1.65)* -5.29 (-1.77)* -4.77 (-1.63) -4.76 (-1.64) TOBINQ 0.50 (2.01)** 0.46 (1.80)* 0.46 (1.79)* 0.48 (1.83)* 0.44 (1.78)* 0.44 (1.77)* CASHHOLDINGS -1.95 (-0.55) -1.29 (-0.38) -1.64 (-0.48) -1.64 (-0.50) -1.55 (-0.48) -1.52 (-0.46) INTCOVERAGE 0.002 (0.32) 0.003 (0.48) 0.003 (0.46) 0.003 (0.44) 0.003 (0.52) 0.003 (0.50) CREDITCRISIS -1.47 (-0.91) -2.04 (-1.25) -2.03 (-1.27) -1.93 (-1.22) -2.20 (-1.35) -2.22 (-1.43) ZSCORE -0.27 (-1.13) -0.28 (-1.22) -0.28 (-1.21) -0.30 (-1.24) -0.27 (-1.20) -0.28 (-1.19) CROSSBORDER -7.83 (-3.76)*** -6.89 (-2.53)** -6.58 (-3.20) -5.91 (-3.45)*** -5.65 (-3.04)*** -5.72 (-3.44)*** RIGHTS 8.86 (1.89)* 8.51 (1.76)* 8.22 (1.77)* 7.51 (1.70)* 7,11 (1.54) 7.17 (1.61) No. Obs 179 179 179 179 179 179 Pseudo R2 15.28% 11.25% 11.21% 11.82% 10.97% 10.98%

Table VII

Tobit Regression Explaining the Portion of Equity Financing in Acquisitions of Private Targets

The estimation is based on a two-boundary Tobit model to reflect lower and upper bound constraints on the percentage of equity used in the transaction. The z-stats are based on QML (Huber/White) heteroskedasticity-consistent standard errors. BIDDERIB equals 1 if the bidder uses an investment banks, 0 otherwise. TARGETIB equals 1 if the target uses an investment banks, 0 otherwise. BIDTOPIB equals 1 if the bidder uses a top tier investment banks, 0 otherwise. TARTOPIB equals 1 if the target uses a top tier investment banks, 0 otherwise. BIDIBSHARE is the market share of the bidder’s investment bank in the previous year. TARIBSHARE is the market share of the target’s investment bank in the previous year. YESBIDNOTAR equals 1 if the bidder uses an investment banks and the target does not use an investment banks, 0 otherwise. NOBIDYESTAR equals 1 if the bidder does not use an investment banks and the target uses an investment banks, 0 otherwise. SIZE is the natural logarithm of total assets of the bidder. PRIOR equals 1 if the bidder acquires a least a private target in the last 10 years, 0 otherwise. RELATED equals 1 if the bidder and seller have the same four-digit SIC code, 0 otherwise. TECHBIDDER equals 1 if the acquirer is a high-tech firm, 0 otherwise. TECHTARGET equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to bidder's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the bidder’s debt ratio. TOBINQ is the Tobin Q’s ratio of the bidder. CASHHOLDINGS is the cash holdings of the bidder scaled by bidder's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the bidder. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the bidder. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. ∗∗∗, ∗∗, and ∗ indicate statistical significance at 0.01, 0.05, and 0.10 level, respectively.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Intercept 1.41 (4.06)*** 1.74 (4.52)*** 1.21 (1.77)* 0.04 (0.01) 1.69 (4.63) 1.16 (1.71)* BIDDERIB 0.83 (1.85)*

134 TARGETIB 4.57 (1.01) BIDTOPIB 1.32 (1.82)* TARTOPIB 16.50 (0.42) BIDIBSHARE 0.05 (1.87)* TARIBSHARE 0.27 (1.07) CASHHOLDINGS 0.27 (1.13) 0.21 (0.82) -0.02 (-0.02) -1.54 (-0.32) 0.26 (1.07) -0.44 (-0.48) LEVERAGE -0.03 (-0.53) -0.02 (-0.31) 0.08 (0.58) -0.08 (-0.26) -0.02 (-0.39) -0.01 (-0.09) INTCOVERAGE 0.001 (1.80)* 0.001 (1.70)* 0.001 (1.20) 0.003 (0.47) 0.001 (1.68)* 0.001 (1.27) BIDDERSIZE -0.26 (-5.40)*** -0.28 (-4.73)*** -0.71 (-1.37) -1.33 (-0.49) -0.28 (-4.95)*** -0.62 (-1.53) TECHBIDDER 0.32 (3.02)*** 0.34 (3.22)*** 0.33 (1.69)* 0.67 (0.81) 0.33 (3.17)*** 0.52 (2.32)** CREDITCRISIS -0.43 (-4.49)*** -0.39 (-3.66)*** -0.27 (-1.04) 0.48 (0.21) -0.39 (-3,84)*** -0.16 (-0.48) ZSCORE 0.002 (0.50) 0.004 (0.76) 0.02 (1.05) 0.01 (0.38) 0.03 (0.69) 0.01 (0.85) TOBINQ 0.002 (0.50) 0.001 (0.26) 0.02 (0.44) 0.002 (-0.28) 0.01 (0.34) 0.001 (0.27) RELSIZE 0.01 (0.48) 0.01 (0.58) -0.01 (-0.34) -0.08 (-0.35) 0.01 (0.51) -0.02 (-0.42) TECHTARGET 0.23 (2.22)** 0.17 (1.47) -0.01 (-0.02) -0.82 (-0.32) 0.19 (1.80)* -0.06 (-0.18) RELATED -0.03 (-0.31) -0.09 (-0.88) -0.09 (-0.51) -0.30 (-0.37) -0.07 (-0.74) -0.11 (-0.62) PRIOR 0.13 (1.35) 0.12 (1.19) 0.10 (0.55) 0.61 (0.48) 0.12 (1.17) 0.25 (1.14) CROSSBORDER -0.03 (-0.08) 0.16 (0.46) -1.14 (-0.82) -4.58 (-0.41) 0.10 (0.29) -1.36 (-0.90) RIGHTS -0.30 (-0.64) -0.48 (-1.02) 1.31 (0.66) 6.73 (0.39) -0.42 (-0.91) 1.85 (0.79) No. Obs 1062 1062 1062 1062 1062 1026

Table VIII

Tobit Regression Explaining the Portion of Equity Financing in Acquisitions of Private Targets

Model 1 Model 2 Model 3 Variable Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Intercept 1.40 (3.59)*** 1.49 (3.73)*** 1.02 (5.17)*** YESBIDNOTAR 1.87 (1.65)* 135 NOBIDYESTAR -2.27 (-1.61) RELIBSHARE 0.03 (1.85)* BIDDERSIZE -0.20 (-6.32)*** -0.11 (-1.98)** -0.10 (-7.38)*** PRIOR 0.09 (0.79) 0.22 (1.62) 0.05 (1.05) RELATED -0.09 (-0.10) -0.02 (-0.17) -0.03 (-0.61) TECHBIDDER 0.28 (2.21)** 0.37 (3.15)*** 0.14 (2.43)** TECHTARGET 0.25 (2.16)** 0.31 (2.53)** 0.12 (2.23)** RELSIZE 0.02 (1.22) 0.02 (0.96) -0.01 (-0.58) LEVERAGE -0.02 (-0.27) -0.10 (-1.23) -0.02 (-0.50) TOBINQ 0.001 (0.01) 0.02 (0.51) 0.001 (0.21) CASHHOLDINGS 0.40 (1.65)* 0.23 (0.81) 0.23 (2.07)** INTCOVERAGE 0.001 (1.12) 0.001 (1.38) 0.001 (1.21) CREDITCRISIS -0.43 (-4.07)*** -0.51 (-4.62)*** -0.20 (-4.10)*** ZSCORE 0.001 (0.12) -0.01 (-0.76) -0.001 (-0.16) CROSSBORDER 0.28 (0.72) 0.15 (0.39) 0.21 (1.11) RIGHTS -0.75 (-1.43) -0.54 (-1.02 -0.43 (-1.68)* No. Obs 1062 1062 1062

Table IX

OLS Regression Explaining the Change in Operating Performance of Bidders in Acquisitions of Private Targets (-1 to +1)

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept -1.51 (-0.09) -2.53 (-0.12) -2.76 (-0.13) -2.20 (-0.12) -2.25 (-0.13) -2.48 (-0.14) BIDDERIB -3.17 (-0.78) 136 TARGETIB 4.22 (0.90) BIDTOPIB -0.20 (-0.09) TARTOPIB 0.09 (0.02) BIDIBSHARE -0.08 (-0.65) TARIBSHARE 0.05 (0.19) YESBIDNOTAR -2.23 (-0.88) NOBIDYESTAR 7.02 (0.61) RELIBSHARE -0.06 (-0.60) SIZE -0.05 (-0.07) 0.17 (0.18) 0.22 (0.23) 0.18 (0.28) -0.06 (-0.09) 0.16 (0.26) EQUITYPMT 4.07 (0.94) 3.95 (0.88) 3.96 (0.88) 3.98 (0.95) 4.19 (0.92) 3.92 (0.93) PRIOR -4.14 (-1.27) -3.96 (-1.23) -3.96 (-1.23) -3.95 (-1.23) -4.39 (-1.29) -3.93 (-1.23) RELATED -3.16 (-1.05) -3.15 (-1.07) -3.08 (-1.06) -3.14 (-1.05) -3.16 (-1.06) -3.10 (-1.03) TECHBIDDER -0.94 (-0.33) -1.28 (-0.54) -1.19 (-0.51) -1.15 (-0.44) -1.05 (-0.37) -1.16 (-0.46) TECHTARGET 0.61 (0.36) 0.84 (0.54) 0.87 (0.55) 0.79 (0.48) 0.60 (0.33) 0.84 (0.50) RELSIZE -7.07 (-1.67)* -7.08 (-1.68)* -7.07 (-1.66)* -7.07 (-1.67)* -7.09 (-1.69)* -7.09 (-1.69)* LEVERAGE 16.02 (0.50) 16.48 (0.50) 16.51 (0.50) 16.31 (0.50) 16.30 (0.51) 16.45 (0.50) TOBINQ 1.10 (1.19) 1.01 (1.18) 1.01 (1.18) 1.01 (1.20) 1.01 (1.19) 1.01 (1.20) CASHHOLDINGS 0.88 (0.23) 1.32 (0.29) 1.24 (0.27) 1.31 (0.31) 0.61 (0.19) 1.21 (0.29) INTCOVERAGE 0.01 (0.54) 0.01 (0.62) 0.01 (0.64) 0.01 (0.65) 0.01 (0.42) 0.01 (0.66) CREDITCRISIS 3.86 (0.61) 3.76 (0.62) 3.75 (0.62) 3.72 (0.60) 4.02 (0.61) 3.77 (0.61) ZSCORE -1.07 (-0.96) -1.09 (-0.98) -1.09 (-0.98) -1.08 (-0.98) -1.07 (-0.95) -1.09 (-0.98) CROSSBORDER 0.50 (0.24) 1.27 (0.45) 1.13 (0.39) 1.01 (0.47) 1.10 (0.51) 1.00 (0.46) RIGHTS -3.84 (-1.02) -4.68 (-0.97) -4.54 (-0.93) -4.38 (-1.03) -4.54 (-1.05) -4.38 (-1.04) No. Obs 801 801 801 801 801 801 McFadden R2 11.05% 10.94% 10.95% 10.96% 11.09% 10.95%

Table X

OLS Regression Explaining the Change in Operating Performance of Bidders in Acquisitions of Private Targets (-1 to +2)

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept 11.70 (0.86) 12.34 (0.88) 12.19 (0.87) 13.44 (0.92) 12.81 (0.90) 12.90 (0.90) BIDDERIB -2.34 (-1.14) 137 TARGETIB -1.50 (-0.79) BIDTOPIB -2.40 (-0.88) TARTOPIB 0.70 (0.32) BIDIBSHARE -0.13 (-1.00) TARIBSHARE 0.06 (0.48) YESBIDNOTAR -3.27 (-1.06) NOBIDYESTAR -2.68 (-0.70) RELIBSHARE -0.10 (-0.97) SIZE 0.33 (0.51) 0.02 (0.04) 0.05 (0.09) -0.10 (-0.16) -0.03 (-0.04) -0.10 (-0.17) EQUITYPMT 0.70 (0.35) 0.49 (0.24) 0.50 (0.25) 0.51 (0.25) 0.39 (0.19) 0.43 (0.21) PRIOR -4.28 (-1.12) -4.18 (-1.11) -4.13 (-1.11) -4.11 (-1.10) -3.94 (-1.12) -4.08 (-1.10) RELATED -1.72 (-0.98) -1.57 (-0.95) -1.55 (-0.94) -1.69 (-0.97) -1.74 (-0.98) -1.58 (-0.95) TECHBIDDER -2.13 (-1.24) -2.10 (-1.29) -2.03 (-1.26) -1.96 (-1.22) -2.30 (-1.29) -1.97 (-1.22) TECHTARGET 1.06 (0.67) 1.11 (0.70) 1.07 (0.68) 0.88 (0.60) 1.06 (0.65) 0.95 (0.62) RELSIZE -3.43 (-1.08) -3.46 (-1.08) -3.47 (-1.08) -3.49 (-1.09) -3.50 (-1.09 -3.49 (-1.09) LEVERAGE -14.65 (-0.88) -14.85 (-0.89) -14.80 (-0.89) -15.22 (-0.90) -14.84 (-0.89) -14.97 (-0.89) TOBINQ 0.60 (1.70)* 0.61 (1.70)* 0.61 (1.71)* 0.61 (1.71)* 0.61 (1.71)* 0.61 (1.70)* CASHHOLDINGS -2.30 (-0.72) -2.53 (-0.75) -2.58 (-0.76) -2.47 (-0.74) -2.18 (-0.70) -2.66 (-0.78) INTCOVERAGE 0.01 (0.85) 0.01 (0.87) 0.01 (0.87) 0.01 (0.88) 0.01 (0.90) 0.01 (0.88) CREDITCRISIS 0.38 (0.18) 0.44 (0.22) 0.48 (0.24) 0.36 (0.17) 0.29 (0.14) 0.50 (0.24) ZSCORE -1.22 (-0.93) -1.22 (-0.93) -1.22 (-0.93) -1.21 (-0.93) -1.23 (-0.93) -1.22 (-0.93) CROSSBORDER 0.33 (0.18) -0.68 (-0.34) -0.67 (-0.33) -1.20 (-0.57) -0.62 (-0.35) -1.08 (-0.52) RIGHTS -2.58 (-0.71) -1.46 (-0.49) -1.43 (-0.48) -0.99 (-0.35) -1.39 (-0.47) -0.95 (-0.34) No. Obs 703 703 703 703 703 703 McFadden R2 14.63% 14.58% 14.59% 14.61% 14.58% 14.59%

Table XI

OLS Regression Explaining the Increase in Systematic Risk of Bidders in Acquisitions of Private Targets

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept 0.09 (0.79) 0.10 (0.84) 0.09 (0.76) 0.13 (1.15) 0.13 (1.11) 0.13 (1.09) BIDDERIB -0.06 (-0.88) 138 TARGETIB -0.04 (-0.62) BIDTOPIB -0.05 (-0.54) TARTOPIB -0.02 (-0.28) BIDIBSHARE -0.01 (-1.18) TARIBSHARE 0.01 (0.12) YESBIDNOTAR -0.05 (-0.49) NOBIDYESTAR -0.03 (-0.36) RELIBSHARE -0.01 (-0.91) SIZE -0.0001 (-0.01) -0.004 (-0.29) -0.003 (-0.19) -0.01 (-0.60) -0.01 (-0.57) -0.01 (-0.60) EQUITYPMT 0.03 (0.40) 0.02 (0.32) 0.03 (0.37) 0.02 (0.24) 0.02 (0.22) 0.02 (0.24) PRIOR 0.11 (1.59) 0.11 (1.58) 0.11 (1.60) 0.11 (1.62) 0.11 (1.63) 0.11 (1.64) RELATED 0.04 (0.73) 0.04 (0.73) 0.05 (0.78) 0.04 (0.67) 0.04 (0.67) 0.04 (0.71) TECHBIDDER 0.08 (0.89) 0.07 (0.84) 0.08 (0.88) 0.07 (0.85) 0.07 (0.80) 0.08 (0.88) TECHTARGET -0.10 (-1.13) -0.10 (-1.11) -0.10 (-1.12) -0.11 (-1.19) -0.11 (-1.19) -0.11 (-1.20) RELSIZE 0.16 (1.97)** 0.14 (1.76)* 0.14 (1.85)* 0.13 (1.67)* 0.12 (1.62) 0.12 (1.65)* LEVERAGE -0.06 (-0.53) -0.06 (-0.48) -0.06 (-0.48) -0.06 (-0.49) -0.05 (-0.45) -0.05 (-0.44) TOBINQ 0.002 (3.46)*** 0.002 (3.50)*** 0.002 (3.56)*** 0.002 (3.58)*** 0.002 (3.51)*** 0.002 (3.61)*** CASHHOLDINGS -0.28 (-2.38)** -0.27 (-2.31)** -0.28 (-2.38)** -0.27 (-2.30)** -0.26 (-2.26)** -0.27 (-2.38)** INTCOVERAGE -0.001 (-1.02) -0.001 (-1.01) -0.001 (-1.01) -0.001 (-0.99) -0.001 (-0.97) -0.001 (-0.99) CREDITCRISIS -0.06 (-0.97) -0.07 (-0.98) -0.07 (-1.00) -0.06 (-0.96) -0.06 (-0.96) -0.06 (-0.95) ZSCORE 0.01 (1.44) 0.01 (1.50) 0.01 (1.51) 0.01 (1.52) 0.01 (1.49) 0.01 (1.55) CROSSBORDER -0.37 (-1.16) -0.37 (-1.17) -0.37 (-1.18) -0.39 (-1.23) -0.38 (-1.21) -0.39 (-1.23) RIGHTS 0.35 (1.07) 0.35 (1.07) 0.35 (1.08) 0.37 (1.14) 0.36 (1.11) 0.37 (1.14) No. Obs 833 833 833 833 833 833 McFadden R2 4.10% 3.98% 4.08% 3.95% 3.93% 4.01%

Table XII

OLS Regression Explaining the Increase in Unsystematic Risk of Bidders in Acquisitions of Private Targets

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept 0.01 (0.38) 0.001 (0.33) 0.002 (0.45) 0.002 (0.44) 0.002 (0.44) 0.001 (0.37) BIDDERIB -0.0027 (-1.73)* 139 TARGETIB 0.002 (0.98) BIDTOPIB -0.003 (-1.65)* TARTOPIB 0.003 (0.97) BIDIBSHARE -0.001 (-0.77) TARIBSHARE 0.001 (0.84) YESBIDNOTAR -0.002 (-0.87) NOBIDYESTAR 0.004 (1.72)* RELIBSHARE -0.001 (-1.26) SIZE 0.001 (0.46) 0.001 (0.39) 0.001 (0.19) 0.001 (0.34) 0.001 (0.01) 0.001 (0.34) EQUITYPMT 0.004 (1.75)* 0.004 (1.74)* 0.004 (1.68)* 0.004 (1.64) 0.004 (1.59) 0.004 (1.66)* PRIOR 0.001 (0.03) 0.001 (0.14) 0.001 (0.15) 0.001 (0.11) -0.001 (-0.05) 0.001 (0.14) RELATED 0.001 (0.37) 0.001 (0.47) 0.001 (0.41) 0.001 (0.36) 0.001 (0.33) 0.001 (0.41) TECHBIDDER 0.001 (0.66) 0.001 (0.63) 0.001 (0.62) 0.001 (0.58) 0.001 (0.62) 0.001 (0.62) TECHTARGET -0.004 (-1.55) -0.003 (-1.51) -0.003 (-1.56) -0.003 (-1.56) -0.003 (-1.62) -0.003 (-1.57) RELSIZE -0.001 (-0.08) -0.001 (-0.22) -0.001 (-0.33) -0.001 (-0.25) -0.001 (-0.21) -0.001 (-0.28) LEVERAGE -0.002 (-0.52) -0.002 (-0.48) -0.001 (-0.42) -0.002 (-0.49) -0.001 (-0.38) -0.001 (-0.43) TOBINQ -0.002 (-0.79) -0.002 (-0.80) -0.002 (-0.86) -0.002 (-0.86) -0.002 (-0.91) -0.002 (-0.85) CASHHOLDINGS -0.01 (0.37) -0.01 (-1.24) -0.004 (-1.20) -0.004 (-1.16) -0.01 (-1.24) -0.004 (-1.21) INTCOVERAGE 0.001 (1.45) 0.001 (1.57) 0.001 (1.54) 0.001 (1.54) 0.001 (1.34) 0.001 (1.54) CREDITCRISIS -0.001 (-0.58) -0.001 (-0.57) -0.001 (-0.56) -0.001 (-0.60) -0.001 (-0.52) -0.001 (-0.59) ZSCORE 0.002 (1.75)* 0.002 (1.67)* 0.002 (1.68)* 0.002 (1.68)* 0.002 (1.84)* 0.002 (1.68)* CROSSBORDER -0.02 (-3.77)*** -0.02 (-3.84)*** -0.02 (-3.78)*** -0.02 (-3.79)*** -0.02 (-3.72)*** -0.02 (-3.81)*** RIGHTS 0.02 (3.45)*** 0.02 (3.51)*** 0.02 (3.47)*** 0.02 (3.46)*** 0.02 (3.41)*** 0.02 (3.49)*** No. Obs 833 833 833 833 833 833 McFadden R2 3.47% 3.43% 3.26% 3.18% 3.53% 3.25%

Table XIII

OLS Regression Explaining the Increase in Total Risk of Bidders in Acquisitions of Private Targets

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept 0.001 (0.29) 0.001 (0.33) 0.001 (0.48) 0.001 (0.26) 0.001 (0.28) 0.001 (0.22) BIDDERIB -0.001 (-1.34) 140 TARGETIB 0.001 (0.97) BIDTOPIB -0.001 (-0.75) TARTOPIB 0.001 (0.73) BIDIBSHARE -0.001 (-0.21) TARIBSHARE 0.001 (0.73) YESBIDNOTAR -0.001 (-0.47) NOBIDYESTAR 0.001 (1.67)* RELIBSHARE -0.001 (-1.06) SIZE 0.001 (0.64) 0.001 (0.46) 0.001 (0.23) 0.001 (0.58) 0.001 (0.27) 0.001 (0.59) EQUITYPMT 0.001 (1.91)* 0.001 (1.96)** 0.001 (1.94)* 0.001 (1.79)* 0.001 (1.77)* 0.001 (1.82)* PRIOR -0.001 (-0.13) -0.001 (-0.01) 0.001 (0.01) -0.001 (-0.05) -0.001 (-0.22) -0.001 (-0.03) RELATED 0.001 (0.08) 0.001 (0.13) 0.001 (0.07) 0.001 (0.08) 0.001 (0.05) 0.001 (0.11) TECHBIDDER 0.002 (0.80) 0.002 (0.78) 0.002 (0.77) 0.002 (0.74) 0.002 (0.80) 0.003 (0.77) TECHTARGET -0.001 (-1.31) -0.001 (-1.26) -0.001 (-1.31) -0.001 (-1.32) -0.001 (-1.37) -0.001 (-1.33) RELSIZE 0.001 (0.45) 0.001 (0.34) 0.001 (0.23) 0.001 (0.34) 0.001 (0.40) 0.001 (0.33) LEVERAGE -0.001 (-0.79) -0.001 (-0.74) -0.001 (-0.69) -0.001 (-0.78) -0.001 (-0.69) -0.001 (-0.74) TOBINQ -0.001 (-0.07) -0.001 (-0.14) -0.001 (-0.22) -0.001 (-0.14) -0.001 (-0.16) -0.001 (-0.12) CASHHOLDINGS -0.001 (-1.44) -0.001 (-1.42) -0.001 (-1.39) -0.001 (-1.38) -0.001 (-1.46) -0.001 (-1.42) INTCOVERAGE 0.001 (0.99) 0.001 (1.14) 0.001 (1.14) 0.001 (1.09) 0.001 (0.82) 0.001 (1.10) CREDITCRISIS 0.001 (0.38) 0.001 (0.37) 0.001 (0.38) 0.001 (0.37) 0.001 (0.43) 0.001 (0.37) ZSCORE 0.001 (1.39) 0.001 (1.31) 0.001 (1.33) 0.001 (1.31) 0.001 (1.50) 0.001 1.32) CROSSBORDER -0.002 (-3.42)*** -0.002 (-3.42)*** -0.002 (3.39)*** -0.002 (-3.40)*** -0.002 (-3.37)*** -0.002 (-3.45)*** RIGHTS 0.002 (3.06)*** 0.002 (3.10)*** 0.002 (3.10)*** 0.002 (3.03)*** 0.002 (3.01)*** 0.002 (3.07)*** No. Obs 833 833 833 833 833 833 McFadden R2 2.58% 2.49% 2.47% 2.38% 2.74% 2.42%

Table XIV

Definition of Variables

Summary of the variables that are used in the paper

Variable Name Variable Definition BUYERIB equals 1 if there is the use of an investment bank by the buyer and 0 otherwise SELLERIB equals 1 if there is the use of an investment bank by the seller and 0 otherwise BUYTOPIB equals 1 if there is at least 1 top tier investment bank advising the buyer and 0 otherwise SELTOPIB equals 1 if there is at least 1 top tier investment bank advising the seller and 0 otherwise BUYIBSHARE the market share of the most reputable investment bank advising the buyer SELIBSHARE the market share of the most reputable investment bank advising the seller YESBUYNOSEL equals 1 if the buyer uses at least one and the seller does not use an investment bank, 0 otherwise NOBUYYESSEL equals 1 if the buyer does not use and the seller uses at least one investment bank, 0 otherwise RELIBSHARE the difference between the market share of the most reputable investment bank advising the buyer and the most reputable investment bank advising the seller CAR (-1, +1) cumulative abnormal return of the buying firm’s stock in the three-day event window (-1, +1) where 0 is the announcement day. The returns are calculated using the market model with the market model parameters estimated over the period starting 180 days and ending 45 days prior to the announcement. the industry adjusted change in operating income scaled by sales of the buyer RISK the risk-shifts of the buyer following the transaction (shifts in either systematic risk, unsystematic risk, or total risk) ALLCASH equals 1 if the buyer uses 100 percent cash to finance the transaction SIZE the natural logarithm of the transaction value EXPERIENCE equals 1 if the buyer involves in at least one assets selloff in the last 10 years, 0 otherwise RELATED equals 1 if the buyer and seller have the same four-digit SIC code, 0 otherwise TECHBUYER equals 1 if the buyer is a high-tech firm, 0 otherwise TECHSELLER equals 1 if the seller is a high-tech firm, 0 otherwise RELSIZE the relative size of the transaction to buyer's market value of equity, as of four weeks prior to the announcement LEVERAGE the buyer’s total liabilities divided by market value of equity, as of four weeks prior to the announcement TOBINQ Tobin Q’s ratio of the buyer. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis (from Q1/2001 to Q4/2002 and from Q3/2007 to Q4/2010) ZSCORE the Altman Z-Score of the buyer INTCOVERAGE interest coverage ratio of the buyer CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise FREEDOM natural logarithm of the economic freedom rating of the seller country RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise COMMON equals 1 if the seller is from a country with a common law system, 0 otherwise

141

Table XV

Descriptive Statistics

This table provides the descriptive statistics for the samples that are used in the paper. ALLCASH equals 1 if the buyer uses 100 percent cash to finance the transaction. SIZE is the natural logarithm of the transaction value. PRIOR equals 1 if the buyer acquires at least 1 private seller in the previous 10-year, 0 otherwise. CASHHOLDINGS is the buyer’s cash holdings scaled by total assets. LEVERAGE is the debt ratio of the buyer. INTERESTRATIO is the interest coverage ratio of the buyer. CREDITCRISIS equals 1 if the transactions happen during 2001- 2002 and 2007 crisis. RELATED equals 1 if the buyer and seller have the same four-digit SIC code, 0 otherwise. TECHBUYER equals 1 if the acquirer is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to buyer’s market value of equity, as of four weeks prior to the announcement. TOBINQ is the Tobin Q’s ratio of the buyer. TECHSELLER equals 1 if the seller is a high-tech firm, 0 otherwise. OWNERSHIP is the percentage of the ownership of insiders. ZSCORE is the Altman Z score of the buyer. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. FREEDOM is the natural logarithm of the economic freedom rating of the seller country. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. COMMON equals 1 if the seller is from a country with a British legal tradition, 0 otherwise. BUYERIB equals 1 of the buyer hires an investment bank, 0 otherwise. SELLERIB equals 1 of the seller hires an investment bank, 0 otherwise. BIDTOPIB equals 1 of the buyer hires a top-tier investment bank, 0 otherwise. TARTOPIB equals 1 of the seller hires a top-tier investment bank, 0 otherwise. YESBUYNOSEL equals 1 of the buyer hires an investment bank and the seller does not hire an investment bank, 0 otherwise. NOBUYYESSEL equals 1 of the buyer does not hire an investment bank and the seller hires an investment bank, 0 otherwise. BIDIBSHARE is the market share of the buyer’s investment bank. TARIBSHARE is the market share of the seller’s investment bank. RELIBSHARE is the difference between the market share of the buyer’s investment bank and the market share of the seller’s investment bank.

Buyers with Investment Buyers without Investment Sellers with Investment Sellers without Investment All Sample Banks Bank Banks Bank 142 Mean Median Mean Median Mean Median Mean Median Mean Median ALLCASH 0.61 1 0.56 1 0.64 1 0.64 1 0.59 1 SIZE 142.14 45.5 324.95 117.6 48.52 30.56 226.63 88.5 53.82 28.79 PRIOR 0.22 0 0.21 0 0.22 0 0.24 0 0.20 0 CASHHOLDINGS 0.19 0.08 0.18 0.08 0.20 0.08 0.19 0.08 0.20 0.08 LEVERAGE 0.49 0.48 0.50 0.50 0.48 0.47 0.49 0.48 0.49 0.48 INTERESTRATIO 92.63 3.31 66.92 3.73 105.79 3.13 133.90 4.42 63.34 2.64 CREDITCRISIS 0.28 0 0.28 0 0.27 0 0.30 0 0.26 0 RELATED 0.42 0 0.38 0 0.43 0 0.37 0 0.45 0 TECHBUYER 0.20 0 0.21 0 0.19 0 0.20 0 0.20 0 RELSIZE 0.37 0.22 0.54 0.41 0.28 0.16 0.45 0.29 0.32 0.18 TOBINQ 1.46 0.94 1.34 0.93 1.52 0.94 1.38 0.93 1.52 0.94 TECHSELLER 0.20 0 0.20 0 0.20 0 0.20 0 0.20 0 ZSCORE -1.14 3.15 -1.20 3.14 -1.10 3.15 5.39 3.34 -7.10 2.93 CROSSBORDER 0.14 0 0.19 0 0.12 0 0.15 0 0.14 0 FREEDOM 4.34 4.35 4.34 4.35 4.34 4.35 4.34 4.35 4.34 4.35 RIGHTS 0.79 1 0.83 1 0.77 1 0.80 1 0.78 1 COMMON 0.93 1 0.88 1 0.95 1 0.92 1 0.93 1 BUYERIB 0.34 0 1 1 0 0 0.53 1 0.20 0 SELLERIB 0.42 0 0.65 1 0.30 0 1 1 0 0 BUYTOPIB 0.16 0 0.48 0 0 0 0.29 0 0.07 0 SELTOPIB 0.20 0 0.37 0 0.11 0 0.47 0 0 0 YESBUYNOSEL 0.12 0 0.35 0 0 0 0 0 0.20 0 NOBUYYESSEL 0.20 0 0 0 0.30 0 0.47 0 0 0 BUYIBSHARE 3.54 0 10.44 9.1 0 0 6.22 0 1.63 0 SELIBSHARE 4.75 0 8.91 1 2.63 0 11.45 11.20 0 0

RELIBSHARE -0.01 0 0.02 0.01 -0.03 0 -0.05 -0.02 0.02 0 NUMBER OF OBSERVATION 809 809 275 275 534 534 337 337 472 472

143

Table XVI

Correlation Matrix

ALLCASH is a dummy variable equals 1 if there is 100 percent cash payment, 0 otherwise. SIZE is the natural logarithm of the transaction value. PRIOR equals 1 if the buyer acquires a least a private target in the last 10 years, 0 otherwise. RELATED equals 1 if the buyer and seller have the same four-digit SIC code, 0 otherwise. TECHBUYER equals 1 if the acquirer is a high- tech firm, 0 otherwise. TECHSELLER equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to buyer's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the buyer’s debt ratio. TOBINQ is the Tobin Q’s ratio of the buyer. CASHHOLDINGS is the cash holdings of the buyer scaled by buyer's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the buyer. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the buyer. FREEDOM is the natural logarithm of the economic freedom rating of the target country in the year prior to the transaction. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. COMMON equals 1 if the target country is a common law country, 0 otherwise.

INT CASH RELATE TECH TECH CREDIT CROSS SIZE ALLCASH PRIOR LEVERAGE COVERAGE HOLDINGS D BUYER TARGET RELSIZE TOBINQ ZSCORE CRISIS BORDER FREEDOM RIGHTS COMMON SIZE 1 ALLCASH 0.01 1 PRIOR 0.05 0.03 1 LEVERAGE 0.11 -0.02 0.11 1 INTCOVERAGE -0.01 0.04 -0.01 -0.11 1 CASHHOLDINGS -0.02 -0.05 -0.02 0.01 -0.01 1 RELATED 0.08 -0.03 0.01 -0.01 0.06 -0.03 1

144 TECHBUYER -0.15 -0.11 -0.03 -0.24 0.02 -0.02 -0.07 1 TECHSELLER -0.14 -0.10 -0.01 -0.23 -0.01 -0.02 -0.07 0.62 1

RELSIZE 0.39 -0.13 -0.09 0.14 -0.04 0.12 -0.07 -0.09 -0.10 1 TOBINQ 0.03 -0.17 -0.13 -0.25 0.02 -0.02 0.01 0.18 0.14 -0.16 1 ZSCORE 0.08 0.05 0.02 -0.04 0.01 0.01 0.03 0.02 -0.07 0.02 0.03 1 CREDITCRISIS -0.02 0.05 0.04 -0.06 0.01 -0.03 -0.03 0.01 -0.02 -0.10 -0.09 0.02 1 CROSSBORDER 0.03 -0.01 0.05 -0.05 0.02 -0.02 -0.05 0.10 0.01 -0.04 -0.01 0.02 0.07 1 FREEDOM -0.01 0.03 -0.05 -0.01 0.03 0.01 0.02 -0.03 0.04 0.05 0.03 -0.01 0.01 -0.51 1 RIGHTS 0.05 -0.02 0.04 -0.06 0.03 -0.02 -0.07 0.14 0.03 -0.04 0.01 0.01 0.07 0.91 -0.41 1 COMMON -0.05 0.03 -0.05 0.03 0.02 0.01 0.07 -0.06 0.06 -0.01 0.03 -0.01 -0.01 -0.71 0.69 -0.66 1

Table XVII

Logit and Tobit Regression Explaining the Decision to Hire Investment Banks in Assets Sell-off Transactions

The estimations are based on Logit and Tobit regression models. The z-stats are based on QML (Huber/White) heteroskedasticity-consistent standard errors. BUYERIB equals 1 if the buyer uses an investment banks, 0 otherwise. SELLERIB equals 1 if the seller uses an investment banks, 0 otherwise. BUYTOPIB equals 1 if the buyer uses a top tier investment banks, 0 otherwise. SELTOPIB equals 1 if the seller uses a top tier investment banks, 0 otherwise. BUYIBSHARES is the market share of the buyer’s investment bank in the previous year. SELIBSHARES is the market share of the seller’s investment bank in the previous year. ALLCASH is a dummy variable equals 1 if there is 100 percent cash payment, 0 otherwise. . SIZE is the natural logarithm of the transaction value. PRIOR equals 1 if the buyer acquires a least a private seller in the last 10 years, 0 otherwise. RELATED equals 1 if the buyer and seller have the same four-digit SIC code, 0 otherwise. TECHBUYER equals 1 if the acquirer is a high-tech firm, 0 otherwise. TECHSELLER equals 1 if the seller is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to buyer's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the buyer’s debt ratio. TOBINQ is the Tobin Q’s ratio of the buyer. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the buyer. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. ∗∗∗, ∗∗, and ∗ indicate statistical significance at 0.01, 0.05, and 0.10 level, respectively.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 (BUYERIB) (BUYTOPIB) (SELLERIB) (SELTOPIB) (BUYIBSHARE) (SELIBSHARE) Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. z-Stat Coeff. z-Stat Intercept -4.12 (-5.87)*** -8.65 (-8.84)*** -2.86 (-4.64)*** -4.66 (-5.94)*** -39.93 (-9.64)*** -30.27 (-6.60)*** ALLCASH -0.55 (-2.95)*** -0.52 (-2.10)** 0.05 (0.28) -0.04 (-0.16) -2.82 (-2.18)** 0.49 (0.35) PRIOR -0.21 (-1.00) -0.30 (-1.06) -0.01 (-0.02) 0.03 (0.11) -2.01 (-1.35) -1.22 (-0.80) 145 SIZE 0.92 (9.78)*** 1.33 (9.09)*** 0.74 (8.67)*** 0.94 (8.70)*** 8.19 (13.83)*** 7.11 (11.54)*** RELATED -0.35 (-1.92)* -0.07 (-0.29) -0.34 (-2.03)** -0.03 (-0.15) -1.62 (-1.29) -1.45 (-1.10) TECHBUYER 0.46 (1.79)* 0.76 (1.97)** 0.14 (0.58) 0.08 (0.24) 3.95 (2.18)** 0.82 (0.42) TECHSELLER 0.23 (0.91) 0.07 (0.17) 0.18 (0.74) 0.27 (0.83) 2.03 (1.11) 0.56 (0.27) TOBINQ -0.02 (-0.54) -0.03 (-0.39) -0.02 (-0.68) -0.09 (-1.12) -0.16 (-0.47) -0.53 (-1.47) RELSIZE 0.72 (2.53)** 0.01 (0.03) -0.03 (-0.14) -0.03 (-0.14) 2.56 (1.75)* -0.26 (-0.17) BUYERIB 0.67 (3.70)*** 0.61 (2.70)*** 5.62 (3.80)*** SELLERIB 0.68 (3.77)*** 0.46 (1.84)* 4.05 (3.04)*** ZSCORE 0.01 (0.42) 0.01 (1.19) 0.01 (0.36) 0.01 (1.22) 0.01 (1.29) 0.03 (3.21)*** LEVERAGE -0.41 (-0.98) 1.19 (2.69)*** -0.17 (-0.54) -0.35 (-0.81) 1.82 (0.72) -0.04 (-0.02) CREDITCRISIS -0.11 (-0.55) -0.37 (-1.28) 0.29 (1.63) -0.60 -2.50)** -2.81 (-1.97)** -1.13 (-0.84) CROSSBORDER 1.03 (1.78)* -0.23 (-0.30) 0.53 (1.00) 0.57 (0.84) 5.11 (1.56) 3.85 (1.02) RIGHTS -0.30 (-0.39) 0.81 (0.78) -0.71 (-0.97) -0.94 (-1.04) -2.62 (-0.58) -5.36 (-1.04) No. Obs 861 861 861 861 861 861 McFadden's R2 27.58% 34.13% 18.21% 23.19% 29.92% 22.96%

Table XVIII

OLS Regression Explaining the Wealth Effect of Buyers in Assets Sell-off Transactions

The estimation is based on a Least Square model. The t-stats are based on White heteroskedasticity-consistent standard errors & covariance. BUYERIB equals 1 if the buyer uses an investment banks, 0 otherwise. SELLERIB equals 1 if the seller uses an investment banks, 0 otherwise. BUYTOPIB equals 1 if the buyer uses a top tier investment banks, 0 otherwise. SELTOPIB equals 1 if the seller uses a top tier investment banks, 0 otherwise. BUYIBSHARE is the market share of the buyer’s investment bank in the previous year. SELIBSHARE is the market share of the seller’s investment bank in the previous year. YESBUYNOSEL equals 1 if the buyer uses an investment banks and the seller does not use an investment banks, 0 otherwise. NOBUYYESSEL equals 1 if the buyer does not use an investment banks and the seller uses an investment banks, 0 otherwise. ALLCASH is a dummy variable equals 1 if there is 100 percent cash payment, 0 otherwise. SIZE is the natural logarithm of the transaction value. PRIOR equals 1 if the buyer acquires a least a private target in the last 10 years, 0 otherwise. RELATED equals 1 if the buyer and seller have the same four-digit SIC code, 0 otherwise. TECHBUYER equals 1 if the acquirer is a high-tech firm, 0 otherwise. TECHSELLER equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to buyer's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the buyer’s debt ratio. TOBINQ is the Tobin Q’s ratio of the buyer. CASHHOLDINGS is the cash holdings of the buyer scaled by buyer's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the buyer. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the buyer. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. ∗∗∗, ∗∗, and ∗ indicate statistical significance at 0.01, 0.05, and 0.10 level, respectively.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept 0.06 (1.37) 0.06 (1.54) 0.06 (1.45) 0.06 (1.39) 0.05 (1.31) 0.05 (1.30) BUYERIB 0.03 (1.99)**

146 SELLERIB -0.02 (-1.72)* BUYTOPIB 0.03 (1.33) SELTOPIB 0.01 (0.86) BUYIBSHARE 0.001 (0.87) SELIBSHARE 0.001 (1.03) YESBUYNOSEL 0.04 (1.95)* NOBUYYESSEL -0.02 (-2.07)** RELIBSHARE -0.02 (-0.37) SIZE 0.01 (0.31) -0.01 (-0.40) -0.002 (-0.32) 0.002 (0.69) 0.004 (0.94) 0.004 (0.94) ALLCASH -0.02 (-2.03)** -0.02 (-2.06)** -0.02 (-2.17)** -0.02 (-2.09)** -0.03 (-2.08)** -0.03 (-2.22) PRIOR 0.001 (0.04) 0.001 (0.01) -0.001 (-0.03) 0.001 (0.05) -0.001 (-0.13) -0.002 (-0.20) RELATED -0.01 (-1.47) -0.01 (-1.34) -0.01 (-1.31) -0.01 (-1.53) -0.01 (-1.46) -0.01 (-1.42) TECHBUYER 0.05 (2.07)** 0.05 (2.06)** 0.05 (2.07)** 0.05 (2.05)** 0.05 (2.07)** 0.05 (2.08)** TECHSELLER -0.03 (-1.37) -0.03 (-1.38) -0.03 (-1.34) -0.03 (-1.35) -0.03 (-1.36) -0.03 (-1.34) RELSIZE 0.002 (1.17) 0.002 (1.18) 0.002 (1.12) 0.002 (1.29) 0.002 (1.07) 0.002 (1.06) LEVERAGE -0.05 (-2.12)** -0.05 (-2.17)** -0.05 (-2.13)** -0.05 (-2.30)** -0.05 (-2.05)** -0.05 (-2.13) TOBINQ -0.02 (-1.30) -0.02 (-1.33) -0.02 (-1.34) -0.03 (-1.45) -0.02 (-1.27) -0.02 (-1.33) CASHHOLDINGS 0.001 (2.97)*** 0.001 (6.39)*** 0.001 (6.26)*** 0.001 (1.95)* 0.001 (5.37)*** 0.001 (5.62)*** INTCOVERAGE 0.001 (1.75)* 0.001 (1.63) 0.001 (1.53) 0.001 (1.46) 0.001 (1.64) 0.001 (1.28) CREDITCRISIS -0.01 (-0.74) -0.01 (-0.59) -0.01 (-0.60) -0.01 (-0.80) -0.01 (-0.77) -0.01 (-0.81) ZSCORE -0.002 (-0.42) -0.001 (-0.56) -0.003 (-0.55) -0.002 (-0.50) -0.002 (-0.47) -0.001 (-0.54) CROSSBORDER -0.04 (-1.50) -0.04 (-1.49) -0.04 (-1.53) -0.04 (-1.42) -0.04 (-1.51) -0.04 (-1.49) RIGHTS 0.04 (0.81) 0.05 (0.90) 0.05 (0.95) 0.04 (0.79) 0.04 (0.85 0.05 (0.88) No. Obs 746 746 746 746 746 746 Pseudo R2 4.47% 4.31% 4.16% 4.51% 3.82% 3.58%

Table XIX

Tobit Regression Explaining the Portion of Cash Financing in Assets Sell-off Transactions

The estimation is based on a two-boundary Tobit model to reflect lower and upper bound constraints on the percentage of cash used in the transaction. The z-stats are based on QML (Huber/White) heteroskedasticity-consistent standard errors. BUYERIB equals 1 if the buyer uses an investment banks, 0 otherwise. SELLERIB equals 1 if the seller uses an investment banks, 0 otherwise. BUYTOPIB equals 1 if the buyer uses a top tier investment banks, 0 otherwise. SELTOPIB equals 1 if the seller uses a top tier investment banks, 0 otherwise. BUYIBSHARE is the market share of the buyer’s investment bank in the previous year. SELIBSHARE is the market share of the seller’s investment bank in the previous year. YESBUYNOSEL equals 1 if the buyer uses an investment banks and the seller does not use an investment banks, 0 otherwise. NOBUYYESSEL equals 1 if the buyer does not use an investment banks and the seller uses an investment banks, 0 otherwise. SIZE is the natural logarithm of total assets of the buyer. PRIOR equals 1 if the buyer acquires divested assets at least 1in the last 10 years, 0 otherwise. RELATED equals 1 if the buyer and seller have the same four-digit SIC code, 0 otherwise. TECHBUYER equals 1 if the acquirer is a high-tech firm, 0 otherwise. TECHSELLER equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to buyer's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the buyer’s debt ratio. TOBINQ is the Tobin Q’s ratio of the buyer. CASHHOLDINGS is the cash holdings of the buyer scaled by buyer's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the buyer. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the buyer. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. ∗∗∗, ∗∗, and ∗ indicate statistical significance at 0.01, 0.05, and 0.10 level, respectively.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Intercept -0.22 (-0.10) 0.37 (0.43) 1.88 (0.91) -0.01 (-0.01) 1.51 (1.23) -0.19 (-0.07) BUYERIB -3.88 (-0.46)

147 SELLERIB -2.27 (-0.55) BUYTOPIB 2.91 (0.55) SELTOPIB -5.32 (-0.35) BUYIBSHARE 0.11 (0.63) SELIBSHARE -0.22 (-0.37) CASHHOLDINGS -0.001 (-0.01) -0.01 (-1.16) -0.01 (-0.88) -0.01 (-0.88) -0.01 (-0.97) -0.01 (-0.92) LEVERAGE -0.29 (-0.36) -0.03 (-0.08) -0.19 (-0.35) 0.03 (0.06) -0.07 (-0.22) 0.12 (0.25) INTCOVERAGE 0.001 (0.84) 0.001 (1.47) 0.01 (0.11) 0.001 (0.21) 0.01 (1.87)* 0.001 (0.49) SIZE 0.76 (0.50) 0.43 (0.64) -0.32 (-0.46) 0.69 (0.38) -0.25 (-0.50) 0.69 (0.41) TECHBUYER 0.13 (0.21) -0.04 (-0.17) -0.31 (-0.88) -0.12 (-0.45) -0.30 (-1.05) (-0.04) (-0.11) CREDITCRISIS 0.06 (0.44) 0.16 (0.67) 0.18 (0.66) -0.40 (-0.31) 0.16 (0.76) -0.29 (-0.32) ZSCORE -0.01 (-1.09) -0.01 (-2.15)** -0.01 (-2.19)** -0.01 (-0.92) -0.01 (-2.48)** -0.01 (-1.51) TOBINQ -0.01 (-0.02) 0.13 (0.54) 0.22 (1.05) 0.33 (0.66) 0.22 (1.23) 0.33 (0.71) RELSIZE -0.01 (-0.38) -0.01 (-0.36) 0.02 (0.36) 0.02 (0.21) 0.01 (0.28) -0.001 (-0.01) TECHSELLER 0.05 (0.16) 0.02 (0.08) -0.12 (-0.63) 0.04 (0.11) -0.09 (-0.61) -0.08 (-0.33) PRIOR -0.17 (-0.33) 0.11 (0.73) 0.19 (0.68) 0.02 (0.09) 0.17 (0.80) -0.02 (-0.07) RELATED -0.41 (-0.57) -0.36 (-0.70) -0.04 (-0.36) -0.30 (-0.47) -0.04 (-0.40) -0.36 (-0.47) CROSSBORDER 0.33 (0.32) -0.17 (-0.51) -0.18 (-0.52) 0.18 (0.18) -0.26 (-0.72) 0.03 (0.04) RIGHTS 0.05 (0.08) 0.10 (0.22) -0.09 (-0.19) -0.56 (-0.32) 0.12 (0.28) -0.32 (-0.30) No. Obs 809 809 809 809 809 809

Table XX

Tobit Regression Explaining the Portion of Cash Financing in Assets Sell-off Transactions

The estimation is based on a two-boundary Tobit model to reflect lower and upper bound constraints on the percentage of cash used in the transaction. The z-stats are based on QML (Huber/White) heteroskedasticity-consistent standard errors. BUYERIB equals 1 if the buyer uses an investment banks, 0 otherwise. SELLERIB equals 1 if the seller uses an investment banks, 0 otherwise. BUYTOPIB equals 1 if the buyer uses a top tier investment banks, 0 otherwise. SELTOPIB equals 1 if the seller uses a top tier investment banks, 0 otherwise. BUYIBSHARE is the market share of the buyer’s investment bank in the previous year. SELIBSHARE is the market share of the seller’s investment bank in the previous year. YESBUYNOSEL equals 1 if the buyer uses an investment banks and the seller does not use an investment banks, 0 otherwise. NOBUYYESSEL equals 1 if the buyer does not use an investment banks and the seller uses an investment banks, 0 otherwise. SIZE is the natural logarithm of total assets of the buyer. PRIOR equals 1 if the buyer acquires a least a private target in the last 10 years, 0 otherwise. RELATED equals 1 if the buyer and seller have the same four-digit SIC code, 0 otherwise. TECHBUYER equals 1 if the acquirer is a high-tech firm, 0 otherwise. TECHSELLER equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to buyer's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the buyer’s debt ratio. TOBINQ is the Tobin Q’s ratio of the buyer. CASHHOLDINGS is the cash holdings of the buyer scaled by buyer's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the buyer. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the buyer. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. ∗∗∗, ∗∗, and ∗ indicate statistical significance at 0.01, 0.05, and 0.10 level, respectively.

Model 1 Model 2 Model 3 Variable Coeff. z-Stat Coeff. z-Stat Coeff. z-Stat Intercept 0.93 (0.82) 1.90 (0.18) 0.95 (2.22)** YESBUYNOSEL 9.15 (0.24) 148 NOBUYYESSEL -17.70 (-0.11) RELIBSHARE 7.25 (0.64) SIZE -0.14 (-0.17) 0.17 (0.16) 0.06 (0.97) PRIOR 0.46 (0.27) 0.69 (0.12) 0.11 (0.84) RELATED -0.30 (-0.32) -0.29 (-0.14) -0.15 (-1.12) TECHBUYER -0.44 (-0.34) 0.001 (0.01) -0.21 (-1.24) TECHSELLER 0.08 (0.12) -0.27 (-0.13) -0.08 (-0.59) RELSIZE 0.05 (0.20) -0.10 (-0.11) 0.01 (0.21) LEVERAGE -0.73 (-0.22) 2.52 (0.11) -0.01 (-0.01) TOBINQ -0.20 (-0.12) 1.39 (0.12) 0.26 (1.33) CASHHOLDINGS -0.01 (-0.32) -0.08 (-0.18) -0.01 (-1.12) INTCOVERAGE 0.001 (0.63) 0.01 (0.14) 0.01 (1.86)* CREDITCRISIS 0.16 (0.29) 0.60 (0.11) 0.01 (0.08) ZSCORE -0.01 (-0.25) 0.01 (0.06) -0.01 (-2.52)** CROSSBORDER -0.76 (-0.27) -1.36 (-0.12) -0.16 (-0.57) RIGHTS 0.21 (0.16) 0.19 (0.07) -0.03 (-0.07) No. Obs 809 809 809

Table XXI

OLS Regression Explaining the Change in Operating Performance of Buyers in Assets Sell-off Transactions (-1 to +1)

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept -27.41 (-1.10) -27.47 (-1.10) -27.38 (-1.10) -25.95 (-1.10) -26.05 (-1.10) -26.11 (-1.10) BUYERIB -2.42 (-0.95) 149 SELLERIB -3.69 (-1.06) BUYTOPIB -3.54 (-0.99) SELTOPIB -0.37 (-0.30) BUYIBSHARE -0.16 (-0.97) SELIBSHARE -0.05 (-0.69) YESBUYNOSEL -2.70 (-0.97) NOBUYYESSEL -3.52 (-1.06) RELIBSHARE -3.13 (-0.58) SIZE 0.12 (0.24) -0.40 (-0.82) -0.31 (-0.68) -0.87 (-1.11) -0.90 (-1.12) -0.93 (-1.12) ALLCASH 2.26 (0.88) 2.10 (0.87) 2.17 (0.87) 2.29 (0.88) 2.72 (0.92) 2.40 (0.89) PRIOR -4.98 (-1.04) -5.01 (-1.04) -5.02 (-1.04) -4.98 (-1.04) -4.81 (-1.04) -4.89 (-1.04) RELATED -2.23 (-0.94) -1.62 (-0.87) -1.64 (-0.87) -1.51 (-0.85) -1.62 (-0.87) -1.51 (-0.85) TECHBUYER 0.39 (0.24) 0.15 (0.10) 0.24 (0.15) -0.02 (-0.01) -0.001 (-0.01) -0.03 (-0.02) TECHSELLER 1.78 (0.85) 1.59 (0.82) 1.53 (0.81) 1.53 (0.80) 1.57 (0.81) 1.55 (0.81) RELSIZE -0.85 (-0.68) -0.78 (-0.63) -0.81 (-0.65) -0.82 (-0.66) -0.92 (-0.73) -0.81 (-0.66) LEVERAGE 49.07 (1.06) 49.63 (1.06) 49.59 (1.06) 49.53 (1.06) 49.66 (1.06) 49.32 (1.06) TOBINQ 4.76 (0.90) 5.04 (0.99) 5.03 (0.89) 5.11 (0.90) 5.15 (0.90) 4.97 (0.90) CASHHOLDINGS -0.001 (-0.61) -0.002 (-0.91) -0.002 (-0.91) 0.001 (0.51) -0.002 (-0.90) -0.001 (-0.71) INTCOVERAGE 0.001 (0.97) 0.001 (0.97) 0.001 (0.97) 0.001 (0.97) 0.001 (0.97) 0.001 (0.97) CREDITCRISIS 9.31 (1.04) 9.02 (1.03) 8.96 (1.03) 9.08 (1.03) 9.29 (1.04) 9.19 (1.03) ZSCORE 0.19 (1.20) 0.19 (1.18) 0.19 (1.19) 0.19 (1.19) 0.20 (1.23) 0.19 (1.19) CROSSBORDER -0.03 (-0.01) 0.01 (0.01) 0.16 (0.05) 0.19 (0.06) -0.39 (-0.13) -0.01 (-0.01) RIGHTS 1.47 (0.31) 1.36 (0.29) 0.98 (0.21) 1.03 (0.21) 1.38 (0.29) 1.17 (0.24) No. Obs 699 699 699 699 699 699 McFadden R2 6.02% 5.90% 5.91% 5.87% 5.93% 5.84%

Table XXII

OLS Regression Explaining the Change in Operating Performance of Buyers in Assets Sell-off Transactions (-1 to +2)

The estimation is based on a Least Square model. The t-stats are based on White heteroskedasticity-consistent standard errors & covariance. BUYERIB equals 1 if the buyer uses an investment banks, 0 otherwise. SELLERIB equals 1 if the seller uses an investment banks, 0 otherwise. BUYTOPIB equals 1 if the buyer uses a top tier investment banks, 0 otherwise. SELTOPIB equals 1 if the seller uses a top tier investment banks, 0 otherwise. BUYIBSHARE is the market share of the buyer’s investment bank in the previous year. SELIBSHARE is the market share of the seller’s investment bank in the previous year. YESBUYNOSEL equals 1 if the buyer uses an investment banks and the seller does not use an investment banks, 0 otherwise. NOBUYYESSEL equals 1 if the buyer does not use an investment banks and the seller uses an investment banks, 0 otherwise. ALLCASH is a dummy variable equals 1 if there is 100 percent cash payment, 0 otherwise. . SIZE is the natural logarithm of the transaction value. PRIOR equals 1 if the buyer acquires a least a private target in the last 10 years, 0 otherwise. RELATED equals 1 if the buyer and seller have the same four-digit SIC code, 0 otherwise. TECHBUYER equals 1 if the acquirer is a high-tech firm, 0 otherwise. TECHSELLER equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to buyer's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the buyer’s debt ratio. TOBINQ is the Tobin Q’s ratio of the buyer. CASHHOLDINGS is the cash holdings of the buyer scaled by buyer's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the buyer. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the buyer. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. ∗∗∗, ∗∗, and ∗ indicate statistical significance at 0.01, 0.05, and 0.10 level, respectively.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept -6.68 (-1.48) -7.05 (-1.48) -6.97 (-1.48) -6.63 (-1.47) -6.71 (-1.48) -6.62 (-1.47) BUYERIB -0.14 (-0.50) 150 SELLERIB -0.76 (-1.46) BUYTOPIB -0.47 (-1.01) SELTOPIB -0.37 (-0.97) BUYIBSHARE -0.02 (-0.84) SELIBSHARE -0.02 (-1.05) YESBUYNOSEL -0.24 (-0.68) NOBUYYESSEL -0.83 (-1.45) RELIBSHARE 0.38 (0.29) SIZE -0.09 (-0.63) -0.13 (-0.84) -0.13 (-0.82) -0.24 (-1.30) -0.24 (-1.29) -0.25 (-1.30) ALLCASH 0.11 (0.29) 0.04 (0.11) 0.06 (0.16) 0.07 (0.18) 0.17 (0.42) 0.10 (0.25) PRIOR -0.64 (-1.19) -0.65 (-1.21) -0.65 (-1.20) -0.64 (-1.19) -0.61 (-1.19) -0.63 (-1.19) RELATED 0.06 (0.15) 0.15 (0.39) 0.14 (0.38) 0.16 (0.45) 0.15 (0.41) 0.16 (0.44) TECHBUYER -0.44 (-0.73) -0.48 (-0.78) -0.47 (-0.76) -0.51 (-0.83) -0.50 (-0.82) -0.52 (-0.83) TECHSELLER 0.40 (1.15) 0.38 (1.13) 0.36 (1.09) 0.36 (1.08) 0.38 (1.12) 0.36 (1.08) RELSIZE -0.40 (-1.52) -0.39 (-1.49) -0.39 (-1.49) -0.39 (-1.50) -0.42 (-1.52) -0.39 (-1.49) LEVERAGE 7.06 (1.12) 7.47 (1.14) 7.43 (1.14) 7.29 (1.13) 7.41 (1.14) 7.25 (1.13) TOBINQ 0.86 (0.54) 1.20 (0.68) 1.17 (0.67) 1.05 (1.02) 1.13 (0.66) 1.03 (0.62) CASHHOLDINGS -0.001 (-0.41) -0.001 (-0.33) -0.001 (-0.29) 0.001 (0.45) -0.001 (-0.28) -0.001 (-0.01) INTCOVERAGE 0.001 (0.70) 0.001 (0.58) 0.001 (0.60) 0.001 (0.64) 0.001 (0.76) 0.001 (0.64) CREDITCRISIS 1.11 (1.05) 1.02 (0.99) 1.03 (1.01) 1.06 (1.02) 1.11 (1.05) 1.07 (1.02) ZSCORE 0.17 (1.21) 0.16 (1.20) 0.17 (1.20) 0.16 (1.20) 0.17 (1.22) 0.17 (1.20) CROSSBORDER -1.34 (-1.15) -1.30 (-1.13) -1.28 (-1.11) -1.30 (-1.12) -1.41 (-1.19) -1.32 (-1.14) RIGHTS 3.99 (1.17) 3.95 (1.17) 3.89 (1.15) 3.93 (1.16) 4.00 (1.18) 3.94 (1.16) No. Obs 622 622 622 622 622 622 McFadden R2 11.23% 11.09% 11.08% 11.00% 11.22% 10.99%

Table XXIII

OLS Regression Explaining the Increase in Systematic Risk of Buyers in Assets Sell-off Transactions

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept -0.68 (-1.88)* -0.71 (-1.95)* -0.71 (-1.94)* -0.68 (-1.88)* -0.69 (-1.90)* -0.68 (-1.87)* BUYERIB -0.03 (-0.33) 151 SELLERIB 0.05 (0.61) BUYTOPIB -0.07 (-0.71) SELTOPIB -0.03 (-0.35) BUYIBSHARE -0.01 (-0.43) SELIBSHARE -0.01 (-0.89) YESBUYNOSEL 0.05 (0.42) NOBUYYESSEL 0.11 (1.22) RELIBSHARE 0.12 (0.38) SIZE -0.03 (-0.69) -0.02 (-0.33) -0.01 (-0.27) -0.03 (-0.76) -0.03 (-0.75) -0.03 (-0.73) ALLCASH 0.03 (0.32) 0.02 (0.27) 0.03 (0.29) 0.03 (0.37) 0.02 (0.26) 0.03 (0.37) PRIOR -0.05 (-0.59) -0.05 (-0.60) -0.05 (-0.60) -0.05 (-0.54) -0.05 (-0.61) -0.05 (-0.55) RELATED 0.07 (0.75 0.06 (0.67) 0.06 (0.64) 0.06 (0.69) 0.06 (0.74) 0.06 (0.70) TECHBUYER 0.07 (0.48) 0.07 (0.52) 0.07 (0.52) 0.06 (0.47) 0.06 (0.48) 0.06 (0.47) TECHSELLER -0.23 (-1.82)* -0.23 (-1.82)* -0.24 (-1.83)* -0.23 (-1.80)* -0.23 (-1.82)* -0.23 (-1.81)* RELSIZE -0.01 (-0.33) -0.01 (-0.35) -0.01 (-0.31) -0.01 (-0.27) -0.01 (-0.30) -0.01 (-0.27) LEVERAGE 0.44 (2.06)** 0.45 (2.10)** 0.45 (2.09)** 0.44 (2.03)** 0.43 (1.99)** 0.44 (2.04)** TOBINQ 0.46 (2.59)*** 0.47 (2.61)*** 0.47 (2.61)*** 0.46 (2.58)*** 0.46 (2.56)*** 0.47 (2.59) CASHHOLDINGS -0.001 (-1.34) -0.01 (-1.74)* -0.001 (-1.76)* -0.001 (-1.67)* -0.001 (-1.54) -0.001 (-1.62) INTCOVERAGE -0.001 (-2.04)** -0.001 (-2.00)** -0.001 (-2.00)** -0.001 (-1.92)* -0.001 (-2.26)** -0.001 (-1.91)* CREDITCRISIS 0.20 (2.53)** 0.20 (2.52)** 0.20 (2.51)** 0.21 (2.61)*** 0.20 (2.52)** 0.21 (2.59)*** ZSCORE -0.002 (-0.97) -0.002 (-0.89) -0.002 (-0.89) -0.002 (-0.92) -0.002 (-1.04) -0.002 (-0.92) CROSSBORDER -0.30 (-1.07) -0.30 (-1.05) -0.29 (-1.03) -0.30 (-1.07) -0.30 (-1.06) -0.30 (-1.07) RIGHTS 0.55 (1.54) 0.54 (1.51) 0.53 (1.48) 0.54 (1.52) 0.55 (1.54) 0.54 (1.52) No. Obs 697 697 697 697 697 697 McFadden R2 2.99% 3.01% 3.03% 2.96% 3.09% 2.95%

Table XIV

OLS Regression Explaining the Increase in Unsystematic Risk of Buyers in Assets Sell-off Transactions

The estimation is based on a Least Square model. The t-stats are based on White heteroskedasticity-consistent standard errors & covariance. BUYERIB equals 1 if the buyer uses an investment banks, 0 otherwise. SELLERIB equals 1 if the seller uses an investment banks, 0 otherwise. BUYTOPIB equals 1 if the buyer uses a top tier investment banks, 0 otherwise. SELTOPIB equals 1 if the seller uses a top tier investment banks, 0 otherwise. BUYIBSHARE is the market share of the buyer’s investment bank in the previous year. SELIBSHARE is the market share of the seller’s investment bank in the previous year. YESBUYNOSEL equals 1 if the buyer uses an investment banks and the seller does not use an investment banks, 0 otherwise. NOBUYYESSEL equals 1 if the buyer does not use an investment banks and the seller uses an investment banks, 0 otherwise. ALLCASH is a dummy variable equals 1 if there is 100 percent cash payment, 0 otherwise. SIZE is the natural logarithm of the transaction value. PRIOR equals 1 if the buyer acquires a least a private target in the last 10 years, 0 otherwise. RELATED equals 1 if the buyer and seller have the same four-digit SIC code, 0 otherwise. TECHBUYER equals 1 if the acquirer is a high-tech firm, 0 otherwise. TECHSELLER equals 1 if the target is a high-tech firm, 0 otherwise. RELSIZE is the relative size of the transaction to buyer's market value of equity, as of four weeks prior to the announcement. LEVERAGE is the buyer’s debt ratio. TOBINQ is the Tobin Q’s ratio of the buyer. CASHHOLDINGS is the cash holdings of the buyer scaled by buyer's market value of equity, as of four weeks prior to the announcement. INTCOVERAGE is the interest coverage ratio of the buyer. CREDITCRISIS equals 1 if the transactions happen during 2001-2002 and 2007 crisis. ZSCORE is the Altman Z-Score of the buyer. CROSSBORDER equals 1 if the deal is a cross-border deal, 0 otherwise. FREEDOM is the natural logarithm of the economic freedom rating of the seller country. RIGHTS equals 1 if the anti-director rights index is three or above, 0 otherwise. ∗∗∗, ∗∗, and ∗ indicate statistical significance at 0.01, 0.05, and 0.10 level, respectively.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept 0.002 (0.25) 0.003 (0.33) 0.003 (0.35) 0.002 (0.21) 0.002 (0.27) 0.002 (0.29) 152 BUYERIB -0.003 (-0.84) SELLERIB 0.004 (1.73)* BUYTOPIB 0.001 (0.25) SELTOPIB 0.002 (0.89) BUYIBSHARE 0.001 (0.54) SELIBSHARE 0.001 (0.48) YESBUYNOSEL -0.006 (-1.50) NOBUYYESSEL 0.002 (1.03) RELIBSHARE 0.001 (0.04) SIZE -0.001 (-0.60) -0.001 (-0.81) -0.001 (-0.81) -0.001 (-0.56) -0.001 (-0.69) -0.001 (-0.68) ALLCASH 0.002 (0.94) 0.003 (1.10) 0.003 (1.11) 0.002 (0.94) 0.002 (0.97) 0.002 (1.03) PRIOR -0.001 (-0.07) 0.001 (0.06) 0.001 (0.09) -0.001 (-0.07) 0.001 (0.01) 0.001 (0.05) RELATED 0.002 (0.85) 0.002 (0.79) 0.002 (0.79) 0.002 (0.83) 0.002 (0.77) 0.002 (0.75) TECHBUYER -0.001 (-0.03) -0.001 (-0.05) -0.001 (-0.07) 0.001 (0.04) -0.001 (-0.04) -0.001 (-0.04) TECHSELLER 0.001 (0.22) 0.001 (0.23) 0.001 (0.25) 0.001 (0.19) 0.001 (0.24) 0.001 (0.24) RELSIZE 0.002 (0.54) 0.001 (0.59) 0.002 (0.64) 0.001 (0.46) 0.001 (0.62) 0.001 (0.62) LEVERAGE -0.006 (-1.32) -0.006 (-1.32) -0.006 (-1.35) -0.005 (-1.17) -0.006 (-1.37) -0.006 (-1.33) TOBINQ -0.004 (-1.56) -0.004 (-1.61) -0.004 (-1.60) -0.004 (-1.52) -0.004 (-1.62) -0.004 (-1.60) CASHHOLDINGS 0.001 (4.57)*** 0.001 (5.39)*** 0.001 (5.40)*** 0.001 (4.25)*** 0.001 (5.19)*** 0.001 (5.15)*** INTCOVERAGE -0.0001 (-2.26)** -0.0001 (-1.80)* -0.0001 (-1.83)* -0.0001 (-1.87)* -0.0001 (2.14)** -0.0001 (-1.89)* CREDITCRISIS -0.009 (-3.01)*** -0.008 (-2.88)*** -0.008 (-2.89)*** -0.008 (-2.98)*** -0.008 (-2.99)*** -0.008 (-2.96)*** ZSCORE -0.0001 (-0.16) -0.0001 (-0.04) -0.0001 (-0.03) -0.0001 (-0.04) -0.0001 (-0.10) -0.0001 (-0.02) CROSSBORDER -0.006 (-0.85) -0.006 (-0.88) -0.006 (-0.87) -0.006 (-0.88) -0.006 (-0.84) -0.006 (-0.85) RIGHTS 0.011 (1.25) 0.011 (1.23) 0.011 (1.22) 0.011 (1.27) 0.011 (1.21) 0.011 (1.19) No. Obs 697 697 697 697 697 697 McFadden R2 2.86% 2.53% 2.52% 2.97% 2.55% 2.46%

Table XXV

OLS Regression Explaining the Increase in Total Risk of Buyers in Assets Sell-off Transactions

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Variable Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Coeff. t-Stat Intercept 0.001 (0.62) 0.001 (0.82) 0.001 (0.83) 0.001 (0.58) 0.001 (0.70) 0.01 (0.73) BUYERIB -0.001 (-0.76) 153 SELLERIB 0.001 (2.17)** BUYTOPIB 0.001 (1.01) SELTOPIB 0.001 (0.57) BUYIBSHARE 0.001 (1.23) SELIBSHARE 0.001 (0.29) YESBUYNOSEL -0.002 (-1.54) NOBUYYESSEL 0.001 (0.81) RELIBSHARE 0.001 (0.73) SIZE -0.001 (-0.91) -0.001 (-1.22) -0.001 (-1.24) -0.001 (-0.92) -0.001 (-1.09) -0.001 (-1.08) ALLCASH 0.001 (0.54) 0.001 (0.74) 0.001 (0.75) 0.001 (0.51) 0.001 (0.59) 0.001 (0.65)) PRIOR -0.001 (-0.11) 0.001 (0.11) 0.001 (0.14) -0.001 (-0.15) 0.001 (0.01) 0.001 (0.06) RELATED 0.001 (1.02) 0.001 (0.94) 0.001 (0.95) 0.001 (0.99) 0.001 (0.91) 0.001 (0.89) TECHBUYER -0.001 (-0.20) -0.001 (-0.25) -0.001 (-0.28) -0.001 (-0.11) -0.001 (-0.21) -0.001 (-0.22) TECHSELLER 0.001 (0.60) 0.001 (0.63) 0.001 (0.65) 0.001 (0.57) 0.001 (0.63) 0.001 (0.63) RELSIZE 0.0001 (0.42) 0.0001 (0.62) 0.0001 (0.65) 0.0001 (0.23) 0.0001 (0.55) 0.0001 (0.60) LEVERAGE -0.001 (-1.16) -0.001 (-1.25) -0.001 (-1.26) -0.001 (-0.91) -0.001 (-1.22) -0.001 (-1.23) TOBINQ -0.001 (-1.17) -0.001 (-1.22) -0.001 (-1.21) -0.001 (-1.12) -0.001 (-1.23) -0.001 (-1.21) CASHHOLDINGS 0.0001 (1.50) 0.0001 (1.69)* 0.0001 (1.70)* 0.0001 (1.70)* 0.0001 (1.46) 0.0001 (1.42) INTCOVERAGE -0.0001 (-2.81)*** -0.001 (-1.96)** -0.0001 (-1.96)** -0.0001 (-2.04)** -0.0001 (-2.44)** -0.0001 (-2.13)** CREDITCRISIS -0.001 (-1.66)* -0.001 (-1.56) -0.001 (-1.54) -0.001 (-1.65)* -0.001 (-1.62) -0.001 (-1.62) ZSCORE -0.0001 (-0.29) -0.0001 (-0.11) -0.0001 (-0.09) -0.0001 (-0.12) -0.0001 (-0.15) -0.0001 (-0.07) CROSSBORDER -0.001 (-0.35) -0.001 (-0.37) -0.001 (-0.40) -0.001 (-0.39) -0.001 (-0.34) -0.001 (-0.34) RIGHTS 0.002 (1.26) 0.002 (1.16) 0.002 (1.19) 0.002 (1.32) 0.002 (1.18) 0.002 (1.14) No. Obs 697 697 697 697 697 697 McFadden R2 1.98% 1.61% 1.66% 2.42% 1.53% 1.53%

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