The Role of Equity Underwriting Relationships in Mergers and Acquisitions*

Hsuan-Chi Chen Anderson School of Management, University of New Mexico Albuquerque, NM 87131, USA E-mail: [email protected]

Keng-Yu Ho Department of Finance, National Taiwan University Taipei City 106, Taiwan E-mail: [email protected]

Pei-Shih Weng Department of Finance, National Sun Yat-sen University Kaohsiung City 804, Taiwan E-mail: [email protected]

Chia-Wei Yeh Department of Banking and Finance, National Chi Nan University Nantou County 545, Taiwan E-mail: [email protected]

This draft: September 17, 2020

* This paper has benefited from comments and suggestions from Sevinc Cukurova, Ying-Chou Lin, and Hilmi Songur. We thank seminar and conference participants at National Central University and the meetings of Desert Finance Festival, Eastern Finance Association, and Financial Management Association for their helpful comments and suggestions. Hsuan-Chi Chen gratefully acknowledges support from Anderson School of Management at the University of New Mexico.

Address for correspondence: Hsuan-Chi Chen, Anderson School of Management, University of New Mexico, Albuquerque, NM 87131, USA. E-mail: [email protected]

1 The Role of Equity Underwriting Relationships in Mergers and Acquisitions

Abstract We examine the role of equity underwriting relationships in subsequent mergers and acquisitions (M&As). Firms, either the bidders or targets, tend to choose their M&A advisors with prior equity underwriting relationships. Consistent with the cost-saving hypothesis, retaining their prior underwriters as future advisors is related to cost reduction in the M&A advisory. Firms also experience shorter deal duration if they hire relationship advisors. This study contributes to the further understanding of how firms derive value from investment bank relationships.

Keywords: Underwriting; Financial Advisor; Merger and Acquisition JEL Classification Codes: G00; G20; G30

2 1. Introduction

The study on relationship banking has received considerable attention in the literature. For example, prior bank-firm relationship can increase the likelihood of winning subsequent securities underwriting business (e.g. Yasuda (2005); Ljungqvist,

Marston, and Wilhelm (2006, 2009), among others). Extending the line of research on relationship banking, this paper investigates how the prior underwriting relationship affects subsequent M&A advisory for the same underwriter-client pair.

Our particular interest on M&A advisory is also motivated by the fact that one major source of revenues for many banks comes from the provision of corporate mergers and acquisition advisory services. According to the calculation of Golubov, Petmezas, and

Travlos (2012), financial advisors were involved in M&As worth more than $4 trillion in 2007 alone (accounting for more than 85% of all transactions by dollar amount) and earned advisory fees of about $40 billion. Furthermore, M&As are one of relevant corporate decision making for firm operation. Cartwright and Schoenberg (2006) conduct a survey of the thirty years of mergers and acquisitions research and point out a broad range of management disciplines related to M&As. In addition, based on the data from SDC platinum of Thomson Reuters, though the financial crisis hit the global economy in 2008, the number of M&A deals announced was over 38,000 globally and the value of worldwide M&As was aggregated to $2 trillion for the following year.

Both the evolving nature of M&A activity and its impact on the revenues of advising investment banks make it important and relevant to investigate the association between the prior underwriting relationship and subsequent M&A advisory. Our study explores the effects of prior equity underwriting relationships on subsequent M&A advisory by examining (1) the chance of winning M&A advisory business for prior underwriting banks; (2) the advisory fees paid by firms; and (3) the quality of merger advisory.

Overall, we find that the established underwriting relationship significantly affects

3 subsequent M&A advisory. Our findings, combined with the results of previous studies

(e.g. Ljungqvist et al. (2009)), suggest that a firm’s underwriting and M&A advisory relationship with banks exhibit roundtrip effects.

When a firm issues stocks in the public market, the issuer establishes its underwriting relationship with banks that underwrite its shares. There are several reasons for studying equity underwriting relationships. First, James (1992) proposes that underwriters may invest in relationship-specific assets with the IPO underwriting client from due diligence, which would be helpful in underwriting subsequent equity offerings. Second, Hansen and Torregrosa (1992) suggest that underwriters gain valuable information as they monitor the underwriting process and investigate issuing firms with the purpose of improving performance and disciplining errant management.

Third, by examining the impacts of failure or near failure of investment banking firms on their industrial clients, Fernando, May, and Megginson (2012) and Kovner (2012) provide evidence supporting the importance of equity underwriting relationships.

To the extent that both theories apply to subsequent M&A advisory, either the theory of relationship-specific assets or the theory of underwriter monitoring predicts that the equity underwriting relationship increases the likelihood of advising subsequent mergers and acquisitions for prior underwriting banks. For the research question that whether an established underwriting relationship between banks and issuing firms affects the costs of M&A advisory, we develop the cost-saving hypothesis from both the relationship-specific capital and underwriter monitoring viewpoints, respectively.

First, if the underwriting bank acquires a better understanding of the client’s operation and learns to work more effectively with the client from due diligence in the underwriting process, the benefits of such soft information may be helpful for financial advisors in subsequent information collection activities when they engage in their prior clients’ M&As. Second, the monitoring component of the underwriting relationship

4 may partially fulfill the monitoring function that is required in the advisory process.

Advisory costs will be lower as long as issuing firms can capture part of cost savings.

Overall, both the relationship-specific capital and the underwriter monitoring viewpoints predict that the underwriting relationship can reduce the cost of subsequent

M&A advisory to issuers if cost savings are shared between advising banks and issuing firms.

In addition to examining the effect of prior underwriting relationship on the advisory fees, we also analyze the effect of underwriting relationship on the time to resolution (deal duration). Similar to Golubov et al. (2012), we posit that financial advisors with better understanding of the company via prior underwriting relationship would provide higher quality service. Therefore, the relationship M&A transactions will exhibit better service quality than otherwise.

The literature also indicates that prior underwriting relationship could be valuable to both firms and underwriting banks. Relevant to our research question, Ljungqvist et al. (2006) find that the main determinant in securing underwriting mandates is the strength of prior underwriting and lending relationships rather than aggressive analyst behavior. Burch, Nanda, and Warther (2005) examine the nature of underwriter-client relationships and their impact on the pricing of subsequent underwriting services for both common stock and debt offers. They find that loyalty to an underwriting bank reduces fees for common stock offers but increases fees for debt offers. Similarly, Ellis,

Michaely, and O’Hara (2011) provide empirical results suggesting that if firms have used the banks in prior debt underwriting or lending, the banks are more likely to be retained. Moreover, Chen, Ho, and Weng (2013) investigate the relation between the

IPO underwriting and subsequent lending. They find that when a bank underwrites a firm’s IPO, the bank is more likely to provide the issuer with future loans at a lower cost, compared to banks without an IPO underwriting relationship. Their findings imply

5 that the underwriting banks share information surplus with the IPO firms in the post-

IPO loans.

While the above studies tend to suggest that prior underwriting relationship plays a beneficial role for subsequent corporate financing, including lending and underwriting, the effect of prior underwriting relationships on the merger advisory mandate are unexplored. Also, some studies have examined the effect of non-underwriting relationship on the likelihood of winning a merger advisory mandate. For instance,

Saunder and Srinivasan (2001) examine the relationship between merger advisor reputation and its ability to retain clients. They find that firms are more likely to switch if their advisor is not a top tier investment bank. Hayward (2003) posits that if a firm has used an investment bank in a previous merger, the bank would abuse the power to induce the client to hire it again for subsequent stock-financed acquisitions. Bao and

Edmans (2011) suggest that merger advisory mandates are given on the basis of past market share league tables, but such mandates result in significant negative returns for the acquirers. Forte, Iannotta, and Navone (2010) find that the decision to hire an advisor in M&As is related to the intensity of the previous banking relationship. More recently, Chang, Shekhar, Tam, and Yao (2016) find that acquirers are more likely to switch their advisors if their former advisors have advisory relationship with their rivals.

Different from the existing literature, we employ a sample of post-underwriting

M&As advised by the firms’ underwriting banks during the period 2000-2018 and present additional evidence to compliment the literature on the bank-firm relationship.

By merging several data sources, we identify the equity issuing firms, underwriters

(financial advisors), M&A transactions, and the characteristics of M&As. Furthermore, we identify whether financial advisors of M&As are also underwriting banks for the same issuing firms. The investigation of the equity underwriting relationship sheds light on whether underwriting banks increase the probability of attracting future M&A

6 advisory business from their underwriting clients, whether these banks charge their prior underwriting clients differently than non-underwriting clients, and how the underwriting relationship affects the service quality of merger transactions in terms of time to resolution.

Our results show that an equity underwriting relationship significantly increases the likelihood that the equity issuing firm will return to the same bank for its future merger advisory needs. Using “what-if” analysis, we find that retaining prior equity underwriters as future M&A advisors is related to cost reduction in the M&A advisory.

Specifically, the cost of retaining prior equity underwriters as M&A advisors is lower than it would have been if an alternative had been chosen, which is consistent with the implication of the cost-saving hypothesis. Furthermore, our subsample analyses show that for target firms who do not hire top-tier banks as their advisors, the underwriting relationship helps reduce their advisory fees; meanwhile, for both acquiring and target firms, the prior equity underwriting relationship helps reduce advisor fees in large deals.

Finally, we find that M&A firms could experience shorter deal duration if they hire relationship advisors.

This study makes several contributions to the literature of relationship banking and

M&As. First, our findings complement with the previous studies that focus on the prior lending relationship (e.g., Yasuda (2005); Ljungqvist et al. (2006)), on the prior IPO underwriting relationship (e.g., Chen et al. (2013)), and on the advisor choice in mergers and acquisitions (e.g., Saunders and Srinivasan (2001); Forte et al. (2010); Chang et al.

(2016)). In particular, we extend the literature to shed new light on the role of equity underwriting relationships and provide linkage for the equity underwriting market and

M&A advisory market. Second, Fernando et al. (2012) and Kovner (2012) provide evidence supporting the importance of equity underwriting relationships. By exploring the relation between the prior equity underwriting relationships and subsequent M&A

7 advisory, our study also contributes to the further understanding of how firms derive value from investment bank relationships.

The remainder of the paper proceeds as follows. Section 2 develops the main hypotheses. Section 3 describes the data and methodology. Section 4 presents the empirical results. Finally, Section 5 concludes the study.

2. Hypothesis Development

In this paper we examine the following three research questions. First, does the prior underwriting relationship affect the likelihood of advising subsequent M&As of the issuers for underwriting banks? Second, does the established underwriting relationship affect the costs of M&A advisory? Third, does the established underwriting relationship affect the service quality of merger transactions? We develop the testable hypotheses relevant to these questions in the following subsections.

2.1 The underwriting relationship and the likelihood of winning subsequent M&A advisory

Previous theoretical models emphasize the information production role of banks through screening and monitoring (e.g., Diamond (1991); Leland and Pyle (1977);

Chemmanur and Fulghieri (1994); Rajan and Winton (1995)). By offering a broader set of financial service, banks can further enhance their information advantage and establish a deeper and longer relationship with their clients (e.g., Petersen and Rajan

(1994); Yasuda (2005)). From another viewpoint, Hayward (2003) finds that if a firm has been advised by an investment bank in previous mergers and acquisitions, the bank would abuse the power gained from its specialized expertise and lead the client towards complex problems, inducing the client to hire that specific investment bank again.

These studies state conclusively that a bank with the established relationship with its

8 client is more likely to involve in future corporate events than one without such relationship.

Indeed, the empirical findings of Bharath, Saunders, and Srinivasan (2007) show that the probability of a relationship lender providing a future loan is 42%, while the probability of being chosen for a non-relationship lender is only 3%. They also find that the firm with greater information asymmetry is more likely to obtain future loans from its previous relationship bank. Furthermore, Chen et al. (2013) show that the bank that underwrites a firm’s IPO is more likely to engage in future lending business with the same firm than other banks that did not underwrite its IPO. In this paper we consider prior equity underwriting relationships between firms and banks, and we expect that in the presence of such underwriting relationship, a candidate bank is more likely to win a particular merger advisory mandate in the future.

Our conjecture is also in line with the real world practice. According to the ranking of Carter, Dark and Singh (1998), Saunders and Srinivasan (2001), and Golubov et al.

(2012), most of the top merger advisors are also top underwriters. In addition, Kovner

(2012) indicates that underwriters interact with and potentially monitor their clients following the equity issues in several ways. For example, lead underwriters continue to discuss the prospects of security offerings and the opportunities of mergers and acquisitions with the post-IPO clients. Underwriters typically provide analyst coverage for their IPO firms, and they may organize post-deal roadshows following the IPOs of their clients.

Overall, one testable implication for the impact of equity underwriting relationship on subsequent M&A advisory is that an underwriting bank is more likely to engage in the M&A advisory business with its underwriting client than others without such relationship. We formalize the hypothesis as follows.

H1: (The increasing retention likelihood hypothesis) The bank that underwrites a firm’s

9 IPO/SEOs is more likely to engage in future M&A advisory business with the same firm than other banks that do not have this underwriting relationship.

2.2 The underwriting relationship and M&A advisory fees

Our second research question investigates the effect of a prior underwriting relationship on merger advisory fees for either bidders or targets. Burch et al. (2005) find that loyal firms pay lower fees for debt and follow-on equity offerings. However, the literature on the relation between merger advisory fees and bank-firm relationships is relatively sparse. Hunter and Jagtiani (2003) find that the payment of larger advisory fees does not improve the likelihood of completing a deal, suggesting that fees are not related to service quality. In light of the results reported in the underwriting fee literature, one would expect merger advisory fee to be negatively related to the existence of prior bank-firm relationships because prior engagement can reduce the advisor’s cost of information production (Chang et al. (2016)). Both Burch et al. (2005) and Chang et al.

(2016) support the interpretation of relationship-specific capital as we discuss earlier.

In the course of providing intermediation services or through an ongoing effort, the underwriter-client relationship becomes a valuable asset that lowers the cost and improves the quality of the services provided. These benefits emerge if the bank acquires a better understanding of the client’s operations and learns to work more effectively with the client from due diligence in the underwriting process. The argument implies that the underwriting relationship can reduce the advisory fees paid by the acquirers or target firms in subsequent M&As if the firms can capture part of these cost savings from their financial advisors.

There is some support for the cost-saving argument in the literature. For example,

Drucker and Puri (2005) find evidence that prior lending relationships, in general, are associated with significantly lower underwriting fees due to the existence of

10 information scope economies between lending and underwriting.1 Schenone (2004) shows that IPO firms with pre-IPO banking relationships tend to exhibit lower underpricing than firms without such banking relationships. These studies conclude that companies may benefit from the prior lending relationship with banks due to less information asymmetry or lower monitoring costs imposed in the subsequent business in terms of banks’ compensation for risks.

However, firms may still pay higher fees to advisors with whom they had relationships in the past if they worry that their advisors may abandon them and advise their merger counterparties (Chang et al. (2016)). On the other hand, contrary to the cost-saving argument, Petersen and Rajan (1994) point out that banks may not share saving with the clients, particularly if they have informational advantage. Furthermore,

Degryse and Van Cayseele (2000) report that financing costs increase with the length of the lending relationship, suggesting that banks exploit their information advantage from small firms. All the arguments tend to suggest that banks who have previously underwritten a firm’s equity offerings charge higher advisory fees.

The two different implications regarding the M&A advisory fees charged by relationship underwriting banks give rise to the following competing hypotheses.

H2a: (The cost-saving hypothesis) Banks that subsequently advise their underwriting clients will charge lower advisory fees than the banks without prior underwriting relationship.

H2b: (The rent expropriation hypothesis) M&A firms will pay higher advisory fees to their financial advisors with underwriting relationship than to those without such relationship.

1 Chen et al. (2013) find that established IPO underwriting relationship can lower the lending costs of post-IPO loans.

11 2.3 The underwriting relationship and time to resolution

Advisory fees can be viewed as the prices of M&A advisory services. A natural research question is how the underwriting relationship affects the quality of such services. To answer this question, we use the time to resolution, which is the time it takes from deal announcement to deal completion or withdrawal, as a measure of service quality.

The predicted direction of the relationship between the equity underwriting relationship and the time to resolution is less clear. On one hand, if a bank has prior underwriting relationship with a M&A client, it has access to private information regarding the firm’s cash flows, financial resources, and other crucial factors, which can facilitate a more accurate valuation about the deal relatively quickly. Also, if the bank learns to work more effectively with the advisory client through the prior underwriting process, the bank might be able to work through the deal in a shorter period of time. On the other hand, since the bank has more relationship capital at stake, the bank might spend more time to complete the deal by carefully evaluating the terms of the M&A transaction and negotiating favorable terms for the advisory client. More due diligence and negotiation might help the bank accumulate the relationship capital continuously and secure more subsequent businesses in the future. We formalize the two hypotheses on the time to resolution as follows.

H3a: (The efficiency hypothesis) Banks that subsequently advise their underwriting clients will complete the M&A deal more quickly than the banks without prior underwriting relationship.

H3b: (The relationship capital hypothesis) Banks that subsequently advise their underwriting clients will spend more time to complete the M&A deal than the banks without prior underwriting relationship.

12 3. Data and Methodology

3.1 Data source and sample

We collect data by matching the equity-issuing firms, including IPOs and SEOs, with both the acquirers and target firms in M&A transactions using the U.S. Domestic

New Issues and M&A Database of Thomson Reuters. We also identify all required information on lead underwriters and financial advisors. To control for other characteristics of firms and underwriters/advisors, we use COMPUSTAT and CRSP as the primary data sources for the relevant variables.

The sample of equity issuing firms consists of all IPOs and SEOs between 1995 and 2018. Among them, we exclude American Depository Receipts, real estate investment trusts, closed-end funds, financial firms (SIC code 6000-6999), utility firms

(SIC code 4900-4999), and unit offers from the initial sample. The selection results in

10,211 equity offerings issued by 5,718 firms. The M&A sample includes mergers and acquisitions between U.S. firms from January 2000 and December 2018. For each post- underwriting merger transaction, we identify the prior equity underwriting relationship using a five-year window.2 We exclude the deals that have missing SIC codes for either the acquirers or the targets, and exclude transactions that involve financial companies

(SIC code 6000-6999) and utility companies (SIC code 4900-4999). We also exclude the deals that do not have transaction values in the SDC and transactions in which neither the acquirer nor the target firm appoints an advisor. Finally, we adopt the filter used in Chang et al. (2016) to exclude “buybacks”, “exchange offers”, and

“recapitalizations” as indicated by the SDC, and privatizations in which acquirers and targets have the same CUSIP. The merger sample consists of 13,572 transactions.

We merge the M&A data and underwriting data for the same firm. For either

2 That is, within five years prior to the announcement of a firm’s M&A, if the advisors of merger transaction also served as the underwriters in past IPOs or SEOs for the bidders or targets, the transactions are designated as relationship M&As, and the advisors are designated as relationship advisors.

13 acquiring firms or target firms, only the M&A transactions within five years after the equity insurance are included because our focus is to examine the effect of prior equity underwriting relationship on M&As. This criterion excludes the issuing firms that have no M&A after their equity underwriting and the M&A transactions take place beyond the five-year post-underwriting window. Our match and selection yield a sample of 678 acquiring firms and a sample of 750 target firms that each firm has at least one M&A transaction including withdrawn cases from 2000 to 2018. Overall, there are 1,049

M&As associated with the 678 acquiring firms and 798 M&As associated with the 750 target firms.3

To investigate the effect of a prior underwriting relationship on the likelihood of winning M&A advisory business, we follow Hellmann, Lindsey, and Puri (2008) and utilize a bank-acquirer (bank-target) pair analysis.4 For each of acquiring firms (target firms), we consider all possible matches of their M&A transactions with the 25 banks that are involved in at least five equity offerings as underwriter and five M&As as financial advisor between January 2000 and December 2018. These 25 banks are listed in Appendix 1. Such combination generates a total of 26,225 observations for the acquirers and 19,950 observations for the targets, and allows us to classify the underwriting and advisory relationship between a specific firm and a bank. For the regression models of advisory fees, the observation unit is an M&A deal, including relationship M&As and non-relationship M&As.

3 Some target firms seem to have been acquired more than once. One reason for this is due to the withdrawn cases. For example, National Dairy Holdings LP (CUSIP=63561M) announced to acquire Milk Products of Alabama LLC (CUSIP=59980K) on 2004/07/15, but the deal was withdrawn on 2004/09/15. Furthermore, Dean Foods Co (CUSIP=242370) announced to acquire Milk Products of Alabama LLC on 2004/09/15, and the deal was completed on 2004/10/15. Another reason is because some target firms become subsidiaries without changing their company names after the M&A transactions. For example, Performance Food Group Co (CUSIP=713755) announced to acquire Fresh Express Inc. (CUSIP=35804X) on 2001/08/09, and the deal was completed on 2001/10/16. After that event, Chiquita Brands International (CUSIP=170032) announced to acquire Fresh Express Inc. on 2005/02/23, and Fresh Express Inc. was sold to Chiquita Brands International on 2005/06/28. 4 For brevity, we use “bank-firm pair” as a general term which stands for either bank-target pair or bank- acquirer pair in the following sections.

14

3.2 Methodology

3.2.1 Basic probit models

To examine whether an underwriting relationship translates into a higher probability of winning future advisory business from the same firm, we estimate a probit model to examine the likelihood that underwriters participate in the issuer’s subsequent M&A advisory. In term of model setting, we focus on bank j’s likelihood of winning the advisory business of firm i. Our data set consists of 26,225 (19,950) bank-firm pairs for acquirers (targets), and each pair represents one observation in the following probit model:

�������, = � + ������������, + ������������, ∗ ���,

+������������, ∗ �������, + ������������, ∗ �� _ � ����,

������������, ∗ ���8 + ���� _ � ��, + ∑ �(�������). (1)

The dependent variable, Advisor, takes the value of one if bank j is the financial advisor of issuing firm i in any of its mergers and acquisition, and zero otherwise. Underwriter is a dummy variable that takes the value of 1 if bank j served as a lead underwriter of firm i within the 5 years prior to the M&A announcement, and zero otherwise. TOP8 is a dummy variable that equals 1 for a bank-firm pair if the bank in the pair is designated as a top 8 M&A advisory bank in the current deal for the firm. Specifically, TOP8 equals

1 if the M&A advisor is ranked as top eight in the league table as of the prior calendar year before the M&A announcement, and zero otherwise. IPO is a dummy variable that equals 1 if bank j served as an IPO underwriter of firm i within the prior 5 years, and zero otherwise. PROCEED is the total equity offering proceeds if bank j served as an underwriter of firm i within the prior 5 years. NO_ISSUE is the number of times that bank j served as an underwriter of firm i within the prior 5 years. PRE_ADV is a dummy variable that equals 1 if the bank was the advisor of the firm’s M&A within the past 5

15 years, and zero otherwise. Specifically, each firm might get involved in multiple M&A transactions within a 5-year window. Given the current M&A transaction and a bank- firm pair, if the bank advises the firm within the prior 5 years, then PRE_ADV takes the value of one for this bank-firm pair, and zero otherwise. We control for this variable by assuming that a bank is more likely to be chosen as an M&A advisor by the firm if the bank has advised the earlier M&A deals of the firm.

Other control variables in the probit model are defined as follows.

IndustryExpertise proxies for the bank’s expertise in merger parties’ industries.

Following Chang et al. (2016) and Sibilkov and McConnell (2014), we define IndustryExpertise as the number of M&As advised by the bank for the firm’s industry divided by the total number of M&As in the firm’s industry within five years before the announcement date of its first M&A after an equity offering. DAYS is the number of days between the equity offering date and the merger announcement date for each firm.5 ASSET is the book value of the assets of the firm at fiscal year-end prior to the announcement date of the first M&A after an equity offering. LEVERAGE is the ratio of the book value of total debt to the book value of total assets of the firm at fiscal year-end prior to the announcement date of the first M&A after an equity offering. To avoid outliers driving our results, we winsorize all continuous variables at the 1st and

99th percentiles. The definitions of the variables used in Equation (1) are collected in

Appendix 2. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the M&A and of equity issuance.6 In addition, because we use a sample of bank-firm pairs, a correlation may exist in the error terms within firms. Thus, throughout all regression analysis, we cluster standard errors by

5 We calculate the number of days between the nearest equity offering date and the M&A announcement date if there are multiple equity offerings prior to a specific M&A transaction. If there is only one equity offering before the M&A deal, DAYS simply measures the number of days from the IPO/SEO date to the M&A announcement date. 6 For brevity, we do not report the coefficient estimates of these dummy variables in the tables.

16 firm to control for such a bias.

3.2.2 Bivariate probit models

Although Equation (1) allows us to establish a correlation between the prior underwriting relationship and winning subsequent M&A advisory business for banks, we note that Underwriter may be endogenous and correlated with factors that affects advisory relationship. Therefore, following the procedure of Hellmann et al. (2008), we regress the main probit model together with an additional equation to perform simultaneous estimation. Because the additional equation is adopted for factors affecting Underwriter, we use a bivariate probit model to estimate the coefficients. In the additional regression, we incorporate an instrumental variable which affects the prior underwriting relationship between firms and banks but is not directly related to the subsequent relationship in the M&A advisory services. We use the number of underwriting co-managers of a specific bank as such an instrument. The definition of this variable is the average number of co-managers joined in the prior year (t–1) for each underwriter in year t.7 According to Corwin and Schultz (2005), this variable reflects the success probability (or networking of institutional investors) of equity offering. That is, more co-managers, more investors’ attraction. Since this variable represents the higher success probability of equity offering, a bank with higher networking ability will make it more likely to be selected as an underwriter. On the other hand, the merger market is more related to acquirers and targets. Underwriters’

(and co-managers’) networking or ability of cooperation seems not relevant in the merger advisory market, where usually there is a single bank (no co-managers) providing the service. Therefore, a bank with a larger number of co-managers should

7 For example, underwriter A has led 3 IPOs/SEOs last (calendar) year (t-1). The underwriter has 5, 4, and 0 co-managers. Thus, the measure for underwriter A in year t = (5+4+0)/3 = 3.

17 be more likely to attract underwriting business, but this variable is not related to the likelihood of winning advisory business. This characteristic makes it suitable for serving as an instrumental variable. We denote the instrumental variable as NO_COMR.

3.2.3 Multivariate regressions

To examine whether the underwriting relationship affects the costs of M&A advisory, we compare the advisory fees of relationship M&As with those of non- relationship M&As. We identify 1,195 transactions for acquirers and 928 transactions for targets, in which the data of all control variables are available.8 Because advisory fees may be affected by firm characteristics and M&A transaction characteristics, we employ a multivariate regression model using firm characteristics and transaction characteristics as control variables to examine advisory fees. The basic regression model is specified as:

�������� ��� = � + ������ + ����8 + ������ ∗ ���8

+����_�� + ∑ �(�������), (2) where Advisory fee is the advisory fee expressed as the percentage of M&A transaction value. RELMA equals 1 if the M&A transaction is a relationship transaction, and zero otherwise. TOP8 equals 1 if any advisor of the firm in an M&A transaction is a top 8 advisory bank, and zero otherwise.9 PRE_MA equals 1 if the current advisor for the

M&A transaction also served in previous transactions for the acquirer or the target. The independent variables including firm and M&A transaction characteristics are

8 Because the data of advisory fees in SDC are not always available, only 113 (588) out of 1,195 (928) transactions for the acquirers (targets) are adopted in the regressions. 9 The definition of TOP8 in Equation (2) is slightly different from that used in Equation (1). An observation in Equation (1) is a bank-firm pair. TOP8 equals 1 for any bank-firm pair if the bank in the pair is designated as a top 8 M&A advisory bank. In Equation (1), we have multiple bank-firm pairs in an M&A transaction since we match a list of banks to a company. Therefore, there could be more than one bank-firm pair with top advisors in an M&A transaction because the annual top 8 M&A advisors are always included in our list of 25 banks.

18 illustrated as follows. DAYS is the number of days between the equity offering date and the M&A announcement date for each firm, which has the same definition as used in

Equation (1).10 MA_SIZE is the logarithm of the M&A transaction value in USD million. NO_ADV is the number of financial advisors in the M&A transaction hired by the acquiring (target) firm. CASH is a dummy variable that equals 1 if the M&A transaction in the consideration is entirely in cash, and zero otherwise. COMM is a dummy variable that equals 1 if the M&A transaction in the consideration is entirely in common stock, and zero otherwise. ASSET is the book value of the assets of the firm at fiscal year-end prior to the announcement date of the M&A transaction. LEVERAGE is the ratio of the book value of total debt to the book value of total assets of the firm at fiscal year-end prior to the announcement date of the M&A transaction. To avoid outliers driving our results, we winsorize all continuous variables at the 1st and 99th percentiles. We also collect the definitions of the variables used in Equation (2) in

Appendix 2.

The literature suggests that selection bias may arise in such regression (Benzoni and Schenone, 2010; Li and Prabhala, 2007). If the underwriter’s decision on whether to advise the prior issuer in subsequent M&As is non-random with unobserved private information held by the underwriting banks, the results we obtain from Equation (2), without correcting self-selection bias, would be biased. Although the market cannot directly observe this private information, it can use the underwriting bank’s decision to update its belief about the bank’s private information. In line with the previous studies, this update is defined as the conditional expectation of the bank’s private information, given its decision on advisory, and is denoted by λ. The conditional expectation can be interpreted as the information revealed by the bank’s advising decision. Therefore, to

10 In unreported analysis, we also calculate the average number of days for multiple equity offerings and find that the alternative definition provides qualitatively similar results.

19 control for potential self-selection bias, we add λRELMA in the OLS regression model for advisory fees. The new specification is the Heckman (1979) two-stage method explicitly stated as:11

�������� ��� = � + ������ + �� + ����8 + ����_��

+ ∑ �(�������), (3) and f(Zgˆ) f(Zgˆ) l = RELMA - (1- RELMA) , (4) RELMA j F(Zgˆ) 1- F(Zgˆ) where gˆ is the first-stage probit estimate of the selection model, and Z is the corresponding vector of explanatory variables in the probit regression. For the selection variable Z, we need to add at least one additional variable as an instrument. In line with the likelihood estimation in Section 3.2.2, again we adopt the average number of co- managers (NO_COMR) joined with the underwriting bank that also serves as financial advisor in a subsequent M&A transaction as the selection variable.12 A significant coefficient of λRELMA implies that the advisory fees for relationship M&As are influenced by the sample selection problem.

When examining whether the underwriting relationship affects the time to resolution of M&A deals, we use similar multivariate regression models with time to resolution as the dependent variable and compare the deal durations of relationship

M&As with those of non-relationship M&As. Time to resolution is measured as the number of days between the announcement date and resolution (effective or withdrawn)

11 The Heckman (1979) two-stage procedure corrects the selection bias. The first stage is to specify a model of self-selection using a probit model. The variable RELMA serves as the dependent variable that is a function of selection variables (Z) in the probit regression. We use the estimated parameters (gˆ ) for selection variables (Z) to calculate Heckman λ, which is then included as an additional explanatory variable with other explanatory variables to perform the ordinary least squares estimation in the second stage. Hoechle, Schmid, Walter, and Yermack (2012) and Song, Wei, and Zhou (2013) also use this two- stage procedure to control for endogeneity. 12 The calculation of NO_COMR here is slightly different from that used in probit regression. Because our analysis here is based on each merger transaction, not the bank-firm pair observation in Section 3.2.2, we use the average value of NO_COMR for each transaction when a transaction has more than one financial advisor.

20 date and divided by 365 when used in the regression models.

4. Empirical Results

4.1 Description statistics

Table 1 reports the descriptive statistics of the sample based on bank-firm pairs.

The acquirer sample includes 26,225 observations, generated from 25 banks and 678 acquiring firms, and 19,950 observations for the target sample, generated from 25 banks and 750 target firms. We report the average value and the percentage value of main variables for the acquirer and target sample separately and also present the results for two subgroups of banks that participate in subsequent merger advisory and those do not, respectively.

[Insert Table 1 Here]

The percentage of the underwriting relationship (Underwriter) for acquirers

(targets) is 8.0% (7.1%) in terms of bank-firm pairs. On average, for our 25 bank candidates, each acquirer (target) tends to have the established underwriting relationship with one or two banks. Similarly, the percentage of Advisor is around 4% for acquirers and targets, suggesting that each acquirer (target) also has, on average, one advisor in their M&A transactions. This is consistent with the finding of previous studies that more than 60% of M&A transactions have only one financial advisor involved. The percentage of IPO is 2.8% (3.6%) for the acquirers (targets); compared with the percentage of Underwriter, the numbers show that around 36% (50%) of the underwriting relationships are based on IPO underwriting in the sample of acquiring

(target) firms.

Other statistics also provide insights for the sample structure. 32% of advisors, either for acquirers or targets, are top eight M&A advisors in the advisory market; the average time length between equity issuance and subsequent M&A advisory is around

21 two years; 4.8% of banks are hired as the financial advisors in the previous M&A transactions for the same bidders, whereas 2.8% of banks are hired for the same target firms. Finally, the comparison among two subgroups, Advisor = 1 and Advisor = 0, reveals that, for both acquirers and targets, the advisory relationship is related to the underwriting relationship.

Table 2 reports the summary statistics for M&A transactions with prior equity issuance within 5 years only.13 It shows that, the percentage of nonrelationship M&As is greater than that of relationship M&As. More than 52% of firms hire the top-tier financial advisor for their M&A transactions, suggesting that advisor reputation is an important factor for the advisor choice. For acquirers, 46% (19%) of transactions are entirely cash (common stock) mergers, whereas 55% (20%) of transactions are entirely cash (common stock) mergers for targets. 23% (16%) of acquiring (target) firms hire at least one advisor who had been hired in their previous M&As.

[Insert Table 2 Here]

The M&A transactions with the underwriting relationships seem to be different from those without the underwriting relationship in a few characteristics. Both acquiring and target firms with underwriting relationship pay lower advisory fees. The average advisory fee for relationship acquiring (target) firms is 0.75% (1.17%), whereas that paid by non-relationship acquiring (target) firms are 2.11% (1.44%).

In sum, relevant to our major research questions, the statistics for bank-firm pairs or M&A transactions imply that banks with the underwriting relationship with the firms are more likely to be retained as the advisors in the firms’ subsequent mergers and acquisitions. In addition, the retained advisors tend to charge lower advisory fees than those do not have the prior underwriting relationship.

13 We also use the sample without this restriction on all tests as robustness check. The corresponding results are qualitatively similar.

22

4.2 Probit model for selecting M&A advisor

Our model setting is similar to the choice model of Ljungqvist et al. (2006) but we analyze it using the approach of bank-firm pairs, which is also applied in Hellmann et al. (2008) and Chen et al. (2013). That is, a bank-firm pair is one unit of observation in the probit model. As specified in Equation (1), the significantly positive coefficient on the variable Underwriter suggests that the underwriting relationship is associated with a higher probability for winning future M&A advisory business for banks. In Table 3 we report the results of advisor choice for both the acquirers and targets and find that the prior underwriting relationship increases the likelihood of a bank being further chosen as the financial advisor in a subsequent M&A. In addition to statistical significance, the effect of the underwriting relationship on winning future M&A advisory business is of economic significance. The marginal effect suggests that the prior underwriting relationship can boost the chance for a bank to serve as an acquirer’s

(target’s) advisor by 5% (8%) in the future. Other variables show no marginal effect as high as Underwriter except for IndustryExpertise that is documented in Chang et al.

(2016).

[Insert Table 3 Here]

For the equity underwriting relationship, the variable, NO_ISSUE, has an extra effect on the advisor choice. The interaction term of Underwriter*NO_ISSUE is positively and significantly related to merger advisory relationship, indicating that the strength of underwriting relationship is also crucial to the selection of M&A advisors.

The interaction term of Underwriter*IPO is significant for selecting advisors of acquiring firms, suggesting that IPO underwriting relationship is more important than

SEO underwriting relationship in the sample of acquirers. The variable TOP8 is significantly positive and is in line with the findings in the previous studies which show

23 that advisor reputation or ranking in terms of market share is crucial to mergers and acquisitions (e.g. Bowers and Miller (1990); Daniels and Phillips (2007), Forte et al.

(2010); Golubov et al. (2012); Kale, Kini, and Ryan (2003), among others). The finding is also consistent with Saunders and Srinivasan (2001) who find that firms are more likely to switch if their M&A advisor is not a top tier investment bank. However, the interaction term of Underwriter*TOP8 is significantly negative for acquiring firms only, indicating that a top M&A advisor that was a former underwriter is less likely to be hired as an advisor by acquiring firms. This finding suggests that a substitution effect may exist between the equity underwriting relationship and advisor reputation. In contrast, the interaction effect is weak for target firms as shown by the insignificant coefficient of Underwriter*TOP8.

Overall, the results in Table 3 support the hypothesis, H1, suggesting that M&A firms, either acquirers or targets, take prior underwriting relationship into account when choosing their financial advisors for subsequent M&A transactions. In other words, having built the underwriting relationship, a bank is more likely to win subsequent

M&A advisory business from issuing firms than other banks lacking such established relationship. The finding is also the complementary evidence to the bank-firm relationship literature (e.g. Bharath et al. (2007); Chang et al. (2016); Drucker and Puri

(2005); Forte et al. (2010); Yasuda (2005)).

4.3 Bivariate probit model for choosing M&A advisor

As mentioned earlier, the previous literature suggests the possible endogeneity bias between relationship dummies when using a simple probit equation. In this subsection, we further estimate the likelihood of winning subsequent M&A advisory business using a bivariate probit model and report the results in Table 4. For both acquirers and targets, the results are consistent with those in Table 3. The coefficient of

24 Underwriting is still significantly positive for target firms in the bivariate probit model.

For acquiring firms, IPO underwriting relationship can increase the likelihood of securing subsequent advisory business. Furthermore, frequent underwriting relationship (Underwriter*NO_ISSUE) can increase the likelihood of being chosen as an advisor for both acquiring firms and target firms. Again, the interaction term of

Underwriter*TOP8 is significantly negative for acquiring firms but weak for target firms. For other variables, TOP8, IndustryExpertise, and PRE_ADV are significantly positive, which are consistent with the findings in prior literature.

In addition, the estimate of ρ is significantly positive at the 1% level in bivariate probit models for both acquirers and targets. The problem of endogeneity indeed exists for advisor selection, and the effect of endogeneity is much stronger for acquiring firms than for target firms because of a larger correlation coefficient in the sample of acquiring firms. We also note that the coefficients of NO_COMR are significantly positive in the underwriter selection models for both acquirers and targets. This finding is consistent with our assumption that the number of underwriting co-managers is related to the networking ability of institutional investors, and a bank with a larger

NO_COMR will make it more likely be selected as an underwriter. Moreover, we calculate the correlation coefficients of NO_COMR and Advisor for both acquirers and targets and find that the correlation coefficients are merely 0.0378 and 0.0280, respectively. Given that NO_COMR is significant at the 1% level in the underwriter selection model, but much less correlated to the dependent variable in the advisor selection model, our instrument should be valid as expected. Overall, the results in both

Table 3 and Table 4 consistently answer our first research question and indicate the beneficial role of the prior underwriting relationship in subsequent M&A advisory.

[Insert Table 4 Here]

25 4.4 The costs of subsequent M&A advisory

The results thus far indicate that the underwriting relationship offers great opportunities to attract subsequent M&A advisory business. We now examine the costs of M&A advisory. In the hypothesis H2a, we posit that the underwriting relationship saves the advisors some costs or efforts in subsequent M&As because this relationship helps lower information asymmetry between banks and firms and reduce monitoring efforts required in the advising process. Alternatively, in the hypothesis H2b, we posit that such a relationship may cause the issuing firms to face higher switching costs or bring more opportunities for information rent extraction.

[Insert Table 5 Here]

Table 5 reports the results of multivariate regressions for acquirer’s and target’s advisory fees, respectively. The coefficients of RELMA are negative for both acquiring and target firms, and it is significant for target firms. The results suggest that the prior underwriting relationship help reduce target firms’ advisor fees in subsequent M&As.

Furthermore, we find that target firms tend to pay lower advisory fees in larger deals due to economies of scale. The evidence from target firms supports the cost-saving hypothesis (H2a).

Using Equations (3) and (4), we further estimate the effect of the underwriting relationship on the advisory fees when considering possible selection bias. In Table 6 we find that the coefficients of RELMA are not significant for both acquiring and target firms, indicating that the prior equity underwriting relationship is not a major determinant of M&A advisory fees even though the sample selection bias is under control. The evidence in Table 6 supports neither the cost-saving hypothesis (H2a) nor the rent expropriation hypothesis (H2b).

[Insert Table 6 Here]

Furthermore, prior studies suggest that prior underwriting relationship plays a

26 beneficial role for subsequent corporate financing. We then examine whether stock acquisitions benefit more from prior equity underwriting relationship than cash acquisitions.14 In Panel A of Table A2 (Appendix 3), we add an interaction term,

RELMA*COMM, to examine whether stock acquisitions have an additional impact on advisory fees. The dummy variable, COMM, equals one if the M&A deal is fully paid in stock, and zero otherwise. If the coefficient of the interaction term is negative

(positive), it indicates that stock acquisitions benefit more (less) from prior equity underwriting relationship than other types of M&A deals (i.e., cash M&As and mixed

M&As). However, we find no significant interaction effect of stock acquisition on advisory fees. Stock acquisitions with prior equity underwriters as M&A advisors do not receive significantly lower advisory fees than cash or mixed deals.

We also follow Change et al. (2016) to use CAR (-1, +1) as the dependent variable and add an interaction term, COMM*RELMA, to examine whether stock acquisitions have an additional impact on shareholders’ wealth. If the coefficient of the interaction term is positive (negative), it indicates that stock acquisitions benefit more (less) from prior equity underwriting relationship than other types of M&A deals. We report the results in Panel B of Table A2. However, the coefficient of RELMA*COMM is insignificant for both acquiring and target firms, suggesting that the shareholders of acquiring firms do not benefit more or less in their stock returns from prior underwriting relationship in stock acquisitions.

4.5 The retention of underwriting banks and the difference of advisory fees

The analysis proposed in Section 4.4 suggests that the retention of underwriting banks as financial advisors in subsequent M&As does not help acquiring or target firms

14 We thank the referee for suggesting this analysis.

27 to save advisory fees. However, it is plausible that the difference of advisory costs can initially affect the retention of underwriters as subsequent M&A advisors. In the spirit of Dunbar (1995), the retention choice can be illustrated by the following model:

I j = d0 +d1(Fsj - Fnj ) -e j , (5) where Ij equals one if firm j retains its relationship underwriter as financial advisor, and zero otherwise; Fnj are advisory fees if the advisor has prior underwriting relationship with firm j; Fsj are advisory fees if the advisor has no prior underwriting relationship with firm j. The expression in parentheses is the reduction in advisory fees if the acquirers or targets switch their underwriting banks in M&As after equity issuance. Estimating this choice model can help us to specifically clarify the relation between advisory costs and the retention decision made by an M&A firm. As illustrated in

Appendix 4, the cost-saving hypothesis will predict a positive coefficient, δ1, in Equation (5), but the rent-extraction hypothesis will predict a negative coefficient. However, Equation (5) cannot be estimated directly because we cannot observe what advisory fees would have been if the alternative arrangement had been chosen by firm j.

Applying the two-stage procedure of Lee (1978), Dunbar (1995) suggests a feasible way to solve the problem and project what these advisory fees would have been if the M&A firm had chosen an alternative. Following Dunbar’s procedure, we further address the association between the likelihood of retaining underwriting banks and the cost difference of switching them. The description of the two-stage procedure is relegated to Appendix 4.

In Table 7, we report the results for the probit model in the first and fourth column for acquiring and target firms, respectively. Based on the estimates of probit models, we calculate the inverse Mills ratios (IMR, hereafter) and analyze the determinants of advisory fees by adding the IMR to the second-stage OLS regressions for relationship

M&As and non-relationship M&As. We report the results incorporating the IMR in the second and third column of Table 7 for acquirers and in the last two columns of Table

28 7 for targets. For both relationship M&As and non-relationship M&As, MA_SIZE has significant influence on advisory costs for target firms. The coefficients of the IMR are not significant for both acquirers and targets, suggesting that self-selection bias is less important in this model setting.15

[Insert Table 7 Here]

The strength of Dunbar’s procedure is that we can obtain projected advisory fees for the M&As given that the firms have the alternative arrangement to retain or to switch their prior underwriting banks in subsequent M&As. This is so-called “what-if” analysis. We report the estimates in Table 8. For acquiring firms, the advisory fees of relationship M&As are lower than the corresponding projected advisory fees if the firms had switched their underwriting banks. Similarly, the advisory fees of non- relationship M&As are higher than the projected advisory fees if the firms had retained their underwriting banks. Furthermore, the proportion of cases where the actual advisory fees are less than the projected fees for relationship M&As (non-relationship

M&As) is 76% (61%) for acquiring firms and 30% (58%) for target firms, suggesting that the firms in most of the deals may reconsider their bank retention decisions if advisory cost is one of major concerns when choosing their M&A advisors.

[Insert Table 8 Here]

Finally, using the same approach as in Table 8, we can obtain the projected net

ˆ ˆ reduction of advisory fees, (Fsj - Fn j ) , to estimate the structural model of Equation

(5).16 We show the results for acquirers and targets as follows, respectively:

15 Lee (1978) indicates that the coefficients of the inverse Mills ratios provide an indication of the significance and direction of any selection bias. For example, if the cost-saving hypothesis holds, a negative coefficient on IMR for the relationship M&As and a positive coefficient for non-relationship M&As are expected. 16 To ensure consistent estimation, Dunbar (1995) suggests that the projected values are used to measure all costs. For example, the estimates of OLS in the relationship M&As are used to project advisory fees for those M&As which have underwriting relationship (rather than using their actual advisory fees in Table 8).

29 � = 0.830 + 0.967(� − �); (6)

� = 1.016 + 1.696(� − �). (7)

Both the coefficients of net reduction in the advisory fees is significantly positive, as implied by the cost-saving hypothesis. For acquirers and targets, retaining their prior underwriters as future advisors is related to cost reduction in the M&A advisory, which is consistent with the cost-saving hypothesis.

4.6 The underwriting relationship and time to resolution

In addition to advisory fees, previous studies suggest that the time to resolution is related to the characteristics of M&A advisors. In this section we further examine the impact of relationship advisors on the time to resolution. Again, we focus on the relationship M&A transactions (RELMA) and the role of the top eight advisory banks

(TOP8). We first examine the impact of RELMA and TOP8 on the time to resolution

(Time to resolution) of M&A transactions. Time to resolution is measured as the number of days between the announcement date and resolution (effective or withdrawn) date and divided by 365 when used in the regressions. We follow the regression model of advisory fees by replacing the dependent variable with Time to resolution.

Panel A of Table 9 presents the results for multivariate regressions. We find that

TOP8 has a negative and significant influence on the time to resolution for target firms, suggesting that top-tier advisors hired by target firms spend less time to complete M&A deals. Furthermore, the coefficient of the interaction term, RELMA*TOP8, is significantly negative, indicating that relationship top-tier advisors can get M&A deals done in much shorter durations. We also find that RELMA has a negative and significant influence on the time to resolution for the acquirers, suggesting that relationship M&A deals have shorter durations for acquiring firms.

30 To address the issue of self-selection bias, we further apply the Heckman (1979) two-stage procedure to examine the impact of RELMA on the time to resolution for both acquirers and targets. We report the results in Panel B of Table 9. Different from the findings in Panel A, we find no significant influence of RELMA on the time to resolution for acquiring firms. In contrast, relationship M&A deals for target firms tend to exhibit shorter deal durations as shown by the significant and negative coefficient of RELMA.

That is, after correcting the problem of sample selection, if target firms select their prior equity underwriting banks as advisors in subsequent M&A deals, they tend to enjoy the benefits of a shorter time to resolution for the transactions. Consistent with the efficiency hypothesis H3a, the finding also suggests that when banks continue to provide M&A advisory services to their underwriting clients, the efforts saved in due diligence process can help banks spend less time to complete the deal.

[Insert Table 9 Here]

4.7 Subsample analysis

In this subsection we conduct several subsample analyses to examine the effect of equity underwriting relationship on the costs and time to resolution of subsequent

M&As. In Table 10, we classify an M&A transaction in the subsample of top-tier advisors if any M&A advisor in the transaction is ranked as top 8 in the league table as of the prior calendar year before the M&A announcement (TOP8 = 1); otherwise, a transaction is classified in the subsample of non-top-tier advisors (TOP8 = 0). We then run the regressions separately for deals with top-tier advisors and non-top-tier advisors.

Panel A of Table 10 shows that the coefficient of RELMA is negative and significant in the subsample of deals with non-top-tier advisors for target firms, which supports the cost-saving hypothesis (H2a). In Panel B, the regression results show that the coefficient of RELMA is negative and significant in the subsample of deals with non-

31 top-tier advisors for acquiring firms and the subsample of deals with top-tier advisors for target firms, which is consistent with the efficiency hypothesis (H3a).

[Insert Table 10 Here]

In addition, we also classify M&A transactions into different subsamples based on their transaction value. An M&A transaction is classified in the subsample of large deals if it has deal value (MA_SIZE) greater than the median in a given year; otherwise, a transaction is classified in the subsample of small deals. We present the regression results for this classification in Table 11. Panel A of Table 11 shows that the coefficient of RELMA is negative and significant in the subsample of large deals for both acquiring and target firms, indicating that the effect of prior equity underwriting relationship on advisory fees is important in large deals. This result is consistent with the cost-saving hypothesis (H2a). In Panel B, the regression results show that the coefficient of RELMA is positive and marginally significant in the subsample of small deals for target firms, suggesting that advisors with prior underwriting relationship spend more time to complete M&A deals for target firms in small deals. This finding is consistent with the relationship capital hypothesis (H3b).

[Insert Table 11 Here]

5. Conclusion

This paper provides evidence on the role of prior equity underwriting relationship in M&As. First, we examine the impact of such relationship on the firm’s choice of financial advisors in subsequent mergers and acquisitions. If the role of an investment bank in an M&A transaction is to provide information, then the likelihood of hiring an advisor should be related to the prior equity underwriting relationship. We find that the likelihood of hiring a financial advisor is mainly influenced by the prior underwriting relationship between the banks and the firms. That is, a firm tends to choose the bank

32 with prior equity underwriting relationships as the financial advisor in subsequent

M&A transactions. Complementing with previous studies indicating that the prior

M&A advisory relationship, the prior lending relationship, the complexity of the deal, and the reputation of advisors are crucial in choosing advisors for M&A deals, our finding suggests that the prior equity underwriting relationship also plays an important role when firms select their M&A advisors.

In addition, our study examines advisory fees and the time to resolution. We find that the prior equity underwriting relationship is a major determinant of M&A advisory fees for target firms. Using “what-if” analysis or switching regression analysis, we also find that retaining their prior equity underwriters as future advisors is related to cost reduction in the M&A advisory, which is consistent with the implication of the cost- saving hypothesis. Controlling for sample selection bias, we find that prior equity underwriting relationship can benefit target firms in terms of shorter deal duration, but the effect is not significant for acquiring firms. Overall, by exploring the relation between the prior equity underwriting relationship and subsequent M&A advisory, our study contributes to the further understanding of how firms derive value from investment bank relationships.

33 Appendix 1 Table A1: Bank List The table presents the ultimate bank names of 25 banks in our sample and predecessor bank names in the case of mergers. We start with the full sample of banks labeled as advisors in M&A transactions. We then calculate the number of deals completed by each bank and select banks that have served as M&A advisors at least 5 times and equity underwriters at least 5 times during the sample period. In cases in which each surviving bank may reflect the merger of two or more predecessor banks, all predecessor banks are also included in the list.

Ultimate Bank Acquired Bank Allen & Company 1997/10/01 Robertson Stephens & Company Bank of America 2004/04/01 FleetBoston Financial 2009/01/01 Merrill Lynch Barclays Capital 2008/09/22 Lehman Brothers Canadian Imperial Bank of Commerce 1997/11/03 Oppenheimer Citigroup Credit Suisse First Boston 2000/11/03 Donaldson Lufkin & Jenrette Deutsche Bank Friedman Billings Ramsey Goldman Sachs Jefferies 2000/12/31 Chase Manhattan JP Morgan 2008/05/30 Bear Stearns Companies Lazard Morgan Stanley Needham & Company Piper Jeffray 1998/05/01 US BanCorp Raymond James 2012/04/02 Morgan Keegan Robert W. Baird & Company Royal Bank of Canada 2001/01/10 Dain Rauscher Smith Barney Societe Generale 1998/06/30 Cowen & Company Stifel Financial Thomas Weisel Partners Union Bank of Switzerland 2007/02/09 McDonald Investments Wells Fargo 2008/12/31 Wachovia William Blair

34 Appendix 2: Variable definitions Panel A: Bank-firm pair sample Variable Definition Advisor Dummy variable, which equals 1 if the bank served as a financial advisor of the firm in any of its mergers and acquisition, and 0 otherwise. Underwriter Dummy variable, which equals 1 if the bank served as an underwriter of the firm within 5 years prior to the announcement date of its first M&A after an equity offering, and 0 otherwise. IPO Dummy variable, which equals 1 if the bank served as an IPO underwriter of the firm within 5 years prior to the announcement date of its first M&A after an equity offering, and 0 otherwise. PROCEED Total proceeds of equity issuing if the bank served as an underwriter of the firm within 5 years prior to the announcement date of its first M&A after an equity offering. NO_ISSUE Number of times that the bank served as an underwriter of the firm within 5 years prior to the announcement date of its first M&A after an equity offering. PRE_ADV Dummy variable, which equals 1 if the bank served as an advisor of the firm within 5 years prior to the announcement date of its first M&A after an equity offering, and 0 otherwise. DAYS Number of days between the most recent equity offering date and the M&A announcement date if there are multiple equity offerings prior to a specific M&A transaction; Divided by 365 in regression analyses. TOP8 Dummy variable, which equals 1 if the bank is ranked as top 8 in the league table before the M&A announcement, and 0 otherwise. IndustryExpertise Number of M&As advised by the bank for the firm's industry divided by the total number of M&As in the firm's industry within 5 years prior to the announcement date of its first M&A after an equity offering. ASSET The book value of the assets of the firm at fiscal year-end prior to the announcement date of the first M&A after an equity offering. LEVERAGE The ratio of the book value of total debt to the book value of total assets of the firm at fiscal year- end prior to the announcement date of the first M&A after an equity offering. NO_COMR Average number of co-managers joined with the bank in a year prior to the latest equity offering before the M&A announcement date. Panel B: M&A transaction sample Variable Definition Advisory fee The advisory fee as the percentage of total M&A transaction size. Time to resolution Number of days between announcement date and resolution (effective or withdrawn) date; Divided by 365 in regression analyses. RELMA Dummy variable, which equals 1 if the M&A transaction is a relationship transaction, and 0 otherwise. PRE_MA Dummy variable, which equals 1 if any current advisor for the transaction also served as an advisor in previous M&A transactions for the acquirer or the target. DAYS The same definition as that of DAYS in Panel A; Divided by 365 in regression analyses. TOP8 Dummy variable, which equals 1 if any M&A advisor in the transaction is ranked as top 8 in the league table as of the prior calendar year before the M&A announcement, and 0 otherwise. MA_SIZE The deal value of the M&A in USD billion. NO_ADV Number of financial advisors in the M&A transaction hired by the acquiring (target) firm. CASH Dummy variable, which equals 1 if the M&A transaction in the consideration is entirely cash, and 0 otherwise. COMM Dummy variable, which equals 1 if the M&A transaction in the consideration is entirely common stock, and 0 otherwise. ASSET The book value of the assets of the firm at fiscal year-end prior to the announcement date of the M&A transaction. LEVERAGE The ratio of the book value of total debt to the book value of total assets of the firm at fiscal year- end prior to the announcement date of the M&A transaction. NO_COMR Average number of co-managers joined with the underwriting bank that also serves as financial advisor in a subsequent M&A transaction.

35 Appendix 3 Table A2: The underwriting relationship and stock acquisition: Multivariate regressions This table presents how the underwriting relationship affects the costs of subsequent M&As and shareholders’ wealth, when interacting with stock acquisition. Cumulative abnormal returns (CAR) is measured over the period (−1, +1) around the M&A announcement date. The market model parameters are estimated over the period (−210, −11) with the CRSP value-weighted return as a proxy for the market return. Refer to Appendix 2 for the definitions of variables. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the mergers as control variables. T-statistics based on standard errors robust to clustering by firms are reported in parentheses. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Advisory fee Acquirer Target Dependent variable Advisory fee Advisory fee Intercept 9.159 2.099*** (1.337) (4.678) RELMA -4.449 -0.133 (-0.990) (-0.542) CASH 0.126 -0.108 (0.070) (-0.541) COMM 0.072 0.230 (0.026) (1.310) TOP8 -2.229 0.281 (-0.866) (1.024) RELMA*CASH 1.787 0.396* (0.530) (1.719) RELMA*COMM 5.189 -0.199 (0.984) (-0.843) RELMA*TOP8 4.342 0.014 (1.166) (0.034) PRE_MA -0.547 -0.047 (-0.411) (-0.489) DAYS 0.388 0.060* (0.982) (1.696) MA_SIZE 0.236 -0.026** (0.821) (-2.350) NO_ADV 2.052 -0.121* (1.255) (-1.662) ASSET -1.421 -0.241*** (-1.008) (-4.518) LEVERAGE -3.475 0.501*** (-0.758) (2.748)

MA_YEAR Yes Yes INDUSTRY Yes Yes

Observation 102 547 Adj. R2 0.00 0.16 (continued on next page)

36 Table A2. (continued)

Panel B: Cumulative abnormal returns Acquirer Target Dependent variable CAR(−1, +1) CAR(−1, +1) Intercept -0.004 0.515*** (-0.171) (6.525) RELMA -0.031* 0.041 (-1.940) (0.699) CASH -0.013 0.004 (-1.200) (0.116) COMM 0.011 0.092*** (1.140) (2.626) TOP8 -0.001 -0.082* (-0.046) (-1.723) RELMA*CASH 0.030* 0.005 (1.763) (0.091) RELMA*COMM 0.001 -0.030 (0.058) (-0.602) RELMA*TOP8 -0.059* 0.015 (-1.904) (0.256) PRE_MA -0.002 0.015 (-0.223) (0.533) DAYS 0.001 -0.007 (0.304) (-0.889) MA_SIZE -0.004** 0.003 (-2.109) (1.607) NO_ADV -0.001 -0.056** (-0.194) (-2.336) ASSET -0.004 -0.044*** (-1.128) (-3.829) LEVERAGE 0.038** -0.090 (2.008) (-1.227)

MA_YEAR Yes Yes INDUSTRY Yes Yes

Observation 1,136 861 Adj. R2 0.03 0.13

37 Appendix 4 The two-stage procedure to estimate the structure model in Equation (5) Formally the advisory fee equations are:

� = � + �� + �; (A1)

� = � + �� + �, (A2) where X is a vector of firm characteristics, M&A deal characteristics, and other control variables that we have applied in Section 4.4. Then, we apply the two-stage procedure of Lee (1978) to derive consistent estimates of parameters for the above system. The procedure is illustrated as follows. First, Equation (A1) and Equation (A2) are substituted into Equation (5) to obtain a reduced-form choice model. Consistent estimates of the parameters in Equation (A1) and Equation (A2) are the then obtained from OLS regression with a variable added to each regression to adjust for the potential selection bias. This added variable, referred to as the “inverse Mills ratio”, is defined as -f(yˆ)/F(yˆ) when the M&A is a relationship deal and f(yˆ)/1-F(yˆ) when the M&A is a non-relationship deal. In the above expression, f is the standard normal density function, F is the standard normal cumulative distribution function, and yˆ is the reduced-form probit model estimate. Next, the estimated coefficients of Equation (A1) and Equation (A2) are used to estimate the net reduction in the advisory fees given that an underwriting bank is retained as the subsequent advisor. Finally, the estimated cost reduction is substituted into Equation (5) for probit estimation. While the cost-saving hypothesis (H2a) implies that the likelihood of the retention of underwriters is positively related to the reduction in advisory cost given the relationship, the rent-extraction hypothesis (H2b) implies the opposite. Thus, the cost-saving hypothesis will predict a positive coefficient, δ1, in Equation (5), but the rent-extraction hypothesis will predict a negative coefficient.

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42 Table 1. Descriptive statistics: Bank-firm pairs

This table presents the descriptive statistics of our sample. The unit of observation is a bank-firm pair. All variables are defined in Appendix 2. The value in each cell for the first five variables is the number of observations, and the percentage is reported in the brackets. The value in each cell for the last six variables is the average value, and the standard deviation is reported in the parentheses. All continuous variables are winsorized at the 1st and 99th percentiles.

Variable Acquirer Target Whole Whole Advisor=1 Advisor=0 Advisor=1 Advisor=0 sample sample Advisor 946 774 [0.036] [0.039] Underwriter 2,095 490 1,605 1,418 410 1,008 [0.080] [0.518] [0.063] [0.071] [0.530] [0.053] IPO 744 186 558 715 203 512 [0.028] [0.197] [0.022] [0.036] [0.262] [0.027] PRE_ADV 1,270 249 1,021 565 141 424 [0.048] [0.263] [0.040] [0.028] [0.182] [0.022] TOP8 8,392 640 7,752 6,384 553 5,831 [0.320] [0.677] [0.307] [0.320] [0.714] [0.304] IndustryExpertise 0.008 0.019 0.007 0.008 0.020 0.008 (0.014) (0.020) (0.013) (0.013) (0.020) (0.013) PROCEED 47.193 273.077 38.740 36.926 225.138 29.329 (237.009) (503.176) (216.401) (316.370) (684.548) (289.399) NO_ISSUE 0.110 0.724 0.087 0.105 0.780 0.077 (0.391) (0.772) (0.349) (0.418) (0.964) (0.354) DAYS 693.811 703.408 693.452 817.560 804.005 818.107 (528.449) (530.441) (528.381) (490.659) (484.647) (490.904) ASSET ($million) 3014.485 3638.593 2991.130 1089.622 1409.246 1076.721 (6577.342) (7734.772) (6529.069) (2323.744) (2795.649) (2301.822) LEVERAGE 0.220 0.235 0.219 0.206 0.224 0.205 (0.217) (0.224) (0.217) (0.254) (0.259) (0.253)

Observation 26,225 946 25,279 19,950 774 19,176

43 Table 2. Descriptive statistics: Relationship and nonrelationship mergers and acquisitions

This table presents the descriptive statistics of M&A transactions with prior equity issuance within 5 years. The unit of observation is an M&A transaction. All variables are defined in Appendix 2. The value in each cell for the first five variables is the number of observations, and the percentage is reported in the brackets. The value in each cell for the last five variables is the average value, and the standard deviation is reported in the parentheses. All continuous variables are winsorized at the 1st and 99th percentiles.

Variable Acquirer Target All All RELMA=1 RELMA=0 RELMA=1 RELMA=0 transactions transactions RELMA 450 383 [0.377] [0.413] PRE_MA 278 178 100 149 97 52 [0.233] [0.396] [0.134] [0.161] [0.253] [0.095] TOP8 616 360 256 540 317 223 [0.515] [0.800] [0.344] [0.582] [0.828] [0.409] CASH 550 221 329 513 211 302 [0.460] [0.491] [0.442] [0.553] [0.551] [0.554] COMM 221 85 136 187 66 121 [0.185] [0.189] [0.183] [0.202] [0.172] [0.222] Deal duration 74.187 78.487 71.591 102.858 104.619 101.620 (80.702) (83.909) (78.646) (72.440) (79.326) (67.229) Advisory fees (%) 1.589 0.745 2.108 1.326 1.168 1.442 (5.686) (0.759) (7.170) (1.303) (1.073) (1.440) DAYS 707.485 598.087 773.565 848.570 717.112 940.952 (533.975) (484.994) (551.382) (492.636) (461.023) (493.549) NO_ADV 1.183 1.309 1.107 1.278 1.405 1.189 (0.509) (0.677) (0.351) (0.556) (0.644) (0.465) MA_SIZE ($million) 758.769 1038.348 589.897 1751.548 2466.151 1249.359 (1891.759) (2420.927) (1459.614) (4012.149) (5164.325) (2844.361)

Observation 1,195 450 745 928 383 545 Observation 113 43 70 588 250 338 (Advisory fees)

44 Table 3. The effect of the underwriting relationship on the advisor choice: Probit regression

This table presents the results of a probit regression for the base model. The unit of observation is a bank-firm pair. All variables are defined in Appendix 2. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the mergers and equity issuance as control variables. Z-statistics based on standard errors robust to clustering by firms are reported in parentheses. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

Acquirer Target Dependent variable Advisor Advisor Coeff. Marginal effect Coeff. Marginal effect Intercept -2.585*** -2.948*** (-9.473) (-10.955) Underwriter 0.752*** 0.047 1.196*** 0.075 (2.753) (10.073) TOP8 0.403*** 0.025 0.389*** 0.024 (8.585) (7.459) Underwriter*IPO 0.220*** 0.014 0.038 0.002 (3.136) (0.473) Underwriter*PROCEED -1.570* -0.099 -0.116** -0.007 (-1.724) (-2.273) Underwriter*NO_ISSUE 0.755*** 0.048 0.163*** 0.01 (3.131) (3.293) Underwriter*TOP8 -0.356*** -0.022 -0.107 -0.007 (-3.728) (-0.991) IndustryExpertise 3.843*** 0.242 8.197*** 0.516 (3.426) (6.537) PRE_ADV 0.579*** 0.036 0.613*** 0.039 (9.590) (8.063) DAYS 0.007 0.000 0.059 0.004 (0.257) (1.457) ASSET 0.005 0.000 0.017 0.001 (0.481) (1.373) LEVERAGE 0.058 0.004 0.108* 0.007 (0.814) (1.683)

MA_YEAR Yes Yes Yes Yes ISSUING_YEAR Yes Yes Yes Yes INDUSTRY Yes Yes Yes Yes

Observation 26,225 19,550 # of firms 678 750

45 Table 4. The effect of the underwriting relationship on the advisor choice: Bivariate probit regression

This table presents the results of a bivariate probit regression. The variables used are the same as those in Table 3, except for the inclusion of an additional instrument, NO_COMR. The unit of observation is a bank-firm pair. All variables are defined in Appendix 2. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the mergers and equity issuance as control variables. Z-statistics based on standard errors robust to clustering by firms are reported in parentheses. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

Acquirer Target Dependent variable Underwriter Advisor Underwriter Advisor Marginal Marginal Coeff. Coeff. Coeff. Coeff. effect effect Intercept -2.335*** -2.577*** -2.761*** -2.996*** (-9.679) (-10.617) (-14.682) (-11.580) Underwriter -0.257 -0.006 0.433* 0.007 (-0.865) (1.769) TOP8 0.762*** 0.507*** 0.020 0.670*** 0.445*** 0.014 (19.729) (10.357) (16.210) (7.615) Underwriter*IPO 0.187*** 0.004 0.032 0.000 (2.917) (0.425) Underwriter*PROCEED -0.995 -0.021 -0.102** -0.002 (-1.192) (-2.121) Underwriter*NO_ISSUE 0.684*** 0.015 0.160*** 0.002 (3.055) (3.354) Underwriter*TOP8 -0.173* -0.004 0.028 0.000 (-1.924) (0.266) IndustryExpertise 10.864*** 6.534*** 0.272 11.492*** 10.114*** 0.280 (11.385) (5.290) (10.678) (8.050) PRE_ADV 0.901*** 0.780*** 0.028 0.765*** 0.743*** 0.020 (13.645) (13.029) (11.302) (9.003) DAYS -0.051* -0.003 -0.001 -0.076*** 0.043 -0.000 (-1.903) (-0.120) (-2.668) (1.082) ASSET 0.011 0.007 0.000 0.034*** 0.022* 0.001 (0.858) (0.700) (3.187) (1.751) LEVERAGE 0.072 0.072 0.002 -0.012 0.108* 0.001 (1.042) (1.051) (-0.212) (1.745) NO_COMR 0.120*** 0.116*** (7.504) (7.447)

MA_YEAR Yes Yes Yes Yes Yes Yes ISSUING_YEAR Yes Yes Yes Yes Yes Yes INDUSTRY Yes Yes Yes Yes Yes Yes

ρ = 0.491*** ρ = 0.350*** Observation 26,225 19,550

46 Table 5. The underwriting relationship and M&A advisory fees: Multivariate regressions

This table presents how the underwriting relationship and top-tier advisor affects the costs of subsequent M&As. The unit of observation is an M&A transaction. All variables are defined in Appendix 2. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the mergers as control variables. T-statistics based on standard errors robust to clustering by firms are reported in parentheses. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

Acquirer Target Dependent variable Advisory fee Advisory fee Intercept 10.916 1.626*** (1.275) (3.952) RELMA -3.127 -0.271* (-1.132) (-1.760) TOP8 -0.316 -0.151 (-0.197) (-0.848) RELMA*TOP8 2.336 0.416** (0.769) (2.080) PRE_MA 0.487 -0.025 (0.666) (-0.283) DAYS 0.184 0.066* (0.758) (1.890) MA_SIZE 0.070 -0.026** (0.449) (-2.232) NO_ADV 1.041 -0.105 (1.032) (-1.543) CASH 1.399 0.155 (0.802) (1.563) COMM -0.559 0.279 (-0.442) (1.392) ASSET -1.103 -0.230*** (-0.880) (-4.670) LEVERAGE -2.167 0.489*** (-0.557) (2.661)

MA_YEAR Yes Yes INDUSTRY Yes Yes

Observation 113 588 Adj. R2 0.00 0.17

47 Table 6. The underwriting relationship and M&A advisory fees: Heckman two-stage procedure

This table reexamines the analysis reported in Table 5 by the Heckman (1979) two-stage procedure, which accounts for firm selection. The variables used are the same as those in Table 5, except for the replacement of RELMA with λRELMA to correct for the selection bias, and the inclusion of NO-COMR as an instrument in the selection model. The unit of observation is an M&A transaction. All variables are defined in Appendix 2. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the mergers as control variables. Z-statistics (or t- statistics) based on standard errors robust to clustering by firms are reported in parentheses. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

Acquirer Target Dependent variable RELMA Advisory fee RELMA Advisory fee Intercept 1.064 9.701 -1.054** 1.998*** (0.686) (1.171) (-2.227) (5.401) RELMA -1.917 -0.286 (-0.709) (-0.442) λRELMA 0.801 0.184 (0.440) (0.507) TOP8 3.219*** 0.420 1.171*** 0.091 (5.092) (0.190) (8.502) (0.289) PRE_MA 2.719*** 0.442 0.492*** 0.015 (4.199) (0.266) (2.848) (0.121) DAYS -0.494*** 0.086 -0.169*** 0.049 (-2.830) (0.259) (-3.463) (1.035) MA_SIZE 0.069 0.082 0.014 -0.024* (0.931) (0.466) (0.873) (-1.960) NO_ADV 0.731** 1.377 0.136 -0.097 (2.172) (0.967) (1.120) (-1.332) CASH -1.395 1.596 -0.111 0.147 (-1.338) (0.714) (-0.719) (1.545) COMM 1.359** -0.595 -0.448** 0.242 (2.415) (-0.305) (-2.324) (1.078) ASSET -0.678*** -1.504 -0.072 -0.232*** (-3.376) (-0.932) (-1.250) (-4.475) LEVERAGE 0.489 -3.110 0.261 0.527*** (0.434) (-0.672) (0.875) (2.685) NO_COMR 1.736*** 0.318*** (4.640) (3.285)

MA_YEAR Yes Yes Yes Yes INDUSTRY Yes Yes Yes Yes

Observation 89 587 Pseudo/Adj. R2 0.61 0.00 0.27 0.17

48 Table 7. The reduced-form choice model and advisory fee estimates

This table reports the estimates based on the approach proposed in Dunbar (1995). The sample and variables used are the same as those in Table 6. In the first stage, the reduced-form equation is estimated using a probit model (the first column for Acquirer and the fourth column for Target). The dependent variable of the probit model is a dummy variable to indicate the firm’s choice on the merger advisor, which equals one if the merger advisor served as the underwriter of the firm’s prior equity issuance, and zero otherwise (RELMA). The estimated parameters are used to calculate the inverse Mills ratio, which is then included as an additional explanatory variable in the OLS estimation for the advisory fees of relationship M&As and non-relationship M&As. The unit of observation is an M&A transaction. All variables are defined in Appendix 2. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the mergers as control variables. Z-statistics (or t-statistics) based on standard errors robust to clustering by firms are reported in parentheses. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

Acquirer Target Rel. Non-rel. Rel. Non-rel. Probit Probit M&As M&As M&As M&As Dependent variable RELMA Advisory fee Advisory fee RELMA Advisory fee Advisory fee Intercept 0.827 -0.191 -2.160** -1.049** -0.617 0.461 (0.537) (-0.130) (-2.044) (-2.195) (-1.034) (1.311) TOP8 3.197*** -0.102 -0.236 1.168*** 0.281 0.005 (5.031) (-0.112) (-0.495) (8.494) (1.066) (0.024) PRE_MA 2.607*** 0.563 -1.193 0.499*** 0.149 0.146 (4.211) (0.701) (-1.356) (2.892) (1.179) (1.025) DAYS -0.489*** 0.079 0.213** -0.179*** -0.023 0.018 (-2.756) (0.510) (2.100) (-3.542) (-0.440) (0.438) MA_SIZE 0.044 0.134 0.079 0.012 -0.097*** -0.115*** (0.692) (1.025) (1.041) (0.465) (-4.248) (-4.533) NO_ADV 0.772** -0.583 1.793* 0.128 -0.008 -0.069 (2.564) (-1.288) (1.964) (1.029) (-0.102) (-0.670) CASH -1.432 0.918 1.491*** -0.118 0.191* 0.220** (-1.454) (0.656) (3.288) (-0.763) (1.685) (2.106) COMM 1.410** -0.328 0.190 -0.446** 0.222 -0.101 (2.541) (-0.834) (0.397) (-2.310) (1.294) (-0.644) ASSET -0.627*** -0.013 -0.219 -0.064 -0.112* -0.128*** (-3.196) (-0.043) (-1.491) (-1.061) (-1.723) (-3.341) LEVERAGE 0.477 -2.511** -1.118 0.247 0.310 0.169 (0.433) (-2.157) (-1.185) (0.826) (1.516) (1.159) NO_COMR 1.710*** 0.319*** (4.655) (3.295) Inverse Mills ratio 0.623 -0.127 -0.266 -0.196 (0.895) (-0.285) (-0.835) (-0.658)

MA_YEAR Yes Yes Yes Yes Yes Yes INDUSTRY Yes Yes Yes Yes Yes Yes

Observation 89 38 51 587 249 338 Pseudo/Adj. R2 0.61 0.21 0.49 0.27 0.42 0.37

49 Table 8. What-if analysis: Actual advisory fees and the forecasts

This table compares the forecasts and actual value of advisory fees. The advisory fees are measured as the percentage of total merger transaction size. The unit of observation is an M&A transaction. The difference between actual advisory fees and projected advisory fees in means is tested by the t-test. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

The underwriting relationship and M&A advisory fees

Acquirer Target Relationship Non-relationship Relationship Non-relationship M&As M&As M&As M&As (38) (51) (249) (338) Mean actual advisory fees 0.728 2.550 1.165 1.442 Mean expected advisory fees if the alternative is 0.847 1.428 0.829 1.312 chosen Mean value of actual advisory fees minus -0.119*** 1.123 0.336*** 0.131* expected advisory fees Number of cases where actual minus expected 29 31 75 195 advisory fees is negative Percentage of cases where actual minus expected 76% 61% 30% 58% advisory fees is negative

50 Table 9. The underwriting relationship and time to resolution

Panel A of this table presents how the underwriting relationship affects the time to resolution of subsequent M&As. Panel B reexamines the analysis using the Heckman (1979) two-stage procedure, which include NO_COMR as an instrument to accounts for firm selection. The unit of observation is an M&A transaction. All variables are defined in Appendix 2. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the mergers as control variables. Z-statistics (or t-statistics) based on standard errors robust to clustering by firms are reported in parentheses. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Multivariate regressions Acquirer Target Dependent variable Time to resolution Time to resolution Intercept 0.130 -0.077 (0.530) (-1.272) RELMA -0.034** 0.031 (-1.994) (1.172) TOP8 0.024 -0.031** (1.450) (-2.037) RELMA*TOP8 0.039 -0.071** (1.639) (-2.290) PRE_MA -0.013 0.041** (-0.825) (2.166) DAYS 0.010* 0.002 (1.924) (0.399) MA_SIZE 0.025*** 0.007* (6.616) (1.940) NO_ADV 0.028** 0.035** (2.006) (2.579) CASH -0.054*** -0.100*** (-3.663) (-6.465) COMM 0.069*** 0.014 (4.087) (0.803) ASSET 0.010** 0.027*** (1.978) (3.931) LEVERAGE 0.117*** 0.038 (3.096) (1.300)

MA_YEAR Yes Yes INDUSTRY Yes Yes

Observation 1,195 928 Adj. R2 0.18 0.26 (continued on next page)

51 Table 9. (continued)

Panel B: Heckman two-stage procedure for RELMA Acquirer Target Dependent variable RELMA Time to resolution RELMA Time to resolution Intercept -1.423** 0.121 -1.002*** 0.127*** (-2.343) (0.496) (-2.719) (2.707) RELMA 0.025 -0.098* (0.332) (-1.818) λRELMA -0.020 0.049 (-0.453) (1.528) TOP8 1.274*** 0.023 1.047*** -0.024 (12.220) (0.666) (9.187) (-1.051) PRE_MA 0.791*** -0.023 0.511*** 0.056*** (6.580) (-0.990) (3.734) (2.589) DAYS -0.227*** 0.011* -0.223*** -0.003 (-7.041) (1.674) (-5.426) (-0.517) MA_SIZE 0.019 0.024*** 0.009 0.007* (0.642) (6.559) (0.645) (1.928) NO_ADV 0.254*** 0.026* 0.233** 0.040*** (2.887) (1.725) (2.457) (2.759) CASH 0.179* -0.056*** -0.075 -0.103*** (1.679) (-3.784) (-0.619) (-6.547) COMM 0.056 0.070*** -0.117 0.010 (0.434) (4.130) (-0.784) (0.549) ASSET -0.110*** 0.011** -0.042 0.026*** (-3.000) (2.017) (-0.908) (3.709) LEVERAGE -0.290 0.122*** 0.109 0.040 (-1.368) (3.221) (0.488) (1.374) NO_COMR 0.312*** 0.466*** (3.788) (4.864)

MA_YEAR Yes Yes Yes Yes INDUSTRY Yes Yes Yes Yes

Observation 1,195 927 Pseudo/Adj. R2 0.27 0.18 0.26 0.26

52 Table 10. Subsample analysis: top-tier advisors versus non-top-tier advisors

This table presents how the underwriting relationship affects the costs and time to resolution of subsequent M&As in different subsamples. An M&A deal is classified in the subsample of top-tier advisor if any M&A advisor in the transaction is ranked as top 8 in the league table as of the prior calendar year before the M&A announcement (TOP8=1); otherwise, a transaction is classified in the subsample of non-top-tier advisor (TOP8=0). The dependent variable in Panel A is Advisory fee, and the dependent variable in Panel B is Time to resolution. The unit of observation is an M&A transaction. All variables are defined in Appendix 2. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the mergers as control variables. T-statistics based on standard errors robust to clustering by firms are reported in parentheses. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Subsample analysis on Advisory fee Acquirer Target Top-tier Non-top-tier Top-tier Non-top-tier Sample Advisors Advisors Advisors Advisors Dependent variable Advisory fee Advisory fee Advisory fee Advisory fee Intercept 2.015** 14.894 1.238** 2.545*** (2.031) (1.191) (2.298) (2.965) RELMA -0.053 1.917 0.169 -0.323** (-0.240) (0.373) (1.408) (-2.031) PRE_MA 0.296 -8.241 -0.055 0.066 (1.517) (-0.665) (-0.510) (0.314) DAYS 0.107* -0.141 0.092** 0.051 (1.796) (-0.105) (2.151) (0.791) MA_SIZE -0.034 3.811 -0.022* -0.067*** (-1.230) (0.842) (-1.711) (-2.600) NO_ADV 0.031 13.745 -0.051 -0.135 (0.236) (1.337) (-0.620) (-0.732) CASH 0.075 -2.132 0.158 0.184 (0.121) (-0.301) (1.279) (1.003) COMM -0.157 -1.790 0.089 0.747 (-0.851) (-0.313) (0.473) (1.558) ASSET -0.152** -3.607 -0.212*** -0.219*** (-2.369) (-1.104) (-3.250) (-3.135) LEVERAGE -0.996 -9.467 0.475* 0.607* (-1.663) (-0.823) (1.885) (1.944)

MA_YEAR Yes Yes Yes Yes INDUSTRY Yes Yes Yes Yes

Observation 66 47 341 247 Adj. R2 0.48 0.00 0.15 0.15 (continued on next page)

53 Table 10. (continued)

Panel B: Subsample analysis on Time to resolution Acquirer Target Top-tier Non-top-tier Top-tier Non-top-tier Sample Advisors Advisors Advisors Advisors Dependent variable Time to resolution Time to resolution Time to resolution Time to resolution Intercept 0.359 -0.060 -0.098 0.102 (1.213) (-0.933) (-1.141) (1.377) RELMA 0.002 -0.038** -0.037** 0.017 (0.092) (-1.968) (-2.468) (0.688) PRE_MA -0.021 0.019 0.043* 0.043 (-1.202) (0.668) (1.960) (1.071) DAYS 0.005 0.016*** 0.004 -0.002 (0.645) (2.631) (0.604) (-0.351) MA_SIZE 0.024*** 0.037*** 0.008** 0.002 (5.325) (3.378) (1.978) (0.850) NO_ADV 0.041*** -0.012 0.048*** -0.003 (2.685) (-0.541) (2.798) (-0.134) CASH -0.067*** -0.040** -0.085*** -0.120*** (-3.125) (-2.146) (-4.302) (-4.875) COMM 0.026 0.126*** 0.012 0.016 (1.172) (4.489) (0.505) (0.533) ASSET 0.007 0.010 0.021** 0.036*** (1.063) (1.380) (2.056) (4.473) LEVERAGE 0.089* 0.156*** 0.072 0.005 (1.839) (2.947) (1.576) (0.140)

MA_YEAR Yes Yes Yes Yes INDUSTRY Yes Yes Yes Yes

Observation 616 579 540 388 Adj. R2 0.18 0.17 0.30 0.21

54 Table 11. Subsample analysis: large deals versus small deals

This table presents how the underwriting relationship affects the costs and time to resolution of subsequent M&As in different subsamples. An M&A deal is classified in the subsample of large deal if it has deal value (MA_SIZE) greater than the median in a given year; otherwise, a transaction is classified in the subsample of small deal. The dependent variable in Panel A is Advisory fee, and the dependent variable in Panel B is Time to resolution. The unit of observation is an M&A transaction. All variables are defined in Appendix 2. All regressions include dummy variables for one-digit SIC codes and dummy variables for the calendar year of the mergers as control variables. T-statistics based on standard errors robust to clustering by firms are reported in parentheses. *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

Panel A: Subsample analysis on Advisory fee Acquirer Target Large Small Large Small Sample Deals Deals Deals Deals Dependent variable Advisory fee Advisory fee Advisory fee Advisory fee Intercept 1.387** 0.828 0.663** 1.337* (2.173) (0.016) (2.409) (1.807) RELMA -0.755*** 24.453 -0.143* -0.066 (-2.987) (0.380) (-1.661) (-0.245) TOP8 -0.131 13.516 -0.061 0.259 (-0.809) (0.961) (-0.870) (0.523) RELMA*TOP8 0.836** -13.426 0.172 0.473 (2.555) (-0.261) (1.606) (0.849) PRE_MA 0.417** -8.933 0.093 -0.385 (2.065) (-0.416) (1.376) (-1.317) DAYS 0.054 3.937 0.011 0.001 (1.383) (0.737) (0.572) (0.013) MA_SIZE -0.019 -67.755 -0.025*** -2.444*** (-1.187) (-1.342) (-3.779) (-3.482) NO_ADV -0.003 15.337 -0.010 -0.120 (-0.047) (1.081) (-0.262) (-0.480) CASH 0.295 -22.277 0.201*** 0.234 (0.992) (-0.711) (3.318) (1.131) COMM -0.147 -10.935 0.031 0.350 (-1.086) (-0.425) (0.404) (0.733) ASSET -0.093 -2.368 -0.067** 0.136 (-1.580) (-0.364) (-2.075) (1.374) LEVERAGE -0.610 -2.302 0.160 0.513 (-1.557) (-0.116) (1.330) (1.362)

MA_YEAR Yes Yes Yes Yes INDUSTRY Yes Yes Yes Yes

Observation 73 40 350 238 Adj. R2 0.50 0.00 0.33 0.11 (continued on next page)

55 Table 11. (continued)

Panel B: Subsample analysis on Time to resolution Acquirer Target Large Small Large Small Sample Deals Deals Deals Deals Dependent variable Time to resolution Time to resolution Time to resolution Time to resolution Intercept 0.337 0.063 -0.143* 0.254*** (1.162) (1.193) (-1.850) (3.123) RELMA -0.017 -0.019 -0.001 0.057* (-0.413) (-0.945) (-0.028) (1.746) TOP8 0.004 -0.004 -0.051** 0.007 (0.153) (-0.217) (-2.403) (0.317) RELMA*TOP8 -0.007 0.065** -0.026 -0.120*** (-0.144) (2.056) (-0.644) (-3.056) PRE_MA -0.004 -0.020 0.028 0.056 (-0.224) (-0.922) (1.194) (1.642) DAYS -0.002 0.023*** -0.005 0.006 (-0.302) (3.269) (-0.787) (0.694) MA_SIZE 0.021*** 0.528*** 0.007* -0.098* (4.848) (4.981) (1.934) (-1.798) NO_ADV 0.037** -0.022 0.040** 0.028 (2.177) (-1.451) (2.359) (1.374) CASH -0.071*** -0.010 -0.086*** -0.117*** (-2.598) (-0.599) (-4.179) (-5.031) COMM 0.012 0.107*** 0.010 0.018 (0.464) (4.807) (0.417) (0.655) ASSET 0.010 -0.013** 0.040*** 0.034*** (1.229) (-2.057) (3.949) (3.070) LEVERAGE 0.152** 0.065 0.072 -0.047 (2.519) (1.383) (1.489) (-1.461)

MA_YEAR Yes Yes Yes Yes INDUSTRY Yes Yes Yes Yes

Observation 541 654 519 409 Adj. R2 0.18 0.14 0.32 0.21

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