The Changing Nature of Investment Banking Relationships

Shane A. Corwin* Mendoza College of Business

University of Notre Dame Notre Dame, IN 56556

scorwin@nd.edu

Mike Stegemoller Hankamer School of Business

Baylor University Waco, TX 76798

michael_stegemoller@baylor.edu

April 2013 * We thank Jack Cooney for comments. Steven Carroll, Brian Ford, and Travis Johnson provided excellent research assistance. Any remaining errors are the responsibility of the authors.

The Changing Nature of Investment Banking Relationships

Abstract

Using a sample of over 20,000 M&A transactions and public and private security issues by U.S. firms

between 1996 and 2009, we provide a comprehensive analysis of the relationships between firms and

their investment banks, both within and across different investment banking functions. We address three

broad research questions. First, what are the characteristics and determinants of IB relationships? Second,

how have relationships between firms and their investment banks changed over time? And, third, to what

extent do relationships carry over across the various aspects of the investment banking business? We

show that the nature of investment banking relationships has changed significantly over time. For all

types of transactions, we find that changes to the underlying economics of the investment banking

business have led to an increase in the number of investment banking relationships and a decrease in

relationship exclusivity over time. Analyzing both investment bank hiring decisions and investment bank

retention, we find strong evidence of a firm-wide relationship component that spans the functional areas

of investment banking. In addition, the cross-over between debt and equity relationships has increased

significantly over time, as has the link between lending and investment banking relationships. Consistent

with informational theories, we show that investment banking relationships are more exclusive for

younger firms, smaller firms, and firms with more growth options and idiosyncratic volatility. Despite

these firm-wide relationships, however, we identify significant transaction-type specific components to

relationships, with relationships being strongest for equity transactions and weakest for M&A

transactions. Our results support both relationship-based and transaction-based theories of investment

bank selection.

JEL classification: G10, G24, G34, L14

Keywords: Investment Banking, Underwriting, M&A Advising, Underwriter Relationships

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1. Introduction

Relationships between firms and their investment banks are generally managed centrally within the

investment bank. However, much of the prior literature analyzes only a specific area of investment banking,

such as equity or debt underwriting, or M&A advising. In addition, much of the previous research focuses on

factors that influence the decision either to hire a specific investment bank or to switch lead underwriters on

consecutive deals. In this paper, we provide a more comprehensive analysis of investment banking (IB)

relationships both within and across the various investment banking functions by analyzing over 20,000

M&A transactions and public and private security issues by U.S. exchange-listed firms between 1996 and

2009. These data allow us to address three broad research questions. First, what are the characteristics and

determinants of IB relationships? Second, how have relationships between firms and their investment banks

changed over time? And, third, to what extent do relationships carry over across the various aspects of the

investment banking business?

An IB relationship can constitute a wide range of functions, including securities underwriting, M&A

advising, derivatives contracts, and transaction services such as cash management. We focus our analysis on

securities underwriting and M&A advisory services. While we separate some of our analysis by transaction

type, we focus much of our analysis on IB relationships across these investment banking functions. We

believe this level of analysis is appropriate for two reasons. First, while the responsibility for various

investment banking activities may fall to different people within a firm, top level executives will generally be

involved in the selection of the underwriters and M&A advisors that are the focus of our analysis. Second,

from the perspective of the investment bank, these relationships tend to be handled on a firm-wide basis with

one banker acting as the point of contact for the firm and bringing in personnel from specific product teams

as appropriate.

The extent to which relationships encompass multiple IB functions will depend largely on what

drives the relationship from the perspectives of both the firm and the investment bank. The literature suggests

that the value of IB relationships derives primarily from information production. For example, James (1992)

provides evidence that set-up costs in the due diligence process lead to reduced underwriting costs for IPO

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firms that are expected to issue additional securities in the future. Information effects lead to switching costs

in IB hiring decisions and may also cause firms to avoid using the same IB as their primary product market

rivals (Asker and Ljungqvist (2010)). Prior research suggests that information plays a more important role in

equity relationships than debt relationships (see, Burch, Nanda, and Warther (2005) and Fernando, May, and

Megginson et al. (2012)). Thus, we expect stronger relationship effects in equity than in debt transactions and

we expect equity relationships to be a primary determinant of firm-wide IB relationships.1 While information

is also likely to play a predominant role in M&A transactions, these transactions rely first and foremost on

ideas, which may or may not come from the primary relationship bank. We expect this difference in

transaction initiation to lead to weaker relationships in M&A advisory services than in underwriting services

and a weaker link between M&A advisory relationships and firm-wide IB relationships. In contrast to these

information-based stories, Fernando, Gatchev, and Spindt (2005) argue for transaction rather than

relationship-based associations between firms and underwriters, with the two sides choosing to part ways

when their characteristics no longer align. One possible interpretation of their results is that firm’s will select

the investment bank most suited to a particular transaction type, rather than relying on a firm-wide

relationship. In this paper, we examine directly the extent to which relationships carry over across different

investment banking functions.

Our sample period encompasses a number of important changes to the economics of investment

banking. The passage of the Gramm-Leach-Bliley Act in 1999 led to a substantial increase in the role of

commercial banks in investment banking. This change also resulted in more direct ties between lending and

underwriting relationships (see, for example, Schenone (2004), Drucker and Puri (2005), and Barath, Dahiya,

Saunders, and Srinivasan (2007)).2 This link between lending and IB relationships has been magnified by the

1 Throughout the paper, we use the term “firm-wide” relationship to denote the component of IB relationships that spans the functional areas within an investment bank. IB relationships that are related only to a specific transaction type, such as equity, debt, or M&A, are referred to as “transaction-type specific” relationships. 2 A substantial literature exists addressing the entry of commercial banks into investment banking and the resulting competition between commercial banks and traditional investment banks. See, for example, Ang and Richardson (1994), Puri (1996, 1999), Saunders (1999), Gande, Puri, Saunders, and Walter (1997), Gande, Puri, and Saunders (1999), Roten and Mullineaux (2002, 2005), Fields, Fraser, Bhargava (2003), Song (2004), Drucker (2005), Kim, Palia, Saunders (2008), Chaplinsky and Erwin (2009), and Papaioannou (2011).

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recent financial crisis, as firms work to secure additional sources of capital. The Dot-Com bubble and the

subsequent Global Settlement in 2003 also led to considerable changes in the business models of investment

banks and in the related determination of underwriting mandates. Against this backdrop, several studies

document significant changes in the structure of underwriting syndicates. For example, Hu and Ritter (2007)

document a rise in the use of multiple bookrunners in IPOs. At the same time, Corwin and Schultz (2005)

document an increase in the number of comanagers in IPOs and a simultaneous decrease in the number of

non-managing syndicate members. Shivdasani and Song (2011) document a similar increase in the frequency

of joint leads in the debt underwriting market. Our analysis allows us to examine the impact of these changes

on the characteristics and determinants of IB relationships, both within and across transaction types.

A large number of prior papers address the questions of why and how often firms switch

underwriters, especially following the IPO. Taken together, these studies find that the frequency of

underwriter switching has increased over time, from approximately 25-30% in the 1980s, to roughly 60% by

the early 2000s (see, for example, Beatty and Ritter (1986), Welch (1989), James (1992), Dunbar (2000),

Krigman, Shaw, and Womack (2001), Lungqvist and Wilhelm (2005), and Burch, Nanda, and Warther

(2005)). Over our entire sample period, we find that firms retain at least one lead underwriter (advisor) in

49.7% of consecutive M&A deals, 55.0% of debt deals, 72.1% of SEOs following the IPO, and 56.0% of

SEOs following other SEOs. However, our data also allow us to analyze investment bank retention across

transactions of different types. For example, at least one IPO lead underwriter is retained in 51.1% of

subsequent M&A deals and 63.9% of subsequent debt deals.3 A probit model of lead banker retention shows

that the likelihood of retention for equity and debt deals increases significantly if the prior deal was of the

same type. Thus, while we find evidence of substantial carry-over in relationships across IB functions, there

also appears to be a significant transaction-type specific component, with retention being strongest for equity

deals and weakest for M&A deals. We also find evidence that the likelihood of IB retention has increased

3 The conclusions are generally similar if comanager and non-advisory roles are included, with 70.1% of M&A deals, 87.2% of debt deals, and 90.8% of equity deals retaining at least one lead or co-manager from the IPO. Krigman, Shaw, and Womack (2001) find that 48.9% of firms that switch underwriters retain the previous lead underwriter as a comanager, and 25.6% of switchers use a previous comanager as the new lead.

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during our sample period, especially for debt issues.4 This finding is consistent with the large increase over

our sample period in the size of debt underwriting syndicates and may also reflect an increased link between

the underwriting of public debt issues and the provision of lending services.

Next, we provide a more comprehensive analysis of the characteristics and determinants of IB

relationships. Using rolling three-year windows, we define several relationship measures both within and

across transaction types. We also measure relationships both including and excluding comanagers and non-

advisor roles. We find that both the total number of IB relationships and the number of strong relationships

increase over time for all types of transactions, but this trend is most significant for debt transactions.

Assigning each lead underwriter/advisor full credit for each deal, we find weak evidence of an increase over

time in market share for the top relationship bank. However, after adjusting for joint leads and joint advisory

roles, we find a significant decrease in market share for the top bank, in combined market share for the top

three banks, and in a market share Herfindahl index. These results suggest that the changing economics of

the investment banking business have led to an increase in the number of IB relationships per firm, but these

relationships are less exclusive, on average.

Using panel regressions, we find that relationships are less exclusive for large firms and firms that

are more active in terms of equity, debt, and M&A transactions. These results are consistent with less

exclusive relationships for firms that have more bargaining power. In addition to small firms, we find that

relationships are more exclusive for young firms and firms with high market-to-book ratios. Equity

relationships are also more exclusive for firms with high idiosyncratic volatility. These results are consistent

with the hypothesis that relationships are more exclusive in cases where information is more important.

Finally, we find that debt relationships are less exclusive for firms with higher leverage ratios. This finding is

consistent with high debt firms maintaining less exclusive IB relationships in order to provide additional

channels to needed capital. Even after controlling for these relationship determinants, we find strong

evidence that the exclusivity of both firm-wide and transaction-type specific IB relationships decrease

4 In part, this trend may reflect the increasing size of underwriting syndicates over time. However, the conclusions are generally similar if we restrict the sample to deals with only a single lead underwriter or if we analyze the proportion of underwriters retained on consecutive deals.

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significantly during our sample.

Prior research suggests that past relationships with firms have a significant effect on the selection of

underwriters and M&A advisors. For example, Ljungqvist, Marston, and Wilhelm (2006), Drucker and Puri

(2005), Yasuda (2005), and Barath et al. (2007) find that lending relationships increase the likelihood of a

bank being awarded debt and equity underwriting business.5 In addition, after controlling for past lending,

Ljungqvist et al. (2006) and Barath et al. (2007) find that the likelihood of winning a debt or equity

underwriting mandate is positively related to past underwriting relationships.6 For M&A transactions, Bao

and Edmans (2011) find that past M&A market share is the primary determinant of advisor selection and

Forte, Iannotta, and Navone (2010) find that firms are more likely to select as advisor the investment bank

with which they have the strongest past relationship.7 We extend these prior studies by analyzing the firm-

wide nature of investment banking relationships and the extent to which relationships in one functional area

carry over to other functional areas. In a frequency analysis for firms that have at least two deals of each

type, we find that the top equity IB is also the top debt IB in 61.3% of cases and the top M&A advisor in

55.1% of cases. Similarly, the top debt IB is also the top M&A advisor in 51.3% of cases. Like the IB

retention results, these findings suggest that relationships span the functional areas within the investment

bank. However, we also find numerous cases where the top bank in one area plays no role in other areas.

For example, the top equity IB fails to show up as a debt underwriter in 19.8% of cases and fails to show up

as an M&A advisor in 31.9% of cases. Again, there appears to be a significant transaction-type specific

component in IB relationships, with the carry-over in relationships being strongest between equity and debt

5 Previous studies also examine the relation between lending relationships and IPO underpricing (James and Wier (1990) and Schenone (2004)), the long-run performance of IPOs (Benzoni and Schenone (2010)), SEO pricing (Narayanan, Rangan, and Rangan (2004), and SEO underwriting fees (Drucker and Puri (2005)). In addition, Krishnan (2013) finds that banks with debt underwriting capabilities use bundling to attract future business, by offering more lending at better rates. 6 While Ljungqvist et al. (2006) find that aggressive analyst coverage does not affect the likelihood of winning lead underwriting mandates, Ljungqvist, Marston, and Wilhelm (2009) find that it does improve an underwriter’s chances of being selected as a comanager. Corwin and Schultz (2005) also find that an underwriter’s chances of being included in an IPO syndicate increases if the underwriter has a previous relationship with the IPO lead underwriter. 7 Bao and Edmans (2011) find a persistent advisor-specific component in M&A performance, but find that this component has little impact on M&A market share. Prior evidence on the performance of investment bank advisors is somewhat mixed. Servaes and Zenner (1996) examine the decision to hire an investment bank as advisor and find that acquirers are more likely to employ investment banks when they have less prior acquisition experience and when transaction complexity is high. While Bowers and Miller (1990) and Rau (2000) find little evidence that top-tier investment banks provide superior acquisition performance, Kale, Kini, and Ryan (2003) and Boone and Mulherin (2008) find evidence that prestigious advisors provide benefits to the firms that hire them.

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underwriting and weakest for M&A advisory services. Subperiod test suggest that the carry-over between

equity and debt relationships has increased over time, while the carry-over between M&A advisory services

and other areas has decreased.

Probit models for IB selection support the importance of both firm-wide and transaction-type

specific relationships. For example, the likelihood of being selected as a lead or comanager in an equity

transaction is positively affected by past equity relationships, but both past debt and M&A relationships have

significant incremental effects. In addition, the importance of debt relationships in the choice of equity

underwriters has increased over time, suggesting a strengthening of the firm-wide relationship component.

Playing a lead role on the prior transaction has a significant positive effect on the likelihood of being hired on

the current transaction. This effect is strongest when the prior deal is of the same type, but continues to be

significant when the deals are of different types.8 Unlike the effects of IB relationships, the effects of IB

reputation appear to be exclusively transaction-type specific. An investment bank’s chances of being hired

in an equity, debt, or M&A transaction are positively affected by transaction-type specific market share, but

unaffected by market share in other transaction types. Overall, our results point to important relationship

effects in investment bank hiring decisions which incorporate both firm-wide and transaction-type specific

relationships, with the cross-over of relationships between equity and debt increasing over time.

Our paper contributes to a large literature addressing underwriter and advisor selection and

investment banking relationships, but is most closely related to Burch et al. (2005), Ljungqvist et al. (2006),

and Barath et al. (2007). Using a sample of securities issues from 1975-2001, Burch et al. find that the

strength of IB relationships decreased during the 1980s and 1990s for both equity and debt issues. They also

find that stronger relationships are associated with reduced underwriting fees for equity issues but higher fees

for debt issues. Ljungqvist et al. (2006) focus on the effects of analyst coverage on the selection of lead

underwriters in equity and debt offers from 1993 through 2002, but also control for the effects of previous

lending and underwriting relationships. Their results suggest that both past underwriting relationships and

8 Consistent with Asker and Ljungqvist (2010), we also find that the effects of past relationships are weakened when the investment bank in question is the primarily relationship bank of the firm’s main product market rival.

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lending relationships have significant effects on underwriter selection, with same-transaction-type

underwriting relationships having a larger impact than cross-market relationships. Finally, Barath et al.

(2007) use a sample of issuers with lending relationships from 1986 through 2002 to analyze the effects of

lending relationships on a bank’s ability to win future lending and underwriting business. Their results

support the importance of past lending and underwriting relationships in winning future business, but suggest

that the economic impact of cross-market relationships is small. Our analysis differs from these prior studies

in several important respects. First, we examine both securities issues and M&A activity for a broad sample

of firms and investment banks, allowing us to provide a more comprehensive analysis of IB relationships and

to address whether these relationships carry over across transaction types. Second, our sample period

includes the period from the end of the dot-com bubble through the financial crisis, during which substantial

changes occurred in the investment banking industry. Finally, we incorporate additional analysis of

comanaging and non-advisor roles, which have become increasingly important over time.

The remainder of the paper is organized as follows. In Section 2, we describe the data and sample

characteristics, including the time trends in underwriting syndicate and M&A advisory group structure.

Section 3 provides an analysis of investment banker retention and Section 4 provides an analysis of the

characteristics and determinants of IB relationships. In Section 5, examine the extent to which IB

relationships carry over across deals types and the impact of these relationships on IB selection decisions.

Section 6 concludes.

2. Data and Sample Characteristics

A. Sample Firms and Transactions

The data for our analyses come from three primary data sources. Firm characteristics are identified

using CRSP, data on securities issues and M&A events are taken from SDC’s new issues and M&A

databases, and data on lending is from Reuters’ DealScan database. Our goal is to identify all security issue

and M&A events for which a firm might hire an investment banker. To identify firms, we use CRSP to

collect the sample of U.S. firms with listed common stock (CRSP share codes 10 or 11) on any dates

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between 1996 and 2009. Because our analysis is at the firm level, we identify CRSP firms based on the

permanent company number (PERMCO). This search produces an initial sample of 15,381 firms.

The security issue and M&A events for these firms are identified using data from SDC. To begin, we

use the SDC new issues database to collect all public and private security issues by U.S. firms between 1996

and 2009. This produces a sample of 18,229 equity issues, 268,784 debt issues, and 4,536 issues of other

securities types, such as preferred stock, units, etc. Next, we use the SDC M&A database to collect all

events with effective dates between 1996 and 2009 for which either the target or acquirer is a U.S. firm

(public or private). The initial M&A sample includes 140,677 events.

Because we are working at the firm level with multiple types of securities, we cannot match CRSP

and SDC data based simply on Ticker symbol or CUSIP. To begin the matching process, we identify the date

range associated with every unique PERMCO-Ticker-CUSIP combination in the CRSP data. An analogous

process can be performed with the SDC data using a firm-level identifier called CIDGEN.9 Specifically, we

identify the earliest and latest event dates for each unique CIDGEN-Ticker-CUSIP combination in the equity,

debt, and M&A event samples. To create PERMCO-CIDGEN matches, we begin by matching the equity,

debt, and M&A samples to the CRSP sample based on CUSIP and Ticker. For unmatched CIDGENs, we

then attempt to match based on CUSIP only, and then based on Ticker only. For each PERMCO, we then

keep the highest-level CIDGEN match we can identify across the three event samples. Finally, we manually

check all CUSIP-only and Ticker-only matches, as well as matches for which the CRSP date range does not

correspond to the SDC date range for that firm. After making corrections, this process results in 14,480

PERMCO-CIDGEN matches, of which 13,018 (89.9%) are matched based on CUSIP and Ticker, 1,274

(8.8%) based on CUSIP only, and 188 (1.3%) based on Ticker only.

The PERMCO-CIDGEN matches described above allow us to identify all SDC events associated

9 CIDGEN is a 10-digit firm-level identifier provided for every event in SDC. While the last three digits can change across events, the first seven digits are unique to the firm and appear to change only as the result of major firm-level changes, such as reorganizations or mergers. While changes in 7-digit CIDGENs tend to correspond with changes in CRSP PERMCOs, this is not always the case. We choose to define firms based on the CRSP PERMCO. In those cases where the CIDGEN number changes and the PERMCO does not, we match both CIDGENs to the same PERMCO. In the small number of cases where the PERMCO changes and the CIDGEN does not, we redefine the CIDGENs to create separate SDC identifiers for events involving the two PERMCOs. Our firm-level matching process utilizes the first six digits of the CUSIP.

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with each firm (or each unique CRSP PERMCO). Using this process, we are able to assign a PERMCO to

80.9% of equity issues, 39.6% of debt issues, 64.4% of other security issues, 40.9% of M&A acquirers, and

22.0% of M&A targets. Once SDC events are matched to firms, we apply several additional restrictions.

First, we exclude financials, utilities, and government agencies. We also exclude firms that are included in

CRSP for less than one calendar year. These restrictions reduce the sample to 8,322 firms. We further restrict

the set of SDC events by applying the following criteria: (1) combine simultaneous offers and remove

duplicate events, (2) exclude purely secondary equity issues, (3) exclude M&A events for which the acquirer

owns more than 50% of the target prior to the event or owns less than 50% after the event and restrict M&A

form to “acquisition”, “merger”, “acquisition of majority interest”, or “acquisition of assets”10, (5) exclude all

events associated with captive finance subsidiaries11, and (6) exclude all events that occur more than five

days outside the CRSP date range associated with that PERMCO. These additional restrictions result in a

final sample of 64,006 events, including 7,319 equity issues, 11,052 debt issues, 662 securities issues of

other types, 32,489 M&A acquirer events, and 12,484 M&A target events. The matching details and sample

restrictions are described in more detail in Table A1 in the appendix.

To analyze lending activity for our sample firms, we match the firm’s identified above to DealScan

using the link table provided by Michael Roberts and Wharton Research Data Services (see Chava and

Roberts (2008)).12 Our initial DealScan sample includes all loan facilities by U.S. firms from 1996 through

2009, excluding loans by financial firms, utilities, and government agencies. After matching to the sample

firms, the final loan sample includes 29,469 loans by 4,964 firms.

The sample of transactions is described in more detail in the first three columns of Table I. The

sample of public and private security issues represents an aggregate transaction value of $3.54 trillion from

10 We exclude M&A events classified as acquisitions of “certain assets”, “partial interest”, or “remaining interest”. 11 We view captive finance subsidiaries as distinct entities that are similar to financial firms and may have distinct relationships with investment bankers. As a result, we separate them from their parent firms and exclude them from the sample. The most active captive finance subsidiaries in our sample are GE Capital, GMAC, Caterpillar Financial Services, and John Deere Capital. For a discussion of captive finance subsidiaries, see Bodnaruk, O’Brien, and Simonov (2012). We thank Andriy Bodnaruk for providing data related to these subsidiaries. 12 DealScan consists primarily of large syndicated loans by medium and large firms. According to Carey and Hrycray (1999), DealScan covered 50-75% of U.S. commercial loans in the early 1990s, with coverage increasing from 1995 onwards. For a more complete description of the DealScan data, see Ivashina (2009) and Chava and Roberts (2008).

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1996 through 2009. Debt transactions dominate the security issue sample, accounting for 49% of all issues

and 77% of total transaction value. Within the equity sample, public seasoned offerings dominate, accounting

for 43% of issues and over 60% of aggregate transaction value. IPOs account for about one third of the

equity issues by both frequency and aggregate transaction value. While private equity offers account for

about 20% of total equity issues, these offers tend to be small, representing only 6% of aggregate equity

transaction value. The debt sample is concentrated in nonconvertible offers, with public nonconvertible debt

accounting for 45% of transactions and 59% of total transaction value and private nonconvertible debt

accounting for 35% of transactions and 27% of aggregate transaction value. Aggregate transaction value in

the M&A sample is significantly larger than that in the security issue sample. Of the 32,451 acquirer events

in the sample, transaction values are available for 17,547 (54%) with an aggregate value of $5.50 trillion.13

Of the 12,427 sample target events, transaction values are available for 8,172 (66%), reflecting an aggregate

value of $6.35 trillion. For comparison, the last row of Table I provides a description of lending activity. The

aggregate loan value of $8.07 trillion is nearly double the aggregate security issue value, with an average

loan size of $273.8 million.

The time series of aggregate transaction values for each transaction type are plotted in Figure 1.

While security issuance frequencies tend to decrease over time, aggregate transaction values follow more

variable patterns, with equity issuance peaking in 1999-2000 and debt issues being higher in the early 2000s

and toward the end of the sample period. M&A transaction values increase sharply in the late 1990s,

decrease through the mid-2000s, and peak again in 2006-2007.

Table II describes firm-level activity for the sample of 8,322 firms. Of these firms, all but 768 have

at least one transaction during the sample period, with the mean (median) firm having 7.3 (4.0) transactions.

The firm at the 99th percentile is involved in 57 combined security issue and M&A transactions, with the

most active firm accounting for 225 combined transactions. Of the sample firms, 64.3% have at least one

security issue, 67.1% are acquirers in at least one M&A event, and 58.9% are targets or target parents in at

13 In general, transaction values must be reported for M&A transactions that are considered “material” to the firm. See Rodrigues and Stegemoller (2007) for a discussion of these reporting requirements. In addition, Netter, Stegemoller, and Wintoki (2011) provide a comprehensive discussion of data screens related to the analysis of M&A transactions.

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least one M&A event. Examining securities issues by type, 50.9% of the sample firms have at least one

equity issue and 27.5% have at least one debt issue. The last row in Table II describes lending activity. Of

the 8,322 firms in our sample, 4,964 have at least one loan in the sample, with the mean (median) firm

having 3.5 (1.0) leans during the sample period.

Event frequencies for the most active firms in the sample are described in more detail in Table A2 in

the appendix. The most frequent security issuers in the sample are Union Pacific, United Parcel Service, and

IBM, with 71, 70, and 68 securities issues, respectively. The most active acquirers in the sample are Cisco

Systems with 133 acquisitions, IBM with 102 acquisitions, and Microsoft with 101 acquisitions. General

Electric is the most frequent target (target parent) with 126 target events, followed by Alderwoods Group

with 69 target events, and AT&T with 62 target events.

Sample firms are described in more detail in Table III. The average sample firm has a market value

of $1.47 billion, an average stock price of $15.06, and average daily trading volume of 444 thousand

shares.14 The number of years from a firm’s first listing in CRSP to the end of our sample period (or the

firm’s delisting date, if earlier) averages 18.9 years. The number of years in the sample averages 6.89, and

ranges from 1.84 to 14.00. Because firms enter and leave the sample at different times, it is useful to

examine transaction frequencies and deal values per year. On average, sample firms experience 0.18 equity

issues per year, 0.14 debt issues per year, and 0.82 M&A transactions per year (as either acquirer or target).

The average transaction value per year equals $16.8 million for equity, $40.0 million for debt, and $241.6

million for M&A transactions. Summary statistics related to lending are listed in the last two rows of Table

III. The average firm in the sample is involved in 0.57 loans per year with an average loan value per year of

$119.5 million.

In most subsequent tests, we wish to focus on the set of transactions for which a full-service

investment bank was both considered and willing to participate in the transaction. This allows us to analyze

IB relationships that span the functional areas within the investment bank and also accounts for the two-sided

14 For the purposes of this table, market value, stock price, and trading volume are averaged for each firm across all trading days in the sample period. The table then reports the cross-sectional average of these sample period averages. Market values are not adjusted for inflation. Again, we require firms to be listed on CRSP for at least one calendar year during our sample period.

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matching problem described in Fernando et al. (2005). To identify this sample, we impose the restriction that

a transaction must have a value of at least $1 million and employ a full-service bank in some role on the

deal.15 As shown in Table A1 of the Appendix, these additional restrictions result in a sample of 22,490

transactions, including 6,043 equity issues, 7,342 debt issues, 437 security issues of other types, 4,394 M&A

acquirer events, and 4,274 M&A target events. Summary statistics for this restricted sample are provided in

the last three columns of Table I. As the table shows, the restricted sample accounts for nearly 99% of

aggregate security issue value, 83.5% of reported M&A acquirer transaction value, and 94.3% of reported

M&A target transaction value.

B. Syndicate Characteristics

Using SDC, we collect data on all underwriting syndicate members for equity, debt, and other

security offerings, as well as all advisors utilized by targets and acquirers in M&A transactions. Across the

full sample of 60,458 offers, we are able to identify 96,329 individual underwriter and advisor observations.

As shown in Table A1 in the appendix, we identify underwriters for 89.0% of equity issues, 98.8% of debt

issues, and 74.8% of other security issues. In addition, we identify advisors for 17.3% of M&A acquirer

events and 44.8% of M&A target events.

To clean the underwriter/advisor data, we first combine all variations of the same

underwriter/advisor name into a single name. We then adjust the underwriter names to account for mergers

and acquisitions among underwriting/advising firms. We identify underwriter/advisor combinations using

several different sources. We begin with the samples of underwriter mergers identified in Corwin and

Schultz (2005), Asker and Ljungqvist (2010), and Bao and Edmans (2011). We then verify these

combinations and identify additional combination events using SDC’s parent information for each firm, news

stories, and the SDC mergers and acquisitions database. In total, we identify 181 combination events during

our sample period.

In some cases, such as the acquisition of Hampshire Securities by Gruntal & Company in 1997, our

15 As described below, we define a full-service investment bank as one that participates in at least one deal of each type (equity, debt, and M&A) during our sample period. There are 195 such investment banks in our sample.

13

interpretation is that one firm is subsumed by another. In these cases, the acquired underwriter/advisor exits

our sample on the event date and the acquiring firm is assumed to absorb all market share and relationships

of the acquired firm. In other cases, such as the acquisition of Dillon Read by SBC Warburg in 1997, our

interpretation is that two firms combine to form a new firm. In this example, the new firm is Warburg Dillon

Read. A new firm is created starting on the event date and this firm absorbs all market share and

relationships from both predecessor firms. To illustrate this process, Figure A1 in the appendix shows all of

the predecessor firms and combination events associated with what ultimately become Bank of America

Merrill Lynch and UBS Paine Webber.

After cleaning, the data include 1,436 unique underwriter/advisor names. Of these, 492 are involved

in equity issues in some capacity, but play no role in debt or M&A events during the sample period. Another

117 are involved only in debt events and 417 are involved only in M&A events. Ninety-eight of the sample

underwriters/advisors are involved in securities issues but not M&A events, and 195 participate in at least

one transaction of each type. This sample of 195 underwriters/advisors is what we refer to as “full-service”

investment banks. The ten most active banks for each transaction type are listed in Table A4 of the appendix.

Using SDC’s role descriptions, we classify underwriters as leads, comanagers, or non-managing

syndicate members and we classify M&A advisory group members as either “advisor” or “other”.16,17

Throughout the paper, we combine these roles into two broad categories. The first category, lead roles,

includes all lead underwriters and all “advisor” roles in M&A transactions. The second category, important

roles, includes lead roles plus all comanaging underwriters and all other M&A advisory roles.

16 We identify an underwriter as a lead if that manager is assigned an SDC underwriter role of global coordinator, book runner, joint book runner, lead manager, joint lead manager, lead placement agent, or joint lead placement agent. We identify an underwriter as a comanager if that manager is assigned an SDC underwriter role of co-manager or co-placement agent. We identify non-managing syndicate members using the SDC role “syndicate member”. Because Corwin and Schultz (2005) find that these non-managing syndicate members play only a minor role in the underwriting process and the use of these syndicate members has decreased substantially over time, we exclude them from our analysis of IB relationships. Hu and Ritter (2007) distinguish between lead managers and book running managers, noting that all book runners are leads, but not all leads are book runners. Following much of the prior research, we do not distinguish between book running and non-book running lead managers. 17 For M&A target (acquirer) events, 89.3% (92.2%) of advisory group members in the data have “advisor” listed as one of their roles. Among those not listed as “advisor”, the vast majority are identified as providing a fairness opinion. For target advisors, 32.9% of those listed as “advisor” have at least one other listed role, with over 90% of these providing a fairness opinion. Of those not listed with an “advisor” role, 58.4% provide fairness opinions and the rest are primarily listed as representing the seller or a majority shareholder. For acquirer advisors, 12.6% of those listed as “advisor” have at least one other listed role, with 100% of these providing a fairness opinion. Of those not listed with an “advisor” role, 77.6% provide fairness opinions and the rest have no listed role.

14

To identify lending relationships between firms and their IBs, we hand match all IB names that

appear in the SDC sample to the DealScan lender IDs associated with our sample loans. Following Gopalan,

Nanda, and Yerramilli (2011), we focus on the subset of lenders who are assigned lead arranger credit and

divide the credit for each loan equally in cases with multiple lead arrangers.18 Using this method, at least one

SDC-identified IB is assigned lead arranger credit in 77.3% of the sample loans, accounting for 89.6% of the

aggregate lending in our sample.

As noted above, previous research identifies several important changes in equity syndicates over

time. Before turning to the question of what impact these patterns have on IB relationships, we first examine

the patterns for our more recent sample period and compare the equity and debt syndicate patterns to those

for M&A advising groups. Panels A and B of Figure 2 plot the average number of lead, comanaging, and

non-managing syndicate members by year for the full sample of equity issues and the subsample of IPOs.

The patterns are consistent with those reported in prior research and show that the previously-documented

trends have continued in the late 2000s. For equity issues overall, the average number of non-managing

syndicate members decreased from approximately 9 in 1997 to 1 in 2003, and decreased to essentially zero

by the end of the sample period. At the same time, the average number of leads increased from 1 to 1.6 and

the average number of comanagers increased from approximately 1.5 to over 3. The patterns for IPOs are

similar, with a slightly larger increase in leads and comanagers and a sharper decrease in non-managing

syndicate members.

Underwriting syndicates for debt issues and other types of security issues are characterized in Panels

C and D of Figure 2, respectively. While debt issues tend not to include non-managing syndicate members,

the patterns in leads and comanagers are even more striking than those for equity issues. During the sample

period, the average number of leads increases from approximately 1 to over 3, and the average number of

comanagers increases from just under 1 to over 5. Syndicate characteristics for other types of security issues

18 Based on DealScan’s lead arranger credit variable, 80.0% of the sample loans have one lead and 19.3% of two. The maximum number of leads is 11, but only 26 loans (0.09%) have more than five leads. Of the lenders with lead arranger credit, DealScan identifies 62% as “Administrative Agent”, 16% as “Syndication Agent”, 11% as “Agent”, and 9% as “Arranger”. The remaining 2% are spread across 42 different role descriptions. Ivashina (2009) describes the difficulty in interpreting lender roles by title, noting that different titles don’t necessarily correspond to different roles.

15

are difficult to interpret due to the small number of issues per year, especially toward the end of the sample

period. In general, however, the patterns are similar to those for equity and debt. The average numbers of

leads and comanagers increase through 2007, and there is little evidence of non-managing syndicate member

participation after the middle of the sample period.19

The characteristics of M&A advisory groups are illustrated in Panel E of Figure 2. From 1996

through 2004, the number of advisors in M&A transactions averaged close to 1.1, and was slightly higher for

acquirers than for targets. This number increased through the end of the sample period, reaching nearly 1.25

for targets and 1.35 for acquirers. While these trends are small relative to those in the equity and debt

syndicates, they do suggest a notable shift in the composition of M&A advising groups over time.

To provide additional detail, Figure 3 plots the proportions of security issues with multiple lead

underwriters (panel A), the proportion of M&A deals with multiple advisors (panel B), and the proportion of

security issues with zero non-managing syndicate members (panel C). For both IPOs and debt issues, the

proportion of deals using multiple leads increases steadily from close to zero at the beginning of the sample

period to over 80% in 2009. For all equity deals, the proportion levels off at just over 40% after 2004. The

increased numbers of leads and comanagers in equity syndicates is accompanied by a significant decrease in

the number of non-managing syndicate members. For both IPOs and seasoned equity offerings, the

proportion of issues with zero non-managing syndicate members increased from 20%-30% in the late 1990s

to nearly 100% by 2009. Debt underwriting syndicates do not tend to include non-managing syndicate

members. These results suggest that the trends reported in Corwin and Schultz (2005) have continued

through the mid and late 2000s. The proportion of M&A acquirers with multiple advisors increases from just

under 10% to over 15%. In comparison, the proportion of M&A targets with multiple advisors increases

through the mid-2000s before dropping off again in the late 2000s.

The results in this section point to several important time trends in the characteristics of underwriting

syndicates and M&A advisory groups. In particular, there is a significant increase over time in the number

19 Syndicate characteristics for other security issues in 2008 and 2009 are difficult to interpret, as the number of sample issues in this category drops to 9 in 2008 and 14 in 2009.

16

of leads and comanagers in underwriting syndicates, with a corresponding trend toward zero non-managing

syndicate members. M&A advisory groups have also grown over time, but to a much smaller extent than

underwriting syndicates. In the next three sections, we examine various aspects of IB relationships, including

the extent to which these trends in syndicate structure have affected the characteristics and determinants of

IB relationships. We begin with an analysis of underwriter retention on consecutive deals. We then provide a

more comprehensive analysis of the characteristics and determinants of IB relationships both across firms

and across time. Finally, we examine the extent to which IB relationships carry over across different

investment banking functions and the role these relationships have on investment bank hiring decisions.

3. Investment Bank Retention

The strength of IB relationships is revealed, in part, through a firm’s decision to retain an investment

bank from one deal to the next. As noted above, the decision to switch lead underwriters has been studied

extensively in the prior literature, especially with respect to the debt and equity issues that follow a firm’s

IPO. We extend this literature by analyzing IB retention during our more recent sample period and across

transactions of different types. While most prior studies define retention as any case where the lead on one

deal is also the lead on the next deal, the increase over time in jointly led deals requires us to use a modified

definition. We define IB retention as any case where at least one IB in a lead (or important) role on the prior

transaction is hired in a lead (or important) role on the current transaction.20

Retention rates for the full sample period are described in Panel A of Table IV. For each transaction

type pair, the top line lists the average retention rate based on lead roles and the second line lists the average

retention rate based on important roles (in brackets). We present results both for the full sample of events that

have an identifiable prior transaction by the same firm and for the subsample of paired events where we

impose the restriction that the prior event be no more than three years prior to the current event. We also

provide separate results categorizing both the current and prior events by transaction type.

20 It is possible that this definition will lead to an increase in retention rates over time simply due to the fact that the number of leads on a deal increases over time. However, our conclusions are generally similar if we instead focus on the subsample of deals with only one lead or if we analyze the proportion of IBs retained on consecutive transactions.

17

Ignoring transaction type, we find that at least one lead IB is retained in 48.8% of consecutive deals

and at least on important IB is retained in 62.1% of consecutive deals. These rates increase to 51.7% and

65.1% if the deals are required to be within three years of each other. Focusing on consecutive deals of the

same type, we find that at least one lead IB is retained in 49.6% of consecutive M&A deals, 55.5% of

consecutive debt deals, 65.1% of SEOs following an IPO, and 49.2% of SEOs following other SEOs. For

equity and debt deals, retention is particularly strong if non-lead roles are considered and if a 3-year

restriction is imposed. For example, for deals no more than three years apart, at least one important IB is

retained in 78.3% of consecutive debt deals, 90.8% of SEOs following the IPO, and 75.8% of SEOs

following other SEOs. These results suggest that ongoing relationships are particular important for equity

and debt issues.

Our data also allow us to analyze investment bank retention across transactions of different types.

For example, following IPOs, at least one lead IB is retained in 42.2% of subsequent M&A transactions,

56.7% of subsequent debt transactions, and 65.1% of subsequent equity transactions. More generally, the

results suggest that retention rates tend to be higher for equity and debt transactions than for M&A

transactions, and retention tends to be highest following the IPO, regardless of the subsequent transaction

type. In all cases, differences across transaction types are statistically significant. However, even for deals of

different types, at least one lead IB is generally retained in at least 40% of deals. These results are consistent

with the importance of both firm-wide and transaction-type specific components in IB relationships. In all

cases, retention rates increase if we impose a maximum restriction of three years between deals, suggesting

that IB relationships weaken as time passes between deals.

Panel B of Table IV provides separate results for deals during 1996-2002 and 2003-2009. Across all

transactions, the results show that retention rates for both lead roles and important roles increase over time.

However, these differences appear to be driven by retention rates related to debt transactions. For example, at

least on lead (important) IB is retained in 65.6% (84.5%) of consecutive debt transactions during 2003-2009,

compared to 49.5% (67.8%) during 1996-2002. Similarly, at least on lead (important) IB is retained in 64.2%

(78.8%) of equity transactions that follow debt transactions during 2003-2009, compared to 21.5% (73.7%)

18

during 1996-2002. These results suggest that the increased use of multiple leads and larger groups of

comanagers has increased the likelihood that a particular IB is retained from one deal to the next, especially

for debt deals. The results may also reflect an increased link between the underwriting of public debt issues

and the provision of lending services.

To control for other factors that may affect the decision to retain an IB on consecutive deals, we

estimate a probit model for IB retention. The dependent variable is defined as one if at least one IB from the

prior transaction is retained on the current transaction and we estimate the model separately using lead roles

(panel A) and important roles (panel B). We also estimate the model separately for equity, debt, and M&A

deals both using the full sample of prior deals and eliminating all deals of different types. The dependent

variables include several firm and transaction characteristics, as well as time period effects. The results are

presented in Table V.

In all models except the equity-equity model, the coefficient on firm age is negative and statistically

significant. Retention is also higher for the transaction immediately following the IPO, regardless of the post-

IPO transaction type. These results are consistent with the retention rates following IPOs documented in

Table IV and suggest that relationships are strongest for young firms. In some cases, multiple deals of

different types or multiple M&A events for the same firm may occur on the same date. The coefficient on a

dummy variable identifying these cases is positive and significant, showing that firms tend to use the same

IB on events that occur on the same day. Similarly, the coefficient on years since prior transaction is negative

and significant, confirming our earlier finding that the likelihood of retention decreases as time passes

between transactions.

Transaction value is positive and significant in the combined deal models and in the equity-equity

models, suggesting that larger deals of these types are more likely to retain IBs from the prior deal. More

interestingly, however, the interaction terms suggest that retention likelihood decreases when the current deal

is substantially larger or substantially smaller than the prior deal. These results are consistent with Fernando

et al. (2005) who find evidence in support of transaction-based underwriter selection decisions. To control

for the increase over time in numbers of leads, comanagers, and advisors per transaction, we include in the

19

model the number of leads on the deal. As expected, the coefficient on this variable is positive and

significant suggesting that the larger the current underwriting syndicate or advisory group, the more likely

that at least one IB is retained from the prior deal. After controlling for this trend, the results from the time

period dummies are somewhat mixed, but are generally consistent with the increase in retention rates for debt

transactions identified in Table IV.

To examine the importance of transaction type, we include two additional variables. First, in the

transaction-type specific models, we include a dummy variable to identify those cases where a transaction of

another type occurs between the two same-type deals. The coefficient on this variable is negative and

significant in the merger-merger model, suggesting that retention likelihood decreases if an equity or debt

deal occurs between the two merger deals. However, the variable is insignificant in the debt model and

positive and marginally significant in the equity model. In the combined deal models, we instead include a

variable to identify whether the current transaction and the prior transaction are of the same type. The results

show that for equity and debt transactions, retention is significantly higher when the prior transaction is of

the same type. In addition, this transaction-type specific effect has increased over time for debt issues.

Panel B provides a similar analysis of retention for IBs in important roles. In general, the results are

similar to those presented in Panel A. However, the results incorporating important roles provide additional

evidence that the debt-specific component of IB relationships has increased over time, while the equity-

specific component has decreased over time.

Overall, the retention analysis in this section points to substantial differences across deal types and

over time. As expected, relationships appear to be strongest for equity issues and weakest for M&A

transactions. The results also suggest that the strength of debt relationships and the debt-specific component

of IB relationships increases over time. In contrast, we find little evidence of an M&A-specific component to

IB relationships and we find weak evidence that the equity-specific component declines over time.

4. The Characteristics and Determinants of Investment Banking Relationships

In this section, we move beyond the type of switching analysis in prior research to provide a more

20

detailed characterization of IB relationships and their determinants. To examine possible changes in

relationships over time, we measure relationships using three-year rolling windows. Because there may be

many different aspects to these relationships, we define several alternative relationship measures. In addition,

for each measure, we create four alternative versions based on lead roles and four alternative versions based

on important roles. The four versions include a firm-wide measure based on all transactions by the firm, and

three transaction-type specific measures based on equity, debt, and M&A transactions, respectively.

To define the relationship measures, we first create two measures of market share for each IB-firm

pair based on the dollar value of transactions done by the firm during the three-year window. The first

measure of IB-firm market share is defined as the proportion of the firm’s aggregate transaction value for

which the IB acted in a lead (or important) role, where each IB in a lead (or important) role is given full

credit for the deal. Adjusted IB-firm market share is defined analogously, but with each IB in a lead (or

important) role receiving credit for a proportion 1/N of deal value. To capture the number of IB relationships

maintained by the firm, we calculate the total number of IB relationships and the number of strong IB

relationships, where we define a strong relationship as any investment bank that earns at least a 50% IB-firm

market share. To focus on the top IB relationships for each firm, we define the market share of the firm’s top

IB, the adjusted market share of the firm’s top IB, and the aggregate adjusted market share of the firm’s top

three IBs. Finally, to capture the exclusivity of the firm’s IB relationships, we define a Herfindahl index

based on the adjusted market shares of all IBs that work with the firm. For the analysis to follow, we analyze

IB relationships only for those firms with at least two relevant deals during a three-year window.

To illustrate the time-series patterns in these relationship measures, Figure 4 plots the cross-sectional

average of each relationship measure across all three-year windows ending from 1998 through 2009.21

Figures A through D show that firms have increased both the total number of IB relationships they maintain

and the number of strong relationships they maintain, whether measured based on lead roles only or based on

all important roles. While these patterns are evident across all types of deals, the patterns are most striking

21 Table A3 in the Appendix provides are more detailed description of the cross-sectional distribution of various relationship measures for the three-year windows ending in 1998 and 2009.

21

for debt relationships and weakest for M&A relationships. For example, the number of different IBs hired in

lead roles on debt transactions increases from an average of 2.3 per firm for transactions in 1996-1998 to 4.9

per firm for transactions in 2007-2009. Likewise, the average number of IBs hired in important roles on debt

transactions increases over this period from 4.9 to 12.0 per firm. The patterns for equity issues are similar,

though less dramatic, with the average number of lead IB relationships increasing from 1.4 to 2.4 and the

average number of important relationships increasing from 3.9 to 5.8. These results show that the changes in

equity and debt syndicate structure documented in Section 2 led to a related increase in the number of

separate investment banks hired by firms in both lead and important roles. While a similar pattern exists for

M&A advisory relationships, the average number of important relationships in this category increase only

from 2.1 to 2.4 per firm.

Panels E through H of Figure 4 provide results related to the strength and exclusivity of IB

relationships. When each lead IB is assigned full credit for each deal, Panel E shows that there is very little

change over time in the market share of the top relationship bank for equity and M&A transactions, and a

modest increase over time for debt transactions. However, when market shares are adjusted to account for

multiple leads, the conclusions change dramatically. As Panel F shows, the average adjusted market share of

the top relationship bank drops significantly over time for all types of deals. On average, the top IB’s

adjusted market share drops from 73.7% to 40.8% for debt transactions and from 86.5% to 64.4% for equity

transactions. Although more modest, a decrease is also evident for M&A transactions, for which adjusted

market share decreases from an average of 78.2% to 72.3%. While the unadjusted market shares illustrated

in Panel E may closely track league table results, the adjusted market shares shown in Panel F may be a

better representation of the division of fees across investment banks. Thus, our results are consistent with a

change over time in the economics of the investment banking business, in which IBs earn fees from more

firms but earn a smaller portion of fees from each individual firm.

Although the results in Panel F suggest that the adjusted market shares of the top relationship IB

have decreased over time, Panel G shows that IB relationships continue to be dominated by a small number

of banks. At the beginning of our sample period, the top three relationship IBs in all transaction types earn an

22

aggregate adjusted market share of at least 98%. This aggregate market share decreases only slightly over

time for equity and M&A transactions. Debt transactions are significantly less concentrated by the end of the

sample period than the other transaction types. Even for debt transactions, however, the top three relationship

IBs earn an aggregate adjusted market share of 80.1% in 2007-2009. These results suggest that IB

relationships have evolved over time from a single primary bank toward a small group of primary

relationship banks.22 Results based on the market share Herfindahl index are consistent with the adjusted

market share results and suggest that the exclusivity of IB relationships has decreased significantly over time,

especially for relationships involving equity and debt transactions.

To better understand the determinants of IB relationships and the related time series patterns, we

provide a pooled time-series and cross-sectional regression of IB relationship measures on several firm and

investment bank characteristics. To measure the firm’s bargaining power, we include the log of market

capitalization and the relative transaction value of the firm. Relative transaction value is defined for each

relevant transaction type (i.e., all transactions, equity, debt, or M&A) as the aggregate transaction value for

the firm during the three-year window divided by the aggregate transaction value of all firms during the same

period. We expect larger firms and more active firms to have more bargaining power, allowing them to

maintain a higher number of less exclusive IB relationships. Firm size may also be related to the firm’s

information environment, where we expect more exclusive relationships in those cases where private

information is more valuable. As additional measures of the information environment, we include the log of

firm age, the market-to-book ratio, and idiosyncratic volatility. To the extent that information production

drives IB relationships, we expect fewer and more exclusive relationships for small firms, young firms, and

firms with high market-to-book ratios and idiosyncratic volatility. We also expect these effects to be

strongest for equity relationships. We include the firm’s leverage ratio to capture the demand for sources of

capital. A higher leverage ratio may also proxy for firms that access debt markets more frequently and

therefore have additional bargaining power. We expect firms with high leverage ratios to have a higher

22 If we separate the adjusted market shares of the top three relationship banks in debt and equity transactions, we find that while the top IB’s adjusted market share decreases over time (Figure 4 Panel F), the adjusted market shares of the second- and third-ranked IBs increase over time. This patterns is much weaker for M&A transactions.

23

number of less exclusive relationships, especially with respect to debt relationships. To capture the

bargaining power of the investment bank, we include the bank’s market share in the relevant type of

transaction across all issuers during the three-year window. We expect investment banks with higher market

share to command more exclusive relationships. We also include year fixed effects to capture any time trends

unrelated to variation in the other explanatory variables.

The results from the panel regression are described in Table VI. As expected, we find that large firms

and more active firms have a higher number of less exclusive relationships across all investment banking

functions. These results are consistent with a bargaining power explanation of relationships. The firm size

effect may also reflect that the informational benefits of IB relationships are more important for small firms.

Consistent with this explanation, we find that older firms have fewer IB relationships. In most specifications,

firms with higher market-to-book ratios have fewer and more exclusive relationships. In addition, firms with

higher idiosyncratic volatility have more exclusive equity and debt relationships. Taken together, these

results are consistent with the informational benefits of IB relationships. In general, we find that firms with

higher leverage ratios have less exclusive relationships. These findings are consistent with high debt firms

having more bargaining power and searching out more sources of capital. Finally, we find we some evidence

that the overall market share of the firm’s top IB is positively related to relationship concentration. However,

this effect is not evident in the models for equity- and debt-specific relationships. Taken together, the results

in Table VI are consistent with the informational benefits of IB relationships and suggest that firms with

more bargaining power are able to maintain less exclusive IB relationships.

5. Investment Bank Relationships across Deal Types

One of the advantages of our data is the ability to examine the extent to which relationships between

firms and their investment banks carry over across transaction types. The results in Sections 3 and 4 provide

evidence of both firm-wide and transaction-type specific components in IB relationships. In this section, we

provide a more detailed analysis of the links between relationships in the different functional areas of

investment banking.

24

We begin by analyzing the frequencies with which the top bank in one transaction type is one of the

top three banks in another transaction type. For this test, we use a variation of the top bank market share

measure described in Section 4, where we define market shares based on the full sample period (or

subperiod) rather than with three-year windows. Using this measure, we identify the top three relationship

IBs for each firm for each transaction type, based on lead roles.23 We then compare the top relationship IBs

in different functional areas. The results are provided in Table VII. For example, there are 328 firms that

have at least two debt transactions and two equity transactions during the sample period. For these firms, we

find that the top debt relationship bank is also the top equity relationship bank in 61.3% of cases.

There appears to be substantial crossover of relationships from one transaction type to another. As

noted above, the top debt relationship bank is also the top equity relationship bank in 61.3% of cases, and

this bank is one of the top three equity relationship banks in 73.8% of cases. Similarly, the top equity

relationship bank falls in the top three for debt in 76.9% of cases. While these results are consistent with a

substantial crossover in relationships between functional areas, there are also a surprising number of cases

where the top bank in one area plays no role in other areas. For example, for firms with at least two deals of

each type, the top debt bank fails to lead an equity issue in 25.9% of cases and the top equity bank fails to

lead a debt issue in 19.8% of cases.

Results involving M&A relationships reflect a weaker crossover in relationships between functional

areas. For example, the top M&A bank fails to lead an equity issue in 32.8% of cases and fails to lead a debt

issue in 23.6% of cases. Even in these cases, however, the top M&A bank is also the top equity bank in

55.1% of eligible cases and is the top debt bank in 51.3% of eligible cases. Thus, the firm-wide IB

relationship appears to have an important impact on the selection of M&A advisors in addition to debt and

equity underwriters.

Panels B and C of Table VII provide separate results based on transactions from 1996-2002 and from

23 The incorporation of important roles has little impact on the results involving M&A relationships. However, for equity

and debt relationships, the incorporation of important roles decreases the likelihood that the top IB in one area does not appear in the other area. Results are also generally consistent if we require four deals of each type rather than two. However, among these more frequent issuers, there is a slight decrease in the likelihood that the top bank in one area is also the top bank in another area, accompanied by an increase in the likelihood that the bank is ranked second in the other transaction type.

25

2003-2009. In addition, to analyze the impact of lending relationships on the firm-wide nature of IB

relationships, we also separate cases where the firm has a lending relationship with their top IB at some point

during the sample period from those cases where no lending relationship exists. For these tests, we require

that a firm have at least two transactions during the subperiod in each of the relevant transaction types.

Ignoring lending relationships, there are several notable patterns in the subperiod results. First, the likelihood

that the top equity bank and top debt bank are the same increases over time. This likelihood is 61.1% in the

first subperiod and 73.8% in the second subperiod. At the same time, the likelihood that the top M&A bank

is also the top equity or debt bank decreases. For M&A and equity lead roles, this likelihood decreases from

68.5% to 62.0%. For M&A and debt roles, this likelihood decreases from 56.8% to 47.5%.

When we separate results based on whether a lending relationship exists between the firm and their

IB, we find that the presence of a lending relationship leads to a much stronger cross-over in relationships,

especially during the second half of the sample period. For example, for cases in the second subperiod where

a lending relationship exists, the top equity bank was also the top 82.4% of cases and appears on a debt deal

in all but 5.9% of cases. The comparable numbers in the absence of a lending relationship are 67.4% and

15.2%. The results across other transaction types are similar.

Taken together, the results in Table VII suggest that there is a strong tendency for IB relationships to

carry over across transaction types, but these patterns differ by transaction type and across time. The

crossover of equity and debt relationships appears to have increased over time, while the crossover between

M&A relationships and those related to security issuance has decreased over time. In addition, the presence

of a lending relationship is associated with a stronger firm-wide component in the IB relationship, especially

in the second half of the sample period.

As a final test, we provide a probit model for underwriter/advisor selection in equity, debt, and M&A

transactions. For each transaction in the sample, the set of eligible IBs for this model includes all full-service

investment banks that were in business at the time of the transaction and could therefore have been

considered for inclusion in the underwriting syndicate or M&A advising group. Because the model includes

multiple observations per transaction, the standard errors are clustered by transaction. We include the log of

26

transaction size to allow for differences between large and small deals. To account for changes in syndicate

structure over time, we include year fixed effects. The primary IB characteristics in the model are IB market

shares in aggregate equity, debt, M&A, and lending transactions over the year prior to the current

transaction. These variables are intended to capture the bank’s reputation or expertise in a particular business

area. We also include IB fixed effects to capture any unidentified IB characteristics that are not capture by

our other variables.

The primary variables of interest are related to relationships between firms and their investment

banks. The first three variables are the investment bank’s share of the firm’s equity, debt, and M&A

transactions over the three years immediately prior to the current transaction. To examine the impact of

lending relationships on IB selection, we include an analogous measure of the IB’s lending relationship with

the firm over the prior three years. To capture the importance of more recent transactions, we also include

dummy variables identifying IBs that had lead roles (or advising roles for M&A deals) and comanaging roles

(or non-advising roles for M&A deals) on the prior transaction. We expect both market share in the firm’s

prior deals and involvement in the preceding deal to improve an investment bank’s chances of being hired on

the current deal. We also separate the relationship variables by type in order to test whether investment bank

selection in one functional area is affected by past relationships in other functional areas.

Asker and Ljungqvist (2010) find that firms are reluctant to hire investment banks that are utilized by

their product market rivals. To capture this effect, we first rank firms by sales in each 4-digit SIC industry.

We then identify the top relationship bank for the top-ranked and second-ranked firm in each industry based

on the proportion of that firm’s combined equity, debt, and M&A transactions over the prior three years for

which the bank held a lead role. For sample firms ranked second or lower in an industry, we define the

competitor bank as the top bank of the number one firm in the same industry. For firms ranked first in an

industry, we define the competitor bank as the top bank of the number two firm. To test whether the effects

of IB relationships are impacted when an investment bank is used by a firm’s competitors, we interact the top

competitor bank dummy with the lead on prior deal and comanager on prior deal dummies. To the extent that

prior relationships improve a banks chances of being hired in a particular transaction, we expect this effect to

27

be reduced in those cases where the IB has a strong relationship with the firm’s primary product market rival.

The results for equity, debt, and M&A deals are provided in Panels A through C of Table VIII,

respectively. Models (1) and (2) provide results based on lead roles, and model (3) provides comparable

results based on important roles. In models (2) and (3), all coefficients are interacted with subperiod

dummies representing transactions from 1996-2000, 2001-2004, and 2005-2009. Examining the IB market

share variables, we find that equity market share improves a bank’s chances of being included on an equity

deal, debt market share improves the likelihood of inclusion on debt deals, and M&A market share improves

the likelihood of inclusion on M&A deals. This suggests that investment banking expertise is transaction-

type specific. After controlling for type-specific market share, there is little evidence that market share in

unrelated transaction types improves an investment bank’s chances of being hired. Examining changes across

time, we find that the coefficient on debt market share in the debt model increases significantly over time and

the coefficient on M&A market share in the M&A model is significantly lower in the middle subperiod than

in the earlier or later periods. For equity deals, the coefficient on equity market share decreases over time, but

this time trend is not statistically significant. Finally, overall lending market share is positive and significant

in some equity and M&A models, but is generally insignificant in the debt models.

Turning to the relationship variables, we find evidence of substantial cross-over in relationships

across functional areas. This is consistent with the results in Table VII and in sharp contrast to the results for

overall market share. In all of the models, we find that all three types of IB relationships matter. However,

equity relationships are more important for inclusion in equity deals, debt relationships are more important

for debt deals, and M&A relationships are more important for M&A deals. In addition, holding a lead or

important role on the prior deal has a positive and significant effect on the likelihood of inclusion, even after

controlling for the continuous relationship variable. we also find that prior lending relationships have a

significant positive effect on the likelihood of earning future equity and debt underwriting mandates. This

finding is consistent with the results of Ljungqvist et al. (2006) and Barath et al. (2007) for equity and debt

offers and suggests that the effects of lending relationships also carry over to the selection of M&A advisors.

Examining changes over time, we find that the effects of prior debt relationships increases over time

28

in the equity model, and the effects of lending relationships increase over time for all transaction types.

There is also weak evidence that the impact of prior equity relationships in the debt model increases over

time. Together, this evidence suggests that both the firm-wide component of IB relationships and the link

between this component and lending has increased over time. These findings contrast with Barath et al. who

find that, while cross-over relationships have a statistically significant effect on underwriter choice in equity

and debt transactions, their economic importance is small.

Consistent with Asker and Ljungqvist (2010), we find for both equity and debt deals that the effects

of past relationships are weaker in cases where the bank under consideration is the utilized by the firm’s

primary product market rival. While this effect is consistent across time in the debt issue model, the effect in

the equity model is not statistically significant in the first subperiod and appears to gain significance through

time. We find weak evidence of a similar effect in the M&A model. However, the subperiod interactions

suggest that this effect is significant only in the first subperiod.

As a final test, we run a joint model including all transaction types. This model is presented in Panel

D. The advantage of this model is that it allows us to examine the interactions of different deal types and to

test the importance of the sequence of transaction types. To examine the relative importance of the various

investment bank market share measures, we interact each transaction-type specific market share measure

with a dummy variable to identify transactions that are not of the same type. As in Panels A through C, the

results suggest that IB market share has a significant positive effect on the likelihood of being included in an

underwriting syndicate or M&A advising group. However, this effect is completely negated or even reversed

for transactions that are not of the same type. Further, it appears that the relative importance of debt market

share increases over time for both debt and non-debt transactions. The results also suggest that lending

relationships tend to have a significant positive effect on the selection of investment banks.

To examine the separate importance of past equity, debt, and M&A relationships between a firm and

its investment banks, we focus on the lead on prior deal variable and we break this variable down by

transaction type in two ways. First, we separately define dummy variables for whether an investment bank

held a lead role on a prior deal that was an equity, debt, or M&A deal. Second, we interact these dummy

29

variables with indicators for whether the current deal is of the same type. As controls, we also include the

overall IB relationship measure, based on all transaction types, and the IB lending relationship measure. As

expected, the coefficient on these relationship measures are positive and significant. In addition, as in Panels

A through C, the importance of lending market share appears to increase over time.

The results show that being a lead on a prior equity deal has a positive and significant effect on lead

choice in the current deal. While this effect is reduced if the current deal is not equity, it is not eliminated.

Specifically, the coefficient on lead on prior equity deal is 1.32 and the interaction with current deal is not

equity is -0.23. This suggests that having an equity relationship with a firm helps the investment bank to

earn lead roles in all types of future deals, though the effect is greatest for future equity deals. The results for

lead on prior M&A deal are similar. The effect is positive and significant, but is reduced by nearly half if the

current deal is not an M&A deal. Surprisingly, it appears that the positive effect of being a lead on a prior

debt deal is not diminished when the current deal is not a debt deal, though the effect is diminished in the

model that incorporates important roles.

The results in Tables VII and VIII suggest that there is substantial crossover in relationships across

transactions of different types. While prior equity, debt, and M&A relationships have a larger impact on

future roles of the same type, these relationships also carry over to transactions of different types. Together

with the findings in the previous sections, these results suggest that IB relationships constitute both firm-

wide and transaction-type specific components.

6. Summary and Conclusions

Although relationships between firms and their investment banks are generally managed centrally

within the investment bank, academic research tends to focus on only a specific area of investment banking,

such as equity or debt underwriting, or M&A advising, and on the factors that influence the decision to hire a

specific investment bank. As a result, very little is known about the extent to which relationships span the

functional areas of investment banking. Using a sample of over 20,000 M&A transactions and public and

private security issues by U.S. exchange-listed firms between 1996 and 2009, we provide a comprehensive

30

analysis of investment banking relationships both within and across functional areas. We address three broad

research questions. First, what are the characteristics and determinants of IB relationships? Second, how have

relationships between firms and their investment banks changed over time? And, third, to what extent do

relationships carry over across the various aspects of the investment banking business?

Our sample period encompasses a number of important changes to the economics of the investment

banking business. Most notably, there have been significant changes over time in the structure of

underwriting syndicates, with both debt and equity syndicates being increasingly characterized by multiple

lead underwriters and large numbers of comanagers. In addition, the repeal of Glass-Steagall restrictions

have led to an increased role for commercial banks in investment banking. Our evidence suggests that these

changes have led to substantial changes in investment banking relationships. For all types of transactions, we

find that firms have increased the number and decreased the exclusivity of their investment banking

relationships. In part, these changes may reflect the increasing link between lending and more traditional

investment banking functions, as firms seek to develop additional sources of capital.

We provide evidence of a strong firm-wide component in investment banking relationships. For

example, while retention of lead underwriters and advisors tends to be strongest on consecutive deals of the

same type, at least one IB in a lead role is retained in at least 40% of consecutive deals, regardless of

transaction type. Despite this firm-wide component of relationships, we also find evidence of significant

transaction-type specific relationship components, with relationships being strongest for equity deals and

weakest for M&A deals. In addition, the crossover between equity and debt relationships appears to have

increased over time. Consistent with information-based explanations for IB relationships, we find that

relationships tend to be more exclusive for small firms, young firms, and firms with high market-to-book

ratios and idiosyncratic volatility. However, we also find evidence that relationships are less exclusive for

firms that are more active in terms of equity, debt, and M&A transactions, suggesting that these firms have

bargaining power in negotiations with investment banks. Overall, our evidence supports both relationship

and transaction-based explanations of investment bank hiring decisions.

31

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35

Table I – Transaction Summary Statistics The table the number of deals, average deal size, and aggregate deal value by transaction type. The sample includes 8,322 firms, as identified by CRSP Permco, and related security issue and M&A events from 1996 through 2009 that meet the sample restrictions described in Section 2. Results are provided for the full sample of deals and for the restricted sample of deals larger than $1 million that involved at least one full-service investment bank. The table also provides summary statistics for loan facilities by the sample firms from 1996 through 2009, as identified by Dealscan.

Full Transaction Sample Restricted Transaction Sample

Number of Deals

Mean Deal Size

($mil)

Aggregate Deal Value

($mil)

Number of

Deals

Mean Deal Size

($mil)

Aggregate Deal Value

($mil) Equity:

Public IPO 2,625 93.94 246,584 2,381 102.62 244,336

Public SEO 3,181 136.30 433,555 2,986 142.85 426,539

Private 1,510 26.59 40,157 676 38.52 26,041

All Equity Issues 7,316 98.45 720,296 6,043 115.33 696,916

Debt:

Public Nonconvertible 3,439 467.87 1,609,012 3,431 468.74 1,608,249

Public Convertible 293 374.93 109,854 288 379.38 109,263

Private Nonconvertible 2,683 275.63 739,519 2,611 280.31 731,887

Private Convertible 1,199 221.45 265,516 1,012 255.77 258,842

All Debt Issues 7,614 357.75 2,723,901 7,342 368.87 2,708,240

Other Security Issues:

Public 156 316.53 49,378 146 337.50 49,276

Private 494 96.33 47,588 291 147.11 42,810

All Other Security Issues 650 149.18 96,966 437 210.72 92,086

Total Security Issues 15,580 227.29 3,541,163 13,822 253.02 3,497,242

M&A Transactions:

Acquirer Events 17,547 (32,451)

313.59 5,502,599 4,394 1045.82 4,595,342

Target Events 8,172 (12,427)

776.55 6,345,968 4,274 1400.43 5,985,419

Dealscan Loan Facilities 29,469 273.81 8,068,901 - - -

36

Table II – Summary of Firm-Level Activity The table describes total and per firm event frequencies across all types of sample events. The sample includes 8,322 firms, as identified by CRSP Permco, and all security issue and M&A events from 1996 through 2009 that meet the sample restrictions described in Section 2. The table also describes loan facilities by the sample firms from 1996 through 2009, as identified by Dealscan.

Total

Sample Events

Activity Per Firm (N=8,322 firms) N Zero Activity

% Zero Activity

Mean Median Min 99th Perc. Max

All Event Types 60,458 768 9.2% 7.26 4.0 0.0 57.0 225.0

Security Issue Events 15,580 2,971 35.7% 1.87 1.0 0.0 15.0 71.0

M&A Events 44,878 1,522 18.3% 5.39 2.0 0.0 47.0 184.0

Acquirer Events 32,451 2,738 32.9% 3.90 1.0 0.0 35.0 133.0

Target Events 12,427 3,417 41.1% 1.49 1.0 0.0 15.0 126.0

Equity Issues 7,316 4,088 49.1% 0.88 1.0 0.0 6.0 15.0

IPOs 2,628 5,694 68.4% 0.32 0.0 0.0 1.0 1.0

SEOs 3,178 6,208 74.6% 0.38 0.0 0.0 4.0 12.0

Private Equity Issues 1,510 7,429 89.3% 0.18 0.0 0.0 3.0 9.0

Debt Issues 7,614 6,035 72.5% 0.91 0.0 0.0 13.0 70.0

Convertible Debt Issues 1,492 7,310 87.8% 0.18 0.0 0.0 3.0 7.0

Non-convertible Debt Issues 6,122 6,706 80.6% 0.75 0.0 0.0 13.0 70.0

Other Security Issues 650 7,808 93.8% 0.08 0.0 0.0 2.0 9.0

Loan Facilities 29,469 3,358 40.4% 3.54 1.0 0.0 23.0 69.0

37

Table III – Firm Summary Statistics The table provides summary statistics for sample firms, as identified by CRSP Permco. Events include all security issue and M&A events from 1996 through 2009 that meet the sample restrictions described in Section 2. Firm characteristics are taken from CRSP and are measured across all days during the sample period on which the firm was listed on CRSP. Market Value and Volume for each firm are defined as the average across all CRSP days. For firms with multiple classes of shares, both market value and volume are summed across the share classes on each trading day. Price is the average across all CRSP days of the closing stock price for the firm’s most active share class. Firm Age is defined as the number of years between the firm’s initial listing on CRSP and the end of our sample period in 2009. Years in Sample is defined as the total number of days between the first CRSP date and the last CRSP date, divided by 365. The table also describes loan facilities by the sample firms from 1996 through 2009, as identified by Dealscan.

N Mean Median Min 10th Perc. 90th Perc. Max Market Value ($mil) 8,322 1,471.60 157.60 0.63 15.71 1,917.53 315,889.59 Price 8,322 15.06 10.18 0.09 2.36 33.65 708.12

Volume (000) 8,322 443.80 111.85 0.06 11.48 836.34 48,116.12

Firm Age 8,322 18.94 15.04 1.03 5.99 37.07 84.06

Years in Sample 8,322 6.89 5.36 0.23 1.84 14.00 14.00

Total Deals/year 8,322 1.15 0.72 0.00 0.07 2.50 69.97

Equity Deals/year 8,322 0.18 0.07 0.00 0.00 0.53 4.35

Debt Deals/year 8,322 0.14 0.00 0.00 0.00 0.32 54.05

M&A Deals/year 8,322 0.82 0.47 0.00 0.00 1.93 18.84

Total Deal Value/year ($mil) 8,322 300.15 29.70 0.00 0.00 519.41 37,011.80

Equity Deal Value/year ($mil) 8,322 16.84 0.43 0.00 0.00 41.82 1,472.87

Debt Deal Value/year ($mil) 8,322 40.04 0.00 0.00 0.00 60.16 23,062.95

M&A Deal Value/year ($mil) 8,322 241.61 12.57 0.00 0.00 398.16 36,108.30

Loan Facilities/year 8,322 0.57 0.23 0.00 0.00 1.47 18.98

Loan Value/year ($mil) 8,322 119.52 2.50 0.00 0.00 267.47 12,014.00

38

Table IV – The Retention of Investment Banks from Transaction t–1 to Transaction t This table shows the frequency with which any investment bank from transaction t-1 appears in transaction t. The top number in each cell represents the frequency when we examine only the lead investment bank role, and the bracketed number is the frequency when we examine important investment bank roles. Rows represent the type of transaction at time t-1 and columns represent the type of transaction at time t. In Panel A we report frequencies from the entire sample and from the entire sample when we restrict the time between t and t-1 to three years. In Panel B, we report frequencies for 1996-2002 and 2003-2009. In the last column of each sample we report the p-value from a difference in means test that tests whether the frequencies in each of the previous columns are equal. In Panel B, we report the significance of the difference in frequencies between 2003-2009 and 1996-2002 by asterisks: ***, **, * representing statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A – Full Sample Period Results

First Transaction

Second Transaction

All Transactions 3-Year Restriction

All M&A Debt SEO p-value for difference

across types

All M&A Debt SEO p-value for difference

across types

All 48.8% [62.1%] 16,051

43.8% [52.0%] 6,469

51.1% [68.2%] 6,448

53.9% [72.1%] 2,781

.0001 [.0001]

51.7% [65.1%] 14,089

47.8% [55.9%] 5,461

52.0% [69.0%] 5,903

58.8% [76.8%] 2,388

.0001 [.0001]

M&A 43.4% [49.9%] 4,870

46.5% [46.3%] 2,469

38.3% [51.7%] 1,735

47.2% [61.2%]

585

.0001 [.0001]

45.6% [52.3%] 4,315

49.7% [49.3%] 2,148

39.4% [53.1%] 1,567

49.1% [63.6%]

523

.0001 [.0001]

Debt 50.5% [67.0%] 6,398

41.0% [53.8%] 2,083

55.0% [73.5%] 3,625

55.1% [73.9%]

568

.0001 [.0001]

51.7% [68.4%] 5,858

43.2% [56.3%] 1,869

55.0% [73.6%] 3,361

58.6% [78.3%]

510

.0001 [.3066]

Equity

IPO 53.5% [72.2%] 1,665

42.2% [59.5%]

751

56.7% [81.2%]

215

65.1% [84.4%]

674

.0001 [.0001]

62.0% [81.2%] 1,321

51.1% [70.1%]

542

63.9% [87.2%]

180

72.1% [90.8%]

574

.0001 [.0001]

SEO 51.3% [67.6%] 2,763

45.4% [58.0%] 1,043

59.8% [77.4%]

727

49.2% [70.1%]

898

.0001 [.0001]

57.6% [73.3%] 2,268

53.0% [65.2%]

795

62.4% [80.0%]

655

56.0% [75.8%]

730

.0032 [.0001]

39

Table IV – Continued

Panel B – Subperiod Results

First Transaction

Second Transaction

1996-2002 2003-2009

All M&A Debt Equity p-value for difference

across types

All M&A Debt Equity p-value for difference

across types

All 47.8%*** [63.1%]***

9,095

45.0%** [55.2%] 3,624

47.0%*** [65.2%]***

3,832

55.8%* [77.9%]***

1,383

.0001 [.0001]

55.5% [66.2%] 5,101

48.6% [54.0%] 2,020

60.2% [75.7%] 1,937

59.2% [72.5%] 1,065

.0001 [.0001]

M&A 43.3%* [51.1%] 2,635

48.3% [48.4%] 1,303

35.1%*** [49.9%]***

946

49.8% [67.3%]*

327

.0001 [.0001]

47.6% [53.2%] 1,650

49.4% [48.4%]

854

45.1% [58.0%]

586

47.9% [60.3%]

194

.4475 [.0003]

Debt 46.9%*** [63.9%]***

3,762

39.9%*** [53.7%]*

1,144

49.5%*** [67.8%]***

2,260

51.5%*** [73.7%]

268

.0001 [.0001]

58.4% [74.1%] 2,046

46.3% [58.2%]

741

65.6% [84.5%] 1,030

64.2% [78.8%]

246

.0001 [.0001]

Equity

IPO 56.0%*** [77.8%] 1,110

45.6%** [66.5%]

509

56.5%** [84.2%]

154

68.7% [89.8%]*

425

.0001 [.0001]

66.7% [79.0%]

309

58.1% [67.3%]

105

80.0% [93.3%]

30

70.0% [84.5%]

171

.0886 [.0010]

SEO 52.3%*** [72.7%]**

1,335

48.9% [65.0%]***

575

57.6%** [79.5%]

368

48.9%** [78.0%]**

331

.0429 [.0001]

58.1% [68.5%] 1,022

49.3% [56.3%]

302

67.2% [78.3%]

256

57.2% [70.4%]

437

.0004 [.0001]

40

Table V – Probit Analysis of Investment Bank Retention from Transaction t–1 to Transaction t

This table examines the frequency with which an investment bank is on consecutive transactions. The current transaction is denoted as t and the previous transaction is t-1. Panels A and B present probit models in which the dependent variable is a dummy taking on the value of one if any lead bank (in Panel A) or a bank in an important role (in Panel B) is the same for both transactions and is zero otherwise. The model in column (1) includes all transaction types. In columns (2), (3), and (4) paired transactions in which transaction t is equity, debt, and merger, respectively. In column (5) – (7) we examine paired transactions of the same transaction type. Firm age is the year in which t occurs minus the start date of the firm’s CRSP Permco. Last transaction was an IPO is equal to one if t-1 is an IPO and is zero otherwise. Same day is equal to one if the paired transactions occur on the same day and is zero otherwise. Years since t-1 is the date of t minus the date of t-1. Transaction value is the issue size or deal value reported in SDC for transaction t. The variables t ≤ .5*t-1 and t ≥ 2*t-1 are dummy variables taking on the value of one if the current transaction value is half (or less) of the last transaction and is at least twice the size of the last transaction, respectively. Number of leads is the number of lead investment banks for transaction t. 1996-2000 and 2005-2009 are dummy variables equal to one if the transaction t occurs in the specified year span. Interim transaction is a dummy variable equal to one if there is a transaction between the pair analyzed in the model. Same transaction type is a dummy equal to one if the type of transaction (e.g., equity, debt, etc.) is the same for the pair. p-values based on robust standard errors are presented in parentheses below the coefficients. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

41

Table V - Continued

Panel A. Lead Roles (1)

All (2)

t = Debt (3)

t = Equity (4)

t = Merger

(5) t = Debt

t-1 = Debt

(6) t = Equity

t-1 = Equity

(7) t = Merger

t-1 = Merger Intercept -0.201***

(.000) -0.328*** (.000)

-0.174 (.113)

-0.244*** (.000)

-0.146*** (.010)

-0.093 (.364)

-0.245*** (.001)

Firm age (years) -0.005*** (.000)

-0.005*** (.000)

-0.006** (.016)

-0.004*** (.000)

-0.004*** (.000)

0.000 (.931)

-0.002* (.084)

Last transaction was IPO 0.239*** (.000)

0.256*** (.006)

0.338*** (.000)

0.142*** (.009)

- 0.323*** (.000)

-

Same day 0.914*** (.000)

0.866*** (.000)

0.654*** (.000)

1.030*** (.000)

- - 1.054*** (.000)

Years since t-1 -0.162*** (.000)

-0.074*** (.000)

-0.278*** (.000)

-0.182*** (.000)

-0.060*** (.000)

-0.254*** (.000)

-0.149*** (.000)

Transaction value 0.012*** (.000)

-0.074** (.022)

0.546** (.025)

0.009*** (.008)

-0.058* (.068)

0.944*** (.001)

0.005 (.189)

t ≤ .5*t-1 -0.192*** (.000)

-0.159*** (.000)

-0.125* (.061)

-0.167*** (.000)

-0.088* (.058)

-0.389*** (.000)

-0.205*** (.000)

t ≥ 2*t-1 -0.174*** (.000)

-0.379*** (.000)

-0.226*** (.004)

-0.018 (.626)

-0.356*** (.000)

-0.324*** (.000)

-0.109** (.027)

Number of leads 0.292*** (.000)

0.296*** (.000)

0.380*** (.000)

0.309*** (.000)

0.331*** (.000)

0.412*** (.000)

0.322*** (.000)

1996-2000 -0.038 (.267)

0.015 (.803)

-0.095 (.316)

-0.039 (.442)

-0.070 (.110)

0.012 (.882)

0.101* (.052)

2005-2009 0.105*** (.005)

0.020 (.755)

0.117 (.255)

0.162*** (.003)

0.145*** (.004)

0.078 (.274)

0.089* (.090)

Interim transaction - - - - 0.020 (.646)

0.125* (.080)

-0.116*** (.010)

Same transaction type 0.204*** (.000)

0.357*** (.000)

0.201** (.039)

0.067 (.298)

- - -

Same transaction type * 1996-2000 -0.011 (.821)

-0.168** (.026)

0.113 (.37)

0.107 (.192)

- - -

Same transaction type * 2005-2009 0.004 (.934)

0.130 (.137)

-0.121 (.361)

-0.028 (.746)

- - -

N 16,051 6,448 2,781 6,469 5,212 2,285 4,120 Pseudo R2 .073 .072 .134 .060 .064 .160 .070

42

Table V – Continued

Panel B – Important Roles (1)

All (2)

t = Debt (3)

t = Equity (4)

t = Merger

(5) t = Debt

t-1 = Debt

(6) t = Equity

t-1 = Equity

(7) t = Merger

t-1 = Merger Intercept 0.187***

(.000) -0.028 (.668)

0.074 (.515)

0.039 (.533)

0.467*** (.000)

0.143 (.161)

-0.237*** (.001)

Firm age (years) -0.006*** (.000)

-0.008*** (.000)

-0.006** (.023)

-0.004*** (.000)

-0.006*** (.000)

-0.003 (.453)

-0.002* (.077)

Last transaction was IPO 0.441*** (.000)

0.670*** (.000)

0.239*** (.005)

0.269*** (.000)

- 0.152* (.054)

-

Same day 0.849*** (.000)

0.898*** (.000)

0.953*** (.000)

0.957*** (.000)

- - 1.068*** (.000)

Years since t-1 -0.152*** (.000)

-0.070*** (.000)

-0.232*** (.000)

-0.173*** (.000)

-0.076*** (.000)

-0.211*** (.000)

-0.144*** (.000)

Transaction value 0.019*** (.000)

-0.050 (.265)

0.239 (.491)

0.011*** (.006)

0.083 (.249)

0.595 (.152)

0.004 (.252)

t ≤ .5*t-1 -0.241*** (.000)

-0.179*** (.000)

-0.109 (.122)

-0.157*** (.000)

-0.032 (.537)

-0.100 (.283)

-0.228*** (.000)

t ≥ 2*t-1 -0.302*** (.000)

-0.486*** (.000)

-0.394*** (.000)

-0.141*** (.000)

-0.605*** (.000)

-0.565*** (.000)

-0.143*** (.002)

Number of leads 0.148*** (.000)

0.141*** (.000)

0.220*** (.000)

0.336*** (.000)

0.167*** (.000)

0.344*** (.000)

0.307*** (.000)

1996-2000 0.113*** (.001)

0.194*** (.001)

0.000 (.999)

0.080 (.105)

-0.109** (.024)

0.406*** (.000)

0.111** (.025)

2005-2009 0.088** (.025)

0.029 (.665)

0.052 (.631)

0.103* (.053)

0.205*** (.001)

-0.001 (.995)

0.084* (.095)

Interim transaction - - - - -0.025 (.611)

0.111 (.185)

-0.089** (.038)

Same transaction type 0.237*** (.000)

0.732*** (.000)

0.440*** (.000)

-0.247*** (.000)

- - -

Same transaction type * 1996-2000 -0.158*** (.002)

-0.426*** (.000)

0.386*** (.006)

0.007 (.926)

- - -

Same transaction type * 2005-2009 -0.068 (.217)

0.130 (.188)

-0.149 (.290)

-0.016 (.846)

- - -

N 16,506 6,460 2,795 6,897 5,212 2,285 4,479 Pseudo R2 .116 .141 .213 .070 .148 .270 .068

43

Table VI – The Determinants of Investment Banking Relationships This table describes panel regressions in which the dependent variable is one of three measures of underwriter relationships. The first measure is a Herfindahl index (HH) based on the adjusted market shares of all investment banks (IBs) who led at least one deal for the firm. The second measure, Top, is the adjusted market share of the firm’s top IB. The third measure is the number of IBs that are involved in 50% or more of the firm’s transactions (N50), based on unadjusted market shares. Adjusted and unadjusted market shares are calculated for each category of transactions and across all transactions based on lead roles over three-year rolling windows, where adjusted market shares are adjusted for the number of leads on the deal. The independent variables are computed at end of the last year in the three-year window. Ln MV is the natural log of the firm’s equity value. Relative transaction value is the ratio of the dollar value of the firm’s transactions scaled by the total value of transactions for the entire market. M/B is the firm’s market value to book value of equity. Ln Age is the natural log of the difference between the last year of the three-year window and the initial CRSP listing date for the firm. Idiosyncratic volatility is the standard deviation of the market-adjusted residuals of daily stock returns from 205 days to 6 days prior to the announcement. Leverage is debt (defined as the sum of long-term debt, the current portion of long-term debt, and notes payable) scaled by debt plus the book value of equity. IB adjusted market share is the adjusted market share of the firm’s top IB across the transactions being analyzed in the particular model (i.e., all transactions, equity transactions, debt transactions, or mergers) during the three-year window. Each model contains year fixed effects. The p-values based on robust standard errors are presented in parentheses below the coefficients. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

All Transactions Equity Transactions Debt Transactions Mergers

HH Top N50 HH Top N50 HH Top N50 HH Top N50

Intercept 0.827*** (.000)

0.860*** (.000)

1.333*** (.000)

0.577*** (.000)

0.645*** (.000)

1.522*** (.000)

0.793*** (.000)

0.814*** (.000)

2.064*** (.000)

0.868*** (.000)

0.869*** (.000)

1.401*** (.000)

Ln MV -0.037*** (.000)

-0.032*** (.000)

0.059*** (.000)

-0.002 (.660)

-0.003 (.473)

0.054*** (.000)

-0.046*** (.000)

-0.042*** (.000)

0.054*** (.002)

-0.022*** (.000)

-0.017*** (.000)

0.015* (.069)

Rel. Transaction Value

-7.598* (.059)

-12.245*** (.002)

137.176*** (.000)

-31.722*** (.000)

-30.280*** (.000)

103.978*** (.000)

-55.527*** (.000)

-52.170*** (.000)

133.234** (.010)

-21.625*** (.000)

-26.254*** (.000)

157.948*** (.000)

Ln Age -0.004 (.122)

-0.001 (.542)

-0.076*** (.000)

-0.007 (.313)

-0.003 (.579)

-0.079*** (.000)

0.000 (.998)

0.002 (.606)

-0.068*** (.008)

0.005 (.337)

0.004 (.354)

-0.053*** (.000)

M/B 0.010*** (.000)

0.009*** (.000)

-0.019*** (.000)

-0.001 (.545)

0.000 (.737)

-0.017*** (.000)

0.026*** (.000)

0.022*** (.000)

-0.028** (.021)

0.011*** (.000)

0.009*** (.000)

-0.019*** (.000)

Idiosyncratic Volatility

0.321* (.057)

0.273* (.071)

-0.697 (.127)

1.277*** (.000)

1.095*** (.000)

-2.435*** (.000)

1.800*** (.000)

1.759*** (.000)

-3.548* (.074)

-0.295 (.399)

-0.156 (.620)

-0.323 (.674)

Leverage -0.128*** (.000)

-0.112*** (.000)

0.256*** (.000)

-0.023 (.161)

-0.028* (.062)

0.111*** (.007)

-0.112*** (.000)

-0.101*** (.000)

0.255*** (.000)

-0.110*** (.000)

-0.090*** (.000)

0.144*** (.000)

IB Adjusted Market Share

0.246*** (.000)

0.274*** (.000)

-0.364*** (.001)

-0.092 (.435)

-0.013 (.898)

0.382 (.166)

-0.082 (.359)

-0.036 (.666)

0.414 (.294)

0.169*** (.001)

0.178*** (.000)

-0.348*** (.009)

N 10,911 10,911 10,911 2,201 2,201 2,201 3,247 3,247 3,247 3,193 3,193 3,193 Adjusted R2 0.1722 0.1623 0.1100 0.1637 0.1598 0.2089 0.3161 0.3246 0.1944 0.0649 0.0704 0.1284

44

Table VII – Top Investment Banks Across Functional Areas The table exames the frequencies with which the firm’s top investment bank in one transaction type is among the firm’s top investment banks in other transaction types. For each transaction type comparison, firms are required to have at least two transactions of each type during the sample period or during the specified subperiod, with the last column in each grouping listing the number of firms with the required number of transactions. Results in Panel A are based on transactions during the entire sample period from 1996 through 2009. Results in Panels B and C are based on transactions during 1996-2002 and 2003-2009, respectively. The column labeled “Did Not Appear” shows the frequency with which the firm’s top investment bank in one transaction type did not lead a deal in the other transaction type.

Equity IB Rank Debt IB Rank Merger IB Rank

1 2 3 Did Not Appear

N 1 2 3 Did Not Appear

N 1 2 3 Did Not Appear

N

Panel A: Firms with at least two deals of each type from 1996-2009 Equity Top IB - - - - - 61.3% 12.2% 3.4% 19.8% 328 55.1% 9.6% 2.4% 31.9% 457

Debt Top IB 61.3% 11.3% 1.2% 25.9% 328 - - - - - 51.3% 12.9% 3.0% 30.7% 704

Merger Top IB 55.1% 10.5% 1.5% 32.8% 457 51.3% 13.1% 5.8% 23.6% 704 - - - - -

Panel B: Firms with at least two deals of each type from 1996-2002 All Firms: Equity Top IB - - - - - 61.1% 12.7% 4.0% 19.1% 126 68.5% 4.5% 1.7% 24.7% 178

Debt Top IB 61.1% 10.3% 0.8% 27.8% 126 - - - - - 56.8% 10.4% 3.2%1 29.1% 347

Merger Top IB 68.5%

8.4% 0.0% 23.0% 178 56.8% 9.2% 4.6% 25.1% 347 - - - - -

With Lending Relationship: Equity Top IB - - - - - 81.3% 9.4% 3.1% 0.0% 32 81.5% 3.7% 0.0%

11.1% 27

Debt Top IB 50.0% 11.5% 0.0% 38.5% 52 - - - - - 55.1% 5.1% 3.1% 35.7% 98

Merger Top IB 78.6% 10.7% 0.0% 10.7% 28 74.0% 12.3% 6.9% 5.5% 73 - - - - -

Without Lending Relationship: Equity Top IB - - - - - 54.3% 13.8% 4.3% 25.5% 94 66.2% 4.6% 2.0% 27.2% 151

Debt Top IB 68.9% 9.5% 1.4%

20.3% 74 - - - - - 57.4% 12.5% 3.2% 26.5% 249

Merger Top IB 66.7% 8% 0.0% 25.3% 150 52.2% 8.4% 4.0% 30.3% 274 - - - - -

45

Table VII Continued

Equity IB Rank Debt IB Rank Merger IB Rank

1 2 3 Did Not Appear

N 1 2 3 Did Not Appear

N 1 2 3 Did Not Appear

N

Panel C: Firms with at least two deals of each type from 2003-2009 All Firms: Equity Top IB - - - - - 73.8% 8.8% 3.8% 11.3% 80 62.0% 10.1% 2.5% 25.3% 79

Debt Top IB 73.8% 7.5% 1.3% 16.3% 80 - - - - - 47.5% 13.1% 3.3% 34.8% 244

Merger Top IB 62.0% 13.9% 1.3% 22.8% 79 47.5% 15.6% 7.0% 23.4% 244 - - - - -

With Lending Relationship: Equity Top IB - - - - - 82.4% 8.8% 2.9% 5.9% 34 72.0% 12.0% 8.0% 8.0% 25

Debt Top IB 87.5% 0.0% 0.0% 9.4% 32 - - - - - 47.7% 16.4% 4.7% 28.9% 128

Merger Top IB 72.0% 12.0% 0.0% 16.0% 25 63.5% 24.0% 5.2% 2.1% 96 - - - - -

Without Lending Relationship: Equity Top IB - - - - - 67.4% 8.7% 6.5% 15.2% 46 57.4% 9.3% 0.0% 33.3% 54

Debt Top IB 64.6% 12.5% 2.1% 20.8% 48 - - - - - 47.4% 9.5% 1.7% 41.4% 116

Merger Top IB 57.4% 14.8% 0.0% 25.9% 54 37.2% 10.1% 8.1%2 37.2% 148 - - - - -

46

Table VIII – Probit Analysis of investment Bank Selection This table presents probit models in which the dependent variable is equal to one if a particular investment bank is the lead bank on a particular transaction and is zero otherwise. In Panels A, B, C, and D we examine equity transactions, debt transaction, mergers, and all transaction types, respectively. Models (2) and (3) in every panel interact all independent variables with sub-period dummies, which represent 1997-2000 (Subperiod 1), 2001-2004 (Subperiod 2), and 2005-2009 (Subperiod 3). We throw out all transaction from 1996 so that the market share and relationship variables contain at least one year of observations. Ln(Transaction size) is the natural logarithm of issue size or deal value reported in SDC. IB’s Equity (Debt, M&A, Lending) Market Share is the percentage of the total market represented by the particular investment bank in the year prior to the transaction being analyzed. IB’s Share of Firm’s Equity (Debt, M&A, Lending) Deals is the value of the firm’s transactions represented by a particular bank over the three years prior to the particular transaction. In Panel D IB’s Share of Firm’s Transactions is the value of the firm’s transactions represented by a particular bank over the three years prior for all the firm’s transactions. Lead on prior deal is a dummy variable equal to one if the IB was a lead bank on the firm’s prior transaction or security issue. Competitor’s Top IB is a dummy variable equal to one if the IB is the most used bank by the firm with the highest revenue amount for a particular four digit SIC code. For the top firm in the industry Competitor’s Top IB is equal to one for the top bank of the firm with second-highest revenue. Comanager on Prior Deal is equal to one if the investment bank is a comanager on the firm’s prior transaction. In Panel D we include interactions with dummies for whether the transaction is not a specific transaction type. Each model contains both year and investment bank specific fixed effects. Robust standard errors, which are clustered by transaction, are presented in parentheses and the significance of the coefficient is denoted by ***, **, and * for coefficients that are significant at the 1%, 5%, and 10% levels, respectively.

47

Table VIII – Probit Analysis of Investment Bank Selection

Panel A – Equity Deals

(1)

Lead

(2)

Lead

(3) Important

Variable ×

Subperiod 1 Variable ×

Subperiod 2 Variable ×

Subperiod 3 Variable ×

Subperiod 1 Variable ×

Subperiod 2 Variable ×

Subperiod 3 Intercept -3.058***

(.000) -3.048***

(.000) -3.246***

(.000)

Ln(Transaction Size) -0.015* (.056)

-0.052*** (.000)

-0.026** (.015)

0.007a (.592)

0.135*** (.000)

0.169*** (.000)

0.160*** (.000)

IB’s Equity Market Share 2.439*** (.000)

3.389*** (.000)

2.13*** (.002)

2.088** (.025)

12.525*** (.000)

11.323*** (.000)

9.731*** (.000)

IB’s Debt Market Share 1.548** (.025)

1.215 (.259)

1.291 (.253)

-0.719 (.552)

-1.301 (.377)

-5.768*** (.000)

-5.803***b (.001)

IB’s M&A Market Share -0.460 (.336)

-0.188 (.834)

-0.377 (.516)

-0.650 (.406)

-0.646 (.331)

-0.001 (.999)

-0.556 (.417)

IB’s Lending Market Share 1.161 (.132)

2.609*** (.005)

1.164 (.207)

2.092* (.072)

1.094 (.157)

1.817** (.013)

2.226** (.016)

IB’s Share of Firm’s Equity Deals 0.873*** (.000)

0.971*** (.000)

0.558*** (.000)

0.863***c (.000)

0.922*** (.000)

0.608*** (.000)

0.820***c (.000)

IB’s Share of Firm’s Debt Deals 0.758*** (.000)

0.408** (.040)

0.78*** (.000)

0.899*** (.000)

0.499*** (.005)

0.633*** (.000)

1.013***b (.000)

IB’s Share of Firm’s M&A Deals 0.858*** (.000)

0.993*** (.000)

0.612*** (.000)

1.070***c (.000)

0.851*** (.000)

0.751*** (.000)

1.000*** (.000)

IB’s Share of Firm’s Loans 0.899*** (.000)

0.509* (.073)

0.88*** (.000)

1.094*** (.000)

0.601** (.012)

1.128*** (.000)

0.974*** (.000)

Lead on Prior Deal 1.300*** (.000)

1.636*** (.000)

1.355*** (.000)

1.171***b (.000)

1.488*** (.000)

1.195*** (.000)

1.136***b (.000)

Lead on Prior Deal × Competitor’s Top IB

-0.335*** (.003)

-0.109 (.608)

-0.341 (.106)

-0.495*** (.005)

-0.176 (.402)

-0.334* (.071)

-0.468** (.012)

Comanager on Prior Deal 0.887*** (.000)

1.009*** (.000)

0.886*** (.000)

0.806*** (.000)

0.077 (.280)

-0.131* (.069)

-0.160**b (.014)

Comanager on Prior Deal × Competitor’s Top IB

-0.005 (.981)

-0.603 (.231)

0.240 (.405)

-0.033 (.926)

0.113 (.789)

-0.129 (.690)

-0.218 (.573)

Year Fixed Effects Yes Yes Yes IB Fixed Effects Yes Yes Yes # of Transactions (not unique) 2,232 2,232 2,232 # of Observations 151,961 151,961 216,703 Pseudo R2 .403 .408 .400

48

Table VIII – Continued

Panel B – Debt Deals

(1)

Lead

(2)

Lead

(3) Important

Variable ×

Subperiod 1 Variable ×

Subperiod 2 Variable ×

Subperiod 3 Variable ×

Subperiod 1 Variable ×

Subperiod 2 Variable ×

Subperiod 3 Intercept -2.721***

(.000) -2.474***

(.000) -3.255***

(.000)

Ln(Transaction Size) 0.058*** (.000)

-0.001 (.854)

0.098*** (.000)

0.109***a (.000)

0.214*** (.000)

0.249*** (.000)

0.220***b (.000)

IB’s Equity Market Share -0.426 (.130)

-0.682 (.201)

-0.005 (.989)

-1.338**c (.015)

-2.442*** (.005)

-0.545 (.423)

-5.119***a (.000)

IB’s Debt Market Share 3.127*** (.000)

2.408*** (.000)

3.151*** (.000)

4.112*** (.000)

7.100*** (.000)

6.933*** (.000)

9.456*** (.000)

IB’s M&A Market Share -0.632** (.016)

-0.182 (.722)

-1.032*** (.003)

-0.294 (.531)

0.542 (.209)

-0.821*** (.006)

0.058a (.880)

IB’s Lending Market Share 0.265 (.475)

0.683 (.146)

0.318 (.490)

0.504 (.387)

1.396*** (.000)

-0.139 (.703)

0.471a (.315)

IB’s Share of Firm’s Equity Deals 0.514*** (.000)

0.432*** (.000)

0.425*** (.000)

0.694***c (.000)

0.582*** (.000)

0.254*** (.000)

0.618***a (.000)

IB’s Share of Firm’s Debt Deals 0.983*** (.000)

1.537*** (.000)

0.847*** (.000)

0.657***a (.000)

1.384*** (.000)

0.920*** (.000)

0.838***a (.000)

IB’s Share of Firm’s M&A Deals 0.419*** (.000)

0.415*** (.000)

0.403*** (.000)

0.394*** (.000)

0.394*** (.000)

0.366*** (.000)

0.379*** (.000)

IB’s Share of Firm’s Loans 1.001*** (.000)

0.678*** (.000)

1.057*** (.000)

1.256***a

(.000) 0.579*** (.000)

1.068*** (.000)

1.281***a (.000)

Lead on Prior Deal 0.705*** (.000)

0.577*** (.000)

0.732*** (.000)

0.727*** (.000)

0.594*** (.000)

0.651*** (.000)

0.837***a (.000)

Lead on Prior Deal × Competitor’s Top IB

-0.186*** (.000)

-0.200* (.056)

-0.173** (.039)

-0.157* (.050)

-0.200* (.056)

-0.251*** (.002)

-0.216** (.012)

Comanager on Prior Deal 0.702*** (.000)

0.673*** (.000)

0.708*** (.000)

0.708*** (.000)

-0.325*** (.000)

0.047 (.301)

0.117**a (.013)

Comanager on Prior Deal × Competitor’s Top IB

-0.023 (.730)

-0.212 (.206)

-0.203* (.089)

0.169*b (.090)

0.063 (.730)

0.007 (.960)

0.104 (.429)

Year Fixed Effects Yes Yes Yes IB Fixed Effects Yes Yes Yes # of Transactions (not unique) 4,931 4,931 4,931 # of Observations 238,356 238,356 450,065 Pseudo R2 .437 .442 .471

49

Table VIII – Continued

Panel C – Mergers

(1)

Lead

(2)

Lead

(3) Important

Variable ×

Subperiod 1 Variable ×

Subperiod 2 Variable ×

Subperiod 3 Variable ×

Subperiod 1 Variable ×

Subperiod 2 Variable ×

Subperiod 3 Intercept -2.705***

(.000) -2.768***

(.000) -2.799***

(.000)

Ln(Transaction Size) 0.044*** (.000)

0.048*** (.000)

0.035*** (.000)

0.047*** (.000)

0.052*** (.000)

0.035*** (.000)

0.050*** (.000)

IB’s Equity Market Share -0.153 (.699)

-0.093 (.889)

-0.495 (.390)

0.696 (.329)

0.593 (.629)

1.746 (.150)

1.586 (.213)

IB’s Debt Market Share 0.891* (.075)

0.555 (.479)

1.106 (.200)

-1.561*b

(.095) -0.255 (.828)

-1.336 (.352)

-1.244 (.446)

IB’s M&A Market Share 2.161*** (.000)

2.722*** (.000)

1.864*** (.000)

3.148*** (.000)

2.712*** (.000)

1.617*** (.000)

2.756***c (.000)

IB’s Lending Market Share 1.603*** (.005)

0.897 (.195)

1.650** (.015)

2.017** (.018)

0.422 (.527)

1.872*** (.002)

1.258*b (.085)

IB’s Share of Firm’s Equity Deals 0.664*** (.000)

0.830*** (.000)

0.569*** (.000)

0.551***b

(.000) 0.611*** (.000)

0.393*** (.000)

0.483***a (.000)

IB’s Share of Firm’s Debt Deals 0.569*** (.000)

0.674*** (.000)

0.523*** (.000)

0.537*** (.000)

0.532*** (.000)

0.434*** (.000)

0.388***b (.000)

IB’s Share of Firm’s M&A Deals 0.431*** (.000)

0.379*** (.000)

0.345*** (.000)

0.429*** (.000)

0.379*** (.000)

0.353*** (.000)

0.381*** (.000)

IB’s Share of Firm’s Loans 0.643*** (.000)

0.496*** (.000)

0.688*** (.000)

0.740*** (.000)

0.440*** (.000)

0.687*** (.000)

0.819***b (.000)

Adviser on Prior Deal 1.133*** (.000)

1.355*** (.000)

1.102*** (.000)

1.016***a (.000)

1.366*** (.000)

1.130*** (.000)

1.053***a (.000)

Adviser on Prior Deal × Competitor’s Top IB

-0.121* (.085)

-0.293** (.023)

-0.002 (.987)

-0.052 (.652)

-0.412*** (.001)

-0.094 (.439)

-0.095c (.398)

Year Fixed Effects Yes Yes Yes IB Fixed Effects Yes Yes Yes # of Transactions (not unique) 3,611 3,611 3,906 # of Observations 291,804 291,804 338,173 Pseudo R2 .358 .360 .363

50

Table VIII – Continued

Panel D – All transaction types

(1)

Lead

(2) Lead

(3) Important

Variable ×

Subperiod 1 Variable ×

Subperiod 2 Variable ×

Subperiod 3 Variable ×

Subperiod 1 Variable ×

Subperiod 2 Variable ×

Subperiod 3 Intercept -2.772***

(.000) -2.798***

(.000) -2.797***

(.000)

Ln(Transaction Size) 0.046*** (.000)

0.042*** (.000)

0.044*** (.000)

0.050*** (.000)

0.091*** (.000)

0.122*** (.000)

0.096*** (.000)

IB’s Equity Market Share 3.541*** (.000)

3.784*** (.000)

3.324*** (.000)

3.855*** (.000)

14.453*** (.000)

14.122*** (.000)

16.073*** (.000)

IB’s Equity Market Share × Current Deal is Not Equity

-3.792*** (.000)

-3.847*** (.000)

-3.416*** (.000)

-4.542*** (.000)

-14.972*** (.000)

-15.603*** (.000)

-20.445*** (.000)

IB’s Debt Market Share 5.253*** (.000)

4.011*** (.000)

4.557*** (.000)

5.911*** (.000)

9.430*** (.000)

11.959*** (.000)

18.193*** (.000)

IB’s Debt Market Share × Current Deal is Not Debt

-5.939*** (.000)

-4.762*** (.000)

-4.734*** (.000)

-9.125*** (.000)

-13.456*** (.000)

-17.857*** (.000)

-28.055*** (.000)

IB’s M&A Market Share 2.643*** (.000)

3.091*** (.000)

1.979*** (.000)

3.653*** (.000)

3.266*** (.000)

2.74*** (.000)

4.827*** (.000)

IB’s M&A Market Share × Current Deal is Not M&A

-3.554*** (.000)

-3.619*** (.000)

-2.988*** (.000)

-4.369*** (.000)

-3.647*** (.000)

-4.341*** (.000)

-6.966*** (.000)

IB’s Lending Market Share 0.481* (.060)

0.645** (.037)

0.496 (.115)

0.933** (.021)

1.048*** (.000)

0.473* (.068)

0.813** (.012)

IB’s Share of Firm’s Transactions 0.983*** (.000)

1.209*** (.000)

0.851*** (.000)

0.843*** (.000)

1.191*** (.000)

0.894*** (.000)

0.957*** (.000)

IB’s Share of Firm’s Loans 1.006*** (.000)

0.875*** (.000)

1.052*** (.000)

1.119*** (.000)

0.835*** (.000)

1.032*** (.000)

1.110*** (.000)

Lead on Prior Deal that is Equity 1.364*** (.000)

1.484*** (.000)

1.355*** (.000)

1.335*** (.000)

1.583*** (.000)

1.242*** (.000)

1.201*** (.000)

Lead on Prior Deal that is Equity × Current Deal is Not Equity

-0.259*** (.000)

-0.289*** (.000)

-0.292*** (.000)

-0.315*** (.000)

-0.838*** (.000)

-0.468*** (.000)

-0.373*** (.000)

Lead on Prior Deal that is Debt 0.768*** (.000)

0.907*** (.000)

0.804*** (.000)

0.572*** (.000)

0.711*** (.000)

0.849*** (.000)

0.945*** (.000)

Lead on Prior Deal that is Debt × Current Deal is Not Debt

-0.071* (.053)

-0.073 (.228)

-0.089 (.158)

0.061 (.352)

-0.160*** (.001)

-0.400*** (.000)

-0.464*** (.000)

Lead on Prior Deal that is M&A 1.145*** (.000)

1.152*** (.000)

1.199*** (.000)

1.101*** (.000)

1.128*** (.000)

1.075*** (.000)

0.987*** (.000)

Lead on Prior Deal that is M&A × Current Deal is Not M&A

-0.504*** (.000)

-0.570*** (.000)

-0.529*** (.000)

-0.469*** (.000)

-0.372*** (.000)

-0.402*** (.000)

-0.297*** (.000)

Year Fixed Effects Yes Yes Yes IB Fixed Effects Yes Yes Yes # of Transactions (not unique) 14,406 14,406 14,767 # of Observations 1,487,285 1,487,285 1,677,405 Pseudo R2 .416 .419 .402

51

0

100

200

300

400

500

600

700

800

900

1,000

1,100

Aggregate Deal Value ($ bil)

Equity Debt Other Issues M&A Acquirer M&A Target

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

5,000

Number of Deals

Equity Debt Other Issues M&A Acquirer M&A Target

Panel A – Aggregate Deal Frequency by Year

Panel B – Aggregate Deal Value by Year ($bil)

Figure 1 – Total Event Activity by Year

52

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

NLEAD NCOMGR NSYND

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

NLEAD NCOMGR NSYND

Panel A – Equity Syndicate Characteristics by Year Panel B – IPO Syndicate Characteristics by Year

Panel A – Debt Syndicate Characteristics by Year Panel D – M&A Advisory Group Characteristics by Year

Figure 2 – Syndicate Characteristics by Year

53

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

EQUITY IPO DEBT

Panel A – Proportion of Deals with Multiple Lead Underwriters Panel B – Proportion of Deals with Multiple Advisors

Panel C – Proportion of Deals with Zero Non-Managing Syndicate Members

Figure 3 – Frequency of Multiple Lead Underwriter/Multiple Advisor Use by Year

54

0.00

1.00

2.00

3.00

4.00

5.00

All Transactions Equity Transactions Debt Transactions Merger Transactions

0.00

1.00

2.00

3.00

4.00

5.00

All Transactions Equity Transactions Debt Transactions Merger Transactions

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

12.0

All Transactions Equity Transactions Debt Transactions Merger Transactions

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

11.00

12.00

All Transactions Equity Transactions Debt Transactions Merger Transactions

Panel A – Number of IB Relationships (Lead Roles) Panel B – Number of IB Relationships (Important Roles)

Panel C –Number of Strong Relationships (Lead Roles) Panel D – Number of Strong Relationships (Important Roles)

Figure 4 – Investment Banking Relationships by Year

55

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

All Transactions Equity Transactions Debt Transactions Merger Transactions

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

All Transactions Equity Transactions Debt Transactions Merger Transactions

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

All Transactions Equity Transactions Debt Transactions Merger Transactions

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

All Transactions Equity Transactions Debt Transactions Merger Transactions

Panel E –Market Share Top IB (Lead Roles) Panel F –Adjusted Market Share Top IB (Lead Roles)

Panel G –Adjusted Market Share Top 3 IBs (Lead Roles) Panel H - Herfindahl Index (Lead Roles)

Figure 4 – Continued

56

APPENDIX

Table A1 – Data Matching and Sample Restrictions Events include all security issue and M&A events from 1996 through 2009, as identified by SDC. CRSP firms include all firms with at least one class of common stock listed during the period from 1996 through 2009.

Panel A – CRSP Matching and Sample Restrictions

CRSP Firms

Equity Issues

Debt Issues

Other Issues

M&A Acquirers

M&A Targets

All Events

Total SDC Events - 18,229 268,784 4,536 140,677 140,677 432,226

Initial Matched Sample based on all CRSP firms

15,381 14,743 106,393 2,921 57,542 30,936 212,535

Exclude financials, utilities, and government agencies

8,849 8,769 14,809 893 40,252 19,398 84,121

Combine simultaneous offers and remove duplicates

8,849 8,701 10,427 869 40,185 19,377 79,559

Exclude firms in sample less than one year

8,322 8,598 10,323 863 39,844 18,862 78,490

Exclude purely secondary issues and restrict M&A form/% acquired

8,322 7,810 10,323 863 35,529 14,991 69,516

Exclude transactions by captive finance subsidiaries

8,322 7,807 8,239 851 35,492 14,935 67,324

Exclude events more than five days outside CRSP date range

8,322 7,316 7,614 650 32,451 12,427 60,458

Exclude events that do not involve at least one full-service I-bank

8,322 6,046 7,344 438 4,755 4,799 23,382

Include only those events with value of at least $1 million

8,322 6,043 7,342 437 4,394 4,274 22,490

Panel B – Advisor/I-Bank Information CRSP

Firms Equity Issues

Debt Issues

Other Issues

M&A Acquirers

M&A Targets

All Events

Exclude events more than five days outside CRSP date range

8,322 7,316 7,614 650 32,451 12,427 60,458

Events with I-Bank/Advisor Info 6,512(89.0%)

7,443 (98.8%)

486 (74.8%)

5,611 (17.3%)

5,562 (44.8%)

25,614 (42.4%)

57

Table A2 – Firm-Level Activity The table describes security issuance and M&A activity for CRSP firms from 1996 through 2009, where financial firms, utilities, and government agencies are excluded. The transaction sample includes all security issuance and M&A events identified in SDC that could be matched to a CRSP firm and meet the sample restrictions described in Section 2. The firm sample includes 8,322 CRSP firms, of which 768 have no security issues or M&A events during the sample period.

Panel A - Most Active Firms based on Securities Issues

Firm Total Events Union Pacific Corp. 71

United Parcel Service Inc. 70

IBM Corp. 68

Burlington Northern Santa Fe Corp. 57

Comdisco Inco. 57

Walt Disney Co. 50

Chesapeake Energy Corp. 45

Centex Corp. 43

Ryder Systems Inc. 43

AT&T Inc. 41

Panel B - Most Active Firms based on M&A Acquirer Events Firm Total Events Cisco Systems Inc. 133

IBM Corp. 102

Microsoft Corp. 101

CBIZ Inc. 99

IKON Office Solutions 97

Airgas Inc. 92

Illinois Tool Works Inc. 91

Parker Hannifin Corp. 87

Quanta Services Inc. 76

Auto Nation Inc. 72

Panel C - Most Active Firms based on M&A Target Events Firm Total Events General Electric Co. 126

Alderwoods Group Inc. 69

AT&T Corp. 62

IBM Corp. 55

Allied Signal Inc. 52

Starwood Hotels & Resorts 48

CBS Corp. 43

Du Pont & Co. 42

Procter & Gamble Co. 39

Columbia HCA Healthcare Corp. 39

58

Table A3 – Summary Statistics for IB-Firm Relationships

1998 2009 N Min 25th Perc Median 75th Perc Max N Min 25th Perc Median 75th Perc Max

Panel A – All Transactions # of Lead IB Relationships 1,423 1.00 1.00 2.00 3.00 12.00 659 1.00 2.00 3.00 4.00 18.00

# of Important IB Relationships 1,423 1.00 3.00 4.00 5.00 27.00 659 1.00 3.00 5.00 10.00 58.00

# of Strong IB Relationships 1,423 0.00 1.00 1.00 1.00 4.00 659 0.00 1.00 1.00 2.00 8.00

Herfindahl Index - Leads 1,423 0.16 0.51 0.67 1.00 1.00 659 0.10 0.32 0.50 0.69 1.00

Herfindahl Index – Important 1,423 0.10 0.28 0.42 0.63 1.00 659 0.03 0.18 0.34 0.55 1.00

Top Lead IB Market Share 1,423 0.28 0.66 0.88 1.00 1.00 659 0.42 0.72 0.96 1.00 1.00

Top Lead IB Adj. Market Share 1,423 0.24 0.59 0.79 1.00 1.00 659 0.12 0.40 0.57 0.81 1.00

Top 3 Lead IB Aggr. Market Share 1,423 0.60 1.00 1.00 1.00 1.00 655 0.37 0.87 1.00 1.00 1.00

Panel A – Equity Transactions # of Lead IB Relationships 291 1.00 1.00 1.00 2.00 3.00 136 1.00 2.00 2.00 3.00 7.00

# of Important IB Relationships 291 1.00 3.00 4.00 4.00 12.00 136 1.00 3.00 5.00 7.00 32.00

# of Strong IB Relationships 291 0.00 1.00 1.00 1.00 2.00 136 0.00 1.00 1.00 2.00 6.00

Herfindahl Index - Leads 291 0.33 0.56 1.00 1.00 1.00 136 0.17 0.39 0.51 0.66 1.00

Herfindahl Index – Important 291 0.12 0.25 0.29 0.36 1.00 136 0.03 0.18 0.26 0.46 1.00

Top Lead IB Market Share 291 0.35 0.71 1.00 1.00 1.00 136 0.44 0.67 1.00 1.00 1.00

Top Lead IB Adj. Market Share 291 0.35 0.68 1.00 1.00 1.00 136 0.23 0.50 0.62 0.78 1.00

Top 3 Lead IB Aggr. Market Share 291 1.00 1.00 1.00 1.00 1.00 136 0.59 1.00 1.00 1.00 1.00

59

Table A3 – Continued

1998 2009 N Min 25th Perc Median 75th Perc Max N Min 25th Perc Median 75th Perc Max

Panel C – Debt Transactions # of Lead IB Relationships 413 1.00 1.00 2.00 3.00 12.00 203 1.00 3.00 5.00 6.00 18.00

# of Important IB Relationships 413 1.00 3.00 4.00 6.00 27.00 203 1.00 7.00 11.00 16.00 58.00

# of Strong IB Relationships 413 0.00 1.00 1.00 1.00 4.00 203 0.00 2.00 2.00 3.00 14.00

Herfindahl Index - Leads 413 0.14 0.50 0.60 1.00 1.00 203 0.06 0.19 0.27 0.38 1.00

Herfindahl Index – Important 413 0.05 0.21 0.29 0.40 1.00 203 0.03 0.08 0.11 0.19 1.00

Top Lead IB Market Share 413 0.28 0.62 0.85 1.00 1.00 203 0.38 0.78 1.00 1.00 1.00

Top Lead IB Adj. Market Share 413 0.21 0.56 0.73 1.00 1.00 203 0.07 0.25 0.33 0.50 1.00

Top 3 Lead IB Aggr. Market Share 413 0.56 1.00 1.00 1.00 1.00 203 0.22 0.65 0.83 1.00 1.00

Panel D – Merger Transactions # of Lead IB Relationships 395 1.00 1.00 2.00 2.00 6.00 192 1.00 1.00 2.00 3.00 11.00

# of Important IB Relationships 395 1.00 1.00 2.00 3.00 6.00 192 1.00 1.00 2.00 3.00 11.00

# of Strong IB Relationships 395 0.00 1.00 1.00 1.00 3.00 192 0.00 1.00 1.00 2.00 8.00

Herfindahl Index - Leads 395 0.25 0.51 0.70 1.00 1.00 192 0.11 0.43 0.67 1.00 1.00

Herfindahl Index – Important 395 0.25 0.51 0.69 1.00 1.00 192 0.11 0.40 0.59 1.00 1.00

Top Lead IB Market Share 395 0.41 0.73 0.96 1.00 1.00 192 0.42 0.79 0.97 1.00 1.00

Top Lead IB Adj. Market Share 395 0.27 0.60 0.81 1.00 1.00 192 0.16 0.50 0.79 1.00 1.00

Top 3 Lead IB Aggr. Market Share 395 0.81 1.00 1.00 1.00 1.00 192 0.38 1.00 1.00 1.00 1.00

60

Table A4 – Investment Bank and Advisor Activity The table describes underwriting and M&A advising activity by investment bank from 1996 through 2009. The transaction sample includes all security issuance and M&A events identified in SDC that could be matched to a CRSP firm and meet the sample restrictions described in Section 2. Financial firms, utilities, and government agencies are excluded. An investment bank is associated with a transaction if that firm shows up anywhere in the underwriting syndicate or advising group.

Panel A - Most Active Investment Banks/Advisors Involved in All Event Categories

Firm Total Events Merrill Lynch 4,031

Goldman Sachs 4,017

Citi Salomon Smith Barney 3,498

Morgan Stanley Dean Witter 3,458

CS First Boston 3,403

BancAmerica 3,127

JP Morgan Chase 2,847

Lehman 2,829

Bear Stearns 2,104

Deutsche Alex Brown 2,021

Panel B - Most Active Investment Banks/Advisors Involved in Security Issues Only Firm Total Events RBS 463

Williams 391

Edward Jones 276

Mizuho 253

Soundview 236

Charles Schwab 190

Utendahl 187

Blaylock 185

CL King 183

Comerica 153

Panel C - Most Active Investment Banks/Advisors Involved in M&A Only Firm Total Events Broadview 176

Sagent Advisors 126

Evercore 77

Daniels & Associates 76

PWC 65

Alliant Partners 57

Peter J. Solomon 55

Grant Thornton 54

Updata 54

Greenhill & Co. 45

61

Table A4 – Continued

Panel D - Most Active Investment Banks/Advisors Involved in Equity Deals Only Firm Total Events WIT Capital 137

EOffering 134

Brad Peery 78

First Southwest 57

Johnston 56

Dominick 55

ETrade 55

Arnhold Bleich 48

Nutmeg Securities 43

Hoefer 40

Panel E - Most Active Investment Banks/Advisors Involved in Debt Deals Only Firm Total Events InCapital LLC 42

Fleet 22

H&R Block Financial Advisors 22

Caboto 18

GE Capital 15

Pershing Securities Ltd 13

Sumitomo Mitsui 12

Banc Boston 8

NAB 8

Gibraltar Securities 5

62

Panel A – Bank of America Merrill Lynch

Panel B – UBS Paine Webber

Figure A1 – Underwriter/Advisor Combination Events