The Impact of CEO Connections for Equity Issuing Firms

Don M. Autore College of Business, Florida State University

Olubunmi Faleye D’Amore-McKim School of Business, Northeastern University

Tunde Kovacs Manning School of Business, University of Massachusetts Lowell

ABSTRACT:

We study the impact of CEOs’ personal connections on the cost of issuing equity. Using fixed effect estimations and an instrumental variables approach, we report that CEOs with a greater degree of social, work, and educational connections are able to negotiate lower gross underwriting spreads and smaller offer price discounts in their firm’s seasoned equity offers. Further tests reveal several channels through which CEO connectedness reduces SEO issuance costs, including institutional buying, analyst following, and underwriter reputation. We argue that these channels lead to increased demand and reduced information asymmetry. Our results emphasize the benefits of broader CEO connectedness and provide support for the hypothesis that personal connections facilitate information flow enabling better corporate decision making, which is recognized by investment banks, investors, and lenders.

The Impact of CEO Connections for Equity Issuing Firms

ABSTRACT:

1. Introduction

It has long been known that personal connections can have significant benefits. For example, individuals who are more socially connected are known to have improved health.

House, Landis, and Umberson (1988) argue that “social relationships, or the relative lack thereof, constitute a major risk factor for health—rivaling the effect of well-established health risk factors such as cigarette smoking, blood pressure, blood lipids, obesity and physical activity.” Recently, a growing number of studies examine how corporations are affected by the personal connections of their executives. Some papers reports harmful effects of executive connections for shareholder value (see, for example, Hwang and Kim, 2009; Ang, Nagel, and Yang, 2012; Fracassi and Tate,

2012; Kramarz and Thesmar, 2013; El-Khatib, Fogel, and Jandik, 2015), while others highlight the benefits of executive connections (for example, McDonald, Khanna, Westphal, 2008;

Engelberg, Gao, and Parsons, 2012; Larcker, So, Wang, 2013; Faleye, Kovacs, and

Venkateswaran, 2014; Chuluun, Prevost, and Puthenpurackal, 2014).

We focus on the impact of executive connections on the cost of issuing new stock via seasoned equity offers (SEOs). Our central premise is that CEOs with more extensive networks can negotiate higher selling prices (i.e. smaller offer price discounts) and lower gross underwriting spreads. Our key network variable, CEO Network, reflects the CEO’s overall degree of connectedness through social, work, and educational connections provided by the

BoardEx database. In fully specified models that include a battery of control variables and fixed effects for issue year and industry, the estimates imply that increasing CEO Network by one standard deviation decreases the offer price discount by 37 basis points. This figure implies a savings of roughly $570,000 for a typical sample offer. The findings are not driven by direct

3 relationships between SEO issuing firms and their underwriters; the results hold when we control for connections between the CEO of the issuing firm and the firm’s lead underwriter.

A potential concern is the possibility of reverse causality; i.e. that a CEO gains connections as a result of lower SEO issuance costs. This concern is largely mitigated by the key feature of our data that CEO connections are established over many years and therefore a large number of connections are already in place several years before any sample SEOs take place.

Another concern is that an omitted variable is associated with both increased CEO connections and lower SEO issuance costs. We address this potential issue by implementing an instrumental variable approach. A successful instrument should influence the extent of a CEO’s network but affect SEO costs only via its effect on the CEO’s network. We use a combination of two instruments: a binary variable indicating that the CEO holds an MBA degree and the natural logarithm of the number of industries in which the CEO has worked. We find that both of these variables have a positive influence on CEO Network, and both meet the exclusion criteria for an acceptable instrument. In second stage estimates, the instrumented CEO Network has a negative impact on both SEO offer price discounting and gross spreads, and the Hansen J statistic indicates that the instruments do not affect discounting beyond their effect through CEO connections. These results support our underlying hypothesis that more connected CEOs can acquire external equity financing on cheaper terms.

Why are larger CEO networks associated with lower SEO issuance costs? We conjecture that greater CEO connectedness is associated with increased institutional demand for newly issued shares, better information flow resulting in reduced information asymmetry, and more complete underwriter certification, all of which can reduce issuance costs.1 First, we argue that

1 According to Merton’s (1987) investor recognition hypothesis, marketing and widening the demand for the firms’ shares during security offerings provide long-standing benefits for the firm. For example, increasing media coverage

4 well-connected CEOs have exceptional interpersonal and communications skills and are therefore more likely to have relationships with institutional investors who are potential buyers of the offered SEO shares. This can result in increased institutional demand for new shares and lead to greater pre- to post-SEO increases in total institutional ownership and a larger number of new institutional owners. The increased demand for newly issued shares can allow the issuer to obtain a higher offer price resulting in a smaller offer price discount, and can lessen the marketing and placement effort of the underwriter resulting in a lower gross spread. Second, the size of the CEO network can arguably influence issue costs through better information flow.

CEOs with larger networks are arguably more visible and have more direct communication with investors and analysts. As a result, SEOs by more connected CEOs are arguably associated with increased demand for information and therefore are likely to exhibit larger increases in analyst coverage leading to greater transparency. Third, prior literature finds that CEO connections facilitate information flow between corporations and therefore the adoption of best practices in operations. This effect increases the likelihood that CEOs with larger networks have access to more reputable underwriters, whose presence in the issue can help certify the offer and lead to lower issue costs (e.g. Chemmanur and Fulghieri 1994; McLaughlin, Safiedine, and Vasudevan

2000; Cooney, Kato, and Schallheim 2003; Pandes 2010).

Our empirical tests support these arguments. First, we find that firms with greater CEO connectedness exhibit greater increases in institutional ownership around SEOs and greater increases in the number of new institutions that hold shares after the SEO but did not hold shares before the SEO announcement. Second, firms with more connected CEOs exhibit greater increases in analyst following from pre- to post-SEO. Finally, SEOs by firms with more

during IPOs (Liu, Sherman, and Zhang, 2014) and broadening the breadth of ownership during SEOs (Autore and Kovacs, 2014) enhance long-term firm value and liquidity.

5 connected CEOs employ more highly ranked investment banks. We estimate the predicted value of each channel variable from a model that includes CEO Network and controls; i.e., we take the fitted value from an estimation of the channel variable as a function of CEO Network and controls. In a second-stage estimation, we find that a higher predicted value of a particular channel variable (change in institutional ownership, number of new institutional owners, change in analyst following, or underwriter rank) is associated with lower SEO issuance costs, measured by either the gross spread or offer price discount. The results suggest that increased demand, reduced information asymmetry, and more credible certification serve as channels through which

CEO connectedness translates to lower issuance costs.

A related study by Engelberg, Gao, and Parsons (2012) reports that firms having a personal relationship with a lender are able to secure lower borrowing costs from that lender, due to either better information flow or better monitoring. Our paper is similar in the sense that we report lower issuance costs as a result of CEO connections. Our findings suggest that the effect of CEO connections on external financing costs goes beyond direct relationships. Importantly, our tests control for direct connections between CEOs and the SEO underwriting firm, although in our sample of SEO firms there is a very low incidence of direct CEO-underwriter relations. In supplementary analyses, we find that a larger CEO network reduces the spread on syndicated loans, after controlling for a direct issuer-lender connection.

This study also contributes to a large SEO literature that examines the determinants of

SEO issuance costs. It is well-established that lower information asymmetry and increased demand are associated with lower issuance costs. Our results suggest that a firm who employs a well-connected CEO can reduce SEO issuance costs by increasing demand by institutional investors, improving information flow; i.e. reducing information asymmetry, and improving

6 issue-related certification. Thus, hiring a well-connected CEO is a mechanism through which a firm can capitalize on the known benefits of increased demand and low information uncertainty.

Overall, the results of this study are important and suggest that firms who hire CEOs with more extensive personal networks can reduce external financing costs, thereby increasing shareholder value.

The paper is organized as follows. Section 2 describes our sample and data. Sections 3 and 4 present the main analysis and Section 5 concludes.

2. Sample

We collect our sample of seasoned equity offers (SEOs) from the Securities Data

Company’s (SDC) Global New Issues Database, and obtain data on CEO connections from

BoardEx, which provides detailed biographical information on directors and top executives beginning in 1997. The BoardEx data starts in 1997, and accordingly our sample of SEO events starts in that year and extends through 2008. We begin with all available data from non-financial companies listed on the NYSE, AMEX, or Nasdaq and follow the previous literature in making adjustments to preserve data integrity and to ensure data availability. Specifically, we use the following requirements. From the sample of SEOs we exclude exclusively secondary offers, non- underwritten offers, and offers with proceeds less than $10 million. All sample SEOs must have non-missing values for offer proceeds and the gross underwriting spread, as well as matching

CEO connections data from BoardEx and firm / offer characteristics used as control variables

(listed in Appendix A) and obtained from Compustat, SDC, and the Center for Research in

Security Prices (CRSP).

We measure CEO connections, similarly to Faleye, Kovacs, and Venkateswaran (2014), as the total number of individuals with whom the CEO shares a common employment,

7 educational, or social history in the BoardEx universe. This measure is re-calculated each year.

Employment-based connections are the number of people with whom the CEO shared the same employer or served on the same board in or prior to the measure’s estimation year. Following

Faleye, Kovacs, and Venkateswaran (2014), we exclude connections that the CEO obtained as

CEO in his current position, because these connections are more likely to result from the position itself rather than reflecting personal connections. For educational connections, we follow Cohen,

Frazzini, and Malloy (2008) and require that two individuals attended the same institution, graduated within two years of each other, and earned a similar type of degree. Social connections are connections established via other organizations (e.g. clubs, charities, sporting, or other not- for-profit organizations). In these organizations, we require that both individuals’ roles go beyond membership, except for organizations categorized as social clubs. Almost 90% of non- corporate associations have a missing starting date, and for these we assume a start prior to 1997 to avoid losing these observations. The network variable exhibits a mechanical trend introduced because connections are added but never deducted over time. Following Faleye, Kovacs, and

Venkateswaran (2014), in our regressions we transform all network variables to purge out the upward trend. Specifically, all network variables are regressed on a trend line, then we add one plus the absolute minimum value of the residual, and finally take the natural logarithm of the de- trended sum. All other variables are collected from CRSP, Compustat, IBES, and Thompson 13f filings.

The final sample of events contains 1,309 SEOs by 906 firms under 939 CEOs. Table 1 presents the annual number of events and total proceeds raised. The years with the greatest SEO frequency are 2004 (149), 2000 (144), and 2003 (135). The sum of total proceeds raised was

8 largest in 2000 ($28,940 million). The sample year with the fewest SEOs is 2008, likely due to weak equity markets as a result of the financial crisis.

Table 2 displays descriptive statistics on issuance costs, firm traits, and CEO network variables. Gross underwriting spreads reflect compensation for certification, marketing and monitoring services. The average gross spread equals 4.9% of issue proceeds, although there is considerable variation across offers. The offer price discount reflects the extent to which SEOs are priced at a discount from the secondary market trading price. The discount is defined following prior studies as the close-to-offer return from the previous day’s closing transaction price to the offer price, multiplied by negative one. Following Safieddine and Wilhelm (1996) and others, we identify the offer date by applying a volume based offer date correction. In particular, if trading volume on the day following the SDC offer date is more than twice the volume on the SDC offer date, and is more than twice the average daily volume during the prior

250 trading days, then the offer date is assigned as the day following the SDC offer date. The average SEO is discounted by 3.2% compared to the prevailing market price at the prior day’s close. This variable also exhibits considerable cross-sectional variation.

The average (median) firm has assets of $1,316 (266) million and is followed by 5.4 (4) analysts. Roughly 66% of sample firms are listed on the Nasdaq. The average (median) relative offer size is 18.6% (16%) of shares outstanding. Most offers are syndicated with an average of

6.3 managing underwriters. Overnight offers constitute about 15% of sample SEOs -- these offers are not announced prior to the issue date.

The average (median) size of a CEO’s network equals 122 (71) after excluding connections that the CEO obtained by becoming part of the current firm. It is rare to find a direct connection between the firm and the underwriter. Only 2% of the SEOs feature a direct

9 connection between the CEO and the lead underwriter and in only 0.5% of the cases is a representative of the lead underwriter a board member for the firm. All variables are defined in

Appendix A.

3. Main Results

3.1. The impact of CEO network on SEO issuance costs

We specify multivariate estimations to identify whether the extent of a CEO’s network is associated with lower issuance costs, while controlling for firm and offer characteristics and including issue year fixed effects to account for the possibility of year-specific patterns in the variables and industry fixed effects using historical 2-digit Standard Industry Classification (SIC) codes to control for industry specific patterns.

Table 3 presents the estimates of regressions that explain the SEO gross underwriting spread (Models 1 and 2) and the offer price discount (Models 3 and 4). Heteroskedasticity- adjusted standard errors, clustered by industry, are reported in parentheses. The key explanatory variable is CEO Network, which equals the total number of a CEO’s connections, excluding connections that the CEO acquired in his current position. This total number reflects the total number of individuals with whom the CEO shares a common employment, educational, or social history in the BoardEx universe. In all regression analyses, this variable is transformed by regressing it on a trend line, then adding one plus the absolute minimum value of the residual, and finally by taking the natural logarithm of the de-trended sum. This procedure follows the method of Faleye, Kovacs, and Venkateswaran (2014) to purge out the natural upward trend. A

CEO with a greater number of connections is more likely to have a direct connection with the underwriter. Engelberg, Gao, and Parsons (2012) find that, in case of syndicated loans, direct

10 connections between the firm and the creditor tend to lower financing costs. To separate the effect of direct connections from the size of the CEO’s network, we introduce two control variables. We include a binary indicator for whether the CEO is connected to the lead underwriter, Direct Connection, which equals one if the issuing firm’s CEO has a direct link to an executive or to an executive board member of the lead underwriter(s)/ arranger(s). We also add a binary indicator for a board connection, Underwriter on Board, which is a binary variable indicating that one of the executives or board members of the lead underwriter(s)/ arranger(s) serves as a supervisory board member of the issuing firm.

Additional controls include firm and offer characteristics. Specifically, SEO cost estimations include a binary indicator for overnight offers,2 the relative offer size, the number of managers, the secondary percentage of the offering, the firm’s prior SEO experience including the current offer (Experience), firm size, 180-day stock price runup, stock volatility, share turnover, and a Nasdaq listing indicator. Finally, following Corwin (2003), in the offer discount regressions we control for the cumulative market-adjusted stock returns in the five days immediately prior to the offer, measured separately for positive and negative values, and an indicator reflecting integer offer prices.

In Table 3, the key result is that the extent of the CEO Network is significantly associated with SEO direct issuance costs. In particular, SEO issuing firms with CEOs who have broader social networks pay significantly lower gross spreads and incur significantly smaller offer price discounts, after controlling for other factors. A one standard deviation increase in the number of

CEO connections leads to a 13 basis point decline in the gross spread and a 37 basis point

2 Overnight offers (which are a subset of shelf registered offers) are studied by Autore, Kumar, and Shome (2008), Bortolotti, Megginson, and Smart (2008), Bethel and Krigman (2008), Gao and Ritter (2010), Autore (2011), and Autore, Hutton, and Kovacs (2011), among others.

11 decline in the discount, all else equal. These figures are significant in economic terms, especially for the discount representing a 12% decline.

A direct connection between the underwriter and the firm’s CEO has a marginally negative impact on SEO issue costs. In Table 3, Direct Connection enters negatively and statistically significant (10% level) in the discount estimation. Despite the weak statistical effect, the economic magnitude is rather large. If the CEO is directly connected to the lead underwriter, the firm has a 1.07 percentage point smaller offer price discount, ceteris paribus. There is no evidence that the underwriter’s presence on the firm’s board decreases SEO issue costs, perhaps due to the very small number of such cases in the sample.

The estimates also provide strong evidence that overnight offers are associated with lower gross spreads, likely due to less marketing effort; and deeper offer price discounts, at least partially due to the discount including the underwriter’s expectation of the stock price decline at the issue (in overnight offers the issue day is when the announcement effect occurs). Larger offers relative to firm size are associated with greater spreads and discounts, and more managing underwriters are associated with increased spreads but smaller discounts. Greater analyst following, stock return runup, and firm liquidity are all associated with lower gross spreads.

SEOs by smaller firms and firms with more stock volatility are associated with higher gross spreads and offer price discounts. Integer pricing and the absolute magnitude of pre-offer five day returns are associated with larger discounts.

Overall, the results support the hypothesis that firms with more connected CEOs receive better pricing in terms of gross spread and discounting in seasoned equity issues.

3.2. Instrumental variables approach

The tests in the prior section demonstrate that more extensive CEO networks are associated with lower SEO issuance costs. This association, however, is subject to an endogeneity concern, particularly that a separate unobserved variable drives the negative relation between CEO networks and SEO issue costs. To mitigate the possibility of such an omitted variable bias, in this subsection we implement an instrumental variable estimation, and in subsequent sections we use alternative definitions of CEO Network and also investigate the channels through which a CEO’s network can influence capital raising costs.

In this section we use an instrumental variable approach to demonstrate that SEO issuance costs are impacted by the degree of CEO connectedness. A suitable instrument should be related to CEO Network and should influence issue costs only via its effect on CEO Network.

We use a combination of two instruments: a binary variable indicating that the CEO holds an

MBA degree, and the natural logarithm of the number of industries in which the CEO has worked. In our sample, an MBA degree is held by 28% of CEOs. Having an MBA degree is correlated with the size of a CEO’s network because it increases the CEO’s education and work related network as alumni relations can lead to sustained relations in the business world.

However, the mere fact that a CEO holds an MBA should not directly impact SEO issuance costs, because the extent to which an MBA improves an executive’s skills and therefore enhances firm value should be priced by the capital markets prior to and independent of the firm’s SEO.

The second instrument is Industry Experience, defined as the natural logarithm of the number of industries in which the CEO has worked. The average CEO in our sample worked in

1.1 industries and the CEO with the most industry experience worked in four industries. Broader cross-industry experience can affect the size of the CEO’s network because it expands the pool

13 of potential contacts. But Industry Experience does not have a straightforward effect on SEO issuing costs (unrelated to network size) because it is unclear whether broad cross-industry knowledge is more important than focused knowledge of a specific industry to create an advantage in selling seasoned shares.

We implement a two-stage regression approach to conduct our instrumental variable analyses. In the first-stage OLS regression, we regress our key variable, CEO Network, on our two instrumental variables together with the set of standard controls from our baseline regressions in Table 3. This specification helps to identify whether holding an MBA and industry experience influence the degree of a CEO’s network that is not captured by the publicly observable control variables.

In the second-stage estimation, we regress the SEO cost variable (either gross spread or offer price discount) on the instrumented CEO Network from the first-stage regression and controls. In the first stage, we expect the instrumental variables, MBA and Industry Experience, to have a statistically significant coefficient in explaining CEO Network, and in the second stage we expect the instrumented CEO Network to have a significant negative coefficient in predicting

SEO issuance costs.

The first-stage estimates (unreported) are consistent with our expectations. We find that an MBA degree and Industry Experience are significantly positively related to the size of the

CEO’s network. The Kleibergen-Paap statistic verifies that the instruments are relevant while the

Hansen J statistic suggests that the instruments pass the endogeneity requirements and have no effect on issue costs after controlling for the size of the CEO’s network.

In Table 4, we report the second-stage estimates of the coefficients on the instrumented

CEO Network. As expected, for both gross spreads and offer price discounts, we find that the

14 coefficient of the instrumented CEO Network is significantly negative suggesting that a higher instrumented CEO Network predicts lower issuance costs. Thus, the instrumental variable estimations support the hypothesis that CEO connectedness impacts SEO issuance costs.

3.3 Alternative definitions of CEO networks

Our primary measure of the size of a CEO’s network reflects the number of people the

CEO is connected to via shared employment and educational history or shared social interest.

However, Faleye, Kovacs, and Venkateswaran (2014) point out that other dimensions of CEO connections could be important. We use two alternative measures following their approach. First, we use the combined network size of the CEO’s connections. This variable captures the number of people that the CEO can reach out to through his direct connections, potentially increasing the firm’s ability to market the security to a wider range of investors. We proxy for this effect with the variable Combined Network, defined as the combined network size of all the CEO’s connections, de-trended and logged. Second, we use the number of firms the CEO is linked to through direct connections. Our motivation for this variable is that the CEO could use direct connections with other firms to lower issuance costs by obtaining information, learning best practices, and finding connections to experienced underwriters and security issue lawyers. We proxy for this effect with the variable Firm Network, defined as the number of firms at which people in the CEO’s network are employed, de-trended and logged.

Table 5 presents the results. The estimations show that defining CEO networks in alternative ways yields the same conclusions. Both a larger Combined Network and more Firm

Connections are significantly associated with lower SEO gross spreads and smaller SEO discounts. The results are robust to controlling for direct CEO-underwriter connections and

15 underwriter presence on the firm’s board. The findings suggest that there is a potential for multiple channels through which CEO connectedness can result in lower equity issue costs. We explore this issue next.

3.4. Channels through which CEO networks lower SEO issue costs

We propose multiple channels through which greater CEO connections can lower SEO issuance costs. Firms with more connected CEOs arguably are more visible to institutional investors resulting in increased institutional demand, are more followed by analysts resulting in better information flow and reduced information asymmetry, and are better able to connect with high quality underwriters.

The first channel for lower direct costs of issuing equity is that CEOs with more connections can create extra demand for the offer among potential buyers. This can occur either because the CEO has direct connections to institutional investors, or because connections are directly related to the

CEO’s interpersonal or communications skills, which is helpful in SEO marketing. We test for this channel by examining the impact of CEO connections on two variables related to institutional buying around SEOs. First, we define ∆Institutional Holdings as the percentage of shares held by institutions at the first quarter-end after issuance minus the percentage of shares held by institutions at the quarter-end prior to issuance. In our sample, the average SEO is associated with a 10 percentage point increase in institutional ownership from pre- to post- issuance. Although this measure reflects pre- and post-issue buying (and selling) in addition to

SEO share allocation, the evidence presented by Chemmanur, He, and Hu (2009) suggests that this measure provides a reasonable approximation of a sorting that is based on actual share

16 allocations. Second, we define #New Institutions as the number of institutions that hold shares in the firm after the SEO issue but not prior to the SEO announcement.

A second mechanism for how the size of the CEO network can influence issue costs involves information channels. More connected CEOs may have better interpersonal or communication skills that enhances information flow and transparency in their communications to investors and analysts. We test this information channel by examining analyst following around the SEO. We define ∆Analyst Following as the number of analysts following the firm in the last month of the fiscal year that ends after the issue date minus the number of analysts following the firm in the last month of the fiscal year that ends prior to the announcement date.

In our sample the average SEO results in a gain of a little more than two analysts following the firm.

Finally, CEO connections could facilitate information flow between corporations and therefore lead to the adoption of best practices in operations. More connected CEOs have access to other executives, lawyers, and firms that have experience in conducting SEOs and have already established relationships with various underwriters. It follows that CEOs with larger networks should have access to more reputable underwriters, whose presence can more fully certify the validity of the issuing firm’s stock price, leading to lower issue costs.3 We examine this possible underlying mechanism by exploring the relation between underwriter rank and CEO networks. Underwriter Rank is defined as the underwriter rank on a 0-9 scale downloaded from

Jay Ritter’s website4 and described in Loughran and Ritter (2004). For each SEO we use the highest ranked lead underwriter for this variable. About 55% of sample SEOs employ an

3 Underwriter certification is studied by Chemmanur and Fulghieri (1994), McLaughlin, Safiedine, and Vasudevan (2000), Cooney, Kato, and Schallheim (2003), Pandes (2010), and Autore, Hutton, and Kovacs (2011), among others. 4 We would like to thank Jay Ritter for making the underwriter reputation data available on his website.

17 underwriter with the highest possible rank of 9 and only 9% employ underwriters with a rank 6 or below. There are 45 observations for which we did not find a rank; these are coded as zero.

In Table 6, Panel A presents results of coefficients estimates from regressing

∆Institutional Holding, #New Institutions, ∆Analyst Following, and Underwriter Rank, respectively, on CEO Network. Models (1) – (4) use the controls for gross spreads and models

(5) – (8) use the controls for discounting. The estimations include control variables and fixed effects for issue year and industry. The coefficient estimates on CEO Network are positive and significant at the 5% or 10% level in all models. A one standard deviation increase in CEO

Network increases institutional holdings by 1.2 percentage points and the number of analysts following by 0.47, all else equal. Compared to the gain in an average SEO, these numbers are substantial. Finally, underwriter ranking increases by 0.26 with one standard deviation increase in the CEO Network variable.

Panel B of Table 6 tests for the effect of CEO network via these channels on SEO issuance costs. Specifically, we use the fitted values of the dependent variable from each model in Panel A as our primary explanatory variable to predict issuance costs. For each particular specification, the control variables are the same in the two Panels, and therefore the coefficients on the fitted values from Panel A capture the expected effect of CEO networks on issue costs due to changes in institutional holdings, the number of new institutional investors, the change in analyst following, and underwriter reputation. In the gross spread estimations, the coefficient on each of the fitted predicted variables is negative and significant at the 5% level. In the discounting estimations, each fitted predictive variable enters negatively and is significant at the

1% level. Thus, the results indicate that a greater CEO network reduces SEO issuance costs by increasing the firm’s institutional ownership and analyst following around SEOs, and by utilizing

18 underwriters with more prestigious reputations. The results provide direct evidence consistent with the hypotheses that employing a well-connected CEO (i) helps with marketing and generating demand for the SEO; (ii) helps with information production; and (iii) lowers certification costs due to access to reputable underwriters; all of which lower costs associated with SEO issues. These effects are consistent with our key result that broader CEO connections are associated with lower SEO gross spreads and offer price discounts.

3.5. Additional tests

3.5.1. The effect of the CFO’s connections

So far we have explored the relation between the CEO’s network and equity issue costs.

However, the CEO is not the only influential executive during the SEO process. The Chief

Financial Officer (CFO) of a corporation also takes a major role in shaping the firm’s financing.

Most importantly, the CFO is tasked with a range of important functions from the planning of long-term capital structure strategy all the way to the operative execution of capital raising events. For example, in many cases the CFO helps selecting and serves as immediate contact for the underwriter, prepares SEO documents, contributes to the pricing decision, and is a member of the road show senior management team. Given the important role of the CFO in the SEO process, we test whether the CFO’s connections influence SEO issuance costs.

Table 7 displays results from Gross spread (Models 1-4) and Discount (Models 5-8) estimations while introducing CFO network and CFO direct connections to the underwriter as explanatory variables. CFO network is the (natural logarithm of the de-trended) number of connections the CFO has via past work experience, education, and social organization in the

BoardEx dataset. CFO Direct Connection indicates that the CFO is linked via a connection to the

19 lead underwriter of the SEO. The regressions employ the control variables of Table 3, issue year and industry fixed effects, and some estimations control for CEO network effects. The results indicate that neither the size of the CFO’s network nor any direct connections between the CFO and the underwriter influence issue costs. However, including CFO network variables in the regression does not influence the negative significant relation between CEO network and gross spread, or CEO network and discount. The results indicate that CFO networks are much less important to the costs of issuing compared to CEO networks.

3.5.2. The effect of each component of the CEO’s network

The variable CEO network combines the CEO’s connections from three distinct areas of life: work, education, and social life. A natural question to ask is: Which types of connections matter most for SEO issuance costs?

In Table 8, we address this question by estimating regressions of SEO issuance costs separately for variables measuring work, educational, and social connectedness, respectively.

CEO Work Network is the (natural logarithm of the de-trended) number of people in the

BoardEx database with whom the CEO shared the same employer or served on the same board in or prior to the measure’s estimation year. CEO Educational Network is the (natural logarithm of the de-trended) number of people who attended the same institution, graduated within two years, and earned a similar type of degree (Ph.D., law degree, medical degree, MBA, other graduate degree, bachelor’s degree) as the CEO. This definition follows that in Cohen et al. (2008). CEO

Social Network is the (natural logarithm of the de-trended) number of connections established via non-work and non-educational organizations (e.g. clubs, charities, sporting, or other not-for-

20 profit organizations). Following prior literature, we require that in social organizations both individuals’ roles go beyond membership, except for organizations categorized as social clubs.

In our SEO sample, the average CEO has 90 work-related connections, 22 education- based connections, and 16 social connections. The regression estimations are presented in Table

8 and include all control variables as well as fixed effects to control for issue year and industry- specific variations. All three network variables have a negative coefficient both when predicting

Gross spread and Discount, indicating that greater connectedness points is associated with lower issuance costs. However, not all network coefficients are significant. CEOs with more connections established via common education significantly lowers both the Gross spread and the Discount for the firm’s SEO. The number of the CEO’s work-based connections significantly lowers the Discount, but does not affect significantly the gross spread paid to the underwriter.

Social connections do not significantly affect issue costs. Thus, work and educational connections appear to drive the inverse relation between CEO networks and SEO issuance costs.

4. Additional evidence: Syndicated loan spreads

The evidence presented so far suggests that firms that employ a CEO with a large network are able to obtain cheaper equity financing. A natural question is whether this effect extends to non-equity financing. Engelberg, Gao, and Parsons (2012) present evidence that direct connections between the firm’s CEO and creditors results in lower spreads in syndicated loans due to either better information flow and/or monitoring. In this section we examine whether the

CEO’s general connectedness – not just direct connections to lenders – results in lower syndicated loan spreads.

Table 9 provides estimates from regressing the natural logarithm of syndicated loans’ all in drawn spread on CEO Network. The regressions control for important firm and loan characteristics known in the literature to affect loan spreads, such as return on assets (ROA),

ROA volatility, leverage, fixed assets, loan size, maturity, credit rating, whether the loan is collateralized, performance pricing, the number of covenants, and indicator variables for loan type and purpose. The source for loan specific variables is Dealscan and firm specific variables are taken from Compustat.5 A description of Dealscan is provided in several papers, e.g. Chava and Roberts (2008). We require that each loan facility has a non-missing all-in drawn spread and loan amount. Our sample of loans intersecting BoardEx, Compustat, and Dealscan consists of

11,901 observations. Detailed variable definitions are presented in the Appendix.

In Table 9, the coefficient on CEO Network is negative and significant at the 5% level, suggesting that syndicated loans by firms with CEOs that have broader connections are associated with significantly lower loan spreads. The coefficient on Direct Connection and

Underwriter on Board are both negative, although not significant, in our sample. More importantly, including these variables does not change the sign or significance of the CEO

Network variable. Therefore, the effect of CEO connectedness on loan spreads goes beyond the direct relationship between the loan provider and the firm or its CEO. Examining other variables, larger and more profitable firms have lower loan spreads while firms with higher ROA volatility, worse credit rating, and more leverage pay more for syndicated loans.

The results overall suggest that the CEO network effect affects debt financing costs in addition to equity issuance costs, and that this effect is incremental to the direct connection effect documented by Engelberg, Gao, and Parsons (2012).

5 We would like to thank Michael Roberts for making Dealscan-Compustat links available on his website.

5. Conclusions

This paper documents a strong association between the extent of a CEO’s external connections and the firm’s cost of raising equity. Gross spreads and offer price discounts in seasoned equity offers are significantly reduced given a more extensive CEO network based on social, work, and educational connections. We use instrumental variable estimations, and provide results that are robust to controlling for direct connections with the underwriter employed in the offer.

We report several channels through which CEO connectedness reduces SEO issuance costs: Institutional buying, analyst following, and underwriter rank. We argue that these channels lead to increased demand for newly issued shares, better information flow resulting in reduced information asymmetry, and enhanced underwriter certification, all of which can reduce issuance costs.

Finally, we find that the CEO network effect also applies to debt financing costs as well, and that this effect is separate from a direct firm–lender connection reported by Engelberg, Gao, and Parsons (2012). Overall, our results emphasize the benefits of broader CEO connectedness and provide support for the hypothesis that personal connections facilitate information flow enabling better corporate decision making, which is recognized by investment banks, investors, and lenders.

Appendix A: Variable definitions

Network-related variables

CEO Network: the number of connections a CEO has, except connections the person acquired via the CEO’s corporation after becoming the CEO. For use in regressions, the variable is transformed by regressing it on a trend line, then adding one plus the absolute minimum value of the residual, and finally by taking the natural logarithm of the de-trended sum.

Direct Connection: binary variable indicating that the issuing firm’s CEO has a direct link to an executive or to an executive board member of the lead underwriter(s)/arranger(s).

Underwriter on Board: binary variable indicating that one of the executives or board members of the lead underwriter(s)/arranger(s) serves as a supervisory board member of the issuing firm.

Combined Network: the natural logarithm of the de-trended combined network size of all the CEO’s connections.

Firm Network: the natural logarithm of the de-trended number of firms at which people in the CEO’s network are employed.

CFO Network: the number of connections a CFO has, except connections the person acquired via the CFO’s corporation after becoming the CFO. For use in regressions, the variable is transformed by regressing it on a trend line, then adding one plus the absolute minimum value of the residual, and finally by taking the natural logarithm of the de-trended sum.

CFO Direct Connection: binary variable indicating that the issuing firm’s CFO has a direct link to an executive or to an executive board member of the lead underwriter(s)/arranger(s).

CEO Work Network: the number of people with whom the CEO shared the same employer or served on the same board in or prior to the measure’s estimation year. For use in regressions, the variable is transformed by regressing it on a trend line, then adding one plus the absolute minimum value of the residual, and finally by taking the natural logarithm of the de-trended sum.

CEO Educational Network: the number of people who attended the same institution, graduated within two years, and earned a similar type of degree (Ph.D., law degree, medical degree, MBA, other graduate degree, bachelor’s degree) as the CEO. For use in regressions, the variable is transformed by regressing it on a trend line, then adding one plus the absolute minimum value of the residual, and finally by taking the natural logarithm of the de-trended sum.

CEO Social Network: the number of connections established via non-work and non- educational organizations (e.g. clubs, charities, sporting, or other not-for-profit organizations). In these organizations, we require that both individuals’ roles go beyond membership, except for organizations categorized as social clubs. For use in regressions, the variable is transformed by regressing it on a trend line, then adding one plus the absolute minimum value of the residual, and finally by taking the natural logarithm of the de-trended sum.

Industry Experience: the natural logarithm of the number of industries in which the CEO has worked.

MBA: binary variable indicating that the CEO has earned an MBA degree.

Costs of raising equity

Gross Spread: The gross spread paid in a public equity offering, as a percentage of total proceeds.

Discount: Closing price on the day prior to the offer minus the offer price, scaled by the closing price on the day prior to the offer.

Other dependent variables

∆Institutional Holdings: The percentage of shares held by institutions at the first quarter-end after issuance minus the percentage of shares held by institutions at the quarter-end prior to the SEO announcement. These variables are obtained from Thomson- Reuters 13f institutional filings database.

#New Institutions: The number of institutions that hold shares in the firm after the SEO issue but not prior to the SEO announcement, taken from the Thomson- Reuters 13f institutional filings database.

∆Analyst Following: The number of analysts following the firm in the last month of the fiscal year that ends after the issue date minus the number of analysts following the firm in the last month of the fiscal year that ends prior to the announcement date. This data is collected from the IBES summary files.

Underwriter Rank: The underwriter rank on a 0-9 scale downloaded from Jay Ritter’s website and described in Loughran and Ritter (2004). For each SEO we use the highest ranked lead underwriter for this variable

Control variables

Firm size: natural logarithm of book assets at the end of the fiscal year.

Overnight: binary variable indicating that the offer is conducted overnight, without a prior announcement.

Relative Offer Size: number of shares offered divided by the number of shares outstanding.

# Managers: the natural logarithm of the number of lead underwriters in the SEO.

Missing Underwriter Rank: binary variable indicating that Underwriter Rank is not available.

% of Secondary Shares: secondary shares as a percentage of all shares offered.

Experience: number of SEOs since the firm’s original IPO.

Analyst Following: the natural logarithm of one plus the number of analysts following the firm in the last month of the fiscal year that ends prior to the filing date, taken from the IBES summary files.

180-day Runup: market adjusted buy-and-hold returns over the 180 days prior to the offer.

Volatility: 30-day standard deviation of market model residuals prior to the offer.

Turnover: 250-day average daily volatility divided by the number of shares outstanding.

Nasdaq: binary variable indicating that Nasdaq is the primary trading venue for the firm’s shares.

Negative Pre-5 CAR: negative one times the pre-issue 5-day market adjusted cumulative returns, if the latter is negative, or else, zero.

Positive Pre-5 CAR: pre-issue 5-day market adjusted cumulative returns, if positive, or else, zero.

Integer Pricing: binary variable indicating that the SEO offer price is an integer.

Loan Spread regression variables

Loan Spread: All-in drawn spread in syndicated loans.

ROA Average: income before extraordinary items scaled by the end-of-year book assets, the average from years [-3, 0].

ROA Volatility: income before extraordinary items scaled by the end-of-year book assets, standard deviation from years [-3, 0].

Leverage: the sum of long-term debt and current portion of long-term debt; scaled by end-of- year book assets.

Fixed assets: Net Property, Plant, and Equipment divided by total assets.

Loan Size: natural logarithm of the loan facility’s amount.

Maturity: natural logarithm of the loan’s maturity in months.

Credit Rating: Standard and Poor’s credit ratings coded as numbers according to the following scale: AAA=1; AA=2; A=3; BBB=4; BB=5; B, below, or missing=6.

Not Rated: binary variable indicating that no S&P credit rating is available.

# Covenants: number of covenants attached to the loan.

Performance Pricing: binary variable indicating that the facility employs performance pricing.

Collateral: binary variable indicating that the loan is collaterized.

Loan Type binary variables: Revolving loan; 364-Day Facility.

Loan Purpose binary variables: Backup; Repay; LBO/MBO; Acquisition.

All non-binary variables are winsorized at the 1% level (each side).

References

Ang, J.S., Nagel, G.L., Yang, J., 2012. The effect of social pressures on CEO compensation. Working paper. Florida State University.

Autore, D.M., 2011. Does Rule 10b-21 increase SEO discounting? Journal of Financial Intermediation 20, 231-247.

Autore, D.M., Hutton, I., Kovacs, T., 2011. Accelerated equity offers and firm quality. European Financial Management 17, 835-859.

Autore, D.M., Kovacs, T., 2014. Investor recognition and seasoned equity offers. Journal of Corporate Finance 25, 216-233.

Autore, D.M., Kumar, R., Shome, D.K., 2008. The revival of shelf-registered corporate equity offerings. Journal of Corporate Finance 14, 32-50.

Bethel, J., and Krigman, L., 2008. Managing the costs of issuing common equity: The role of registration choice. Quarterly Journal of Finance and Accounting 47, 57-85.

Bortolotti, B., Megginson, W.L., Smart, S.B., 2008. The rise of accelerated seasoned equity underwritings. Journal of Applied Corporate Finance 20, 35-57.

Chava, S., Roberts, M., 2008. How does financing impact investment? The role of debt covenants. Journal of Finance 63, 2085-2121.

Chemmanur T.J., and Fulghieri, P., 1994. Investment bank reputation, information production, and financial intermediation, Journal of Finance 49, 57-79.

Chemmanur, T.J., He, S., Hu, G., 2009. The role of institutional investors in seasoned public equity offerings. Journal of Financial Economics 94, 384-411.

Chuluun, T., Prevost, A., Puthenpurackal, J., 2014. Board ties and the cost of corporate debt. Financial Management 43, 533-568.

Cohen, L., Frazzini, A., Malloy, C., 2008. The small world of investing: board connections and mutual fund returns. Journal of Political Economy 116, 951-979.

Cooney, J.W., Kato, H.K., and Schallheim, J.S., 2003. Underwriter certification and Japanese seasoned equity issues. Review of Financial Studies 16, 949-982.

Corwin, S., 2003. The determinants of underpricing for seasoned equity offers. Journal of Finance. 58, 2249-2279.

El-Khatib, R., Fogel, K., Jandik, T. 2015. CEO network centrality and merger performance. Journal of Financial Economics 116, 349-382.

Engelberg, J., Gao, P., and Parsons, C.A. 2012. Friends with money. Journal of Financial Economics 103, 169–188.

Faleye, O., Kovacs, T., and Venkateswaran, A. 2014. Do better-connected CEOs innovate more? Journal of Financial and Quantitative Analysis 49, 1201-1225.

Fracassi, C., Tate, G., 2012. External networking and internal firm governance. Journal of Finance 67, 153–194.

Gao, X., and Ritter, J.R., 2010. The marketing of seasoned equity offerings. Journal of Financial Economics 97, 33-52.

House, J.S, Landis, K.R., Umberson, D., 1988. Social relationships and health. Science 241, 540–545.

Hwang, B., Kim, S., 2009. It pays to have friends. Journal of Financial Economics 93, 138–158.

Kramarz, F., Thesmar, D., 2013. Social networks in the boardroom. Journal of the European Economic Association 11, 780-807.

Larcker, D.F., So, E.C., Wang, C.C.Y., 2013. Boardroom centrality and firm performance. Journal of Accounting and Economics 55, 225-250.

Liu, L.X., Sherman, A.E., Zhang, Y., 2014. The long-run role of the media: evidence from initial public offerings. Management Science 60, 1945-1964.

Loughran, T., and Ritter, J.R., 2004. Why has IPO underpricing changed over time? Financial Management 33, 5-37.

McDonald, M.L., Khanna, P., Westphal, J.D., 2008. Getting them to think outside the circle: Corporate governance, CEOs' external advice networks, and firm performance. Academy of Management Journal 51, 453–475.

McLaughlin, R., Safieddine, A., and Vasudevan, G.K., 2000. Investment banker reputation and the performance of seasoned equity issues. Financial Management 29, 96-110.

Merton, R.C., 1987. A simple model of capital market equilibrium with incomplete information. Journal of Finance 42, 483-510.

Pandes, A., 2010. Bought deals: The value of underwriter certification in seasoned equity offerings. Journal of Banking and Finance 34, 1576-1589.

Safieddine, A., Wilhelm, W.J., 1996. An empirical investigation of short-selling activity prior to seasoned equity offerings, Journal of Finance 51, 729-749.

Schonlau, R. J., Singh, P. V., 2009. Board networks and merger performance. Working paper. Brigham Young University and Carnegie Mellon University.

Table 1 SEO frequency and proceeds by year This table presents the annual number of SEOs and total proceeds raised (in million dollars) per year during1997- 2008.

Year SEOs Proceeds (million $) 1997 122 9487 1998 69 9137 1999 116 20041 2000 144 28940 2001 108 14689 2002 104 15366 2003 135 16958 2004 149 20037 2005 121 14582 2006 92 13017 2007 90 17317 2008 59 17436 Total 1309 197012

Table 2 Descriptive statistics This table displays descriptive statistics on issuance costs, firm and SEO traits, and Network-related variables for SEOs. Variables are defined in the Appendix.

N 25th Median Mean 75th Standard Percentile Percentile deviation Gross Spread (%) 1309 4.49 5.07 4.86 5.76 1.33 Discount (%) 1309 0.74 2.33 3.18 4.65 3.25 Proceeds (m$) 1309 53.646 88.708 150.505 156.498 194.965 Relative Offer Size 1309 0.1022 0.1599 0.1858 0.2354 0.1278 Overnight Offer 1309 0 0 0.1535 0 0.36 # Managers 1309 3 5 6.32 8 5.18 % of Secondary Shares 1309 0 0 5.54 4.73 11.22 Experience 1309 1 2 2.12 3 1.45 Firm Size 1309 86.33 266.08 1315.75 847.60 3351.82 Analyst Following 1309 2 4 5.42 7 5.09 180-day Runup 1309 0.0796 0.3436 0.5913 0.7931 0.8371 Volatility 1309 0.0229 0.0326 0.0373 0.0456 0.0206 Turnover 1309 0.0045 0.0078 0.0098 0.0126 0.0078 Nasdaq 1309 0 1 0.66 1 0.47 CEO Network 1309 30 71.00 121.51 161.00 140.78 Direct Connection 1309 0 0 0.0199 0 0.1396 Underwriter on Board 1309 0 0 0.0046 0 0.0676

Table 3 The effect of CEO network on SEO issuance costs This table presents coefficient estimates from OLS fixed effects regressions that explain the SEO gross underwriting spread (Models 1 and 2) and the offer price discount (Models 3 and 4). Gross spread is the compensation paid to the underwriter as a percentage of total proceeds. Discount is the closing price on the day prior to the offer minus the offer price, scaled by the closing price on the day prior to the offer. CEO Network is the de-trended natural logarithm of CEO connections, defined as the total number of individuals with whom the CEO shares a common employment, educational, or social history in the BoardEx universe, excluding connections the person acquired via the CEO’s corporation after becoming the CEO. Direct Connection is a binary variable indicating a direct connection between the firm’s CEO and the lead underwriter. Underwriter on Board indicates that a representative from the lead underwriter sits on the firm’s board. All other variables are defined in the Appendix. The estimations include issue year fixed effects and industry fixed effects (using two-digit SIC codes). Heteroskedasticity-adjusted standard errors, clustered by industry, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Dependent variable Gross spread Gross spread Discount Discount (1) (2) (3) (4) CEO Network -0.0819** -0.0831** -0.2447*** -0.2269*** (0.0403) (0.0405) (0.0826) (0.0833) Direct Connection - -0.025 -1.071* (0.1149) - (0.5589) Underwriter on Board - -0.1948 0.4791 (0.3162) - (1.3008) Overnight Offer -1.0362*** -1.0361*** 1.1639*** 1.1806*** (0.1362) (0.1366) (0.3681) (0.3614) Relative Offer Size 0.8823*** 0.8848*** 3.8152*** 3.7565*** (0.1709) (0.1717) (0.9145) (0.8968) # Managers 0.2858*** 0.2855*** -0.6733*** -0.6744*** (0.0439) (0.0433) (0.0936) (0.0936) % of Secondary Shares -0.005*** -0.0049*** -0.0034 -0.0035 (0.0016) (0.0016) (0.0068) (0.0069) Experience 0.0035 0.0033 0.0637 0.0641 (0.0288) (0.0285) (0.0951) (0.0948) Firm Size -0.4113*** -0.4109*** -0.2547** -0.2591** (0.0213) (0.0209) (0.1258) (0.1258) Analyst Following -0.2204*** -0.22*** -0.1003 -0.0978 (0.0558) (0.0564) (0.1564) (0.1584) 180-day Runup -0.1613*** -0.1608*** 0.0149 0.025 (0.0233) (0.0232) (0.1099) (0.1128) Volatility 4.0851*** 4.0568*** 32.593*** 32.5193*** (1.2430) (1.2656) (6.8183) (6.7640) Turnover -10.1921*** -10.2222*** -7.6403 -8.1607 (2.7704) (2.7884) (14.5366) (14.4102) Nasdaq 0.0825 0.0802 -0.3627* -0.3743* (0.0822) (0.0816) (0.2234) (0.2258) Negative Pre-5 CAR - - 2.8675** 2.9968*** (1.2340) (1.1664) Positive Pre-5 CAR - - 5.2035* 5.2319* (2.7527) (2.8276)

Dependent variable Gross spread Gross spread Discount Discount (1) (2) (3) (4) Integer Pricing - - 1.2348*** 1.2445*** (0.1603) (0.1658) Fixed effects Yes Yes Yes Yes R-square 0.66 0.66 0.23 0.23 N 1309 1309 1309 1309

Table 4 Instrumental variable regressions This table presents second-stage coefficient estimates from instrumental variable (IV) estimations. In the first-stage (not reported) CEO Network is instrumented by MBA and Industry Experience. Columns 1 and 2 provide second- stage estimates from IV regressions of Gross spread and Discount, respectively, on the instrumented (predicted) CEO Network. Gross spread is the compensation paid to the underwriter as a percentage of total proceeds. Discount is the closing price on the day prior to the offer minus the offer price, scaled by the closing price on the day prior to the offer. CEO Network is the de-trended natural logarithm of CEO connections, defined as the total number of individuals with whom the CEO shares a common employment, educational, or social history in the BoardEx universe, excluding connections the person acquired via the CEO’s corporation after becoming the CEO. MBA indicates that the CEO has earned an MBA degree. Industry Experience is the natural logarithm of the number of industries the CEO has worked in. All other variables are defined in the Appendix. The estimations include issue year fixed effects and industry fixed effects (using two-digit SIC codes). Heteroskedasticity-adjusted standard errors, clustered by industry, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Gross spread Discount

(1) (2) * * CEO Network (IV) -0.2563 -0.6346 (0.1498) (0.3727) *** *** Overnight Offer -1.0245 1.1848 (0.1447) (0.3220) *** *** Relative Offer Size 0.8259 3.6572 (0.1737) (0.8934) *** *** # Managers 0.2906 -0.6662 (0.0391) (0.0895) *** % of Secondary Shares -0.0053 -0.0041 (0.0016) (0.0068) Experience 0.0022 0.0628 (0.0246) (0.0817) *** Firm Size -0.3929 -0.2136 (0.0245) (0.1476) *** Analyst Following -0.2010 -0.0556 (0.0512) (0.1346) *** 180-day Runup -0.1593 0.0123 (0.0196) (0.1105) *** *** Volatility 4.1472 33.1195 (1.0967) (6.5635) *** Turnover -9.3271 -5.8393 (2.4374) (13.2476) * Nasdaq 0.0774 -0.3705 (0.0766) (0.2109) ** Negative Pre-5 CAR 5.3186 - (2.6865) *** Positive Pre-5 CAR 2.9671 - (1.1325) *** Integer 1.2450 (0.1583) Fixed effects Yes Yes Hansen J statistic 0.4196 0.1470 [p-value] [0.5171] [0.7014]

Gross spread Discount

(1) (2) Kleibergen-Paap underidentification test 8.1511 8.1976 [p-value] [0.0170] [0.0166] N 1309 1309

Table 5 Alternative definitions of CEO network This table presents coefficient estimates from OLS fixed effects regressions that explain the SEO gross underwriting spread (Columns 1-4) and the offer price discount (Columns 5-8). Gross spread is the compensation paid to the underwriter as a percentage of total proceeds. Discount is the closing price on the day prior to the offer minus the offer price, scaled by the closing price on the day prior to the offer. Combined Network is defined as the natural logarithm of the de- trended combined size of the network of the CEO’s connections. Firm Network is defined as the natural logarithm of the de-trended number of firms at which people in the CEO’s network are employed. Direct Connection is a binary variable indicating a direct connection between the firm’s CEO and the lead underwriter. Underwriter on Board indicates that a representative from the lead underwriter sits on the firm’s board. All other variables are defined in the Appendix. The estimations include issue year fixed effects and industry fixed effects (using two-digit SIC codes). Heteroskedasticity-adjusted standard errors, clustered by industry, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Dependent variable: Gross spread Dependent variable: Discount (1) (2) (3) (4) (5) (6) (7) (8) Combined network -0.09** -0.092** -0.1698** -0.1456* (0.0386) (0.0386) - - (0.0767) (0.0782) - - Firms in network -0.0709** -0.0723** -0.1514** -0.1321* - - (0.0319) (0.0320) - - (0.0740) (0.0775) Direct Connection -0.0108 -0.0179 -1.0844* -1.0855* - (0.1146) - (0.1155) - (0.5583) - (0.5622) Underwriter on Board -0.225 -0.2069 0.5208 0.5286 - (0.3175) - (0.3119) - (1.3071) - (1.3079) Overnight Offer -1.0339*** -1.0339*** -1.0349*** -1.0349*** 1.1628*** 1.1789*** 1.1626*** 1.179*** (0.1359) (0.1365) (0.1365) (0.1370) (0.3731) (0.3670) (0.3724) (0.3660) Relative Offer Size 0.8645*** 0.8675*** 0.8704*** 0.873*** 3.8183*** 3.7642*** 3.8199*** 3.764*** (0.1700) (0.1704) (0.1699) (0.1706) (0.9073) (0.8903) (0.9071) (0.8897) # Managers 0.2886*** 0.2883*** 0.2877*** 0.2874*** -0.6704*** -0.6724*** -0.6709*** -0.6727*** (0.0431) (0.0424) (0.0433) (0.0427) (0.0937) (0.0937) (0.0933) (0.0934) % of Secondary Shares -0.0051*** -0.0051*** -0.0051*** -0.005*** -0.0035 -0.0035 -0.0034 -0.0035 (0.0016) (0.0016) (0.0016) (0.0016) (0.0068) (0.0069) (0.0068) (0.0069) Experience 0.0029 0.0027 0.0028 0.0026 0.0631 0.0637 0.0625 0.0632 (0.0287) (0.0283) (0.0287) (0.0284) (0.0971) (0.0970) (0.0967) (0.0965)

Dependent variable: Gross spread Dependent variable: Discount (1) (2) (3) (4) (5) (6) (7) (8) Firm Size -0.4084*** -0.4078*** -0.4108*** -0.4103*** -0.2584** -0.264** -0.2606** -0.2657** (0.0212) (0.0207) (0.0216) (0.0212) (0.1265) (0.1259) (0.1261) (0.1258) Analyst Following -0.2215*** -0.2211*** -0.2224*** -0.222*** -0.113 -0.1106 -0.1129 -0.1103 (0.0572) (0.0579) (0.0571) (0.0577) (0.1592) (0.1614) (0.1603) (0.1623) 180-day Runup -0.1614*** -0.161*** -0.1606*** -0.1602*** 0.013 0.0231 0.0151 0.025 (0.0235) (0.0234) (0.0235) (0.0233) (0.1097) (0.1124) (0.1101) (0.1127) Volatility 4.1167*** 4.0861*** 4.1259*** 4.0973*** 32.6734*** 32.5938*** 32.7138*** 32.6316*** (1.2472) (1.2715) (1.2487) (1.2714) (6.8507) (6.7982) (6.8733) (6.8188) Turnover -10.061*** -10.0811*** -10.1802*** -10.2061*** -7.7939 -8.3646 -7.9233 -8.4635 (2.7511) (2.7666) (2.7497) (2.7668) (14.5609) (14.4291) (14.5213) (14.3890) Nasdaq 0.0862 0.0839 0.0881 0.0859 -0.3542 -0.3665* -0.3493 -0.3621* (0.0809) (0.0803) (0.0804) (0.0799) (0.2233) (0.2261) (0.2226) (0.2256) Negative Pre-5 CAR 2.8061** 2.9401*** 2.7928** 2.9281*** - - - - (1.2274) (1.1613) (1.2250) (1.1574) Positive Pre-5 CAR 5.1524* 5.1765* 5.1336* 5.1605* - - - - (2.7704) (2.8431) (2.7685) (2.8434) Integer Pricing 1.2297*** 1.2397*** 1.2293*** 1.2394*** - - - - (0.1603) (0.1659) (0.1604) (0.1659) Fixed effects Yes Yes Yes Yes Yes Yes Yes Yes R-square 0.66 0.66 0.66 0.66 0.23 0.23 0.23 0.23 N 1309 1309 1309 1309 1309 1309 1309 1309

Table 6 Channels through which CEO networks affect SEO costs Panel A presents coefficient estimates from fixed effects OLS regressions that explain the change in institutional holdings, the change in analyst following, and the underwriter rank. Panel B uses fitted values from Panel A to estimate their effect on Gross spread and Discount. ∆Institutional Holdings is the difference between the percent of shares held by institutions at the first quarter ending after the SEO issue and the end of the last quarter ending prior the SEO filing. #New Institutions is the number of institutions that hold shares in the firm after the SEO issue but not prior to the SEO filing. ∆Analyst Following is the difference between the number of analysts following the firm in the last month of the fiscal year that ends after the issue date and the number of analysts following the firms in the last month of the fiscal year that ends prior to the filing date, both taken from the IBES summary files. Underwriter Rank is the variable IPO underwriter reputation rank for the highest ranked lead underwriter downloaded from Jay Ritter’s website and is described in Loughran and Ritter (2004). Gross spread is the compensation paid to the underwriter as a percentage of total proceeds. Discount is the closing price on the day prior to the offer minus the offer price, scaled by the closing price on the day prior to the offer. CEO Network is the de-trended natural logarithm of CEO connections, defined as the total number of individuals with whom the CEO shares a common employment, educational, or social history in the BoardEx universe, excluding connections the person acquired via the CEO’s corporation after becoming the CEO. All other variables are defined in the Appendix. The estimations include issue year fixed effects and industry fixed effects (using two-digit SIC codes). Heteroskedasticity-adjusted standard errors, clustered by industry, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: CEO Network’s effect on the change in institutional holdings and analyst following, and on underwriter rank ∆Institutional #New ∆Analyst Underwriter ∆Institutional #New ∆Analyst Underwriter Holdings Institutions Following Rank Holdings Institutions Following Rank (1) (2) (3) (4) (5) (6) (7) (8) CEO 0.0077* 0.0509** 0.3004* 0.1667** 0.0074* 0.0497** 0.3023* 0.1703** Network (0.0043) (0.0213) (0.1660) (0.0728) (0.0044) (0.0203) (0.1638) (0.0713) Overnight -0.0017 0.1406*** 0.3576 -0.2063 -0.0035 0.1331*** 0.3622 -0.207 Offer (0.0120) (0.0467) (0.3286) (0.2273) (0.0116) (0.0473) (0.3380) (0.2273) Relative 0.2822*** -0.1383 -4.0464*** -0.8032 0.2891*** -0.0826 - -0.8552 Offer Size (0.0377) (0.1878) (0.7267) (0.5897) (0.0363) (0.1859) 3.8942*** (0.5949) # -0.0037 0.1117*** 0.8575*** 0.4693*** -0.0027 0.1172*** 0.8699***(0.7466) 0.4767*** Managers (0.0041) (0.0193) (0.1133) (0.0698) (0.0040) (0.0190) (0.1209) (0.0645) % of 0.0009*** 0.0019* 0.0089 -0.0028 0.0009*** 0.0022** 0.0094 -0.0031 Secondary (0.0002) (0.0010) (0.0070) (0.0050) (0.0002) (0.0009) (0.0067) (0.0049) Shares Experience -0.0033 -0.0106 0.1186 -0.0055 -0.0035 -0.0119 0.1147 -0.0069 (0.0028) (0.0142) (0.1433) (0.0529) (0.0030) (0.0155) (0.1426) (0.0543) Firm Size -0.0104*** 0.1667*** 0.2432** 0.3274*** -0.0105*** 0.1653*** 0.2402** 0.3347*** (0.0036) (0.0209) (0.1035) (0.0575) (0.0037) (0.0201) (0.1005) (0.0584) Analyst -0.0141*** -0.0405 -2.1827*** 0.2201*** -0.0139*** -0.0383 - 0.2006*** Following (0.0049) (0.0405) (0.1310) (0.0585) (0.0050) (0.0407) 2.1858*** (0.0612) 180-day 0.0107** 0.161*** -0.0013 0.0515 0.0106** 0.1594*** (0.1322)0.0011 0.0641* Runup (0.0047) (0.0204) (0.1931) (0.0378) (0.0046) (0.0217) (0.1987) (0.0361) Volatility 0.5274*** 0.2101 -1.3024 4.2283*** 0.5525*** 0.4754 -0.1689 4.1722** (0.1766) (0.8539) (5.9683) (1.5671) (0.1687) (0.8303) (5.7654) (1.7655) Turnover -3.486*** 8.497*** 67.0098*** 28.8427*** -3.6538*** 7.5725*** 66.061*** 29.297*** (0.5742) (2.0853) (25.4288) (10.3824) (0.5710) (2.3401) (25.5291) (10.4953)

∆Institutional #New ∆Analyst Underwriter ∆Institutional #New ∆Analyst Underwriter Holdings Institutions Following Rank Holdings Institutions Following Rank (1) (2) (3) (4) (5) (6) (7) (8)

Nasdaq 0.0097 -0.0488 0.2934 0.0513 0.0099 -0.048 0.3027 0.0654 (0.0092) (0.0353) (0.3234) (0.1469) (0.0090) (0.0333) (0.3239) (0.1476) Negative - - - -0.1233 -0.9674*** - 0.896 Pre-5 CAR - (0.0781) (0.3249) 2.4909*** (0.6836) (0.7278) Positive - - - 0.1511* 0.6327** -0.7176 -0.4417 Pre-5 CAR - (0.0856) (0.3106) (2.0530) (1.3278) Integer - - - -0.0005 0.0208 -0.0047 -0.2483** Pricing - (0.0109) (0.0279) (0.1097) (0.1206) Fixed Yes Yes Yes Yes Yes Yes Yes Yes effects R-square 0.27 0.38 0.26 0.25 0.27 0.39 0.27 0.26 N 1307 1309 1309 1309 1307 1309 1309 1309

Panel B: The effect of CEO Network on issue costs through the predicted change in institutional holdings, predicted change in analyst following, and predicted underwriter ranking Dependent variable Gross spread Discount (1) (2) (3) (4) (5) (6) (7) (8) Pred(∆Institutional Holdings) -10.671** - - -33.026*** (5.2606) - (11.1516) - - - Pred(#New Institutions) - -0.5187*** - - -4.9228*** (0.1990) - (1.6622) - - Pred(∆Analyst Following) - - -0.2727** - -0.8093*** (0.1344) - - (0.2732) - Pred(Underwriter Rank) - - - -0.4916** -1.4368*** (0.2423) - - - (0.4851) Overnight Offer -1.0543*** -0.9669*** -0.9387*** -1.1377*** 1.0487*** 1.8193*** 1.457*** 0.8664** (0.1349) (0.1319) (0.1522) (0.1365) (0.3792) (0.3819) (0.3578) (0.4038) Relative Offer Size 3.8942*** 0.8309*** -0.2213 0.4875* 13.3631*** 3.4086*** 0.6636 2.5865*** (1.4854) (0.1565) (0.5789) (0.2658) (3.6486) (0.8755) (1.1321) (0.8606) # Managers 0.246*** 0.3421*** 0.5197*** 0.5166*** -0.7631*** -0.0962 0.0307 0.0117 (0.0551) (0.0541) (0.1049) (0.1034) (0.0920) (0.2337) (0.2736) (0.2676) % of Secondary Shares 0.0047 -0.0039** -0.0026 -0.0064*** 0.0279** 0.0072 0.0041 -0.0079 (0.0045) (0.0015) (0.0016) (0.0019) (0.0129) (0.0079) (0.0074) (0.0069) Experience -0.0313 -0.0016 0.0358 0.0008 -0.0515 0.0049 0.1565 0.0537 (0.0305) (0.0294) (0.0355) (0.0286) (0.0902) (0.0906) (0.1094) (0.0940) Firm Size -0.5226*** -0.3305*** -0.345*** -0.2503*** -0.6013*** 0.5591* -0.0604 0.2261 (0.0629) (0.0359) (0.0349) (0.0775) (0.1296) (0.3481) (0.1651) (0.2446) Analyst Following -0.3706*** -0.2476*** -0.8157*** -0.1122* -0.5592** -0.289* -1.8693*** 0.1878 (0.1060) (0.0581) (0.3157) (0.0637) (0.2273) (0.1727) (0.6273) (0.1786) 180-day Runup -0.047 -0.0792** -0.1617*** -0.136*** 0.364* 0.7997** 0.0158 0.1071 (0.0465) (0.0358) (0.0234) (0.0189) (0.1871) (0.3204) (0.1100) (0.1242) Volatility 9.7128*** 4.1832*** 3.7299*** 6.1639*** 50.8402*** 34.9333*** 32.4563*** 38.5875*** (2.7365) (1.3146) (1.2986) (1.3955) (10.0319) (7.0135) (6.8095) (7.4769) Turnover -47.3918** -6.0276* 8.0846 3.9883 -128.31*** 29.6379 45.8226* 34.4529* (19.1331) (3.2087) (8.8320) (6.9372) (42.4347) (19.7899) (23.8450) (20.9294) Nasdaq 0.1865*** 0.0577 0.1625** 0.1077 -0.0346 -0.599*** -0.1177 -0.2687 (0.0710) (0.0876) (0.0705) (0.0765) (0.2675) (0.2226) (0.2525) (0.2315)

Dependent variable Gross spread Discount (1) (2) (3) (4) (5) (6) (7) (8) Negative Pre-5 CAR - - - -1.2053 -1.8948 0.8516 4.1549*** - (1.9962) (2.1854) (1.5062) (1.2375) Positive Pre-5 CAR - - - 10.1949*** 8.3183** 4.6227* 4.5688* - (3.8075) (3.3510) (2.6723) (2.6655) Integer Pricing - - - 1.2178*** 1.3374*** 1.231*** 0.878*** - (0.1621) (0.1534) (0.1606) (0.2278) Fixed effects Yes Yes Yes Yes Yes Yes Yes Yes R-square 0.66 0.66 0.66 0.66 0.23 0.23 0.23 0.23 N 1309 1309 1309 1309 1309 1309 1309 1309

Table 7 CFO networks This table presents coefficient estimates from OLS fixed effects regressions that explain the SEO gross underwriting spread (Models 1-4) and the offer price discount (Models 5-8). Gross spread is the compensation paid to the underwriter as a percentage of total proceeds. Discount is the closing price on the day prior to the offer minus the offer price, scaled by the closing price on the day prior to the offer. CFO Network is the de-trended natural logarithm of CFO connections, defined as the total number of individuals with whom the CFO shares a common employment, educational, or social history in the BoardEx universe, excluding connections the person acquired via the CFO’s corporation after becoming the CFO. CFO Direct Connection is a binary variable indicating a direct connection between the firm’s CFO and the lead underwriter. CEO Network is the de-trended natural logarithm of CEO connections, defined as the total number of individuals with whom the CEO shares a common employment, educational, or social history in the BoardEx universe, excluding connections the person acquired via the CEO’s corporation after becoming the CEO. CEO Direct Connection is a binary variable indicating a direct connection between the firm’s CEO and the lead underwriter. Underwriter on Board indicates that a representative from the lead underwriter sits on the firm’s board. All other variables are defined in the Appendix. The estimations include issue year fixed effects and industry fixed effects (using two-digit SIC codes). Heteroskedasticity-adjusted standard errors, clustered by industry, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Dependent variable Gross spread Discount (1) (2) (3) (4) (5) (6) (7) (8) CFO Network 0.0097 0.0109 0.0099 0.011 -0.0118 -0.0083 -0.0126 -0.0114 (0.0098) (0.0094) (0.0100) (0.0097) (0.0305) (0.0306) (0.0316) (0.0333) CEO Network - -0.0835** - -0.0846** -0.2434*** -0.226*** (0.0404) (0.0405) - (0.0840) - (0.0852) CEO Direct Connection - - - -0.0161 -1.1733* (0.1183) - - - (0.6267) CFO Direct Connection - - -0.0562 -0.0426 0.1976 0.5165 (0.1247) (0.1331) - - (0.9635) (0.9494) Underwriter on Board - - -0.1015 -0.1675 0.2139 0.2519 (0.2952) (0.3002) - - (1.0500) (1.2872) Overnight Offer -1.0386*** -1.0328*** -1.0381*** -1.0322*** 1.1453*** 1.1612*** 1.1432*** 1.1718*** (0.1323) (0.1373) (0.1336) (0.1387) (0.3831) (0.3659) (0.3729) (0.3537) Relative Offer Size 0.9113*** 0.8806*** 0.9124*** 0.8822*** 3.9077*** 3.8155*** 3.9076*** 3.7639*** (0.1730) (0.1708) (0.1739) (0.1715) (0.9138) (0.9138) (0.9058) (0.8990) # Managers 0.2837*** 0.2863*** 0.2838*** 0.2862*** -0.6807*** -0.6736*** -0.6811*** -0.6779*** (0.0458) (0.0440) (0.0454) (0.0435) (0.0956) (0.0937) (0.0982) (0.0944) % of Secondary Shares -0.0048*** -0.005*** -0.0048*** -0.0049*** -0.0029 -0.0035 -0.003 -0.0036 (0.0015) (0.0015) (0.0016) (0.0016) (0.0068) (0.0068) (0.0068) (0.0069)

Dependent variable Gross spread Discount (1) (2) (3) (4) (5) (6) (7) (8) Experience 0.0045 0.004 0.0045 0.0039 0.0646 0.0633 0.0645 0.0629 (0.0303) (0.0288) (0.0300) (0.0285) (0.1006) (0.0947) (0.1023) (0.0967) Firm Size -0.4233*** -0.4152*** -0.4232*** -0.4149*** -0.2751** -0.2517** -0.275** -0.2544** (0.0221) (0.0207) (0.0218) (0.0204) (0.1160) (0.1204) (0.1142) (0.1192) Analyst Following -0.2312*** -0.222*** -0.2311*** -0.2217*** -0.1268 -0.0993 -0.1271 -0.0958 (0.0575) (0.0555) (0.0578) (0.0561) (0.1646) (0.1587) (0.1647) (0.1605) 180-day Runup -0.1626*** -0.1606*** -0.1623*** -0.16*** 0.009 0.0145 0.008 0.0235 (0.0260) (0.0231) (0.0260) (0.0229) (0.1100) (0.1093) (0.1114) (0.1119) Volatility 4.0796*** 4.0972*** 4.066*** 4.0735*** 32.5712*** 32.5783*** 32.6108*** 32.4472*** (1.3099) (1.2553) (1.3249) (1.2742) (6.8993) (6.8378) (6.9936) (6.9607) Turnover -10.4341*** -10.0548*** -10.4784*** -10.1033*** -8.8029 -7.7406 -8.6639 -8.0674 (2.8781) (2.7413) (2.9034) (2.7727) (14.7277) (14.6603) (15.0781) (14.9005) Nasdaq 0.085 0.0844 0.0827 0.0815 -0.3614* -0.364* -0.3545 -0.3675* (0.0836) (0.0818) (0.0826) (0.0807) (0.2240) (0.2210) (0.2223) (0.2187) Negative Pre-5 CAR - - - - 2.8417** 2.8842** 2.826** 3.0367** (1.2802) (1.2616) (1.3101) (1.2332) Positive Pre-5 CAR - - - - 5.0895* 5.2079* 5.0697* 5.2743* (2.7628) (2.7540) (2.7047) (2.7861) Integer Pricing - - - - 1.2253*** 1.2326*** 1.2232*** 1.2412*** (0.1617) (0.1609) (0.1621) (0.1668) Fixed effects Yes Yes Yes Yes Yes Yes Yes Yes R-square 0.65 0.66 0.65 0.66 0.23 0.23 0.23 0.23 N 1309 1309 1309 1309 1309 1309 1309 1309

Table 8 Components of CEO networks This table presents coefficient estimates from regressing SEO gross underwriting spread (Models 1-3) and the offer price discount (Models 4-6) on components of the CEOs network based on the source of the connections. CEO Work Network is the number of people with whom the CEO shared the same employer or served on the same board in or prior to the measure’s estimation year. CEO Educational Network is the number of people who attended the same institution, graduated within two years, and earned a similar type of degree (Ph.D., law degree, medical degree, MBA, other graduate degree, bachelor’s degree) as the CEO. CEO Social Network is the number of connections established via non-work and non-educational organizations (e.g. clubs, charities, sporting, or other not- for-profit organizations). In these organizations, we require that both individuals’ roles go beyond membership, except for organizations categorized as social clubs. All three network variables are transformed by regressing it on a trend line, then adding one plus the absolute minimum value of the residual, and finally by taking the natural logarithm of the de-trended sum. Gross spread is the compensation paid to the underwriter as a percentage of total proceeds. Discount is the closing price on the day prior to the offer minus the offer price, scaled by the closing price on the day prior to the offer. All other variables are defined in the Appendix. The estimations include issue year fixed effects and industry fixed effects (using two-digit SIC codes). Heteroskedasticity-adjusted standard errors, clustered by industry, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Dependent variables Gross spread Discount (1) (2) (3) (4) (5) (6)

CEO Work Network -0.0674 - - -0.1842** - - (0.0435) (0.0924) CEO Educational Network - -0.0549*** - - -0.1273*** - (0.0190) (0.0386) CEO Social Network - - -0.0314 - - -0.0873 (0.0281) (0.0949) Overnight Offer -1.0442*** -1.0246*** -1.0361*** 1.1417*** 1.1854*** 1.1644*** (0.1341) (0.1401) (0.1341) (0.3743) (0.3674) (0.3702) Relative Offer Size 0.8952*** 0.8473*** 0.9112*** 3.8589*** 3.7474*** 3.9078*** (0.1729) (0.1613) (0.1723) (0.9102) (0.8952) (0.9290) # Managers 0.2839*** 0.2891*** 0.2845*** -0.679*** -0.6672*** -0.677*** (0.0450) (0.0429) (0.0444) (0.0960) (0.0911) (0.0927) % of Secondary Shares -0.0049*** -0.005*** -0.0048*** -0.0033 -0.0034 -0.0028 (0.0016) (0.0016) (0.0015) (0.0069) (0.0068) (0.0068) Experience 0.0026 0.0046 0.0061 0.0612 0.0666 0.0708 (0.0293) (0.0294) (0.0301) (0.0982) (0.0987) (0.1020) Firm Size -0.4134*** -0.4163*** -0.4144*** -0.2625** -0.2714** -0.265** (0.0212) (0.0226) (0.0209) (0.1227) (0.1233) (0.1295) Analyst Following -0.223*** -0.2245*** -0.2276*** -0.1103 -0.1161 -0.1228 (0.0560) (0.0565) (0.0575) (0.1573) (0.1562) (0.1625) 180-day Runup -0.1627*** -0.1585*** -0.1628*** 0.0111 0.0202 0.011 (0.0242) (0.0243) (0.0259) (0.1112) (0.1094) (0.1097) Volatility 4.1245*** 4.034*** 4.046*** 32.7204*** 32.3991*** 32.5464*** (1.2580) (1.2576) (1.2981) (6.8378) (6.8978) (6.7793) Turnover -10.4113*** -9.8417*** -10.2251*** -8.2826 -7.1294 -7.7765 (2.7365) (2.7992) (2.9395) (14.4559) (14.6582) (14.4798) Nasdaq 0.0832 0.087 0.0795 -0.3601* -0.3518 -0.37* (0.0834) (0.0805) (0.0837) (0.2246) (0.2243) (0.2177)

Dependent variables Gross spread Discount Negative Pre-5 CAR - - - 2.858** 2.9695** 2.7738** (1.2387) (1.2045) (1.3070) Positive Pre-5 CAR - - - 5.1127* 5.3761* 5.0695* (2.7277) (2.8595) (2.7788) Integer Pricing - - - 1.2298*** 1.2288*** 1.2295*** (0.1605) (0.1631) (0.1602) Fixed effects Yes Yes Yes Yes Yes Yes R-square 0.66 0.66 0.65 0.23 0.23 0.23 N 1309 1309 1309 1309 1309 1309

Table 9 CEO networks and syndicated loan spreads This table provides estimations testing the effect of CEO connections on the costs of syndicated loan spreads. Loan spread is the all-in drawn spread of syndicated loans. . CEO Network is the de-trended natural logarithm of CEO connections, defined as the total number of individuals with whom the CEO shares a common employment, educational, or social history in the BoardEx universe, excluding connections the person acquired via the CEO’s corporation after becoming the CEO. Direct Connection is a binary variable indicating a direct connection between the firm’s CEO and the lead underwriter. Underwriter on Board indicates that a representative from the lead underwriter sits on the firm’s board. All other variables are defined in the Appendix. The estimations include issue year fixed effects and industry fixed effects (using two-digit SIC codes). Heteroskedasticity-adjusted standard errors, clustered by industry, are reported in parentheses. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively. Dependent variable Ln(Loan Spread) Ln(Loan Spread) CEO Network -0.025** -0.0244** (0.0115) (0.0113) Direct Connection - -0.0282 (0.0540) Underwriter on Board - -0.0357 (0.0640) Firm Size -0.0241** -0.0239** (0.0104) (0.0104) ROA Average -0.8817*** -0.8797*** (0.1271) (0.1276) ROA Volatility 0.4227** 0.4237** (0.1779) (0.1781) Leverage 0.461*** 0.4612*** (0.0405) (0.0405) Fixed assets 0.0092 0.0091 (0.0682) (0.0681) Loan Size -0.0857*** -0.0857*** (0.0096) (0.0096) Maturity -0.0548*** -0.0545*** (0.0144) (0.0143) Credit Rating 0.3156*** 0.3151*** (0.0179) (0.0178) Not Rated -0.3866*** -0.3859*** (0.0444) (0.0440) # Covenants 0.0314*** 0.0315*** (0.0050) (0.0050) Performance Pricing -0.1172*** -0.1176*** (0.0202) (0.0200) Collateral 0.3842*** 0.3841*** (0.0261) (0.0261) Revolving Loan -0.2289*** -0.2285*** (0.0180) (0.0180) 364-Day Facility -0.4478*** -0.4473*** (0.0289) (0.0291)

Dependent variable Ln(Loan Spread) Ln(Loan Spread) Backup -0.2346*** -0.2354*** (0.0186) (0.0185) Repay 0.0031 0.0033 (0.0163) (0.0164) LBO/MBO 0.7098*** 0.71*** (0.0551) (0.0549) Acquisition 0.1332*** 0.1333*** (0.0150) (0.0150) Fixed effects Yes Yes R-square 0.71 0.71 N 11901 11901