The association between corporate general counsel and firm credit risk

Author’s Accepted Manuscript The association between corporate general counsel and firm credit risk Charles Ham, Kevin Koharki

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To appear in: Journal of Accounting and Economics Received date: 6 April 2015 Revised date: 4 November 2015 Accepted date: 5 January 2016 Cite this article as: Charles Ham and Kevin Koharki, The association between corporate general counsel and firm credit risk, Journal of Accounting and Economics, http://dx.doi.org/10.1016/j.jacceco.2016.01.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

The Association Between Corporate General Counsel and Firm Credit Risk∗ CHARLES HAM, Washington University in St.

Louis

KEVIN KOHARKI, Washington University in St.

Louis

Accepted by SP Kothari. We would like to thank SP Kothari (editor), Stephen Ryan (reviewer), Sam Bonsall, Dane Christensen, Richard Frankel, Karl Muller, and Monica Neamtiu for helpful comments. Koharki and Ham would like to the thank the Olin Business School for its nancial support. ∗

The Association Between Corporate General Counsel and Firm Credit Risk

Abstract

This paper examines whether bond market participants alter their credit risk assessments of rms that appoint the corporate general counsel (GC) to senior management. GCs may place less emphasis on their gatekeeping responsibilities upon appointment to senior management, thus potentially resulting in increased rm credit risk. Using changes in rm-level credit ratings and credit default swap spreads to proxy for changes in credit risk, we nd a positive association between GC promotions to senior management and increases in rm credit risk. Additionally, the increased personal liability for GCs under the Sarbanes-Oxley Act only partially mitigates this association.

Keywords: credit risk; credit rating agency; general counsel JEL Classications: K00, G24, M40

Resolving the tension between being a partner to the CEO and the guardian of the company is at the core of being the General Counsel. Ben Heineman, former General Counsel of General Electric Company (Egon Zehnder International, 2011)

1 Introduction This paper examines whether bond market participants' credit risk perceptions change towards rms that appoint the corporate general counsel (GC) to senior management relative to rms that do not make these appointments. Specically, we examine whether rms that promote a GC to senior management (hereafter GC rms) experience increases in overall credit risk, as measured by changes in rm-level credit ratings and changes in credit default swap (CDS) spreads. The GC has traditionally assumed the role of corporate gatekeeper. This internal corporate governance function includes monitoring the rm and its personnel to ensure that both behave conservatively and operate within the bounds of the law (Hamdani, 2003; Jagolinzer et al., 2011; Kim, 2010; Kwak et al., 2012).

In this sense, the GC acts similarly to other

gatekeepers who are responsible for ensuring rms and markets engage in best practices. For instance, auditors ensure nancial statements are presented accurately, safety inspectors ensure rms' products meet critical quality standards, and credit rating agencies ensure nancial securities' risks are adequately represented.

However, breakdowns in these gate-

keeping functions have undermined the health of the economy and increased the skepticism towards these various gatekeepers from engaged parties such as regulators and the media.

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Over time, the importance of corporate GCs has grown. This is due to increased business complexity and regulation which has required GCs to gain a broader understanding of various regulatory and operating environments.

Because of this, the GC's responsibilities include

For example, the S&L scandal of the 1980s, the dot-com bubble of the 1990s, and the real estate bubble of the 2000s were prompted by breakdowns in gatekeeping functions by various parties such as lenders, investment banks, rating agencies, and auditors. 1

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helping to ensure sustained corporate performance and anticipating how changes in the legal environment will impact the company's business over time. Further, Nelson and Nielsen (2000) suggest that internal counsel often seeks to expand the role that law and legal practice play to generate new sources of growth, as well as to gain an economic edge over competition. 2 These factors have led to an increase in GC appointments to senior management positions within their respective rms (Duggin, 2006; Liggio, 2002; Egon Zehnder International, 2011). In addition, GCs have begun to assume advisory and entrepreneurial responsibilities within the rm.3 Recent survey evidence suggests a keen understanding of business management, project management, sales, and marketing are necessary attributes of contemporary GCs (Association for Corporate Counsel, 2015). Ganguin and Bilardello (2005) expand upon this notion by highlighting that rms' credit risk can be impacted by a reliance on GCs who excessively focus on capital raising, rm restructuring, and rm strategy, as well as GCs who allow the rm to become overly aggressive in dealings with suppliers, customers, and other stakeholders. As the GC takes on these new responsibilities, he/she is likely to place less of an emphasis on the gatekeeping functions and more of an emphasis on the facilitating functions, thereby potentially reducing the eectiveness of the GC's internal monitoring. Understanding the composition, ability, integrity, and risk tolerance of senior management is a key attribute of credit risk analysis. This is due to the fact that these characteristics can signicantly impact rms' future performance, nancial stability, and risk proles. 4 However, it remains unclear whether and to what extent the GC's status is incorporated into the rm's credit risk assessments. We address this question by examining whether GC promotions (e.g., from non-senior manager to senior manager) aect bond market participants' credit risk assessments. If bond market participants account for the possibility that GCs reduce their gatekeeping responsibilities in favor of their facilitating functions upon

Prior literature has referred to this brand of lawyering as penumbra lawyering (Picciotto, 1991) or professional innovation (Powell, 1993). 3 These non-gatekeeping functions are commonly referred to as facilitator functions, consistent with Parker et al. (2009), 4 Ganguin and Bilardello (2005) state that it would be an understatement to say that the role of management is pivotal to a company's performance and therefore its credit quality. 2

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obtaining a senior management position, we predict increases in credit risk when the GCs are appointed to senior management. We test our hypothesis by examining whether increases in rm credit risk, as proxied for by changes in rm-level credit ratings and CDS spreads, are positively associated with the appointment of a GC to senior management using a sample of rms from Execucomp over the period 1994  2013. Consistent with our hypothesis, we nd that bond market participants perceive an increase in credit risk when GCs are appointed to senior management. In addition, we nd that bond market participants adjust their credit risk assessments relatively quickly by focusing on the one- and two-year periods immediately before and after a GC is appointed to senior management, controlling for changes in rm-specic characteristics. Collectively, these ndings support the notion that bond market participants anticipate a potential increase in GCs' facilitating role relative to their gatekeeping role when included among senior management, resulting in increased credit risk for these rms. In supplemental analyses, we examine whether greater regulation mitigates the higher credit risk associated with having a GC in top management. There was concern that the facilitating role of attorneys inuenced the corporate accounting scandals of the early 2000s. In response to these concerns, the Securities and Exchange Commission (SEC) adopted Section 307 of the Sarbanes-Oxley Act of 2002 (SOX) to reinforce GCs' responsibilities with respect to rms' governance, nancial, and operating decisions. Under Section 307, GCs are personally liable for corporate negligence and/or malfeasance and must report any such behavior up-the-ladder to the CEO, audit committee, and/or board of directors. 5 Therefore, we examine whether SOX mitigates the concern that GCs reduce or abandon their gatekeeping functions when appointed to senior management, as reected in rms' credit risk. While we nd evidence that SOX partially reduced the greater credit risk associated with having a GC in senior management, a positive association still exists between GC rms and credit risk relative to non-GC rms post-SOX. Given this, it does not appear that the

By March 2005 the SEC had already led enforcement actions against 76 attorneys under SOX Section 307 (Choudhary et al., 2013; Lowenfels et al., 2006). 5

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potential regulatory threat under SOX was enough to alleviate bond market participants' credit risk concerns for GC rms. We also re-examine our primary analyses in a levels, rather than changes, framework. Consistent with our main results, we nd a positive association between having a GC in senior management and rms' overall credit risk relative to non-GC rms. Firms may accept increased credit risk if the foregone benets associated with not appointing a GC to senior management are greater than the costs associated with higher credit risk. For instance, appointing a GC to senior management could reduce the risks associated with increased operating and regulatory complexity. This is particularly relevant given the aforementioned importance rms place on anticipating the impact that regulatory changes will have on rms' future performance and operations. The bond market constitutes a meaningful setting to examine our research question because bond market participants are sophisticated information users with an asymmetric loss function (Beaver et al., 2006; Cantor and Packer, 1995; Holthausen and Leftwich, 1986; Morgan, 2002). As such, bond market participants are likely to be sensitive to dierences in rm risk brought about by promoting a GC to senior management. 6 Further, bond market participants are adept at incorporating soft information, such as assessments of managerial quality and governance risk, when assessing issuers' credit risk (Kraft, 2015; Standard & Poor's, 2006). This is particularly true for credit rating agencies given their exemption from Regulation Fair Disclosure, as this exemption allows agency personnel to routinely meet with and assess rm management in person. Our study contributes to the literature in several ways. There is mixed evidence as to whether GC rms obtain net benets or costs from appointing the GC to senior management. For instance, while Kwak et al. (2012) nd that GC rms issue more conservative and accurate management earnings forecasts, Hopkins et al. (2015) suggest that GC rms are 6 Private discussions with rating agency personnel support this notion, particularly given the failures of certain rms over time that have suered from a lack of institutional control and/or proper internal monitoring (e.g., Enron, Worldcom, among others).

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more likely to engage in earnings management. Although these studies illustrate the impact of having an inuential GC on rm decisions, our study examines how external market participants view this information. Therefore, we provide the rst evidence that sophisticated market participants view GC promotions to senior management more negatively, and thus penalize GC rms with higher (i.e., worse) credit ratings and CDS spreads. Bond market participants state that management composition and character play a vital role in assessing rms' creditworthiness (Ganguin and Bilardello, 2005; Standard & Poor's, 2006). However, limited empirical research has addressed the eect of soft information on credit risk (Kraft, 2015). Therefore, we extend prior literature by highlighting how bond market participants account for certain qualitative rm characteristics when assessing rms' overall credit risk, thus highlighting a key component of credit assessments. Finally, our ndings highlight that increased personal liability under Section 307 of SOX only partially mitigated the increased credit risk associated with having a GC in senior management. This is signicant as it highlights regulators' limitations in assigning personal liability to corporate ocers. It also provides some evidence as to why periods of corporate malfeasance occur despite regulatory changes. Given this, our study has implications for rms as well as market participants such as investors and regulators. Our study is subject to certain caveats. First, we employ an admittedly crude proxy to capture the changing nature of the GC's role in the rm. We argue that as the GC's status within the rm increases (i.e., by promotion to the executive team), he/she is likely to place less (greater) emphasis on his/her gatekeeping (facilitating) functions. To the extent that there is noise in our measure, this should bias against our ndings. Second, the decision to appoint the GC to senior management is a rm choice and thus our results may capture selection eects rather than treatment eects. While we do not claim to demonstrate causality, our research design attempts to alleviate this concern with several tests: we utilize a changes specication, include rm xed eects in the models, and employ a dierence-in-dierence research design around the enactment of SOX. Although the results of these tests support 5

our hypothesis, we acknowledge that selection issues cannot be entirely ruled out from our study. The remainder of the paper is organized as follows. Section 2 discusses the changing nature of the GC's role and develops the hypothesis. Section 3 describes the research design and sample selection procedure. Section 4 reports the empirical ndings, and Section 5 concludes.

2 The Role of the General Counsel and Hypothesis Development 2.1 The changing nature of the role of the general counsel As the rm's primary legal advisor, GCs have traditionally presided as gatekeepers in their rms with an emphasis on preventing rm personnel from acting inappropriately or illegally. In this sense, GCs have served as an internal corporate governance mechanism by monitoring rm behavior (Choudhary et al., 2013; Coee, 2003; Hamdani, 2003). However, over time GCs have assumed more entrepreneurial roles within their companies to help meet certain medium and long-term business goals (DeMott, 2005; Heineman, 2010). For instance, GCs have more recently focused their eorts on ensuring certain business divisions meet key sales goals, advising CEOs in identifying and/or completing key business transactions, and assisting managers engaged in opportunistic earnings management (Hopkins et al., 2015). These varied roles result from both GCs' experience as legal counsel and their increased business acumen. This is particularly relevant as regulatory environments have become more industry-specic over time (Association for Corporate Counsel, 2015). For instance, having appointed Gerald Quirk as General Counsel and Senior VP of Business Operations in May 2015, Tokai Pharmaceutical Inc. stated: Mr. Quirk brings to Tokai more than twenty years of experience advancing the 6

legal and business interests of public biopharmaceutical companies, including development and commercialization of oncology products. . . With responsibility for legal and intellectual property matters, as well as business operations functions, we expect Gerald will contribute signicantly to the advancement of galeterone through Phase 3 development and commercial launch, as well as the expansion of our Androgen Receptor Degrading Agents platform.

Increased business complexity is also causing the GC to reduce or forfeit his/her responsibilities as corporate secretary, a role that was traditionally assumed by the GC. Because today's corporate environment typically requires two individuals for these positions, it is common for contemporary GCs to engage in greater advisory services to the CEO and the board of directors, while the corporate secretary focuses on regulatory and compliance matters (Egon Zehnder International, 2011). GC promotions to senior management have resulted in substantial increases in status and compensation.

Therefore, these promotions create signicant pressure for the GC to both

appease the CEO and uphold the standards of the legal profession (DeMott, 2005; Liggio, 2002).

In fact, the Association for Corporate Counsel demonstrates GCs' strong interest

to further their roles as liaisons to CEOs concerning key strategic business decisions (i.e., 60% of survey respondents), whereas only 14% of survey respondents expressed dedication to compliance issues (Association for Corporate Counsel, 2013, 2015).

This supports the

notion put forth by Nelson and Nielsen (2000) that GCs wish to be seen as condants to senior management, and thus decrease the emphasis placed on their gatekeeping functions. Therefore, GC promotions to the executive team suggest the reduction of a safeguard that previously led to greater oversight.

2.2 Hypothesis development The bond market is comprised of sophisticated market participants with an asymmetric loss function (Beaver et al., 2006; Cantor and Packer, 1995; Holthausen and Leftwich, 1986;

7

Morgan, 2002). Therefore, these participants should be particularly sensitive to potential changes in rm risk proles resulting from changes in the composition of top management. Further, examining the capability, integrity, and risk tolerance of corporate management is an integral part of credit risk analysis. Ganguin and Bilardello (2005) state that an assessment of management should not only account for their nancial and operational prowess, but also for the amount of risk they are willing to take in order to meet their objectives. This suggests that bond market participants expect corporate personnel to act appropriately and rely on rms' internal monitors, including the GC, to ensure that key personnel are in fact doing so. The rating agencies caution that an over reliance on lobbyists or lawyers can create a corporate culture that is overly aggressive (Ganguin and Bilardello, 2005). This suggests that bond market participants are cognizant of the possibility that GCs are more likely to act as facilitators if they are members of the executive team. Of particular concern is the possibility that GCs use their legal expertise to allow their rms to engage in risky behavior on the fringes of legality (DeMott, 2005; Nelson and Nielsen, 2000). For instance, prior research suggests that GCs can eectively assist senior managers in employing incomeincreasing earnings management or tax-reducing activities (Goh et al., 2014; Hopkins et al., 2015). This concern appears warranted as the SEC has gone so far as to emphasize the gatekeeper role in formal complaints led against former GCs. Further, various attorneys recently stated that they consider their role to be in support of their client, going so far as to suggest that they have little to no obligation to market participants (New York City Bar Association, 2006). Given this, bond market participants may perceive GC appointments to senior management as a signicant credit risk increasing event as such appointments could result in reduced gatekeeping functions by GCs. Thus, we state our primary hypothesis (in the alternative form): 7

8

7 Parker et al. (2009) illustrate this facilitating function: [L]awyers may play games with the law, using their considerable expertise in interpreting and manipulating the law to help their clients avoid or evade the eects of the law. 8 Legal scholars consider this to be a signicant event that highlights the importance of GCs adhering to their professional standards as corporate gatekeepers (Lowenfels et al., 2006).

8

H1: A positive association exists between GC promotions to senior management and increases in rm credit risk. However, because senior managers rely on the GC to help navigate the legal environment, internal legal resources can help ensure that senior management decisions are both impactful and appropriate. In addition, GCs are uniquely positioned to ensure that appropriate corporate governance is applied both consistently and eectively (Veasey, 2004). This is particularly true in the aftermath of the dot-com bubble as new regulations increased the responsibilities and potential liability of various corporate personnel, including the GC. Therefore, if bond market participants perceive no material impact in the GC's gatekeeping responsibilities when promoted to senior management, we would fail to nd support for our hypothesis.

3 Research design and sample selection 3.1 Research design

Credit rating agencies and CDS investors are highly sophisticated market participants. While credit rating agencies have access to non-public information and are responsible for assessing rms' overall creditworthiness, CDS investors typically consist of institutional investors. Because bond market participants have an asymmetric loss function, credit rating agencies and CDS investors are likely sensitive to any changes that could potentially alter rms' internal monitoring and/or gatekeeping functions. Given this, we examine our hypothesis using two distinct research designs. We rst examine whether bond market participants alter their assessments of rms' credit risk when the GC is appointed to senior management using a changes analysis. We conduct 9

our analysis over the 1994 - 2013 time period via the following OLS model: 9

ΔCreditRisk t = α0 + α1 Appointmentt + α2 ΔSizet−1 + α3 ΔLeveraget−1 + α4 ΔT angiblet−1 + α5 ΔIntCvg t−1 + α6 ΔROAt−1 + α7 ΔB/M t−1 + α8 ΔStdRett−1 + α9 ΔStdCF F Ot−1 + α10 ΔLitRisk t−1 + α11 Losst−1 + α12 Y OY t−1 + α13 ΔInstOwnt−1 + α14 ΔEIndext−1 + α15 EIndexDumt−1 + ε

(1)

DCreditRisk is either the change in annual average rm-level credit ratings (DRating) or the change in annual average credit default swap spreads (DCDSSpread) from year t-1 where

to year

t. Rating

is Standard and Poor's (S&P) average annual rm-level credit rating,

which takes an ordinal value of 1 for the highest rated rms (e.g., AAA) and 22 for the lowest rated rms (e.g., D), and is thus increasing in credit risk. Because S&P's rm-level

Rating is the average of a rm's assigned CDSSpread is the ve-year CDS spread and is

credit ratings are provided monthly by Compustat, monthly credit rating in a given scal year.

also increasing in credit risk. We use ve-year CDS spreads because these contracts are the most liquid; thus they provide the most reasonable pricing estimate of the default risk for the underlying entity (Micu et al., 2006; Zhang et al., 2009). 10 Because Markit provides daily CDS spreads,

CDSSpread

is the average of a rm's daily CDS spread in a given scal year.

Appointment is an indicator variable equal to one for the years in which the GC is appointed to senior management, and zero otherwise. We identify whether the GC is a member of the senior management team via the annual job titles provided by Execucomp. We search the annual titles of the available executive-rm-years for the words counsel, law, legal, and similar variants to identify executives who are general counsels. We manually search the executives' titles that are captured by this search and exclude the titles that do not refer to

We present OLS estimates for ease of interpretation throughout our paper. Our inferences remain unchanged when we apply an ordered logit model where applicable. We thank an anonymous reviewer for suggesting the examination of CDS spreads. 9

10

10

legal experts such as tax counsel, investment counsel, etc. We also manually search the job titles that are not captured via the search and code these titles accordingly. The primary variable of interest in equation (1) is on

Appointment

to be positive (

a1

Appointment

. We expect the coecient

> 0) if bond market participants increase their assigned

credit risk assessments for rms that appoint GCs to senior management.

To account for

possible time-series and cross-sectional dependence in the regression error terms, standard

DRating

errors are clustered by rm and year (Gow et al., 2010; Petersen, 2009) when

is the

dependent variable. However, because the CDS spread data encompasses a short time period, standard errors are clustered by rm only when

DCDSSpread

is the dependent variable.

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Throughout the analyses we report the results including industry and year xed eects, as well as including rm and year xed eects, regardless of the dependent variable. Equation (1) includes control variables for several rm characteristics. include

DSize

Specically, we

, dened as the change in the natural log of total assets for year t - 1;

DLeverage

,

dened as the change in the ratio of long-term debt scaled by total assets for year t - 1;

DTangible

, dened as the change in the ratio of gross property, plant, and equipment scaled

by total assets for year t - 1;

DIntCvg

, dened as the change in the ratio of annual operating

income after depreciation and amortization scaled by annual interest expense for year t 1;

DROA

, dened as the change in the ratio of annual income before extraordinary items

scaled by total assets for year t - 1;

DB/M

, dened as the change in the ratio of book value of

equity scaled by market value of equity for year t - 1;

DStdRet

, dened as the change in the

standard deviation of monthly stock returns for the 60 months prior to year t;

DStdCFFO

,

dened as the change in the standard deviation of cash ows from operating activities for the ve years prior to year t;

DLitRisk

, dened as the change in predicted litigation risk for

year t - 1 (see Kim and Skinner, 2012);

Loss

, dened as a dummy variable equal to one if

a rm reports a loss in income before extraordinary items in year t-1, and zero otherwise;

YOY 11

, dened as a dummy variable equal to one if a rm reports an increase in year-over-

Our inferences remain unchanged when we cluster the DRating analyses by rm only. 11

year income before extraordinary items in year t-1, and zero otherwise;

DInstOwn, dened

as the change in the percentage of common equity shares held by institutional investors in year t - 1;

DEIndex, dened as the change in the entrenchment index following Bebchuk

et al. (2009); and

EIndexDum,

which is equal to one when a rm-year observation for the

entrenchment index is missing, and zero otherwise. 12 We also examine our research question over pooled short-window time periods around GC appointments to senior management. Specically, we restrict the sample to periods over which the rm had one or two consecutive years without a GC in senior management, followed by one or two consecutive years with a GC in senior management. Given this, we exclude from these analyses rms that never appointed a GC to senior management, as well as rms that maintained a GC in senior management throughout the sample. These pooled short-window event periods allow us to focus on the within-rm changes around GC appointments via the following OLS model:

ΔCreditRisk t = α0 + α1 GC t + α2 ΔSizet−1 + α3 ΔLeveraget−1 + α4 ΔT angiblet−1 + α5 ΔIntCvg t−1 + α6 ΔROAt−1 + α7 ΔB/M t−1 + α8 ΔStdRett−1 + α9 ΔStdCF F Ot−1 + α10 ΔLitRisk t−1 + α11 Losst−1 + α12 Y OY t−1 +

(2)

α13 ΔInstOwnt−1 + α14 ΔEIndext−1 + α15 EIndexDumt−1 + ε

where DCreditRisk is either the change in annual average rm-level credit ratings or the change in annual average credit default swap spread dened.

GC

(

(

DRating)

DCDSSpread), as previously

is an indicator variable equal to one if the general counsel is a member of the

senior management team, and zero otherwise. Our primary variable of interest in equation (2) is

GC.

We expect the coecient on

GC

to be positive (a1 > 0) if bond market partic-

Because the E-Index is missing for several observations we employ a zero-order regression approach consistent with Greene (1993). In untabulated analyses, our inferences also remain unchanged when we use G-Score (Gompers et al., 2003) rather than the E-Index. 12

12

ipants increase their assigned credit risk assessments for rms that include GCs in senior management. All control variables were previously dened.

3.2 Sample selection The initial sample is comprised of all Execucomp rms that have S&P rm-level credit ratings available on the Compustat database over the period 1994  2013. We obtain ve-year CDS spreads from Markit, rm-level nancial information from Compustat, stock price information from CRSP, institutional ownership data from Thomson Reuters, and entrenchment index information from Institutional Shareholder Services.

Consistent with prior research

(Becker and Milbourn, 2011; Dimitrov et al., 2015), we exclude observations from nancial (SIC 6000  6999) and utility (SIC 4000  4999) industries. To limit the inuence of outliers, we winsorize all continuous variables at the top and bottom 1%. This provides a base sample of 9,878 annual rm-level credit rating observations between 1994 and 2013 and 3,031 annual CDS spread observations between 2001 and 2013 after deleting observations with missing control variables.

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Panel A of Table 1 highlights the number of GC and non-GC rms by year. The percentage of GC rms increases nearly monotonically over the sample period from 21% in 1994 to 47% in 2013, consistent with Ham (2015).

This provides support for the notion that GCs

have consistently been promoted to senior management over time, and is consistent with the GC's evolving role in the rm.

Panel B of Table 1 highlights the number of GC appoint-

ments and removals over the sample period. The total number of appointments and removals remains relatively steady throughout the sample period, with no evidence of clustering by year. We note that our classication of GC appointments and removals could be impacted by data ambiguity because Execucomp covers only the most highly paid executives in each rmyear.

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For instance, large one-time bonuses paid to other executives could eliminate GCs'

The Markit database begins in 2001 resulting in a smaller sample size for the CDS spread analyses. 13

inclusion as a top-ve executive in a given year. Given this, we caution that the number of appointments and removals presented could be overstated, though we address this concern when we present our primary results.

4 Empirical ndings 4.1 Descriptive statistics Panel A of Table 2 reports descriptive statistics for the variables used in our analyses. The sample rms have a mean rm-level credit rating of 9.532, which equates to an assigned credit rating between BBB and BBB- on S&P's rating scale. The sample rms have a mean CDS spread of 171.382 basis points.

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As is typical of rms that issue public debt, rms in

our sample are large with average total assets of $10.3 billion (Alissa et al., 2013; Livingston et al., 2007). These rms are modestly leveraged with long-term debt encompassing 25.5% of lagged total assets.

The sample rms exhibit reasonable nancial strength with a mean

interest coverage ratio of 17.145, operating losses 16.4% of the time, and year-over-year increases in income 62.0% of the time. Panel B of Table 2 reports variable mean dierences for GC and non-GC rms.

GC

rms have higher (i.e., worse) credit ratings and higher CDS spreads than non-GC rms. These dierences are statistically signicant below the 0.01 level.

Specically, GC rms

have rm-level credit ratings that are roughly 0.70 rating notches higher than non-GC rms, where one notch reects a dierence of one rating level (e.g., the dierence between A and A-). In addition, GC rms have CDS spreads that are 30.871 basis points higher than non-GC rms. These descriptives suggest that a positive association exists between having a GC in senior management and rm credit risk.

GC rms also appear to be smaller,

have higher leverage and tangible assets, lower protability, a higher likelihood of reporting

The descriptive statistics are consistent with Beaver et al. (2006) who report average credit ratings between BBB- and BB over the 1996  2002 timeframe and Zhang et al. (2009) who report an average CDS spread of 172.40 basis points over the 2001  2003 timeframe. 14

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operating losses, a lower probability of reporting year-over-year increases in income, a higher entrenchment index, and higher stock returns. GC rms also have higher stock return and cash ow volatility, although we note that these dierences, while statistically signicant, do not appear to be economically meaningful. Collectively, these dierences suggest that GC rms exhibit lower nancial performance than non-GC rms, with the caveat that GC rms do not appear to exhibit economically greater rm risk via stock return and cash ow volatility, and no statistically signicant dierence with regard to litigation risk. Panel B of Table 2 also reports variable means at the industry level for GC and non-GC rms.15 For each industry-level variable we compute a yearly average for all rms within the same 2-digit SIC industry, excluding the given rm from the calculation. We examine the mean dierences of industry-level variables to determine if the characteristics of GC rm industries dier from those of non-GC rm industries. GC rm industries exhibit higher credit risk via credit ratings but not CDS spreads. In addition, GC rm industries are composed of rms that are larger, have more leverage and tangible assets, higher protability, higher stock return and cash ow volatility, a greater (lower) propensity for reporting losses (year-over-year increases in income), and a higher entrenchment index. We also include the average annual cumulative abnormal stock return in our industry sample and conclude that GC rm industries exhibit more positive abnormal stock returns compared to non-GC rm industries. Collectively, this descriptive evidence suggests that GC rm industries are comprised of rms with somewhat lower nancial stability, but greater stock return potential. Changes in GC rms' credit risk proles could be impacted by changes in industryspecic characteristics. To address this possibility we examine whether GC rms' credit risk is statistically dierent from those of non-GC rms after deducting industry average credit risk (0.699 minus 0.092 for credit ratings and 30.871 minus -3.372 for CDS spreads). In untabulated analyses, we nd that the dierences for both rm-level credit ratings and CDS spreads between GC rms and non-GC rms are statistically signicant below the 0.05 level. 15

We thank an anonymous reviewer for suggesting the examination of industry-level variables. 15

Collectively, these ndings suggest that the descriptive results presented in Panel B of Table 2 are not driven by changes in GC rm industries' credit risk proles. Panel C of Table 2 restricts the sample to rms that appoint a GC to senior management within the sample period. We then report variable means for the GC and non-GC rmyears, thus highlighting within-rm dierences. Similar to the ndings presented in Panel B, a positive association exists between having a GC in senior management and rm credit risk. Specically, GC rm-years have rm-level credit ratings that are roughly 0.79 rating notches higher than non-GC rm-years, and CDS spreads that are 16.645 basis points higher than non-GC rm-years. These dierences are statistically signicant below the 0.01 and 0.10 levels, respectively. In addition, GC rm-years are smaller, have higher leverage, lower interest coverage ratios, lower protability, are more likely to report operating losses, have lower institutional ownership, higher entrenchment indices, and higher stock returns. While GC rm-years exhibit higher stock return and cash ow volatility, we again note that these dierences do not appear to be economically meaningful. Collectively, these results suggest that GC rms do alter their nancial proles post-GC appointments, with the caveat that key measures of credit risk such as stock return and cash ow volatility, as well as litigation risk, do not meaningfully change over time. Examination of these variables at the industry-level reveals similar evidence to that presented in Panel B. For instance, while GC rm industries exhibit greater credit risk than non-GC rm industries, this result is only statistically signicant for credit ratings. In addition, dierences in rm size, leverage, ROA, standard deviations of stock returns and operating cash ows, the propensities for reporting losses and year-over-year changes in income, higher entrenchment indices, as well as cumulative abnormal returns are statistically signicant. Collectively, these industry characteristics support those reported in Panel B of Table 2 that GC rm industries have somewhat lower nancial stability, but greater stock return potential. Similar to Panel B of Table 2, we examine whether changes in GC rms' credit risk 16

proles are driven by changes in industry-specic characteristics. To address this possibility we examine whether GC rms' credit risk is statistically dierent from those of non-GC rms after deducting industry average credit risk (0.794 minus 0.174 for credit ratings and 16.645 minus 9.508 for CDS spreads). In untabulated analyses, we nd that the dierences for rm-level credit ratings are statistically signicant below the 0.01 level, while the dierences for CDS spreads are indistinguishable from zero. Collectively, these ndings suggest that the descriptive results presented in Panels B and C of Table 2 are not driven by changes in GC rm industries' credit risk proles and thus provide initial evidence for our hypothesis. Table 3 reports annual variable means for the ve-year period surrounding GC appointments for GC rms. This analysis suggests that GC rm credit risk increases over time as GC rms' credit ratings and CDS spreads both increase over the ve-year period. Interestingly, CDS spreads show a considerable increase in the year of and following a GC appointment. For instance, GC rms' CDS spreads increase 27.1% and 20.2% in the year of and immediately following a GC appointment to senior management, respectively, on average. This suggests that bond market participants alter their perceptions of rms' credit risk relatively quickly with regard to GC appointments to senior management. 16 GC rms also experience changes in most rm-level control variables over the ve-year period examined, with the exception of Leverage, IntCvg, StdRet, and StdCFFO. Collectively, this analysis suggests that GC rms experience changes in their assigned credit risk and nancial proles surrounding GC appointments to senior management. Table 3 also reports annual variable means at the industry-level for the ve-year period surrounding GC appointments. These industries appear to experience greater overall credit risk via higher (i.e., worse) credit ratings and CDS spreads over time. However, the increases in industry credit risk are much weaker economically. Specically, IndCDSSpread increases 4.1% (7.6%) from year t-1 to t (t to t+1 ), whereas CDSSpread increases 27.1% (20.2%) from year t-1 to t (t to t+1 ), as previously discussed. The trend in the IndGC variable suggests

We caution that these univariate ndings could also coincide with announced changes in rms' overall strategies. We attempt to reduce this concern in our multivariate tests. 16

17

that there is industry clustering with respect to GC appointments to senior management. However, GC rm industries do not exhibit an overall improvement or decline in most of our control variables over the ve-year period, with the exception of IndSize, IndLeverage, IndROA, IndB/M, IndInstOwn, and IndEIndex. This is not necessarily unique as rms in

our sample are quite large and thus should be relatively stable over time.

4.2 Primary results Our primary analyses examine whether bond market participants respond to GC promotions to senior management. Panel A of Table 4 presents the results from estimating equation (1) over the full sample period. Columns (1) and (2) present the results for the credit rating sample. The coecient on Appointment is positive and statistically signicant below the 0.10 level in column (1) ( a1 = 0.039), and positive and statistically signicant below the 0.05 level in column (2) ( a1 = 0.047). These ndings are consistent with the notion that credit rating agencies view the appointment of GCs to senior management as a credit risk increasing event, resulting in higher (i.e., worse) rm-level credit ratings. Columns (3) and (4) present the results from estimating equation (1) on the CDS spread sample. The coecient on Appointment is positive and statistically signicant below the 0.05 level in columns (3) and (4) ( a1 = 22.620;

a1

= 22.294). These results support those

reported in columns (1) and (2) and suggest that CDS investors perceive GC appointments to senior management as a credit risk increasing event, resulting in higher CDS spreads for GC rms after a GC is appointed to senior management. As previously discussed, the number of appointments and removals may be be overstated due to data ambiguity, which could bias our results.

To address this possibility we re-

estimate equation (1) but now require our Appointment observations to be preceded by three years without a GC listed in senior management (e.g., GC = 0). We label this modied appointment indicator variable Appointment-3pre and present the results in Panel B of Table 4.

18

Columns (1) and (2) present the results for the credit rating sample. The coecient on Appointment-3pre is positive and statistically signicant below the 0.10 level in columns (1) and (2) (a1 = 0.052; a1 = 0.052). Columns (3) and (4) present the results for the CDS spread sample. The coecient on Appointment-3pre is positive and statistically signicant below the 0.01 level in columns (3) and (4) ( a1 = 32.104; a1 = 32.487). These results support those presented in Panel A, and in fact are economically stronger than those in Panel A. Collectively, the results presented in Table 4 suggest that bond market participants perceive the potential for GCs to place greater emphasis on their facilitating functions and less emphasis on their gatekeeping functions post-appointment to senior management, warranting an increase in assigned credit risk. Panel A of Table 5 reports the results from estimating equation (2) on the credit rating sample. Columns (1) and (2) present the results when we pool the one year before and after a GC appointment to senior management, while columns (3) and (4) present the results when we pool the two years before and after a GC appointment to senior management. The coecient on GC is positive and statistically signicant below the 0.05 level in all four columns. These results indicate that credit rating agencies respond relatively quickly to GC promotions to senior management by assigning GC rms less favorable credit ratings post-promotion. Panel B of Table 5 reports the results from estimating equation (2) on the CDS spread sample. As in Panel A of Table 5, columns (1) and (2) present the results when we pool the one year before and after a GC appointment to senior management, while columns (3) and (4) present the results when we pool the two years before and after a GC appointment to senior management. The coecient on GC is positive and statistically signicant below the 0.10 level in all four columns. These results indicate that CDS investors also respond relatively quickly to GC promotions to senior management by increasing GC rms' CDS spreads post-promotion. Collectively, these ndings provide support for our hypothesis and suggest that bond market participants respond relatively quickly by increasing GC rms' 19

assigned credit risk once GCs are appointed to senior management. We also examine GC removals from senior management in a manner consistent with Tables 4 and 5. In untabulated analyses, we fail to nd statistically signicant results for both the credit rating and CDS spread analyses. We note that this is likely due in part or whole to the aforementioned data ambiguity with respect to GC removals. While these results should be interpreted with caution, one interpretation is that rms are unable to reverse the increased credit risk perceptions associated with appointing a GC to senior management. 17

4.3 Supplemental analyses 4.3.1

Impact of Sarbanes-Oxley on rm credit risk

GCs have shouldered increased scrutiny due to the high-prole corporate failures of the early 2000s. In fact, GCs are considered to have been a primary cause of the corporate malfeasance that occurred in the period leading up to the dot-com bubble crash (Cramton, 2003; DeMott, 2005; U.S. Securities and Exchange Commission, 2002). This suggests that GCs have not always met their responsibilities as corporate gatekeepers. Given this history, regulators have increased their eorts to deter inappropriate corporate behavior by re-establishing the GC's gatekeeping functions. In particular, the SEC adopted Section 307 of SOX, which explicitly mandates GCs' professional responsibilities. These responsibilities include matters concerning not only the rm's legal obligations but also those related to the rm's governance, nancial, and operating decisions. One such requirement of SOX 307 requires GCs to report securities law violations or breaches of duciary duty up-the-ladder to the CEO, audit committee, and/or 17 Specically,

the data limitations are more severe for GC removals than appointments. To illustrate this point, we estimate the likelihood that GC appointments (removals) in year t result in a GC being included in (excluded from) senior management in years t+1, t+2, and t+3. In untabulated analyses, we nd a positive and statistically signicant association for GC appointments in all years, while we nd a negative and statistically signicant association for GC removals only in year t+1. This suggests that GC appointments are sticky over time, while GC removals are not. We also examine the proxy statements of 25 randomly sampled rms with a GC removal and nd that only 10-15% of these GC removals are permanent over time.

20

the board of directors, or suer professional and personal ramications. These penalties can aect the GCs themselves, as well as the rms that employ them. If bond market participants perceive that SOX was successful in its attempt to reign in potentially risky behavior by GCs, we expect the aforementioned increased credit risk for GC rms relative to non-GC rms to be mitigated post-SOX. To test this prediction we employ a dierence-in-dierence research design for S&P rm-level credit ratings via the following OLS regression: 18

M Rating t = α0 + α1 P ost + α2 GC t + α3 P ost ∗ GC t + α4 Sizet−1 + α5 Leveraget−1 + α6 T angiblet−1 + α7 IntCvgt−1

+

α8 ROAt−1 + α9 B/M t−1 + α10 StdRett−1 +

α11 StdCF F Ot−1 + α12 LitRisk t−1 + α13 Losst−1 + α14 Y OY t−1 + α15 InstOwnt−1 + α16 EIndext−1 + α17 EIndexDumt−1 + ε

where

(3)

MRating is the rm-level S&P monthly credit rating. Post is an indicator variable

equal to one if the observation occurs after July 30, 2002, the date in which SOX was enacted, and zero otherwise. All other variables were previously dened. Our primary coecient of interest in equation (3) is the interaction variable (

Post*GC ). If the implementation of SOX

alleviates the concern that GCs, once appointed to senior management, focus less on their gatekeeping roles relative to their facilitating roles, we expect a negative coecient on the interaction term ( a3 < 0). Table 6 presents the results from estimating equation (3). 19 The coecient on

Post is

positive and statistically signicant below the 0.05 level in columns (1) and (2) ( a1 = 0.141;

a1

= 0.093). This indicates an increase in credit risk common to both GC and non-GC rms

post-SOX and is consistent with increased overall credit risk post-SOX. The coecient on

We do not conduct this analysis for CDS spreads as our sample begins in 2001 and thus does not provide a large enough pre-period sample. SOX underwent signicant revisions prior to its enactment on July 30, 2002 which could have impacted rms' interpretation of the law. In untabulated analyses, we re-estimate equation (3) and exclude all observations within 2002. Our inferences remain unchanged in these analyses. 18 19

21

GC

is positive and statistically signicant below the 0.01 level in columns (1) and (2) (

0.282;

a2

a2

=

= 0.225). This suggests that GC rms had higher (worse) credit ratings relative to

non-GC rms pre-SOX. The coecient on the interaction term is negative and statistically signicant below the 0.10 level in columns (1) and (2) (

a3

= -0.177;

a3

= -0.171).

These

results support the notion that SOX was at least partially eective at reducing the concern that GCs will limit or abandon their gatekeeping functions, and that this is reected in a reduction in GC rms' credit risk relative to non-GC rms post-SOX. This is likely due to the fact that GCs have greater personal liability post-SOX, which should incentivize GCs to increase the weight placed on their gatekeeping role relative to the weight placed on their facilitating role. While the coecient on the interaction term is negative, we conduct a series of F-tests to determine the net impact of SOX on credit risk for GC rms relative to non-GC rms. The F-test of the sum of the coecients on

Post

and

Post*GC

is statistically insignicant in

columns (1) and (2). This suggests that GC rms were not signicantly aected by the implementation of SOX. Similarly, the F-test of the sum of the coecients on

GC

and

Post*GC

is statistically insignicant in columns (1) and (2). This suggests that the incremental eect of a GC in the post-SOX period did not signicantly alter rms' credit risk with respect to rm-level credit ratings. and

Post*GC

Finally, the F-test of the sum of the coecients on

Post GC ,

,

is positive and statistically signicant below the 0.01 level in columns (1) and

(2). This result captures the association between rm-level credit ratings and GCs in senior management post-SOX. Given this, SOX does not appear to have eliminated the positive association between having a GC in senior management and higher rm-level credit risk. Collectively, these results suggest that credit rating agencies continue to view GC rms cautiously relative to non-GC rms with respect to overall credit risk even after new regulations were implemented to hold corporate GCs more accountable for acts of corporate malfeasance.

22

4.3.2

Levels analyses

We extend our primary analyses by examining the association between having a GC in senior management and rm credit risk in a levels framework. This analysis provides additional support for our primary analyses because rm-level credit ratings (CDS spreads) are obtained monthly (daily), resulting in a signicantly larger, and potentially more powerful, sample size. We re-estimate equation (2) over our full sample period, replacing DRating with MRating and replacing DCDSSpread with MCDSSpread to reect monthly observations. Firm-level control variables are calculated on a trailing twelve month basis in the quarter immediately preceding that of the credit rating or CDS spread observation. The results are reported in Table 7. Columns (1) and (2) of Table 7 report the results from estimating the augmented version of equation (2) on the credit rating sample. The coecient on GC is positive and statistically signicant below the 0.05 level in columns (1) and (2) ( a = 0.161; a = 0.107), suggesting that a positive association exists between having a GC in senior management and rmlevel credit ratings. Columns (3) and (4) of Table 7 report the results from estimating the augmented version of equation (2) on the CDS spread sample. The coecient on GC is positive and statistically signicant below the 0.10 level in columns (3) and (4) ( a = 9.046; a = 13.982), suggesting that a positive association exists between having a GC in senior management and CDS spreads. Collectively, the results reported in Table 7 suggest that bond market participants perceive GC rms as bearing greater overall credit risk than non-GC rms, providing additional support for our primary analyses. However, we note that our levels analyses suer form reduced causal inferences and more severe self-selection issues as the decision to appoint a GC to senior management is a rm choice, as previously mentioned. 20

1

1

1

1

21

We compute average CDS spreads for each month in our sample period. To alleviate this concern, in untabulated analyses we employ a propensity score matching design for our Table 7 analyses and our inferences remain unchanged. 20 21

23

5 Conclusion This paper examines whether bond market participants' credit risk perceptions change towards rms that promote the general counsel to senior management relative to rms that do not make these promotions. Specically, we examine whether rms that promote a GC to senior management experience increases in overall credit risk, as measured by changes in rm-level credit ratings and changes in CDS spreads. While traditionally a corporate gatekeeper, GCs' responsibilities have increased over time as rms have become more global and regulatory environments have expanded. This has led to a greater propensity for GC promotions to senior management, resulting in GCs also assuming more entrepreneurial and advisory roles to the CEO and board of directors. These actions potentially strain GCs' eectiveness as internal monitors and gatekeepers (Egon Zehnder International, 2011). Recent survey evidence supports this notion as GCs seek to act as advisors to senior managers, rather than act as corporate gatekeepers (Association for Corporate Counsel, 2015). Therefore, bond market participants may increase rms' assigned credit risk once GCs are promoted to senior management. This is particularly relevant when one considers that bond market participants have an asymmetric loss function (Beaver et al., 2006; Cantor and Packer, 1995; Holthausen and Leftwich, 1986; Morgan, 2002), and thus should be especially sensitive to potential reductions in rms' monitoring functions. Using changes analyses for a sample of rms during the 1994  2013 period, we nd that credit rating agencies and CDS investors perceive an increase in GC rms' credit risk relative to non-GC rms upon appointment of a GC to senior management. We also show that bond market participants respond to appointments in the one- and two-year periods immediately following a GC appointment, suggesting that these participants do not perceive GC rms' credit risk as increasing slowly over time, but rather relatively quickly. In supplemental analyses we examine whether SOX aected bond market participants' perceptions of GC rms' credit risk relative to non-GC rms. This is signicant as the SEC criticized the role of corporate in-house counsel in the various corporate failures that 24

occurred during the early 2000s, and thus implemented rules (e.g., SOX Section 307) that increased the personal liability of corporate lawyers for engaging in improper conduct. While our regression estimates suggest a slight reduction in GC rms' credit risk relative to non-GC rms post-SOX, a positive association between having a GC in senior management and rmlevel credit risk still exists in the post-SOX period. Finally, we conduct a levels analysis and nd results consistent with our changes analyses. Specically, we nd a positive association between having a GC in senior management and rm-level credit risk. Extant literature on the GC's impact on rm activities is currently mixed. We build upon this literature by being the rst to highlight how market participants interpret the eects of appointing a GC to a senior management position. Our study also provides insight to the credit rating literature, which has only recently begun to examine the role of soft information used during the credit rating and bond investment processes. By highlighting how bond market participants respond to potential changes in rms' internal monitoring and gatekeeping functions, our study provides evidence as to how sophisticated information users interpret the potential impact of key changes in management composition. Finally, our study sheds light on the perception of SOX's mandate to hold internal counsel accountable for corporate malfeasance. Given this, our ndings should be of signicance to regulators, market participants, and academic researchers. We note that our study has certain limitations. First, because we employ an admittedly crude proxy to capture the changing role of GCs within rms, our measure could suer from considerable noise. Second, our results may be capturing selection eects rather than treatment eects because the decision to appoint a GC to senior management is a rm choice. While we do not claim to demonstrate causality, our research design and certain of our supplemental analyses attempt to alleviate this concern. Although the results of these tests support our hypothesis, we acknowledge that selection issues cannot be entirely ruled out from our study.

25

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28

Appendix  Variable denitions Rating

M Rating

Average annual rm-level credit rating, which takes an ordinal value of 1 for the highest rated rms (e.g., AAA on Standard and Poor's rating scale) and 22 for the lowest rated rms (e.g., D). Monthly rm-level credit rating, which takes an ordinal value of 1 for the highest rated rms (e.g., AAA on Standard and Poor's rating scale) and 22 for the lowest rated rms (e.g., D).

CDSSpread

Average annual ve-year credit default swap spread.

M CDSSpread

Monthly ve-year credit default swap spread.

Appointment

Indicator variable that equals one in the years in which a GC is appointed to a senior management position, and zero otherwise. We identify whether the GC is on the top management team via the annual job titles provided by Execucomp. We search the annual titles of the available executive-rm-years for the words counsel, law, legal, and similar variants to identify executives who are general counsels. We manually search the executives' titles that are captured by this search and exclude the titles that do not refer to legal experts such as tax counsel, investment counsel, etc. We also manually search the job titles that are not captured via the search and code these titles accordingly.

Appointment − 3pre

Indicator variable that equals one in the years in which a GC is appointed to a senior management position, and zero otherwise. This modied appointment indicator variable only equals one when preceded by three years without a GC in senior management.

GC

Size Leverage T angible IntCvg

Indicator variable equal to one if the general counsel is a member of the top management team, and zero otherwise. Natural log of total assets for year t  1 (not logged in Panel A of Table 2). Ratio of long-term debt scaled by total assets for year t - 1. Ratio of gross property, plant, and equipment scaled by total assets for year t - 1. Ratio of annual operating income after depreciation and amortization scaled by annual interest expense for year t - 1. 29

ROA B/M

StdRet StdCF F O LitRisk

Loss

Y OY

InstOwn EIndex

Ratio of annual income before extraordinary items scaled by total assets for year t - 1. Ratio of book value of shareholders' equity scaled by market value of equity for year t 1. Standard deviation of monthly stock returns for the 60 months prior to year t. Standard deviation of operating cash ows over the ve years prior to year t. Litigation risk is measured as the predicted value from following Model 3 in Table 7 of Kim and Skinner (2012). Indicator variable that equals one if the rm reports a loss in income before extraordinary items for year t - 1, and zero otherwise. Indicator variable that equals one if the rm reports an increase in annual income before extraordinary items for year t - 1, and zero otherwise. Percentage of common equity shares held by institutional investors in year t - 1. Entrenchment index following Bebchuk et al. (2009).

EIndexDum

Indicator variable equal to one if the entrenchment index is missing, and zero otherwise.

Rtn IndRating

Average annual cumulative abnormal return. Average annual credit ratings for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndCDSSP read

Average annual credit default swap for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndSize

Average annual rm size for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndLeverage

Average annual leverage ratio for rms in a two-digit SIC industry, excluding the given rm from the calculation. 30

IndT angible

Average annual tangible assets ratio for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndIntCvg

IndROA

IndB/M

IndStdRet

Average annual interest coverage ratio for rms in a two-digit SIC industry, excluding the given rm from the calculation. Average annual return on assets for rms in a two-digit SIC industry, excluding the given rm from the calculation. Average annual book-to-market ratio for rms in a two-digit SIC industry, excluding the given rm from the calculation. Average annual standard deviation of monthly stock returns for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndStdCF F O

Average annual standard deviation of cash ow from operating activities for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndLitRisk

Average annual litigation risk for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndLoss

IndY OY

Average annual Loss for rms in a two-digit SIC industry, excluding the given rm from the calculation. Average annual YOY for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndInstOwn

Average institutional ownership for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndEIndex

Average annual entrenchment index for rms in a two-digit SIC industry, excluding the given rm from the calculation.

IndRtn

Average annual cumulative abnormal return for rms in a two-digit SIC industry, excluding the given rm from the calculation.

31

Table 1 Panel A: GC Firms by Year 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Total

GC Firms 80 89 121 125 137 143 156 172 192 199 213 243 232 214 215 222 225 239 253 106 3,576

21% 23% 28% 28% 29% 30% 32% 34% 35% 35% 37% 43% 41% 39% 40% 41% 42% 45% 46% 47% 36%

Non-GC Firms 302 303 316 328 333 331 326 335 357 371 359 321 335 331 317 314 314 297 293 119 6,302

79% 77% 72% 72% 71% 70% 68% 66% 65% 65% 63% 57% 59% 61% 60% 59% 58% 55% 54% 53% 64%

Total Firms 382 392 437 453 470 474 482 507 549 570 572 564 567 545 532 536 539 536 546 225 9,878

Panel A of Table 1 presents the number of GC rms and non-GC rms in our analyses by year.

32

Table 1  continued Panel B: GC Appointments/Removals by Year 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Total

Appointments 2 24 28 35 30 39 45 38 45 37 46 60 43 45 48 41 39 59 50 36 790

67% 55% 67% 56% 52% 57% 59% 51% 57% 47% 55% 65% 48% 51% 55% 52% 51% 60% 55% 72% 56%

Removals 1 20 14 28 28 29 31 36 34 41 37 32 47 44 39 38 38 39 41 14 631

33% 45% 33% 44% 48% 43% 41% 49% 43% 53% 45% 35% 52% 49% 45% 48% 49% 40% 45% 28% 44%

Total Changes 3 44 42 63 58 68 76 74 79 78 83 92 90 89 87 79 77 98 91 50 1,421

Panel B of Table 1 presents the number of GC appointments and removals from senior management in our analyses by year. Please refer to the Appendix for variable denitions.

33

Table 2 Panel A: Descriptive Statistics Rating t CDSSpreadt GC t Sizet−1 Leveraget−1 T angiblet−1 IntCvg t−1 ROAt−1 B/M t−1 StdRett−1 StdCF F Ot−1 LitRisk t−1 Losst−1 Y OY t−1 InstOwnt−1 EIndext−1

N Mean Median Std Dev 25th Percentile 75th Percentile 9,878 9.532 9.667 3.339 7.000 12.000 3,031 171.382 74.542 464.365 40.110 168.112 9,878 0.385 0.000 0.487 0.000 1.000 9,878 10,297.440 3,340.240 37,630.530 1,531.180 8,746.850 9,878 0.255 0.237 0.162 0.147 0.340 9,878 0.595 0.514 0.408 0.291 0.832 9,878 17.145 5.231 290.432 2.431 10.620 9,878 0.040 0.049 0.096 0.018 0.081 9,878 0.463 0.416 1.364 0.259 0.635 9,878 0.114 0.103 0.053 0.077 0.137 9,878 0.039 0.030 0.034 0.020 0.048 9,878 0.362 0.287 0.258 0.152 0.521 9,878 0.164 0.000 0.370 0.000 0.000 9,878 0.620 1.000 0.485 0.000 1.000 9,878 0.349 0.405 0.390 0.000 0.736 9,878 1.163 1.000 1.351 0.000 2.000

Panel A of Table 2 presents descriptive statistics for the variables used in our analyses. Please refer to the Appendix for variable denitions.

34

Table 2  continued Panel B: Mean Dierences Across GC and Non-GC Firms N

Rating t CDSSpreadt Sizet−1 Leveraget−1 T angiblet−1 IntCvg t−1 ROAt−1 B/M t−1 StdRett−1 StdCF F Ot−1 LitRisk t−1 Losst−1 Y OY t−1 InstOwnt−1 EIndext−1 Rtnt−1 IndGC t IndRating t IndCDSSpreadt IndSizet−1 IndLeveraget−1 IndT angiblet−1 IndIntCvg t−1 IndROAt−1 IndB/M t−1 IndStdRett−1 IndStdCF F Ot−1 IndLitRisk t−1 IndLosst−1 IndY OY t−1 IndInstOwnt−1 IndEIndext−1 IndRtnt−1

3,576 1,273 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 1,273 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576 3,576

GC Firms 9.978 189.287 8.223 0.271 0.609 20.052 0.036 0.445 0.117 0.041 0.358 0.188 0.609 0.334 1.189 0.101 0.457 10.967 177.293 5.070 0.375 0.595 4.370 0.018 0.480 0.157 0.344 0.340 0.426 0.516 0.169 1.269 0.047

N

6,302 1,758 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 1,758 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302 6,302

Mean Non-GC Firms Dierence t-stat 9.279 158.416 8.270 0.246 0.587 15.496 0.043 0.473 0.112 0.038 0.365 0.150 0.627 0.358 1.149 0.097 0.334 10.875 180.665 4.996 0.321 0.579 13.046 0.014 0.492 0.155 0.299 0.342 0.407 0.521 0.175 1.195 0.035

0.699*** 30.871*** -0.047* 0.025*** 0.022*** 4.556 -0.007*** -0.023 0.005*** 0.003*** -0.007 0.038*** -0.018* -0.024 0.040*** 0.004** 0.123*** 0.092*** -3.372 0.074*** 0.054*** 0.016*** -8.676 0.004*** -0.012 0.002* 0.035*** -0.002 0.019*** -0.005** -0.006 0.074*** 0.012**

10.412 2.701 1.772 7.397 2.682 0.767 3.542 1.005 5.132 4.860 1.256 5.066 1.784 0.617 5.557 2.124 37.998 3.489 0.925 3.740 3.012 3.074 1.549 3.260 1.281 1.924 4.180 0.634 6.299 2.412 1.517 4.376 2.508

Panel B of Table 2 presents mean dierences for the variables used in our analyses for GC rms and non-GC rms. *, **, and *** denote statistical signicance at the 0.10, 0.05, and 0.01 levels, respectively. Please refer to the Appendix for variable denitions.

35

Table 2  continued Panel C: Mean Dierences for GC Firms Before/After GC Appointment

Rating t CDSSpreadt Sizet−1 Leveraget−1 T angiblet−1 IntCvg t−1 ROAt−1 B/M t−1 StdRett−1 StdCF F Ot−1 LitRisk t−1 Losst−1 Y OY t−1 InstOwnt−1 EIndext−1 Rtnt−1 IndGC t IndRating t IndCDSSpreadt IndSizet−1 IndLeveraget−1 IndT angiblet−1 IndIntCvg t−1 IndROAt−1 IndB/M t−1 IndStdRett−1 IndStdCF F Ot−1 IndLitRisk t−1 IndLosst−1 IndY OY t−1 IndInstOwnt−1 IndEIndext−1 IndRtnt−1

N GC Firms 3,105 9.674 1,128 174.757 3,105 8.355 3,105 0.262 3,105 0.604 3,105 9.169 3,105 0.039 3,105 0.454 3,105 0.113 3,105 0.040 3,105 0.356 3,105 0.171 3,105 0.616 3,105 0.355 3,105 1.237 3,105 0.097 3,105 0.454 3,105 10.945 1,128 160.908 3,105 5.075 3,105 0.366 3,105 0.590 3,105 4.169 3,105 0.019 3,105 0.287 3,105 0.157 3,105 0.344 3,105 0.341 3,105 0.427 3,105 0.516 3,105 0.180 3,105 1.302 3,105 0.050

N Non-GC Firms 3,828 8.880 1,173 158.112 3,828 8.445 3,828 0.243 3,828 0.593 3,828 14.578 3,828 0.044 3,828 0.427 3,828 0.106 3,828 0.036 3,828 0.355 3,828 0.146 3,828 0.627 3,828 0.366 3,828 1.144 3,828 0.093 3,828 0.334 3,828 10.771 1,173 151.400 3,828 4.967 3,828 0.309 3,828 0.585 3,828 3.177 3,828 0.014 3,828 0.428 3,828 0.155 3,828 0.291 3,828 0.335 3,828 0.412 3,828 0.520 3,828 0.183 3,828 1.198 3,828 0.039

Mean Dierence 0.794*** 16.645* -0.090*** 0.019*** 0.011 -5.409*** -0.005*** 0.027 0.007*** 0.004*** 0.001 0.025*** -0.011 -0.011*** 0.093*** 0.004*** 0.120*** 0.174*** 9.508 0.108*** 0.057*** 0.005 0.992 0.005*** -0.141 0.002** 0.050*** 0.006 0.015*** -0.004* -0.003 0.104*** 0.011**

t-stat 9.984 1.782 2.930 5.223 1.227 2.641 2.686 0.770 6.162 4.852 0.127 2.976 0.905 3.141 7.363 2.810 31.554 5.645 0.287 4.782 2.776 0.894 0.160 3.622 0.515 2.492 4.781 1.633 4.447 1.670 1.375 12.891 2.153

Panel C of Table 2 presents mean dierences for the variables used in our analyses for GC rms both before and after a GC is appointed to senior management. *, **, and *** denote statistical signicance at the 0.10, 0.05, and 0.01 levels, respectively. Please refer to the Appendix for variable denitions.

36

Table 3: Trend in Key Variables Surrounding GC Appointments Rating t CDSSpreadt Sizet−1 Leveraget−1 T angiblet−1 IntCvg t−1 ROAt−1 B/M t−1 StdRett−1 StdCF F Ot−1 LitRisk t−1 Losst−1 Y OY t−1 InstOwnt−1 EIndext−1 Rtnt−1 IndGC t IndRating t IndCDSSpreadt IndSizet−1 IndLeveraget−1 IndT angiblet−1 IndIntCvg t−1 IndROAt−1 IndB/M t−1 IndStdRett−1 IndStdCF F Ot−1 IndLitRisk t−1 IndLosst−1 IndY OY t−1 IndInstOwnt−1 IndEIndext−1 IndRtnt−1

t-2

t-1

t

t+1

t+2

9.328

9.449

9.510

9.678

9.660

129.784

138.580

176.132

211.686

192.194

8.347

8.361

8.304

8.425

8.475

0.254

0.260

0.259

0.267

0.258

0.584

0.594

0.597

0.600

0.620

11.153

4.425

17.501

10.268

11.412

0.044

0.035

0.040

0.036

0.032

0.480

0.299

0.436

0.438

0.426

0.108

0.110

0.113

0.113

0.113

0.037

0.038

0.039

0.041

0.042

0.366

0.364

0.359

0.354

0.382

0.149

0.194

0.167

0.202

0.205

0.615

0.578

0.618

0.596

0.585

0.319

0.335

0.349

0.358

0.351

1.173

1.198

1.213

1.225

1.257

0.099

0.097

0.098

0.098

0.097

0.274

0.271

0.340

0.332

0.340

10.700

10.733

10.800

10.859

10.886

217.028

226.779

236.000

253.853

234.573

4.745

4.783

4.834

4.863

4.890

0.283

0.294

0.339

0.318

0.321

0.571

0.571

0.560

0.567

0.561

30.903

28.626

27.443

22.963

30.427

0.014

0.014

0.016

0.017

0.017

0.401

0.488

0.599

0.535

0.556

0.159

0.160

0.160

0.160

0.160

0.316

0.327

0.329

0.346

0.331

0.341

0.338

0.344

0.353

0.346

0.432

0.434

0.438

0.436

0.442

0.516

0.513

0.513

0.517

0.517

0.165

0.172

0.179

0.185

0.190

1.177

1.234

1.255

1.309

1.367

0.041

0.033

0.059

0.048

0.046

Table 3 examines the trend in rm-level and industry-level variables used in our analyses over the ve-year period surrounding a GC appointment to senior management. Industry-level variables apply to GC rm industries. All variables are calculated as means. Please refer to the Appendix for variable denitions.

37

Table 4: Relation Between General Counsel Appointments and Changes in Credit Risk Panel A: Full Sample ΔRating t

ΔRating t

ΔCDSSpreadt

ΔCDSSpreadt

Industry Fixed Eects Firm Fixed Eects Year Fixed Eects

YES NO YES

NO YES YES

YES NO YES

NO YES YES

Observations Adjusted R2

9,338 0.149

9,338 0.257

2,886 0.281

2,886 0.357

Appointmentt ΔSizet−1 ΔLeveraget−1 ΔT angiblet−1 ΔIntCvg t−1 ΔROAt−1 ΔB/M t−1 ΔStdRett−1 ΔStdCF F Ot−1 ΔLitRisk t−1 Losst−1 Y OY t−1 ΔInstOwnt−1 ΔEIndext−1 EIndexDumt−1

(1) 0.039∗ (0.070) -4.325∗∗∗ (0.000) 0.029∗∗ (0.010) -0.475∗∗∗ (0.000) -0.017∗ (0.064) -0.000 (0.991) -0.019∗ (0.097) -0.786 (0.135) 0.077 (0.807) 0.061∗∗∗ (0.000) 0.485∗∗∗ (0.000) 0.050∗∗ (0.016) 0.011 (0.898) -0.047∗∗ (0.025) 0.005 (0.751)

(2) 0.047∗∗ (0.011) -3.044∗∗∗ (0.000) 0.031∗∗∗ (0.008) -0.375∗∗∗ (0.000) -0.014 (0.128) 0.001 (0.804) -0.032∗∗ (0.023) -0.385 (0.704) 0.495 (0.301) 0.056∗∗∗ (0.000) 0.484∗∗∗ (0.000) 0.070∗∗∗ (0.003) 0.066 (0.456) -0.043 (0.151) -0.088 (0.134)

(3) 22.620∗∗ (0.010) -430.929∗∗ (0.047) 13.535∗∗ (0.011) 41.431 (0.249) -0.684 (0.872) -0.706 (0.595) 17.384∗∗ (0.023) -132.710∗ (0.056) 56.734 (0.620) -6.781 (0.165) 98.339∗∗∗ (0.000) -7.181 (0.111) -33.765 (0.463) -6.741 (0.306) 7.159 (0.127)

(4) 22.294∗∗ (0.024) -385.375 (0.116) 10.850∗ (0.064) 47.302 (0.255) 1.604 (0.752) -0.982 (0.520) 16.253∗∗ (0.035) -414.786∗∗ (0.016) 66.775 (0.721) -7.369 (0.169) 101.500∗∗∗ (0.000) -4.796 (0.347) -48.976 (0.407) -2.417 (0.752) 35.401 (0.579)

Panel A of Table 4 examines the relation between GC appointments to senior management and changes in credit risk for the full sample. To limit the inuence of outliers, we winsorize all continuous variables at the

38

1st and 99th percentiles. In columns (1) and (2) standard errors are clustered by rm and year, while in columns (3) and (4) standard errors are clustered by rm. *, **, and *** denote statistical signicance at the 0.10, 0.05, and 0.01 levels, respectively. Please refer to the Appendix for variable denitions.

39

Table 4  continued Panel B: Three-year Restricted Appointments ΔRating t

ΔRating t

ΔCDSSpreadt

ΔCDSSpreadt

Industry Fixed Eects Firm Fixed Eects Year Fixed Eects

YES NO YES

NO YES YES

YES NO YES

NO YES YES

Observations Adjusted R2

9,338 0.147

9,338 0.255

2,886 0.282

2,886 0.358

Appointment − 3pret ΔSizet−1 ΔLeveraget−1 ΔT angiblet−1 ΔIntCvg t−1 ΔROAt−1 ΔB/M t−1 ΔStdRett−1 ΔStdCF F Ot−1 ΔLitRisk t−1 Losst−1 Y OY t−1 ΔInstOwnt−1 ΔEIndext−1 EIndexDumt−1

(1) 0.052∗ (0.056) -4.313∗∗∗ (0.000) 0.029∗∗ (0.016) -0.463∗∗∗ (0.000) -0.015∗ (0.090) 0.001 (0.731) -0.019∗ (0.081) -0.739 (0.155) 0.281 (0.346) 0.057∗∗∗ (0.000) 0.469∗∗∗ (0.000) 0.051∗∗ (0.013) -0.034 (0.662) -0.053∗∗ (0.012) 0.007 (0.667)

(2) 0.052∗ (0.082) -2.983∗∗∗ (0.000) 0.029∗∗ (0.017) -0.342∗∗∗ (0.000) -0.013 (0.150) 0.002 (0.468) -0.030∗∗ (0.020) -0.305 (0.758) 0.453 (0.332) 0.052∗∗∗ (0.000) 0.477∗∗∗ (0.000) 0.072∗∗∗ (0.002) 0.020 (0.804) -0.038 (0.233) -0.088 (0.132)

(3) 32.104∗∗∗ (0.004) -413.198∗ (0.063) 13.640∗∗ (0.010) 39.043 (0.283) -1.827 (0.684) -0.610 (0.645) 18.061∗∗ (0.017) -129.710∗ (0.061) 63.744 (0.581) -6.657 (0.172) 99.029∗∗∗ (0.000) -7.149 (0.114) -41.959 (0.373) -6.711 (0.309) 7.779∗ (0.100)

(4) 32.487∗∗∗ (0.007) -326.465 (0.190) 10.984∗ (0.059) 47.414 (0.262) 0.645 (0.905) -0.889 (0.561) 16.962∗∗ (0.027) -423.939∗∗ (0.013) 66.914 (0.721) -7.592 (0.155) 102.886∗∗∗ (0.000) -4.695 (0.361) -56.776 (0.343) -2.292 (0.763) 34.569 (0.604)

Panel B of Table 4 examines the relation between GC appointments to senior management and changes

40

in credit risk by requiring three consecutive non-GC years prior to appointment. of outliers, we winsorize all continuous variables at the 1st and 99th percentiles.

To limit the inuence In columns (1) and (2)

standard errors are clustered by rm and year, while in columns (3) and (4) standard errors are clustered by rm. *, **, and *** denote statistical signicance at the 0.10, 0.05, and 0.01 levels, respectively. Please refer to the Appendix for variable denitions.

41

Table 5: Short-Window Relation Between General Counsel Appointments and Changes in Credit Risk Panel A: Credit Rating Sample one-year

one-year

two-year

two-year

ΔRating t

ΔRating t

ΔRating t

ΔRating t

Industry Fixed Eects Firm Fixed Eects Year Fixed Eects

YES NO YES

NO YES YES

YES NO YES

NO YES YES

Observations Adjusted R2

1,580 0.152

1,580 0.549

1,392 0.166

1,392 0.450

GC t ΔSizet−1 ΔLeveraget−1 ΔT angiblet−1 ΔIntCvg t−1 ΔROAt−1 ΔB/M t−1 ΔStdRett−1 ΔStdCF F Ot−1 ΔLitRisk t−1 Losst−1 Y OY t−1 ΔInstOwnt−1 ΔEIndext−1 EIndexDumt−1

(1) 0.047∗∗ (0.014) -4.435∗∗∗ (0.002) 0.035 (0.466) -0.472∗∗ (0.013) -0.027 (0.122) 0.005 (0.423) -0.035 (0.249) 0.007 (0.993) 1.161 (0.308) 0.053 (0.145) 0.435∗∗∗ (0.000) 0.075∗ (0.097) 0.068 (0.748) 0.143 (0.241) -0.071∗ (0.076)

(2) 0.077∗∗∗ (0.001) -2.211 (0.284) 0.031 (0.466) -0.215 (0.382) -0.051∗ (0.090) 0.006 (0.383) -0.059 (0.115) -0.555 (0.744) -1.225 (0.687) 0.022 (0.512) 0.417∗∗∗ (0.000) 0.113∗ (0.061) -0.048 (0.858) 0.135 (0.473) -0.094 (0.595)

42

(3) 0.103∗∗∗ (0.005) -3.081∗∗ (0.011) 0.032 (0.546) -0.349∗∗ (0.019) -0.017 (0.174) 0.003 (0.392) -0.085∗∗∗ (0.001) 0.142 (0.883) 0.091 (0.906) 0.043∗∗ (0.022) 0.426∗∗∗ (0.000) 0.064∗∗ (0.030) -0.295∗∗∗ (0.004) 0.065 (0.465) -0.039 (0.322)

(4) 0.107∗∗ (0.048) -0.598 (0.658) 0.011 (0.841) -0.208 (0.213) -0.004 (0.793) 0.002 (0.600) -0.123∗∗∗ (0.003) 0.755 (0.732) 2.335 (0.142) 0.040∗ (0.091) 0.411∗∗∗ (0.000) 0.161∗∗∗ (0.000) -0.172 (0.115) 0.091 (0.485) -0.568∗∗∗ (0.004)

Panel A of Table 5 examines the relation between GC appointments to senior management and changes in credit risk when credit rating is the proxy for credit risk. Columns (1) and (2) present results when examining the period encompassing the one year immediately before and after a GC is appointed to senior management. Columns (3) and (4) present results when examining the period encompassing the two years immediately before and after a GC is appointed to senior management. To limit the inuence of outliers, we winsorize all continuous variables at the 1st and 99th percentiles. Standard errors are clustered by rm and year. *, **, and *** denote statistical signicance at the 0.10, 0.05, and 0.01 levels, respectively. Please refer to the Appendix for variable denitions.

43

Table 5  continued Panel B: CDS Spread Sample one-year

one-year

two-year

two-year

ΔCDSSpreadt

ΔCDSSpreadt

ΔCDSSpreadt

ΔCDSSpreadt

19.292 (0.056) -503.158 (0.355) 13.435 (0.189) 59.361 (0.500) 1.623 (0.865) 0.413 (0.898) 13.394 (0.410) 423.306 (0.124) 277.464 (0.364) -16.643 (0.122) 98.648∗∗∗ (0.000) -5.786 (0.716) -94.997 (0.577) 20.352 (0.150) 9.611 (0.374)

24.386 (0.037) -531.293 (0.510) 2.127 (0.888) -6.240 (0.962) 10.535 (0.538) -1.927 (0.736) 13.695 (0.410) 589.372 (0.552) 251.296 (0.722) -14.386 (0.354) 83.032∗ (0.069) -2.702 (0.918) -244.669 (0.450) 17.383 (0.436) 244.051 (0.180)

29.096 (0.030) -1331.396 ∗∗ (0.023) 4.127 (0.731) -35.431 (0.597) 5.614 (0.585) -2.273 (0.275) -16.469 (0.509) 788.191∗ (0.062) -223.741 (0.381) -8.813 (0.455) 34.542 (0.206) -13.756 (0.395) 62.283 (0.545) 12.122 (0.538) 30.044∗ (0.076)

33.229∗∗ (0.013) -1204.427 (0.159) -7.905 (0.575) -18.405 (0.839) 15.637 (0.151) -3.067 (0.315) -27.245 (0.443) 1590.028∗ (0.088) 583.184 (0.235) -8.786 (0.473) 16.222 (0.742) -21.168 (0.282) -35.889 (0.797) 29.647 (0.339) 16.951 (0.609)

Industry Fixed Eects Firm Fixed Eects Year Fixed Eects

YES NO YES

NO YES YES

YES NO YES

NO YES YES

Observations Adjusted R2

530 0.378

530 0.603

481 0.333

481 0.610

(1)

GC t ΔSizet−1 ΔLeveraget−1 ΔT angiblet−1 ΔIntCvg t−1 ΔROAt−1 ΔB/M t−1 ΔStdRett−1 ΔStdCF F Ot−1 ΔLitRisk t−1 Losst−1 Y OY t−1

ΔInstOwnt−1 ΔEIndext−1 EIndexDumt−1

∗

(2)

44

∗∗

(3)

∗∗

(4)

Panel B of Table 5 examines the relation between GC appointments to senior management and changes in credit risk when CDS spread is the proxy for credit risk. Columns (1) and (2) present results when examining the period encompassing the one year immediately before and after a GC is appointed to senior management. Columns (3) and (4) present results when examining the period encompassing the two years immediately before and after a GC is appointed to senior management. To limit the inuence of outliers, we winsorize all continuous variables at the 1st and 99th percentiles. Standard errors are clustered by rm. *, **, and *** denote statistical signicance at the 0.10, 0.05, and 0.01 levels, respectively. Please refer to the Appendix for variable denitions.

45

Table 6: Impact of SOX on the Relation Between General Counsel and Credit Risk M Rating t

M Rating t

(1)

(2)

P ost

0.141∗∗∗

GC t

0.282∗∗∗

(0.004) (0.001) -0.177∗

P ost ∗ GC t

(0.065)

Sizet−1 Leveraget−1

(0.011)

0.225∗∗∗ (0.001)

-0.171∗∗ (0.040)

-1.005∗∗∗

-1.067∗∗∗

(0.000)

(0.000)

4.192∗∗∗

3.254∗∗∗

(0.000)

(0.000)

0.016

T angiblet−1

0.093∗∗

(0.919)

-0.621∗∗∗ (0.008)

IntCvg t−1

-7.331∗∗∗

ROAt−1

-0.012∗∗∗

B/M t−1

1.276∗∗∗

0.756∗∗∗

(0.000)

(0.000)

(0.000) (0.000)

StdRett−1

(0.000)

(0.000)

LitRisk t−1

0.660∗∗∗

(0.013)

(0.001)

EIndext−1

(0.316)

0.343∗∗∗ (0.000)

0.178∗∗∗

(0.632)

(0.006)

0.274∗∗∗

0.230∗∗∗

(0.000)

(0.000)

(0.033)

(0.496)

-0.066

-0.039

(0.133)

(0.169)

(0.072)

(0.076)

-0.236∗

EIndexDumt−1

1.144

0.049

-0.297∗∗

InstOwnt−1

(0.000)

14.252∗∗∗

3.707∗∗

Y OY t−1

(0.000)

-0.005∗∗∗

21.512∗∗∗

StdCF F Ot−1

Losst−1

-3.952∗∗∗

0.079

-0.235∗

F-tests:

P ost

+

P ost ∗ GC t

GC t + P ost ∗ GC t

P ost

+

GC t

+

P ost ∗ GC t

46

-0.036

-0.078

(0.618)

(0.160)

0.105

0.054

(0.153)

(0.224)

0.246***

0.147***

(0.001)

(0.000)

YES

NO

Firm Fixed Eects

NO

YES

Year Fixed Eects

YES

YES

79,590

79,590

0.752

0.917

Industry Fixed Eects

Observations Adjusted

R2

Table 6 examines the impact of SOX on the relation between GCs and credit risk, where credit risk is measured monthly. To limit the inuence of outliers, we winsorize all continuous variables at the 1st and 99th percentiles. Standard errors are clustered by rm and year. *, **, and *** denote statistical signicance at the 0.10, 0.05, and 0.01 levels, respectively. Please refer to the Appendix for variable denitions.

47

Table 7: Relation Between General Counsel and Monthly Levels of Credit Risk

GC t

M Rating t

M Rating t

M CDSSpreadt

M CDSSpreadt

(1)

(2)

(3)

(4)

0.161∗∗ (0.012)

Sizet−1

-1.003∗∗∗

Leveraget−1

4.189∗∗∗

(0.000)

(0.000)

T angiblet−1 IntCvg t−1 ROAt−1 B/M t−1

StdCF F Ot−1

(0.000)

3.244∗∗∗ (0.000)

(0.000)

(0.256)

217.709∗∗∗ (0.000)

102.968∗∗∗

(0.913)

(0.009)

(0.004)

(0.004)

-7.331∗∗∗

-3.955∗∗∗

-542.145∗∗∗

-435.478∗∗∗

(0.000)

(0.000)

-0.012∗∗∗

(0.000)

-0.005∗∗∗

(0.000) 0.014

-0.147

(0.000)

(0.000)

(0.947)

(0.516)

1.275∗∗∗

0.758∗∗∗ (0.000)

21.500∗∗∗

14.227∗∗∗

(0.000)

(0.000)

3.711∗∗

0.658∗∗∗ (0.001)

(0.094)

33.518 (0.215)

1344.671∗∗∗

1272.210∗∗∗

0.018∗∗∗

(0.003)

(0.000)

(0.307)

(0.001)

(0.057)

0.342∗∗∗ (0.000)

0.180∗∗∗

Y OY t−1

0.275∗∗∗

0.231∗∗∗

(0.000)

InstOwnt−1

-0.297∗∗

(0.621)

37.738∗

1.174

0.050

EIndexDumt−1

(0.006)

338.328∗∗∗

(0.020) -19.777

62.710∗∗∗

Losst−1

EIndext−1

(0.078)

-22.119∗∗∗

13.982∗∗

-0.614∗∗∗

(0.013)

LitRisk t−1

(0.009)

-1.064∗∗∗

9.046*

0.017

(0.000)

StdRett−1

0.107∗∗∗

(0.006)

25.989 (0.551)

90.005∗∗∗ (0.000)

0.014∗

-47.273∗∗ (0.023)

53.708∗∗∗ (0.000)

27.945∗∗∗

24.107∗∗∗

(0.000)

(0.000)

(0.000)

0.074

-19.604

17.571

(0.033)

(0.520)

(0.129)

-0.066

-0.039

-12.136∗∗∗

(0.209)

(0.133)

(0.168) -0.231∗

(0.001)

-20.747∗

(0.068)

96.004∗∗

(0.070)

(0.080)

(0.080)

(0.029)

-0.237∗

-7.690∗

Industry Fixed Eects

YES

NO

YES

NO

Firm Fixed Eects

NO

YES

NO

YES

Year Fixed Eects

YES

YES

YES

YES

79,590

79,590

36,241

36,241

0.752

0.917

0.562

0.712

Observations Adjusted R2

Table 7 examines the relation between GCs in senior management and credit risk, where credit risk is measured monthly. To limit the inuence of outliers, we winsorize all continuous variables at the 1st and 99th percentiles. In columns (1) and (2) standard errors are clustered by rm and year, while in columns

48

(3) and (4) standard errors are clustered by rm. *, **, and *** denote statistical signicance at the 0.10, 0.05, and 0.01 levels, respectively. Please refer to the Appendix for variable denitions.

49