School of Accounting

Seminar – Session 2, 2011

Enterprise Risk Management Program Quality: Determionants, Value Relevance, and the

Financial Crisis

Professor Jean Bedard Bentley College

Date: Friday 11th

Time: 2:00 to 3:30 pm November

Venue: QUAD2063

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Enterprise Risk Management Program Quality: Determinants, Value Relevance, and the Financial Crisis

Ryan Baxtera Email: rbaxter@bentley.edu

Office: 781-891-3485

Jean C. Bedarda,b Email: jbedard@bentley.edu

Office: 781-891-2410

Rani Hoitasha Email: rhoitash@bentley.edu

Office: 781-891-2588

Ari Yezegela ayezegel@bentley.edu Office: 781-891-2264

aDepartment of Accountancy

Bentley University 175 Forest Street

Waltham, MA 02452-4705

bProfessorial Visiting Fellow University of New South Wales

Acknowledgements: We are grateful for comments and suggestions by Dorothy Feldmann, Steve Fortin, Dana Hermanson, Ronny Hofmann, Udi Hoitash, Doug Prawitt, Zvi Singer, Monte Swain, Desmond Tsang, Ann Vanstraelen, Jeff Wilks, David Wood, Bill Zhang, Mark Zimbelman, and seminar participants at Bentley University, Brigham Young University, Maastricht University, McGill University and Virginia Tech. We also thank the PricewaterhouseCoopers INQuires program and Bentley University for research grant support, and Bentley graduate students Lindsay Bove, Dana Bogen, Tien-Shih Hsieh, and Maximino Rivera for their excellent research assistance.

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Enterprise Risk Management Program Quality: Determinants, Value Relevance, and the Financial Crisis

Abstract

This paper investigates factors associated with high quality Enterprise Risk Management

(ERM) programs, and whether ERM quality enhances performance and signals credibility to the

financial markets. ERM, developed within the accounting profession, provides a framework and

plan to integrate management of all sources of risk. Challenged by measurement difficulties

common to research on management control systems, prior ERM studies present mixed findings.

Using ERM quality (ERMQ) ratings of financial companies by Standard & Poor’s, we find

higher ERMQ associated with greater complexity, less resource constraint, and better corporate

governance. Controlling for such characteristics, we find that higher ERMQ improves

accounting performance and firm value. Results show that market reaction to signals of enhanced

management control from initial ERMQ ratings and rating revisions, and greater response to

earnings surprises for firms with higher ERMQ. Focusing on the recent financial crisis, we find

that ERMQ ratings were positively associated with abnormal returns during the market rebound.

Overall, results reveal that firm performance and value are enhanced by high quality controls that

integrate risk management efforts across the firm, enabling better oversight of managers’ risk-

taking behavior, and aligning that behavior with the strategic direction of the company.

Keywords: Enterprise Risk Management; Firm Value; Stock market returns; Event Study; Earnings Response Coefficient; Global financial crisis

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Enterprise Risk Management Program Quality: Determinants, Value Relevance, and the Financial Crisis

I. INTRODUCTION

Enterprise Risk Management (ERM) programs are designed to enable firms to manage

risk from a wide variety of sources in an integrated manner. ERM is intended to improve risk

management by promoting awareness of all sources of risk, and by aligning strategic and

operational decision-making across the entity with the company’s risk appetite (e.g., COSO

2004; Nocco and Stulz 2006). As such, ERM is a corporate governance mechanism that

constrains and coordinates managers’ behavior. While potential benefits to firm performance and

value (e.g., through improving efficiency and reducing volatility) have been espoused (Beasley

et al. 2008; Hoyt and Liebenberg 2011) there is little available archival evidence on the benefits

of these programs. We investigate factors associated with high quality ERM programs as

measured by ratings produced by Standard & Poor’s (S&P), and whether anticipated benefits of

high quality ERM are realized.

The criteria used by S&P to rate ERM quality (shown in the Appendix) relate to effective

communication of strategy throughout disparate parts of the enterprise, appropriate project

selection, improvement of accounting-based returns and optimization of market response. ERM

development was led by the accounting profession, through the Committee of Sponsoring

Organizations (COSO). The ERM framework reflects the profession’s interest in embedding the

internal control framework and principles (COSO 1992) beyond the financial reporting system,

more broadly and systematically throughout organizations.1

1 ERM programs are often within the purview of a firm’s internal audit function (IIA 2004), linking this research to studies of internal audit effectiveness (Beasley et al. 2005).

While prior research has addressed

determinants of, and market implications associated with, other corporate governance choices

such as implementing effective/ineffective internal controls (e.g., Krishnan 2005; Ogneva et al.

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2007; Hammersley et al. 2008; Ashbaugh-Skaife et al. 2008) and including more expert

personnel in the governance structure (e.g., DeFond et al. 2005), these issues have not been

similarly examined with respect to ERM quality.

This study fits within the accounting literature on corporate governance and contributes

to it in several ways. First, the study of ERM links theoretically to the accounting literature on

corporate governance because ERM is among the “internal” mechanisms that companies can

adopt to reduce the risk that managers undertake activities not well aligned with overall strategy.

Accounting researchers are particularly interested in how corporate control mechanisms affect

allocation and utilization of economic resources (e.g., Bushman and Smith 2001). For instance,

high quality ERM may affect allocation of resources through market participants’ perceptions of

the reliability and persistence of accounting earnings. However, Zimmerman (2001) notes that

the greatest constraint of empirical research on management control systems (MCS) is the lack of

information on what corporations do internally. Concerns over the quality of publicly available

proxies for corporate governance quality are also expressed by Larcker et al. (2007) in their

review of the literature. Further, Davila and Foster (2005; 2007) and Ittner and Larcker (2001)

both note the difficulties of using manager perceptions as indicators of MCS quality. S&P

assessments of communication, action and evaluation processes within companies, in forming

their ERM ratings, address these measurement problems by providing a view by professional

evaluators independent of the firm, with access to nonpublic information.

This research addresses two basic issues regarding ERM common to the empirical

literature on MCS, using a sample of 165 firm-year observations in the banking and insurance

industries with S&P ERM ratings in 2006-2008. First, we investigate characteristics of

companies that implement effective ERM programs. Most prior archival research concentrates

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on the existence of ERM programs (e.g., Liebenberg and Hoyt 2003; Hoyt and Liebenberg

2011). Other research uses survey data to investigate ERM program quality (e.g., Kleffner et al.

2003; Beasley et al. 2005; Beasley et al. 2008), or infers quality from publicly available data

(e.g., greater than expected sales; Gordon et al. 2009). Given the issues with publicly available

proxies and perception data noted above, studying ERM quality using ratings from an

independent, professional source contributes to the literature on ERM implementation.

In this analysis, we develop explanatory constructs using theories from the ERM and

MCS literatures. Because ERM is designed to integrate risk management across disparate parts

of the enterprise, we find that more complex (larger, more diversified) entities have higher

quality programs. In addition, because effective ERM should help also protect against lower tail

outcomes, companies with higher volatility and/or risk of financial distress may demand better

programs. On the other hand, companies with financial risk may lack the personnel and systems

resources to implement a high quality program. Our results support the latter explanation: higher-

risk companies have lower quality ERM, likely due to resource constraints preventing those

firms from investments required for effective ERM. Lastly, we find higher quality ERM among

companies with better corporate governance; i.e., audit committees charged with direct oversight

of risk, less audit-related risk (i.e., stable auditor relationships and effective internal controls),

risk officers/committees, and boards with longer tenure.2

We then study the association of ERM quality with firm value. Limited prior research

finds an association of ERM quality with firm value as measured by Tobin’s Q: Hoyt and

Liebenberg (2011), who identify ERM programs through Lexis-Nexis and SEC filings, and

McShane et al. (2011) who use S&P ratings among insurance companies. We examine how this

2 This analysis not only contributes to the literature in its own right, revealing company characteristics associated with investment in managing risk, but is also needed to control for possible endogeneity in subsequent analyses of the linkage between ERM quality and performance.

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greater valuation occurs, by whether it is based on higher accounting returns achieved through

better risk management, the market’s anticipation of superior future performance when ratings

become available, and/or greater usefulness of earnings, consistent with confidence by market

participants in earnings reliability and persistence. First, we expect and find that ERM quality is

positively associated with operating performance, and replicate findings of prior research that

ERM quality is also associated with greater market valuation in our broader sample. Examining

market reaction to initial ERM ratings announcements and revisions, we find significant reaction

to initial ratings disclosures in the two-day window preceding the announcement (implying

leakage of the ratings news), and significant response to ERM rating changes across various

event windows. We then investigate whether the market places greater value on unexpected

earnings of firms with higher quality ERM programs. Results using earnings response

coefficients support this expectation. This result builds on evidence provided by prior research

showing that investors’ perceptions of the credibility/persistence of earnings are affected by

governance factors such as auditor size (Teoh and Wong 1993), auditor industry specialization

(Balsam et al. 2003), and ineffective internal controls (Beneish et al. 2008).

Lastly, reflecting the purpose of ERM programs to add value by reducing costs from

lower-tail outcomes (Pagach and Warr 2008; Nocco and Stulz 2006; Stulz 1996), we examine

the relation between ERM quality and market returns around the Global Financial Crisis (GFC)

of 2008-2009. While we find no relation prior to and during the market collapse, we find a

positive association of ERM quality with market performance during the market rebound. This

suggests that following the flight from financial services companies during the crisis period,

information contained in ERM quality ratings was used by investors to identify companies more

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likely to recover.3

II. PRIOR RESEARCH AND HYPOTHESIS DEVELOPMENT

In the next section, we review prior research on ERM within the context of

empirical literature in accounting on the impacts of corporate governance and management

control. We then describe empirical methods and results of our tests. In the paper’s final section,

we present our main conclusions and their implications for research.

Enterprise Risk Management: Background and Related Research

The COSO 2004 ERM framework defines ERM as follows:4

“Enterprise risk management is a process, effected by an entity’s board of directors, management and other personnel, applied in strategy setting and across the enterprise, designed to identify potential events that may affect the entity, and manage risk to be within its risk appetite, to provide reasonable assurance regarding the achievement of the entity’s objectives.”

The academic literature on ERM comprises two basic streams, reflecting the literature on

MCS in general. One stream explores drivers and/or impediments to ERM program adoption

while the other asks whether the presence of (or less frequently, the quality of) ERM provides

information to the financial markets. The literature on company characteristics associated with

the presence or level of implementation of ERM programs, considered in this sub-section,

mirrors research on implementation of advanced MCS (Elbashir et al. 2011), and on the

determinants of effectiveness of internal control over financial reporting (e.g., Ashbaugh-Skaife

et al. 2007; Doyle et al. 2007). ERM studies measure adoption/ implementation through surveys

of company personnel, or infer the status of ERM programs from publicly available information.

Ittner and Larcker (2001) and Larcker et al. (2007) discuss the common difficulties associated

3 Regulators also reacted to the financial crisis by requiring public companies to disclose risk oversight and responsibilities of boards of directors and audit committees (SEC 2009). 4 PricewaterhouseCoopers LLP was engaged in 2001 to lead the development of the ERM framework. The ERM project advisory council consisted of Accounting and Finance related Vice Presidents and Controllers, accounting academics, and managing partners of accounting and consulting firms. The oversight committee included representatives of the AAA, AICPA, FEI, IMA, and IIA.

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with each source of information in the MCS literature. Surveys of company personnel provide a

valuable inside view of the firm; however, their responses relative to their own programs could

be biased. Publicly available data often constitute poor proxies for corporate activities, a key

limitation when program quality (not adoption) is the object of study. 5 We address these

limitations through use of S&P’s evaluations of ERM quality. These ratings not only verify the

presence of ERM, but also provide an evaluative ranking by independent parties of ERM quality

based on established criteria.6

In this discussion, we place studies examining company characteristics associated with

ERM adoption/implementation in the context of prior accounting research that investigates

characteristics associated with other internal corporate governance mechanisms involving

management control. First, since ERM is a risk management tool, prior research has investigated

the association of ERM adoption/implementation with two basic sources of risk. Several studies

find that more complex entities are more likely to adopt ERM (Kleffner et al. 2003; Beasley et

al. 2005; Hoyt and Liebenberg 2009). As Liebenberg and Hoyt (2003) note, such companies face

a broader scope of risks, and lack of coordination in response will lead to greater risk in the

aggregate. Thus, large and diversified companies have a greater incentive to move from siloed to

5 For instance, researchers have used announcements in securities filings and/or news databases such as Lexis-Nexis. While public disclosure of ERM indicates the presence of a program, disclosure is not required, and thus there may be error involved in inferring lack of a program from no disclosure. For example, Hoyt and Liebenberg 2011, in supplemental analysis, compare publicly traded property-casualty insurers determined by key word search of news reports and filings, to those shown objectively to have ERM programs by the presence of an S&P rating. Only five of the 17 companies with S&P ERM program ratings appear in their sample. 6 While credit rating agencies are technically independent of management, those agencies have been criticized for inflated ratings on structured finance products, and the inability of those ratings to anticipate the financial crisis of 2008 (Frydman and Goldberg 2009). By using the S&P ERM ratings as an indication of program quality, we test whether those ratings have information content to the financial markets. If the ERMQ ratings were biased upward, variance would be lower; and if those ratings do not represent actual program quality, error is introduced. Both effects would reduce our ability to find significant effects of ERM quality. Further, we test sensitivity in results of our hypothesis testing models to controlling for both selection into the set of companies that are covered by S&P, and determinants of ERM quality ratings, finding little difference in significance. If ratings inflation were based on factors such as company size (presumably affecting fees provided to the rating agency), then the selection controls should mitigate any effects of ratings inflation on our hypothesis tests.

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integrated risk management. Likewise, the literature on MCS (e.g., Elbashir et al. 2011; Davila

and Foster 2005) provides support that complexity should influence development of advanced

controls in general, as larger and more diversified companies have greater information

processing needs.

Second, research on determinants of ERM adoption/implementation also recognizes

financial risk as a source of demand for ERM.7

Third, the COSO 2004 framework suggests that management philosophy is important in

implementation of ERM programs. This implies that high quality ERM programs should be

implemented in companies evidencing effective corporate governance in other ways (e.g.,

Studies have considered both uncertainty (e.g.,

volatility in earnings or stock prices) and financial distress indicators as financial risk indicators,

with weak results. Liebenberg and Hoyt (2003) and Gordon et al. (2009) do not find significance

on measures of uncertainty in CRO appointment and ERM implementation. Pagach and Warr

(2007) find leverage to have a significant positive association with CRO appointment, but

Gordon et al. (2009) do not find an association. This variability in findings may be due to the

dual character of financial risk measures. On one hand, companies with higher financial risk face

higher likelihood of lower-tail outcomes such as bankruptcy (Pagach and Warr 2007), and thus

have more to gain from programs aimed at reducing that likelihood. On the other hand, higher

financial risk is associated with fewer resources to devote to implementing advanced control

systems (Davila and Foster 2005; Elbashir et al. 2011). In the specific context of ERM,

Liebenberg and Hoyt (2003) note that ERM development is often stalled by the lack of

technological tools necessary for implementation. Thus, the direction of the effect of financial

risk indicators on ERM quality is unclear ex ante.

7 Further study of the effects of risk on MCS implementation is recognized by Ittner and Larcker (2001; 394) as a valuable topic for future research.

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Kleffner et al. 2003; Liebenberg and Hoyt 2003).8 Beasley et al. (2009) report that expectations

of the board of directors are a key driver of ERM, due to increased demand for greater risk

transparency.9

In sum, prior research considers the roles of risk and corporate governance in

implementing ERM programs and MCS in general, but does not investigate these factors in

implementing high quality ERM programs. Our first research question is:

Also, boards of companies with superior governance structures should recognize

the need for other mechanisms to control agency conflicts. Prior research shows that ERM

adoption is associated with greater board independence (Beasley et al. 2005), appointment of

Chief Risk Officers (CROs), and use of Big 4 auditors (Kleffner et al. 2003; Beasley et al. 2009).

While we examine these governance mechanisms as determinants of ERM quality, we also

include corporate governance mechanisms specific to the auditing literature not addressed by

prior ERM research. These include internal control effectiveness, stability of audit relationships,

and appointment of expert audit committee members.

RQ 1: Is ERM program quality associated with company complexity, financial risk/resources, and corporate governance?

ERM, Performance, and Firm Value

The second phase of this study concerns the effects of ERM quality. ERM programs are

intended to add value by reducing costs from lower-tail outcomes (Beasley et al. 2008; Nocco

and Stulz 2006; Stulz 1996). For instance, by reducing the likelihood and impact of extreme,

negative financial events, firms avoid direct costs such as losses and bankruptcy, and indirect

costs, such as reputational effects with customers and suppliers (Pagach and Warr 2008). Yet,

8 Seconding this notion in the accounting literature, Larcker et al. (2007) show that structural indicators of governance quality are correlated. 9 Their survey results indicate “that 75% of the full boards and 86% of audit committees are making ‘Moderate’ to ‘Extensive’ to ‘A Great Deal’ of requests for more senior management involvement in risk oversight” (Beasley et al. 2009; 11).

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high quality ERM programs should not only reduce the impact of negative events, but also help

identify untapped opportunities across the enterprise (COSO 2004). If so, capital should be

allocated more efficiently and returns increased (Hoyt and Liebenberg 2011). For both reasons,

ERM quality should improve performance and be valued in the financial markets.

Following implementation, ERM quality could contribute to firm valuation in two ways.

First, effective ERM could enhance financial performance through ability to both recognize

potential for opportunities on the upside and protect against lower-tail outcomes on the

downside, as suggested by the COSO definition cited above. If so, then companies with better

ERM quality should have higher accounting returns, but prior research has not examined this

association. Further, objective evidence supporting the value proposition for ERM quality is very

limited. We test both accounting and market performance with the following hypothesis:

H1: ERM program quality is positively associated with performance and firm value. If the market does value ERM quality, as predicted in H1, the question arises as to

whether market participants recognized the information content of the quality ratings at

announcement. Theory postulates that returns are a function of changes in market participants’

information sets.10

10 Our inquiry is based on a rich empirical literature that uses event study methodology to examine how the announcement of news affects asset returns, and investigates the market mechanism producing adjustment of prices to new information. For example, Fama et al. 1969, Grinblatt et al. 1984, Ikenberry et al. 1996 and Desai and Jain 1997 study the signal conveyed by stock splits and their effects on prices. Lakonishok and Vermaelen 1990 and Ikenberry et al. 1995 study tender offers and open market repurchases. Groth et al. 1979, Bjerring et al. 1983, Elton et al. 1984 and Womack 1996 examine analyst recommendations’ impact on security prices. Michaely et al. 1995 document evidence on dividend initiations and omissions and Bernard and Thomas 1989 examine earnings surprises.

If the market perceives ex ante that higher ERM quality will increase firm

value, public announcements of ERM ratings should be associated with a market response. Prior

research has examined this issue indirectly. Beasley et al. (2008) examine the two-day market

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reaction to CRO hiring announcements from 1992-2003 as a proxy for ERM program adoption.11

They do not find an overall market reaction, but do show a response contingent on industry, size,

etc.12

H2a: Market reactions will be positively associated with the level of ERM quality rating.

Beasley et al. (2008) suggest that lack of overall response in the financial services industry

may be due to regulatory pressure; i.e., those firms “may already have begun engaging in ERM

before the CRO appointment”. If so, the market might react more strongly to information from

S&P on ERM quality, which enables better understanding of the level of commitment to ERM.

We build on prior research by testing market response to variation in disclosed ERM quality

ratings and ratings revisions, through the following hypotheses:

H2b: Market reactions will be positively associated with ERM quality rating revisions. In addition, knowledge of relative ERM quality could assist investors by providing

information that helps assess the usefulness of financial reports in predicting future cash flows.

Prior research finds that investors’ responses to earnings surprises, measured using the earnings

response coefficient (ERC), varies by company characteristics that provide information on the

quality of financial reporting. For instance, Teoh and Wong (1993) find stronger response to

earnings surprises among companies audited by large audit firms, and Balsam et al. (2003) find a

similar result among clients of industry specialist auditors. Anderson and Yohn (2002) find

weaker response to earnings surprises following restatements, and Francis et al. (2002) show that

an observed increase in earnings response over two decades is largely due to better disclosure of

the properties of earnings, implying that nonfinancial disclosures are used to assess earnings

quality. In the ERM context, firms with stronger programs have a more structured approach for

11 CRO announcements are an indirect proxy for ERM programs, because while CROs may lead ERM programs, other corporate officials may do so. Further, ERM implementation may lag CRO appointment. 12 Pagach and Warr 2008 also examine CRO appointment announcements with a somewhat different sample. While they do not find a market response to CRO appointment, they do show that firms announcing appointments have a decline in volatility of stock returns, consistent with reduction of risk.

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capturing and managing risk across the entire organization (COSO 2004). Thus, it follows that

firms with superior ERM programs would likely have greater earnings credibility, resulting in

stronger market response to unexpected earnings. However, prior research has not addressed this

issue. We test the following hypothesis:

H3: Earnings response coefficients will be positively associated with ERM quality.

ERM and the Global Financial Crisis

One primary objective of ERM programs is to help companies avoid catastrophic loss in

profitability and market value. If so, the advantage of an effective risk management program may

be more pronounced at times of crises, when risk is of greater concern. The recent GFC of 2008-

2009 provides a natural laboratory in which to examine market effects of ERM quality, but prior

research has not investigated this issue. While high quality ERM programs may have protected

companies during the crisis, given the depth of the market crash, many investors may have

divested their financial industry holdings regardless of firm-specific factors. Further, as the

market began to recover, investors who desired to return to financial firms might have

considered evidence of effective risk management in deciding where to initially invest. We

propose the following research question:

RQ2: Did firms with stronger ERM programs have better market returns prior to, during, and/or after the global financial crisis?

III. METHOD

Sample Development

Our sample comprises financial services firms (banks and insurance companies) with

coverage in the S&P Ratings Direct database.13

13 At this point, coverage is only available in these industries. Using all available data, our final sample is composed of 28 observations in SIC 60 (Depository Institutions), 6 in 61 (Non-depository Credit Institutions), 11 in 62

From the 404 ERM disclosures from 2006-2008

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in the S&P database, we remove 157 observations without clear or matching company

identifiers, resulting in 247 firm-years. We eliminate 27 companies with missing Compustat

data, and an additional 28 companies with missing internal control disclosures in Audit Analytics.

We manually search Audit Analytics and proxy filings for data on company executives and

corporate governance, omitting 17 additional observations with missing data. Finally, we exclude

real estate investment trusts, shares of beneficial interest and depository units. Our final sample

contains 165 firm-year observations.14

ERM Quality

Our test variable is the S&P’s ERM quality rating, provided as part of credit rating

analysis for some companies since 2006. S&P analysts evaluate companies’ ERM programs and

classify program quality into four categories: Weak, Adequate, Strong, or Excellent. The ERM

rating “includes the assessment of Risk Management Culture, Risk Controls, Emerging Risk

Management, Risk and Capital Models, and Strategic Risk Management” (Santori et al. 2006,

3).15

(Security and Commodity Brokers, Dealers, Exchanges, and Services), 119 in 63 (Insurance Carriers), and 1 in 67 (Holding and Other Investment Offices).

Since the majority of ERM programs are categorized as “Adequate”, we developed a finer

quality scale that divides ratings in the “Adequate” category into three subcategories based on

the content of the analyst’s report. Two of the authors read the section of each S&P credit rating

report with an ERM program categorized as “Adequate”. If the narrative in that section includes

more positive than negative descriptions, we assigned a rating of “Strong-adequate”. For

example, one description notes that a firm “has an adequate enterprise risk management (ERM)

process, with many aspects viewed as strong” (Gaskel et al. 2007). Where descriptions note a

more negative tone, we assigned a rating of “Weak-adequate”. For example, one report notes that

14 Depending on data availability, sample size varies slightly across analyses. 15 The Appendix contains ERM rating criteria for insurers. Criteria for financial institutions are similar, except “emerging risk management” is replaced by “extreme-event management”.

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“the development of its ERM lags behind that of similarly rated peers,” while another states,

“elements remain less developed than those of its peers.” Companies with balanced narratives

remain in the “Adequate” category. Following this approach, the coders agreed on 108 of the 117

reports (92.3 percent, Kappa = 0.834). For the remaining nine reports, the authors jointly

reviewed the text, and agreed on its classification. This process yields an ERM quality scale

ranging from one to six.16

Model Specifications and Variable Definitions

The numbers of observations in the resulting six levels are: weak=1 (n

= 6), weak-adequate=2 (n = 11), adequate=3 (n = 84), strong-adequate=4 (n = 22), strong=5 (n =

35), excellent=6 (n = 7).

Table 1 provides definitions for all variables used in the models.

[Insert Table 1]

Determinants of ERM Quality

Our first research question asks whether company risk and corporate governance quality

are associated with ERM quality among firms rated by S&P. First, we consider company risk in

terms of complexity, which is predicted to be a driver of investment in ERM due to greater need

to integrate risk management across multiple and/or diverse operations. Prior research finds that

ERM adoption is positively associated with company complexity (Kleffner et al. 2003, Beasley

et al. 2005; Hoyt and Liebenberg 2011). Consistent with this research, we expect that more

complex companies will build higher quality ERM programs. To measure complexity, we

include company size as MARKETCAP (the log of market value of equity), SEGMENTS (the

number of business and geographic segments), GLOBAL (an indicator variable that accepts one

16 We discuss the sensitivity of our results to other specifications in the following sections. S&P 2010 has also recently discussed the need for more detailed description of firms initially rated as adequate. Accordingly, in future rankings they will consider two additional categories in the “adequate” range: “adequate with strong risk controls” and “adequate with positive trend.”

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among companies with a presence in most countries, zero otherwise 17

Second, because ERM is intended to manage risk from all sources, companies with

greater financial risk might be more likely to have high quality programs. However, given threats

to solvency, such companies may lack interest in or ability to invest in high quality ERM,

making the likely sign of coefficients difficult to predict ex ante. Following prior accounting

research, we measure risk using indicators of uncertainty and financial distress. We measure risk

as market volatility using the standard deviation of stock returns during the fiscal year, STD-RET

(e.g. Sengupta 1998; Bushee and Noe 2000). We also include the standard deviation of cash

generated from operating activities, STD-OP-CASH. Froot et al. (1993) suggest that more

volatile cash flow can increase the need for external financing which often necessitates more

assurance and better risk management. Companies that experience more frequent losses also face

greater risk. We define LOSS-PROPORTION as the ratio of loss years calculated over five years.

Further, because ERM evaluations are done as part of the credit assessment, we control for credit

risk with CREDIT-RATING. Similar to Ashbaugh-Skaife et al. (2006), we use long-term issuer

credit ratings, ranging from a high of AAA to a low of D. We collapse the ratings into seven

categories.

), and FOREIGN (an

indicator variable equal to one if the firm has foreign operations, zero otherwise).

18 We then examine cash flow from operations deflated by total assets (OP-CASH)

and LEVERAGE. Liebenberg and Hoyt (2003) observe that highly leveraged companies are more

likely to implement ERM programs.19

17 We read 10-K filings for each firm year and coded GLOBAL as one for companies that operate in most countries in the world.

We further control for bankruptcy risk using a measure of

18 Following are S&P ratings with their numerical equivalents: AAA (7), AA+, AA and AA- (6), A+, A, and A- (5), BBB+, BBB, and BBB- (4), BB+, BB, BB- (3), B+, B, and B- (2), CCC+, CCC, CC, C, D or SD (1). 19 We also considered market beta, another measure of company risk, for use in the determinants model, but because it results in a small loss of sample in this model, we do not include it here. When we include beta in the determinants model it is insignificant and other results are unchanged.

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Z-SCORE that is specifically structured for financial firms (Roy 1952, Demirguc-Kunt and

Huizinga 2010).

The remaining construct likely associated with ERMQ is the quality of company

governance. One aspect likely to be related to ERMQ is risk oversight. Companies can address

risk by appointing a CRO and/or by assigning responsibilities for risk management to board

committees.20 Similar to Hoyt and Liebenberg (2011), we collect data on the presence of CROs

and other risk executives by searching Lexis-Nexis and ABI-Inform for news reports and

announcements. To identify risk committees, we manually search Audit Analytics and company

financial reports for the presence of a board committee responsible for risk.21

We also follow the Conference Board’s (Brancato et al. 2006) suggestion that instead of

creating a separate risk committee, companies might opt to assign this responsibility to the audit

committee (AC). We measure direct AC oversight of risk by manually collecting and reading AC

charters from SEC filings, classifying AC-RISK-OVERSIGHT as one if the charter mentioned

that the AC is responsible to oversee risk at the company (i.e., not merely to discuss risk). Next,

we also employ common proxies for AC strength in the form of financial expertise and size. Past

research finds that accounting and executive expertise on the AC is associated with financial

reporting quality (Abbott et al. 2004; Carcello et al. 2008; Dhaliwal et al. 2006; Hoitash et al.

2009). We measure accounting expertise with PAFE (the proportion of audit committee

members who possess accounting expertise, e.g., CPA and CFO experience) and supervisory

Combining this

information, we define RISK-STRUCTURE, which equals one in the presence of either a risk

officer or a risk committee; zero otherwise.

20 Johnson 2010 writes that while the SEC requires companies to disclose in their proxy statements how they address risk, companies’ choices with respect to the allocation of risk responsibilities vary and the boundaries and responsibilities over different risks are not always clear. 21 As key words in this search, we use common risk committee titles, including: risk, risk and compliance, risk management, finance and risk management, among others.

18

expertise with PSFE (the promotion of audit committee members with expertise in supervising

the financial function) expecting that both will be associated with better ERM quality. We also

control for the size of the AC with ACSIZE, based on the argument that larger AC are better

equipped to handle diverse responsibilities and will have greater voting power within the board.

We also study the association of board and management characteristics with ERM

program ratings. Prior research indicates a positive link between overall governance measures

and ERM adoption (Beasley et al. 2009; Beasley et al. 2005; Kleffner et al. 2003), as implied by

COSO 2004. The board not only establishes ERM programs, but also uses information produced

by those programs to assess significant risks and determine whether they are appropriately

addressed. We first control for board independence with BOARD-IND, measured as the

proportion of outsiders who serve on the board. Past research finds that more independent boards

are associated with better financial reporting quality (e.g., Klein 2002; Krishnan 2005), and with

better ERM implementation (Beasley et al. 2005). We also include board tenure, as longer tenure

is associated with higher financial reporting quality (Beasley 1996; Bédard et al. 2004). We

expect a positive association between these board quality measures and ERMQ. The success of

ERM programs could also depend on top management commitment and willingness to be

monitored in that manner. Fama and Jensen (1983) and Goyal and Park (2002), suggest that

separating the CEO and board chair positions contributes to reducing the agency conflict.

Accordingly, we include CEO-DUALITY as an indicator variable that equals one if the CEO is

also the chair of the board and expect an inverse association between CEO duality and ERMQ.

In addition, we develop an indicator of governance based on the external audit. ERM was

developed by the accounting profession as a means of extending the internal control framework

more broadly, into operational risk controls (Santori et al. 2006). Because measures of

19

operational control quality are not publicly available, we use as a proxy the effectiveness of

internal controls over financial reporting, based on part on the independent auditor’s assessment

of the control environment.22 This indicator also includes the stability of company’s relationship

with the external auditor. Past research implies that poorly governed companies are more likely

to switch auditors (Schwartz and Menon 1985). We define AUDIT-RELATED-RISK as an

indicator variable that captures both internal control quality and auditor switches. This variable is

an indicator that equals one for companies that report a material weakness under either Sarbanes-

Oxley Section 404 or 302 or for companies that recently switched auditors; zero otherwise. We

predict a negative association between this variable and ERMQ.23

We further control for FIRM-AGE with the natural logarithm of the number of years that

the firm has Compustat data, with no directional expectation. We also include NYSE, an indicator

variable that equals one for firms listed on the New York Stock Exchange. We expect a positive

coefficient because NYSE regulation could be associated with early adoption of ERM and

greater time to facilitate an increase in ERM program quality. All variables (with the exception

of year and industry dummies) are measured with one-year lags to ensure that the values

correspond to information available to S&P analysts at the time of their ratings.

Model 1, presented below, also includes year and industry fixed effects and is estimated

with standard errors corrected for clustering at the firm and year levels.

ERMQ= α +β1 MARKETCAPt-1+ β2SEGMENTS t-1 + β3 GLOBAL t-1+ β4FOREIGN t-1 + β5 STD-RET t-1 +β6 STD-OP-CASH t-1 + β7 LOSS-PROPORTION t-1+β8CREDIT-RATING t-1 + β9OP-CASH t-1+ β10LEVERAGE t-1+ β11 Z-SCORE t-1 + β12RISK-STRUCTUREt-1+ β13AC-RISK-OVERSIGHTt-1+ β14PAFEt-1+β15PSFEt-1+β16ACSIZEt-

1+ β17 BOARDIND t-1+ β18BOARD-TENURE t-1 + β19CEO-DUALITY t-1 +β20AUDIT-RELATED-RISK t-1 + β21FIRM-AGE t-1 + β22NYSE t-1 + β23-24 YEAR_DUMMIES + β25-28 INDUSTRY_DUMMIES + e

(1)

22 Here we also use the lagged value of disclosed material weaknesses because S&P analysts, and even company management, are not aware of internal control problems until the audit is completed following the end of the year. 23 Because over 97 percent of our sample companies are audited by a Big4 auditor, we do not include auditor size as a control variable in our models. Including a Big 4 indicator variable does not alter our results.

20

ERM and Firm Performance and Value

H1 predicts a positive association of ERM quality with firm performance and value. We

measure financial performance using ROA, the ratio of income before extraordinary items

divided by total assets. The ROA model controls for variables found to explain differences in

firm performance and value, including board size (Yermack 1996), board independence

(Rosenstein and Wyatt 1990), firm size and number of segments (Berger and Ofek 1995; Denis

et al. 2002), operating profitability (Yermack 1996), institutional ownership (McConnell and

Servaes 1990), leverage (Anderson and Reeb 2003), growth opportunities in the form of SALES-

GROWTH (Bhagat and Black 2002) and CAPITAL-OVER-SALES (Yermack 1996), and the

standard deviation of ROA (Liebenberg and Sommer 2008). The regression specification is

presented in Model 2 below.

ROA = α + β1ERMQ + β2 BOARDSIZE + β3BOARDIND + β4 LOGASSETS + β5INST-OWN + β6 SEGMENTS + β7LEVERAGE + β8SALES-GROWTH + β9CAPITAL-OVER-SALES + β10CREDIT-RATING + β11STDROA + β12-13YEAR_DUMMIES + β14-17INDUSTRY_DUMMIES + e

(2)

Next, following Hoyt and Liebenberg (2011) and McShane et al. (2011), we measure

value creation using Tobin’s Q (the book value of assets minus the book value of equity plus the

market value of equity divided by the book value of assets). We employ the same control

variables as in the ROA analysis, adding ROA and removing STDROA from the independent

variables. This regression specification is presented in Model 3 below:

Q = α + β1ERMQ + β2BOARDSIZE + β3 BOARDIND + β4LOGASSETS + β5ROA + β6INST-OWN β7SEGMENTS + β8LEVERAGE + β9SALES-GROWTH + β10CAPITAL-OVER-SALES + β11CREDIT-RATING + β12-13 YEAR_DUMMIES + β14-17INDUSTRY_DUMMIES + e

(3)

Market Reaction to ERM Rating Announcements

H2 proposes that equity market participants will react to information contained in

announcements of ERM quality ratings and revisions of those ratings. Using a standard event

21

study methodology, we examine the market reaction to the initial ERM S&P rating and

subsequent rating revision announcements.24

We expect a positive (negative) market reaction to ERMQ disclosures in the strong and

excellent (weak and adequate) ERM ratings. Similarly, an upgrade (downgrade) in the ERM

rating should be associated with a positive (negative) market reaction.

We define the event day (t) as the date that S&P

publishes the credit rating report containing the ERM quality rating. We compute the abnormal

return on day t as 𝐴𝑅𝑗𝑡 = 𝑅𝑗𝑡 − 𝑅𝑟𝑡, where Rjt is the return on security j on day t and Rrt is the

return on the reference portfolio on day t. Due to the possibility of selection bias in the set of

companies rated by S&P, we primarily rely on a within-sample reference portfolio to compute

abnormal returns, computed as the value-weighted daily returns of all securities in our sample.

However, we also test sensitivity to industry-, size-, and market-based reference portfolios.

Returns on the industry index are computed as the value-weighted returns of all securities within

the relevant two-digit SIC codes. Size and market returns are obtained from the CRSP index files

(crsp.ermport1, crsp.dsi).

25

Finally, following Beasley et al. (2008), we compute cumulative abnormal returns based

on the event windows (-2, +2), (-2, 0) and (0, +2) as follows: 𝐶𝐴𝐴𝑅𝑗(𝑇1,𝑇2) = ∑ 𝐴𝐴𝑅𝑗𝑡𝑇2𝑡=𝑇1 ,

where AARjt is the abnormal return on security j on day t as defined above. T1 is the return

accumulation start day relative to the initial announcement or revision and T2 is the end day. We

test for significance based on parametric (Patell 1976) and nonparametric (Corrado 1989) tests.

To align the market

reaction to positive and negative news, we multiply the returns in response to low ratings and

downgrades by negative one.

24 In order to ensure that the event study analysis is not influenced by contemporaneous credit rating changes, we remove ERM announcements and revisions that take place within a ten day window surrounding credit rating changes. Our results are similar when we do not exclude these observations. 25Due to the limited number of ERM rating revisions in our sample, we collected additional S&P reports that were published after the end-date of our main sample (until the end of the year 2009).

22

ERM Quality and the Earnings Response Coefficient

H3 predicts that the security price reaction to earnings surprises will be greater for firms

with stronger ERM quality, as firms with superior ERM presumably have greater credibility, less

uncertainty and lower earnings volatility. We test this hypothesis by estimating Model 4 using

ordinary least squares (OLS) regression with firm and year clustered standard errors:

CAR= α + β1UE + β2UE×STRONG_ERM + β3UE×CREDIT-RATING + β4NEG + β5UE×NEG + β6UE×B/M + β7UE×BETA + β8UE×COV +

β9-12INDUSTRY_DUMMIES + β13-29FISCAL_QUARTER_DUMMIES + e

(4)

To measure the extent to which share prices respond to earnings surprises, we regress

cumulative abnormal returns for the three-day period centered on earning announcement dates on

unexpected earnings. We primarily rely on sample-adjusted (CAR_SAMP) abnormal returns, but

test sensitivity using industry- (CAR_IND), market- (CAR_MAR) and size-adjusted (CAR_SIZE)

abnormal returns. We measure unexpected earnings (UE) as the difference between reported

earnings and analysts’ earnings expectations scaled by price at the end of the previous fiscal

quarter. STRONG_ERM is an indicator variable that equals one for firms with ERMQ>3. Our

model follows prior ERC research in using the interaction of UE and STRONG_ERM as the test

variable for H3; we expect a positive sign. The model controls for variation in earnings response

due to other factors, including CREDIT-RATING. Following Hackenbrack and Hogan (2002) and

based on the findings of Hayn (1995), we include an indicator for firms reporting negative

income before extraordinary items (NEG) and its interaction with UE. Consistent with the prior

literature (Collins 1989; Lipe 1990; Teoh and Wong 1993) we control for growth (B/M), market

risk (BETA) and analyst coverage (COV) by including the interaction of these variables with UE.

Finally, we include industry and fiscal quarter fixed effects to control for variation in earnings

announcement returns associated with industry and period characteristics of the sample.

23

ERM and the Global Financial Crisis

RQ2 concerns the association of ERM quality ratings with market performance before,

during and after the recent financial crisis. We classify the period from January 1st 2008 to

August 31st, 2008 as pre-crisis; the period from September 1st, 2008 to February 28th, 2009 as the

crisis; and the period from March 1st, 2009 to October 31st, 2009 as post-crisis.26 We obtain

monthly return data from the CRSP monthly file (crsp.msf), and compute buy-and-hold

abnormal returns (BHAR) based on sample, industry, size-decile, and market reference portfolios

for the three crisis periods as follows: ∏ �1 + 𝑅𝑗𝑡� −𝑇2𝑡=𝑇1 ∏ (1 + 𝑅𝑟𝑡)

𝑇2𝑡=𝑇1 , where T1 and T2 are the

first and last months of each period, Rjt is jth security’s raw return on month t and Rrt is the

reference portfolio return on month t. We estimate the following regression model separately for

each period to test the predicted association of ERM quality with abnormal security

performance. 27 While estimating the model below we rely on the most recent ERM ratings

available prior to the beginning of each period of investigation.28

BHAR= α + β1ERMQ + β2CREDIT-RATING + β3TARP + β4BETA + β5MARKETCAP + β6B/M + β7PRET + β8E/P + β9ln(LEVERAGE) + β10-13 INDUSTRY_DUMMIES + ε

(5)

The coefficient of ERMQ in this model measures the average change in market

performance associated with a one-level increase in the ERM rating.29

26 As a sensitivity analysis we replicated these tests using several alternative crisis begin dates including September 1st, 2007, December 1st, 2007, March 1st, 2008 and June 1st, 2008. The regression results based on these alternative crisis begin-dates were qualitatively similar. We find that the ERMQ variable continues to be insignificant in the pre-crisis and crisis periods and significant during the post-crisis period.

The model controls for

other factors that could affect abnormal returns. We control for the possible association with

credit ratings (discussed in the same report that analyzes a firms’ ERM quality) and returns by

27 We use t-statistics based on standard errors clustered by firm to test our hypotheses. 28 For example, when investigating pre-crisis returns we use the most recent ERM quality rating before January 1st, 2008. Similarly we use the most recent ratings before September 1st, 2008 and March 1st, 2009 when we study the crisis and post-crisis periods, respectively. 29SEC requires all firms to submit 10-K filings within 90 days. For large accelerated filers the deadline is 60 days. In order to ensure that accounting data was available in event-time, we compute control variables using data from the most recent fiscal year that ended three months before the period begin date.

24

including the S&P credit rating (CREDIT-RATING) in our regression model. The Capital Asset

Pricing Model posits expected returns to be a function of securities’ market risk. We control for

market risk by including the market model beta (BETA) estimated by regressing excess security

returns on excess market returns for the 60-month period ending one month before the period

beginning date. Prior literature (e.g., Banz 1981; Fama and French 1993) shows that size and

book-to-market significantly explains variation in stock returns. Therefore, we include the

natural logarithm of size (MARKETCAP) and book-to-market ratio (B/M) in our regression

model. We measure size as share price times number of shares outstanding at fiscal-year-end and

book-to-market as the ratio of the book value of common equity and size using Compustat data.

Jegadeesh and Titman (1993) find that past winners outperform past losers. We control for the

relation between future returns and past returns by including the past six-month buy and hold

return (PRET) as of the period start date. Basu (1983) shows that the earnings-to-price ratio

predicts future returns, controlling for size and beta. Therefore, we include earnings-to-price

ratio (E/P) computed as the ratio of earnings per share and price at fiscal-year-end. Because

Bhandari (1988) finds a positive relation between leverage and future returns, we control for

leverage using the natural logarithm of debt to equity ratio (ln(LEVERAGE)). We also include

industry fixed effects to control for possible variation in returns related to industry. Finally,

Model 5 in the post-crisis period includes a control variable for firms that received government

assistance through the Troubled Asset Relief Program (TARP), as such assistance is also likely

to have affected market performance in this period.

As a supplemental analysis, we follow the calendar-time portfolio approach to investigate

the market performance associated with ERM quality. We construct an ERM hedge portfolio that

takes long positions in firms that have ERM ratings of four and above (STRONG_ERM=1) and

25

short positions in firms with ERM ratings less than four (STRONG_ERM=0). We then regress

the returns of the ERM hedge portfolio on the four-factor returns as follows30

Rt= α + β1MKTRFt+ β2SMBt+ β3HMLt+ β4UMDt+ ε, (6)

:

where Rt is the return of the ERM hedge portfolio on day t, MKTRF is the CRSP value weighted

index return minus the risk-free rate, SMB is the average return on the three small portfolios

(value, neutral and growth) minus the average return on the three big portfolios (value, neutral

and growth), HML is the average return on the two value portfolios (small and big) minus the

average return on the two growth portfolios (small and big), UMD is the average return on the

two high prior return portfolios (small and big) minus the average return on the two low prior

return portfolios (small and big) for day t. We use the intercept of equation (6) as an estimate of

the average abnormal return associated with the ERM hedge portfolio.

IV. RESULTS

ERM Determinants

Table 1 presents descriptive statistics for the dependent and independent variables.31

Table 2 reports the estimation results of Model 1 which addresses RQ1 by investigating

the determinants of ERMQ. Regarding company complexity, results presented in Column A

show that larger companies as measured by MARKETCAP have higher quality ERM programs

(p<0.01). Company complexity as measured by SEGMENTS, GLOBAL and FOREIGN are also

The

mean ERM rating is 3.54 on its scale from one to six, with 25.45 percent of companies having

“strong” or “excellent” ratings. The mean credit rating is 4.39 on its scale from one to seven,

where a score below four represents the cutoff for investment grade rating.

30 We use t-statistics based on heteroscedasticity-consistent standard errors to test for statistical significance. 31 For efficiency of presentation, we only discuss in the text the descriptive statistics for test and dependent variables. The highest variance inflation factor among independent variables is for LOSS-PROPORTION (5.23).

26

positively associated with ERMQ (p<0.05). 32

We also investigate the relation between ERM quality and measures of financial distress.

Results show that credit rating (CREDIT-RATING, p<0.01) and Z-score (Z-SCORE, p<0.1) are

significant and positively associated with ERMQ, while LEVERAGE (p<0.01) is negatively

associated with ERMQ. Operating cash-flow (OP-CASH) is not significant. Generally, these

results support the argument that less distressed firms have higher quality ERM programs, due to

availability of resources to invest in such programs, rather than the alternative proposition that

companies with greater risk of distress invest in high quality ERM to mitigate risk of lower-tail

outcomes.

These results are consistent with companies

recognizing that operating complexity creates greater risk inherent in a “silo” management

approach. Regarding the potential association of uncertainty and ERM program quality, Table 2

Column A shows no significance for the standard deviation of daily returns (STD-RET), standard

deviation of operating cash-flow (STD-OP-CASH) and proportion of loss years over the past five

years (LOSS-PROPORTION). These results suggest that firms with greater uncertainty do not

necessarily invest in higher quality ERM programs.

33

[Insert Table 2]

We next investigate the relation between ERM program quality and corporate

governance. RISK-STRUCTURE, a variable combining the existence of a risk officer and/or a

risk committee is positively associated with ERMQ (p<0.01).34

32 We also searched SEC filings and collected data on the number of different SIC codes that the company operates in. When we include this variable it is not statistically significant, while other results are unchanged.

We test the Conference Board’s

(Brancato et al. 2006) suggestion that some companies delegate risk response to the audit

33 We also investigated measuring risk through capital adequacy/information obtained from S&P reports; i.e., capital relative to risk exposure. When this variable is included in the model it is not statistically significant and other results remain unchanged (for seven observations we substituted the sample median for missing values). 34 When RISK-STRUCTURE is separated into two indicator variables that signify the presence of a risk officer or a risk committee, both variables are positive and significant (p<0.05).

27

committee. The coefficient of AC-RISK-OVERSIGHT indicates that firms with audit committees

whose charters indicate direct oversight over risk have higher ERMQ.35 Model results also show

that audit committees with a larger proportion of individuals having more upper management

experience (e.g. CEOs) are positively associated with ERMQ (p<0.1), but audit committee size is

not significant. We also find significant positive association of ERM quality with board tenure

(BOARD-TENURE, p<0.05) but not with other governance variables (BOARDIND and CEO-

DUALITY) pointing to the importance of institutional knowledge for risk management.36

We perform several sensitivity analyses to test the robustness of our findings. First, we

substitute ERMQ with ERMQ_RAW (the original ERM rating collected from S&P). Results,

presented in Column B of Table 2, are consistent with the above results. Further, because ERMQ

ranges from one to six, we also use an ordered logistic regression. Results (presented in Column

C) are similar, as are those of a logistic regression with the dichotomous variable STRONG_ERM

(untabulated). In addition, we address possible nonlinearity in the association of credit ratings

categories with default probability by using rating factors from Moody’s and Fitch, and default

The

lack of association with respect to other board and management characteristics likely implies that

these very general governance attributes are poor proxies for board oversight activities (e.g.,

Larcker et al. 2007). We find a negative coefficient on AUDIT-RELATED-RISK (p<0.01),

implying that companies with strong controls over financial reporting and stable relationships

with external auditors also tend to manage other risks well. Regarding other control variables, we

find that more mature companies in our sample are less apt to invest in higher quality ERM, but

there is no significant association of NYSE listing with ERMQ.

35 Recently, the SEC issued a new rule which enhances proxy disclosures by requiring boards to disclose their leadership structure and the board's role in risk oversight. We read a sample of disclosures following the effective date of this rule (February 2010) and concluded that, for the most part, these first time disclosures were very general in nature and did not reveal much information. 36 We also investigate board size, CEO tenure, and CEO age and found that these variables were not significant.

28

probabilities used by S&P.37

One empirical concern pertains to companies that are excluded from our sample. Not all

companies in the financial services industry are covered by S&P, and many that receive credit

ratings from S&P do not also receive ERM ratings. Selection of companies receiving S&P ERM

coverage is unlikely to be random, which could potentially impact the reliability and external

validity of our results. To test for this possibility, we extract data on 767 additional observations,

including companies within the same industries covered by our primary sample, which receive

S&P credit ratings but do not receive ERM ratings during the same time period.

Analyses using these alternative measures (not tabled) again yield

results that are similar to those reported in Table 2. We also test for survivorship bias by

identifying and removing four companies that delisted after filing for bankruptcy during our

study period. Re-estimating Model 1, results are unchanged. Also, because S&P ERM ratings are

fairly new, we test for a learning curve effect by removing early ratings (those made in 2006),

finding consistent results. Also, since our sample is not a balanced panel, as a sensitivity analysis

we re-estimated Model 1 while including only the most recent observation per company. Again,

our results were qualitatively similar.

38

37 For example, although the difference between an AA rating and AA- ratings by S&P is only one in proximity, the likelihood of default in the AA- category is 50% higher than in the AA rating category.

We employ a

Heckman (1976) selection model to test for bias in OLS regression results. Based on prior

research (e.g., Sufi 2009) investigating factors associated with coverage by rating agencies, we

include company size (MARKETCAP), maturity (FIRM-AGE) and financial risk (MARKET-TO-

BOOK). As the instrument in the first-stage model of S&P credit rating coverage, we use INST-

OWN. As noted by the SEC 2003, debt issuers seek credit ratings to improve marketability and

pricing of their offerings. Greater penetration of a company into the institutional market implies

38 We perform a similar analysis that includes all companies in the same two-digit industry codes that are not necessarily covered by S&P (4,122 observations) and obtain similar results.

29

that the company will seek multiple ratings for its debt, thus increasing the likelihood of rating

by S&P. Estimating Model 1 with the Heckman procedure, we observe (Panel B) that all of the

selection variables affect the likelihood of being censored out of the sample. However, the null

hypothesis that rho=0.51 is not rejected (p=0.475), which indicates that the OLS results in Panel

A are not biased.

ERM and Firm Performance and Value

H1 predicts that ERM quality will be positively associated with firm performance and

value. To test whether ERM quality is associated with firm performance, we use Model 2, whose

dependent variable is ROA. Table 1 shows that the mean ROA among sample companies is 1.9

percent. Results of the OLS regression are presented in Table 3, Column A. The coefficient on

ERMQ is positive and significant (p<0.01), supporting H1. The coefficient of 0.0114 in the ROA

regression implies that a one-level increase in ERMQ is associated with 1.14 percent increase in

ROA from the sample mean.39 We perform a supplemental analysis using 2SLS to determine

whether results are sensitive to controlling for possible endogeneity in the ERM rating. Column

C shows that the coefficient of ERMQ remains significant in this model.40

[Insert Table 3]

H1 also predicts that ERM quality is positively related to firm value. We measure firm

value as Tobin’s Q. Table 1 shows that the mean of Q in the sample is 1.14. Table 3 presents

results of Model 3, testing the predicted association. Column B reveals a positive and significant

association between ERMQ and Tobin’s Q (p<0.05), supporting H1. The coefficient on ERMQ

implies that each increase in ERMQ level is associated with an increase of 3.88 percentage points

39 Substituting ERMQ for ERMQ_RAW yields similar results. Also, our results remain unchanged when estimating this model without 2006 ratings, or using the most recent ERM ratings among unique firms. 40 As robustness we also estimate models wherein the dependent variables are as follows: (1) ROA based on operating income and (2) Net cash flow from operating activities. In both regressions we observe that ERMQ is positive and significant (p<0.1, and p<0.05 respectively).

30

in Tobin’s Q. Compared to the sample average of 1.14, this implies an increase of 3.40 percent in

the typical firm’s Q value, which is economically significant. We further test the sensitivity of

our results to using ERMQ_RAW (the original ratings by S&P) and observe consistent results.41

Our results also indicate no significant association between credit ratings and Tobin’s Q.42 To

investigate whether this result is sensitive to endogeneity due to the non-random nature of ERM

program quality, we estimate an instrumental variable 2SLS regression. We choose instrumental

variables from Model 1 that do not overlap with independent variables in the Tobin’s Q model

and do not give rise to overidentification concerns.43 Specifically, we include CREDIT-RATING,

AUDIT-RELATED-RISK, and FIRM-AGE as instruments.44

Market Reactions to ERM Quality Ratings Disclosures and Revisions

Column D reports the results of the

second stage regression model, showing that as with the OLS results, ERMQ is positive and

significant (p<0.05). The Hansen J statistic for over-identification (Larcker and Rusticus 2010) is

not significant (p=>0.52), implying that the instruments are valid.

We next present results relevant to testing H2, which predicts that market reactions are

associated with the initial ERM rating announcement (Table 4 Panel A) and revisions in ERM

ratings (Table 4 Panel B). As previously noted, we primarily focus on results using sample-

adjusted abnormal returns, but present results based on other reference portfolios as sensitivity

tests. Panel A shows that the cumulative average abnormal return for the event window (-2, +2)

41 Also, using the last available rating among unique firms as well as omitting ratings received in 2006 yields consistent results. 42 The highest variance inflation factor among independent variables is for BOARD-SIZE (2.33). 43 CREDIT-RATING is a valid instrument in this system, as results of Model 1 show that it is associated with ERMQ, but single-stage OLS models of Tobin’s Q and ROA both show no significant effect of CREDIT-RATING. Because CREDIT-RATING and ERMQ are positively correlated (0.49), it is possible that multicollinearity between the measures prevents significance on CREDIT-RATING. To investigate, we regressed CREDIT-RATING on ERMQ, and substituted the residual from this model for CREDIT-RATING in Models 2 and 3. ERMQ remains significant in the presence of the residual credit rating, and the residual credit rating is not significant in either model. Also, we exclude RISK-STRUCTURE from the first stage as its inclusion causes overidentification. 44 In this model, CREDIT-RATING, is only included as instrument and is therefore removed from the Tobin’s q regression model.

31

based on sample-adjusted returns is 1.62 percent, statistically significant using parametric

(p<0.01) but not using non-parametric statistics. This pattern suggests that the overall market

response could be driven by outliers. When we examine market response preceding the

announcement date (-2, 0) we find that both parametric and nonparametric tests are significant at

least at p<0.05. However, in the post-announcement period (0, +2) we find only the parametric

test statistic to be significant. Examining individual observations, we find that the post-

announcement drift is driven by a single company, Colonial Bancgroup. When we exclude this

observation the (0, +2) event window becomes insignificant using both parametric and non-

parametric tests. Taken together, these results imply that the market values ERM quality ratings,

but the information is already incorporated into share prices prior to the S&P announcement. The

remaining columns show that results of parametric testing of the pre-announcement response is

not sensitive to alternative specifications, but the nonparametric result is only found when the

reference portfolio is formed within the financial services industry. Overall, these results show

that information contained within the revelation of ERM quality ratings is important and is used

by investors to revise their average expectations about firm value.

[Insert Table 4]

Results of testing market reaction to changes in ERM quality ratings are reported in Table

4 Panel B. These statistics suggest a strong market reaction to changes in the ERM ratings. The

five-day market reaction (CAAR -2, +2) centered on the date of the ERM rating change, is 4.51

percent based on sample-adjusted abnormal returns. CAARs (-2, +2) computed using other

reference portfolios are also statistically significant using Patell and Rank test statistics across all

32

event windows.45

ERM Quality and the Earnings Response Coefficient

We conclude that H2b is supported, as changes in S&P’s ERM ratings appear

to convey significant information to markets and investors.

H3 predicts that the market reaction to earnings surprises is greater for firms with higher

ERM quality. Table 5 presents results of estimating Model 4, based on earnings announcement

cumulative abnormal returns relative to sample, industry, market and size reference portfolios.

Consistent with prior literature, we find that UE in all models is positive and significant

(p<0.05), indicating an association between unexpected earnings and earnings announcement

returns. The test variable for H3 is the interaction of unexpected earnings UE and

STRONG_ERM, which is positive and significant (p<0.01), supporting H3.46 This indicates that

markets react more strongly to earnings surprises of firms with higher quality ERM programs,

consistent with investors perceiving the earnings of those firms to be of higher quality. Results

are similar using industry-adjusted, market-adjusted, or size-adjusted returns to measure market

reaction to earnings announcements.47

Further, we examine whether our results are affected by non-linearity, through using the

rating factors of Fitch and Moody’s and default probabilities reported by S&P (not tabled),

finding similar results. Finally, we again test sensitivity to learning curve effects on the part of

S&P analysts by including the interaction of year and UE X STRONG_ERM variables in the

models. None of the interaction variables are statistically significant and the coefficient of UE X

STRONG_ERM is unaffected. In conclusion, results of earnings response coefficient analysis are

45 One of the ERM rating revisions in our sample involves a two step change in the ERM quality rating. As a sensitivity analysis, we exclude this observation and find similar results. 46 The variance inflation factors on ERMQ (CREDIT-RATING) in this model are 1.47 (1.04). 47 We repeat the earnings response coefficient analysis using an alternative STRONG_ERM variable that takes a value of one for firms with ERM ratings greater than four and find that the primary interaction variable is statistically significant and that the remaining results are similar

33

insensitive to the use of alternative ERMQ definitions, alternative credit rating specifications and

controlling for the timing of ERM ratings.

[Insert Table 5] ERM and the Global Financial Crisis

Because the advantage of effective ERM is likely to be more pronounced at times of

greater risk, we supplement the above analysis by investigating whether firms with stronger

ERM programs experienced superior market returns in the periods surrounding the recent

financial crisis (RQ2). Table 6 Panels A, B, and C report regression results based on pre-crisis,

crisis and post-crisis return data, respectively. Panel A shows that the coefficient on ERMQ is not

significant, implying no association of ERM quality with equity market performance before the

financial crisis. Thus, prior to the global financial crisis (GFC) when systemic risk was relatively

low, ERM quality was not valued by the market. Results reported in Panel B show that ERMQ is

also insignificant in Panel B, implying that as the crisis deepened, financial services companies

with stronger ERM programs fared no differently than those with weaker programs. In contrast,

Panel C shows that there is a significant association (p<0.05) between ERM quality and market

returns during the post-crisis period.48 In the sample-adjusted model, the coefficient on ERMQ is

0.2152, which indicates that a one-level variation in ERM quality is associated with 21.52

percent change in the post-crisis buy-and-hold abnormal return.49

48 The highest variance inflation factor in the pre-crisis and crisis period regression analyses belongs to MARKETCAP with 2.28 for pre-crisis and 2.53 for the crisis periods. In the post-crisis analysis the highest variance inflation factor is computed for BETA with a value of 5.00. The VIF factors and the correlation structure do not suggest multicolinearity to be an issue.

This suggests that firms with

49 These results are obtained while controlling for credit ratings and several risk measures. The signs and significance of these variables differ by phase of the crisis. For instance, BETA is negative and significant (p < 0.10) in the downturn (Panel B) and positive and significant (p < 0.05) in the rebound period (Panel C), consistent with higher variance in returns for higher-beta companies. Panel C also shows that the receipt of TARP funds by some companies in the sample is positively associated with returns in the rebound period (p < 0.05).

34

superior ERM programs were able to rebound more swiftly from the crisis and regain market

value.50

[Insert Table 6]

In order to ascertain that the results reported in Table 6 are insensitive to methodological

choices, we conduct a number of robustness checks. First, replacing the ERMQ variable with

ERMQ_RAW leaves results unaffected. Replacing the credit rating variable CREDIT-RATING

with Moody’s and Fitch’s ratings factors and S&P’s default probability rates also yields similar

results. Finally, we estimate shifts in effects over time by including the interaction of ERMQ

with year dummy variables. The interaction variables are statistically insignificant and inferences

are unchanged from the original models. We test sensitivity to using the calendar-time portfolio

approach by constructing a portfolio that takes long positions in firms with ERM ratings of four

or above and short positions in firms with ERM ratings less than four. We regress the returns of

this portfolio on excess market returns using a four-factor model (excess market returns and size,

book-to-market, and momentum factor returns). The intercept of the regression serves as an

estimate of the average abnormal returns associated with ERM quality. Results reported in Table

7 Panel A, show intercepts of -0.0001, -0.0008 and 0.0016 for the pre-crisis, crisis and post-crisis

periods, respectively. Only the intercept based on post-crisis data is statistically significant,

suggesting that firms with strong ERM programs outperformed other sample firms by an average

daily return of 0.16% during the post-crisis period.51

50 We supplement our tests of the financial crisis period by testing whether firms with higher ERMQ are less likely to be affected by sharp market fluctuations (i.e., have lower volatility of returns). We investigate this issue by regressing the standard deviation of daily returns within each crisis sub-period on ERMQ and control variables. In contrast to our findings based on buy-and-hold abnormal returns, we do not find any significant association between ERMQ and return volatility for any sub-period of the crisis.

The results are largely similar when we use

the three-factor model (Panel B) or the Capital Asset Pricing Model (Panel C) to estimate

abnormal returns.

51 This corresponds to an annualized abnormal return of approximately 40%.

35

V. CONCLUSIONS AND LIMITATIONS

This paper investigates the determinants of ERM program quality, and the association of

ERM quality with firm performance and value, among financial services companies with ERM

quality ratings provided by Standard & Poor’s during 2006-2008. As such, it follows a line of

accounting literature that seeks to understand why corporations adopt specific corporate

governance mechanisms, and whether those mechanisms achieve their goals. In addition to

advancing knowledge relevant to these questions in general, research in the context of ERM is

also important because it is a product of a joint effort by the professional and academic

accounting communities, expanding the COSO framework beyond the financial reporting system

to management control more broadly defined.

This research is also important due to increasing pressure on firms to invest resources in

improving risk management; for example, the SEC has considered requiring specific disclosures

on the risk qualifications of individual directors. While ERM presents the most widely accepted

framework that entities can use to manage risks, there is limited empirical evidence on the

relation between ERM and firm performance/value. Prior related research tends to examine the

drivers of ERM adoption and its consequences, rather than the effects of ERM program quality.

By using a direct measure of ERM quality, we examine a set of companies known to have ERM

programs, and differentiate them by quality as determined by an independent rater. Given the

challenges to frequently used methods of measuring the presence or quality of corporate

governance mechanisms (e.g., surveys of company personnel or publicly available data), these

quality ratings provide a valuable view of the extent to which each ERM program has adopted

and internalized the integration of risk management across disparate parts of the entity, instead of

a traditional silo-based approach.

36

Prior to discussing our conclusions, we note several limitations of our analysis. First, we

implicitly assume that the components of S&P ERM ratings validly represent aspects of ERM

quality, and that program effectiveness increases in the ERM rating score. If this were not the

case, it would produce a bias against finding the associations we test, and our results are

consistent with this assumption. However, we recognize that there may be features of ERM

quality that are not measurable, which would reduce the power of our tests. Second, ratings

agencies such as S&P have been criticized for their ratings during the financial crisis,

particularly their ratings of financial products. If credit ratings were biased upward during our

sample period, this bias may also have applied to ERM quality ratings. However, the mean rating

in our sample is at the midpoint of the theoretical range, which does not suggest an upward bias.

Third, we limit our analysis to financial services companies, as S&P ERM ratings currently only

cover those types of firms. Future research could explore issues related to ERM quality in other

industries as data become available. Fourth, because S&P’s ERM ratings are part of the credit

ratings process, their analysts focus on the impact that ERM might have on the firm’s ability to

repay debt. While prior research investigates equity investors’ use of credit ratings in making

market decisions, the impact of ERM on the equity markets is less clear. Despite this limitation,

we find strong evidence that equity markets do find the ERM ratings useful.

Our first set of conclusions relates to determinants of ERM program quality. Davila and

Foster (2007), Elbashir et al. (2011; 157) note that studies of management control systems

typically examine adoption, but rarely explore “the variation in quality or depth of the use of

MCS following that adoption”. Similarly, prior ERM research examines ERM adoption through

announcements of risk officer appointments (e.g., Liebenberg and Hoyt 2003) finding few

significant determinants. Our results provide insight in showing that companies with superior

37

ERM programs are more complex, have greater financial resources, and better corporate

governance. Similarly, prior research finds that effective internal controls (as determined by

auditors using the COSO framework) are associated with overall corporate governance quality

and audit committee financial expertise (Hoitash et al. 2009). Further, studies find that resource

constraints inhibit implementation of effective internal controls (e.g., Doyle et al. 2007) and

management control systems (Elbashir et al. 2011). In sum, our results provide insights into the

characteristics of financial services companies that allocate sufficient resources to integrating

risk management activities to achieve a high quality rating by S&P.

We also examine the proposition that high quality ERM programs enhance operating

performance and add value to companies, controlling for the characteristics identified in the

determinants analysis. As noted by Ittner and Larcker (2001), the performance effects of

management control techniques is an important issue that remains unresolved. We find that firm

performance as measured by accounting returns, as well as market valuation using Tobin’s Q, are

higher for firms that invest in higher quality ERM. These results suggest that the improved risk

management inherent in higher quality ERM programs assists performance by helping to

mitigate losses and/or to take advantage of opportunities. Further, while prior research (McShane

et al. 2011) and this paper find that overall market valuation is higher for such companies, there

are various ways in which the higher valuation could occur. We find that when ERM programs

are initially rated by S&P, the average market reaction is higher for strong and excellent ERM

rated firms than for firms with lower ratings. In addition, there is a positive and significant

market reaction to revisions of ERM ratings. These results imply market anticipation of better

future performance by high-quality ERM companies, while prior research does not find an

overall market reaction to CRO announcements as a proxy for ERM implementation (Beasley et

38

al. 2008). Interestingly, the market response to initial ratings appears prior to the announcement,

implying that profits from this information have been taken prior to the information becoming

public. Further, we find that the intensity of investors’ average reactions to earnings surprises

increases for companies with higher quality ERM. This is evidence of increased usefulness of

accounting information, consistent with market perceptions of greater reliability of earnings due

to those companies’ better control over their future activities.

Finally, we examine ERM quality in the recent financial crisis, which our study period

encompasses. Because ERM programs are intended to protect against lower-tail outcomes, the

financial crisis provides a natural setting in which to examine this proposition. We consider how

relative ERM quality affected these financial industry firms during this very challenging time.

We find no association of ERM quality with abnormal returns in the sub-period preceding the

crisis (i.e., January through August 2008). While we cannot definitively explain this lack of

response, it may be that because market returns in that period were generally high, risk was not

as important an issue. We also find no association of ERM quality with returns during the crisis

(September 2008 through February 2009), suggesting that firms with higher-quality ERM were

not differentially protected from the sudden and catastrophic market declines experienced during

the crisis. In contrast, however, we find a strong association of ERM quality and returns in the

initial recovery period (March through October 2009). This result suggests that as the market

rebounded, investors looked to information such as ERM quality, that indicated that some firms

could address future risks in a more systematic and integrated manner.

39

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45

APPENDIX ERM Quality Classification Description

Weak insurer ERM programs cannot consistently control all of an insurer's major risks. Control processes are incomplete for one or more major risks, and these insurers have limited ability to fully identify, measure, or manage major risk exposures.

Adequate insurer ERM programs have fully functioning risk control systems in place for all major risks. The risk management process is solid, classical, and silo-based, and most insurers fall into this category. However, these insurers often lack a clear vision of their overall risk profile and often lack overall risk tolerance. Risk limits for various risks have usually been set independently, and systems for each risk element usually function completely separately, without any significant coordination across silos of its risks. Adequate insurers also lack a robust process for identifying and preparing for emerging risks. Since neither cross-risk view nor overall risk tolerance exists, no process to optimize risk-adjusted return is present either. Standard & Poor's does not expect these companies to experience any unusual losses outside of their separate risk tolerances unless a rapid, major change occurs in the environment related to one or more of their major risks. Insurers can also have Adequate ERM if the insurer has developed a cross-risk view, and an overall risk tolerance uses risk-return considerations for its business decisions and has a process for envisioning the next important emerging risk but does not have fully developed controls. Adequate ERM should not be a negative factor in most insurer ratings.

Strong ERM insurers have exceeded the Adequate criteria for risk control and have a vision of their overall risk profile, an overall risk tolerance, a process for developing the risk limits from the overall risk tolerance that is tied to the risk adjusted returns for the various alternatives, and a goal of optimizing risk-adjusted returns. In addition, Strong programs have robust processes to identify and prepare for emerging risks. Standard & Poor's expects ERM to be a competitive advantage for these insurers over time. The process of selecting choices that have the best risk-adjusted returns should result in lower losses per unit of income over time, allowing these insurers to choose between offering lower prices, paying higher dividends retaining higher capital, or obtaining capital at a lower net cost than competitors without the ERM advantage.

Excellent ERM programs share all the criteria for programs considered Strong but are more advanced in their development, implementation, and execution effectiveness. An Excellent ERM insurer will have developed its process more fully over time, may have implemented it throughout a higher percentage of its group, or may be executing the process more effectively.

Note: This description is taken from Santori et al. (2006, 3-4). Criteria for financial institutions are similar.

46

TABLE 1 Variable Definitions and Descriptive Statistics

Variable Name Variable Definition [source] Mean (Median), S.D. Panel A: ERM Quality Determinants (Model 1)

ERMQ ERM quality measure ranging from 1 (low) to 6 (high) [S&P Ratings Direct] 3.545 (3), 1.134

ERMQ_RAW The raw ERM ratings published by S&P. [S&P Ratings Direct] 2.26 (2), 0.594

MARKETCAP Natural log of the market value of equity [Compustat data PRCC_F × data CSHO] 8.476 (8.362), 1.454

SEGMENTS Sum of reported business and geographic segments [Compustat Segment file] 5.388 (4), 4.957

GLOBAL =1 if the company operates at a global scale [SEC filings]; 0 otherwise 0.103 (0), 0.305

FOREIGN = 1 if the company has non-zero foreign currency translation; 0 otherwise [Compustat data FCA] 0.139 (0), 0.347

STD-RET Standard deviation of daily stock returns during the fiscal year. [CRSP] 0.033 (0.022), 0.024

STD-OP-CASH The standard deviation of operating cash-flows during the past five years. [Compustat] 0.026 (0.014), 0.036

LOSS-PROPORTION The proportion of loss years over the past five years. 0.186 (0.071) 0.276

CREDIT-RATING

An ordinal variable based on credit ratings by S&P with values as follows: AAA (7), AA+, AA and AA- (6), A+, A, and A- (5), BBB+, BBB, and BBB- (4), BB+, BB, BB- (3), B+, B, and B- (2), CCC+, CCC, CC, C, D or SD (1) [S&P credit rating]

4.394 (4), 0.809

OP-CASH Cash flow from operations divided by total assets [Compustat data OANCF] 0.063 (0.038), 0.089

LEVERAGE Ratio of total liabilities to total assets [ Compustat (data DLC+data DLTT)/data AT)] 0.11 (0.059), 0.141

Z-SCORE

The sum of the mean rate of return on assets and the mean equity-to-assets ratio divided by the standard deviation of the return on assets. A minimum of four and maximum of 15 years of historical data were required to compute this measure. [Compustat]

22.191 (16.551), 22.787

RISK-STRUCTURE

=1 if either RISK-OFFICER or RISK-COMMITTEE =1; 0 otherwise. 0.4 (0), 0.491

AC-RISK-OVERSIGHT

=1 if the audit committee has risk oversight. [Proxy statements]; 0 otherwise 0.273 (0), 0.447

PAFE

Proportion of audit committee members that are accounting financial experts, i.e. the biography indicates at least one of the following: CPA, chief financial officer, auditor, chief accounting officer, controller, treasurer or VP-Finance [AuditAnalytics or proxy statements]

0.273 (0.25), 0.242

PSFE

Proportion of audit committee members that are supervisory financial experts, i.e., the biography indicates at least one of the following: CEO, chief operating officer, board chair or company president, and individual is not an accounting financial expert [AuditAnalytics or proxy statements]

0.383 (0.333), 0.241

ACSIZE Number of members of the audit committee in 2004 [IRRC, AuditAnalytics or proxy statements] 4.2 (4), 1.122

BOARDIND Percentage of outsiders as board members [IRRC, Audit Analytics or proxy statements] 0.858 (0.889), 0.083

47

TABLE 1 (continued) Variable Definitions and Descriptive Statistics

Variable Name Variable Definition [source] Mean (Median), S.D. Panel A: ERM Quality Determinants (Model 1) (Continued) BOARD-TENURE

Average tenure of outsiders serving on the board [IRRC, Audit Analytics or proxy statements] 7.842 (7), 3.83

CEO-DUALITY = 1 if the CEO is also the board chair; 0 otherwise [IRRC, Audit Analytics or proxy statements] 0.582 (1), 0.495

AUDIT-RELATED-RISK

= 1 for companies that switched auditors in the current year, or reporting Section 302/404 material weakness; 0 otherwise [AuditAnalytics]

0.091 (0), 0.288

FIRM-AGE Natural logarithm of the number of years the firm has coverage by Compustat [Compustat] 2.774 (2.773), 0.646

NYSE =1 if the company’s shares are traded at the New York Stock Exchange. [CRSP]; 0 otherwise. 0.442 (0), 0.498

Panel B: ERM Quality Determinants (Model 1)

M/B Ratio of market value at fiscal year-end to book value of common equity [Compustat data(PRCC_F × CSHO)/ CEQ] 1.455 (1.192) 1.323

Panel B: ERM Quality, Firm Value and Financial Performance (Models 2 and 3)

ROA Income before extraordinary items divided total assets [Compustat data IB / data AT] 0.019 (0.012), 0.053

TOBIN’S Q (Book value of assets – (book value of equity plus the market value of equity)/ book value of assets) [Compustat data AT – (data CEQ + data PRCC_F × CSHO) / data AT]

1.135 (1.026), 0.428

LOGBOARDSIZE Logarithm of number of members serving on the board of directors [IRRC, AuditAnalytics or proxy statements] 2.429 (2.485), 0.27

LOGASSETS Logarithm of total assets [Compustat data AT]. 9.879 (9.586), 1.711

INST-OWN Percent of shares owned by institutional investors [Compustat Institutional Ownership File] 0.687 (0.718), 0.242

SALES-GROWTH Percentage growth in sales 0.006(0.015) 0.187

CAPITAL OVER SALES

Capital expenditure over sales [Compustat data CAPX/data SALE] 0.015(0.008) 0.025

STDROA The standard deviation of returns on assets calculated over a period of no less than three years and no more than five years [Compustat data IB / data AT]

0.018 (0.011), 0.021

Panel A: ERM and Earnings Response (Model 4)

CAR_SAMP Cumulative sample-adjusted returns for the three-day period centered on the earnings announcement date [CRSP] 0.001 (0.005), 0.072

CAR_IND Cumulative industry-adjusted returns for the three-day period centered on earnings announcement date [CRSP] 0.001 (0.002), 0.072

CAR_MAR Cumulative market-adjusted returns for the three-day period centered on the earnings announcement date [CRSP] 0.003 (0.005), 0.08

CAR_SIZE Cumulative size-adjusted returns for the three-day period centered on the earnings announcement date [CRSP] 0.003 (0.004), 0.079

UE (Reported earnings – analysts’ earnings expectation)/share price at the end of previous fiscal quarter [I/B/E/S and CRSP] -0.009 (0.000), 0.113

STRONG_ERM =1 if the ERM quality is 4 or above; 0 otherwise [S&P Ratings Direct] 0.388 (0), 0.489

NEG Fiscal quarters for which income before extraordinary items are negative [Compustat data IBQ] 0.07 (0), 0.255

48

TABLE 1 (continued) Variable Definitions and Descriptive Statistics

Variable Name Variable Definition [source] Mean (Median), S.D.

Panel A: ERM and Earnings Response (Model 4) (Continued)

B/M Ratio of book value of common equity to market value at fiscal year-end [Compustat data CEQ / (data PRCC_F × data CSHO)]

0.73 (0.683), 0.341

BETA Market model beta estimated using 60 months of returns, ending three months prior to the fiscal quarter end [CRSP] 1.108 (0.992), 0.551

COV Inverse of the number of analysts who made earnings forecasts for the fiscal quarter [I/B/E/S]. 0.193 (0.143), 0.193

Panels A, B, and C: Pre-Crisis, Crisis, and Post-Crisis, respectively (Model 5)

BHAR_SAMP Sample-adjusted buy and hold return [CRSP] 0.07 (0.038), 0.734 BHAR_IND Industry-adjusted buy and hold return [CRSP] 0.141 (0.036), 0.759 BHAR_MAR Market-adjusted buy and hold return [CRSP] 0.113 (-0.047), 0.775 BHAR_SIZE Size-adjusted buy and hold return [CRSP] 0.094 (-0.046), 0.773

PRET Prior six-month buy and hold return as of the period begin date [CRSP] -0.229 (-0.152), 0.296

E/P Ratio of earnings per share to price [Compustat data EPFI / data PRCC_F] 0.067 (0.085), 0.157

PRET Prior six-month buy and hold return as of the period begin date [CRSP] -0.229 (-0.152), 0.296

E/P Ratio of earnings per share to price [Compustat data EPFI / data PRCC_F] 0.067 (0.085), 0.157

Panel A: Four-Factor Model (Calendar-Time Portfolio Approach)

MKTRF CRSP value weighted index return for day t. -0.0004 (0.003), 0.0228

SMB Average return on the three small portfolios (value, neutral and growth) minus the average return on the three big portfolios (value, neutral and growth)

0.0002 (0.0005), 0.0083

HML Average return on the two value portfolios (small and big) minus the average return on the two growth portfolios (small and big)

0.0001 (0.002), 0.0108

UMD Average return on the two high prior return portfolios (small and big) minus the average return on the two low prior return portfolios (small and big)

-0.0012 (0.0006), 0.0201

Notes: This table presents mean (median) and standard deviation of all variables. Each panel contains variables in the respective models that are incremental to those previously described.

49

TABLE 2 Results of Estimating Model 1: ERM Determinants

(A) (B) (C) (D)

Panel A. ERM Determinants Expected Sign

Linear Regression DV=ERMQ

Linear Regression DV =

ERMQ_RAW

Ordered Logistic Regression DV=ERMQ

Heckman Two Stage regression

DV=ERMQ

MARKETCAP + 0.2148*** (3.43)

0.0886*** (2.69)

0.6894*** (2.36)

0.1853 (1.14)

SEGMENTS + 0.0385** (1.95)

0.0133 (1.20)

0.1091* (1.78)

0.0352* (1.46)

GLOBAL + 0.6925** (2.21)

0.4990*** (2.70)

2.2830** (1.65)

0.6900*** (2.48)

FOREIGN + 0.3446** (1.79)

0.2572*** (2.44)

0.7325* (1.29)

0.3551* (1.63)

STD-RET + 1.8087 (0.39)

1.8871** (2.02)

3.0384 (0.23)

2.3232 (0.45)

STD-OP-CASH + 4.7052 (1.00)

3.0519* (1.62)

13.9362* (1.45)

4.9923* (1.28)

LOSS PROPORTION + 0.4577 (1.00)

0.3112* (1.56)

1.6692 (0.93)

0.3897 (0.56)

CREDIT-RATING + 0.2672*** (3.25)

0.1450*** (2.96)

0.5936* (1.62)

0.2599*** (2.39)

OP-CASH + 0.4302 (0.23)

-0.0179 (-0.03)

1.4941 (0.51)

0.7316 (0.44)

LEVERAGE ? -1.2539*** (-3.40)

-0.1962 (-0.77)

-3.9950** (-2.20)

-0.6363 (-0.20)

Z-SCORE + 0.0030* (1.32)

0.0035** (2.24)

0.0120* (1.38)

0.0031* (1.37)

RISK-STRUCTURE + 0.5470*** (4.51)

0.1472*** (8.57)

1.7017*** (3.17)

0.5320*** (2.88)

AC-RISK-OVERSIGHT + 0.2282* (1.61)

0.1330** (1.72)

0.6800* (1.32)

0.2186* (1.46)

PAFE + -0.0028 (-0.01)

0.1578 (0.86)

-0.1677 (-0.16)

0.0617 (0.12)

PSFE + 0.6373* (1.59)

0.2960** (1.72)

1.5350 (1.15)

0.6528** (1.88)

ACSIZE + -0.0468 (-0.74)

-0.0395* (-1.57)

-0.2012 (-0.82)

-0.0413 (-0.59)

BOARDIND + 1.3264 (0.98)

0.6170 (0.79)

4.4153* (1.55)

1.1917 (1.08)

BOARD-TENURE + 0.0343** (1.84)

0.0042 (0.67)

0.1160** (1.81)

0.0363** (1.71)

CEO-DUALITY - 0.2229 (1.44)

0.0890 (1.06)

0.5351 (1.14)

0.2210* (1.52)

AUDIT-RELATED-RISK - -0.6698*** (-4.27)

-0.3152*** (-4.64)

-2.1587*** (-3.21)

-0.6608*** (-3.01)

FIRM AGE ? -0.2986*** (-2.80)

-0.0480 (-0.63)

-0.9606** (-2.11)

-0.3681 (-0.88)

NYSE + -0.0101 (-0.05)

0.0162 (0.21)

-0.0681 (-0.13)

0.0029 (0.01)

Constant -1.7214 (-1.23)

-0.5552 (-0.73)

-0.3278 (-0.07)

Industry and Year Dummies Included Included Included Included Observations 165 165 165 932

(165 Selected) Wald Chi-square/Adjusted R2, or Pseudo R2

0.461 0.387 0.296 194.42***

50

TABLE 2 (continued) Results of Estimating Model 1: ERM Determinants

(A) (B) (C) (D)

Panel B. Results of First Stage Heckman Model

Expected Sign

Linear Regression DV=ERMQ

Linear Regression

DV = ERMQ_RAW

Ordered Logistic Regression DV=ERMQ

Heckman Two Stage regression

DV=ERMQ

M/B +

-0.2214** (-2.09)

MARKETCAP +

0.1556*** (4.69)

FIRM_AGE +

0.0187*** (2.86)

LEVERAGE ?

-2.5181*** (-8.92)

Constant

-1.4899*** (-5.58)

Rho Chi-square 0.51

(p=.475) Notes: This table presents results of estimating Model 1, investigating company characteristics associated with greater investment in ERM, as measured by S&P ERM program quality ratings. Model 1 includes year and industry fixed effects and is estimated with standard errors corrected for clustering at the year and firm level. All variables are defined in Table 1. Numbers in the cells are coefficients (t-statistics), with significance denoted as ***, **, * for one percent, five percent, and ten percent, respectively. One-tailed tests are presented for directional expectations.

51

TABLE 3 Results of Estimating Models 2 and 3: ERM Quality, Firm Value and Financial Performance

OLS Regressions 2SLS Regressions Expected (A) (B)

(C) (D)

Sign Model 2: ROA

Model 3: Tobin's Q

Model 2: ROA

Model 3: Tobin's Q

ERMQ + 0.0114*** (3.72)

0.0388** (2.25)

0.0102*** (4.11)

0.1120* (1.56)

LOGBOARDSIZE - -0.0441*** (-3.26)

0.1074 (1.27)

-0.0466*** (-4.06)

0.0809 (0.99)

BOARDIND ? -0.0127 (-0.53)

0.1339 (0.49)

-0.0095 (-0.45)

0.0570 (0.44)

LOGASSETS ? -0.0064*** (-2.73)

-0.0653* (-1.75)

-0.0074*** (-4.23)

-0.0722* (-1.83)

ROA +

5.1311*** (20.60)

4.8284*** (84.66)

INST-OWN ? 0.0062 (0.50)

0.0467 (0.30)

0.0086 (0.87)

-0.0012 (-0.01)

SEGMENTS ? 0.0020* (1.90)

0.0015 (0.16)

0.0020** (2.29)

0.0002 (0.02)

LEVERAGE ? -0.0052 (-0.12)

0.2331 (0.36)

-0.0067 (-0.19)

0.2946 (0.53)

SALES-GROWTH + 0.0774*** (2.78)

-0.1086 (-0.33)

0.0774*** (3.39)

-0.0735 (-0.33)

CAPITAL OVER SALES

+ -0.5766*** (-3.36)

0.8074 (0.31)

-0.5774*** (-4.06)

0.5679 (0.26)

CREDIT-RATING ? -0.0058* (-1.35)

0.0561 (1.07)

STDROA ? -0.5885 (-0.79)

-0.6003 (-1.01)

Constant 0.2166*** (6.96)

0.9156*** (3.34)

0.2008*** (5.22)

1.1629*** (5.39)

Industry and Year Dummies

Included Included Included Included

Observations 165 165 165 165 Adjusted R2 0.376 0.520 0.375 0.496

Notes: This table presents results of estimating Models 2 (ROA, column A) and 3 (Tobin’s Q, column B) as a function of ERMQ and other company characteristics. Columns C and D, respectively, show results of testing sensitivity to use of two-stage least squares; please see the text for details of model structure. Model 2 includes year and industry fixed effects and is estimated with standard errors corrected for clustering at the year and firm level. All variables are defined in Table 1. Numbers in the cells are coefficients (t-statistics), with significance denoted as ***, **, *, for one percent, five percent, and ten percent, respectively. One-tailed tests are presented for directional expectations.

52

TABLE 4 Market Reaction to the Disclosure of ERM Ratings and Ratings Revisions

Event Window

Expected Sign

Sample-Adjusted Returns

Industry-Adjusted Returns

Market-Adjusted Returns

Size-Adjusted Returns

Panel A: Initial Enterprise Risk Management Rating Announcement (N=107)

(-2,+2) + 1.62% 1.33% 0.74% 0.70%

3.440*** 2.939*** 0.455 0.166

0.151 -0.093 -0.980 -0.836

(-2,0) + 1.27% 1.07% 0.68% 0.63%

4.676*** 4.073*** 1.881** 1.338*

1.896** 1.640* 0.527 0.544

(0,+2) + 0.68% 0.55% 0.22% 0.21%

1.702** 1.572* -0.316 -0.363

-0.814 -0.885 -1.069 -1.018

Panel B: Enterprise Risk Management Rating Revisions (N=21)

(-2,+2) + 4.51% 4.67% 3.52% 3.24%

4.969*** 5.628*** 3.316*** 3.169***

2.105** 2.694*** 1.799** 1.690**

(-2,0) + 2.95% 3.03% 2.60% 2.37%

4.433*** 4.727*** 2.457*** 2.241**

1.227 1.345* 0.894 0.701

(0,+2) + 3.60% 3.78% 2.48% 2.35%

5.078*** 6.128*** 3.789*** 3.844***

2.273** 3.148*** 2.332** 2.375*** Notes: This table presents results of testing the significance of abnormal market returns around initial S&P ratings (Panel A) and ratings revisions (Panel B). The initial S&P rating is the first ERM rating assigned by the S&P for a particular firm (weak, adequate, strong, and excellent). Rating revision is the issuance of a new ERM rating by S&P that results in a change in the previous ERM rating, for example, an upgrade (downgrade) accepts the value of one (minus one) when ratings are revised. To align the market reaction to positive and negative news, we multiply the returns in response to low ratings and downgrades by negative one. All variables are defined in Table 1. Patell and Rank test statistics are shown on the second and third rows, with significance denoted as ***, **, * for one percent, five percent, and ten percent, respectively.

53

TABLE 5 Results of Estimating Model 4: ERM Quality and the Earnings Response Coefficient

Panel A: ERM and Earnings Response

Expected Sign

(A) Sample-Adjusted Returns

(B) Industry-Adjusted Returns

(C) Market-Adjusted Returns

(D) Size-

Adjusted Returns

(CAR_SAMP) (CAR_IND) (CAR_MAR) (CAR_SIZE) UE + 2.6516** 3.2926*** 3.3433*** 2.9821*** (2.17) (3.23) (3.47) (2.70) UE×STRONG_ERM + 3.6620*** 3.3059*** 3.6876*** 3.9342*** (3.58) (2.86) (2.81) (3.16) UE×CREDIT-RATING ? -0.0010 -0.0007 -0.0013 -0.0012 (-0.69) (-0.51) (-0.88) (-0.87) NEG - 0.0165*** 0.0123 0.0092 0.0104 (5.12) (0.94) (0.64) (0.73) UE×NEG - 1.0970 0.8623 1.0250 0.8706 (0.99) (0.92) (0.91) (0.81) UE×B/M - -0.4306 -0.5488 -0.4295 -0.3791 (-0.59) (-0.77) (-0.63) (-0.52) UE×BETA - -0.7447 -0.7982* -0.8094* -0.6901 (-1.21) (-1.41) (-1.40) (-1.14) UE×COV - -2.1822 -3.3850* -4.1488** -3.6990* (-0.92) (-1.34) (-1.90) (-1.61) Constant -0.0057 -0.0081 -0.0024 -0.0034 (-0.43) (-0.59) (-0.18) (-0.25) Industry and fiscal quarter fixed-effects

Included Included Included Included

N 544 544 544 544 R2 0.148 0.147 0.175 0.176 Notes: This table presents estimation results of Model 4, investigating whether the market response to company earnings is greater for high ERMQ companies. The model is estimated with fiscal quarter and industry fixed effects. All variables are defined in Table 1. Numbers in the cells are coefficients (t-statistics based on standard errors clustered by fiscal quarter and firm), with significance denoted as ***, **, * for one percent, five percent, and ten percent, respectively. One-tailed tests are presented for directional expectations.

54

TABLE 6 Results of Estimating Model 5: ERM Quality and Pre-crisis, Crisis and Post-crisis Returns

Panel A: Pre-Crisis Period

Expected Sign

Sample-Adjusted Returns

(BHAR_SAMP)

Industry-Adjusted Returns

(BHAR-IND)

Market-Adjusted Returns

(BHAR_MAR)

Size-Adjusted Returns

(BHAR_SIZE) ERMQ + -0.0075 -0.0106 -0.0075 0.0062 (-0.36) (-0.52) (-0.36) (0.27) CREDIT-RATING ? -0.0069 0.0007 -0.0069 -0.0187 (-0.21) (0.02) (-0.21) (-0.52) BETA + 0.0706* 0.0675* 0.0706* 0.0674 (1.44) (1.38) (1.44) (1.08) MARKETCAP - -0.0516*** -0.0519** -0.0516*** -0.0538** (-2.34) (-2.32) (-2.34) (-2.23) B/M + 0.0840 0.0689 0.0840 0.0411 (0.64) (0.52) (0.64) (0.28) PRET + 0.4707*** 0.4567*** 0.4707*** 0.4549*** (3.08) (3.00) (3.08) (2.80) E/P + 0.5013 0.4093 0.5013 0.3589 (0.79) (0.63) (0.79) (0.53) ln(Leverage) + 0.0127 0.0022 0.0127 0.0115 (0.38) (0.07) (0.38) (0.33) Constant 0.4901*** 0.4741*** 0.3645** 0.3890** (2.80) (2.65) (2.08) (1.78) Industry Fixed-Effects

Included Included Included Included

N 101 101 101 96 Adjusted R2 0.204 0.195 0.204 0.194

55

TABLE 6 (continued) Results of Estimating Model 5: ERM Quality and Pre-crisis, Crisis and Post-crisis Returns

Panel B: Crisis Period

Expected Sign

Sample-Adjusted Returns

(BHAR_SAMP)

Industry-Adjusted Returns

(BHAR-IND)

Market-Adjusted Returns

(BHAR_MAR)

Size-Adjusted Returns

(BHAR_SIZE) ERMQ + -0.0169 -0.0196 -0.0186 -0.0022 (-0.57) (-0.66) (-0.64) (-0.07) CREDIT-RATING ? 0.0697** 0.0767** 0.0714** 0.0725** (2.17) (2.29) (2.19) (2.12) BETA + -0.0960** -0.1113** -0.0952** -0.1038** (-1.74) (-1.95) (-1.70) (-1.75) MARKETCAP - -0.0664*** -0.0594** -0.0623** -0.0940*** (-2.45) (-2.11) (-2.31) (-3.77) B/M + -0.2814*** -0.2475*** -0.2709*** -0.3145*** (-3.27) (-2.80) (-3.09) (-3.56) PRET + 0.1039 0.1210 0.1360 0.1157 (0.81) (0.96) (1.06) (0.81) E/P + 0.0082 0.0034 0.0080 0.0188 (0.05) (0.02) (0.05) (0.11) ln(Leverage) + -0.1054*** -0.0930*** -0.1073*** -0.1157*** (-2.93) (-2.57) (-2.95) (-2.76) Constant 0.9669*** 0.7645*** 0.7313*** 0.9878*** (4.73) (3.58) (3.56) (4.19) Industry Fixed-Effects

Included Included Included Included

N 102 102 102 96 Adjusted R2 0.273 0.226 0.270 0.305

56

TABLE 6 (continued) Results of Estimating Model 5: ERM Quality and Pre-crisis, Crisis and Post-crisis Returns

Panel C: Post-Crisis Period

Expected Sign

Sample-Adjusted Returns

(BHAR_SAMP)

Industry-Adjusted Returns

(BHAR-IND)

Market-Adjusted Returns

(BHAR_MAR)

Size-Adjusted Returns

(BHAR_SIZE) ERMQ + 0.2152*** 0.2149*** 0.2152*** 0.2225*** (2.51) (2.47) (2.51) (2.37) CREDIT-RATING ? 0.0182 0.0083 0.0182 0.0307 (0.17) (0.08) (0.17) (0.29) TARP 0.7610** 0.7239** 0.7610** 0.7497** (2.10) (1.93) (2.10) (1.90) BETA + 0.8142** 0.8439** 0.8142** 0.9107*** (2.11) (2.13) (2.11) (2.41) MARKETCAP - -0.0529 -0.0651 -0.0529 0.0354 (-0.50) (-0.60) (-0.50) (0.29) B/M + 0.5133 0.4756 0.5133 0.5766* (1.18) (1.08) (1.18) (1.28) PRET + -0.8640 -0.8291 -0.8640 -0.5823 (-1.20) (-1.12) (-1.20) (-0.82) E/P + -0.1812 -0.1796 -0.1812 -0.0293 (-0.19) (-0.18) (-0.19) (-0.03) ln(Leverage) + 0.2605* 0.2325* 0.2605* 0.3627** (1.64) (1.44) (1.64) (2.02) Constant -3.4225*** -2.9621*** -2.9561*** -4.0623*** (-3.57) (-2.99) (-3.08) (-4.05) Industry Fixed-Effects

Included Included Included Included

N 99 99 99 94 Adjusted R2 0.482 0.484 0.482 0.513 Notes: This table presents results of estimating Model 5, which investigates the association of buy-and-hold abnormal market returns with ERM program quality in three sub-periods of the recent financial crisis (2008-2009). All variables are defined in Table 1. Numbers in the cells are coefficients (t-statistics based on standard errors clustered by firm), with significance denoted as ***, **, *, for one percent, five percent, and ten percent, respectively. One-tailed tests are presented for directional expectations.

57

TABLE 7 Calendar-Time Portfolio Approach Results

Panel A: Four-Factor Model

Expected Sign Pre-Crisis Crisis Post-Crisis

Intercept + -0.0001 -0.0008 0.0016** (-0.27) (-0.46) (2.20) MKTRF ? 0.1227*** 0.0334 0.3148*** (2.75) (0.56) (2.68) SMB ? -0.4223*** -0.5068** -0.5614*** (-3.99) (-2.15) (-3.43) HML ? -0.0373 0.2073 0.1975 (-0.43) (0.88) (1.42) UMD ? -0.1295** -0.3905*** -0.1239 (-2.52) (-3.38) (-1.49) N 168 124 171 Adjusted R2 0.244 0.455 0.492

Panel B: Three-Factor Model Expected

Sign Pre-Crisis Crisis Post-Crisis Intercept + -0.0003 -0.0002 0.0018*** (-0.58) (-0.10) (2.51) MKTRF ? 0.1831*** 0.1563*** 0.3957*** (4.64) (2.95) (3.80) SMB ? -0.3497*** -0.4599** -0.5979*** (-3.20) (-2.11) (-3.61) HML ? 0.1314* 0.5359*** 0.2774** (1.70) (3.19) (2.31) N 168 124 171 Adjusted R2 0.213 0.411 0.484

Panel C: Capital Asset Pricing Model Expected

Sign Pre-Crisis Crisis Post-Crisis Intercept + -0.0004 -0.0002 0.0018** (-0.83) (-0.09) (2.24) MKTRF ? 0.1973*** 0.3175*** 0.4605*** (5.12) (5.90) (6.53) N 168 124 171 Adjusted R2 0.136 0.268 0.371

Notes: This table presents results of the calendar-time portfolio approach which examines the performance of a hedge portfolio that is long on high quality ERM firms (ERMQ>3) and short on low quality ERM firms (ERMQ<=3). The daily hedge portfolio returns are regressed on excess market returns (MKTRF), size (SMB), book-to-market (HML) and momentum (UMD) factor returns. All variables are defined in Table 1. Numbers in the cells are coefficients (t-statistics based on heteroscedasticity-consistent standard errors), with significance denoted as ***, **, *, for one percent, five percent, and ten percent, respectively. One-tailed tests are presented for directional expectations.