Original Article
The Relationship Between
Chief Risk Officer Expertise,
ERM Quality, and Firm
Performance
Journal of Accounting,
Auditing & Finance
1–25
ÓThe Author(s) 2019
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DOI: 10.1177/0148558X19850424
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Cristina Bailey1
Abstract
Financial volatility and the most recent financial crisis turned the spotlight on enterprise
risk management (ERM), yet, high-quality ERM is difficult to define and in many ways,
poorly understood. Furthermore, little is known about the individuals occupying key risk
management positions within the firm and their relationship to ERM system quality and outcomes. Although the resource-based theory of the firm suggests that expertise in key risk
management positions will be beneficial, institutional theories of corporate governance suggest that these positions might be created simply as ‘‘window dressing’’ in response to pressure from regulators and investors. This study examines expertise in the chief risk officer
(CRO) role. I examine seven individual expertise areas, as well as broad-based expertise
measures. Results show that supervisory and industry expertise of the CRO, as well as an
MBA degree and internal promotion are associated with higher ERM quality. Risk and
actuarial expertise are associated with higher levels of return on assets (ROA), whereas
financial expertise, supervisory expertise, and an MBA degree are associated with higher
levels of Tobin’s Q. Broad-based CRO expertise measures are also associated with ERM
quality and firm value. Additional results show that expertise in the CRO role was particularly important during the financial crisis. Findings should inform investors and regulators of
the importance of the individuals occupying key risk management roles within the firm and
help further understanding of the determinants and benefits of high-quality ERM.
Keywords
enterprise risk management, S&P ERM system ratings, insurance industry, chief risk officer
Introduction
The most recent financial crisis increased the focus on enterprise risk management (ERM),
yet, practically speaking, high-quality ERM is difficult to define and in many ways, poorly
understood. Furthermore, research related to the individuals occupying key risk management positions within the firm and their relationship to ERM system quality is sparse.
1
Boise State University, ID, USA
Corresponding Author:
Cristina Bailey, Assistant Professor of Accounting, College of Business and Economics, Boise State University, 1910
W. University Drive, Boise, ID 83725, USA.
Email: cristinabailey@boisestate.edu
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Journal of Accounting, Auditing & Finance
Researchers have begun to examine the determinants and implications of ERM, but largely
consider binary measures of ERM (i.e., existence of a chief risk officer [CRO] or ERM disclosures), which do not allow for variations in quality. ERM adoptions continue to increase
(Deloitte Touche Tohmatsu Limited, 2011), decreasing variation in binary measures. In this
study, I investigate whether the quality and benefits of ERM are conditional on the expertise of the individuals in charge of risk management. Specifically, I contend that the expertise of the CRO is significantly associated with the success of an ERM system, and,
further, leads to higher levels of firm value. Measures considering expertise examine not
only the existence of the CRO, but also allow for variation within the role.
Recent research on risk management has heavily focused on the financial crisis of 2008.
Studies show that strong risk cultures and risk management practices helped firms to
weather the crisis and rebound more quickly (Baxter, Bedard, Hoitash, & Yezegel, 2013;
Ellul & Yerramilli, 2013). With more firms adopting ERM in direct response to the both
the financial crisis and ensuing regulatory pressure (KPMG, 2009), binary measures lose
variation, suggesting a need to explore more detailed proxies for risk management quality.
In addition, these binary measures fail to examine the quality or depth of the risk management systems (Baxter et al., 2013). Although some studies have used the ERM score issued
by Standard & Poor’s (S&P), which does allow for variation in quality, this measure is
only available for select firms. Thus, this study aids both in response to calls for greater
understanding of the details related to how ERM creates value and in the refinement of
measures attempting to capture ERM quality.
The resource-based view of the firm (Penrose, 1959) suggests that expertise in risk management roles will facilitate the development of higher quality ERM systems. Prior
research highlights expertise as an intangible asset that creates value at both the chief executive officer (CEO) and chief financial officer (CFO) positions (e.g., Aier, Comprix,
Gunlock, & Lee, 2005; Hutchison, 2014; Kaplan, Klebanov, & Sorensen, 2012; Li, Sun, &
Ettredge, 2010), yet, research to date has been largely silent in regards to the CRO.
Furthermore, Security and Exchange Commission (SEC) Rule 33-9089 suggests greater
regulator interest in the qualifications of directors involved in risk management activities
(SEC, 2010). Taken together, these arguments suggest that measuring and incorporating
expertise of the CRO into research designs will strengthen tests of the effect of ERM on
risk outcomes and other benefits to the firm.
Despite the theoretical underpinnings of the value of expertise, institutional theories of
corporate governance question the economic benefits of practices or policies that are advocated by parties outside of the firm (Menon & Williams, 1994; Westphal & Graebner,
2010). Regulators and investors have been increasing their scrutiny surrounding ERM processes and have been pressuring firms to increase ERM quality (KPMG, 2009). Because
hiring a CRO is commonly associated with higher quality ERM (Baxter et al., 2013;
Beasley, Clune, & Hermanson, 2005), it is possible that firms create these positions as
‘‘window dressing’’ to appease these outside pressures. The implication of this explanation
is that risk management activities engaged in by these CROs may fail to create any real
value for the firm.
Although early ERM research examines a broad industry base (i.e., Beasley et al., 2005;
Liebenberg & Hoyt, 2003; Pagach & Warr, 2011), more recent work focuses on financial
service firms and shows clear benefits of ERM (i.e., Baxter et al., 2013; Ellul &
Yerramilli, 2013; McShane, Nair, & Rustambekov, 2011). The insurance industry provides
an ideal environment for studies related to ERM for several reasons. Insurers generally
shoulder risk related to other areas of the market and have been leaders in ERM adoption
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3
(Nair, Rustambekov, McShane, & Fainshmidt, 2013; S&P, 2012). In addition, and likely
due to their position in the market, insurance firms have embraced ERM more fully than
firms in other industries. The existence of the S&P ERM score for insurance firms also provides a consistent ERM proxy by which expertise of the CRO may be compared.
Accordingly, I focus my study in the insurance industry.
To examine the impact of the individuals occupying the CRO role, I construct measures
of expertise using biographical information from firm proxy statements and other publicly
available information. Although prior research has utilized the presence of a CRO as proxy
for ERM (i.e., Beasley, Pagach, & Warr, 2008; Pagach & Warr, 2010), I suggest that distinguishing CRO expertise from the sole presence of the role is necessary to fully understand
firm-level benefits. In addition, I argue that ERM benefits related to CRO expertise are not
fully captured by the S&P ERM score.
Because of the broad-based impact of ERM, I examine CRO expertise across multiple
dimensions (financial, supervisory, industry, risk, actuarial, internal promotion, and earning
of an MBA degree). I examine each CRO expertise area individually and then create a
broad-based measure, calculated as (a) a simple count of expertise areas the CRO possesses
(CRO_EXP) and (b) an indicator variable equal to one, if the CRO has an expertise count
greater than or equal to the sample median (CRO_HIGH). Results related to ROA are
robust to both measures, whereas results related to ERM quality and Tobin’s Q are only
significant for the CRO_EXP specification.
I first examine a known measure of ERM quality from S&P to determine whether expertise in risk management roles is associated with this measure. My sample is limited to
firms that have appointed a CRO and also have a rating available from S&P. Using the
S&P ERM score, I show that supervisory and industry expertise of the CRO, as well as an
MBA degree and internal promotion are positively associated with ERM quality. In addition, CRO_EXP is significantly associated with ERM quality. I next examine the relationship between CRO expertise and firm value.
I examine individual CRO expertise areas and their relationship with ROA and Tobin’s
Q (TOBIN_Q). Results show that prior risk and actuarial expertise of the CRO are positively related to ROA. Financial and supervisory expertise, as well as the existence of an
MBA degree, are positively associated with TOBIN_Q, whereas actuarial expertise and
internal promotion are negatively associated with this measure of firm value. This difference in significance for expertise areas suggests that CRO expertise used to generate a
return on assets (ROA) is not directly associated with investor expectations of future value.
Turning to broad-based measures of expertise, both CRO_EXP and CRO_HIGH are
positively associated with ROA. I include the S&P ERM score in all tests to account for
other sources of ERM quality and to examine the incremental information contained in
CRO expertise.1 After controlling for endogeneity related to the decision to hire a highquality executive, CRO expertise is highly significant, whereas the S&P ERM score is only
marginally significant. This suggests incremental information is provided by examining
CRO expertise. CRO_EXP is significantly associated with TOBIN_Q, but CRO_HIGH is
not significant.
Extending prior work related to the financial crisis, the sample is next divided to examine years before, during, and after the financial crisis of 2008. Because ERM is focused on
avoiding catastrophic events (Committee of Sponsoring Organizations of the Treadway
Commission [COSO] 2004), this crisis presents a scenario where high-quality ERM should
have a strong impact. Results show that the broad-based CRO_EXP measure is significant
both during and after the financial crisis for ROA. Examining individual expertise areas
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Journal of Accounting, Auditing & Finance
shows that risk and actuarial expertise are positively associated with ROA during the financial crisis, whereas only risk expertise is significantly related to ROA in the postcrisis
period. This suggests that in more recent years, it may be most desirable to hire a CRO
with prior experience in a similar role, while in times of economic turmoil, actuarial skills
may also be desirable. Supervisory expertise and an MBA degree are positively associated
with TOBIN_Q during the crisis. In the postcrisis period financial, supervisory and risk
expertise are positively associated with TOBIN_Q. Taken together, results highlight the
importance of CRO expertise both during and after the crisis, but suggest that different
expertise areas may be beneficial in different economic climates. Results highlighting the
importance of the CRO during the financial crisis are consistent with prior work suggesting
that high-quality ERM firms were able to recover more quickly after the crisis (Baxter
et al., 2013).
The main contribution of my study is to examine a more granular, expertise-based proxy
for ERM, allowing for variation in quality. As opposed to the ERM score provided by
S&P, which is only available for select firms, CRO expertise measures can be easily measured for any firm where a CRO is present. This presents the potential to extend research
into other industries. To my knowledge, no previous study has explicitly considered the
expertise of the CRO in relation to ERM quality and outcomes. As more firms adopt ERM,
variation in binary ERM measures will eventually reach zero. Considering expertise in risk
management roles not only provides variation, but also highlights the value of intangible
knowledge and people-based resources. My study contributes to the ERM literature and to
the literature examining expertise of key executives. Although prior work has focused primarily on expertise of the CEO and CFO, my findings highlight the important role that the
CRO plays in the execution of ERM. Findings suggest that firms are not simply appointing
the CRO position as ‘‘window dressing’’ to appease regulators and investors, but that these
roles are associated with tangible benefits for the firm, specifically, stronger ERM quality
and higher levels of firm value.
Background
ERM
ERM systems are constantly evolving, and effective ERM systems must reach beyond the
surface level to integrate with all of the firm’s processes and operations (Mikes & Kaplan,
2013). With this in mind, ERM quality complements and interacts with both internal controls and with broader firm-level governance. The COSO ERM framework encompasses
internal controls (COSO, 2004), with ERM operating in a broader capacity with regard to
firm-level controls. In turn, ERM can be considered a component of governance, with governance encompassing the risk management function (McNally & Tophoff, 2014; Protiviti
Inc., 2006). The broad scope of ERM and abstract nature of the COSO framework suggest
that large variation will exist between firms.
Generally, a firm-implementing risk management system will employ executive-level
risk management personnel (the CRO function or equivalent) and/or create a separate
board-level risk committee to address risk (Protiviti Inc., 2006). Although high-quality
ERM is implemented through firmwide processes that affect multiple levels of individuals
within the firm, ultimate responsibility and ownership begin with executive management
and the board of directors (Protiviti Inc., 2006; SEC, 2010). This suggests that a firm may
seek out highly qualified individuals with specialized expertise for the CRO role.
Bailey
5
ERM Literature Review
Although the theory surrounding ERM quality and its relation to firm value is clear,
researchers have struggled to fully understand how ERM adds value in practice. The
increased focus on ERM processes has led to calls for opening the ‘‘black box’’ of overall
governance and specific ERM practices in an effort to better understand how these activities add value to the firm (Adams, Hermalin, & Weisbach, 2010). Recent ERM research
has focused on the existence of the CRO position.2
Early research examining the relationship between the CRO and firm value uses a
broad-based sample including multiple industries and provides mixed results. For instance,
research shows that neither stock market reaction (Beasley et al., 2008) nor firm performance (Pagach & Warr, 2010) is significantly related to the hiring of a CRO, which acts as
proxy for ERM adoption. More recent work focuses on a single-industry setting and provides more promising results.
Two studies within the insurance industry examine the direct link between ERM and
firm-level risk measures. Eckles, Hoyt, and Miller (2014) show that firms implementing
ERM exhibit a reduction in stock return volatility and that the relationship gets stronger
over time. Berry-Stölzle and Xu (2018) show a negative relationship between ERM and the
cost of equity capital. Both studies rely on newswires and firm disclosures, including the
hiring of a CRO, to determine ERM implementation. ERM is coded as a binary variable,
equal to one, if a CRO is present or firm disclosures indicate that ERM is present. This reliance represents a key limitation to their studies, as it does not allow examination of ERM
system levels or quality.
Additional research within the insurance industry documents a positive relationship
between firm performance and both the presence of a CRO and the quality of the ERM
system (Baxter et al., 2013; Hoyt & Liebenberg, 2011; McShane et al., 2011). Both
McShane et al. (2011) and Baxter et al. (2013) use the S&P ERM score to proxy for ERM
quality, allowing for variation in ERM quality. This score, however, is not available for all
insurance firms and represents a limitation in these studies.3 Survey work within the insurance industry also suggests that the presence of a CRO is associated with higher levels of
operational performance (Grace, Leverty, Phillips, & Shimpi, 2014). The recent focus on
firm valuation highlights the overall goal of ERM to add both short- and long-term value
for investors (COSO, 2004).
Taken together, the prior literature displays the pervasive nature of ERM and its influence on both firm-level risk and performance outcomes. Proxies for ERM in the majority
of prior studies are based on the existence of a CRO or ERM disclosures and have not
allowed for variations in ERM quality. More recent work utilizes the ERM score provided
by S&P, but scores are available only for a limited number of firms within the insurance
industry, suggesting the need for a proxy that could be applied more broadly.
Key Executive Expertise Literature
Prior research examines the qualifications and expertise of key executive officers. Research
to date focuses heavily on the CEO and CFO. The CEO is generally tasked with strategic
oversight and value creation within the firm. Prior research shows that general ability
(Kaplan et al., 2012) and expertise (Hutchison, 2014) of the CEO are associated with
higher firm value. The CFO is generally tasked with oversight of financial reporting,
including internal controls. Research shows that CFOs with prior financial experience are
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Journal of Accounting, Auditing & Finance
less likely to produce accounting errors (Aier et al., 2005). Less qualified CFOs are associated with higher instances of material weaknesses and remediation requires hiring a better
qualified CFO (Liu & Jiraporn, 2010). Research to date has been largely silent on the CRO
position and how qualifications and expertise at this position can create value for the firm.
Hypothesis Development
The resource-based view of the firm asserts that more successful firms will leverage both
their tangible and intangible assets in developing systems and processes that are critical to
the future success of the firm (Penrose, 1959). This suggests that knowledge and expertise
of the individuals occupying key risk management roles will facilitate the development of
‘‘holistic,’’ higher quality ERM systems. In addition, SEC 33-9089, calling for detailed disclosures related to board member experience and qualifications (SEC, 2010), implicitly
endorses the importance of expertise to investors and regulators. In contrast, institutional
theories of corporate governance (Menon & Williams, 1994; Westphal & Graebner, 2010)
suggest that the CRO position may be created in response to pressure from outside investors and regulators, decreasing the true economic benefits to the firm. This leaves open the
question of whether the existence of a highly qualified individual in the CRO role adds
value for the firm.
Research related to key executive expertise suggests that firms seek out specific intangible assets when appointing executives, which in turn creates value for the firm. A separate
stream of literature examines the ‘‘managerial effect’’ and suggests that individual characteristics of the person occupying a role determine both the quality of work and influence
over corporate processes (Goodwin & Wu, 2014). Along with the CEO, other top executives have strong influence over firm-level practices (Bertrand & Schoar, 2003). Taken
together, these two streams of literature suggest that the individual occupying the CRO role
will exert individual-specific influence over risk management practices and ERM quality.
This leads to my first hypothesis.
Hypothesis 1 (H1): CRO expertise is positively associated with ERM quality.
The COSO framework highlights the importance of ‘‘an appropriate degree of management, technical and other expertise’’ for individuals responsible for risk oversight (COSO,
2004). With the strategic and operational components of the framework in mind, prior literature suggests that firms implementing high-quality ERM will utilize resources more effectively and avoid catastrophic negative events (Baxter et al., 2013; COSO, 2004; McShane
et al., 2011), leading to increased accounting and market valuation. This leads to my
second hypothesis.
Hypothesis 2 (H2): CRO expertise is positively associated with accounting performance and market valuation.
Sample Selection and Model Specification
Sample Selection
An initial sample is drawn from publicly traded firms in the insurance industry in the
Compustat and CRSP databases (firms with Standard Industry Classification 6311 through
6411). The insurance/reinsurance industry is ideal for this type of study as the industry
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7
appears to have embraced ERM to a greater degree than other industries (McShane et al.,
2011). Furthermore, the level of financial assets and financial instruments and the resulting
inherent level of risk in the industry likely increase the power of my tests. All main tests
focus on the 196 firm-year observations where a CRO is present and an ERM rating is
available from S&P.
Institutional ownership data are gathered from the Thompson Reuters Institutional
Holdings database and board tenure information is gathered from BoardEx. S&P ERM
scores are collected from S&P reports. CRO expertise is collected from company financial/
proxy statements and news releases. Because of the availability of S&P ratings, I start my
sample in 2006 and end in 2012.
Measures of Expertise
Because ERM involves broad processes throughout the entire firm, several types of expertise seem relevant to the risk management function. In discussing the desired skill set of
the CRO, Protiviti Inc. (2006) suggests that the CRO should possess strong strategic skills
and have prior experience interacting with the board. In addition, ‘‘previous experience in
auditing, risk assessment or risk management’’ is desirable. I begin with measures examined in prior literature related to key executives.
Breaking the Protiviti Inc. (2006) prescription into two parts, I first examine expertise
related to strategy and board interaction. I include CRO_SUP, equal to one if the CRO has
experience in a high-level supervisory role.4 Individuals in this type of role are responsible
for firm strategy and should also interact frequently with the board of directors. I include
CRO_IND, equal to one if the CRO has prior experience as an employee or board member
of another insurance company. Prior experience in the same industry likely leads to a better
understanding of industry strategy and may be beneficial in the identification of risk exposures in multiple areas (Cohen, Krishnamoorthy, & Wright, 2008).
I next examine expertise related to auditing and risk management. Due to the strong
overlap between the COSO internal control and ERM frameworks, it seems that the same
types of expertise should be beneficial to both the CFO and risk managers. Prior research
examines financial experience, the possession of an MBA, and prior experience in another
similar role in relationship to the CFO (Aier et al., 2005). Considering the overlap with risk
management, I measure financial expertise of the CRO (CRO_FIN)5 and the possession of
an MBA degree (CRO_MBA). I also include CRO_RISK, equal to one if the CRO has experience in a prior high-level risk management position. I include INT_PROMOTE, equal to
one, if the CRO was promoted from within the firm, accounting for firm-specific expertise.
All insurance firms must estimate loss reserves for estimated future settlement of claims,
and these loss reserves are usually the largest liabilities on the firm’s balance sheets.
Calculation of these reserves is complex for most insurers (Grace & Leverty, 2011). Due to
the specialized nature of reserve calculations and the risk involved with inadequate estimation, I include CRO_ACTUARY, equal to one, if the CRO is an actuary. In total, seven
CRO expertise areas are examined. Although actuarial expertise may be more common in
the insurance industry, other expertise areas measured are likely relevant to other
industries.
Dhaliwal, Naiker, and Navissi (2010) and Protiviti Inc. (2006) suggest that individuals
with a broad expertise base, as opposed to one single area of expertise, are beneficial to the
firm. Thus, I consider a broad expertise measure, as well as individual expertise areas. The
broad-base measure is created using (a) a count of expertise areas of the CRO (CRO_EXP)
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Journal of Accounting, Auditing & Finance
and (b) an indicator equal to one if CRO_EXP is greater than or equal to the sample
median (CRO_HIGH). The two measures are created to provide a robust analysis of broadbased CRO expertise. The first measure acts as a naı̈ve count, assuming that all types of
expertise are weighted equally. This measure is appealing due to its ease of calculation and
is justified by the lack of prior evidence related to unequal weighting of certain CRO
expertise areas. The second measure uses the sample data to create a split between CROs
with more or less broad-based expertise.
The CFO is generally tasked with risk oversight in cases where a CRO is not present
(Ellul & Yerramilli, 2013). Although all firms in my sample do have a CRO present, it is
possible that the CFO is tasked with some risk management functions. I include a broadbased CFO expertise variable (CFO_EXP) in all models to account for this possibility.6
Prior research shows that individuals in executive roles may succumb to pressure from the
firm CEO (Feng, Ge, Luo, & Shevlin, 2011). Following the ‘‘reputation hypothesis’’ and
assuming longer tenured CEOs exert more power over operations (Liu & Jiraporn, 2010), I
include CEO_TENURE, measured as the number of years the CEO has served in the current role.7
Determinants of ERM Quality
In 2006, S&P added an ERM component to its credit rating evaluation for companies in
the insurance and reinsurance industry. Insurance firms are rated at least once per year and
also after certain events such as a merger or debt issuance. S&P initiates discussions with
target company’s CEOs, CFOs, and business unit managers to evaluate each insurer on several components of their ERM system.8 Because of the detailed nature of these discussions,
ratings are likely to impound information not available in public disclosures. According to
S&P ratings criteria, these scores do not implicitly rate specific expertise areas of key executives, but rather focus on the firm’s understanding and management of key risks (S&P,
2006).9
ERM scores from S&P fall into four categories (weak, adequate, strong, and excellent).
Following prior research (Baxter et al., 2013; McShane et al., 2011), I translate the ERM
scores into a numeric variable ERM_RATING equal to 0 if the firm is rated Weak, 1 if the
firm is rated Adequate, 2 if the firm is rated Strong and 3 if the firm is rated Excellent.10
To examine the relationship between expertise and ERM quality, I use an ordered logistic regression. Model 1 presented below includes indicators for four-digit SIC industry and
fiscal year and is estimated with heteroscedasticity-consistent standard errors:
ERM RATINGi, t = b0 + b1 CROExpertisei, t + lk ðControlsÞi, t + ei, t
ð1Þ
where CRO Expertise is comprised of either individual expertise areas or a single broadbased measure and Controls includes variables consistent with Baxter et al. (2013). Control
variables capture firm size, complexity, and risk factors that are likely associated with
ERM quality. Controls are listed in the Appendix section. In addition to control variables
noted in prior literature, I include CFO_EXP and CEO_TENURE, as earlier defined, to
account for risk management activities from other executives. H1 predicts a positive coefficient on b1 for CRO Expertise.
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CRO, Firm Performance, and Valuation
H2 predicts a positive relationship between CRO expertise and both firm performance and
market valuation. I utilize established measures of accounting performance (ROA) and
market valuation (TOBIN_Q). Although ROA captures operational performance through
strategic use of assets, TOBIN_Q captures future expectations of the market (Hoyt &
Liebenberg, 2011), providing additional insights into the benefits of strong ERM. I examine
both dependent variables at year t + 1 to incorporate the time lag between ERM activities
and recognition of operational benefits (Eckles et al., 2014).
Model 2, presented below, examines ROA and includes CRO Expertise, along with
CFO_EXP and CEO_TENURE to account for value creation in these executive roles. Fourdigit SIC industry and year controls are also included. The model is as follows:
ROAi, t + 1 = b0 + b1 CROExpertisei, t + lk ðControlsÞi, t + e
ð2Þ
where Controls are defined in accordance with prior literature. H2 predicts a positive coefficient on b1 for CRO Expertise.
Following Baxter et al., 2013, I include board size (BOARD_SIZE) and independence
(BOARD_IND), company size (LOGASSETS), institutional ownership (INST_OWN), complexity (SEGMENTS), leverage (LEV), growth opportunities (SALES_GROWTH), capital
expenditures over sales (CAPOVERSALES), credit rating (CR_RATE), and the 5-year standard deviation of ROA (STDROA). I include an indicator variable equal to one if a risk
committee (R_COMM) is present to account for risk management at the board level. I also
include PSE_AC, to account for supervisory expertise of the audit committee.
Model 3, presented below, examines Tobin’s Q. Four-digit SIC industry and year controls are also included. The model is as follows:
TOBIN Qi, t + 1 = b0 + b1 CROExpertisei, t + lk ðControlsÞi, t + e
ð3Þ
where Controls are defined in accordance with prior literature. H3 predicts a positive coefficient on b1 for CRO Expertise. Following Baxter et al. (2013), I include the same controls
from Model 2, but replace STDROA with ROA.
Selection Bias—Heckman Two-Stage Model
The choice to appoint a high-expertise CRO is likely determined by firm-specific factors,
where a firm may have a propensity to appoint executives and committee members of a
certain caliber. Thus, the firm-level decision related to appointment of risk management
personnel is likely to be endogenously determined, leading to a potential bias in my regression coefficients. To address this bias, I estimate Models 2 and 3 using a Heckman (1979)
two-stage selection model for the presence of a high-expertise CRO. I utilize the first-stage
model as follows:
PROB(CRO HIGHi, t = 1) = b0 + b1 INDUSTRY CROHIGHi, t + lk ðControlsÞi, t + ei, t
ð4Þ
The Heckman model requires identification of a variable that is exogenous and independent in the first stage that can be excluded from the second stage. Governance decisions
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Journal of Accounting, Auditing & Finance
Table 1. Descriptive Statistics—Expertise Areas by Position.
Panel A: Expertise Areas for the Chief Risk Officer.
CRO_FIN (Financial)
CRO_SUP (Supervisory)
CRO_IND (Industry)
CRO_RISK (Risk)
CRO_MBA (MBA degree)
CRO_ACTUARY (Actuarial)
INT_PROMOTE (internal promotion)
Count
M
66
39
162
152
16
65
86
0.34
0.20
0.83
0.78
0.08
0.33
0.44
Count
M
182
46
132
7
40
69
0.93
0.23
0.67
0.04
0.20
0.35
Panel B: Expertise Areas for the Chief Financial Officer.
Financial
Supervisory
Industry
Risk
MBA degree
Internal promotion
Note. This table presents descriptive analysis of individual expertise areas for the chief risk officer and chief
financial officer roles. Panel A provides information for the chief risk officer role and Panel B provides information
for the chief financial officer role.
are likely influenced by the actions of other firms in the same industry (Bostrom, 2002;
Hines, Masli, Mauldin, & Peters, 2015). I include the average presence of a high-expertise
CRO (INDUSTRY_CROHIGH) by year and four-digit SIC code (excluding the firm itself)
in my first stage, as the firm-level decision to maintain a high-expertise CRO is likely
influenced by practices of industry peers. I exclude this variable from my second stage, as
the average industry presence of a high-expertise CRO is not likely to affect individual
firm-level performance.11
I utilize all control variables from Model 1, as factors that influence the likelihood of
prioritizing high-quality ERM should also influence the decision to appoint a higher expertise CRO. I include the inverse Mills ratio (LAMBDA) in the second-stage regression for
Models 2 and 3.
Results
Descriptive Statistics and Correlations
Table 1 reports descriptive statistics for expertise areas of the CRO and CFO. Panel A presents information for the CRO position. Industry expertise is the most prevalent, with 83%
of CROs possessing this expertise. Risk expertise is present for 78% of CROs, which is not
surprising given the nature of the CRO role, and 44% of CROs were internally promoted.
Financial expertise is present for 34% of CROs, reinforcing the tie between financial
reporting controls and risk management. Roughly one third of the CROs have experience
as an actuary (33%), whereas fewer CROs possess supervisory expertise (20%)12 or an
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MBA degree (8%). Panel B presents information for the CFO position. The majority of
CFOs possess financial expertise (93%) and industry expertise (67%).
Table 2 presents descriptive statistics for Models 1 to 3. Model 1 variables are presented
in Panel A. The mean ERM rating is 1.327, showing that on average, firms are rated in the
Adequate category. This is consistent with prior studies using the S&P ERM score. The
mean CRO_EXP is 3.097 out of seven possible expertise areas. Mean CFO_EXP is 2.429
out of six possible expertise areas. Just over half the sample has a board-level risk committee (R_COMM). Panel B reports descriptive statistics for Models 2 and 3 variables. Means
for ROA and TOBIN_Q are generally in line with prior research (i.e., Baxter et al., 2013;
McShane et al., 2011).
Regression Results
Table 3 reports ordered logistic results from Model 1, with dependent variable
ERM_RATING. Column 1 reports results examining individual expertise areas. Results
show that CRO supervisory and industry expertise, as well as the possession of an MBA
degree and internal promotion are positively associated with ERM quality. Surprisingly,
risk expertise is negatively associated with ERM quality. This may be because firms with
lower quality ERM are more likely to hire a CRO with prior experience in a similar role.
Column 2 reports results using the broad-based CRO expertise measure. Results show
that CRO_EXP is positively associated with ERM quality (coefficient = 0.676, p = .010).
CFO_EXP and CEO_TENURE are not significant, suggesting that these additional executive traits are not associated with ERM quality, as measured by S&P.13 Turning to control
variables, R_COMM is positively related to ERM quality under this specification. Although
control variables are generally consistent with prior literature, BOARD_TENURE and
FOREIGN are negative and AUD_REL_RISK is positive. This may be due to differences in
sample years. Untabulated results show that the coefficient for CRO_HIGH is positive, but
not significant (p = .136). Generally, results from Table 3 provide support for H1, suggesting that CRO expertise is positively associated with ERM quality. Although these results
are informative in terms of the types of expertise that are associated with ERM quality, it
is still possible that firms appoint certain CROs with the intention of impressing the S&P
raters. I next turn to analysis of firm value to more directly address the ‘‘window dressing’’
argument.
Table 4 reports results for individual CRO expertise variables for Models 2 and 3.
Column 1 reports results with dependent variable ROA. Only CRO_RISK and
CRO_ACTUARY are significantly associated with ROA. In contrast, TOBIN_Q results presented in column 2 show that financial and supervisory expertise of the CRO, as well as
possession of an MBA degree are positively associated with market valuation. Actuarial
expertise and internal promotion are negatively associated with TOBIN_Q. The difference
in significant expertise areas suggests that expectations of market value related to CRO
expertise are not explicitly linked to accounting value creation measured as ROA. It is possible that the market is still unclear as to what types of CRO expertise best translate to
operational performance and future valuation.
Table 5 reports results using broad-based measures of CRO expertise. Columns 1 and 2
examine Model 2 related to ROA. CRO_EXP is significant in column 1 using ordinary least
squares (OLS; coefficient = 0.007, p = .056). ERM_RATING and CFO_EXP are also positive and significant.14 CEO_TENURE is negative and significant, suggesting that CEOs
who have been in their role for a longer tenure may actually detract from firm accounting
12
Journal of Accounting, Auditing & Finance
Table 2. Descriptive Statistics, Models 1 to 3.
Panel A: ERM Determinants (Model 1).
Variable
ERM_RATING
CRO_EXP
CFO_EXP
CEO_TENURE
MKTCAP
SEGMENTS
GLOBAL
FOREIGN
STOCK_VOL
STD_OP_CASH
LOSS_PROP
CR_RATE
OP_CASH
LEV
ZSCORE
R_COMM
PFE_AC
PSE_AC
AC_SIZE
BOARD_IND
BOARD_TENURE
CEO_DUAL
AUD_REL_RISK
LOGAGE
NYSE
M
Mdn
SD
1.327
3.097
2.429
7.281
8.424
2.254
0.184
0.362
0.026
0.023
0.154
4.347
0.028
0.069
15.646
0.505
0.290
0.564
3.872
0.833
7.747
0.475
0.020
2.907
0.250
1.000
3.000
2.000
5.000
8.287
2.236
0.000
0.000
0.019
0.012
0.200
4.000
0.031
0.056
14.151
1.000
0.250
0.667
4.000
0.889
7.823
0.000
0.000
2.917
0.000
0.727
1.135
0.709
7.574
1.249
0.522
0.388
0.482
0.020
0.029
0.199
0.993
0.051
0.043
9.144
0.501
0.254
0.290
1.826
0.119
3.628
0.501
0.142
0.546
0.434
M
Mdn
SD
0.013
0.951
3.097
2.429
7.281
0.505
0.564
2.332
0.833
10.117
0.746
2.254
0.069
0.508
0.006
4.347
0.027
0.015
0.995
3.000
2.000
5.000
1.000
0.667
2.398
0.889
9.820
0.861
2.236
0.056
0.014
0.001
4.000
0.013
0.050
0.248
1.135
0.709
7.574
0.501
0.290
0.243
0.119
1.464
0.294
0.522
0.043
6.622
0.018
0.993
0.035
Panel B: ROA and Tobin’s Q (Models 2 and 3).
Variable
ROA
TOBIN_Q
CRO_EXP
CFO_EXP
CEO_TENURE
R_COMM
PSE_AC
BOARD_SIZE
BOARD_IND
LOGASSETS
INST_OWN
SEGMENTS
LEV
SALES_GROWTH
CAPOVERSALES
CR_RATE
STDROA
Note. This table presents the mean, median, and standard deviation for all variables used in Models 1 to 3. Panel A
provides descriptive statistics for Model 1 and Panel B provides descriptive statistics for Models 2 and 3. All
variables are defined in the Appendix section. ERM = enterprise risk management; ROA = return on assets.
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13
Table 3. Results of Estimating Model 1: ERM Determinants.
(1)
CRO_FIN
CRO_SUP
CRO_IND
CRO_RISK
CRO_ACTUARY
CRO_MBA
INT_PROMOTE
CRO_EXP
CFO_EXP
CEO_TENURE
MKTCAP
SEGMENTS
GLOBAL
FOREIGN
STOCK_VOL
STD_OP_CASH
LOSS_PROP
CR_RATE
OP_CASH
LEV
ZSCORE
R_COMM
PFE_AC
PSE_AC
AC_SIZE
BOARD_IND
BOARD_TENURE
CEO_DUAL
AUD_REL_RISK
LOGAGE
NYSE
Observations
Pseudo R2
(2)
Coefficient
p
–0.487
3.782***
3.407**
–2.091***
–0.408
3.684**
1.924***
.374
.001
.015
.006
.362
.029
.008
1.413
0.342
0.488
0.233
–82.915*
30.099
–11.030**
1.975***
30.157***
–33.347
–0.093
–0.044
1.010
–0.003
0.051
5.736
–0.197
–2.934***
7.857***
–2.293***
–2.258**
196
.661
.188
.833
.683
.773
.065
.529
.040
.001
.001
.265
.158
.959
.369
.999
.823
.100
.295
.001
.000
.002
.038
Coefficient
p
0.676**
–0.398
–0.060
0.945*
0.086
1.630*
–1.448**
–59.735
7.406
–9.901***
1.427**
26.502***
–15.527
–0.027
1.477**
–0.549
2.543
–0.014
6.225**
–0.303**
–2.025**
4.804**
–1.956***
–2.248**
196
.596
.010
.389
.382
.100
.918
.085
.043
.133
.741
.001
.014
.006
.283
.523
.045
.588
.105
.934
.032
.045
.034
.016
.002
.020
Note. This table reports ordered logistic results for Model 1, examining the determinants of ERM quality. Results
for ERM variables are reported as one-tailed tests and all other variables are presented as two-tailed tests with
*p \ .1. **p \ .05. ***p \ .01. All variables are defined in the Appendix section. ERM = enterprise risk
management.
value. Supervisory expertise of the audit committee (PSE_AC) is positive and significant,
whereas the existence of a risk committee (R_COMM) is not significantly related to ROA.
Column 2 utilizes the Heckman two-stage analysis described above to control for endogeneity related to the decision to hire high-quality executives. This model substitutes
CRO_HIGH for CRO_EXP to provide a suitable first-stage probit dependent variable.
First-stage results are presented in Panel B.15 Five control variables are significantly related
to the existence of a high-expertise CRO. In addition, the instrumental variable
(INDUSTRY_CROHIGH) is positive and significant, providing support for use of this
14
Journal of Accounting, Auditing & Finance
Table 4. Results of Estimating Models 2 and 3: ROA and Tobin’s Q Using Individual CRO Expertise
Areas.
CRO_FIN
CRO_SUP
CRO_IND
CRO_RISK
CRO_ACTUARY
CRO_MBA
INT_PROMOTE
ERM_RATING
BOARD_SIZE
BOARD_IND
LOGASSETS
INST_OWN
SEGMENTS
LEV
SALES_GROWTH
CAPOVERSALES
CR_RATE
STDROA
ROA
INTERCEPT
Observations
Adjusted R2
Model 2: ROA
Model 3: Tobin’s Q
(1)
(2)
Coefficient
p
Coefficient
p
0.002
–0.007
–0.000
0.019**
0.009*
–0.004
–0.003
0.014**
0.010
–0.004
–0.012***
0.005
0.006
0.039
–0.001***
0.384**
0.008
–0.597**
.444
.235
.491
.023
.094
.380
.285
.049
.559
.872
.001
.590
.610
.645
.000
.020
.242
.023
0.066**
0.151***
–0.006
0.044
–0.106***
0.160***
–0.062**
0.021
–0.206**
0.009
–0.067***
0.074
0.181***
–1.718***
–0.001
2.171
–0.070**
.047
.001
.468
.221
.008
.000
.025
.494
.029
.951
.000
.207
.000
.007
.270
.107
.033
0.166
2.225***
196
.289
.724
.000
0.039
196
.406
.312
Note. This table presents OLS results for Model 2 (column 1) and Model 3 (column 2), including individual
expertise areas for the CRO. Results for ERM variables are reported as one-tailed tests and all other variables are
presented as two-tailed tests with *p \ .1. **p \ .05. ***p \ .01. All variables are defined in the Appendix
section. ROA = return on assets; CRO = chief risk officer; OLS = ordinary least squares; ERM = enterprise risk
management.
measure. Returning to second-stage results, Panel A shows that CRO_HIGH is highly significant under this specification. ERM_RATING remains marginally significant, whereas
variables related to other executive roles (CFO_HIGH, CEO_TENURE) and the audit committee (PSE_AC) are no longer significant. The inverse Mills ratio is significant, suggesting
that bias exists in the OLS results.16 After controlling for this bias, CRO_HIGH shows a
significant association with ROA. In addition, a t-test between the coefficients on
CRO_HIGH and ERM_RATING is significant, suggesting incremental value creation by the
CRO outside of that captured by the S&P ERM quality score.
Column 3 examines Model 3 related to TOBIN_Q. OLS results show a significant coefficient on CRO_EXP (p = .093) and ERM_RATING (p = .058). CFO_EXP is also positive
and significant. In contrast to ROA results, CEO_TENURE is positive and significant, suggesting that longer tenured CEOs create a positive signal for market valuation. Supervisory
expertise of the audit committee (PSE_AC) and the existence of a risk committee
(R_COMM) are not significant. Untabulated results utilizing a Heckman two-stage analysis
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15
Table 5. Results of Estimating Models 2 and 3: ROA and Tobin’s Q Using a Broad-Based CRO
Measure.
Panel A: Results of Estimating Models 2 and 3.
CRO_EXP
CRO_HIGH
ERM_RATING
CFO_EXP
CFO_HIGH
CEO_TENURE
R_COMM
PSE_AC
BOARD_SIZE
BOARD_IND
LOGASSETS
INST_OWN
SEGMENTS
LEV
SALES_GROWTH
CAPOVERSALES
CR_RATE
STDROA
ROA
INVERSE_MILLS
INTERCEPT
Observations
Adjusted R2
(1)
(2)
(3)
OLS: ROA
Heckman Stage 2: ROA
OLS: Tobin’s Q
Coefficient
p
0.007*
.056
0.007*
0.009**
.093
.029
–0.001**
0.006
0.023**
0.003
–0.016
–0.016***
0.012
0.011
0.133
–0.001***
0.331**
0.012
–0.727**
.025
.386
.039
.815
.405
.000
.169
.220
.119
.000
.037
.121
.010
0.044
196
.413
.149
Coefficient
p
0.034***
0.008*
.003
.075
0.006
–0.001
–0.004
0.014
–0.002
–0.030
–0.014***
0.005
0.007
0.093
–0.001***
0.323*
0.013***
–0.609***
.396
.182
.656
.245
.902
.293
.000
.610
.275
.351
.005
.055
.005
.000
–0.019**
0.077*
196
.415
.032
.058
Coefficient
p
0.020*
.093
0.047*
0.067**
.058
.013
0.004**
–0.013
0.030
–0.053
0.180
–0.055***
0.077
0.154***
–2.004***
–0.001
1.910*
–0.082***
.050
.773
.517
.620
.132
.000
.242
.003
.000
.460
.076
.005
0.040
.927
1.465***
196
.195
.000
Panel B: Results of First-Stage Heckman Model.
(1)
INDUSTRY_CROHIGH
MKTCAP
SEGMENTS
GLOBAL
FOREIGN
STOCK_VOL
STD_OP_CASH
LOSS_PROP
CR_RATE
OP_CASH
LEV
ZSCORE
R_COMM
PFE_AC
PSE_AC
Coefficient
p
10.203**
1.340***
0.660
0.026
0.304
–2.996
43.057**
–5.401**
15.066*
10.794
–0.009
0.974
–1.276
–0.359
0.011
.032
.003
.426
.980
.707
.884
.026
.020
.082
.190
.872
.100
.192
.724
.957
(continued)
16
Journal of Accounting, Auditing & Finance
Table 5. (continued)
Panel B: Results of First-Stage Heckman Model.
(1)
Coefficient
AC_SIZE
BOARD_IND
BOARD_TENURE
CEO_DUAL
AUD_REL_RISK
LOGAGE
NYSE
INTERCEPT
Observations
Pseudo R2
1.424
0.046
–1.085*
2.492
–0.429
–0.085
–0.411
–18.326
196
.421
p
.476
.669
.091
.148
.573
.907
.639
.973
Note. This table presents results in Panel A for Model 2 (columns 1-2) and Model 3 (column 3), including broadbased measures for CRO expertise. Columns 1 and 3 present OLS results, whereas column 2 presents secondstage results using a Heckman two-stage analysis. Panel B presents first-stage results for the Heckman two-stage
analysis. Results for ERM variables are reported as one-tailed tests and all other variables are presented as twotailed tests with *p \ .1. **p \ .05. ***p \ .01. All variables are defined in the Appendix section. ROA = return
on assets; CRO = chief risk officer; OLS = ordinary least squares; ERM = enterprise risk management.
report an insignificant inverse Mills ratio, suggesting that OLS results are appropriate in
this case. Additional untabulated results show that the coefficient on CRO_HIGH is positive, but not significant under the OLS specification.
Taken together, results generally support H2, suggesting that expertise of the CRO is
associated with higher accounting value, although only one broad-based measure
(CRO_EXP) is significant for both ROA and TOBIN_Q. It appears that expertise in the
CRO role does create value for the firm and these roles are not simply created as ‘‘window
dressing.’’
Additional Analysis
Prior ERM research focuses heavily on the financial crisis of 2008-2009, as strong ERM
should assist firms in avoiding catastrophic events (COSO, 2004). Extending prior work, I
next examine expertise in the CRO role before, during and after this crisis. Results are presented in Table 6. I examine both individual expertise areas and CRO_EXP. Only coefficients are reported and control variables are suppressed for brevity. Panel A, focusing on
CRO_EXP, examines ROA in column 1 and TOBIN_Q in column 2. Column 1 shows that
expertise of the CRO is not significant during the precrisis period, but is highly significant
during the crisis and marginally significant in the postcrisis period. CFO_EXP and
ERM_RATING are also significant during and postcrisis, whereas CEO_TENURE is not
significant during any of the time periods. These results highlight the importance of expertise in risk management both during and after the crisis. Column 2 shows that CRO_EXP is
not significantly related to TOBIN_Q during any of the time periods examined. CFO_EXP,
CEO_TENURE, and ERM_RATING are significant only in the postcrisis period. ROA
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17
Table 6. Results of Estimating Models 2 and 3 During the Precrisis, Crisis, and Postcrisis Periods.
Panel A: Analysis of CRO_EXP Before, During, and After the Financial Crisis.
CRO_EXP
CFO_EXP
CEO_TENURE
ERM_RATING
Observations
(1)
(2)
Model 2: ROA
Model 3: Tobin’s Q
Pre
Crisis
Post
Pre
Crisis
Post
0.002
0.043
0.003
0.004
30
0.020***
0.015*
–0.001
0.013*
55
0.006*
0.010***
–0.000
0.012**
111
0.013
0.037
–0.002
–0.019
30
–0.020
0.092
0.003
0.095
55
0.025
0.061*
0.005*
0.054*
111
Panel B: Analysis of Individual Expertise Areas Before, During, and After the Financial Crisis.
CRO_FIN
CRO_SUP
CRO_IND
CRO_RISK
CRO_ACTUARY
CRO_MBA
INT_PROMOTE
CFO_EXP
CEO_TENURE
ERM_RATING
Observations
(1)
(2)
Model 2: ROA
Model 3: Tobin’s Q
Pre
Crisis
Post
Pre
Crisis
Post
0.546
–0.301
–0.113
–0.044
1.008*
–0.597**
0.172
–0.216
0.046
–0.100
30
0.037
–0.004
0.039
0.024*
0.018*
0.020
–0.002
0.011
–0.001*
0.006
55
0.007
0.000
0.000
0.016***
0.003
–0.006
0.006
0.009**
–0.000
0.013**
111
0.031
0.062
0.004
–0.037
–0.074
0.104
0.054
0.066
–0.002
–0.022
30
0.094
0.367***
0.114
–0.164
–0.117
0.229*
0.050
0.133*
0.005
–0.099
55
0.159**
0.171***
–0.110
0.155**
–0.142**
0.116
–0.023
0.107**
0.004
0.080**
111
Note. This table reports regression results for Models 2 and 3, with dependent variables ROA and TOBIN_Q,
partitioned by precrisis, crisis, and postcrisis years. Pre includes years 2006-2007, crisis includes 2008-2009, and
post includes years 2010-2012. Column 1 (2) in each panel reports results for Model 2 (Model 3). Only coefficients
are reported, with *p \ .1. **p \ .05. ***p \ .01. Panel A reports results for a broad-based expertise measure
(CRO_EXP), whereas Panel B reports results for individual CRO expertise areas. ROA = return on assets; CRO =
chief risk officer.
results are consistent with prior research suggesting that high-quality ERM helped firms to
recover more quickly from the financial crisis (Baxter et al., 2013).
Panel B, focuses on individual expertise areas. Column 1 shows that risk expertise is
positive and significant during the crisis and in the post-crisis period. Actuarial expertise is
significant in the precrisis and crisis periods. These differing results suggest that accounting
value was influenced by different expertise areas over the different time periods. Column 2
shows that supervisory expertise and an MBA degree are positively associated with
TOBIN_Q during the financial crisis. In the postcrisis period, financial, supervisory, and
risk expertise are positive and significant. This suggests that the market differentially
valued individual expertise areas during and after the financial crisis. Taken together, these
results suggest that CRO expertise areas most beneficial during a financial downturn may
differ from those that are most beneficial during other time periods.
18
Journal of Accounting, Auditing & Finance
One argument related to CRO expertise variables is that another characteristic of the
CRO is actually driving results. I consider two variables related to power of the CRO
within the firm to account for this possibility. I include an indicator variable (CRO_TOP5)
equal to one if the CRO is listed as a top five paid executive and an indicator variable
(CRO_EXEC) equal to one if the CRO is listed as a member of the executive team (Ellul
& Yerramilli, 2013). Although these variables proxy for power of the CRO within the firm,
they are not explicitly measuring individual-specific expertise of the CRO. Untabulated
results for CRO_EXP in Model 1 (ERM_RATING) and Model 2 (ROA) are robust to inclusion of each of these measures, whereas results for Model 3 (TOBIN_Q) are only robust to
inclusion of the CRO_EXEC measure. I also consider CRO learning over time on the job. I
include CRO_TENURE, equal to the number of years the CRO has occupied the current
position. Results for Models 1 to 3 are robust to inclusion of this measure. CRO_TENURE
is only significant in Model 1 (ERM_RATING) with a coefficient of 0.322 (p = .007).
Next, I create a measure for CRO_EXP that does not include actuarial experience to
examine only measures that are likely to be common outside of the insurance industry.17 I
include CRO_ACTUARY separately in each model, along with the new CRO_EXP variable.
Results are quantitatively similar for Models 1 to 3 with two exceptions. First, CRO_EXP
is not significant in Model 2 (ROA), although CRO_HIGH remains significant. Second,
CRO_HIGH becomes significant in Model 3 (TOBIN_Q). CRO_ACTUARY is positive and
significant only in Model 2, reporting a coefficient similar to that reported in Table 4.
These results provide some evidence that broad-based CRO expertise is robust to specifications that may apply outside of the insurance industry.
Last, I consider differences in the types of insurance firms within my sample. The
sample is comprised of both life insurance and property/casualty insurance firms. I replace
SIC dummy variables with an indicator variable equal to one if the firm provides life insurance (LIFE). Results for Models 1 to 3 are robust to inclusion of LIFE. I next run separate
regressions for nonlife and life insurance firms. CRO_EXP remains significant in both samples for Model 1.18 Turning to firm value, CRO_EXP remains significant in relationship to
ROA in both samples. CRO_EXP is not significantly associated with TOBIN_Q in either
sample.19
Conclusion
This study examines the effect of CRO expertise on both ERM quality and firm value in
the insurance industry. I provide evidence that CRO supervisory and industry expertise, as
well as an MBA degree and internal promotion are significantly related to ERM quality. A
broad-based count of CRO expertise areas is also significantly associated with ERM quality. Turning to firm value, risk and actuarial expertise are positively associated with ROA,
whereas financial expertise, supervisory expertise, and an MBA degree are positively associated with TOBIN_Q. Both broad-based measures of CRO expertise examined show a significant relationship to ROA, while only the CRO_EXP specification is significantly related
to TOBIN_Q. All models include variables related to the CFO and CEO executive roles, as
well as the S&P ERM score. Results suggest that CRO expertise is capturing information
outside of that captured by the S&P ERM score. Examining measures that do not rely on a
rating from S&P also introduces the potential to proxy for ERM quality in additional
industries.
Although prior ERM research relies heavily on binary proxies for ERM adoption, my
study develops a more granular proxy based on the expertise of the CRO. As more firms
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19
adopt ERM, variation in binary measures approaches zero, suggesting a need for more
refined measures. Although S&P does provide a more granular ERM score, this score is
not available for all insurance firms. Measures related to CRO expertise are appealing due
to ease of calculation and wider availability. This study contributes to the literature examining the determinants and benefits of high-quality ERM and the literature examining characteristics of individuals in executive positions. Despite pressure from regulators and claims
that lack risk management fueled the most recent financial crisis, some firms have resisted
adoption of high-quality ERM systems. Because adoption and system improvement can
bear a high cost to the firm, research examining inputs and outcomes of ERM is increasingly important. The COSO framework emphasizes the importance of executive members
in implementation of high-quality ERM.
My study is subject to several limitations. First, my data collection relies on firm disclosures and publicly available information. If a firm does not disclose the existence of the
CRO function or if public information does not fully disclose the background of the individual occupying that role, then this would bias my sample. Second, my study focuses on a
single industry, and it is possible that results do not apply to firms in other industries.
Despite this single-industry focus, results provide initial evidence related to the importance
of expertise in the CRO role. Although actuarial expertise may be less common outside of
the insurance industry, other expertise areas examined likely benefit CROs in other industries. Results are generally robust to exclusion of actuarial expertise, providing some promise for an expertise measure that could be used outside of the insurance industry. Future
research could examine the types of expertise that add value for a broader set of firms.
Overall, results support the resource-based view of the firm and suggest that expertise in
the CRO position is an intangible asset used by the firm to create value. This suggests that
firms are not appointing risk managers as simple ‘‘window dressing,’’ but are in fact using
these positions to improve ERM system quality, resulting in higher levels of firm value.
These results inform the discussion on how the strategic component of ERM interacts with
the accounting function, highlighting the importance of the individuals occupying key
roles. This study should be of interest to regulators and investors, as well as firms seeking
additional support for the adoption and improvement of ERM.
Although this study takes initial steps to identify the types of expertise that are beneficial to an ERM system, future research may focus on additional characteristics of individuals in key risk management roles and specific ERM processes that create value for the
firm.
Appendix
Definition of Variables
Logistic regression variables (Model 1).
ERM_RATING: A numerical translation of the S&P ERM rating as follows: 0 =
weak, 1 = adequate, 2 = strong, 3 = excellent [Standard and Poor’s]
CRO_FIN: Equal to 1 if the CRO has financial expertise
CRO_SUP: Equal to 1 if the CRO has supervisory expertise
CRO_IND: Equal to 1 if the CRO has experience in a prior role within the insurance industry
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Journal of Accounting, Auditing & Finance
CRO_RISK: Equal to 1 if the CRO has expertise in a prior high-level risk management role
CRO_ACTUARY: Equal to 1 if the CRO has expertise as an actuary
CRO_MBA: Equal to 1 if the CRO possesses an MBA degree
INT_PROMOTE: Equal to 1 if the CRO was promoted internally within the firm
CRO_EXP: A count of individual expertise areas (financial, supervisory, industry,
risk, actuarial, MBA degree, and internal promotion) possessed by the CRO
CRO_HIGH: Equal to 1 if the CRO_EXP is greater than or equal to the sample median
CFO_EXP: A count of the individual expertise areas (financial, supervisory, industry, risk, MBA degree, and internal promotion) possessed by the CFO
CFO_HIGH: Equal to 1 if the CFO_EXP is greater than or equal to the sample
median
CEO_TENURE: Equal to the number of years the CEO has occupied the current
role
MKTCAP: The natural log of the market value of equity [COMPUSTAT data
PRCC_F x CSHO]
SEGMENTS: The number of business segments
GLOBAL: Equal to 1 if the company reports global operations
FOREIGN: Equal to 1 if the company has nonzero foreign currency translation
[COMPUSTAT data FCA]
STOCK_VOL: The standard deviation of daily stock returns during the fiscal year
[CRSP]
STD_OP_CASH: The standard deviation of operating cash flows during the prior 5
years [COMPUSTAT]
LOSS_PROP: The proportion of losses over the prior 5 years [COMPUSTAT]
CR_RATE: Based on the S&P credit rating as follows: AAA (7), AA + , AA, and
AA– (6), A + , A, and A– (5), BBB + , BBB, and BBB– (4), BB + , BB, and BB–
(3), B + , B, and B– (2), CCC + , CCC, CC, C, D or SD (1) [S&P credit rating]
OP_CASH: Operating cash flows scaled by total assets [COMPUSTAT data
OANCF / AT]
LEV: The firm leverage, book value of debt divided by total assets [COMPUSTAT
data (DLTT + DD1) / AT]
ZSCORE: The sum of the mean rate of return on assets and the mean equity-toassets ratio divided by the standard deviation of the return on assets. A minimum
of 4 years and a maximum of 15 years of historical data are required
[COMPUSTAT]
R_COMM: Equal to 1, if a risk committee is present
PFE_AC: Equal to the proportion of audit committee members with financial
expertise
PSE_AC: Equal to the proportion of audit committee members with supervisory
expertise
AC_SIZE: The number of members of the audit committee
BOARD_IND: The proportion of independent board members
BOARD_TENURE: The average tenure of members serving on the board of directors [BoardEx]
CEO_DUAL: Equal to 1, if the CEO is also chairman of the board
AUD_REL_RISK: Equal to 1, if the company switched auditors or reported a material weakness for the year [Audit Analytics]
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21
LOGAGE: The natural log of the number of years the firm has coverage in
COMPUSTAT
NYSE: Equal to 1, if the firm is listed on the New York Stock Exchange
Regression variables (Models 2 and 3).
ROA: Income before extraordinary items divided by total assets [COMPUSTAT
data IB / AT]
TOBIN_Q: Book value of assets—(book value of equity + the market value of
equity) / book value of assets [COMPUSTAT data AT—(CEQ + PRCC_F 3
CSHO) / AT]
BOARD_SIZE: Equal to the log of the number of board members
LOGASSETS: The natural log of total assets [COMPUSTAT data AT]
INST_OWN: Equal to the percentage of institutional ownership. [Thomson Reuters]
LEV: Ratio of total liabilities to total assets [COMPUSTAT data (DLC + DLTT) /
AT]
SALES_GROWTH: Percentage sales growth
CAPOVERSALES: Equal to capital expenditures over sale [COMPUSTAT data
CAPX/SALE]
STDROA: Equal to the standard deviation of return on assets calculated over a
period of no less than 3 and no more than 5 years [COMPUSTAT data IB/AT]
All other variables defined above
Heckman Stage 1 variables (Model 4).
INDUSTRY_CROHIGH: Equal to the mean presence of CRO_HIGH by four-digit
SIC industry and year (excluding the firm itself)
All other variables defined above.
Author’s Note
This article is based on the author’s dissertation from Texas Tech University. She is currently an
Assistant Professor at the University of New Mexico (Email: cristinabailey@unm.edu).
Acknowledgments
The author thanks Denton Collins, Steve Buchheit, Laurie Corradino, Josh Filzen, Juan Manuel
Sanchez, Jason Triche, and Emily Xu for insights and suggestions. She also thanks workshop participants at Texas Tech University, University of Montana, University of New Hampshire, University of
Rhode Island, Lehigh University, University of Nevada-Reno, and Boise State University and conference participants at the 2016 AAA Annual Meeting for helpful comments.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/
or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or
publication of this article: The author acknowledges the financial support of the Jerry S. Rawls
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Journal of Accounting, Auditing & Finance
College of Business Administration at Texas Tech University, the Peter T. Paul College of Business
and Economics at the University of New Hampshire, and the College of Business and Economics at
Boise State.
Notes
1. I thank an anonymous reviewer for this suggestion.
2. See Bromiley, McShane, Nair, and Rustambekov (2015) or Viscelli, Beasley, and Hermanson
(2016) for an in-depth review of the enterprise risk management (ERM) literature.
3. My study also utilizes the ERM score provided by Standard & Poor’s (S&P), as well as expertise
measures related to the chief risk officer (CRO).
4. I classify supervisory expertise as prior experience as a chief executive officer (CEO), company
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
president, chief operating officer, or chairman of the board (Dhaliwal, Naiker, & Navissi, 2010;
G. V. Krishnan & Visvanathan, 2008).
I classify financial expertise as experience as a certified public accountant, chief financial officer
(CFO), vice president of finance, controller, or any other major accounting position (J. Krishnan,
2005).
CFO_EXP is measured similar to CRO_EXP, but does not examine actuarial expertise.
Results are robust to including a broad-based CEO expertise measure similar to that of the CFO,
although prior literature does not support the notion that the same types of expertise will create
value in both the CEO and CFO roles.
Components include (a) risk-management culture, (b) risk controls, (c) emerging risk management, (d) risk and capital models, and (e) strategic risk.
S&P (2006) does specify that ERM functions should be led by a ‘‘qualified’’ senior executive,
but explicit consideration of specific expertise areas is not noted in ratings criteria.
Starting in 2009, S&P adopted a more granular approach to rating the adequate category.
Because this rating scheme is not consistent across my full sample, I code all adequate categories
the same. Results are robust to creating a more granular translation of the ERM score coded as
follows and focusing on 2009 forward where the additional rating category is available: 0 =
weak, 1 = adequate, 2 = adequate with strong risk controls or adequate with positive trend, 3 =
strong, and 4 = excellent. This specification matches the coding of McShane et al. (2011).
I note that 6 firm-year observations do not have any four-digit industry peers.
INDUSTRY_CROHIGH is calculated as zero for these observations. Results for ROA are robust
to excluding these observations.
Although the transition from CEO to CRO may seem like a demotion, a review of the sample
biographies shows that the most common transition was from CEO of a smaller company to
CRO of a larger company.
Consistent with prior research (Baxter et al., 2013), results are robust to utilizing a Heckman
(1976) selection model to test for bias associated with receiving a rating from S&P.
Results are robust to replacing CFO_EXP with CFO_FIN, equal to 1 if the CFO has prior financial expertise and CFO_MBA, equal to 1 if the CFO has an MBA degree (Aier et al., 2005).
The area under the receiver operating characteristic (ROC) curve is 0.892, suggesting a good fit.
Untabulated ordinary least squares (OLS) results using CRO_HIGH report a coefficient of 0.016,
p = .082.
I thank an anonymous reviewer for this suggestion.
MKTCAP, GLOBAL, OP_CASH, ZSCORE, and BOARD_TENURE are excluded from the
ordered logistic in Model 1 when running separate regressions for these subsamples due to collinearity and to avoid overidentification in the life insurance sample.
I note that there are only 37 firm-year observations in the life insurer category, potentially limiting the power of this test.
Bailey
23
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