Identifying and Managing Risk
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THE VALUATION IMPLICATIONS OF ENTERPRISE RISK MANAGEMENT MATURITY Mark Farrell Ronan Gallagher
ABSTRACT Enterprise Risk Management (ERM) is the discipline by which enterprises monitor, analyze, and control risks from across the enterprise, with the goal of identifying underlying correlations and thus optimizing the risk-taking behavior in a portfolio context. This study analyzes the valuation implications of ERM Maturity. We use data from the industry leading Risk and Insurance Management Society Risk Maturity Model over the period from 2006 to 2011, which scores firms on a five-point maturity scale. Our results suggest that firms that have reached mature levels of ERM are exhibiting a higher firm value, as measured by Tobin’s Q. We find a statistically significant positive relation to the magnitude of 25 percent. Upon decomposition of the maturity score, we find that the most important aspects of ERM from a valuation perspective relate to the level of top–down executive engagement and the resultant cascade of ERM culture throughout the firm. Firms that have successfully integrated the ERM process into both their strategic activities and everyday practices display superior ability in uncovering risk dependencies and correlations across the entire enterprise and as a consequence enhanced value when undertaking the ERM maturity journey ceteris paribus.
INTRODUCTION While the tools of portfolio theory are ubiquitous in the practice of finance, the same cannot be said of risk management practice at the enterprise level. Over the past decade, attention has turned to this very issue. Enterprises are subject to risks in many forms and the ultimate goal of Enterprise Risk Management (ERM) is to model, measure, analyze, and respond to these risks in a holistic manner, treating each risk exposure not in isolation, but rather in a portfolio context (Gordon, Loeb, and Tseng, 2009). It is now widely recognized that for a firm to control its risk taking, it is necessary to set risk budgets among the various firm divisions and thus aggregate all
Mark Farrell is at the Management School, Queens University, Belfast. Ronan Gallagher is at the Business School, University of Edinburgh. The authors can be contacted via e-mail: mark. [email protected] and [email protected], respectively. The authors declare that no potential conflicts of interest arise in this work with respect to the research, authorship, and/or publication of this article.
© 2014 The Journal of Risk and Insurance. 82, No. 3, 625–657 (2015). DOI: 10.1111/jori.12035
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the types of risk it is exposed to into one consistent framework (Lleo, 2010). The portfolio-based approach to risk management helps reduce inefficiencies caused by a lack of coordination between different risk management departments as well as exploiting natural hedges that may occur across the enterprise. As a consequence, ERM programs can lead to significant enterprise cost savings through avoidance of duplication of risk management expenditure (Hoyt and Liebenberg, 2011). Imple- mentation of a comprehensive risk management framework such as an ERM program will be subject to material costs,1 in terms of both monetary expenditure and opportunity sacrifice, which must therefore be weighed against the benefits of the program to the firm, to ensure the undertaking of ERM is a value-additive economically justified activity.
The Casualty Actuarial Society (2003) highlights the ultimate value increasing goal of ERM by defining ERM as “the discipline by which an organization in any industry assesses, controls, exploits, finances and monitors risks from all sources for the purpose of increasing the organization’s short and long-term value to its stakeholders.” If ERM maturity improves risk–return optimization at the enterprise level in a cost-effective manner, it is reasonable to conjecture that it should indeed be value additive. The purpose of our study is to address this very question. We seek to ascertain whether firms with more mature ERM programs, experience enhanced value. Moreover, we examine which aspects and attributes of ERM enhancement are most value additive. Analysis of the relative importance of ERM facets is particularly important given that the concept is nascent and generally found to be broadly defined.2 Heterogeneity in the valuation implications across ERM attributes provides important information for firm risk management as the discipline itself evolves and matures.
This study contributes to the emerging field of research on ERM by analyzing the valuation implications of ERM using a detailed ERM maturity assessment score, obtained from the widely utilized Risk and Insurance Management Society Risk Maturity Model (RIMS RMM).3 The RIMS RMM ranks the overall ERM maturity of enterprises from many sectors. Our study utilizes these composite scores and we have been able to decompose the overall scores to observe the relative maturity of important attributes that drive overall ERM maturity (hitherto described in the third section).
1A key finding from the 1,776 participants of the Professional Risk Managers’ International Association’s (PRMIA) 2008 “ERM: A Status Check on Global Best Practices” survey found that 51 percent of respondents said that their firm spends under 2 percent of its operational costs on its ERM program on an ongoing basis, with 27 percent in the 2–5 percent range and 22 percent choosing 5–7 percent.
2Some of the more popular definitions put forward for ERM include those from the Committee of Sponsoring Organizations of the Treadway Commission (COSO) (2004), the Casualty Actuarial Society (2003), the Institute of Internal Auditors (IIA) (2004), and the Risk and Insurance Management Society (RIMS).
3The authors would like to acknowledge and thank Carol Fox of The Risk and Insurance Management Society Inc. and Steven Minsky of Logic Manager Inc. for providing access to the data for this study.
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Current research on ERM firm value implications is relatively limited. This is not surprising given that the formalized discipline of ERM has only been in existence for just over a decade. Furthermore, as a result of a lack of ERM disclosure requirements, research to date has been limited in terms of the lack of an appropriate measurement of the extent of ERM engagement at the firm level. The relevant research has typically utilized a binary proxy variable (such as the appointment of a chief risk officer [CRO] or public ERM-related announcement) to indicate whether the firm is currently undertaking an ERM program (see Pagach and Warr, 2010; Hoyt and Liebenberg, 2011; Lin, Wen, and Yu, 2012). Since 2008, the rating agency, Standard & Poor’s, has included an ERM analysis as part of its global corporate credit rating process for insurance companies (Standard & Poor’s, 2008). Use of this rating allowed for a more sophisticated “extent of ERM implementation” construct variable to be used (see McShane, Nair, and Rustambekov, 2011). To date, analyses have been limited to U.S. and Bermudian insurance companies and suffer from small sample sizes and inability to investigate the constructs of the overall rating, which this study seeks to overcome (as discussed in the third section). To our knowledge, this study is the first to decompose an ERM maturity rating and examine the specific aspects of ERM, which are adding value to the firm.
Consistent with prior research (Hoyt and Liebenberg, 2011), we find a highly significant valuation premium is associated with firms that have undertaken an ERM program (in our case measured by a maturity score as discussed in the third section) as part of their corporate strategy. Furthermore, our study highlights that the valuation premium is being driven by the embedding of risk culture and integration of ERM processes within the organization as well as the degree to which the ERM process is viewed as an integral element in strategy and planning activities.
This article is structured as follows. First, we discuss the evolution of risk management from the traditional approach to the modern-day holistic-based ERM approach. We examine the theoretical underpinning of ERM and why it is proposed to add value above the traditional risk management approach. Subsequently, we describe the data and model used and we then empirically examine the relationship between ERM maturity attributes and firm value. Finally, we present our empirical results followed by discussion of the results and concluding sections.
THEORETICAL BACKGROUND From Traditional Risk Management to Enterprise Risk Management The Miller and Modigliani (1958) seminal contribution on the irrelevance of an organization’s capital structure implies that in perfect capital markets risk management activities also do not create value. Furthermore, the capital asset pricing model (Sharpe, 1964) asserts that well-diversified investors are able to hold portfolios that will have already eliminated the idiosyncratic-specific risks of the firm, thus rendering risk management efforts irrelevant in terms of value creation.
However, there are various counter arguments suggesting that risk management can and does indeed add value to the firm. First, as highlighted by Grace et al. (2010), the commercial environment has many market imperfections in terms of taxes (Miller and Modigliani, 1963), bankruptcy costs (Kraus and Litzenberger, 1973), external capital
VALUATION IMPLICATIONS OF ERM 627
costs (Froot, Scharfstein, and Stein, 1993), and agency costs (Jensen and Meckling, 1976), which can be exploited allowing risk management to add value within the organization. This view is also echoed by Pagach and Warr (2011) who again highlight that attempting to reduce idiosyncratic risk is not a negative net present value project, due to the numerous market frictions and imperfections that exist within the corporate world. Other arguments include recognition of the fact that well-diversified investors in fact do not exist (Shimko, 2001) and that risk management enhances firm value by improving the value of expected cash flows (Shapiro and Titman, 1998; Nocco and Stulz, 2006). Various studies have also statistically shownthat risk management appears tobe adding value in the presence of these market imperfections (e.g., Smith and Stulz, 1985; MacKay and Moeller, 2007).
Theworld has, however, changed at a rapid rateover the last two decades and withit the role that risk management plays within the organization. An increasingly complex layer of connected risks has called for the adoption of an integrated approach to risk management. Corporate risk management has expanded beyond financial and hazard risk mitigation practices such as using insurance and financial hedging instruments to now include the consideration of a multitude of other risk types such as operational risk, reputational risk, and strategic risk. Risk is also no longer being viewed as an activity that can be carried out within the traditional silos of operation that may have existed in the past. Whereas historically risk management activities were compartmentalized and uncoordinated witha focus on using insurance and derivative instruments to protect the firm against hazard and financial risks, a holistic approach has emerged that instead aims to achieve a coordinated management of all significant risk exposures the organization faces (McShane, Nair, and Rustambekov, 2011). This emerging integrated approach, to the aggregation of risk, is generally referred to as ERM.
ERM and Firm Value It is generally recognized that ERM attempts to create shareholder value by allowing firms to achieve a more optimized risk–return trade-off. This view is shared by Meulbroek (2002) who argues, “The goal of risk management is not to minimize the total risk faced by a firm per se, but to choose the optimal level of risk to maximize shareholder value.” Taking an integrated framework approach to managing risk aids the goal of optimal risk taking. Nocco and Stulz (2006) contend that an evaluation of risk and return at the project level does not allow for optimization at the corporate level as risk diversification and correlations are ignored, thus leading to suboptimal decision making. As a key component of ERM is the examination of the risk interactions and their aggregation it is therefore posited that ERM improves internal decision making and hence ultimately contributes to firm value through more efficient capital allocation (Myers and Read, 2001). Furthermore, Nocco and Stulz (2006) argue that ERM can lead to a reduction in the probability of large detrimental cash flow shortfalls (which are economically burdensome to the firm in terms of future growth implications), costly capital acquisition, and relinquishing of profitable investments.
A key aspect of ERM (and difference from the traditional risk management approach) is that the firm’s major risks, from all sources, are aggregated together in a “portfolio” of risks, thus embracing a holistic approach to risk management. Rosenburg and
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Schuermann (2006) use a copula-based method to show that a firm’s total amount of risk differs from the sum of the enterprise’s individual risks. McShane, Nair, and Rustambekov (2011) emphasize the benefits of the ERM risk aggregation approach, attesting that hedging residual risk, rather than independent risks, maximizes value by allowing the organization to benefit from a risk diversification effect or recognition of natural risk hedges. Thus, only the remaining risk needs to be dealt with which should be less onerous than the amount of risk mitigation required if each risk was dealt with independently.4 This benefit is also recognized by Hoyt and Liebenberg (2011) who point out that the integration of risks helps firms to avoid duplication of risk management outlay.
In addition, viewing the company’s risks as a portfolio should be beneficial to the firm as it should improve both the senior management’s and the board’s ability to oversee the enterprise’s risk (Beasley, Clune, and Hermanson, 2005). An improvement in the understanding and transparency of the aggregate level of firm risk, right up to the board level, should allow an efficient level of strategic decision making in line with an optimal risk-taking strategy (Chapman, 2006). Hoyt and Liebenberg (2011) posit that this improved understanding, at board level, enhances resource allocation, capital efficiency, and equity return.
It should also be noted that ERM goes beyond focusing on just risk avoidance activities in recognition of the value to be gained from exposure to risks for which the firm has a strategic competitive advantage. This is partly in recognition of the fact that the desire for risk avoidance may actually increase the volatility and fragility of financial markets as a whole via certain investment products (Jacobs, 2004).
Further noted, value-added benefits of ERM include reduced cost of capital via improved ratings from credit rating agencies (Samanta, Azarchs, and Martinez, 2004; Hoyt and Liebenberg, 2011), improved insights into different types of risk (Meulbroek, 2002), enhanced capacity to inform outsiders such as regulators and investors of the firm’s risk profile (Hoyt and Liebenberg, 2011) and better capital structure decision making (Graham and Rogers, 2002).
METHODOLOGY Sampling Data Data Source: The RIMS RMM for Enterprise Risk Management. The data in our analysis originated from an online survey assessment model developed and collated by RIMS with the support of codeveloper LogicManager Inc. RIMS is a not-for-profit professional association representing more than 3,500 industrial, service, nonprofit, charitable, and government entities throughout the world. RIMS provides networking, professional development, and education opportunities to its
4This is an application of portfolio theory generally first conceptualized by Markowitz (1952) whereby it was recognized that an investor can reduce portfolio risk simply by holding combinations of instruments, which are not perfectly positively correlated. In a similar fashion, the application for ERM assumes that the risks are not 100 percent correlated.
VALUATION IMPLICATIONS OF ERM 629
membership of more than 11,000 risk management professionals who operate in more than 60 countries (RIMS, 2013).
The RIMS RMM data collection was the result of a coordinated marketing effort by RIMS over the 2006–2011 period. RIMS sent the survey assessment invitation to its entire marketing database of roughly 50,000 risk management individuals (the invitation was purposely not limited to the RIMS membership of approximately 10,000). Targeted search engine traffic from terms such as “risk maturity models” drove a significant additional number of visitors to take the survey. As a result, RIMS members represented less than 50 percent of survey participants. Recognizing the prominence of the RIMS RMM, the authors approached RIMS in 2011 to provide access to the data, for research purposes, and this was facilitated via Logic Manager Inc. in a confidential format.
Data Survey Risk Maturity Model: The RIMS RMM. The survey itself was designed as a tool (via an online assessment medium5) for executives in risk management to develop sustainable ERM programs via the scoring of their risk programs and the provision of a real-time report serving as an organizational roadmap for improvement. It has subsequently been widely used throughout the world as a benchmark of ERM maturity, providing organizations with a mechanism for measuring ERM maturity.
Developed in 2006, the RIMS RMM was conceptualized based upon the Capability Maturity Model Integration, a highly utilized maturity model developed by the Software Engineering Institute. It encompasses various applicable risk management frameworks such as ISO 31000, BS31100, FERMA (2002), and COSO (2004) (RIMS, 2011). Maturity models are used in numerous industries for the purposes of assessment and benchmarking as they allow organizations to measure their relative performance position on a pathway to maturity representing an optimal state. As recognized by Lindberg and Seifert (2011), the RIMS RMM is one of the most prominent models for ERM, in existence.
Since ERM involves the complex task of the systematic evaluation of all the significant risks facing an organization and how they affect the organization in aggregate, the ERM process cannot be simply characterized by one or two defining components or attributes. The RIMS RMM for ERM encompasses a total of seven attributes, which describe the fundamental characteristics of an effective ERM program, as shown in Table 1.
As discussed in the RIMS State of ERM Report (2008), survey respondents are asked to rate each of these seven attributes in three dimensions (effectiveness of ERM activities, degree of proactivity, and coverage/pervasiveness throughout the organization), via a number of competency drivers. The data provided by RIMS were a 1–10 scoring of each dimension of each competency driver making up the attribute in question (again, refer to RIMS State of ERM Report, 2008, for further
5The RIMS RMM maturity assessment can be accessed from the following link: http://www. rims.org/resources/erm/pages/RiskMaturityModel.aspx.
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TA B LE
1 RI M S RM
M A ttr ib ut e D es cr ip tio
ns
A tt ri b u te
D e sc ri p ti o n
F o cu
s Jo u rn e y D e sc ri p ti o n
M e a su
re s o f M a tu ri ty
1 E R M -b a se d
a p p ro a ch
E x te n t a n d le v e l o f e x e cu
ti v e
su p p o rt
fo r E R M
F ro m
co n fo rm
in g w it h re g u la ti o n
to v a lu e e x tr a ct io n fo cu
s C o m m u n ic a ti o n in
d a il y d e ci si o n
m a k in g
R is k m a n a g e m e n t co m p e te n cy
a s
p re re q u is it e fo r p ro m o ti o n to
le a d e rs h ip
p o si ti o n
2 E R M
p ro ce ss
m a n a g e m e n t
H o w
w e ll th e E R M
p ro ce ss
is b e in g in te g ra te d in to
e v e ry d a y
p ra ct ic e s.
T h e e x te n t to
w h ic h
re p e a ta b le
a n d sc a la b le
ri sk
m a n a g e m e n t p ro ce ss e s h a v e
b e e n in co rp o ra te d in to
th e
v a ri o u s b u si n e ss
u n it s a id e d b y
q u a li ta ti v e a n d q u a n ti ta ti v e ri sk
m a n a g e m e n t a n a ly se s,
st ro n g
ri sk
m a n a g e m e n t re p o rt in g a n d
cl e a r ro le s a n d h ie ra rc h y o f ri sk -
re la te d re sp
o n si b il it y
F u rt h e r e m b e d d in g o f ri sk
cu lt u re
w it h in
th e o rg a n iz a ti o n
E n te rp ri se -w
id e co m m u n ic a ti o n
o f ri sk -b a se d in it ia ti v e s
E n co u ra g in g e m p lo y e e s to
ta k e a
m o re
ri sk -a w a re
a p p ro a ch
to th e ir
b u si n e ss
a ct iv it ie s a n d
ta sk s
F in a ll y tr u e e m p lo y e e ri sk
m a n a g e m e n t a cc o u n ta b il it y
3 R is k a p p e ti te
m a n a g e m e n t
T h e o rg a n iz a ti o n ’s
d e g re e o f
u n d e rs ta n d in g a n d
a cc o u n ta b il it y to w a rd
th e ri sk –
re w a rd
tr a d e -o ff
S e tt in g e ff e ct iv e p ri o ri ti z a ti o n
cr it e ri a fo r th e o rg a n iz a ti o n in
te rm
s o f it s st ra te g ic
g o a ls
a n d
m a n a g e m e n t o f a ss o ci a te d ri sk s
E n h a n ce d o rg a n iz a ti o n a l
u n d e rs ta n d in g a n d
a cc o u n ta b il it y to w a rd
th e
ri sk – re w a rd
tr a d e -o ff
T h e q u a n ti ty
o f ri sk
e x p o su
re th a t
th e o rg a n iz a ti o n is
w il li n g to
u n d e rt a k e a n d th e o p ti m a l
m a x im
iz a ti o n o f v a lu e o r
o p p o rt u n it y fr o m
th e a p p o in te d
ri sk
a m o u n t
(C on ti n u ed )
VALUATION IMPLICATIONS OF ERM 631
TA B LE
1 C on
tin ue
d
A tt ri b u te
D e sc ri p ti o n
F o cu
s Jo u rn e y D e sc ri p ti o n
M e a su
re s o f M a tu ri ty
4 R o o t ca u se
d is ci p li n e
A b il it y to
p ro a ct iv e ly
id e n ti fy
cr it ic a l tr e n d s in
o rd
e r to
b o th
m in im
iz e o r p re v e n t im
p a ct
o f
a d v e rs e e v e n ts
a n d m a x im
iz e
v a lu e e x tr a ct io n fr o m
o p p o rt u n it ie s
Im p ro v e d u n d e rs ta n d in g o f ri sk
e x p o su
re s o f th e o rg a n iz a ti o n
A tt ri b u te
is sc o re d h ig h ly
if d e co n st ru ct io n o f p a st
e v e n ts
is ca rr ie d o u t a lo n g w it h
th o ro u g h a n a ly si s o f
li k e li h o o d a n d fr e q u e n cy
o f
id e n ti fi e d ri sk s a s p a rt
o f
ro u ti n e ri sk
m a n a g e m e n t
a ct iv it ie s
T h e e x te n t o f d is ci p li n e a n d e ff o rt
th a t is
d ir e ct e d to w a rd
u n d e rs ta n d in g th e so u rc e o f a
p ro b le m
(i ts
ro o t ca u se )
5 U n co v e ri n g a n d
id e n ti fy in g
ri sk s
C a p a b il it y in
o n g o in g ri sk
m e a su
re m e n t a n d re p o rt in g
T h e q u a li ty
a n d co v e ra g e g iv e n to
d o cu
m e n ti n g ri sk s a n d
o p p o rt u n it ie s th ro u g h o u t th e
o rg a n iz a ti o n to
a id
e ff e ct iv e ri sk
id e n ti fi ca ti o n a n d m it ig a ti o n o r
e x p lo it a ti o n
E n su
ri n g th a t th e ri sk
m a n a g e m e n t p ro ce ss
is k e e p in g
a b re a st
w it h n e w
e m e rg in g
ri sk s a n d fa ci li ta ti n g
id e n ti fi ca ti o n o f ri sk
a n d
o p p o rt u n it y th ro u g h fr o n t- li n e
e m p lo y e e e n g a g e m e n t
T h e p e n e tr a ti o n a ch
ie v e d in
e ff e ct iv e ly
o b ta in in g ri sk
in fo rm
a ti o n fr o m
d if fe re n t
a re a s su
ch a s e m p lo y e e
e x p e rt is e , d a ta b a se s, a n d o th e r
e le ct ro n ic
fi le s w it h th e g o a l o f
u n co v e ri n g d e p e n d e n ci e s a n d
co rr e la ti o n a cr o ss
th e
e n te rp ri se
S p e ci a l a tt e n ti o n is
p a id
to cr it ic a l ri sk
in d ic a to rs
a n d th e
e ff ic a cy
to w h ic h th e y a re
re g u la rl y re v ie w e d a lo n g w it h
th e re v ie w
o f th e im
p a ct
a n d
li k e li h o o d ri sk
sc o ri n g u se d b y
v a ri o u s b u si n e ss
u n it s
(C on ti n u ed )
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TA B LE
1 C on
tin ue
d
A tt ri b u te
D e sc ri p ti o n
F o cu
s Jo u rn e y D e sc ri p ti o n
M e a su
re s o f M a tu ri ty
6 P e rf o rm
a n ce
m a n a g e m e n t—
m a n a g in g
u n ce rt a in ty
T h e d e g re e to
w h ic h th e
o rg a n iz a ti o n is
a b le
to e x e cu
te o n v is io n a n d st ra te g y a lo n g si d e
th e ir ri sk
m a n a g e m e n t a ct iv it ie s
T h e E R M
p ro ce ss
is v ie w e d a s a n
in te g ra l e le m e n t in
st ra te g y a n d
p la n n in g a ct iv it ie s
L e v e l o f co m m u n ic a ti o n o f th e
b u si n e ss
g o a ls
th ro u g h o u t th e
o rg a n iz a ti o n
D e v ia ti o n fr o m
st a te d g o a ls
a re
m e a su
re d a s w e ll a s b e in g
re p o rt e d a n d a li g n in g th e
g o a ls
a ss o ci a te d w it h th e E R M
p ro g ra m
w it h th e
o rg a n iz a ti o n ’s
st ra te g ic
g o a ls
a n d o b je ct iv e s
7 B u si n e ss
re si li e n ce
a n d
su st a in a b il it y —
m a n a g in g lo w -
p ro b a b il it y /
h ig h -
co n se q u e n ce
e v e n ts
O rg a n iz a ti o n ’s
a b il it y to
re co v e r
q u ic k ly
fr o m
se tb a ck s
O rg a n iz a ti o n ’s
a b il it y to
m a in ta in
so m e th in g o f v a lu e
D e n o te s th e e x te n t to
w h ic h th e
o rg a n iz a ti o n in te g ra te s th e se
tw o co m p o n e n ts
fo r it s
o p e ra ti o n a l p la n n in g in to
it s
E R M
p ro ce ss
C o n ti n u o u s a d a p ta ti o n is
a k e y
re q u ir e m e n t to
e n su
re a n
a p p ro p ri a te
re sp
o n se
to ch
a n g in g b u si n e ss
co n d it io n s is
a ch
ie v e d
B a la n ce
b e tw
e e n sh
o rt -t e rm
d e li v e ra b le s a n d lo n g e r- te rm
v a lu e
E n g a g in g in
a ct iv it ie s su
ch a s
st re ss
te st in g a n d sc e n a ri o
a n a ly si s to
u n d e rs ta n d w h a t
ca n g o w ro n g u n d e r v a ry in g
sc e n a ri o s a n d h o w
it m ig h t
th e re fo re
b e a v o id e d to
a ll o w
b u si n e ss
co n ti n u it y a n d
g ro w th
VALUATION IMPLICATIONS OF ERM 633
information on the competency drivers), across all three previously mentioned dimensions. We proceeded to score the attributes on a 1–10 scale after equally weighting the scoring across the three dimensions and also averaging the competency drivers, as can be seen in the hypothetical example in Figure 1. The overall maturity score, calculated by the authors, was the simple average of the attribute scores.
The 1–10 scoring scale corresponds to five distinct levels of maturity depicted in the RIMS RMM, as described in Table 2.
Essentially, the RIMS RMM produces a 1–5 maturity scale assessment for each of the seven ERM attributes as well as a final ERM maturity score, as described above. We examine each of these attributes in isolation in our study as well as the overall ERM maturity score, which we simplify using a binary dummy variable (see the “Empirical Analysis” section) for analysis purposes.
Data Cleaning and Validity. As the assessment data were taken from an open online medium, it was important to clean the data boththoroughly andappropriately. We were provided with over 2,000 assessment responses from across the corporate spectrum of public and private firms. We initially removed those where the corporate e-mail address was not provided. Our firm value study focuses on market value and therefore we analyze the subsample of public-listed companies, so that we had access to market- based measures of value. Analysis of public-listed firms also has the benefit of looking at firms, which have more mature ERM systems, as shown by Paape and Spekle (2012). We then proceeded to remove respondents for whom the job title6 was not deemed to be at a level whereby the respondent could adequately provide the required information on the
FIGURE 1 ERM Attribute Maturity Scoring Example
6Appropriate Job titles included titles such as chief risk officer, director of risk management, director of enterprise risk management, chief actuary, director internal audit, and so on.
634 THE JOURNAL OF RISK AND INSURANCE
overall organization’s risk policies, frameworks, and methodologies. Our final sample size of ERM maturity assessments was 225 unique respondents.
Modeling ERM via a maturity model provides additional useful and unique information above the Standard & Poor’s rating upon which some previous ERM research has been based (see McShane, Nair, and Rustambekov, 2011). Whereas the Standard & Poor’s rating is used to evaluate credit worthiness (Standard & Poor’s, 2012) (and therefore only examines a narrow band of ERM characteristics as they pertain to management’s risk practices), the RIMS RMM index is a more comprehensive look at an organization’s key risk management attributes (discussed below), inclusive of risk appetite management and performance. It should therefore be noted that the Standard & Poor’s rating focuses primarily on a limited number of subfactors, which are rated “positive, negative or neutral” based on its analysts’ review for comprehensiveness and performance standards. It can be argued that such a review does not provide a thorough indication of the organization’s risk management practices and capabilities and hence overall ERM maturity levels. In contrast, the RIMS RMM assessment is completed by a high-level employee with a thorough and strategic oversight of the risk management activities of the organization. The self-reported assessments are available to Internal Audit depart- ments to use as a guide to evaluate and verify the effectiveness of their ERM programs as is required in the Internal Audit mandate. The Internal Audit function helps to provide the assurance that the senior-level risk manager self-reporting is indeed
TABLE 2 RIMS RMM Levels
Maturity (Level) Maturity-Level Characteristics
Ad hoc (1) Implies an extremely primitive level of ERM maturity where risk management typically depends on the actions of specific individuals, with improvised procedures and poorly understood processes.
Initial (2) Risk is managed in silos, with little integration or risk aggregation. Processes typically lack discipline and rigor. Risk definitions often vary across the silos.
Repeatable (3) A risk assessment framework is generally in place with the Board of Directors being provided with risk overviews. Approaches to risk management are established and repeatable.
Managed (4) Enterprise wide risk management activities, such as monitoring, measurement, and reporting are integrated and harmonized with measures and controls established.
Risk procedures are communicated and fully understood throughout the organization with the risk management principles integrated fully within the management process.
Leadership (5) Risk-based discussions are embedded to a strategic level, such as long- term planning, capital allocation, and decision making. Risk appetite and tolerances are clearly understood with alerts in place to ensure the board of directors and executive management is made aware when risk thresholds are exceeded.
Source: Adapted from RIMS (2006), Marks (2011), and Lindberg and Seifert (2011).
VALUATION IMPLICATIONS OF ERM 635
accurate. Further, since all results are fully confidential and primarily used for self- improvement, there is no incentive for firms to overstate their actual capabilities.
Data Characteristics. Table 3 highlights the distribution of the overall maturity scores among our 225 observations.
When we decompose the overall maturity score we observe heterogeneity among attribute maturities. Table 4 shows the number of firms that have obtained at least a certain level of maturity for each attribute. It can be noted that attribute 7 (Business Resilience and Sustainability) leads the other attributes in terms of progression toward
TABLE 3 ERM Maturity Breakdown
Overall Maturity Level (ERMMaturity) Count Percentage
Ad hoc (1) 19 8.44 Initial (2) 64 28.44 Repeatable (3) 93 41.33 Managed (4) 48 21.33 Leadership (5) 1 0.44 Total 225 100.00
Note: ERMMaturity is defined in Table 6 and the maturity levels are described in Table 2.
TABLE 4 Maturity Distribution by Attribute
Attribute-Specific
Maturity Level
(ERMMaturity_
Attributei) Attribute1 Attribute2 Attribute3 Attribute4 Attribute5 Attribute6 Attribute7
Ad hoc (1) 33 36 35 39 28 36 18 Initial (2) 65 67 77 79 75 81 66
Repeatable (3) 85 77 70 73 85 72 81
Managed (4) 38 44 37 31 36 33 56
Leadership (5) 4 1 6 3 1 3 4
Total 225 225 225 225 225 225 225
Attribute Description
1 ERM-based approach
2 ERM process management
3 Risk appetite management
4 Root cause discipline
5 Uncovering and identifying risks
6 Performance management—managing uncertainty
7 Business resilience and sustainability—managing low-probability/high-consequence
events
Note: ERMMaturity_Attributei is defined in Table 6 and the attribute description is summarized above.
636 THE JOURNAL OF RISK AND INSURANCE
the upper maturity Levels, 4 and 5. It should also be noted that overall, relatively few firms have reached the “Leadership” level across the seven attributes, indicating there is still much scope for attribute maturation in the future. This is not surprising given that the discipline of ERM is still in its infancy and organizations are still going through the implementation and refinement journey as the discipline continues to evolve.
The distribution of data by sector, country, and year is shown in Table 5.
We note from Table 5 that manufacturing firms make up almost 40 percent of those sample. A further 20 percent come from Transportation, Communications, Electric, Gas, and Sanitary Services sector while almost 17 percent are services firms. Firms domiciled in the United States make up the overwhelming majority of the data set with over 75 percent of respondents. Finally, survey responses are more concentrated in early years of the sample, when the survey was first made available, and decline over time with a significant drop off in responses between 2009 and 2010.
TABLE 5 Characteristics of Responding Firms
Freq. % Cum. %
Panel A: Distribution of Data by Sector
Industry Agriculture, forestry, and fishing 2 0.89 0.89 Finance, insurance, and real estate 14 6.22 7.11 Manufacturing 87 38.67 45.78 Mining and construction 18 8 53.78 Services 38 16.89 70.67 Transportation, communications, electric, gas, and sanitary services 44 19.56 90.22 Wholesale and retail trading 22 9.78 100
Panel B: Distribution of Data by Countrya
Australia 7 3.11 3.11 Canada 16 7.11 10.22 United Kingdom 5 2.22 12.44 United States 170 75.56 88.00 Other 27 12 100
Panel C: Distribution of Data by Yearb
2006 60 26.67 26.67 2007 53 23.56 50.22 2008 48 21.33 71.56 2009 38 16.89 88.44 2010 14 6.22 94.67 2011 12 5.33 100 Total 225 100 aCountries constituting less than 2 percent of data set included in “Other.” bBased upon the year the survey was completed.
VALUATION IMPLICATIONS OF ERM 637
TA B LE
6 D efi
ni tio
n of
M od
el V ar ia bl es
N a m e
D e sc ri p ti o n
H y p o th e si z e d Im
p a ct
o n D e p e n d e n t V a ri a b le
(T o b in sQ
)a
H y p o th e si z e d Im
p a ct
o n D e p e n d e n t V a ri a b le
(E R M _ E n g a g e m e n t_
D u m m y )a
E R M M a tu ri ty
O v e ra ll E R M
1 – 5 m a tu ri ty
le v e l ca lc u la te d fr o m
th e
R IM
S ’ R M M
m a tu ri ty
sc o re s (s e e T a b le
2 )
E R M M a tu ri ty _
A tt ri b u te
i
E R M
A tt ri b u te
m a tu ri ty
sc o re
(s e e T a b le
2 ) fo r
a tt ri b u te
i (a tt ri b u te s 1 – 7 ; se e T a b le
1 )
E R M _ E n g a g e m e n t_
D u m m y
E R M _ E n g a g e m e n t_
D u m m y ¼ 0 if E R M M a tu ri ty
le v e l is
ad ho c o r
in it ia l (i .e ., L e v e ls
1 o r 2 a s d e sc ri b e d in
T a b le
2 )
E R M _ E n g a g e m e n t_ D u m m y ¼ 1 if E R M M a tu ri ty
le v e l is
re p e a ta b le , m a n a g e d o r le a d e rs h ip
(i .e .,
L e v e ls
3 , 4 , o r 5 a s d e sc ri b e d in
T a b le
2 )
E R M A tt ri b u te
i_ E n g a g e m e n t_ D u m m y
E R M A tt ri b u te
i_ E n g a g e m e n t_ D u m m y ¼
0 if E R M M a tu ri ty _ A tt ri b u te
i
le v e l is
ad ho c o r in it ia l
(i .e ., L e v e ls
1 o r 2 a s d e sc ri b e d in
T a b le
2 )
E R M A tt ri b u te
i_ E n g a g e m e n t_ D u m m y ¼ 1 if
E R M M a tu ri ty _ A tt ri b u te
i le v e l is
re p e a ta b le ,
m a n a g e d o r le a d e rs h ip
(i .e ., L e v e ls
3 , 4 , o r 5 a s
d e sc ri b e d in
T a b le
2 )
S iz e
N a tu ra l lo g a ri th m
o f th e b o o k v a lu e o f to ta l a ss e ts
N e g a ti v e (L a n g a n d S tu lz ,
1 9 9 4 ; A ll a y a n n is
a n d
W e st o n , 2 0 0 1 )
P o si ti v e (C
o lq u it t, H o y t,
a n d L e e , 1 9 9 9 ; B e a sl e y ,
C lu n e , a n d
H e rm
a n so n , 2 0 0 5 ; P a a p e
a n d S p e k le , 2 0 1 2 )
S a le sG
ro w th
(S a le s in
y e a r t m in u s sa le s in
y e a r t � 1 )/ sa le s in
y e a r t � 1
P o si ti v e (T it m a n a n d
W e ss e ls , 1 9 8 8 )
P o si ti v e (c o n je ct u re d b y th e
a u th o rs )b
(C on ti n u ed )
638 THE JOURNAL OF RISK AND INSURANCE
TA B LE
6 C on
tin ue
d
N a m e
D e sc ri p ti o n
H y p o th e si z e d Im
p a ct
o n D e p e n d e n t V a ri a b le
(T o b in sQ
)a
H y p o th e si z e d Im
p a ct
o n D e p e n d e n t V a ri a b le
(E R M _ E n g a g e m e n t_
D u m m y )a
L e v e ra g e
B o o k v a lu e o f li a b il it ie s/
m a rk e t v a lu e o f e q u it y
A m b ig u o u s (J e n se n a n d
M e ck li n g , 1 9 8 6 ; F a m a
a n d F re n ch
, 2 0 0 2 ; D e
W e t, 2 0 0 6 ; S h a rm
a , 2 0 0 6 )P
o si ti v e (P a g a ch
a n d
W a rr , 2 0 1 1 )
F in a n ci a lS la ck
C a sh
a n d sh
o rt -t e rm
in v e st m e n ts / b o o k v a lu e o f
a ss e ts
A m b ig u o u s (P a g a ch
a n d
W a rr , 2 0 1 0 )
E a rn V a ri a b il it y
C o e ff ic ie n t o f v a ri a ti o n o f e a rn in g s b e fo re
in te re st
a n d ta x e s,
in th e fi v e fi n a n ci a l y e a rs
u p to
a n d
in cl u d in g th a t co rr e sp
o n d in g to
su rv e y
co m p le ti o n
A m b ig u o u s (L ie b e n b e rg
a n d H o y t, 2 0 0 3 ; P a g a ch
a n d W a rr , 2 0 1 0 )
A ss e tO
p a ci ty
In ta n g ib le
a ss e ts / b o o k v a lu e o f a ss e ts
P o si ti v e (L ie b e n b e rg
a n d
H o y t, 2 0 0 3 )
V a lu e C h a n g e
(F ir m
v a lu e in
y e a r t m in u s fi rm
v a lu e in
y e a r t � 1 )/
fi rm
v a lu e in
y e a r t � 1
N e g a ti v e (P a g a ch
a n d
W a rr , 2 0 1 1 )
In si d e rs
P e rc e n ta g e o f o u ts ta n d in g sh
a re s o w n e d b y in si d e rs
A m b ig u o u s
In si d e rS q
In si d e rs � in si d e rs
A m b ig u o u s
D iv id e n d P a y m e n tS ta tu s
G iv e n a n in d ic a to r v a lu e 1 if fi rm
p a id
d iv id e n d s in
th a t y e a r, 0 o th e rw
is e
A m b ig u o u s
R e tu rn O n E q u it y
N e t in co m e / m a rk e t v a lu e o f e q u it y
P o si ti v e (A
ll a y a n n is
a n d
W e st o n , 2 0 0 1 ;
P a n d le y , 2 0 0 5 )
B e ta
C o v a ri a n ce
(f ir m
e x ce ss
re tu rn s,
m a rk e t re tu rn s) /
v a ri a n ce
(m a rk e t re tu rn s)
o v e r 5 y e a rs
N e g a ti v e (c a p it a l a ss e t
p ri ci n g m o d e l;
S h a rp e , 1 9 6 4 )c
P o si ti v e (c o n je ct u re d b y th e
a u th o rs )d
(C on ti n u ed )
VALUATION IMPLICATIONS OF ERM 639
TA B LE
6 C on
tin ue
d
N a m e
D e sc ri p ti o n
H y p o th e si z e d Im
p a ct
o n D e p e n d e n t V a ri a b le
(T o b in sQ
)a
H y p o th e si z e d Im
p a ct
o n D e p e n d e n t V a ri a b le
(E R M _ E n g a g e m e n t_
D u m m y )a
In d D iv
In d u st ri a l d iv e rs if ic a ti o n in d ic a to r (e q u a l to
1 if
fi rm
is in d u st ri a ll y d iv e rs if ie d a s e x h ib it e d b y
T h o m p so n O n e B a n k e r’ s W o rl d sc o p e d a ta b a se
sh o w in g sa le s in
S IC
co d e s in
m o re
th a n o n e
in d u st ry )
P o si ti v e (B h a ra d w a j,
B h a ra d w a j, a n d
K o n sy n sk i, 1 9 9 9 )
P o si ti v e (S ta n d a rd
& P o o r’ s,
2 0 0 5 )
In tl D iv
In te rn a ti o n a l d iv e rs if ic a ti o n in d ic a to r (e q u a l to
1 if
fi rm
is in te rn a ti o n a ll y d iv e rs if ie d a s e x h ib it e d b y
T h o m p so n O n e B a n k e r’ s W o rl d sc o p e d a ta b a se
sh o w in g fo re ig n sa le s)
P o si ti v e (B h a ra d w a j,
B h a ra d w a j, a n d
K o n sy n sk i, 1 9 9 9 )
P o si ti v e (S ta n d a rd
& P o o r’ s,
2 0 0 5 )
T im
e D u m
D u m m y v a ri a b le
fo r y e a r a ss e ss m e n t ca rr ie d o u t
In d D u m
D u m m y v a ri a b le
fo r in d u st ry
ty p e
C tr y D u m
D u m m y v a ri a b le
fo r co u n tr y
In v e rs e M il ls
P ro b a b il it y d e n si ty
fu n ct io n / cu
m u la ti v e
d is tr ib u ti o n fu n ct io n fr o m
fi rs t- st a g e e q u a ti o n
(d is cu
ss e d in
th e “ E m p ir ic a l A n a ly si s”
se ct io n )
T o b in sQ
A p p ro x im
a ti o n o f T o b in ’s
Q d e fi n e d a s p e r th e
“ D e p e n d e n t a n d In d e p e n d e n t V a ri a b le
o f
In te re st ” se ct io n
L o g T o b in sQ
N a tu ra l lo g a ri th m
o f T o b in sQ
e A n i. i. d e rr o r te rm
w it h m e a n 0
a S u p p o rt in g li te ra tu re
is in
p a re n th e se s.
b It is co n je ct u re d th a t co m p a n ie s th a t a re
fa ce d w it h o p p o rt u n it ie s to
e x p a n d a n d su
b se q u e n tl y u n d e rg o in g ra p id
g ro w th
w il l h a v e a g re a te r n e e d fo r
m o re
a d v a n ce d E R M
p ra ct ic e s.
c T h e ca p it a l a ss e t p ri ci n g m o d e l (S h a rp e ,1 9 6 4 ) a ss e ts th a t a h ig h e r le v e l o f sy st e m a ti c o r n o n d iv e rs ifi a b le ri sk
re su
lt s in
a h ig h e r e x p e ct e d ra te o f re tu rn .I n
o th e r w o rd
s, in v e st o rs
w il l d is co u n t th e fu tu re
ca sh
fl o w s a t a h ig h e r ra te , w h ic h re su
lt s in
a lo w e r fi rm
v a lu e . W e th e re fo re
e x p e ct th e fi rm
’s b e ta , w h ic h
p ro x ie s th e fi rm
’s sy st e m a ti c ri sk , to
b e n e g a ti v e ly
re la te d to
fi rm
v a lu e .
d W e e x p e ct
fi rm
s w it h v a ry in g le v e ls
o f v o la ti li ty , a n d th u s sy st e m a ti c ri sk , to
h a v e g re a te r n e e d fo r a n d th e re fo re
le v e ls
o f E R M
so p h is ti ca ti o n a n d
m a tu ri ty
a s a d ir e ct
re su
lt o f th e g re a te r ri sk
le v e ls , b y d e fi n it io n .
640 THE JOURNAL OF RISK AND INSURANCE
Dependent and Independent Variable of Interest The central focus of this study is to observe the valuation implications of ERM maturity. The natural logarithm of an approximation of Tobin’s Q serves as the dependent variable in our model, representing firm value. Tobin’s Q was chosen as it has been widely used in empirical risk management studies (Smithson and Simkins, 2005) and in particular three recent, directly applicable, ERM value implication studies performed by Hoyt and Liebenberg (2011), McShane, Nair, and Rustambekov (2011) and Lin, Wen, and Yu (2012). We calculate the approximate Q ratio using the method performed by Chung and Pruitt (1994). The Tobin’s Q approximation in this context is measured as:
Tobin0s Q ¼ MVE þ PS þ D TA
;
where
MVE ¼ market value of equity; PS ¼ value of preferred stock; D ¼ Debt ¼ (Current Liabilities � Current Assets) þ Long Term Debt; TA ¼ total assets.
Our main independent variable of interest is an ERM engagement variable (see the “Empirical Analysis” section) based upon the previously discussed RIMS RMM maturity rating (see Table 1). The other independent variables utilized in our empirical model are described in the empirical analysis below.
It should be noted that for consistency and data availability purposes, all financial data utilized in the study are from the financial year closest to the survey filing. For example, if a company has a December 31st fiscal year-end and completed the survey on January 5, 2007, the 2006 year-end financial data are used. On the other hand, if the company had alternatively completed the survey on September 15, 2007, the 2007 financial year-end data are used.
Empirical Analysis When dealing with survey/response-based data, it is important to acknowledge and analyze selection bias (Heckman, 1979). Firms with ERM programs are not a random sample of all firms. Respondents to the survey have self-selected their status as an organization that has instigated at least preliminary engagement with risk management practice. Heckman (1979) notes that the least squares estimator of the population variance is downward biased under such circumstances. The solution that Heckman proposes is sometimes referred to as a two-step model.
We conjecture that firms that have limited engagement with ERM practices may have latent differences from those that have more formal risk management practice, and these latent factors could possibly impact upon firm value. To ignore this would be to
VALUATION IMPLICATIONS OF ERM 641
run the risk of sample selection bias, as noted above. As a consequence, in the first- stage regression, we model the level of ERM in a probit setting.
Looking at the definition of ERM categories from the RIMS RMM, we can draw a clear distinction between maturity levels 1 and 2 and maturity levels 3, 4, and 5, as shown in Table 2.
Maturity levels 1 and 2 feature a risk management process that lacks discipline but more specifically lack enterprise wide coordination. Silo-based risk activities are dominant. Both of these descriptions point toward a corporate environment that has not yet engaged in a mature ERM program, which at its heart aims to break down silos and instead bring about a holistic enterprise-wide coordinated approach to risk manage- ment activities. In contrast, Levels 3–5 have established, integrated, and repeatable risk management practices in place throughout the enterprise with engagement coming from the top of the firm. Additionally, a risk framework is generally in place and risk- based discussions are embedded to a strategic level with effective communication throughout the organization. Clearly, firms at Levels 3–5 are engaged in a more holistic portfolio approach to risk, which aligns with the general aims of ERM. As such in our first-stage regression, we state that firms have primitive ERM engagement until they reach Level 3, where they move to disciplined ERM engagement.
In our first estimating equation, we examine the determinants of ERM to allow us to perform the two-step approach to overcome sample selection bias. The hypothesized impact of these variables, on ERM maturity, is shown in Table 6. In order to properly specify the selection process (i.e., the drivers of ERM maturity), we turn to the extant literature (e.g., Miccolis and Shah, 2000; Hoyt, Merkley, and Thiessen, 2001; Lam, 2001; Liebenberg and Hoyt, 2003; Beasley, Clune, and Hermanson, 2005; Standard & Poor’s, 2005; Pagach and Warr, 2010) and decide upon a specification including firm size, financial leverage, sales growth, financial slack, industrial diversification, international diversification, earnings variability, asset opacity, change in firm value, and a series of dummies controlling for industry and time.
The resulting first-stage specification is, therefore, a probit model estimated with time and industry fixed effects as follows:
ERM Engagement Dummy ¼ a þ b1 � Size þ b2 � Leverage þ b3 � SalesGrowth þb4 � FinancialSlack þ b5 � IndDiv þ b6 � IntlDiv þ b7 � EarnVariability
þb8 � AssetOpacity þ b9 � ValueChange þ Swt � TimeDumt þ Sgi � IndDumi þ e; where
ERM_Engagement_Dummy ¼ 0 if ERM maturity level (ERMMaturity) is ad hoc or initial (i.e., Levels 1 or 2)
ERM_Engagement_Dummy ¼ 1 if ERM maturity level (ERMMaturity) is repeat- able, managed, or leadership (i.e., Levels 3, 4, or 5)
The first step of the two-step procedure allows the researcher to generate an inverse Mills ratio, which is the ratio of the probability density function over the cumulative distribution function calculated using the probit output.
642 THE JOURNAL OF RISK AND INSURANCE
In the second stage, the inverse Mills ratio is inserted into our specification as an explanatory variable to correct for selection bias in our firm value models.
In controlling for the drivers of firm value, we again turn to the extant literature and include control variables such as firm size, financial leverage, return on equity, sales growth, systematic risk (beta), industrial diversification, international diversification, insider share ownership (and its squared value), and a dividend payment status indicator. These control variables are also augmented with a series of dummy variables focusing on year of assessment completion, as previously described, and the country and industry of the firm surveyed. RIMS RMM data were matched to the specific firm operational and financial characteristics downloaded from Thomson One Banker’s Worldscope database.
The second-stage model is, therefore, estimated (with time, industry, and country fixed effects) as:
LogTobinsQ ¼ a þ b1 � ERM Engagement Dummy þ b2 � Size þ b3 � Leverage þb4 � ReturnOnEquity þ b5 � SalesGrowth þ b6 � Beta þ b7 � IndDiv
þb8 � IntlDiv þ b9 � Insiders þ b10 � InsidersSq þ b11 � DividendPaymentStatus þb12 � InverseMills þ Swt � TimeDumt þ Sgi � IndDumi þ SliCtryDumi þ e:
It should be noted that the second-step estimating equation is log linear. In addition, by estimating in a two-step Heckman manner, the second equation is robust to selection bias in the ERM categorization.
Table 6 defines the variables used in our model.
RESULTS AND DISCUSSION Table 7 provides summary statistics on the variables, which are utilized in the study.
The average ERM maturity of firms in the sample is 2.77. Firms on average have positive sales growth to the magnitude of around 7.44 percent and the average firm sampled has a systematic risk coefficient (beta as estimated from a market model regression) of 1.15. As many as 75 percent of firms included in the study are diversified across industrial lines, while 64 percent are diversified internationally and do business in more than one international market.
Table 8 profiles the Pearson correlations between variables used across the two-step estimation procedure. The results of the two-step baseline specification estimations are shown in Table 9.
It can be noted that the key explanatory variable in modeling ERM maturity (via the previously discussed ERM_Engagement_Dummy variable) is that of firm size. Larger firms tend to be farther along the ERM maturity spectrum. This finding is consistent with Beasley, Clune, and Hermanson (2005) who found that ERM implementation advancement is positively related to entity size. It is conjectured that larger firms benefit from economies of scale and division of labor to the extent that risks can be more closely dissected and monitored from board level right down throughout the
VALUATION IMPLICATIONS OF ERM 643
firm. We also find that firms that are internationally diversified tend to have lower ERM maturity scores ceteris paribus. Again this is consistent with prior findings, such as Hoyt and Liebenberg (2011). It is suggested that the complexity of doing business across international lines can dilute the operational performance and consistency of formal ERM programs. As ERM is still a relatively new discipline the practices and processes associated with it are continuing to evolve (Frigo and Anderson, 2011). We expect that firms that experience difficulties in implementing ERM across geographical boundaries will improve considerably over the next decade, as ERM process sophistication and efficiency improves.
In the second-stage regression, our explanatory variable of focus is that of ERM maturity (again via the previously discussed ERM_Engagement_Dummy variable). It can be noted that firms with upper levels of ERM Maturity engagement (i.e., Levels 3– 5 that are captured via ERM_Engagement_Dummy ¼ 1, as discussed in the “Empirical Analysis” section) are associated with a 22.5 percent rise in firm value as measured by our logged approximate Tobin’s Q firm value proxy. This equates to a
TABLE 7 Summary Statistics
Variables Mean SD 1st Quartile Median 3rd Quartile
TobinsQ 1.2563 0.7576 0.7492 1.0983 1.5672 LogTobinsQ 0.0606 0.6122 �0.2887 0.0938 0.4493 ERMMaturity 2.7689 0.8964 2.0000 3.0000 3.0000 ERMMaturity_Attribute1 2.6222 0.9885 2.0000 3.0000 3.0000 ERMMaturity_Attribute2 2.5867 0.9923 2.0000 3.0000 3.0000 ERMMaturity_Attribute3 2.5644 1.0249 2.0000 3.0000 3.0000 ERMMaturity_Attribute4 2.4667 0.9774 2.0000 2.0000 3.0000 ERMMaturity_Attribute5 2.5867 0.9175 2.0000 3.0000 3.0000 ERMMaturity_Attribute6 2.4933 0.9734 2.0000 2.0000 3.0000 ERMMaturity_Attribute7 2.8311 0.9533 2.0000 3.0000 4.0000 ERM_Engagement_Dummy 0.6311 0.4836 0.0000 1.0000 1.0000 Size 3.6869 0.6656 3.3158 3.7309 4.1362 Leverage 1.0344 1.4693 0.3493 0.6363 1.2015 SalesGrowth 0.0744 0.2006 �0.0128 0.0747 0.1538 FinancialSlack 0.3776 4.0613 0.0245 0.0571 0.1478 IndDiv 0.7511 0.4333 1.0000 1.0000 1.0000 IntlDiv 0.6444 0.4797 0.0000 1.0000 1.0000 EarnVariability 0.6083 6.1729 0.1718 0.3052 0.5824 AssetOpacity 0.1988 0.1928 0.0331 0.1510 0.3143 ValueChange 0.0789 0.4984 �0.2589 0.0655 0.2993 ReturnOnEquity �0.1012 2.4907 0.0635 0.1314 0.2052 Beta 1.1480 0.8322 0.6126 1.0013 1.4600 Insiders 0.1735 0.1993 0.0082 0.1152 0.2529 InsidersSq 0.0697 0.1320 0.0001 0.0133 0.0640 DividendPaymentStatus 0.7378 0.4408 0.0000 1.0000 1.0000
Note: Variables are defined in Table 6.
644 THE JOURNAL OF RISK AND INSURANCE
TA B LE
8 Pe
ar so n C or re la tio
n M at rix
L o g T o b in sQ
E R M M a tu ri ty
E R M _
E n g a g e m e n t_
D u m m y
S iz e
L e v e ra g e
R e tu rn O n E q u it y
S a le sG
ro w th
B e ta
In d D iv
In tl D iv
In si d e rs
In si d e rs S q
F in a n ci a lS la ck
E a rn V a ri a b il it y
A ss e tO
p a ci ty
V a lu e C h a n g e
D iv id e n d P a y m e n tS ta tu s
L o g T o b in sQ
1
E R M M a tu ri ty
0 .0 7 5 4
1
E R M _ E n g a g e m e n t_
D u m m y
0 .0 6 0 5
0 .8 5 2 9
1
S iz e
�0 .0 1 5 9
0 .2 1 5 4
0 .1 8 1 8
1
L e v e ra g e
�0 .5 7 0 3
0 .0 3 6
0 .0 5 0 3
0 .1 5 9 6
1
R e tu rn O n E q u it y
�0 .0 2 1 9
0 .0 5 5 2
0 .0 6 5 1
0 .0 4 1 8
�0 .0 3 2 5
1
S a le sG
ro w th
0 .2 1 8 7
�0 .0 6 7 7
�0 .0 8 8 4
�0 .0 4 1 9
�0 .1 1 8 4
0 .1 0 7 9
1
B e ta
�0 .0 5 8 9
�0 .0 6 3 7
�0 .0 5 8 3
0 .0 0 4 8
0 .0 9 6 1
�0 .0 6 3 3
�0 .0 4 2 4
1
In d D iv
�0 .1 1 6 7
�0 .0 2 2 3
0 .0 0 7 3
0 .1 4 2 1
0 .1 2 5 3
0 .1 2 7
�0 .0 3 4 7
�0 .0 2 0 6
1
In tl D iv
0 .0 4 8 8
�0 .0 3 6 2
�0 .0 4 8 3
0 .2 9 0 9
�0 .0 0 4
�0 .0 3 4 8
�0 .1 1 8 1
0 .1 1 0 9
0 .2 1 6 7
1
In si d e rs
�0 .1 0 1 4
�0 .0 0 0 7
0 .0 0 0 2
�0 .2 5 5
0 .0 4 5 5
0 .0 2 0 7
0 .0 5 2 2
0 .0 2 2 4
�0 .1 0 7 6
0 .0 2 0 6
1
In si d e rs S q
�0 .0 5 9 1
�0 .0 0 3
�0 .0 0 7 7
�0 .2 0 0 2
0 .0 0 8 4
0 .0 2 8 9
0 .0 6 6 7
�0 .0 4 4 4
�0 .1 5 5 8
0 .0 2 0 3
0 .9 3 5 2
1
F in a n ci a lS la ck
�0 .0 3 1 4
�0 .0 5 4 9
�0 .0 8 5 7
�0 .2 6 5 4
0 .0 1 1 6
0 .0 0 8 4
0 .0 1 4 3
0 .0 0 1 4
0 .0 3 4 7
�0 .0 8 3 4
�0 .0 2 5 5
�0 .0 3 0 1
1
E a rn V a ri a b il it y
�0 .0 3 2 5
0 .0 4 0 4
0 .0 7 2 9
�0 .0 5 5 7
�0 .0 0 2 1
0 .0 0 1 7
�0 .1 3 5 9
�0 .0 7 1 7
�0 .0 5 4 6
0 .0 7 8 8
0 .0 7 6
0 .1 0 0 2
�0 .0 0 3 7
1
A ss e tO
p a ci ty
0 .1 4 7 2
�0 .0 0 2 3
0 .0 2 3 3
0 .0 6 4 3
�0 .1 2 6 6
0 .0 4 5 4
0 .0 0 3 4
�0 .1 1 8 5
0 .0 0 0 4
0 .1 5 6 7
�0 .0 0 9 1
�0 .0 2 5 6
�0 .0 7 0 6
�0 .0 7 3 7
1
V a lu e C h a n g e
0 .3 5 0 2
0 .0 1 7 9
�0 .0 3 3 6
�0 .0 2 4 8
�0 .2 6 0 1
0 .1 1 2 6
0 .1 5 0 8
0 .0 3 6 2
�0 .0 9 2 1
�0 .0 8 0 4
0 .0 1 3 6
0 .0 3 4 4
�0 .0 4 7 7
0 .0 1 3 7
�0 .0 7 9 3
1
D iv id e n d P a y m e n tS ta tu s
0 .0 8 2 4
0 .0 2 6 7
0 .0 0 4 9
0 .2 5 0 1
�0 .0 1 5 4
0 .0 0 3 5
�0 .0 3 4 7
�0 .1 9 3 6
0 .0 3 0 7
0 .1 0 6
�0 .0 2 5 3
�0 .0 0 6 8
0 .0 3 1 8
�0 .0 1 5
�0 .1 5 5 9
0 .0 6 4 1
1
N ot e: V a ri a b le s a re
d e fi n e d in
T a b le
6 .
VALUATION IMPLICATIONS OF ERM 645
25.3 percent (exp(0.225) � 1) increase in the unlogged Tobin’s Q variable. This result is statistically significant at the 1 percent significance level. The adjusted R2 of the model is around 47 percent and the specification passes the test of joint significance.
Further to the baseline specification, the assessment data collected and compiled by RIMS facilitate the decomposition of the drivers of ERM maturity using the seven key attributes of ERM in their RMM. Table 10 presents the pairwise correlations between ERM attribute maturities.
It is clear that there is a significant degree of correlation between the maturity scores for the various attributes, with correlations generally in the range of between 70 and 80 percent. Given the composition of the RIMS RMM, this finding is to be expected. ERM is a multifaceted corporate objective and one might reasonably expect, for example, that those firms that have significant board-level engagement with their ERM programs (attribute 1), more closely integrate risk management objectives with broader corporate objectives (attribute 6), monitor risk more effectively (attribute 5), and focus on root causes of risk events (attribute 4). Owing to the high degree of correlation between the factors, they cannot be used simultaneously in a specification
TABLE 9 Overall ERM Engagement Regression Results
Variable ERM_Engagement_Dummy LogTobinsQ
ERM_Engagement_Dummy 0.22544��� (0.07176) Size 0.51653��� (0.16884) �0.00755 (0.10768) Leverage �0.02067 (0.07158) �0.20968��� (0.02453) ReturnOnEquity �0.01453 (0.01267) SalesGrowth �0.58314 (0.51717) 0.57587��� (0.21298) Beta 0.00104 (0.03917) IndDiv �0.00648 (0.22073) �0.10308 (0.07963) IntlDiv �0.59482�� (0.23605) 0.18088 (0.12268) Insiders �0.84017 (0.53254) InsidersSq 0.97445 (0.86288) DividendPaymentStatus 0.18926�� (0.08032) FinancialSlack �0.02715 (0.06187) EarnVariability 0.02676 (0.03520) AssetOpacity �0.23319 (0.54383) ValueChange �0.02556 (0.24702) InverseMills 0.16108 (0.27985) Constant �1.18440 (1.08397) �0.14528 (0.86713) Observations 225 225 R2 0.57820 Adj. R2 0.47216 Log-likelihood �131.97404 x 2 32.31467
Note: Variables are defined in Table 6. Standard errors are in parentheses. ��� and �� denote statistical significance at the 1 and 5 percent levels, respectively.
646 THE JOURNAL OF RISK AND INSURANCE
TA B LE
1 0
Pe ar so n C or re la tio
n M at rix
by ER
M A ttr ib ut e M at ur ity
E R M M a tu ri ty
E R M M a tu ri ty
E R M M a tu ri ty
E R M M a tu ri ty
E R M M a tu ri ty
E R M M a tu ri ty
E R M M a tu ri ty
_ A tt ri b u te
1 _ A tt ri b u te
2 _ A tt ri b u te
3 _ A tt ri b u te
4 _ A tt ri b u te
5 _ A tt ri b u te
6 _ A tt ri b u te
7
E R M M a tu ri ty _ A tt ri b u te
1 1
E R M M a tu ri ty _ A tt ri b u te
2 0 .8 2 3 2
1 E R M M a tu ri ty _ A tt ri b u te
3 0 .7 1 3 7
0 .7 2 6 4
1 E R M M a tu ri ty _ A tt ri b u te
4 0 .7 4 2 3
0 .7 8 8 9
0 .7 8 7 6
1 E R M M a tu ri ty _ A tt ri b u te
5 0 .7 5 2 4
0 .8 0 6 9
0 .7 2 3 9
0 .7 9 8 5
1 E R M M a tu ri ty _ A tt ri b u te
6 0 .8 0 2 3
0 .7 8 9 8
0 .7 3 5 4
0 .7 7 5 2
0 .7 6 4 2
1 E R M M a tu ri ty _ A tt ri b u te
7 0 .7 7 9 9
0 .7 8 4 8
0 .6 8 7 4
0 .7 7 0 1
0 .7 9 2 6
0 .7 6 3 7
1
A tt ri b u te
D e sc ri p ti o n
1 E R M -b a se d a p p ro a ch
2 E R M
p ro ce ss
m a n a g e m e n t
3 R is k a p p e ti te
m a n a g e m e n t
4 R o o t ca u se
d is ci p li n e
5 U n co v e ri n g a n d id e n ti fy in g ri sk s
6 P e rf o rm
a n ce
m a n a g e m e n t—
m a n a g in g u n ce rt a in ty
7 B u si n e ss
re si li e n ce
a n d su
st a in a b il it y — m a n a g in g lo w -p ro b a b il it y / h ig h -c o n se q u e n ce
e v e n ts
N ot e: E R M M a tu ri ty _ A tt ri b u te
i is
d e fi n e d in
T a b le
6 a n d th e a tt ri b u te
d e sc ri p ti o n is
su m m a ri z e d a b o v e .
VALUATION IMPLICATIONS OF ERM 647
due to variance inflation concerns.7 As a consequence, we conduct our supplementa- ry estimations for each separate attribute in turn.
In order to test the impact of these attributes on firm value, we again turn to the two- step model, this time modeling the selection equation in terms of each ordinal ERM attribute. As a consequence, our specifications are as follows:
ERMAttributei Engagement Dummy ¼ a þ b1 � Size þ b2 � Leverage þ b3 � SalesGrowth þb4 � FinancialSlack þ b5 � IndDiv þ b6 � IntlDiv
þb7 � EarnVariability þ b8 � AssetOpacity þ b9 � ValueChange þSwt � TimeDumt þ Sgi � IndDumi þ e;
where
ERMAttributei_Engagement_Dummy ¼ 0 if ERM attribute maturity level (ERM- Maturity_Attributei) is ad hoc or initial (i.e., Levels 1 or 2)
ERMAttributei_Engagement_Dummy ¼ 1 if ERM attribute maturity level (ERM- Maturity_Attributei) is repeatable, managed, or leadership (i.e., Levels 3, 4, or 5)
and
LogTobinsQ ¼ a þ b1 � ERMAttributei Engagement Dummy þ b2 � Size þb3 � Leverage þ b4 � ReturnOnEquity þ b5 � SalesGrowth
þb6 � Beta þ b7 � IndDiv þ b8 � IntlDiv þ b9 � Insiders þb10 � InsidersSq þ b11 � DividendPaymentStatus
þb12 � InverseMills þ Swt � TimeDumt þ Sgi � IndDumi þSliCtryDumi þ e:
The results of these supplementary attribute estimations are shown in Table 11.
Again focusing on the first-stage selection equation, the most significant variable in explaining variation in ERM attribute-level maturities is that of firm size. Larger firms consistently outscore their smaller peers when it comes to ERM attribute maturity, further echoing the work of Beasley, Clune, and Hermanson (2005). The effect of size on attribute maturity is most pronounced and also most significant for attributes 1 (ERM-based approach), 2 (ERM Process Management), 3 (Risk Appetite Manage- ment), 5 (Uncovering Risks), and 6 (Performance Management). Once again, firms that are internationally diversified tend to have lower ERM attribute scores, indicative of the difficulties in maintaining consistent ERM practice while operating across international boundaries.
7In the case of the overall ERM maturity regression, separate attributes are collapsed into a single measure by weighting (see the “Data Survey Risk Maturity Model: The RIMS RMM” section) and as a consequence enter the specification in the form of a single regress and negating colinearity concerns.
648 THE JOURNAL OF RISK AND INSURANCE
TA B LE
1 1
A ttr ib ut e‐ Sp
ec ifi c Re
gr es si on
Re su lts
V a ri a b le
E R M A tt 1 _
E n g a g _
D u m m y
E R M A tt 2 _
E n g a g _
D u m m y
E R M A tt 3 _
E n g a g _
D u m m y
E R M A tt 4 _
E n g a g _
D u m m y
E R M A tt 5 _
E n g a g _
D u m m y
E R M A tt 6 _
E n g a g _
D u m m y
E R M A tt 7 _
E n g a g _
D u m m y
E R M A tt i_ E n g a g _
D u m m y
0 .1 5 6 6 2 ��
0 .1 8 2 5 6 ��
� 0 .1 1 1 2 3
0 .1 4 8 6 0 ��
0 .1 4 2 2 5 ��
0 .2 0 5 9 0 ��
� 0 .0 6 5 4 7
(0 .0 7 1 3 6 )
(0 .0 6 9 8 9 )
(0 .0 7 1 5 0 )
(0 .0 6 8 5 4 )
(0 .0 7 0 3 0 )
(0 .0 7 2 1 0 )
(0 .0 7 2 4 3 )
S iz e
0 .4 2 7 7 8 ��
� �0
.0 1 7 6 9
0 .6 2 2 9 6 ��
� �0
.1 4 9 7 0
0 .5 3 8 4 7 ��
� �0
.0 9 5 7 8
0 .3 8 7 3 0 ��
�0 .0 4 8 8 5
0 .4 4 9 1 9 ��
� �0
.0 3 8 2 4
0 .5 9 1 7 7 ��
� �0
.1 3 2 1 4
0 .3 5 5 9 1 ��
�0 .0 3 6 7 4
(0 .1 6 3 9 8 )
(0 .0 8 7 2 5 )
(0 .1 6 7 0 2 )
(0 .1 4 5 1 8 )
(0 .1 6 1 8 7 )
(0 .1 0 9 8 1 )
(0 .1 5 6 7 5 )
(0 .0 9 6 4 4 )
(0 .1 6 2 0 6 )
(0 .0 9 6 6 7 )
(0 .1 6 9 3 7 )
(0 .0 9 5 8 3 )
(0 .1 6 6 4 6 )
(0 .0 6 6 1 4 )
L e v e ra g e
0 .0 3 8 3 6
�0 .2 1 2 9 5 ��
� �0
.0 4 4 8 7
�0 .1 9 9 7 2 ��
� 0 .1 0 6 4 3
�0 .2 2 0 1 5 ��
� 0 .0 1 8 8 9
�0 .2 1 1 7 2 ��
� �0
.0 1 2 2 0
�0 .2 1 0 8 1 ��
� �0
.0 3 6 8 4
�0 .1 9 9 7 7 ��
� 0 .0 3 3 1 0
�0 .2 0 8 9 6 ��
�
(0 .0 6 9 7 6 )
(0 .0 2 5 4 0 )
(0 .0 6 7 8 7 )
(0 .0 2 6 8 1 )
(0 .0 8 2 6 6 )
(0 .0 2 7 0 1 )
(0 .0 6 6 3 4 )
(0 .0 2 4 8 0 )
(0 .0 6 7 2 8 )
(0 .0 2 4 8 4 )
(0 .0 8 1 0 7 )
(0 .0 2 5 1 6 )
(0 .0 7 3 6 6 )
(0 .0 2 5 1 1 )
R e tu rn O n E q u it y
�0 .0 1 5 1 6
�0 .0 1 4 9 6
�0 .0 1 1 8 5
�0 .0 1 2 0 9
�0 .0 1 3 9 5
�0 .0 1 5 3 4
�0 .0 1 2 8 3
(0 .0 1 2 9 3 )
(0 .0 1 2 7 9 )
(0 .0 1 2 8 9 )
(0 .0 1 2 8 2 )
(0 .0 1 2 9 0 )
(0 .0 1 2 7 0 )
(0 .0 1 2 9 9 )
S a le sG
ro w th
�0 .0 5 8 2 4
0 .5 7 6 8 1 ��
� �0
.5 9 0 8 0
0 .6 7 6 0 7 ��
� �0
.5 7 9 6 5
0 .6 5 6 7 7 ��
� �0
.4 2 7 0 2
0 .5 8 1 8 9 ��
� �1
.5 8 6 3 5 ��
� 0 .6 3 7 6 3 ��
�0 .7 7 4 8 4
0 .7 2 9 3 2 ��
� �0
.5 1 8 1 1
0 .6 1 7 1 4 ��
�
(0 .4 9 4 9 5 )
(0 .1 8 8 8 7 )
(0 .5 1 9 5 8 )
(0 .2 0 6 0 7 )
(0 .5 1 3 6 7 )
(0 .2 0 3 6 2 )
(0 .5 0 8 3 7 )
(0 .1 9 7 0 5 )
(0 .5 4 5 3 3 )
(0 .3 1 8 9 8 )
(0 .5 2 8 2 5 )
(0 .2 1 1 6 7 )
(0 .5 3 3 9 5 )
(0 .1 9 5 8 5 )
B e ta
0 .0 0 4 7 2
�0 .0 0 8 3 4
�0 .0 0 4 3 1
�0 .0 0 2 6 0
0 .0 0 0 9 3
0 .0 1 1 8 2
�0 .0 1 0 4 8
(0 .0 3 9 9 8 )
(0 .0 3 9 9 7 )
(0 .0 3 9 9 4 )
(0 .0 4 0 2 0 )
(0 .0 4 0 0 3 )
(0 .0 3 9 5 8 )
(0 .0 4 4 0 7 )
In d D iv
�0 .2 1 6 4 4
�0 .0 8 6 7 6
�0 .1 4 2 9 9
�0 .0 5 9 4 1
0 .0 2 5 3 3
�0 .0 9 3 2 5
0 .0 2 9 9 7
�0 .0 9 0 1 1
�0 .0 1 3 9 1
�0 .0 9 1 1 0
�0 .1 8 6 1 2
�0 .0 4 9 8 6
�0 .2 2 4 7 2
�0 .0 6 9 6 4
(0 .2 2 0 2 6 )
(0 .0 8 8 8 4 )
(0 .2 1 8 7 4 )
(0 .0 8 6 0 1 )
(0 .2 1 7 1 2 )
(0 .0 8 0 9 7 )
(0 .2 1 3 8 3 )
(0 .0 8 0 4 8 )
(0 .2 1 7 8 1 )
(0 .0 8 1 1 2 )
(0 .2 2 2 4 0 )
(0 .0 8 5 8 0 )
(0 .2 2 6 9 0 )
(0 .0 8 9 7 4 )
In tl D iv
�0 .4 8 6 3 0 ��
0 .1 8 6 6 6 �
�0 .5 0 0 6 4 ��
0 .2 8 9 4 2 ��
�0 .7 0 7 1 9 ��
� 0 .2 7 0 7 8 ��
�0 .6 6 1 1 9 ��
� 0 .2 4 2 6 3 �
�0 .3 3 6 5 3
0 .1 9 9 7 6 ��
�0 .6 2 6 1 4 ��
� 0 .2 8 8 4 2 ��
�0 .5 0 8 1 6 ��
0 .2 1 9 5 8 ��
(0 .2 2 9 5 1 )
(0 .1 0 6 5 8 )
(0 .2 2 8 7 3 )
(0 .1 3 0 9 8 )
(0 .2 3 0 1 9 )
(0 .1 3 1 3 7 )
(0 .2 2 6 0 1 )
(0 .1 4 0 7 1 )
(0 .2 2 5 4 6 )
(0 .0 9 9 0 0 )
(0 .2 3 9 7 1 )
(0 .1 1 1 2 6 )
(0 .2 3 3 6 9 )
(0 .1 0 2 5 3 )
In si d e rs
�0 .8 0 1 0 9
�0 .8 2 6 5 7
�0 .7 8 5 9 7
�0 .7 6 1 1 8
�0 .8 0 2 2 3
�0 .7 8 2 1 1
�0 .7 2 8 5 2
(0 .5 4 2 4 1 )
(0 .5 3 4 5 9 )
(0 .5 4 7 1 5 )
(0 .5 3 7 7 9 )
(0 .5 4 3 8 0 )
(0 .5 4 2 0 5 )
(0 .5 4 5 2 2 )
In si d e rs S q
1 .0 1 5 8 0
1 .0 2 8 9 4
0 .8 5 9 1 6
0 .8 5 9 1 0
0 .8 6 4 7 3
0 .8 8 4 5 6
0 .8 1 5 8 7
(0 .8 8 3 7 1 )
(0 .8 6 6 5 6 )
(0 .8 9 0 3 1 )
(0 .8 6 9 6 8 )
(0 .8 8 2 4 2 )
(0 .8 7 6 6 2 )
(0 .8 7 8 2 9 )
D iv id e n d P a y m e n tS ta tu s
0 .1 9 0 2 4 ��
0 .1 8 9 8 9 ��
0 .2 0 1 1 5 ��
0 .2 0 0 5 6 ��
0 .1 9 4 3 0 ��
0 .1 9 2 7 9 ��
0 .1 9 9 4 5 ��
(0 .0 8 1 3 1 )
(0 .0 8 0 5 2 )
(0 .0 8 2 0 1 )
(0 .0 8 1 2 6 )
(0 .0 8 1 3 4 )
(0 .0 8 0 0 5 )
(0 .0 8 3 2 4 )
F in a n ci a lS la ck
�0 .0 3 0 7 1
�0 .0 1 4 3 2
�0 .0 2 8 6 6
�0 .0 2 5 8 3
�0 .0 2 5 4 1
�0 .0 2 3 8 8
2 .0 9 4 8 5 ��
(0 .0 5 6 5 1 )
(0 .0 5 0 5 6 )
(0 .0 9 5 6 0 )
(0 .0 5 4 0 4 )
(0 .0 5 5 8 8 )
(0 .0 7 1 8 7 )
(0 .9 1 8 8 2 )
E a rn V a ri a b il it y
0 .0 3 8 9 3
�0 .0 1 3 0 2
0 .0 3 1 0 7
�0 .0 0 3 7 9
0 .0 2 5 6 1
0 .1 1 1 1 2
0 .0 2 5 0 0
(0 .0 4 3 9 9 )
(0 .0 1 6 8 9 )
(0 .0 3 0 2 4 )
(0 .0 1 6 8 1 )
(0 .0 3 2 5 4 )
(0 .0 8 2 5 6 )
(0 .0 3 2 3 3 )
A ss e tO
p a ci ty
�0 .6 4 9 6 6
�0 .2 7 1 0 9
�0 .1 9 1 0 8
�0 .5 2 5 4 7
�0 .6 4 6 9 2
�0 .0 7 3 2 7
�0 .3 1 7 5 8
(0 .5 3 0 1 9 )
(0 .5 3 3 6 6 )
(0 .5 2 8 2 0 )
(0 .5 3 0 5 1 )
(0 .5 2 7 0 7 )
(0 .5 4 7 0 1 )
(0 .5 4 2 7 5 )
V a lu e C h a n g e
0 .1 5 3 4 8
0 .2 6 1 3 4
0 .2 8 0 0 5
0 .2 7 9 2 0
0 .1 5 9 9 4
0 .1 6 7 3 7
0 .2 5 4 3 7
(0 .2 2 7 4 8 )
(0 .2 6 1 6 3 )
(0 .2 5 3 1 1 )
(0 .2 4 5 7 2 )
(0 .2 4 7 0 8 )
(0 .2 5 0 2 7 )
(0 .2 7 2 2 1 )
(C on ti n u ed )
VALUATION IMPLICATIONS OF ERM 649
TA B LE
1 1
C on
tin ue
d
V a ri a b le
E R M A tt 1 _
E n g a g _
D u m m y
E R M A tt 2 _
E n g a g _
D u m m y
E R M A tt 3 _
E n g a g _
D u m m y
E R M A tt 4 _
E n g a g _
D u m m y
E R M A tt 5 _
E n g a g _
D u m m y
E R M A tt 6 _
E n g a g _
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650 THE JOURNAL OF RISK AND INSURANCE
The key variable of interest in the second-stage regressions is ERMAttributei_ Engagement_Dummy, indicating whether or not ERM maturity for attribute i has progressed to the upper levels of maturity as described above.
It can be noted that five of the seven ERM attributes significantly explain variation in firm value (as proxied by Tobin’s Q) in their own right. Of the five significant ERM attributes, most significance is attached to attribute 2 (ERM Process Management) and attribute 6 (Performance Management—managing uncertainty) (both significant at the 1 percent level). Mature engagement in attribute 2 (which measures how well the ERM process is being integrated into everyday practices) is associated with a 20.0 percent (18.3 percent logged) increase in firm value, while mature engagement in attribute 6 (which measures the degree to which the organization is able to execute on vision and strategy alongside their risk management activities) is associated with a 22.9 percent (20.6 percent logged) increase in firm value. Attributes 1 (ERM-based Approach), 4 (Root Cause Discipline), and 5 (Uncovering and Identifying Risks) are all statistically significant at the 5 percent level with marginal valuation effects of 17.0 percent (15.7 percent logged), 16.0 percent (14.9 percent logged), and 15.3 percent (14.2 percent logged), respectively. The positive and significant results of attributes 1, 2, and 6 are particularly noteworthy. Attribute 1 has a focus on the extent and level of executive support for ERM while attribute 2 examines the further embedding of risk culture within the organization to achieve effective ERM integration and attribute 6 considers whether ERM is viewed as an integral element in strategic activities. This finding is consistent with Sobel and Reding (2004) who acknowledge active participation by an organization’s board of directors is positively related to an effective ERM program and the findings of Gordon, Loeb, and Tseng (2009) who find the relation between ERM and firm performance is contingent upon the appropriate match between ERM board of director’s monitoring.
It is reasonable to assert that these attributes are complementary, and indeed is evidenced in our correlation analysis. Maturity progression through one attribute is not in isolation and will only be truly effective if the appropriate supporting attributes are in place. ERM is a function of multiple facets measured in the RIMS RMM model. Our estimations do, however, reveal that attributes 3 (Risk Appetite Management) and 7 (Business Resilience and Sustainability) are insignificant in explaining variation in firm value within our sample.
In terms of the explanatory power of the overall model, we note the adjusted R2 is consistently around 45 percent for all seven second-stage regressions. All regression variants pass the test of joint significance.
CONCLUSION This study seeks to build on the nascent literature on the valuation implications of ERM maturity. We use a data set of 225 publicly listed firms, across various sectors that have completed the RIMS ERM Maturity Model assessment over the 2006–2011 period. Our results suggest significant evidence of a valuation premium attached to enhanced ERM maturity. The RIMS RMM assessment ranks firms on a 1–5 scale and our results suggest that for firms that have fully engaged in ERM so as to be
VALUATION IMPLICATIONS OF ERM 651
considered mature in their ERM approach, there is a highly significant firm value increase of around 25 percent.
The structure of our data allows for further decomposition of the ERM maturity score along seven key themes: the corporate approach to ERM, process management, risk appetite management, root cause discipline, efficacy of uncovering risks, ongoing performance management, and business resilience and sustainability. Each attribute is given an equal weighting when forming the composite ERM maturity score and thus the marginal valuation impact of an individual attribute can be greater than that of the overall ERM score.
Of the individual attributes, our results suggest that the strongest valuation effects are associated with ongoing performance management, process management, the corporate approach to ERM, root cause discipline, and the efficacy of uncovering risks, respectively. We do not find evidence of value relevance of risk appetite management or business resilience and sustainability in our sample.
MANAGERIAL IMPLICATIONS The findings outlined in this study have clear corporate policy implications. Of all 225 firms sampled, only 83 had progressed to the upper levels of ERM maturity. For the majority of sampled firms, there is scope for ERM maturation and thus shareholder value creation. For those entities that have not yet embraced ERM, the arguments to do so are compelling. An ERM maturity transition from a silo-based risk management process that lacks discipline and enterprise wide coordination to a mature ERM environment with established ERM routines and engagement from the top of the firm could create a value improvement of as much as 25 percent. The findings of this study lend weight to the argument that the improvement of formal ERM programs is consistent with the ISO 31000 Risk Management Principles (International Organiza- tion for Standardization, 2009), which state that risk management should create and protect value and should be an integral part of all business.
The decomposition of ERM maturity into its constituent attributes provides an interesting extension that has insights for entities wanting to maximize the value- creating potential of ERM maturity. Of paramount importance is an integrated ERM approach consistent with the broader corporate strategic agenda. Support must be from the executive level down and cascade throughout the organization, with ERM aligned with the organization’s vision and strategy. In accordance with the attribute definition set out in the RIMS RMM, the board of directors, senior management, and senior risk officers must communicate the importance of risk management in daily decision making to each business function throughout the firm. Much deeper engagement is required to maximize the value of ERM maturity. For many organizations, this will necessitate a change in culture to ensure that the board’s vision and risk initiatives flow effectively throughout the firm hierarchy with the board members, senior management, and lower level employees all playing an important role in managing risks in an optimal manner. Furthermore, our analysis relating to ongoing performance management highlights that in order to attain maturity progression firms must clearly articulate goals to all business units. The goals must be specific, measurable, attainable, realistic, and trackable. Deviations
652 THE JOURNAL OF RISK AND INSURANCE
from these goals must be measured, reported, and acted upon on an ongoing basis. Again, the attainment of these ERM goals must be aligned with the broader corporate agenda.
We provide evidence of the strong valuation implications of a firm’s ability to uncover and track risk. Best practice with regard to this ERM facet includes explicit risk ownership by business units, formalized measures of risk, the collection of knowledge from employee expertise and electronic databases and documentation of risks and opportunities. The ultimate goal of uncovering and tracking risk information via these mechanisms is to enhance the organization’s ability to uncover correlations and dependencies across the entire enterprise. Given the important role that correlations now play within risk management (Lo, 1999), an important consideration for risk managers to keep in mind is that these risk correlations may change drastically in times of market turmoil (Bookstaber, 2010). This aspect is especially important for ERM given its dynamic nature and requirement to capture emerging risks. As a consequence an effective ERM program should achieve a heightened managerial awareness of market volatility.
Finally, it is important to note that these attributes exist not in isolation, but rather as part of a holistic approach to risk management. The ERM maturity journey is a multifaceted one. That said, the heterogeneous valuation implications among attributes may provide a useful tool for corporate boards when they seek to concentrate on the value maximizing facets. Here, the message is clear: there must be a consistent “tone from the top,” an embedding of risk culture throughout the organization, alignment of strategy and risk management activities, and ongoing documentation of current and emerging risks with clear goal setting and reporting.
LIMITATIONS AND FUTURE RESEARCH The discipline of ERM is still in its infancy and is continuing to evolve both in its practical application and in terms of the academic and practitioner research aiding our understanding. Increasing numbers and diversity of firms initiating ERM programs will provide a natural extension of the data set in both the cross-sectional and time-series dimensions. We also acknowledge that the maturity data used in our study are based upon self-reported assessments based on the responses of a single individual per firm. Self-reporting is subject to obvious bias (Bertrand and Mullainathan, 2001). To overcome this limitation, we suggest further study that independently measures ERM maturity. A thorough, valid, and clear picture of the firm’s ERM status would be best achieved through independent assessment of risk procedures and practices as well as group and individual interviews conducted throughout different levels of the organization. This type of assessment would further our understanding of ERM immensely.
Finally, our study was limited by the number of firms that had reached the uppermost level of ERM maturity. As the discipline evolves, we expect a higher percentage of firms to have attained a high degree of ERM maturity. Similar studies in the future will, therefore, enhance our understanding of ERM valuation implications at the uppermost levels of ERM maturity.
VALUATION IMPLICATIONS OF ERM 653
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