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The Effect of Operational Control Quality on Operational Efficiency
and Cost of Capital: Evidence from U.S. Bank Holding Companies
by
Sasan Saiy
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Graduate Department of Joseph L. Rotman School of Management
University of Toronto
© Copyright by Sasan Saiy (2016)
ii
The Effect of Operational Control Quality on Operational Efficiency
and Cost of Capital: Evidence from U.S. Bank Holding Companies
Sasan Saiy
Doctor of Philosophy
Rotman School of Management
University of Toronto
2016
Abstract
Recent high profile and costly operational risk events have focused the attention of bank managers and regulators
on operational risk management practices since the early 2000s. This led the Basel II Accord to recognize
operational risk as a separate risk. In this study, I examine whether operational control quality is associated with
operational efficiency and the costs of debt and equity capital for a large sample of U.S. bank holding companies. I
measure banks’ operational control quality using two measures: (1) the incidence of actual operational risk events
as an ex-post observable proxy for weaknesses in operational controls, and (2) an index-based measure of
operational risk management quality (𝑂𝑅𝑀𝑄) as an ex-ante proxy, created via textual analyses of Form 10-K
filings. First, I find that operational efficiency, derived from a frontier analysis, is significantly higher among banks
with higher operational control quality. Second, I find that banks with stronger operational controls are associated
with lower costs of debt and equity capital. These results are incremental to controlling for the quality of the
internal control over financial reporting. Furthermore, in the changes analyses, I find that remediating firms exhibit
higher operational efficiency and lower cost of capital estimates, while non-remediating banks are associated with
no significant change in their operational efficiency estimate but exhibit a significant higher cost of capital. I also
examine the net effect of operational control quality on equity valuation and find a positive association between
operational control quality and equity prices. In addition, I observe that banks with higher operational control
quality exhibit higher earnings persistence. Overall, the findings of this thesis suggest that operational controls have
significant effects on banks’ operations and cost of capital, and that the operational risk information in banks’ Form
10-K filings is credible.
iii
ACKNOWLEDGMENTS
I would like to thank the members of my dissertation committee: Partha Mohanram (Co-
chair), Gordon Richardson (Co-chair), Dushyantkumar Vyas, and M. H. Franco Wong for their
generous support and for patiently guiding me throughout my dissertation.
I owe special gratitude to the following faculty members of the Rotman School of
Management for their consistent support and insightful comments on my dissertation: Francesco
Bova, Jeffrey L. Callen, Gus De Franco, Alex Edwards, Ole-Kristian Hope, and Aida Sijamic
Wahid. I would also like to thank my colleagues in the PhD program. They made life during the
PhD program much more enjoyable and helped me immensely in my research. These people
include Hila Fogel Yaari, Heather Li, Leila Peyravan, Danqi Hu, Barbara Su, Na Li, Yu Hou,
Kevin Jason Veenstra, Ross Lu, Stephanie F. Chang, Wuyang Zhao, and Mahfuz Chy. Last, I
thank the Statistical Analyses System (SAS) Institute, and Identify Theft Resources Center
(ITRC) for providing data on operational risk events and breaches, respectively.
Last but not least, I deeply appreciate the unconditional love, support, and
encouragement of my wife, Niloo, my parents, Homa and Moe, my sister, Layla, and my twin
brother, Saman. Without them, I could not have achieved what I have thus far.
iv
TABLE OF CONTENTS
CHAPTER 1 INTRODUCTION ............................................................................................ 1
CHAPTER 2 INSTITUTIONAL BACKGROUND……………. …………………………11
2.1. Committee of Sponsoring Organizations of the Treadway Commission Internal
Control Framework ................................................................................................................. 11
2.2 Operational Risk Under Basel II ................................................................................ 12
2.3 U.S. U.S. Bank Holding Companies and Basel II ...................................................... 15
CHAPTER 3 LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT ............ 17
3.1 Relation between Operational Control Quality and Operational Efficiency .............. 17
3.2 Relation between Operational Control Quality and Cost of Capital .......................... 19
CHAPTER 4 DATA AND RESEARCH DESIGN .............................................................. 23
4.1 Data and Sample Selection ......................................................................................... 23
4.2 Main Dependent Variables ......................................................................................... 25
4.2.1 Operational Efficiency Measure ................................................................................. 25
4.2.2 Cost of Debt Capital Measure .................................................................................... 27
4.2.3 Cost of Equity Capital Measure .................................................................................. 28
4.3 Main Independent Variables ....................................................................................... 29
4.3.1 Operational Risk Avoidance Metric ........................................................................... 30
4.3.2 Operational Risk Management Quality Metric .......................................................... 31
4.4 Research Design ......................................................................................................... 33
4.4.1 Operational Control Quality and Operational Efficiency Model ............................... 33
4.4.2 Operational Control Quality and Cost of Debt Capital Model ................................... 35
4.4.3 Operational Control Quality and Cost of Equity Capital Model ................................ 36
v
CHAPTER 5 EMPIRICAL RESULTS ................................................................................ 38
5.1 Results for Operational Control Quality and Operational Efficiency ........................ 38
5.2 Results for Operational Control Quality and Cost of Capital ..................................... 40
5.2.1 Results for Operational Control Quality and Cost of Debt Capital ............................ 40
5.2.2 Results for Operational Control Quality and Cost of Equity Capital ......................... 41
CHAPTER 6 ADDITIONAL ANALYSES .......................................................................... 43
6.1 Price-level Analysis .................................................................................................... 43
6.2 Earnings Persistence Analysis .................................................................................... 44
6.3 Changes Analyses ....................................................................................................... 46
6.4 Operational Risk Event Types Analyses .................................................................... 51
CHAPTER 7 CONCLUSION ............................................................................................... 54
REFERENCES ....................................................................................................................... 57
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LIST OF APPENDICES
APPENDIX A: OPERATIONAL RISK MANAGEMENT QUALITY (ORMQ) INDEX ... 63
APPENDIX B: PRINCIPLES FOR SOUND PRACTICES FOR THE MANAGEMENT
AND SUPERVISION OF OPERATIONAL RISK (BCBS 2003, 2011) ............................... 66
APPENDIX C: VARIABLE DEFINITIONS ......................................................................... 68
APPENDIX D: IMPLIED COST OF EQUITY CAPITAL MODELS .................................. 72
APPENDIX E: ISS QuickScore Corporate Governance Metric ............................................. 75
vii
LIST OF TABLES
Figure 1: Frequency of operational risk event types ............................................................... 76
Table 1: Sample Composition ................................................................................................. 77
Table 2: Descriptive Statistics ................................................................................................ 78
Table 3: Correlations between Efficiency and Operational Control Quality Measures ......... 82
Table 4: Operational Control Quality and Operational Efficiency (H1) ................................ 83
Table 5: Operational Control Quality and Cost of Debt Capital (H2a) .................................. 85
Table 6: Operational Control Quality and Cost of Equity Capital (H2b) ............................... 86
Table 7: Price-level Analysis .................................................................................................. 87
Table 8: Earnings Persistence Analysis .................................................................................. 88
Table 9: Changes Analyses ..................................................................................................... 89
Table 10: Operational Risk Event Types Analysis ................................................................. 97
1
CHAPTER 1
INTRODUCTION
Since the early 2000s, the Basel Committee on Banking Supervision (BCBS) and
banking supervisors throughout the world have increasingly focused their attention on the
importance of sound operational controls. The BCBS’s “Framework for Internal Control
Systems in Banking Organizations” (BCBS 1998a) states that “a system of strong internal
controls can help to ensure that the goals and objectives of a banking organization will be met,
that the bank will achieve long-term profitability targets, and maintain reliable financial and
managerial reporting.” Moreover, Section 404 of the Sarbanes-Oxley Act (SOX), which became
effective in 2002, mandates that public companies assess and publicly report on the
effectiveness of their internal control over financial reporting (ICFR). While there is an overlap
between internal controls over financial reporting and operations, SOX may have had an
unintended consequence of overshadowing internal control over operations (Tysiac 2012).1,2
However, as U.S. public companies have been adjusting to the requirements of SOX over the
past decade, companies as well as regulators have discovered that SOX requirements can be
1 A recent survey reveals that while most managers feel that Section 404 of SOX has improved their firms’ financial
reporting quality, they do not believe that the regulation has improved their firms’ operations (Alexander et al.
2013).
2 David Landsittel, the former chairman of the Committee of Sponsoring Organizations of the Treadway
Commission (hereafter COSO), also raised this concern in an interview in 2012: “People think of internal controls
and they think of controls over books and records and accounting. They think of SOX 404. And we just want to
emphasize the fact that there’s an opportunity here to apply our framework in other, broader ways as well” (Tysiac
2012). Also, COSO’s newly revised framework in 2013 emphasizes the importance of internal controls to achieve
not just financial reporting objectives, but objectives relating to the operations of the business and compliance with
laws and regulations (COSO 2013).
2
used as a springboard to better integrate risk management into firm operations and to delve
deeper into operational controls and processes.3
Consistent with these developments, a body of literature on SOX is emerging, which
examines: (1) the spillover effect of ICFR on firms’ operations (e.g., Bauer 2014; Cheng, Wee
Goh, and Kim 2014; Feng, Li, McVay, and Skaife 2014), and (2) the association between
operational control deficiencies and financial reporting risk as well as audit risk (e.g., Altamuro,
Gray, and Zhang 2014; Lawrence, Minutti-Meza, and Vyas 2014). I add to this growing
literature by investigating whether operational control quality is associated with higher
operational efficiency and lower costs of debt and equity capital.
The BCBS defines operational risk as “the risk of loss resulting from inadequate or failed
internal processes, people and systems or from external events” (BCBS 2003b). BCBS breaks
operational risk events into seven categories (BCBS 2003b): (1) internal fraud; (2) external
fraud; (3) employment practices and workplace safety; (4) clients, products, and business
practices; (5) damage to physical assets; (6) business disruption and system failures; and (7)
execution, delivery, and process management (see Section 2.2 for more details).
Operational control deficiencies have led to costly operational risk events in the past two
decades. For example, rogue trading led to a $1.3 billion loss and the eventual bankruptcy of
Barings Bank. Unauthorized trading at Societe Generale in 2008 resulted in a $7.2 billion loss.
Trading errors and excessive risk-taking at JPMorgan Chase gave rise to a $6.2 billion trading
fiasco in 2012 (the “London Whale”). Furthermore, data breach incidences (i.e., cyber-security
attacks) are among important and pervasive types of operational risk events (Lawrence et al.
2014). The major cyber-attack that infiltrated JPMorgan Chase’s network in 2014 is a recent
3 “Leveraging 10 Years of SOX for Stronger Risk Management”
(http://deloitte.wsj.com/riskandcompliance/2013/12/17/leveraging-10-years-of-sox-for-stronger-risk-management/)
3
example indicating that weaknesses in operational controls could lead to external fraud in the
form of cyber-security attacks. Kieran Poynter, the former U.K. chairman of
PriceWaterhouseCoopers, states that “organizations with weak data security are generally also
weak in terms of wider risk management and governance.”4
In addition to anecdotal evidence,
prior studies attribute operational risk events to agency problems and breakdown of internal
controls (Barakat, Chernobai, and Wahrenburg 2014; Lawrence et al. 2014). Overall, both
anecdotal and empirical evidence suggest that operational risk events are potentially
manifestations of operational control weaknesses.
My study builds on two streams of research emerging from a new wave of SOX 404
studies. The first stream examines whether more pervasive ICFR weaknesses have broader
implications beyond financial reporting quality (e.g., Cheng et al. 2014; Feng et al. 2014). These
studies predict and find that company-level ICFR weaknesses have a spillover effect on firm
operations by giving rise to agency problems (Jensen and Meckling 1976), and to low-quality
managerial and financial reporting. For example, Cheng et al. (2014) document that firms with
effective ICFR have higher operational efficiency. In a similar study, Feng et al. (2014) find that
firms with effective ICFR over inventory have systematically higher inventory turnover and a
lower likelihood and magnitude of inventory impairments. Consistent with these studies and
BCBS’s “Framework for Internal Control Systems in Banking Organizations” (BCBS 1998a), I
argue that an effective operational control system further curtails managerial rent-seeking
behaviour within the firm, and enhances the accuracy and timeliness of internal reporting. In
addition, since operational control is a component of the bank’s overall management control
system (MCS), operational control quality may reflect the overall quality of the MCS. Taken
4 “Data security is not just a matter of technology” (http://www.ft.com/cms/s/0/525bc6ec-526d-11dd-9ba7-
000077b07658.html#axzz3JoxuLFGZ)
4
together, I predict that banks with higher operational control quality are associated with higher
operational efficiency. It is unclear, a priori, whether stronger operational controls lead to higher
operational efficiency for two reasons. First, operational control is one of the subcomponents of
MCS, thus an effective operational control system may not be fully reflective of the overall
effectiveness of the firm’s MCS that ensures optimal resource allocation and reliable internal
reporting. Second, the potential benefits of effective operational controls may not offset their
high implementation and compliance costs. In addition, excessive operational controls may
create a burden for managers and stifle operations.
The objectives of MCS are to ensure that a firm achieves optimal resource allocation and
reliable internal management reporting. MCS encompasses not only operational controls, but
also controls pertaining to budgeting, monitoring profits by product line, financial reporting, and
so on. These subcomponents are distinct but overlapping. On the one hand, these
subcomponents are likely to be correlated with each other. One the other hand, it is plausible
that a firm invests in effective controls along some of these subcomponents while
overshadowing other subcomponents. For example, Tysiac (2012) discusses COSO’s concerns
that SOX 404 increased the effectiveness of ICFR with an unintended consequence of
overshadowing internal control over operations. Prior SOX studies ignore this possibility and
use ICFR quality as a proxy for firm’s overall MCS quality. In order to examine the incremental
impact of operational controls beyond ICFR and to mitigate concerns that the documented
results in this study may be due to these correlated subcomponents, I control for both ICFR
quality and overall MCS quality. Specifically, I proxy for ICFR quality using the SOX
disclosures, and for MCS quality using the firm’s overall corporate governance quality. Lastly, it
is plausible that pervasive management culture or “tone at the top” drives MCS quality and, in
5
turn, operational control quality. While empirical proxies for “tone at the top” are tough to
obtain, overall corporate governance quality implicitly reflects “tone at the top.” As I control for
overall corporate governance quality, this mitigates the concerns about this potentially correlated
omitted variable problem. Finally, I include firm fixed effect in the main analyses, and conduct
changes analyses as well.
The second stream of research investigates the association between financial reporting
noncompliance and operational noncompliance (Altamuro, Gray, and Zhang 2014), and the
impact of operational control deficiencies on financial reporting risk (Lawrence et al. 2014).
Altamuro et al. (2014) focus on the medical device and pharmaceutical industries that are
subject to the U.S. Food and Drug Administration (FDA) guidelines and examine the association
between accounting restatements (i.e., proxy for financial reporting noncompliance) and adverse
outcomes from the FDA’s manufacturing plant inspections (i.e., proxy for operational
noncompliance).5 They find that there is a contemporaneous association between financial
reporting and operational noncompliance. They also document that the impact of financial
reporting noncompliance on the stock market, audit fees, and CEO turnover is greater in the
presence of operational noncompliance. Furthermore, Lawrence et al. (2014) use data breach
incidences as a manifestation of operational control weaknesses, and find that firms with such
weaknesses exhibit lower financial reporting quality (i.e., higher information risk), and higher
audit fees. In addition to giving rise to information risk, operational control weaknesses may
lead to higher business risk. In particular, operational control deficiencies may increase the risk
of undetected operational losses arising from, for example, fraud and data breach. They may
5 Examples of FDA violations include failure to adhere to the company’s required written procedures, failure to
properly investigate discrepancies and complaints, and failure to properly validate new and modified controls and
procedures. These failures result in defective products reaching the trade, recalls and/or seizure of the products,
regulators fines, and plant closures (Altamuro et al. 2014).
6
also lead to excessive risk-taking by the bank. Both the increased likelihood of undetected
operational losses and excessive risk-taking increase the probability of business failure (i.e.,
business risk). Consequently, to the extent that an effective operational control system reduces
business risk and information risk, I expect a negative relation between operational control
quality and the costs of debt and equity capital.
I measure banks’ operational control quality using two metrics: (1) the incidence of
actual operational risk events as an ex-post observable proxy for weaknesses in operational
controls, and (2) an index-based measure of operational risk management quality (𝑂𝑅𝑀𝑄) as an
ex-ante proxy created through textual analyses of Form 10-Ks in the SEC EDGAR database (see
Section 4.3.2 for more details). The disadvantage of the ex-post measure is that it does not
distinguish operational risk events due to bad luck from such events due to poor operational
control quality. While the ex-ante measure does not suffer from this caveat, it may be subject to
the boilerplate measurement problem since it is based on the information disclosed in annual
reports (see Section 4.3 for more details).
I use two proprietary databases to obtain the actual operational risk events: (1) the SAS
OpRisk Global Data, and (2) the Identity Theft Resource Center (ITRC) database.6
The former
contains operational risk events categorized based on BCBS operational risk event classification.
The latter contains data breach incidences (i.e., cyber-security attacks), which are a form of
external fraud forced on a firm. Both vendors gather information from public sources such as
regulatory agencies (e.g., the SEC, the Financial Industry Regulatory Authority [FINRA], and
the Federal Deposit Insurance Corporation [FDIC]), and major financial newspapers (e.g., the
6 Operational risk events from SAS OpRisk Global Data and ITRC database are obtained with permission from
their vendors, the Statistical Analysis System (SAS) Institute, and Identity Theft Resource Center (ITRC),
respectively.
7
Wall Street Journal). As a result, the source of operational risk events announcements are not
the banks themselves, thus mitigating concerns about selective disclosure. I extract a total of 110
material operational risk events on 98 banks from 2003 to 2013 with necessary data availability
for my dependent and control variables. The sample also includes other public bank holding
companies that do not have operational risk events during the sample period. In total, the sample
consists of 287 U.S. banks, of which 98 have and 189 do not have operational risk events.7
I focus on the banking industry for the following reasons. First, while operational risk
events do exist in all industries and thus are important for all firms, the banking industry is the
first to formally recognize operational risk as a standalone risk and to define best practices for
sound operational risk management (please refer to Section 2.2 for more details). Second and
related to the first point, although the SAS OpRisk Global Data includes operational risk events
across all industries, the majority of the events pertain to banks, and more importantly, the
events details are most complete for operational risk events relating to banks. Third, I construct
the 𝑂𝑅𝑀𝑄 index based on the best practices and principles for the sound management of
operational risk developed particularly for banks by the BCBS (please refer to Section 4.3.2 for
more details). As a result, the 𝑂𝑅𝑀𝑄 is motivated by and most relevant for banks. Fourth, the
inputs and outputs selected for the Data Envelopment Analysis to measure operational efficiency
is based on the business structure of banks that is inherently different from that of non-banking
firms. Therefore, I focus on the banking industry.
To examine whether operational control quality is positively associated with operational
efficiency, I use a frontier analysis technique—Data Envelopment Analysis (hereafter DEA)—
7 To construct a more homogenous sample, I focus on bank holding companies (BHCs) only. They make up a large
fraction of total banking industry assets in the United States (Avraham et al. 2012). For example, the 72 largest
BHCs in terms of book value of total assets at the end of 2007 accounted for 78% of the total book value of assets
of the U.S. banking systems (Ellul and Yerramilli 2013).
8
as the proxy for operational efficiency. Consistent with my prediction, the results show that
higher operational control quality is associated with significantly higher operational efficiency.8
This finding holds after controlling for the quality of ICFR. Prior studies (e.g., Baik et al. 2013;
Greene and Segal 2004) document a positive association between profitability and operational
efficiency. Building on these studies, I contend that an effective operational control system is a
source of firm value enhancement by improving operational efficiency and hence performance
(i.e., “numerator” effect). In addition, to provide further empirical evidence on the numerator
effect, I show that banks with higher operational control quality exhibit higher earnings
persistence. Finally, I perform a price-level analysis based on the Collins, Maydew, and Weiss’s
(1997) valuation model and find a strong positive association between operational control
quality and equity prices. This latter result provides support for the net equity valuation impact
of operational control quality.
Next, I examine the association between operational control quality and costs of debt and
equity capital. I use bond spreads, measured as the difference between offering yield of the bond
issue minus the yield on the Treasury bill with comparable maturity and coupon rate to measure
the cost of debt. For the cost of equity capital, I use implied cost of capital. Consistent with my
prediction that strong operational controls mitigate information risk and business risk, I find that
banks with stronger operational controls exhibit significantly lower costs of debt and equity
capital. These results are robust after controlling for ICFR quality. Consequently, I conclude that
reducing cost of capital (i.e., “denominator” effect) is the second channel by which strong
operational controls enhance firm value (Clarkson, Fang, Li, and Richardson 2013).
8 Financial analysts also rank financial institutions based on simple measures of operational efficiency. For example,
a common measure used is the NIX ratio, which is the ratio of noninterest expense to revenue. As a robustness
check, I use the inverse of the NIX ratio (i.e., the ratio of revenue to noninterest expense) as an alternative measure
of operational efficiency. In an untabulated analysis, I find that results continue to hold using this measure. Revenue
is calculated as the sum of net interest income and noninterest income less loan loss provision.
9
Moreover, I conduct additional analyses and find several interesting insights. First, in the
changes analyses, I find that (1) remediating banks (i.e., those banks that improve their
operational control quality following the occurrence of an operational risk event) are associated
with improvement in their operational efficiency and cost of capital estimates, and (2) non-
remediating banks (i.e., those banks that do not improve their operational control quality
following the occurrence of an operational risk event) are associated with no significant change
in their operational efficiency estimate but exhibit a significant increase in their cost of capital
estimates. Overall, these results provide further empirical evidence that changes in operational
control quality lead to predictable changes in operational efficiency and cost of capital
consistent with the results documented by the levels analyses. Second, I explore whether
operational control deficiencies arising from the different operational risk event categories
differentially impact operational efficiency and cost of capital. The findings provide weak
evidence that operational control deficiencies arising from internal fraud have a stronger adverse
effect on operational efficiency and cost of equity capital compared with deficiencies arising
from other operation risk event types.9
This study contributes to and complements the emerging literature on SOX (e.g.,
Altamuro et al. 2014; Cheng et al. 2014; Lawrence et al. 2014). One major difference between
these studies, particularly Cheng et al. (2014), and my study is that they focus on the spillover
effect of ICFR on firms’ operations, while I directly study the effect of operational controls on
firm’s operations and cost of capital. Specifically, throughout the entire study, I control for the
9 In addition, in an attempt to distinguish operational risk events arising from fundamentally deficient operational
controls from events arising from bad luck, I partition the sample of banks with operational risk events into
subsamples of banks with 𝑂𝑅𝑀𝑄 below and above the sample median. The untabulated results suggest that the
magnitude of change for operational efficiency and cost of capital estimates pre-and post-operational risk events is
larger among the subsample of banks with 𝑂𝑅𝑀𝑄 below the sample median relative to the subsample of banks with
𝑂𝑅𝑀𝑄 above the sample median. These untabulated results also serve to provide construct validity for the 𝑂𝑅𝑀𝑄
index.
10
quality of ICFR using SOX 404 disclosures and provide evidence on the incremental impact of
operational controls on banks’ operational efficiency and cost of capital. Furthermore, because
banks are inherently different from other industries, these noted studies focus only on
nonbanking industries. The importance and sheer size of the banking sector, and the significance
of operational risk management for banks (as evidenced by Basel II), warrant a study focusing
on the banking sector. Taken together, the findings in this thesis suggest that effective
operational controls enhance firm value by both improving operational performance (i.e.,
“numerator” effect), and reducing cost of capital (i.e., “denominator” effect). These results are
important, given the increased attention on operational controls by BCBS and other banks
supervisors. Thus, these findings should be of interest to the Basel Committee, bank supervisors,
as well as banks.
The rest of my thesis is organized as follows. Chapter 2 provides the institutional
background. Chapter 3 develops my hypotheses, building on results from prior literature.
Chapter 4 describes the data, and explains the measurement of key variables and model
specifications. Chapter 5 presents the results, Chapter 6 includes additional analyses, and
Chapter 7 concludes the thesis.
11
CHAPTER 2
INSTITUTIONAL BACKGROUND
2.1. Committee of Sponsoring Organizations of the Treadway Commission Internal
Control Framework
In 1992, the Committee of Sponsoring Organizations of the Treadway Commission
(COSO) developed a model for evaluating internal controls. This model has been adopted as the
generally accepted framework for internal control and is widely recognized as the definitive
standard against which organizations measure the effectiveness of their internal control systems.
Moreover, it has been adopted by banking authorities worldwide. For example, the U.S.
Department of the Treasury Office of the Comptroller of the Currency (OCC) issued guidance
on the importance of establishing and maintaining sound internal controls (OCC 2000, 2013)
based on the COSO Framework. In addition, the BCBS’s “Framework for Internal Control
Systems in Banking Organizations” is based on the COSO Framework. According to the BCBS,
performance objectives for internal controls pertain to “the effectiveness and efficiency of the
bank in using its assets and other resources and protecting the bank from loss. The internal
control process seeks to ensure that personnel throughout the organization are working to
achieve its goals with efficiency and integrity, without unintended or excessive cost of placing
other interests (such as those for employees, vendors or customers) before those of the bank
(BCBS 1998a, p. 8).”
COSO defines internal control as “a process, affected by an entity’s board of directors,
management, and other personnel, designed to provide reasonable assurances regarding the
achievement of objectives in the following categories:
12
1. Effectiveness and efficiency of operations;
2. Reliability of financial reporting; and,
3. Compliance with applicable laws and regulations.”10
The first objective, which is the focus of my study, pertains to the effectiveness and
efficiency of operations such as the performance goals and ways of safeguarding assets against
loss. The second objective, which is primarily the focus of SOX, relates to reporting reliability,
including internal and external financial and nonfinancial reporting. Finally, the compliance
objective pertains to complying with applicable laws and regulations (COSO 1992, 2013).11
According to COSO, an effective internal control system consists of five essential interrelated
entity-level components to support the achievement of these three objectives (COSO 1992,
2013): (1) control environment, (2) risk management, (3) information and communication, (4)
monitoring activities, and (5) control activities.
2.2 Operational Risk Under Basel II
Following a series of costly operational risk events and pursuing widespread recognition
of the importance of operational risk, BCBS conducted a number of studies related to
operational risk management beginning in 1998. First, it released the document “Operational
Risk Management” (BCBS 1998b), summarizing the results of interviews with over thirty major
banks worldwide on the management of operational risk. The results shows (1) an increased
10
Similarly, Statement of Auditing Standards No. 115, Communicating Internal Control Related Matters Identified
in an Audit, defines internal control as “a process — affected by those charged with governance, management and
other personnel — designed to provide reasonable assurance about the achievement of the entity’s objectives with
regard to the reliability of financial reporting, effectiveness and efficiency of operations, and compliance with
applicable laws and regulations.”
11
According to COSO, these are distinct but overlapping objectives. This is consistent with the emerging literature
on SOX that examines the effect of internal control over financial reporting (ICFR) on firms’ operation (see Section
3 for an over-view).
13
awareness about operational risk among bank board of directors and senior management
associating operational risk events with internal control weaknesses and a lack of compliance
with existing control procedures, and (2) an increased recognition of operational risk as a
separate risk factor, and the growing existence of an operational risk management framework.
Second, the BCBS’s Transparency Group conducted three surveys of the public
disclosure practices in major international banks from 2001 to 2003. In particular, the surveys
focus on the annual reports of 54 financial institutions across 13 countries for the years 1999,
2000, and 2001, and analyze the trends in qualitative and quantitative disclosures. The results of
the surveys revealed that banks voluntarily increased their operational risk disclosures in their
annual reports due to widespread recognition of the importance of the operational risk and in
anticipation of future disclosure requirements (BCBS 2001, 2002, 2003a). Specifically, while
only 63% of these banks in 1999 “disclosed information about the main types of operational risk
and identified and discussed any specific issues considered to be significant” in their annual
reports, this figure increased to 82% in 2000 and 91% in 2001 (BCBS 2003a , p. 23).
Subsequently, BCBS adopted the “Revised Framework on International Convergence of
Capital Measurement and Capital Standards” in 2004, commonly known as the Basel II capital
Accord (Basel II). Basel II classifies operational risk, for the first time, as a self-contained risk
factor separate from credit risk and market risk. The BCBS defines operational risk as “the risk
of loss resulting from inadequate or failed internal processes, people and systems or from
external events” (BCBS 2003b). Operational risk is an inevitable part of doing business (Hull
2012), as it pertains to risk generated by the production of goods and services for the clients of a
financial institution (Cummins, Lewis, and Wei 2006).
14
To help identify the most significant causes of operational risk events and to facilitate
operational risk management, the Basel Committee classifies operational risk events into seven
event types (BCBS 2003b):
(1) Internal fraud: losses due to an act of fraud, misappropriation of property and assets,
or circumvention of regulation, law and company policy. Examples include the
intentional misreporting of positions, employee theft, and insider trading.
(2) External fraud: losses due to an act of fraud, misappropriation of property, or
circumvention of the law by a third party, including robbery, check kiting, and damage
from computer hacking.
(3) Employment practices and workplace safety: losses arising from acts inconsistent
with employment, health, or safety laws or agreements, or due to the payment of
personal injury claims or diversity or discrimination issues. Examples include worker
compensation claims and violations of employee health and safety rules.
(4) Clients, products, and business practices: losses arising from unintentional or
negligent failure to meet a professional obligation to clients and the use of inappropriate
products or business practices. Examples are misuse of confidential customer
information and improper trading activity.
(5) Damage to physical assets: losses due to the loss or damage of physical assets from
natural disasters or other events, such as vandalism, fire, and flooding.
(6) Business disruption and system failures: losses arising from disruption of business or
system failures, such as hardware and software failures and utility outages.
15
(7) Execution, delivery, and process management: losses due to failed transaction
processing or process management and disputes with trade counterparties and vendors.
Examples include data entry errors, incomplete legal documentation, and unapproved
access given to clients’ accounts.
2.3 U.S. Bank Holding Companies and Basel II
Most European banks switched to Basel II during 2008, while major Canadian banks
became Basel II compliant by the end of fiscal year 2007. However, implementation of the
Basel II in the U.S. has been much slower. The U.S. federal banking regulators announced the
final rules for implementation of Basel II in late 2007. Before switching to Basel II, U.S.
regulators have to approve if the bank is in compliance with the final rule. As of 2013, no U.S.
bank received approval from U.S. regulators to switch to Basel II. As a result, all U.S. banks
during the sample period (2003-2013) employed in this study operate under Basel I, which does
not explicitly highlight operational risk as a separate risk factor. Accordingly, all disclosure on
operational risk management practices provided by the U.S. banks in my sample is voluntary
disclosure.
In order to encourage and enhance market discipline, Basel II encouraged mandatory and
systematic operational risk disclosures, and published two influential best practices guidelines
for operational risk management and related disclosures. Basel II guidelines for operational risk
management practices and disclosure are widely used by banks worldwide as well as by the U.S.
banks. There is no specific U.S. regulatory guidance regarding disclosure of operational risk in
Form 10-K filings. The first risk-disclosure requirement in the Form 10-K filings was introduced
by FRR No. 48 in 1997, which required listed firms to discuss “Qualitative and Quantitative
Market Risks” in quarterly and annual reports. It is noteworthy that these disclosure
16
requirements pertained only to market risks. In addition, prior to 2005, risk-factor disclosures
were only required in S-1 registration statements, which were filed before a firm proceeds with a
public offering. Starting in 2005, however, the SEC mandated firms to extend their risk-factor
disclosures in the risk-factor section (Item 1A) to their quarterly and annual reports to describe
“the most significant factors that make the company speculative or risk.” Even though these
risk-factor disclosures are mandatory after 2005 and require disclosure of risk information
beyond market and credit risk, there is no explicit reference to operational risk.
17
CHAPTER 3
LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
3.1 Relation between Operational Control Quality and Operational Efficiency
A growing body of research investigates whether company-level ICFR weaknesses,
which are more pervasive than account-level weaknesses, have broader implications beyond
financial reporting quality. For example, Cheng et al. (2014) study the relation between ICFR
weaknesses and operational efficiency for nonfinancial firms. They find that firms with effective
ICFR have a higher operational efficiency. In a similar study, Feng et al. (2014) examine the
association between inventory-related material weaknesses and firms’ inventory management.
They provide evidence that effective ICFR over inventory is associated with higher inventory
turnover and a lower likelihood and magnitude of inventory impairments. Lastly, Bauer (2014)
finds that ICFR weaknesses disclosed under SOX have a spillover effect on firms’ tax avoidance
objectives.12
These studies argue that company-level ICFR weaknesses affect firms’ operations
via two mechanisms. First, these weaknesses are symptomatic of overall internal control
weaknesses and poor control environment (i.e., weak “tone at the top”) that give rise to agency
problems. Second, they lead to low-quality internal reporting, which leads management who
acts on these reports to make suboptimal operational decisions (e.g., Feng et al. 2014).
The role of the MCS is to ensure that a firm achieves optimal resource allocation and
reliable internal management reporting. MCS includes not only controls over operations, but
also controls relating to budgeting, monitoring profits by product line, financial reporting, and so
on. Weaknesses in any of these subcomponents may be reflective of a deficient MCS that is not
12
Cheng et al. (2013) also show that SOX-related ICWs affect firms’ investment efficiency.
18
able to achieve these objectives. The above studies focus on the financial reporting controls
component, while this study focuses on the operational controls component. In order to examine
the incremental impact of operational controls, I control for the quality of ICFR, and the firm’s
overall corporate governance quality.
Weaknesses in operational controls may lead to agency problems and increase the
propensity of management misappropriation of inputs and resources. Also, such deficiencies
could lead to mismanaged or poorly trained employees, willful misconduct, conflict of interests,
fraud, rogue trading, and so on. Effective operational controls could mitigate these problems. In
addition, banks that invest in an effective operational risk management system are better able to
integrate risk management into their operations and thus build a more comprehensive and
intelligent internal control system. This, in turn, should allow banks to make more optimal
operational decisions and to improve the effectiveness and efficiency of their operations. Based
on these arguments, I conjecture that banks with higher operational control quality are
associated with higher operational efficiency. I state my first hypothesis as follows (alternative
form):
H1: After controlling for the quality of internal control over financial reporting, higher
operational control quality is positively associated with higher operational efficiency.
As stated above, management control systems include not only operational controls, but
also controls pertaining to budgeting, monitoring profits by product line, and internal reward
systems. As a result, the null of H1 could occur because operational control is only one of the
components of MCS; therefore, an effective operational control system may not be reflective of
19
the overall effectiveness of the firm’s MCS that is required to ensure optimal resource allocation
and reliable internal management reporting.13
Additionally, implementation of an effective
operational control system is resource intensive and costly, and thus its potential benefits may
not offset its cost. Relatedly, excessive operational controls may create a burden (e.g.,
bureaucracy and lack of dynamism) for managers, and stifle operations. As a result, it is not
clear a priori whether banks with stronger operational controls are associated with higher
operational efficiency.
3.2 Relation between Operational Control Quality and Cost of Capital
Prior studies find that firms with ICFR weaknesses are associated with higher cost of
debt capital (e.g., Dhaliwal, Hogan, Trezevant, and Wilkins 2011; Kim, Song, and Zhang 2011).
For example, Dhaliwal et al. (2011) use company-level ICFR weaknesses and document that
these weaknesses affect creditors’ assessments of firm risk, and thus cost of debt. They offer
three reasons for this finding. First, weaknesses over financial reporting controls may lead to a
reduction in the quality and precision of financial reporting numbers, which would, in turn,
decrease the reliability of the information creditors need to assess the likelihood of default (i.e.,
estimation risk increases). Because the probability of default is an important factor for cost of
debt, creditors would charge a higher cost of debt to compensate for their decreased ability to
accurately assess the likelihood of default (Bhojraj and Sengupta 2003). The second reason is
that creditors determine compliance with debt covenants using financial reporting numbers
(DeFond and Jiambalvo 1994). Therefore, they charge a higher cost of debt to compensate for
the decrease in the reliability and accuracy of the financial reporting numbers that they use to
13
According to the COSO Framework (1992, 2013), an effective operational control system can only provide
reasonable assurance for the achievement of effectiveness and efficiency of operations.
20
assess compliance. Third, Dhaliwal et al. (2011) suggest that ICFR weaknesses allow managers
to more easily misappropriate cash flows (i.e., misappropriation risk increases), which, in turn,
increases the default risk, and thus the cost of debt.
With regard to the cost of equity capital, extant studies provide a link between ICFR
weaknesses and the cost of equity capital. Specifically, building on prior research that
information risk is priced (Francis et al. 2004, 2005) and that ICFR weaknesses increase
information risk (e.g., Ashbaugh‐Skaife et al. 2008; Doyle, Ge, and McVay 2007), Ogneva,
Subramanyam, and Raghunandan (2007) and Ashbaugh-Skaife et al. (2009) find that firms with
ICFR deficiencies are associated with higher cost of equity capital.
Both empirical and anecdotal evidence suggest that operational control deficiencies lead
to increased information and business risk. A recent study by Lawrence et al. (2014) provides
empirical evidence that firms with operational control deficiencies are more likely to have
restatements and to receive SEC comment letters (i.e., have higher information risk).
Furthermore, anecdotal evidence suggests that operational control weaknesses increase the
probability of default (i.e., default risk). For example, several catastrophic events due to
operational control deficiencies have resulted in major losses (e.g., Societe Generale, and
JPMorgan’s “London Whale”), and the collapse of large financial institutions (e.g., Barings
Bank). As a result, I argue that operational control weaknesses affect costs of debt and equity
capital by giving rise to (1) information risk, and (2) business risk.14
To the extent that an effective operational control system mitigates these risks and
enhances public confidence in firms with sound operational controls and procedures, I expect
that banks with higher operational quality to have lower costs of debt and equity capital. In
addition to reducing the information risk and business risk, an effective operational control
14
Creditors are especially concerned about the downside risk arising from operational control weaknesses.
21
system could lead to lower cost of equity capital by improving financial performance. I state my
second set of hypotheses as follows (alternative form):
H2a: After controlling for the quality of internal control over financial reporting, higher
operational control quality is associated with lower cost of debt capital.
H2b: After controlling for the quality of internal control over financial reporting, higher
operational control quality is associated with lower cost of equity capital.
There is mixed evidence in the literature as to whether idiosyncratic risk such as
accounting information risk and business risk is priced in the equity markets (e.g., Beyer,
Cohen, Lys, and Walther 2010; Shevlin 2013). On the one hand, prior studies such as Easley and
O’Hara (2004) and Francis et al. (2004, 2005) provide theoretical and empirical support for this
link, respectively. In particular, Easley and O’Hara (2004) rely on the argument that differences
in the composition of information between private and public information affect the cost of
capital because uninformed investors (i.e., those with no private information) demand a higher
expected return to protect themselves vis-à-vis informed investors (i.e., those with private
information). This higher expected return arises because informed investors are better able to
shift their portfolio weights to incorporate new information. As a result, Easley and O’Hara
argue that private information induces a new form of systematic risk that cannot be diversified
away by uninformed investors; therefore, in equilibrium, investors require compensation for this
risk. Francis et al. (2004, 2005) provide empirical support for the link between information risk
and cost of equity capital by documenting that firms with higher accounting quality exhibit a
lower cost of equity capital.
22
On the other hand, other studies (e.g., Core, Guay, and Verdi 2008; Hughes, Liu, and Liu
2007; Mohanram, and Rajgopal 2009) have shown that information risk is fully diversifiable in
the capital market and, as such, there exists no link between idiosyncratic information risk and
cost of capital. Particularly, Hughes et al. (2007) extend Easley and O’Hara’s (2004) model to a
large economy to allow for full diversification and conclude that idiosyncratic information risk
is either diversifiable or subsumed by existing risk factors.
In a recent study, however, Hou (2015) finds that accounting information risk that is
idiosyncratic in nature is priced either because the effect is non-diversifiable (due to ambiguity)
or because investors are not fully diversified. Consistent with prior accounting literature such as
Francis et al. (2004, 2005), I define idiosyncratic information risk as “the likelihood that firm-
specific information that is pertinent to investors’ pricing decisions is of poor quality.”
Information risk and business risk arising from operational control deficiencies are mainly
idiosyncratic in nature, although there may be an impact on estimating the systematic risk of a
given bank. Building on Hou (2015), I conjecture that banks with higher operational control
quality are associated with lower cost of capital. Nonetheless, given the mixed results in the
literature, it is unclear, a priori, whether the risks arising from operational control weaknesses
are priced by market participants.
23
CHAPTER 4
DATA AND RESEARCH DESIGN
4.1 Data and Sample Selection
The sample comprises U.S. public bank holding companies for the period 2003–2013.
The sample period begins in 2003, as that is when my access to the OpRisk Global Data for U.S.
financial institutions begins. The ITRC database begins at 2005.
The SAS OpRisk Global Data is maintained by the SAS Institute, and the ITRC database
is maintained by the Identity Theft Resource Center (ITRC). Both vendors gather information
from public sources such as regulatory agencies (e.g., the SEC, the Financial Industry
Regulatory Authority [FINRA], and the Federal Deposit Insurance Corporation [FDIC]), and the
major financial newspapers (e.g., the Wall Street Journal), thus the source of operational risk
events announcements is not banks, which mitigates concerns about banks’ selective disclosure
The SAS OpRisk Global Data is the world’s largest and most comprehensive repository
of information on publicly reported operational risk events. It identifies and categorizes
operational risk events for financial institutions in accordance with BCBS operational risk event
classification (see Section 2.2 for details on BCBS classification). The database provides a
detailed description of each event, including the company name, a detailed account of the event,
and the dates of the event occurrence. The database’s primary clientele are financial
institutions.15
Financial institutions’ own internal operational risk events data provide the most
relevant information for managing operational risk; however, internal data is generally
insufficient for most modelling and statistical analysis purposes, especially for high-
15
The SAS Institute is completing its database for other sectors, such as for insurance companies.
24
severity/low-frequency operational risk events. Financial institutions overcome this shortage by
complementing their internal data with external databases, such as SAS OpRisk Global Data.16
The ITRC database contains data breach information confirmed by various media
sources and state governmental agencies. According to ITRC, “a breach is defined as an event in
which an individual’s name plus Social Security Number (SSN), driver’s license number,
medical record, or a financial record/credit/debit card is potentially put at risk—either in
electronic or paper format.” The database provides a detailed description of the event, the type
of breach, the date the event occurred, and the number of records that were exposed. ITRC
started tracking publicly reported breach events in 2005. The database currently has a total of
4,794 data breach events.17
I obtain the rest of the data from the following sources: (1) accounting and regulatory
data from the consolidated financial statements of bank holding companies (FR Y-9C reports),
retrieved from the SNL Regulated Depositories database, and COMPUSTAT; (2) Form 10-Ks
from the SEC EDGAR database; (3) stock returns and characteristics from the Center for
Research in Security Prices (CRSP) file; (4) consensus (median) one- and two-year-ahead EPS
forecasts from the I/B/E/S database; (5) bond issuance data from the SNL Capital Structure and
Mergent Fixed Income Securities (FISD) databases; and (6) ICFR weaknesses from the Audit
Analytics database.
I obtain 93 operational risk events on 81 banks from the Global OpRisk Data.
Furthermore, I obtain an additional 17 data breach events on 17 additional banks from the ITRC
database that are distinct from those 93 operational risk events obtained from the Global OpRisk
16
“SAS OpRisk Global Data: A Comprehensive Database of Operational Loss Information”
(http://www.sas.com/resources/product-brief/sas-oprisk-globaldata-brief.pdf).
17
“Data Breaches” (http://www.idtheftcenter.org/id-theft/data-breaches.html).
25
Data. Collectively, the sample contains 110 material operational risk events on 98 banks from
2003 to 2013 with necessary data availability for my dependent and control variables. The
sample also includes other public bank holding companies that do not experience operational
risk events during the sample period. In total, the main sample consists of 287 public bank
holding companies, out of which 98 banks experience and 189 banks do not experience
operational risk events. There are a total of 2,525 firm-year observations. Table 1 summarizes
the sample composition. Table 2 presents the descriptive statistics for the main sample. Figure 1
tabulates the frequency of the BCBS’s seven operational risk event types. The following three
operational risk event types have the highest frequency in descending order: (1) internal fraud;
(2) clients, products, and business practices; and (3) external fraud.
4.2 Main Dependent Variables
4.2.1 Operational Efficiency Measure
H1 predicts a positive association between operational control quality and operational
efficiency. One of the features of this study is that I use a frontier analysis technique, DEA, to
measure operational efficiency.
The DEA provides firm-level operational efficiency that is based on the relation between
outputs and inputs. Specifically, it is a nonparametric statistical procedure originally developed
by Charnes, Cooper, and Rhodes (1978) for estimating the relative efficiency of a group of
firms, referred to as “decision making units” (DMUs), that operate in the same industry. In my
study, each bank is a DMU. Each bank converts inputs (e.g., deposits) into outputs (e.g., loans).
DEA efficiency is defined as the ratio of outputs over inputs:
max 𝜃 = ∑ 𝑢𝑖𝑦𝑖𝑘
𝑠𝑖=1
∑ 𝑣𝑗𝑥𝑗𝑘𝑚𝑗=1
𝑘 = 1, … , 𝑛. (1)
26
where 𝑠, 𝑚, and 𝑛 refer to the number of outputs, inputs, and DMUs, respectively. The DEA is
an optimization procedure that uses a linear programming optimization technique to maximize
the ratio of output to input. In particular, DEA uses the inputs and outputs of all DMUs to
determine the optimal weights (𝑢 and 𝑣) for outputs and inputs such that the ratio of outputs to
inputs for each DMU is maximized relative to other DMUs. The derived optimal weights are
then multiplied by their corresponding output and input quantities, as shown in Equation (1).
Lastly, all obtained efficiency scores (𝜃s) are scaled by the highest efficiency score, resulting in
an ordinal ranking of DMUs on relative efficiency, where the most efficient DMUs have an
efficiency score of one (Demerjian, Lev, and McVay 2012). Banks with a relative efficiency
score of one (𝜃 = 1) form the efficient frontier (also referred to as the “best practices” frontier),
while banks located below the frontier are assigned an efficiency score of less than one (0 ≤
𝜃 < 1) and are considered relatively inefficient.
The DEA methodology has been used extensively in economics and banking research
(for a review see Berger and Humphrey 1997; Berger and Mester 1997; and Hughes and Mester
2012).18
Particularly, prior banking research employ this methodology to measure operational
efficiency for both traditional banks (i.e., those that are mainly in the business of borrowing
funds by accepting deposits and lending them in the form of loans) and universal banks (i.e.,
those that in addition to traditional banking activities provide a wide variety of financial services
such as investment banking). The sample in this study consists of U.S. bank holding companies
and thus lends itself to the latter. I draw on prior studies (e.g., Barth et al. 2013; Hughes and
Mester 2012) to select inputs and outputs that account for heterogeneous sources of income. In
particular, I use the following inputs: (1) total deposits, (2) noninterest expense (less loan loss
18
The DEA has also been extensively used in the operations research and management accounting. Financial
accounting researchers have recently started using DEA (e.g., Baik et al. 2013; Cheng et al. 2014; Demerjian, Lev,
and McVay 2012; Demerjian et al. 2013; Koester, Shevlin, and Wangerin 2013).
27
provision), (3) physical capital (total fixed assets), and (4) loan loss provision. The loan loss
provision input is included to capture the loan quality (Laeven and Majnoni 2003). I use the
following outputs: (1) total loans and leases, (2) other earnings assets (e.g., bonds and
investment securities), and (3) noninterest income. The first output accounts for traditional
banking activities, while the latter two outputs account for other financial services that may be
offered by bank holding companies. I label the operational efficiency measure obtained from
this set of inputs and outputs as 𝐸𝐹𝐹, which is the main measure of operational efficiency.
The above DEA efficiency measure (𝐸𝐹𝐹) combines balance sheet items (stock
variables) with income statement items (flow variables) in the output-input ratio. To mitigate
concerns about utilizing stock and flow variables in the ratio, I develop two additional DEA
efficiency measures. One measure is purely based on balance sheet items (𝐸𝐹𝐹_𝐵𝐿), while the
other measure is based on only the income statement items (𝐸𝐹𝐹_𝐼𝑆). For the DEA efficiency
measure based on the balance sheet items (𝐸𝐹𝐹_𝐵𝐿), the inputs are: (1) total deposits, (2) other
liabilities, (3) fixed assets, and (4) loan loss reserve; while the outputs are: (1) total loans and
leases, and (2) other assets generating earnings. For the DEA efficiency measure based on the
income statement items (𝐸𝐹𝐹_𝐼𝑆), the inputs are: (1) noninterest expense (less loan loss
provision), (2) interest expense, and (3) loan loss provision; while the outputs are: (1) net
interest income, and (2) noninterest income.
4.2.2 Cost of Debt Capital Measure
H2a predicts a negative relation between operational control quality and cost of debt
capital. To measure cost of debt, I use the difference between offering yield of the bond issue
28
(i.e, primary market) minus the yield on a U.S. Treasury bill with comparable maturity and
coupon rate. I refer to this variable as 𝑆𝑃𝑅𝐸𝐴𝐷.
4.2.3 Cost of Equity Capital Measure
H2b predicts a negative association between operational control quality and cost of
equity capital. Consistent with related prior research (e.g., Ashbaugh-Skaife et al. 2009; Ogneva,
Subramanyam, and Raghunandan 2007), I use implied cost of equity to proxy for cost of equity
capital. Implied cost of equity is defined as the internal rate of return that equates current stock
prices to expected future payoffs. Because expected future payoffs are unobservable, it is
common practice to use either (1) Value Line’s dividend forecasts or (2) I/B/E/S’s analysts’
earnings forecasts along with the dividend payout assumptions.
Two types of valuation models are commonly used to infer implied cost of equity: (1)
Ohlson’s (1995) residual income model (e.g., Claus and Thomas 2001; Gebhardt, Lee, and
Swaminathan 2001) with different assumptions about the terminal value; and (2) the Ohlson and
Juettner-Nauroth (OJ) model (Easton 2004; Gode and Mohanram 2003) with the assumption that
abnormal earnings growth rates decay asymptotically to a long-term economic growth rate.19
Implied cost of equity metrics suffer from the measurement error problem (Easton and
Monahan 2005). In order to mitigate this problem, I follow prior research (e.g., Hail and Leuz
2006; Mohanram and Gode 2013) to construct an aggregate implied cost of equity metric by
averaging across the following four models: (1) the OJ model as implemented by Gode and
Mohanram (2003), (2) a simplified version of the OJ model, similar to Easton’s (2004) price-
19
Another valuation model is the dividend discount model (Botosan 1997) that uses the target price at the end of the
forecast horizon as the terminal value. This method is less commonly used because it relies on target prices and
forecasts of dividends that are available only for a small subset of firms. For this reason and consistent with recent
research (e.g., Mohanram and Gode 2013), I do not estimate implied cost of equity using the dividend discount
model.
29
earnings-growth (PEG) model, (3) the residual income model as implemented by Claus and
Thomas (2001), and (4) an alternative form of residual income model as implemented by
Gebhardt et al. (2001). Refer to Appendix D for more details. For comparability across time, I
express the aggregate implied cost of equity measure as the implied risk premium (𝑅𝑃_𝐴𝑉𝐺) by
subtracting the prevailing risk-free rate (e.g., Mohanram and Gode 2013).
4.3 Main Independent Variables
A key research design feature of this study is to develop a reliable proxy for operational
control quality. I measure banks’ operational control quality using two novel measures: (1) the
incidence of actual operational risk events as an ex-post observable proxy for weaknesses in
operational controls, and (2) an index-based measure of operational risk management quality
(ORMQ) as an ex-ante proxy, created via content analyses of Form 10-K filings.
Although the advantage of actual operational risk events is that they are the
manifestation of poor operational control systems, the limitation of this ex-post measure is that it
does not distinguish operational risk events that are due to bad luck from events that are a result
of poor operational control quality. More specifically, it could be the case that among the 98
banks in my sample with operational risk events, some have an effective operational control
system and yet experience an operational risk event solely because of bad luck. Similarly, some
of the 189 banks in the sample with no operational risk events may have poor operational
control systems but have not yet experienced an operational risk event during the sample
period.20
20
Focusing on the sample of banks with operational risk events, I examine whether the magnitude of the change
pre- and post-operational risk events differs between banks with 𝑂𝑅𝑀𝑄 above the sample median and banks with
𝑂𝑅𝑀𝑄 below the sample median. The untabulated results indicate that the magnitude of change for operational
efficiency and cost of capital estimates pre- and post-operational risk events is larger among subsample of banks
30
The ex-ante measure does not suffer from the above limitation. However, an empirical
disadvantage of this measure is that the information disclosed in annual reports may be
boilerplate. In addition, this measure comingles the effects of disclosure transparency and
operating control quality. I rely on a maintained assumption that if a particular operating control
is in place, the bank will talk about it in its voluntary annual report disclosures, and hence
silence on that control in such disclosure channels implies that the control is absent. Overall, I
find results consistent with my hypotheses using both measures. In particular, the documented
effects using the ex-post proxy (ORA) cannot be driven by transparency. As a result, the concern
that the ex-ante measure (ORMQ) could merely reflect transparency is mitigated by the fact that
results are the same using both measures.
4.3.1 Operational Risk Avoidance Metric
The first measure of operational control quality is operational risk avoidance (ORA). In
particular, ORA is an indicator variable that equals 1 if bank i does not experience an operational
risk event in year t + 1, and zero otherwise. For the second measure (i.e., ORMQ), a higher
ORMQ index indicates stronger operational control quality. As a result, in order for the two
measures to have the same predicted sign, I define my first measure as operational risk
avoidance (ORA).
My assumption is that operational control weaknesses exist at least in the year
immediately prior to the year the operational risk event materializes. More specifically, an
operational risk event in year t + 1 (i.e., 𝑂𝑅𝐴 = 0) is a manifestation of operational control
weaknesses in year t. This assumption is consistent with prior related studies (Ashbaugh-Skaife
with 𝑂𝑅𝑀𝑄 below the sample median relative to the subsample of banks with 𝑂𝑅𝑀𝑄 above the sample median.
These untabulated results help distinguish the bad luck story from the inherently poor operational control case.
31
et al. 2008; Cheng, Dhaliwal, and Zhang 2013; Dhaliwal et al. 2011; Doyle, Ge, and McVay
2007). Thus, I view the absence of operational risk event in year t + 1 as indicative of effective
operational controls in prior years, in particular in year t.
4.3.2 Operational Risk Management Quality Metric
The second measure of operational control quality is a self-constructed index of
operational risk management quality (ORMQ) created through a textual analysis of the Form 10-
Ks in the SEC EDGAR database. To guide my selection of the items to include in my index, I
appeal to the Basel II “Sound Practices for the Management and Supervision of Operational
Risk” (BCBS 2003b) and “Principles for the Sound Management of Operational Risk” (BCBS
2011). Refer to Appendix B for more details about these principles. The aim of these documents
is to outline “a set of principles that provide[s] a framework for the effective management and
supervision of operational risk, for use by banks and supervisory authorities when evaluating
operational risk management policies and practices” (BCBS 2003b).
The ORMQ index consists of the following eleven items: (1) enterprise risk management
system, (2) chief risk officer, (3) operational risk framework, (4) operational risk committee, (5)
internal operational loss data collection and analysis, (6) external operational loss data collection
and analysis, (7) key performance indicators, (8) key risk indicators, (9) scenario analysis, (10)
risk control self-assessments, and (11) scorecards. The higher the ORMQ metric, the higher is
the operational control quality.
I scan the banks’ 10-K filings from 2003 to 2013 using the Python programming
language to measure their operational risk management quality along the above dimensions.
Specifically, I obtain the number of times each of the eleven items is repeated each year for each
bank. I then scale each item each year by the maximum number of times that item is repeated
32
(see Appendix A for details of the text extraction procedure). Therefore, each item gets a score
between 0 and 1. For example, if the maximum number of times the “enterprise risk
management” appears in 2003 is 20 times and is by Bank of America, then Bank of America
receives a score of 1 for this item in year 2003. Now, if in year 2003 the “enterprise risk
management” appears ten times in JPMorgan Chase’s 10-K filing, and zero times in Wells
Fargo & Company’s 10-K filing, then in year 2003 JPMorgan Chase gets a score of 0.5 while
Wells Fargo & Company receives a score of zero for this item in 2003.21
As a result, the ORMQ index, which is the sum of the eleven items, ranges from zero to
eleven for each bank each year. A higher ORMQ index is indicative of stronger operational
control quality. The notion is that if a bank has invested in its operational risk management
system that points to its strong operational controls, then the bank has all the incentives to
disclose this positive development to the market. Therefore, I assume that the lack of disclosure
on any of these items means that the bank is weak along that dimension.22
21
To validate my ORMQ index, I consulted with a Vice President of the Operational Risk Management division of
a major North American bank and an international bank. These individuals vetted the components of the index and
were of the view that the index has construct validity. The individuals asked to remain anonymous. In addition, I
continue to find results, although weaker, using a less refined form of the ORMQ index, where each of the eleven
components is simply coded as “0” or “1” and the ORMQ index is calculated as the simple sum of the components.
The fact that a more refined measure gives stronger results supports the construct validity of the index. Finally, I
conduct an analysis of pre and post operational risk events where I partition the sample of banks with operational
risk events based on ORMQ sample median. The untabulated results indicate that the magnitude of the change pre-
and post-operational risk events for operational efficiency and cost of capital estimates is greater for the subsample
of banks with ORMQ below the sample median compared with the subsample of banks with ORMQ above the
sample median. These untabulated results also serve to provide construct validity for the index.
22
Basel II was introduced in 2003 and became effective in 2007. It requires that banks make adequate public
disclosure about their operational risk management system to allow shareholders to assess banks’ approach to
operational risk management (see Appendix B for more details). European and Canadian banks switched to a Basel
II regime in 2007. Surveys of public disclosures by banks published by Basel Committee (BCBS, 2001, 2002,
2003a) reveal that banks voluntarily increased their operational risk disclosures in their annual reports since early
2000s due to widespread recognition of the importance of the operational risk and in anticipation of future
disclosure requirements. In addition, Helbok and Wagner (2006) investigate operational risk disclosure practices of
banks in North America, Asia, and Europe and show that the extent and information content of discretionary
operational risk disclosure increased drastically over the span of 1998 to 2001. I randomly selected 40 U.S. banks
and read through their 10-Ks from 1997 to 2013. Consistent with the above studies, I find that beginning early
33
4.4 Research Design
4.4.1 Operational Control Quality and Operational Efficiency Model
H1 predicts a positive association between operational control quality and operational
efficiency. To test this hypothesis, I estimate the following regression model (e.g., Berger and
Mester 1997; Cheng et al. 2014; Chernobai et al. 2011; Demerjian, Lev, and McVay 2012):
EFFICIENCYi,t = α0 + α1OCQi,t or t+1 + α2SIZEi,t + α3AGEi,t + α4ICWAi,t
+α5NONPERF_LOANS / LOANSi,t + α6BIG4i,t + α7CGQi,t
+ α8MERGERi,t + α9FOREIGNi,t + α10LOSSi,t + α11RESTRUCTUREi,t
+α12TRADING_ASSETS /ASSETSi,t + FIRM_FE + TIME_FE + εi,t (2)
where 𝐸𝐹𝐹𝐼𝐶𝐼𝐸𝑁𝐶𝑌𝑖,𝑡 is one of the three measures of operational efficiency(𝐸𝐹𝐹, 𝐸𝐹𝐹_𝐵𝐿, and
𝐸𝐹𝐹_𝐼𝑆) outlined in Section 4.2.1. The variable OCQi,t or t+1 is one of the two proxies (ORAi,t+1
or ORMQi,t) for operational control quality outlined in Sections 4.3.1 and 4.3.2, respectively.
Specifically, the 𝑂𝑅𝐴 is an indicator variable that equals one if bank i does not experience an
operational risk event in year t + 1, which indicates an effective operational control in year t,
and zero otherwise. The 𝑂𝑅𝑀𝑄 is a score from the 𝑂𝑅𝑀𝑄 index measured in the same year as
the three measures of the operational efficiency. H1 predicts a positive coefficient on both 𝑂𝑅𝐴
and 𝑂𝑅𝑀𝑄 proxies.
I follow the prior literature in selecting factors that are shown to affect operational
efficiency. In particular, I control for firm size, life cycle, and geographical complexity (e.g.,
Cheng et al. 2014; Demerjian et al. 2012). First, I expect larger banks to have more market
power and be more effective in negotiating and acquiring loans and deposits on favorable terms.
2000s, U.S. banks began introducing a new section entitled “Operation Risk Management” in their MD&A section
and began disclosing more details on their operational risk management practices.
34
I use the natural logarithm of total assets to control for bank size (SIZE). Second, I expect that
bank life cycle affects management’s opportunity set of possible projects and required start-up
costs of investments. I use the number of years the bank has been listed as a proxy for life cycle
(DeAngelo, DeAngelo, and Stulz 2010; Demerjian et al. 2012). Third, I expect that operating in
multiple countries makes it more difficult for a bank’s management team to efficiently allocate
capital, as it requires a broader knowledge set and reduces the amount of attention management
pays to any single geographical location (Stein 1997). I control for this using an indicator
variable (FOREIGN), which is equal to 1 if the bank has foreign operation.
Given that Cheng et al. (2014) show that firms with effective ICFR have a higher
operational efficiency, it is important to control for the ICFR quality to ensure that my empirical
findings reflect the incremental impact of operational control quality on operational efficiency.23
Therefore, I control for the quality of internal control over financial reporting (ICFR). Internal
control weakness avoidance (ICWA) is an indicator variable that equals 1 if bank i does not
disclose ICFR weaknesses in year t, and zero otherwise.
The results from prior studies suggest that firms with ICFR weaknesses (e.g., Ashbaugh-
Skaife, Collins, and Kinney 2007; Doyle et al. 2007) and operational risk events (e.g., Chernobai
et al. 2011) tend to be smaller, poorly performing, more complex, involved in mergers and
acquisitions or restructuring, audited by Big N auditors, and have lower corporate governance
quality. Therefore, I control for these factors. In particular, I control for the poor performance
using LOSS as an indicator variable that takes a value of 1 if a bank reports a loss in year t, and
zero otherwise. MERGER and RESTRUCTURE are indicator variables that control for merger
and acquisition, and restructuring, respectively. 𝐵𝐼𝐺4 is an indicator variable that equals 1 if the
23
In addition, in my view, combining the strength of internal control over financial reporting and operations yields
better signals for overall firm risk and thus value assessments.
35
bank is audited by one of the Big4 audit firms, and zero otherwise. 𝐶𝐺𝑄 is a measure of
corporate governance quality, which uses the QuickScore metric created by the Institutional
Shareholder Services (ISS).24
In particular, ISS evaluates each firm across four pillars: (1) board
structure, (2) compensation/remunerations, (3) shareholder rights, and (4) audit and risk
oversight (see Appendix E for more details).
In addition, banks may seem more efficient if they issue risky loans (Berger and Mester
1997). To avoid labelling unmeasured differences in loan quality as differences in efficiency, I
control for loan quality using the ratio of nonperforming loans to total loans (Hughes and Mester
2012).25
Lastly, the variable TRADING_ASSETS/ASSETS controls for the heterogeneity in
banking activities.26
4.4.2 Operational Control Quality and Cost of Debt Capital Model
H2a predicts a negative association between operational control quality and the cost of
debt. To test my prediction, I follow prior literature (Kleymenova 2014; Morgan and Stiroh
2001) and estimate the following regression model:
SPREADi,t+1 = α0 + α1OCQi,t+1 + α2ISSUE_AMOUNTi,t+1 + α3BOND_LIFEi,t+1
+ α4CALLABLEi,t+1 + α5ISSUE_RATINGi,t+1 + α6SIZEi,t+1 + α7ROAi,t+1
+ α8ASSET_RISKi,t+1 + α9TIER_RATIOi,t+1 + α10DEPOSITS/ASSETSi,t+1
+ α11ICWAi,t+1 + α12CGQi,t+1 + FIRM_FE + TIME_FE + εi,t+1 (3)
24 This measure is commonly used by both institutional investors and academic researchers (e.g., Vyas 2011).
25
In addition, I control for loan quality in the DEA methodology by including loan loss provision as an input for the
EFF and EFF_IS measures, and loan loss reserve as an input for the EFF_BL measure.
26
I also control for the heterogeneity of banking activities in the DEA methodology. For example, I include “other
earnings assets” and “noninterest income” as outputs in calculating my main measure of operational efficiency
(EFF). Please refer to Section 4.2.1 for more details.
36
where 𝑆𝑃𝑅𝐸𝐴𝐷𝑖,𝑡+1 measures cost of debt capital at t + 1 as outlined in Section 4.2.2. The
variable of interest is 𝑂𝐶𝑄𝑖,𝑡+1, which is one of the two proxies (𝑂𝑅𝐴𝑡+1 or 𝑂𝑅𝑀𝑄𝑡+1) for
operational control quality defined in Sections 4.3.1 and 4.3.2, respectively. H2a predicts a
negative coefficient on both 𝑂𝑅𝐴 and 𝑂𝑅𝑀𝑄.
I follow the prior research (e.g., Kleymenova 2014; Morgan and Stiroh 2001) to include
control variables that are likely to affect the cost of debt capital. Specifically, I control for the
total dollar amount of the face value of each bond at issuance (𝐼𝑆𝑆𝑈𝐸_𝐴𝑀𝑂𝑈𝑁𝑇) and for the
bond maturity in years (𝐵𝑂𝑁𝐷_𝐿𝐼𝐹𝐸). 𝐶𝐴𝐿𝐿𝐴𝐵𝐿𝐸 is an indicator variable that equals 1 if a
bond is callable, and zero otherwise. Consistent with Morgan and Stiroh (2001), I control for
bank-specific characteristics that may affect the cost of issuance. In particular, I control for bank
size using the natural logarithm of total assets (𝑆𝐼𝑍𝐸); the bank’s capitalization, measured as the
ratio of Tier 1 regulatory capital to total assets (𝑇𝐼𝐸𝑅_𝑅𝐴𝑇𝐼𝑂); the overall riskiness of the
bank’s assets, measured as the ratio of risk-weighted assets to total assets (𝐴𝑆𝑆𝐸𝑇_𝑅𝐼𝑆𝐾); the
bank’s reliance on external funding, measured as the ratio of total deposits to total assets
(𝐷𝐸𝑃𝑂𝑆𝐼𝑇𝑆/𝐴𝑆𝑆𝐸𝑇𝑆); and the bank’s profitability, using return on assets (𝑅𝑂𝐴). Lastly, the
variable 𝐶𝐺𝑄 controls for the corporate governance quality.
4.4.3 Operational Control Quality and Cost of Equity Capital Model
Finally, H2b predicts a negative relation between operational control quality and the cost
of equity. To test my prediction, I estimate the following regression model (e.g., Nissim 2013):
RP_AVGi,t+1 = α0 + α1OCQi,t+1 + α2BETAi,t+1 + α3IDIO_RISKi,t+1 + α4BMi,t+1
+ α5SIZE,t+1 + α6TIER_RATIOi,t+1 + α7ASSET_RISKi,t+1 + α8ICWAi,t+1
+ α9CGQi,t+1 + FIRM_FE + TIME_FE + εi,t+1 (4)
37
where 𝑅𝑃_𝐴𝑉𝐺𝑖,𝑡+1 measures the cost of equity capital at t + 1, which is the mean of four
implied cost of capital estimates imputed from commonly used accounting valuation models
outlined in Section 4.2.3. The variable of interest is 𝑂𝐶𝑄𝑖,𝑡+1, which is one of the two proxies
(𝑂𝑅𝐴𝑡+1 or 𝑂𝑅𝑀𝑄𝑡+1) for operational control quality defined in Sections 4.3.1 and 4.3.2,
respectively. H2b predicts a negative coefficient on both 𝑂𝑅𝐴 and 𝑂𝑅𝑀𝑄.
Following the prior literature, I include a set of control variables that are likely to affect
the cost of equity capital. First, I control for systematic risk (𝐵𝐸𝑇𝐴) and idiosyncratic risk
(𝐼𝐷𝐼𝑂_𝑅𝐼𝑆𝐾) and expect that α2 > 0 and α3 > 0.27
Stock returns are positively correlated with
book-to-market equity and are negatively correlated with firm size (e.g., Fama and French
1992). I control for firm size (𝑆𝐼𝑍𝐸) as the natural log of market value. 𝐵𝑀 controls for book-to-
market equity and is constructed as the ratio of the book value of equity divided by the market
value of equity. In addition, I control for the bank’s capitalization (TIER_RATIO), overall
riskiness of assets (ASSET_RISKi), and the quality of ICFR (ICWA). Lastly, the variable 𝐶𝐺𝑄
controls for the corporate governance quality.
27 𝐵𝐸𝑇𝐴 is systematic risk, and 𝐼𝐷𝐼𝑂_𝑅𝐼𝑆𝐾 is idiosyncratic risk obtained from the following market model:
𝐸𝑋𝐶𝐸𝑆𝑆_𝑅𝐸𝑇 = 𝛽0 + 𝛽1𝑅𝑀𝑅𝐹 + 𝜖
where 𝐸𝑋𝐶𝐸𝑆𝑆_𝑅𝐸𝑇 is the bank’s monthly return minus the risk-free rate and 𝑅𝑀𝑅𝐹 is the excess return on the
market. 𝐵𝐸𝑇𝐴 is measured as the coefficient on 𝑅𝑀𝑅𝐹. 𝐼𝐷𝐼𝑂_𝑅𝐼𝑆𝐾 is measured as the standard deviation of the
residuals. This equation is estimated using monthly returns from the CRSP file requiring a minimum of 18 and a
maximum of 60 months over each year and the four previous fiscal years.
38
CHAPTER 5
EMPIRICAL RESULTS
5.1 Results for Operational Control Quality and Operational Efficiency
Table 3 shows the correlations between the three operational efficiency measures
(𝐸𝐹𝐹, 𝐸𝐹𝐹_𝐵𝐿, and 𝐸𝐹𝐹_𝐼𝑆) and the two operational control quality measures (ORA and
ORMQ). Specifically, the main operational efficiency measure (𝐸𝐹𝐹) is positively and
significantly correlated with 𝑂𝑅𝐴 (0.09) and 𝑂𝑅𝑀𝑄 (0.48). In addition, all three operational
efficiency measures are significantly correlated with each other. For example, 𝐸𝐹𝐹 is
significantly positively correlated with 𝐸𝐹𝐹_𝐵𝐿 (0. 95) and 𝐸𝐹𝐹_𝐼𝑆 (0.70). Overall, these
results provide preliminary support for H1.
Table 4 reports the regression results for Equation (2), which tests the relation between
operational control quality (𝑂𝑅𝐴 and 𝑂𝑅𝑀𝑄) and operational efficiency (𝐸𝐹𝐹, 𝐸𝐹𝐹_𝐵𝐿, and
𝐸𝐹𝐹_𝐼𝑆). The three operational efficiency measures are based on DEA methodology and range
from zero to one. Columns 1 through 3 present the results for 𝐸𝐹𝐹, 𝐸𝐹𝐹_𝐵𝐿, and 𝐸𝐹𝐹_𝐼𝑆,
respectively. Consistent with H1, I find that banks with stronger operational controls are
associated with higher operational efficiency. Because the results are very similar across the
three columns, I use 𝐸𝐹𝐹 for illustration. Focusing on column 1 in Panel A of Table 4, the
coefficient estimate on 𝑂𝑅𝐴, the ex-post proxy for operational control quality, is positive
(0.089) and statistically significant (𝑡 = 7.14). In terms of economic significance, this means
that the operational efficiency of banks with no operational risk events is 8.9% (a 12.7% change
relative to mean efficiency) higher than that of banks with operational risk events.
39
Similarly, in Panel B of Table 4, the coefficient estimate on 𝑂𝑅𝑀𝑄, the ex-ante proxy
for operational control quality, is also positive and statistically significant. For example, the
coefficient in column 1 is 0.017 (𝑡 = 9.09). Regarding the economic significance of this
finding, an interquartile range movement (i.e., from the first quartile to the third quartile) of 3.5
in 𝑂𝑅𝑀𝑄 is associated with an increase in operational efficiency of 0.059. Relative to the mean
operational efficiency of 0.70, the interquartile range difference in 𝑂𝑅𝑀𝑄 leads to a 8.4%
change in operational efficiency.
These findings are robust to controlling for ICFR quality, thus highlighting the
incremental impact of operational control quality on operational efficiency over and above the
ICFR quality. Furthermore, the coefficient estimates for the control variables are consistent with
prior literature except for the firm age. In particular, larger banks, those audited by Big4
auditors, and those with higher corporate governance quality exhibit a higher operational
efficiency. In contrast, poor performing banks and those with foreign operations have lower
operational efficiency.
Several studies have examined the implications of operational efficiency—derived from
either a frontier analysis or simple financial ratios—on firms’ current and future profitability.
For example, Greene and Segal (2004) document a contemporaneous association between
profitability, as measured by ROA and ROE, and efficiency for a sample of U.S. life insurance
companies. More recently, Baik et al. (2013) find that higher operational efficiency improves
firms’ profitability forecasts. Based on these findings, the results from this section suggest that
effective operational controls that lead to higher operational efficiency enhance firm value by
improving profitability (i.e., “numerator” effect). To provide further empirical evidence on the
numerator effect, I examine whether banks with higher operational efficiency exhibit higher
40
earnings persistence in Section 6.2. Lastly, to examine the net equity valuation effect of
operational efficiency, I perform a price-level analysis based on Collins et al.’s (1997) equity
valuation model in Section 6.1.
5.2 Results for Operational Control Quality and Cost of Capital
5.2.1 Results for Operational Control Quality and Cost of Debt Capital
Table 5 presents the results for regression Equation (3), which tests the association
between operational control quality (𝑂𝑅𝐴 and 𝑂𝑅𝑀𝑄) on the cost of debt capital (SPREAD).
Column 1 reports the results for the ex-post measure of operational control quality (𝑂𝑅𝐴), and
column 2 reports the results for the ex-ante measure (𝑂𝑅𝑀𝑄). Consistent with H2a, the results
show that banks with a higher operational control quality have a lower cost of debt capital.
Specifically, column 1 shows that banks with no operational risk events receive significantly
lower bond spreads, with an average reduction of 0.903 (𝑡 = −4.71) percentage points.
Similarly, in column 2, the coefficient estimate on 𝑂𝑅𝑀𝑄 is −0.090 (𝑡 = −1.92), indicating a
statistically significant negative relation between 𝑂𝑅𝑀𝑄 and bond spreads. This finding is
economically significant in that an interquartile range movement of 3.7 in 𝑂𝑅𝑀𝑄 is associated
with a decrease in the cost of debt capital of 0.333 (a 16% change relative to the mean cost of
debt capital).
These results are robust to controlling for ICFR quality, indicating that operational
control quality has an effect on the cost of debt equity beyond the effects captured through ICFR
quality (e.g., Dhaliwal et al. 2011). The signs of the coefficients for the control variables are as
expected in that the cost of debt is positively related to bond maturity (𝐵𝑂𝑁𝐷_𝐿𝐼𝐹𝐸), issue
rating (𝑅𝐴𝑇𝐼𝑁𝐺), overall riskiness of bank’s assets (𝐴𝑆𝑆𝐸𝑇_𝑅𝐼𝑆𝐾), and the level of bank’s
41
reliance on outside financing (𝐷𝐸𝑃𝑂𝑆𝐼𝑇𝑆/𝐴𝑆𝑆𝐸𝑇𝑆), and it is negatively related to the face
value of bond issue (𝐼𝑆𝑆𝑈𝐸_𝐴𝑀𝑂𝑈𝑁𝑇), bank size (𝑆𝐼𝑍𝐸), bank’s capitalization
(𝑇𝐼𝐸𝑅_𝑅𝐴𝑇𝐼𝑂), profitability (𝑅𝑂𝐴), and corporate governance quality (𝐶𝐺𝑄).28
5.2.2 Results for Operational Control Quality and Cost of Equity Capital
Table 6 reports the regression results for Equation (4), testing the association between
operational control quality (𝑂𝑅𝐴 and 𝑂𝑅𝑀𝑄) and the cost of equity capital (𝑅𝑃_𝐴𝑉𝐺). Column
1 reports the results for the 𝑂𝑅𝐴 measure, and column 2 reports the results for the 𝑂𝑅𝑀𝑄
measure. The results support H2b that banks with a more effective operational controls exhibit a
lower cost of equity capital. In particular, the coefficient estimate on 𝑂𝑅𝐴 in column 1 is
−3.319 (𝑡 = −8.06), indicating that banks with no operational risk events exhibit a 3.319
percentage point lower cost of equity capital. Similarly, in column 2, the coefficient estimate on
𝑂𝑅𝑀𝑄 is −0.647 (𝑡 = −4.79), signifying a statistically significant negative relation between
𝑂𝑅𝑀𝑄 and the cost of equity capital. In terms of economic significance, an interquartile range
movement of 3.5 in 𝑂𝑅𝑀𝑄 is associated with a decrease in the cost of equity capital of 2.26 (a
27.9% change relative to the mean cost of equity capital).
Similar to the results in Table 5, these results are robust after controlling for ICFR
quality, signifying that operational control quality has an impact on the cost of equity capital
incremental to the ICFR quality documented in prior studies (e.g., Ashbaugh-Skaife et al. 2009).
In addition, the signs of the coefficients on the risk factors are as expected in that the cost of
28
Consistent with the prior literature (e.g., Jorion, Liu, and Shi 2005), I convert the categorical letter credit rating
grades into cardinal scales (𝑅𝐴𝑇𝐼𝑁𝐺). The higher the cardinal scale (𝑅𝐴𝑇𝐼𝑁𝐺), the lower the credit rating. That is
why the coefficient estimate on 𝑅𝐴𝑇𝐼𝑁𝐺 is positive and statistically significant, indicating that a lower credit rating
increases the cost of debt.
42
equity capital is positively related to 𝐵𝑀, 𝐵𝐸𝑇𝐴, and 𝐼𝐷𝐼𝑂_𝑅𝐼𝑆𝐾, and negatively related to
𝑆𝐼𝑍𝐸.29
Taken together, the results from Tables 5 and 6 indicate that reducing cost of capital is
the second channel through which operational controls may enhance firm value (i.e.,
“denominator” effect).
29
I also regress SPREADt+1 and RP_AVGt+1 at time t+1 on ORMQt at time t and continue to get similar results.
43
CHAPTER 6
ADDITIONAL ANALYSES
6.1 Price-level Analysis
In Section 5.1, I rely on prior studies (Baik et al. 2013; Greene and Segal 2004) to
conclude that an effective operational control system is a source of firm value enhancement via
improving operational efficiency. In this section, I perform a price-level analysis to examine
whether there is a significant positive relation between operational control quality and equity
value, and whether higher operational control quality increases the positive impact of book value
and earnings on stock prices. Following Collins, Maydew, and Weiss (1997), I estimate the
value of a firm’s equity as a function of its earnings and book value: 30
Pi,t = α0 + α1BVi,t + α2EARNINGSi,t + α3I_EFFi,t + α4BVi,t×I_EFFi,t
+ α5EARNINGSi,t×I_EFFi,t + FIRM_FE + TIME_FE + εi,t+1 (5)
where 𝑃𝑖,𝑡 is the stock price at the end of fiscal year 𝑡; 𝐵𝑉𝑖,𝑡 is the book value per share at the
end of fiscal year 𝑡; and 𝐸𝐴𝑅𝑁𝐼𝑁𝐺𝑆𝑖,𝑡 is the earnings per share at the end of fiscal year 𝑡. I
partition the sample into high and low operational efficiency based on the sample median.
Specifically, 𝐼_𝐸𝐹𝐹 is an indicator variable that equals 1 for banks with operational efficiency
measure (𝐸𝐹𝐹) above the sample median, and zero otherwise. More importantly, I estimate
Equation (5) using the two measures of operational control quality (𝑂𝑅𝐴 and 𝑂𝑅𝑀𝑄).
30
The Ohlson (1995) valuation model requires the estimation of abnormal earnings to allow discount rates to vary
across firms (i.e., the model includes a term (1+ri,t/ri,t) for discounting the earnings). Maydew (1993) finds that
allowing discount rates to vary across firms does not significantly improve the explanatory power of the model.
Consistent with this, Collins et al. (1997), estimate the value of a firm’s equity as a function of its earnings and
book value.
44
The results are documented in Table 7. Columns 1, 2, and 3 present the results for
𝐼_𝐸𝐹𝐹, 𝑂𝑅𝐴, and 𝑂𝑅𝑀𝑄, respectively. The results for 𝐼_𝐸𝐹𝐹 is obvious as prior studies have
documented that higher operational efficiency leads to higher profitability (e.g., Greene and
Segal 2004; Baik et al. 2013). In column 1, the coefficient estimates on 𝐵𝑉 (0.812,𝑡 = 7.53)
and 𝐸𝑃𝑆 (1.873, 𝑡 = 4.25) are positive and statistically significant. The coefficient on 𝐼_𝐸𝐹𝐹 is
positive but not significant. While I find a positive but insignificant coefficient estimate (0.011,
𝑡 = 0.08) on the interaction term (I_EFF×BV), the coefficient estimate on (I_EFF×EPS) is
positive and statistically significant (1.442, 𝑡 = 1.76). Turning to the results for operational
control quality (𝑂𝑅𝐴 and 𝑅𝑀𝑄), columns 2 and 3 report results similar to that of 𝐼_𝐸𝐹𝐹. In
particular, in column 2, I find a marginally significant positive (0.242, 𝑡 = 1.63) coefficient on
the interaction term (ORMQ×BV), while the coefficient estimate on (ORMQ×EPS) is positive
and significant (1.583, 𝑡 = 2.33). Lastly, in column 3, while the coefficient estimate on
(ORA×BV) is insignificant (0.100, 𝑡 = 0.70), I find a positive and significant (1.047, 𝑡 = 3.82)
coefficient estimate on (ORA×EPS).
Overall, the results indicate that there is a positive relationship between equity valuation
and operational efficiency (𝐼_𝐸𝐹𝐹), and between equity valuation and operational control
quality (𝑂𝑅𝑀𝑄 and 𝑂𝑅𝐴). In addition, the evidence suggests that this positive relation is
obtained mainly through the increased impact of earnings on stock prices. In other words, the
results show that operational efficiency and operational control quality increase the value
relevance of earnings.
6.2 Earnings Persistence Analysis
The results from the price level analysis in Section 6.1 show the net effect of operational
control quality on equity valuation. Increasing the earnings persistence (i.e., “numerator” effect)
45
may be one potential channel through which an effective operational control system enhances
equity valuation. To provide evidence on this channel, I regress one-year-ahead return on assets
(ROAi,t+1) on operational efficiency after controlling for current return on assets (ROAi,t). The
regression model takes the following form:
ROAi,t+1 = α0 + α1ROAi,t + α2I_EFFi,t + α3ROAi,t×I_EFFi,t + FIRM_FE
+ TIME_FE + εi,t (6)
where 𝑅𝑂𝐴 is income before extraordinary items divided by beginning total assets, and I_EFF is
as defined in Section 6.1. Also, I estimate Equation (6) using the two measures of operational
control quality (𝑂𝑅𝐴 and 𝑂𝑅𝑀𝑄).
Table 8 presents the results for this analysis. Columns 1, 2, and 3 present the results for
𝐼_𝐸𝐹𝐹, 𝑂𝑅𝐴, and 𝑂𝑅𝑀𝑄, respectively. Given that prior studies document contemporaneous and
leading relationship between operational efficiency and profitability (e.g., Baike et al. 2013), the
results for 𝐼_𝐸𝐹𝐹 are not surprising. In column 1, the coefficient estimate on current 𝑅𝑂𝐴 is
positive and significant (0.266, 𝑡 = 3.05) and the coefficient estimate on the interaction term
(𝐼_𝐸𝐹𝐹 × 𝑅𝑂𝐴) is positive and statistically significant (0.240, 𝑡 = 2.12), suggesting that banks
with higher operational efficiency exhibit a higher earnings persistence. Regarding the results
for 𝑂𝑅𝑀𝑄 and 𝑂𝑅𝐴, columns 2 and 3 report similar results to the one reported in column 1 for
𝐼_𝐸𝐹𝐹. Specifically, column 2 documents a positive and significant (0.292, 𝑡 = 2.44)
coefficient for current 𝑅𝑂𝐴. However, the coefficient estimate on the interaction term (𝑂𝑅𝑀𝑄 ×
𝑅𝑂𝐴) is positive but insignificant (0.042, 𝑡 = 0.36). Lastly, in column 3, I find a positive and
significant (0.216, 𝑡 = 1.95) coefficient for current 𝑅𝑂𝐴 and a positive and significant
coefficient estimate (0.111, 𝑡 = 1.65) on the interaction term (𝑂𝑅𝐴 × 𝑅𝑂𝐴).
46
Overall, the results suggest that banks with higher operational control quality measured as
𝑂𝑅𝐴 exhibit higher earnings persistence. I interpret these findings as providing evidence for the
cash flow effects (i.e., “numerator” effect) of operational control quality on equity valuation.
6.3 Changes Analyses
In this section, I re-examine the relation between operational control quality and
operational efficiency (H1) and cost of capital (H2) using changes analysis. One advantage of a
changes specification is that it uses the same firm as its own control, thereby mitigating possible
concerns regarding time-invariant and firm-specific omitted correlated variables.
Using the two measures of operational control quality (ORA and ORMQ), I partition the
sample into four subsamples based on the occurrence of an operational risk event (i.e., ORA = O
or 1) at 𝑡 − 1 and whether there is a negative or positive change in operational risk management
quality from 𝑡 − 1 to 𝑡 (i.e., ΔORMQ(t-1,t) = 0 or > 0). Panel A of Table 9 shows the
classification of firms. In particular, NON_EVENT (ORA = 1) indicates bank-year observations
with no operational risk events at 𝑡 − 1. EVENT (ORA = 0) indicates bank-year observations
with operational risk events at 𝑡 − 1. 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 is an indicator variable that equals one for the
subsample of non-event banks (ORA = 1) with no positive improvement in their ORMQ from
𝑡 − 1 to 𝑡. 𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆 is an indicator variable that equals one for the subsample of non-
event banks (ORA = 1) with positive improvement in their ORMQ from 𝑡 − 1 to 𝑡.
𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 is an indicator variable that equals one for the subsample of event banks
(ORA = 0) that experience an operational risk event at 𝑡 − 1 but do not improve their ORMQ
from 𝑡 − 1 to 𝑡. Lastly, 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 is an indicator variable that equals one for the
47
subsample of event banks (ORA = 0) that experience an operational risk event at 𝑡 − 1 but
improve their ORMQ from 𝑡 − 1 to 𝑡.
The above partition strategy is consistent with that of SOX 404 studies (e.g., Ashbaugh-
Skaife et al. 2008). A major difference, however, is that in these studies an independent
auditors’ opinion (i.e., unqualified SOX 404 opinion) provides an unambiguous signal about the
changes in the effectiveness of firms’ ICFR. This forms the basis for identifying the firms that
receive an adverse SOX 404 opinion in the previous year, but remediate their ICFR weakness in
the following year (i.e., remediators). In the absence of such a feature in my setting, I make use
of the ORMQ index to identify remediators and non-remediators. As explained in Section 4.3.2,
my maintained assumption is that if a bank has invested in its operational risk management
system, then the bank has all the incentives to disclose this positive development to the market.
Accordingly, this change should be captured by the ORMQ index.
I use these four distinct subsamples to provide a test of the impact of operational control
quality on operational efficiency (H1) and cost of capital (H2) by examining within-firm
changes in operational efficiency and changes in cost of capital conditional on changes in
operational control quality as confirmed by the ORMQ index. Accordingly, I estimate the
changes specification of Equations (2), (3), and (4). In order to avoid over-identification, I drop
the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 indicator variable in the three regression models. As a result, the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸
subsample serves as the benchmark. Therefore, the intercept in each of the three changes
regression models measures the average change for the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample. The coefficient
estimate for each of the treatment variables (𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆, 𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆, and
𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆) captures changes incremental to the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample.
48
For 𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆, I expect an increase in their operational efficiency subsequent to
improving their operational control quality (ΔORMQ(t-1,t) = > 0). Moreover, enhancing their
operational control quality may lead to a reduction in the level of firm risk perceived by market
participants (i.e., “denominator” effect). As a result, I expect a reduction in their costs of debt
and equity capital. Additionally, because I use implied cost of capital to proxy for the cost of
equity capital, the decrease in the cost of equity may be also due to cash flow effects (i.e.,
“numerator” effect) arising from an increase in operational efficiency.
Turning to 𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆, these banks do not improve their operational control
quality (ΔORMQ(t-1,t) = 0) subsequent to the occurrence of an operational risk event.
Accordingly, I do not expect any significant change in their operational efficiency. However, the
occurrence of an operational risk event increases market’s assessment of firm risk (i.e.,
“denominator” effect). As such, I expect an increase in the costs of debt and equity capital for
𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆.
Lastly, 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 invest in improving their operational control quality (ΔORMQ(t-1,t)
= > 0) after experiencing an operational risk event. As a result, I expect an increase in their
operational efficiency. However, the impact of enhancing their operational control quality on
their cost of capital is unclear. On the one hand, the occurrence of an operational risk event
increases the level of risk perceived by market participants (i.e., “denominator” effect), which
leads to an increase in the costs of debt and equity capital. On the other hand, enhancing their
operational control quality subsequent to the operational risk event may lead to the reduction of
firm risk. Consequently, it is unclear whether the net effect increases or decreases market’s
assessment of firm risk for 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆. In addition, 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 may experience a
49
reduction in their cost of equity capital due to cash flow effects (i.e., “numerator” effect) arising
from an increase in operational efficiency.
Panel A of Table 9 reports that among the non-event banks (ORA = 1) only approximately
thirty percent of the bank-year observations experience a positive improvement in their
operational control quality (ΔORMQ(t-1,t) = > 0). However, among the event banks (ORA = 0)
approximately seventy percent of the bank-year observations experience a positive improvement
in their operational control quality (ΔORMQ(t-1,t) = > 0). This difference in proportion is
statistically different from zero, and highlights the importance of operational risk events to
firms.
Panel B of Table 9 reports the univariate descriptive statistics on changes in operational
efficiency (ΔEFF(t-1,t)) and changes in costs of debt (ΔSPREAD(t-1,t)) and equity (ΔRP_ AVG(t-1,t))
capital.31
The significance levels for the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample indicate that the mean values of
the change in operational efficiency and the change in costs of debt and equity capital are not
statistically different from zero. In contrast, I find that there is a significant increase in the
operational efficiency and a significant reduction in the costs of debt and equity capital for
𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆. Consistent with my expectations, while 𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 do not
experience a significant change in their operational efficiency they exhibit a significant increase
in their costs of debt and equity capital. Conversely, 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 experience a significant
increase in their operational efficiency and a significant reduction in their costs of debt and
equity capital.
Panel C of Table 9 reports the regression results for the effect of changes in operational
control quality on change in operational efficiency. The intercept captures the average
31
The untabulated results for ΔSPREAD(t,t+1) and ΔRP_ AVG(t,t+1) are similar.
50
operational efficiency change for the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample, while the coefficient estimate for
𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆, 𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆, and 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 variables captures
operational efficiency changes relative to the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample. In particular, the
coefficient estimate (−0.012, 𝑡 = −0.37) on 𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 is not significant,
suggesting that banks that had no change in their quality of operational controls following an
operational risk event exhibit no significant change in their operational efficiency measured the
next year, relative to the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample. However, I find a significant positive
coefficient on 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 (0.038, 𝑡 = 5.45). This result indicates that banks that
remediate their operational control weaknesses exhibit an improvement in operational
efficiency, relative to the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample. Consistent with my expectations, the
coefficient estimate on 𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆 (0.033, 𝑡 = 6.41) is also positive and significant.
Panel B also indicates that the coefficient estimate of 𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 is significantly
smaller than the coefficient estimates for 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 (𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.06) and
𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆 (𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.07).
Panel D of Table 9, reports the regression results for the effect of change in operational
control quality on change in cost of debt capital. There is a significant positive coefficient on
𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 (1.011, 𝑡 = 1.89). This finding shows that banks that had no change in
their operational control quality following an operational risk event are subject to a higher cost
of debt capital measured the next year, relative to the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample. The marginally
insignificant negative coefficient on 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 (−0.431, 𝑡 = −1.63) provides some
evidence that banks that remediate their operational control deficiencies exhibit a lower cost of
debt capital in the next period, relative to the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample. Finally, consistent with
my expectations, the coefficient estimate on 𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆 (−0.612, 𝑡 = −2.08) is
51
negative and significant. Panel D also indicates that the coefficient estimate of
𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 is significantly greater than the coefficient estimates for
𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 (𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.002) and 𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆 (𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.000).
Panel E of Table 9 reports the regression results for the effect of changes in operational
control quality on changes in cost of equity capital. The significant positive coefficient on
𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 (3.564, 𝑡 = 5.41) indicates that banks that had no change in their
operational control quality following an operational risk event experience an increase in the cost
of equity capital in the next period, relative to the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample. Moreover, the
significant negative coefficient on 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 (−0.444, 𝑡 = −1.92) shows that banks that
remediate their operational control deficiencies exhibit a reduction in the cost of equity capital in
the next period, relative to the 𝐵𝐴𝑆𝐸𝐿𝐼𝑁𝐸 subsample. Finally, consistent with my expectations,
the coefficient estimate on 𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆 (−1.063, 𝑡 = −4.54) is negative and significant.
Panel D also shows that the coefficient estimate of 𝑁𝑂𝑁_𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 is significantly
greater than the coefficient estimates for 𝑅𝐸𝑀𝐸𝐷𝐼𝐴𝑇𝑂𝑅𝑆 (𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.000) and
𝑅𝐼𝑆𝐾_𝑅𝐸𝐷𝑈𝐶𝐸𝑅𝑆 (𝑝 − 𝑣𝑎𝑙𝑢𝑒 = 0.000).
Overall, the results documented in Table 9 provide empirical evidence that changes in
operational control quality lead to predictable changes in operational efficiency (H1) and
changes in cost of capital (H2).
6.4 Operational Risk Event Types Analyses
In the main analyses, I use the incidence of actual operational risk events as an
observable ex-post proxy for operational control weaknesses. BCBS classifies operational risk
events into seven categories. To explore whether operational control deficiencies arising from
52
these event types differentially impact operational efficiency and cost of capital, I control for the
following operational risk event categories: (1) internal fraud (𝑂𝑅𝐴_𝐼𝑁𝑇_𝐹𝑅𝐴𝑈𝐷); (2) external
fraud (𝑂𝑅𝐴_𝐸𝑋𝑇_𝐹𝑅𝐴𝑈𝐷); (3) clients, products, and business practices (𝑂𝑅𝐴_𝐶𝐿𝐼𝐸𝑁𝑇𝑆); and
(4) the remaining three event types (𝑂𝑅𝐴_𝑅𝐸𝑆𝑇).32
In particular, to test the differential impact of event types on operational efficiency, I
replace the 𝑂𝑅𝐴 metric in Equation (2) with the above four variables. Table 10, Panel A, shows
the results for operational efficiency (𝐸𝐹𝐹). The results show that the coefficient estimates on
internal fraud (𝑂𝑅𝐷_𝐼𝑁𝑇_𝐹𝑅𝐴𝑈𝐷) is larger in magnitude compared with those of the other
three variables (𝑂𝑅𝐷_𝐸𝑋𝑇_𝐹𝑅𝐴𝑈𝐷, 𝑂𝑅𝐷_𝐶𝐿𝐼𝐸𝑁𝑇𝑆, and 𝑂𝑅𝐷_𝑅𝐸𝑆𝑇). Specifically, the
coefficient estimate on 𝑂𝑅𝐴_𝐼𝑁𝑇_𝐹𝑅𝐴𝑈𝐷 is (0.107, 𝑡 = 6.44), while the coefficient estimates
on 𝑂𝑅𝐴_𝐸𝑋𝑇_𝐹𝑅𝐴𝑈𝐷, 𝑂𝑅𝐴_𝐶𝐿𝐼𝐸𝑁𝑇𝑆, and 𝑂𝑅𝐴_𝑅𝐸𝑆𝑇 are (0.102, 𝑡 = 3.40), (0.095,
𝑡 = 6.86), and (0.047, 𝑡 = 2.34), respectively. However, the test of significance indicates that
the coefficient estimate of 𝑂𝑅𝐴_𝐼𝑁𝑇_𝐹𝑅𝐴𝑈𝐷 is only significantly greater than the coefficient
estimate of 𝑂𝑅𝐷_𝑅𝐸𝑆𝑇.
Panel B in Table 10 reports the results for the same analysis on the cost of debt. The
coefficient estimates on the four variables have the expected sign as predicted by H2a.
Specifically, the coefficient estimate on 𝑂𝑅𝐷_𝐼𝑁𝑇_𝐹𝑅𝐴𝑈𝐷 is (−1.151, 𝑡 = −4.20), while the
coefficient estimates on 𝑂𝑅𝐷_𝐸𝑋𝑇_𝐹𝑅𝐴𝑈𝐷, 𝑂𝑅𝐷_𝐶𝐿𝐼𝐸𝑁𝑇𝑆, and 𝑂𝑅𝐷_𝑅𝐸𝑆𝑇 are (−1.075,
𝑡 = −3.94), (−0.718, 𝑡 = −2.28), and (−0.529, 𝑡 = −1.32), respectively. Although, the
coefficient estimate of 𝑂𝑅𝐷_𝐼𝑁𝑇_𝐹𝑅𝐴𝑈𝐷 is greater than those of the other three types, the test
32
The reason I partition my ORA metric into four categories instead of seven is because four of the event types have
small sample sizes (see Figure 1), thus I combine them into one category.
53
of significance indicates that the difference is not statistically significant suggesting that
creditors penalize all types of operational risk events equally.
Lastly, Panel C reports the results for the cost of equity capital. The coefficient estimates
on the four variables are statistically significant with the expected sign as predicted by H2b. The
coefficient estimate on 𝑂𝑅𝐷_𝐼𝑁𝑇_𝐹𝑅𝐴𝑈𝐷 is (−4.450, 𝑡 = −3.58), which is greater than the
coefficient estimates on 𝑂𝑅𝐷_𝐸𝑋𝑇_𝐹𝑅𝐴𝑈𝐷, 𝑂𝑅𝐷_𝐶𝐿𝐼𝐸𝑁𝑇𝑆, and 𝑂𝑅𝐷_𝑅𝐸𝑆𝑇 (−4.262,
𝑡 = −5.27), (−1.374, 𝑡 = −1.87), and (−2.990, 𝑡 = −2.66), respectively. However, the test of
significance shows only the difference between the coefficient estimates of 𝑂𝑅𝐷_𝐼𝑁𝑇_𝐹𝑅𝐴𝑈𝐷
and 𝑂𝑅𝐷_𝐶𝐿𝐼𝐸𝑁𝑇𝑆 is statistically significant.
Taken together, the results from Table 10 provide some support that operational control
deficiencies stemming from internal fraud are more strongly associated with operational
efficiency and cost of equity capital than deficiencies arising from the other operational risk
event types.
54
CHAPTER 7
CONCLUSION
Recent high profile and costly operational loss events have focused the attention of bank
managers and regulators on operational risk management practices. The increasing severity and
frequency of such events has led the BCBS to classify operational risk as a separate risk factor
under the Basel II. Furthermore, in order to encourage and enhance market discipline, Basel II
encouraged mandatory and systematic operational risk disclosures, and published two influential
best practices guidelines for operational risk management and related disclosures. Although
European and Canadian banks switched to Basel II by 2008, all U.S. banks in the sample
deployed in this study operate under Basel I. While not mandatory under Basel I, many U.S.
banks began voluntarily disclosing information on their operational risk management practices
in their Form 10-Ks using the two aforementioned BCBS guidelines since the turn of the
millennium.
This study contributes to this regulatory emphasis on operational risk by empirically
examining whether operational control quality is associated with operational efficiency and the
costs of debt and equity capital for a large sample of U.S. bank holding companies for the period
2003-2013. I measure banks’ operational control quality using two measures: (1) the incidence
of actual operational risk events as an ex-post observable proxy for weaknesses in operational
controls; and (2) an index-based measure of operational risk management quality (𝑂𝑅𝑀𝑄) as an
ex-ante proxy, created via textual analyses of Form 10-K filings.
First, in pooled cross-sectional tests, I find that operational efficiency is significantly
higher in banks with higher operational control quality compared to banks with lower
operational control quality. I also find that banks with effective operational controls exhibit
55
lower costs of debt and equity capital. These findings are incremental to the banks’ ICFR quality
and are robust after controlling for a variety of firm characteristics that prior research shows to
be related to operational efficiency and cost of capital. Second, in the changes analyses, I find
that (1) remediating firms are associated with improvement in their operational efficiency and
cost of capital estimates, and (2) non-remediating banks are associated with no significant
change in their operational efficiency estimate but exhibit a significant higher cost of capital.
In addition, I conduct supplemental analyses and find several interesting insights. First, I
examine the net effect of operational control quality on equity valuation and find a positive
association between operational control quality and equity prices. Second, I find that banks with
higher operational control quality exhibit a higher earnings persistence. Lastly, the results
provides some support that operational control deficiencies stemming from internal fraud are
more strongly associated with operational efficiency and cost of equity capital than deficiencies
arising from the other operational risk event types.
Taken together, the findings in this dissertation suggest that operational controls have
significant effects on banks’ operations and cost of capital, and that the information provided by
banks about their operational control management is credible. Concerning the latter, while
operational risk disclosure for U.S. banks are purely discretionary during my sample period, the
results from the operational efficiency and cost of capital analyses suggest that operational risk
disclosure provides credible information about banks’ strength of operational controls and
operational risk.33
It is not inconceivable that the credibility of such operational risk disclosures
could become even more enhanced under a mandatory disclosure regime.
33
In other words, the operational risk information disclosed by the U.S. banks in their From 10-K filings and
captured by the ORMQ index, credibly ranks firms in terms of their strength of operational controls.
56
This study is subject to the following caveats. First, MCS includes not only operational
controls but also controls pertaining to budgeting, monitoring profits by product lines, internal
reward systems, and so on. Although I consider these subcomponents to be conceptually distinct
from each other, these are likely to be highly correlated with each other. Additionally, it is
plausible that pervasive management culture or “tone at the top” is driving the MCS quality and,
in turn, operational control quality. In order to mitigate these concerns, I control for the bank’s
overall corporate governance quality. Second, both 𝑂𝑅𝐴 and 𝑂𝑅𝑀𝑄 metrics as proxies for
operational control quality are subject to caveats. In particular, 𝑂𝑅𝐴 is an ex-post measure;
therefore, it does not distinguish operational risk events that are due to bad luck from events that
are a result of poor operational control quality. The ex-ante measure (𝑂𝑅𝑀𝑄) does not suffer
from this limitation. However, an empirical limitation of this measure is that the information
disclosed in annual reports may be boilerplate. Moreover, this measure comingles the effects of
disclosure transparency and operational control quality. Despite these caveats, I find results
consistent with my hypotheses using both measures. In particular, the documented effects using
the ex-post proxy (ORA) cannot be driven by transparency. Accordingly, the concern that the ex-
ante measure (ORMQ) could merely reflect transparency is mitigated by the fact that results are
the same using both measures.
57
REFERENCES
Alexander, C. R., S. W. Bauguess, G. Bernile, Y.-H. A. Lee, and J. Marietta-Westberg. 2013.
“Economic Effects of SOX Section 404 Compliance: A Corporate Insider Perspective.”
Journal of Accounting and Economics 56 (2–3): 267–290.
Altamuro, J., J. Gray, and H. Zhang. 2014. “A Commitment to Compliance: An Examination of
Finanical and Operational Noncompliance for FDA-regulated Industries.” Working paper,
Ohio State University.
Ashbaugh-Skaife, H., D. W. Collins, and W. R. Kinney. 2007. “The Discovery and Reporting of
Internal Control Deficiencies Prior to SOX-mandated Audits.” Journal of Accounting and
Economics 44 (1–2): 166–192.
Ashbaugh-Skaife, H., D. W. Collins, W. R. Kinney, and R. LaFond. 2008. “The Effect of SOX
Internal Control Deficiencies and their Remediation on Accrual Quality.” The Accounting
Review 83 (1): 217–250.
Ashbaugh-Skaife, H., D. W. Collins, W. R. Kinney, and R. Lafond. 2009. “The Effect of SOX
Internal Control Deficiencies on Firm Risk and Cost of Equity.” Journal of Accounting
Research 47 (1): 1–43.
Avraham, D., P., Selvaggi, and J. Vickery. 2012. “A Structural View of U.S. Bank Holding
Companies.” Economic Policy Review–Federal Reserve Bank of New York 18(2): 1–10A.
Baik, B., J. Chae, S. Choi, and D. B. Farber. 2013. “Changes in Operational Efficiency and Firm
Performance: A Frontier Analysis Approach.” Contemporary Accounting Research 30 (3):
996–1026.
Barakat, A., A. Chernobai, and M. Wahrenburg. 2014. “Information Asymmetry around
Operational Risk Announcements.” Journal of Banking & Finance 48: 152–179.
Barth, J. R., C. Lin, Y. Ma, J. Seade, and F. M. Song. 2013. “Do Bank Regulation, Supervision
and Monitoring Enhance or Impede Bank Efficiency?” Journal of Banking & Finance 37
(8): 2879–2892.
Bauer, A. M. 2014. “Tax Avoidance and the Implications of Weak Internal Controls.”
Contemporary Accounting Research, forthcoming.
BCBS (Basel Committee on Banking Supervision). 1998a. “Framework for Internal Control
Systems in Banking Organizations.”
———. 1998b. “Operational Risk Management.”
———. 2001. “Public Disclosures by Banks: Results of the 1999 Disclosure Survey.”
58
———. 2002. “Public Disclosures by Banks: Results of the 2000 Disclosure Survey.”
———. 2003a. “Public Disclosures by Banks: Results of the 2001 Disclosure Survey.”
———. 2003b. “Sound Practices for the Management and Supervisions of Operational Risk.”
———. 2011. “Principles for the Sound Management of Operational Risk.”
Berger, A. N., and D. B. Humphrey. 1997. “Efficiency of Financial Institutions: International
Survey and Directions for Future Research.” European Journal of Operational Research 98
(2): 175–212.
Berger, A. N., and L. J. Mester. 1997. “Inside the Black Box: What Explains Differences in the
Efficiencies of Financial Institutions?” Journal of Banking & Finance 21 (7): 895–947.
Beyer, A., D. A. Cohen, T. Z. Lys, and B. R. Walther. 2010. “The Financial Reporting
Environment: Review of the Recent Literature.” Journal of Accounting and Economics 50
(2–3): 296–343.
Bhat, G., S. G. Ryan, and D. Vyas. 2013. “The Implications of Credit Risk Modeling for Banks’
Loan Loss Provision Timeliness and Loan Origination Procyclicality.” Working paper.
Bhojraj, S., and P. Sengupta. 2003. “Effect of Corporate Governance on Bond Ratings and
Yields: The Role of Institutional Investors and Outside Directors.” The Journal of Business
76 (3): 455–475.
Botosan, C. 1997. “Disclosure Level and the Cost of Equity Capital.” Accounting Review 72 (3):
323–349.
Charnes, A., W. W. Cooper, and E. Rhodes. 1978. “Measuring the Efficiency of Decision
Making Units.” European Journal of Operational Research 2 (6): 429–444.
Cheng, M., D. Dhaliwal, and Y. Zhang. 2013. “Does Investment Efficiency Improve after the
Disclosure of Material Weaknesses in Internal Control over Financial Reporting?” Journal
of Accounting and Economics 56 (1): 1–18.
Cheng, Q., B. Wee Goh, and J. B. Kim. 2014. “Internal Control and Operational Efficiency.”
Working paper, Singapore Management University.
Chernobai, A., P. Jorion, and F. Yu. 2011. “The Determinants of Operational Risk in U.S.
Financial Institutions.” The Journal of Financial and Quantitative Analysis 46 (6): 1683–
1725.
Clarkson, P. M., X. Fang, Y. Li, and G. Richardson. 2013. “The Relevance of Environmental
Disclosures: Are Such Disclosures Incrementally Informative?” Journal of Accounting and
Public Policy 32 (5): 410–431.
59
Claus, J., and J. Thomas. 2001. “Equity Premia As Low As Three Percent? Evidence from
Analysts’ Earnings Forecasts for Domestic and International Stock Markets.” The Journal
of Finance 56 (5): 1629–1666.
Collins, D. W., E. L. Maydew, and I. S. Weiss. 1997. “Changes in the Value-Relevance of
Earnings and Book Values over the Past Forty Years.” Journal of Accounting and
Economics 24 (1): 39–67.
Core, J. E., W. R. Guay, and R. Verdi. 2008. “Is Accruals Quality a Priced Risk Factor?”
Journal of Accounting and Economics 46 (1): 2–22.
COSO (Committee of Sponsoring Organizations of the Treadway Commission). 1992. “Internal
Control–Integrated Framework. The Framework.” New York, NY: COSO.
———. 2013. “2013 Internal Control–Integrated Framework.” New York, NY: COSO.
Cummins, J. D., C. M. Lewis, and R. Wei. 2006. “The Market Value Impact of Operational Loss
Events for US Banks and Insurers.” Journal of Banking & Finance 30 (10): 2605–2634.
DeAngelo, H., L. DeAngelo, and R. M. Stulz. 2010. “Seasoned Equity Offerings, Market
Timing, and the Corporate Lifecycle.” Journal of Financial Economics 95 (3): 275–295.
DeFond, M. L., and J. Jiambalvo. 1994. “Debt Covenant Violation and Manipulation of
Accruals.” Journal of Accounting and Economics 17 (1–2): 145–176.
Demerjian, P. R., B. Lev, M. F. Lewis, and S. E. McVay. 2013. “Managerial Ability and
Earnings Quality.” The Accounting Review 88 (2): 463–498.
Demerjian, P., B. Lev, and S. McVay. 2012. “Quantifying Managerial Ability: A New Measure
and Validity Tests.” Management Science 58 (7): 1229–1248.
Dhaliwal, D., C. Hogan, R. Trezevant, and M. Wilkins. 2011. “Internal Control Disclosures,
Monitoring, and the Cost of Debt.” The Accounting Review 86 (4): 1131–1156.
Doyle, J., W. Ge, and S. McVay. 2007. “Determinants of Weaknesses in Internal Control over
Financial Reporting.” Journal of Accounting and Economics 44 (1–2): 193–223.
Easley, D., and M. O’Hara. 2004. “Information and the Cost of Capital.” The Journal of Finance
59 (4): 1553–1583.
Easton, P. 2004. “PE Ratios, PEG Ratios, and Estimating the Implied Expected Rate of Return
on Equity Capital.” The Accounting Review 79 (1): 73–95.
Easton, P. D., and S. J. Monahan. 2005. “An Evaluation of Accounting-based Measures of
Expected Returns.” The Accounting Review 80 (2): 501–538.
60
Ellul, A., and V. Yerramilli. 2013. “Stronger Risk Controls, Lower Risk: Evidence from U.S.
Bank Holding Companies.” The Journal of Finance 68 (5): 1757–803.
Fama, E. F., and K. R. French. 1992. “The Cross-section of Expected Stock Returns.” Journal of
Finance 47: 427–465.
Feng, M., C. Li, S. McVay, and H. Skaife. 2014. “Does Ineffective Internal Control over
Financial Reporting Affect a Firm’s Operations? Evidence from Firms’ Inventory
Management.” The Accounting Review, forthcoming.
Francis, J., R. Lafond, P. M. Olsson, and K. Schipper. 2004. “Cost of Equity and Earnings
Attributes.” The Accounting Review 79 (4): 967–1010.
Francis, J., R. LaFond, P. Olsson, and K. Schipper. 2005. “The Market Pricing of Accruals
Quality.” Journal of Accounting and Economics 39 (2): 295–327.
Gebhardt, W. R., C. M. C. Lee, and B. Swaminathan. 2001. “Toward an Implied Cost of
Capital.” Journal of Accounting Research 39 (1): 135–176.
Gode, D., and P. Mohanram. 2003. “Inferring the Cost of Capital using the Ohlson–Juettner
Model.” Review of Accounting Studies: 399–431.
Greene, W. H., and D. Segal. 2004. “Profitability and Efficiency in the U.S. Life Insurance
Industry.” Journal of Productivity Analysis 21 (3): 229–247.
Hail, L., and C. Leuz. 2006. “International Differences in the Cost of Equity Capital: Do Legal
Institutions and Securities Regulation Matter?” Journal of Accounting Research 44 (3):
485–531.
Helbok, G., and C. Wagner. 2006. “Determinants of Operational Risk Reporting in the Banking
Industry.” Journal of Risk 9: 49–74.
Hou, Y. 2015. “The Role of Diversification in the Pricing of Accruals Quality.” Review of
Accounting Studies, forthcoming.
Hughes, J. S., J. Liu, and J. Liu. 2007. “Information Asymmetry, Diversification, and Cost of
Capital.” The Accounting Review 82 (3): 705–729.
Hughes, J. P., and L. J. Mester. 2012. “Efficiency in Banking: Theory, Practice, and Evidence.”
The Oxford Handbook of Banking (September): 1–15.
Hull, J. 2012. Risk Management and Financial Institutions (Third Edition). Hoboken, NJ:
Wiley.
Jensen, M. C., and W. H. Meckling. 1976. “Theory of the Firm: Managerial Behavior, Agency
Costs and Ownership Structure.” Journal of Financial Economics 3 (4): 305–360
61
Jorion, P., Z. Liu, and C. Shi. 2005. “Informational Effects of Regulation FD: Evidence from
Rating Agencies.” Journal of Financial Economics 76 (2): 309–330.
Kim, J.B., Song, B.Y. and Zhang, L., 2011. "Internal control weakness and bank loan
contracting: Evidence from SOX Section 404 disclosures." The Accounting Review 86 (4):
1157–1188.
Kleymenova, A. 2014. “Consequences of Mandated Bank Liquidity Disclosures.” Working
paper, London Business School.
Koester, A., T. Shevlin, and D. Wangerin. 2013. “Does Operational Efficiency Spill Over onto
the Tax Return?” Working paper, Georgetown University.
Laeven, L., and G. Majnoni. 2003. “Loan Loss Provisioning and Economic Slowdowns: Too
Much, Too Late?” Journal of Financial Intermediation 12 (2): 178–197.
Lambert, R., C. Leuz, and R. E. Verrecchia. 2007. “Accounting Information, Disclosure, and the
Cost of Capital.” Journal of Accounting Research 45 (2): 385–420.
Lambert, R., C. Leuz, and R. E. Verrecchia, 2012. “Information Asymmetry, Information
Precision, and the Cost of Capital.” Review of Finance, 16(1): 1-29.
Lawrence, A., M. Minutti-Meza, and D. Vyas. 2014. “Is Operational Control Risk Informative
of Financial Reporting Risk?” Working paper, University of California, Berkeley.
Maydew, E., I993. “An Empirical Evaluation of Earnings and Book Values in Security
Valuation.” Working paper, University of Iowa.
Mohanram, P., and D. Gode. 2013. “Removing Predictable Analyst Forecast Errors to Improve
Implied Cost of Equity Estimates.” Review of Accounting Studies 18 (2): 443–478
Mohanram, P., and S. Rajgopal. 2009. “Is PIN Priced Risk?” Journal of Accounting and
Economics 47 (3): 226–243.
Morgan, D., and K. Stiroh. 2001. “Market Discipline of Banks: The Asset Test.” Journal of
Financial Services Research 20 (2-3): 195–208.
Nissim, D. 2013. “Relative Valuation of U.S. Insurance Companies.” Review of Accounting
Studies 18 (2): 324–359.
OCC (Office of the Comptroller of the Currency). 2000. “Internal Control—A Guide for
Directors.”
———. 2013. “Internal Control—A Guide for Directors.”
62
Ogneva, M., K. Subramanyam, and K. Raghunandan. 2007. “Internal Control Weakness and
Cost of Equity: Evidence from SOX Section 404 Disclosures.” The Accounting Review 82
(5): 1255–1297.
Ohlson, J. A. 1995. “Earnings, Book Values, and Dividends in Equity Valuation.”
Contemporary Accounting Research 11 (2): 661–687.
SEC (Securities and Exchange Commission). 2007. “Commission Guidance Regarding
Management’s Reports on Internal Control Over Financial Reporting Under Section 13(a)
or 15(d) of the Securities Exchange Act of 1934. Release Nos. 33-8810.” Washington, DC:
SEC June 20.
Shevlin, T. 2013. “Some Personal Observations on the Debate on the Link between Financial
Reporting Quality and the Cost of Equity Capital.” Australian Journal of Management 38
(3): 447–473.
Stein, J. C. 1997. “Internal Capital Markets and the Competition for Corporate Resources.”
Journal of Finance 52 (1): 111–133.
Tysiac, K. 2012. “COSO Chair Says Updated Framwork is a ‘Refresh’.” Journal of
Accountancy, January 23, 2012.
Vyas, D. 2011. “The Timeliness of Accounting Write-Downs by U.S. Financial Institutions
during the Financial Crisis of 2007–2008.” Journal of Accounting Research 49 (3): 823–
860.
63
APPENDIX A: OPERATIONAL RISK MANAGEMENT QUALITY (ORMQ) INDEX
The following table outlines the construction of the ORMQ index. The first column lists the
eleven items that I search for in banks’ Form 10-K filings. The second column lists the keyword
that I look for in each sentence. I scan the banks’ 10-K filings from 2003 to 2013 using the
Python programming language to measure their operational risk management quality along the
above dimensions. Specifically, I obtain the number of times each of the eleven items is
repeated each year for each bank. I then scale each item each year by the maximum number of
times that item was repeated. Therefore, each item gets a score between 0 and 1. As a result, the
ORMQ index, which is the sum of the eleven items, ranges from zero to eleven for each bank
each year. For example, if the maximum number of times “enterprise risk management” appears
in 2003 is 20 times and is by the Bank of America, then Bank of America receives a score of 1
for this item in year 2003. Now, if in year 2003 “enterprise risk management” appears 10 times
in JPMorgan Chase’s 10-K filing, and zero times in Wells Fargo & Company’s 10-K filing, then
in year 2003 JPMorgan Chase gets a score of 0.5 while Wells Fargo & Company receives a
score of 0.
No. Data Item Keywords
1 Enterprise Risk Management
(score: between 0 and 1)
“ERM” OR “enterprise risk management” OR “Enterprise Risk
Management” OR “Enterprise risk management”
2 Chief Risk Officer
(score: between 0 and 1) “Chief Risk Officer” OR “Chief risk officer” OR “CRO”
3 Operational Risk Framework
(score: between 0 and 1)
“Operational risk framework” OR “Operational Risk Framework”
OR “operational risk framework” OR “OR Framework” OR “OR
framework” OR (“OR” OR “Operational Risk” OR “Operational risk”
OR “operational risk”) AND (“framework” OR “Framework”)
4 Operational Risk Committee
(score: between 0 and 1)
(“Operational Risk” AND “Committee”) OR (“Operational Risk”
AND “committee”) OR (“Operation risk” AND “committee”) OR
(“operational risk” AND “committee”) OR (“Op Risk” AND
“Committee”) OR (“Op Risk” AND “committee”) OR (“Op risk”
AND “Committee”) OR (“Op risk” AND “committee”) OR (“OR”
AND “Committee”) OR (“OR” AND “committee”) OR “ORC” OR
“ORC*”
64
APPENDIX A (continued):
No. Data Item Keywords
Operational Risk Identification and Assessment
5
Backward-looking
identification: internal loss
data
(score: between 0 and 1)
“External loss data” OR “external loss data” OR “External loss event
data” OR “external loss event data” OR “External operational risk event
data” OR “external operational risk event data” OR “External op risk
event data” OR “external op risk event data” OR “External op risk data”
OR “external op risk data” OR “External operational risk data” OR
“external operational risk data” OR “External OR event data” OR
“external OR event data” OR “External OR data” OR “external OR
data” OR (“External” OR “external” AND (loss data) OR (“External”
OR “external” AND (loss event data) OR (“External” OR “external”)
AND (“operational risk data”) OR (“External” OR “external”) AND
(“operational risk event data”) OR (“External” OR “external”) AND (op
risk data) OR (“External” OR “external”) AND (op risk event data) OR
(“External” OR “external”) AND (OR data) OR (“External” OR
“external”) AND (OR event data)
6
Backward-looking
identification: external loss
data
(score: between 0 and 1)
“Internal loss data” OR “internal loss data” OR “Internal loss event
data” OR “internal loss event data” OR “Internal operational risk event
data” OR “internal operational risk event data” OR “Internal op risk
event data” OR “internal op risk event data” OR “Internal op risk data”
OR “internal op risk data” OR “Internal operational risk data” OR
“internal operational risk data” OR “Internal OR event data” OR
“internal OR event data” OR “Internal OR data” OR “internal OR data”
OR (“Internal” OR “internal” AND (loss data) OR (“Internal” OR
“internal” AND (loss event data) OR (“Internal” OR “internal”) AND
(“operational risk data”) OR (“Internal” OR “internal”) AND
(“operational risk event data”) OR (“Internal” OR “internal”) AND (op
risk data) OR (“Internal” OR “internal”) AND (op risk event data) OR
(“Internal” OR “internal”) AND (OR data) OR (“Internal” OR
“internal”) AND (OR event data)
7
Backward-looking
identification: key
performance indicators
(score: between 0 and 1)
(“Key performance indicator*” OR “key performance indicator*” OR
“KPI*”) AND (“Operational risk*” OR “operational risk*” OR “OR”)
65
APPENDIX A (continued):
No. Data Item Keywords
8
Present-looking identification: key risk
indicators
(score: between 0 and 1)
(“Key risk indicator*” OR “key risk indicator*” OR “KRI*”)
AND (“Operational risk*” OR “operational risk*” OR “OR”)
9
Forward-looking identification: scenario
analysis
(score: between 0 and 1)
(“Scenario analy*” AND “operational risk”) OR (“scenario
anal*” AND “operational risk”) OR (“Scenario analy*” AND
“OR”) OR (“scenario analy*” AND “OR”) OR (“Scenario
analy*” AND “op risk”) OR (“scenario anal*” AND “op
risk”)
10 Risk control self-assessments
(score: between 0 and 1)
“Risk control self-assessment*” OR “Risk control self-
assessment*” or “RCSA*”
11 Scorecards
(score: between 0 and 1)
(“operational risk” OR “Operational risk” OR “OR”) AND
(“scorecard*” OR “Scorecard*”)
66
APPENDIX B: PRINCIPLES FOR SOUND PRACTICES FOR THE MANAGEMENT
AND SUPERVISION OF OPERATIONAL RISK (BCBS 2003, 2011)
Developing an Appropriate Risk Management Environment
Principle 1: The board of directors should be aware of the major aspects of the bank’s
operational risks as a distinct risk category that should be managed, and it should approve and
periodically review the bank’s operational risk management framework. The framework should
provide a firm-wide definition of operational risk and lay down the principles of how
operational risk is to be identified, assessed, monitored, and controlled/mitigated.
Principle 2: The board of directors should ensure that the bank’s operational risk management
framework is subject to effective and comprehensive internal audit by operationally
independent, appropriately trained, and competent staff. The internal audit function should not
be directly responsible for operational risk management.
Principle 3: Senior management should have responsibility for implementing the operational
risk management framework approved by the board of directors. The framework should be
consistently implemented throughout the whole banking organization, and all levels of staff
should understand their responsibilities with respect to operational risk management. Senior
management should also have responsibility for developing policies, processes, and procedures
for managing operational risk in all of the bank’s material products, activities, processes, and
systems.
Risk Management: Identification, Assessment, Monitoring, and Mitigation/Control
Principle 4: Banks should identify and assess the operational risk inherent in all material
products, activities, processes, and systems. Banks should also ensure that before new products,
activities, processes, and systems are introduced or undertaken, the operational risk inherent in
them is subject to adequate assessment procedures.
67
APPENDIX B (continued):
Principle 5: Banks should implement a process to regularly monitor operational risk profiles
and material exposures to losses. There should be regular reporting of pertinent information to
senior management and the board of directors that supports the proactive management of
operational risk.
Principle 6: Banks should have policies, processes, and procedures to control and/or mitigate
material operational risks. Banks should periodically review their risk limitation and control
strategies, and should adjust their operational risk profile accordingly using appropriate
strategies, in light of their overall risk appetite and profile.
Principle 7: Banks should have in place contingency and business continuity plans to ensure
their ability to operate on an ongoing basis and limit losses in the event of severe business
disruption.
Role of Supervisors
Principle 8: Banking supervisors should require that all banks, regardless of size, have an
effective framework in place to identify, assess, monitor, and control/mitigate material
operational risks as part of an overall approach to risk management.
Principle 9: Supervisors should conduct, directly or indirectly, a regular independent evaluation
of a bank’s policies, procedures, and practices related to operational risks. Supervisors should
ensure that there are appropriate mechanisms in place that allow them to remain apprised of
developments at banks.
Role of Disclosure
Principle 10: A bank’s public disclosures should allow stakeholders to assess its approach to
operational risk management.
68
APPENDIX C: VARIABLE DEFINITIONS
Dependent Variables
Operational Efficiency Measures
EFFi,t = A measure of firm efficiency for fiscal year 𝑡 based on the Data Envelopment
Analysis (DEA) methodology. Inputs are: (1) total deposits, (2) noninterest expense, (3)
physical capital (total fixed assets), and (5) loan loss provision. Outputs are: (1) total
loans and leases, (2) other earnings assets (e.g., bonds and investment securities), and (3)
other noninterest income.
EFF_BLi,t = A measure of firm efficiency for fiscal year 𝑡 based on the DEA
methodology. Inputs and outputs are balance sheet items (stock variables). Inputs are: (1)
total deposits, (2) other liabilities, (3) fixed assets, and (4) loan loss reserve. Outputs are:
(1) total loans and leases, and (2) other earnings assets.
EFF_ISi,t = A measure of firm efficiency for fiscal year 𝑡 based on the DEA
methodology. Inputs and outputs are income statement items (flow variables). Inputs are:
(1) noninterest expense, (2) interest expense, and (3) loan loss provision. Outputs are: (1)
interest income, and (2) noninterest income.
Cost of Debt Capital Measure
SPREADi,t+1 = Difference between the bond’s yield-to-maturity at issuance in fiscal year
𝑡 + 1 and a government bond with a comparable maturity.
Cost of Equity Capital Measure
RP_AVGi,t+1 = Risk premium in fiscal year 𝑡 + 1 obtained by taking the average of risk
premiums based on the Ohlson and Juettner-Nauroth (OJ) model as implemented by
Gode and Mohanram (2003), Easton’s (2004) price-earnings-growth (PEG) model, and
69
APPENDIX C (continued):
the residual income model as implemented by Clause and Thomas (2001) and Gebhardt,
Lee, and Swaminathan (2001). Refer to Appendix D for more details.
Firm Equity Measure
Pi,t = Stock price at the end of fiscal year 𝑡.
Earnings Measure
ROAi,t+1 = Income before extraordinary items divided by beginning total assets.
Independent Variables
Operational Control Quality (OCQ) Measures
ORAi,t+1 = Indicator variable that equals 1 if the bank does not experience an operational
risk event in fiscal year 𝑡 + 1, and zero otherwise.
ORMQi,t or t+1 = Score from the operational risk management quality index in fiscal year
𝑡 or 𝑡 + 1 obtained via content analysis of Form 10-K filings. Refer to Appendix A for
more details.
I_EFF = Indicator variable that equals 1 for the subsample of banks with operational
efficiency (EFF) above the sample median, and zero otherwise.
Control Variables
BMi,t+1 = Ratio of book value of equity to market value of equity in fiscal year 𝑡 + 1.
SIZEi,t or t+1 = Natural logarithm of total assets in fiscal year 𝑡 or natural logarithm of
market value of equity in fiscal year 𝑡 + 1.
BETAi,t+1 = Systematic volatility in fiscal year 𝑡 + 1 computed using the market model.
70
APPENDIX C (continued):
IDIO_RISKi,t+1 = Idiosyncratic risk in fiscal year 𝑡 + 1 using the standard deviation of
the residuals from the market model.
DEPOSITS / ASSETSi,t+1 = Ratio of deposits to assets in fiscal year 𝑡 + 1.
TIER_RATIOi,t+1 = Ratio of tier 1 equity to total assets in fiscal year 𝑡 + 1.
ASSET_RISKi,t+1 = Ratio of risk-weighted assets (RWAs) to total assets in fiscal year
𝑡 + 1.
ISSUE_AMOUNTi,t+1 = Total dollar face value of the bond issued in fiscal year 𝑡 + 1.
BOND_LIFE i,t+1 = Bond maturity (in years) of the bond issued in fiscal year 𝑡 + 1.
CALLABL i,t+1 = Indicator variable that equals 1 if the bond issued in fiscal year 𝑡 + 1 is
callable, and zero otherwise.
ICWAi,t or t+1 = Indicator variable that equals 1 if the bank does not report material SOX-
related internal control weaknesses in fiscal year 𝑡 or 𝑡 + 1, and zero otherwise.
ROAi,t+1 = Return on assets (net income divided by average total assets) in fiscal year
𝑡 + 1.
AGEi,t = Natural logarithm of the age of the bank reported in the SNL database as of
fiscal year 𝑡.
BIG4i,t = Indicator variable that equals 1 if the bank is audited by a Big4 audit firm in
fiscal year 𝑡, and zero otherwise.
NONPERFORMING LOANS/LOANSi,t = Ratio of nonperforming loans to total loans
in fiscal year 𝑡.
BVi,t = Book value per share at the end of fiscal year t.
EARNINGS i,t = Earnings per share at the end of fiscal year 𝑡.
MERGER i,t = An indicator variable that takes a value of 1 if a bank reports sales from
merger and acquisition (Compustat data item AQC) for fiscal year t, and zero otherwise.
71
APPENDIX C (continued):
FOREIGNi,t = An indicator variable that takes a value of 1 if a bank reports a non-zero
value for foreign currency adjustment (Compustat data item FCA) for fiscal year t, and
zero otherwise.
LOSSi,t = An indicator variable that takes a value of 1 if a bank reports a loss (Compustat
data item IB) in fiscal year t, and zero otherwise.
RESTRUCTUREi,t = An indicator variable that takes a value of 1 if a bank was involved
in a restructuring (i.e., if any Compustat data items RCP, RCA, RCEPS, and RCD is
non-zero) , and zero otherwise.
TRADING ASSETS / TOTAL ASSETS i,t = Total trading assets in fiscal year 𝑡 divided
by total assets in fiscal year 𝑡.
72
APPENDIX D: IMPLIED COST OF EQUITY CAPITAL MODELS
Implied Cost of Equity Capital Based on the Ohlson-Juettner (OJ) Model
The OJ model as implemented by Gode and Mohanram (2003) is based on the following
equation:
𝑃𝑖,𝑡 = 𝑒𝑝𝑠𝑖,𝑡+1
𝑟𝑂𝐽+
𝑒𝑝𝑠𝑖,𝑡+2 − 𝑒𝑝𝑠𝑖,𝑡+1 − 𝑟𝑂𝐽 ∗ (𝑒𝑝𝑠𝑖,𝑡+1 − 𝑑𝑝𝑠𝑖,𝑡+1))
𝑟𝑂𝐽 ∗ (𝑟𝑂𝐽 − 𝑔)
where 𝑃𝑖,𝑡 is the current price per share at the time of forecasts, 𝑒𝑝𝑠𝑖,𝑡+1 is the one-period-ahead
median forecast of accounting earnings per share, 𝑒𝑝𝑠𝑖,𝑡+2 is the two-period-ahead median
forecast of accounting per share, 𝑔 is the long-run growth in abnormal earnings changes, and 𝑟𝑂𝐽
is the implied cost of equity capital based on the OJ model. Lastly, 𝑑𝑝𝑠𝑖,𝑡+1 is the expected one-
year-ahead dividend per share, defined as 𝑒𝑝𝑠𝑖,𝑡+1 times payout ratio. The assumption is that
dividends (𝑑𝑝𝑠) are a constant fraction of forecasted earnings. Payout is estimated as the ratio of
the most recent dividends to net income. The following expression solves for 𝑟𝑂𝐽:
𝑟𝑂𝐽 = 𝐴 + √𝐴2 + 𝑓𝑒𝑝𝑠1
𝑃0∗ (𝑆𝑇𝐺 − (𝛾 − 1))
where
𝐴 = 1
2(((𝛾 − 1)) +
𝑑𝑝𝑠1
𝑃0 ) 𝑎𝑛𝑑 𝑆𝑇𝐺 =
𝐸𝑃𝑆2
𝐸𝑃𝑆2 − 1
Consistent with Gode and Mohanram (2003), (𝛾 − 1) is 𝑟𝑓 − 1, where 𝑟𝑓is the annual yield on a
ten-year Treasury. Lastly, to reduce the noise in 𝑆𝑇𝐺 (i.e., short-term growth), I use the
geometric mean of two-year growth and long-term growth (𝐿𝑇𝐺) from I/B/E/S as my measure
73
APPENDIX D (continued):
of short-term growth. If two-year growth is lower than 𝐿𝑇𝐺, I set 𝑆𝑇𝐺 to 𝐿𝑇𝐺 (Gode and
Mohanram 2003).
Implied Cost of Equity Capital Based on the Price-Earnings-Growth (PEG) Model
Easton’s (2004) PEG model is a simplified version of the OJ model. In particular, the
PEG model is derived from the OJ model by setting 𝛾 = 1 and ignoring dividends. The PEG
model is expressed based on the following equation:
𝑟𝑃𝐸𝐺 = √𝑓𝑒𝑝𝑠1
𝑃0∗ 𝑆𝑇𝐺
where all variables are defined as above.
Implied Cost of Equity Capital Based on the Residual-Income Valuation (RIV) Model
Both Gebhardt, Lee, and Swaminathan (2001) and Claus and Thomas (2001) use the
Residual-Income Valuation (RIV) model to estimate implied cost of equity with different
assumptions about the terminal value. They use earnings per share (𝑓𝑒𝑝𝑠) estimates for the
future two years and expected dividend payout to derive book value and return on equity
forecast. Beyond the forecast horizon, Gebhardt, Lee, and Swaminathan assume that return on
equity declines to the industry median return on equity by year twelve and remains constant
thereafter. Claus and Thomas, however, assume that earnings grow at the analyst’s consensus
long-term growth rate until year five and at the inflation rate (𝑟𝑓 − 3) subsequently. In both
cases, the cost of equity is computed by equating current stock price to the sum of the current
74
APPENDIX D (continued):
book value per share and the present value of future residual earnings. In particular, the Clause
and Thomas (2001) is based on the following model:
𝑃𝑖,𝑡 = 𝑏𝑝𝑠𝑖,𝑡 + ∑(𝑅𝑂𝐸𝑖,𝑡+𝜏 − 𝑟𝐶𝑇) × 𝑏𝑝𝑠𝑖,𝑡+𝜏−1
(1 + 𝑟𝐶𝑇)𝜏
4
𝜏=1
+ (𝑅𝑂𝐸𝑖,𝑡+5 − 𝑟𝐶𝑇) × 𝑏𝑝𝑠𝑖,𝑡+4 × (1 + 𝛾)
(𝑟𝐶𝑇 − 𝛾) × (1 + 𝑟𝐶𝑇)11
It assumes that residual income grows at rate 𝛾 after 𝑇 = 5. 𝑅𝑂𝐸𝑖,𝑡+𝜏 = 𝑒𝑝𝑠𝑖,𝑡+𝜏 𝑏𝑝𝑠𝑖,𝑡+𝜏−1⁄ ,
where for 𝜏 > 2, 𝑒𝑝𝑠𝑖,𝑡+𝜏 = 𝑒𝑝𝑠𝑖,𝑡+2 × (1 + 𝑙𝑡𝑔)𝜏−2. 𝑙𝑡𝑔 is I/B/E/S consensus long term
growth rate. 𝑏𝑝𝑠𝑖,𝑡+𝜏 = 𝑏𝑝𝑠𝑖,𝑡+𝜏−1 + 𝑒𝑝𝑠𝑖,𝑡+𝜏 × (1 − 𝐾), where 𝐾 is the payout ratio. 𝛾 is the
10-year government bond rate less 3% (i.e., adjusted for inflation). Lastly 𝑟𝐶𝑇, is implied cost of
equity capital as implemented by Clause and Thomas.
The Gebhardt, Lee, and Swaminathan model is based on the following equation:
𝑃𝑖,𝑡 = 𝑏𝑝𝑠𝑖,𝑡 + ∑(𝑅𝑂𝐸𝑖,𝑡+𝜏 − 𝑟𝐺𝐿𝑆) × 𝑏𝑝𝑠𝑖,𝑡+𝜏−1
(1 + 𝑟𝐺𝐿𝑆)𝜏
11
𝜏=1
+ (𝑅𝑂𝐸𝑖,𝑡+12 − 𝑟𝐺𝐿𝑆) × 𝑏𝑝𝑠𝑖,𝑡+11
𝑟𝐺𝐿𝑆 × (1 + 𝑟𝐺𝐿𝑆)11
where 𝑃𝑖,𝑡 is current price per share, 𝑏𝑝𝑠𝑖,𝑡 is current book value of equity per share, 𝑏𝑝𝑠𝑖,𝑡+𝜏−1
is future book value of equity per share calculated using the clean surplus assumption. In
particular, 𝑏𝑝𝑠𝑖,𝑡+𝜏 = 𝑏𝑝𝑠𝑖,𝑡+𝜏−1 + 𝑒𝑝𝑠𝑖,𝑡+𝜏 × (1 − 𝐾). The assumption is that residual income
converges to industry-specific median return from 𝑇 = 3 to 𝑇 = 12. After 𝑇 = 12, residual
income in assumed to remain constant. For 𝜏 = 1,2, 𝑅𝑂𝐸𝑖,𝑡+𝜏 = 𝑒𝑝𝑠𝑖,𝑡+𝜏 𝑏𝑝𝑠𝑖,𝑡+𝜏−1⁄ . For 𝜏 >
2, 𝑅𝑂𝐸𝑖,𝑡+𝜏 = 𝑅𝑂𝐸𝑖,𝑡+𝜏 − 𝐷𝑒𝑐𝑙𝑖𝑛𝑒, where 𝐷𝑒𝑐𝑙𝑖𝑛𝑒 = ( 𝑅𝑂𝐸𝑖,𝑡+𝜏−2 − 𝐻𝐼𝑅𝑂𝐸𝑡). 𝐻𝐼𝑅𝑂𝐸𝑡 is the
industry median 𝑅𝑂𝐸 from 𝑡 − 4 to 𝑡. Lastly, 𝑟𝐺𝐿𝑆 is implied cost of equity capital.
75
APPENDIX E: ISS QuickScore Corporate Governance Metric
The following list outlines the items used by Institutional Shareholder Services (ISS) to create
the QuickScore corporate governance metric. Companies are assessed across four pillars: Board
Structure, Compensation/Remuneration, Shareholder Rights, and Audit and Risk Oversight. In
particular, each pillar consists of the following items:
Board Structure
1. Board Compensation
2. Composition of Committees
3. Board Practices
4. Board Policies
5. Related Party Transactions
Compensation and Remuneration
6. Pay for Performance
7. Non-Performance-Based Pay
8. Use of Equity
9. Equity Risk Mitigation
10. Non-Executive Pay
11. Communications and Disclosure
12. Termination
13. Controversies
Shareholder Rights
14. One Share One Vote
15. Takeover Defenses
16. Voting Issues
17. Voting Formalities
18. Other Shareholder Rights Issues
Audit Practices
19. External Auditor
20. Audit and Accounting Controversies
21. Other Audit Issues
76
Figure 1: Frequency of operational risk event types
The Basel Committee classifies operational risk events into seven categories: (1) internal fraud; (2) clients, products, and business practices; (3) external fraud;
(4) execution, delivery, and process management; (5) damage to physical assets; (6) employment practices and workplace safety; and (7) business disruption and
system failures (refer to Section 2.2 for more details). The SAS database categorizes operational risk events according to this classification scheme. Consistent
with the definition of “external fraud,” I classify data breach incidences obtained from the Identity Theft Resource Center (ITRC) database as external fraud.
This figure shows the frequency of operational risk event types in the main sample.
0
5
10
15
20
25
30
35
40
45
Internal Fraud Clients, Products
& Business
Practices
External Fraud Execution,
Delivery &
Process
Management
Damage to
Physical Assets
Employment
Practices and
Workplace Safety
Business
Disruption and
System Failures
77
Table 1: Sample Composition
Firm-Years Firms
No. of
Operational
Risk Events
Operational risk event sample SAS OpRisk Global data
81 93
ITRC Database
17 17
852 98 110
Non-operational risk event sample 1,673 189 0
Final sample 2,525 287 110
The operational risk event data and the breach data are obtained with permission from the SAS Institute and the
Identity Theft Resource Center (ITRC), respectively. The SAS OpRisk Global data identifies and categorizes
operational risk events for financial institutions in accordance with the Basel Committee on Banking Supervision
(BCBS) operational risk event types (refer to Section 2.2 for more details). The SAS database provides a detailed
description of each event such as the company name, a detailed account of the event, the dates of event occurrence
and settlement. The ITRC database provides a detailed description of the event, the type of breach, the dates of the
event occurrence and disclosure, and the number of records exposed. The sample period begins in January 2003 and
continues until the end of fiscal year 2013.
78
Table 2: Descriptive Statistics
Panel A: Bank Holding Companies Characteristics (Full Sample)
Variable (1) (2) (3) (4)
N Mean Median SD
LOG (ASSETS) 2,525 9.358 9.27 0.50
LOANS / ASSETS 2,525 0.644 0.66 0.13
FIXED ASSETS / ASSETS 2,525 0.141 0.02 0.49
REVENUE / ASSETS 2,525 0.569 0.04 2.12 AVG OTHER INTEREST EARNING ASSETS / ASSETS 2,525 1.013 0.02 6.22
NON-INTEREST INCOME / ASSETS 2,525 0.279 0.01 1.15
INTEREST INCOME / ASSETS 2,525 0.485 0.05 1.74
NON-INTEREST EXPENSE / ASSETS 2,525 0.409 0.03 1.49
INTEREST EXPENSE / ASSETS 2,525 0.171 0.01 0.72
NET INCOME / ASSETS 2,525 0.100 0.01 0.52
TIER RATIO 2,525 0.091 0.09 0.02
ASSET RISK 2,525 0.727 0.74 0.12
DEPOSITS/ ASSETS 2,525 0.766 0.78 0.09
ROA 2,525 0.01 0.01 0.01
NON-PERFORMING LOANS / LOANS 2,525 0.02 0.01 0.04
This panel presents summary statistics for bank holding company (BHC) characteristics for the 2003 to 2013
period. Variable definitions are included in Appendix C.
79
Table 2 (continued):
Panel B: Variables Used in the Operational Efficiency Analysis (H1)
Variable (1) (2) (3) (4)
N Mean Median SD
EFF 2,086 0.70 0.68 0.14
EFF_BL 2,086 0.67 0.65 0.14
EFF_IS 2,086 0.73 0.72 0.15
ORA 2,086 0.96 1.00 0.21
ORMQ 2,086 1.90 0.99 2.19
CGQ 2,086 4.87 5.00 1.91
BIG4 2,086 0.48 0.00 0.50
SIZE 2,086 9.38 9.30 0.50
NON-PERFORMING LOANS /LOANS 2,086 0.02 0.01 0.04
TRADING_ASSETS /ASSETS 2,086 0.00 0.00 0.02
MERGER 2,086 0.30 0.00 0.46
FOREIGN 2,086 0.11 0.00 0.31
LOSS 2,086 0.16 0.00 0.37
RESTRUCTURE 2,086 0.10 0.00 0.30
This panel presents summary statistics for the main variables used for the operational efficiency analysis using the
time period 2003 to 2013. Variable definitions are included in Appendix C.
80
Table 2 (continued):
Panel C: Variables Used in the Cost of Debt Analysis (H2a)
Variable (1) (2) (3) (4)
N Mean Median SD
BOND SPREAD (%) 690 2.02 1.39 1.63
LOG (ISSUE AMOUNT) 690 5.29 5.70 0.97
BOND LIFE 690 10.72 8.99 7.24
CALLABLE 690 0.34 0.00 0.47
RATING 690 6.55 7.00 2.87
LOG (ASSETS) 690 8.20 8.25 0.93
ASSET RISK (%) 690 71.17 72.68 13.97
TIER RATIO (%) 690 7.67 7.32 2.02
DEPOSITS /ASSETS (%) 690 62.22 64.48 14.08
ROA (%) 690 0.22 0.24 0.24
ORA 690 0.76 1.00 0.43
ORMQ 690 2.55 1.90 2.35
CGQ 690 5.09 6.00 2.76
This panel presents summary statistics for the main variables used for the cost of debt capital analysis using the
time period 2003 to 2013. Variable definitions are included in Appendix C.
81
Table 2 (continued):
Panel D: Variables Used in the Cost of Equity Analysis (H2b)
Variable (1) (2) (3) (4)
N Mean Median SD
RP_AVG (%) 1,834 8.12 6.72 2.99
SIZE 1,834 96.40 94.72 6.60
BM 1,834 0.83 0.72 0.53
BETA 1,834 0.68 0.33 1.01
IDIO_RISK 1,834 0.09 0.09 0.05
TIER RATIO (%) 1,834 9.21 8.99 1.93
ASSET RISK (%) 1,834 73.27 74.13 11.98
ORA 1,834 0.96 1.00 0.20
ORMQ 1,834 1.92 0.99 2.16
CGQ 1,834 4.80 5.00 2.01
This panel presents summary statistics for the main variables used for the cost of equity capital analysis using the
time period 2003 to 2013. Variable definitions are included in Appendix C.
82
Table 3: Correlations between Efficiency and Operational Control Quality Measures
EFF EFF_BL EFF_IS ORA ORMQ ICWA CGQ
EFF
0.93 0.77 0.09 0.44 0.07 0.17
EFF_BL 0.95
0.76 0.11 0.46 0.06 0.16
EFF_IS 0.70 0.78
0.09 0.35 0.05 0.17
ORA 0.09 0.10 0.09
0.19 0.07 0.06
ORMQ 0.48 0.49 0.37 0.20
0.01 0.08
ICWA 0.06 0.05 0.05 0.07 0.01
0.03
CGQ 0.15 0.14 0.15 0.07 0.09 0.02
This table reports Pearson (Spearman) correlations below (above) the diagonal between the four operational
efficiency measures and the two proxies for operational control quality as well as of ICFR quality (ICWA) and
corporate governance quality (CGQ). See Appendix C for variable definitions. The correlation coefficients in bold
are significant at the 10% level or better.
83
Table 4: Operational Control Quality and Operational Efficiency (H1)
Panel A: Operational Risk Avoidance (ORA) Measure (ex-post OCQ proxy)
Variable (1) (2) (3)
EFFt EFF_BLt EFF_ISt
ORAt+1 H1(+) 0.089*** 0.085*** 0.080***
(7.14) (7.10) (7.52)
SIZEt 0.051* 0.048 0.047*
(1.65) (1.59) (1.96)
LOG(AGE)t –0.061*** –0.057*** –0.059***
(–3.13) (–2.61) (–2.95)
ICWAt 0.027*** 0.028*** 0.024
(2.93) (2.99) (1.55)
BIG4t 0.029** 0.026** 0.018
(2.47) (2.13) (1.09)
CGQt 0.003*** 0.003*** 0.003***
(3.99) (3.98) (3.24)
NONPERF_LOANS /LOANSt –0.090 –0.114 –0.089
(–0.85) (–1.28) (–0.96)
MERGERt 0.008* 0.006 0.005
(1.92) (1.41) (0.79)
FOREIGNt –0.007 –0.007 –0.012
(–0.40) (–0.43) (–0.78)
LOSSt –0.033*** –0.030*** –0.035***
(–3.62) (–3.63) (–3.21)
RESTRUCTUREt –0.011 –0.008 –0.009
(–0.88) (–0.63) (–0.66)
TRADING_ASSETS /ASSETSt 0.015 0.024 –0.177
(0.06) (0.09) (–0.68)
CONSTANT 0.195 0.208 0.243
(0.61) (0.66) (1.00)
Time FE Yes Yes Yes
Firm FE Yes Yes Yes
N 2,086 2,086 2,086
Adjusted R2
0.802 0.812 0.817
This table presents the results of the analyses examining the relation between operational efficiency and operational
control quality proxied by 𝑂𝑅𝐴 metric. The 𝑂𝑅𝐴 metric is an indicator variable that equals 1 if the BHC does not
experience an operational risk event in year 𝑡 + 1, and zero otherwise. See Appendix C for variable definitions. All
continuous variables are winsorised at the utmost 1% tails of their respective distributions to adjust for the effects of
extreme observations. All 𝑡 −statistics are calculated using two-way clustered standard errors (by firm and year).
Numbers inside parentheses are 𝑡 −statistics. ***, **, and * indicate significance levels at 1%, 5%, and 10%,
respectively.
84
Table 4 (continued):
Panel B: Operational Risk Management Quality (ORMQ) Measure (ex-ante OCQ proxy)
Variable (1) (2) (3)
EFFt EFF_BLt EFF_ISt
ORMQt H1(+) 0.017*** 0.016*** 0.013***
(9.09) (8.48) (4.42)
SIZEt 0.055* 0.052* 0.050**
(1.86) (1.82) (2.29)
LOG(AGE)t –0.054*** –0.051** –0.054***
(–2.73) (–2.26) (–2.63)
ICWAt 0.030** 0.031*** 0.027
(2.54) (2.64) (1.62)
BIG4t 0.030*** 0.026** 0.018
(2.69) (2.28) (1.13)
CGQt 0.003*** 0.003*** 0.003***
(4.75) (4.61) (3.52)
NONPERF_LOANS /LOANSt –0.107 –0.131* –0.102
(–1.11) (–1.68) (–1.20)
MERGERt 0.005 0.003 0.002
(1.06) (0.72) (0.37)
FOREIGNt –0.007 –0.007 –0.012
(–0.33) (–0.35) (–0.65)
LOSSt –0.025*** –0.023*** –0.029***
(–2.71) (–2.68) (–2.58)
RESTRUCTUREt –0.015 –0.012 –0.012
(–1.24) (–0.97) (–0.90)
TRADING_ASSETS /ASSETSt –0.012 –0.002 –0.198
(–0.05) (–0.01) (–0.76)
CONSTANT 0.208 0.220 0.261
(0.67) (0.73) (1.14)
Time FE Yes Yes Yes
Firm FE Yes Yes Yes
N 2,086 2,086 2,086
Adjusted R2 0.813 0.822 0.820
This table presents the results of the analyses examining the relation between operational efficiency and operational
control quality proxied by ORMQ metric. ORMQ is an index-based measure of operational risk management
quality. It is an ex-ante proxy created through a textual analysis of Form 10-K filings. Operational efficiency
measures at time t are regressed on ORMQ at time t. See Appendix C for variable definitions. All 𝑡 −statistics are
calculated using two-way clustered standard errors (by firm and year). All continuous variables are winsorised at
the utmost 1% tails of their respective distributions to adjust for the effects of extreme observations. Numbers inside
parentheses are 𝑡 −statistics. ***, **, and * indicate significance levels at 1%, 5%, and 10%, respectively.
85
Table 5: Operational Control Quality and Cost of Debt Capital (H2a)
Variable (1) (2)
SPREADt+1 SPREADt+1
ORAt+1 H2a (–) –0.903***
(–4.71)
ORMQt+1 H2a (–) –0.090*
(–1.92)
ISSUE_AMOUNTt+1 –0.252*** –0.241***
(–3.59) (–3.13)
BOND_LIFEt+1 0.005 0.005
(0.60) (0.57)
CALLABLEt+1 0.329* 0.288
(1.70) (1.57)
RATINGt+1 0.106** 0.115**
(2.18) (2.40)
SIZEt+1 –1.091* –0.931
(–1.70) (–1.09)
ASSET_RISKt+1 0.015 0.014
(1.26) (1.10)
TIER_RATIOt+1 –0.060 –0.048
(–0.56) (–0.48)
DEPOSITS/ASSETSt+1 0.005 0.004
(0.31) (0.26)
ROAt+1 –0.869*** –0.936**
(-2.65) (-2.53)
ICWAt+1 –1.092* –0.935*
(–1.93) (–1.75)
CGQt+1 –0.080** –0.091**
(–2.32) (–2.49)
CONSTANT 9.794* 8.139
(1.81) (1.22)
Time FE Yes Yes
Firm FE Yes Yes
N 690 690
Adjusted R2 0.761 0.737
This table presents the results of the analyses examining the relation between cost of debt capital and operational
control quality proxied by ORA and ORMQ metrics. Column 1 reports results for the ORA measure and column 2
presents the results for the OMQ measure. See Appendix C for variable definitions. All 𝑡 −statistics are calculated
using two-way clustered standard errors (by firm and year). All continuous variables are winsorised at the utmost
1% tails of their respective distributions to adjust for the effects of extreme observations. Numbers inside
parentheses are 𝑡 −statistics. ***, **, and * indicate significance levels at 1%, 5%, and 10%, respectively.
86
Table 6: Operational Control Quality and Cost of Equity Capital (H2b)
Variable (1) (2)
RP_AVGt+1 RP_AVGt+1
ORAt+1 H2b (–) –3.319***
(–8.06)
ORMQt+1 H2b (–) –0.647***
(–4.79)
SIZEt+1 –0.766* –0.698*
(–1.78) (–1.71)
BMt+1 1.587* 1.558**
(1.94) (1.98)
ICWAt+1 –5.398*** –5.656***
(–3.34) (–3.73)
CGQt+1 –0.076 –0.089
(–1.23) (–1.47)
BETAt+1 0.974*** 1.016***
(2.61) (2.63)
IDIO_RISKt+1 10.084*** 9.724***
(3.65) (3.79)
TIER_RATIOt+1 –0.674*** –0.710***
(–3.13) (–3.41)
ASSET_RISK t+1 0.123*** 0.132***
(4.20) (4.35)
CONSTANT 34.742* 25.190*
(1.88) (1.76)
Time FE Yes Yes
Firm FE Yes Yes
N 1,834 1,834
Adjusted R2 0.689 0.695
This table presents the results of the analyses examining the relation between cost of equity capital and operational
control quality proxied by ORA and ORMQ metrics. Column 1 reports results for the ORA measure and column 2
presents the results for the ORMQ measure. See Appendix C for variable definitions. All 𝑡 −statistics are calculated
using clustered standards errors by firm. The result for the ORMQ measure becomes marginally significant using
two-way clustering. All continuous variables are winsorised at the utmost 1% tails of their respective distributions
to adjust for the effects of extreme observations. Numbers inside parentheses are 𝑡 −statistics. ***, **, and *
indicate significance levels at 1%, 5%, and 10%, respectively.
87
Table 7: Price-level Analysis
Variable (1) (2) (3)
PRICEt PRICEt PRICEt
BVt 0.812*** 1.067*** 0.744***
(7.53) (5.28) (6.21)
EPSt 1.873*** 1.472** 0.048
(4.25) (2.49) (0.31)
I_EFF 0.008
(0.01)
I_EFF × BV 0.011
(0.08)
I_EFF × EPS 1.442*
(1.76)
ORMQ 2.757
(1.48)
ORMQ × BV 0.242
(1.63)
ORMQ × EPS 1.583**
(2.33)
ORA 1.954
(1.24)
ORA × BV 0.100
(0.70)
ORA × EPS 1.047***
(3.82)
CONSTANT 22.936*** 20.925*** 31.422***
(6.02) (4.17) (9.77)
Time FE Yes Yes Yes
Firm FE Yes Yes Yes
N 1,989 1,989 1,989
Adjusted R2 0.887 0.878 0.882
This table presents the results for the price-level analysis based on Collins, Maydew, and Weiss’s (1997) equity
valuation model. Column 1, reports the results for the association between operational efficiency (I_EFF) and
equity valuation. I_EFF is an indicator variable that equals one for the subsample of banks with operational
efficiency above the sample median, and zero otherwise. Columns 2 and 3, report the results for the association
between operational control quality and equity valuation using the ORMQ and ORA measures, respectively. See
Appendix C for variable definitions. All 𝑡 −statistics are calculated using two-way clustered standard errors (by
firm and year). All continuous variables are winsorised at the utmost 1% tails of their respective distributions to
adjust for the effects of extreme observations. Numbers inside parentheses are 𝑡 −statistics. ***, **, and * indicate
significance levels at 1%, 5%, and 10%, respectively.
88
Table 8: Earnings Persistence Analysis
Variable (1) (2) (3)
ROAt+1 ROAt+1 ROAt+1
ROAt 0.266*** 0.292** 0.216*
(3.05) (2.44) (1.95)
I_EFF 0.145
(1.05)
I_EFF × ROAt 0.240**
(2.12)
ORMQ 0.157
(1.18)
ORMQ × ROAt 0.042
(0.36)
ORA 0.345**
(1.97)
ORA × ROAt 0.111*
(1.65)
CONSTANT 0.903*** 0.938*** 0.744***
(11.45) (5.56) (4.42)
Time FE Yes Yes Yes
Firm FE Yes Yes Yes
N 1,989 1,988 1,988
Adjusted R2 0.505 0.488 0.490
This table presents the results for the earnings persistence analysis. Colum 1, documents the results for the
association between operational efficiency (I_EFF) and earnings persistence. I_EFF is an indicator variable that
equals 1 for the subsample of banks with operational efficiency above the sample median, and zero otherwise.
Columns 2, and3, document the results for the association between operational control quality and earnings
persistence using the ORMQ and ORA measures, respectively. See Appendix C for variable definitions. All
𝑡 −statistics are calculated using two-way clustered standard errors (by firm and year). All continuous variables are
winsorised at the utmost 1% tails of their respective distributions to adjust for the effects of extreme observations.
Numbers inside parentheses are 𝑡 −statistics. ***, **, and * indicate significance levels at 1%, 5%, and 10%,
respectively.
89
Table 9: Changes Analyses
Panel A: Proportion of non-event and event banks with and without improvement in their
ORMQ
(1) (2)
ΔORMQ(t-1,t) = 0 Δ ΔORMQ(t-1,t) = > 0
NON_EVENTt-1 (ORA = 1) 1,219
(BASELINE)
470
(RISK REDUCERS)
EVENTt-1 (ORA = 0) 30
(NON_REMEDIATORS)
71
(REMEDIATORS)
Pearson Chi-squared Test: Test of difference in proportion
H0: The proportion of banks with and without improvement in their ORMQ is the same for non-
event and event banks.
Pearson chi-squared Test: P–value = 0.000
This panel presents the proportion of non-event and event banks with no improvement (ΔORMQ(t-1,t) = 0) and
improvement (ΔORMQ(t-1,t) = > 0) in their operational risk management quality (ORMQ) from 𝑡 − 1 to 𝑡. Non-
event banks (ORA = 1) indicate bank-year observations with no operational risk event at 𝑡 − 1 and event banks
(ORA = 0) indicate bank-year observations with operational risk events at 𝑡 − 1. The sample is partitioned into four
subsamples as follows. BASELINE represents the subsample of non-event banks (ORA = 1) that do not improve
their (ORMQ). RISK REDUCERS represents the subsample of non-event banks (ORA = 1) that despite not
experiencing an operational risk event at 𝑡 − 1 improve their (ORMQ) from 𝑡 − 1 to 𝑡. NON_REMEDIATORS
represents the subsample of event banks (ORA = 0) that despite experiencing an operational risk event at 𝑡 − 1 do
not improve their (ORMQ) from 𝑡 − 1 to 𝑡. Lastly, REMEDIATORS represents the subsample of event banks (ORA
= 0) that experience an operational risk event at 𝑡 − 1 but do improve their (ORMQ) from 𝑡 − 1 to 𝑡.
90
Table 9 (continued):
Panel B: Univariate Tests of Within-Firm Changes in Operational Efficiency and Changes in
Costs of Debt and Equity Capital Conditional on Changes in Operational Control Quality
Mean p-value
BASELINE
ΔEFF(t-1,t) –0.02 0.290
ΔBOND_SPREAD(t-1,t) –0.02 0.746
ΔRP_ AVG(t-1,t) –0.15 0.330
RISK_REDUCERS
ΔEFF(t-1,t) 0.01 0.002
ΔBOND_SPREAD(t-1,t) –1.32 0.004
ΔRP_ AVG(t-1,t) –0.87 0.000
NON_REMEDIATORS
ΔEFF(t-1,t) –0.04 0.194
ΔBOND_SPREAD(t-1,t) 1.98 0.023
ΔRP_ AVG(t-1,t) 3.82 0.000
REMEDIATORS
ΔEFF(t-1,t) 0.02 0.021
ΔBOND_SPREAD(t-1,t) –0.56 0.029
ΔRP_ AVG(t-1,t) –0.78 0.000
This panel presents the univariate descriptive statistics on ΔEFF, ΔBOND_SPREAD, and ΔRP_ AVG for the four
subsamples.
91
Table 9 (continued):
Panel C: The Effect of Changes in Operational Control Quality on Changes in Operational
Efficiency (H1)
Variable
ΔEFF(t-1,t)
RISK_REDUCERS(t-1,t) (1 > 0) 0.033***
(6.41)
NON_REMEDIATORS(t-1,t) (2 = ?) –0.012
(–0.37)
REMEDIATORS(t-1,t) (3 > 0) 0.038***
(5.45)
ΔSIZE(t-1,t) 0.038
(1.22)
ΔICWA(t-1,t) 0.020**
(2.15)
ΔBIG4(t-1,t) 0.021*
(1.85)
ΔCGQ(t-1,t) 0.001**
(1.97)
ΔNONPERF_LOANS/LOANS(t-1,t) 0.050
(0.45)
ΔMERGER(t-1,t) –0.001
(–0.20)
ΔLOSS(t-1,t) –0.024*
(–1.95)
ΔRESTRUCTURE(t-1,t) –0.010
(–0.88)
ΔTRADING_ASSETS/ASSETS(t-1,t) –0.010
(–0.06)
CONSTANT 0.024***
(3.14)
N 1,790
Adjusted R2 0.101
F-Test 𝑯𝟎: 𝜶𝟐 < 𝜶𝟏 𝑯𝟎: 𝜶𝟐 < 𝜶𝟑
Prob > F (1-tailed) 0.07 0.06
This panel documents the results for the effect of the changes in operational control quality (ΔORMQ(t-1,t)) on the
changes in the operational efficiency (ΔEFF(t-1,t)). BASELINE represents the subsample of non-event banks (ORA =
1) that do not improve their (ORMQ). RISK REDUCERS represents the subsample of non-event banks (ORA = 1)
92
that despite not experiencing an operational risk event at 𝑡 − 1 improve their (ORMQ) from 𝑡 − 1 to 𝑡.
NON_REMEDIATORS represents the subsample of event banks (ORA = 0) that despite experiencing an operational
risk event at 𝑡 − 1 do not improve their (ORMQ) from 𝑡 − 1 to 𝑡. Lastly, REMEDIATORS represents the subsample
of event banks (ORA = 0) that experience an operational risk event at 𝑡 − 1 but do improve their (ORMQ) from
𝑡 − 1 to 𝑡. See Appendix C for variable definitions. All t −statistics are calculated using two-way clustered
standard errors (by firm and year). All continuous variables are winsorised at the utmost 1% tails of their respective
distributions to adjust for the effects of extreme observations. Numbers inside parentheses are t −statistics. ***, **,
and * indicate significance levels at 1%, 5%, and 10%, respectively.
93
Table 9 (continued):
Panel D: The Effect of Changes in Operational Control Quality on Changes in the Cost of Debt
Capital (H2a)
Variable
ΔSPREAD(t-1,t)
RISK_REDUCERS(t-1,t) (1 < 0) –0.612**
(–2.08)
NON_REMEDIATORS(t-1,t) (2 > 0) 1.011*
(1.89)
REMEDIATORS(t-1,t) (3 = ?) –0.431
(–1.63)
ΔISSUE_AMOUNT(t-1,t) –0.248***
(–3.49)
ΔBOND_LIFE(t-1,t) 0.004
(0.46)
ΔCALLABLE(t-1,t) 0.216
(1.02)
ΔRATING(t-1,t) 0.085*
(1.89)
ΔSIZE(t-1,t) 0.967*
(1.68)
ΔASSET_RISK(t-1,t) 0.027
(0.66)
ΔTIER_RATIO(t-1,t) –0.030
(–1.55)
ΔDEPOSITS/ASSETS(t-1,t) 0.016
(0.83)
ΔROA(t-1,t) –0.690*
(–1.87)
ΔICWA(t-1,t) –0.507
(–0.93)
ΔCGQ(t-1,t) –0.060
(–0.62)
CONSTANT –0.074
(–1.07)
N 493
Adjusted R2 0.478
F-Test 𝑯𝟎: 𝜶𝟐 > 𝜶𝟏 𝑯𝟎: 𝜶𝟐 > 𝜶𝟑
Prob > F (1-tailed) 0.000 0.002
94
This panel documents the results for the effect of the changes in operational control quality (ΔORMQ(t-1,t)) on the
changes in the cost of debt capital (ΔBOND_SPREAD(t-1,t)). BASELINE represents the subsample of non-event
banks (ORA = 1) that do not improve their (ORMQ). RISK REDUCERS represents the subsample of non-event
banks (ORA = 1) that despite not experiencing an operational risk event at 𝑡 − 1 improve their (ORMQ) from 𝑡 − 1
to 𝑡. NON_REMEDIATORS represents the subsample of event banks (ORA = 0) that despite experiencing an
operational risk event at 𝑡 − 1 do not improve their (ORMQ) from 𝑡 − 1 to 𝑡. Lastly, REMEDIATORS represents
the subsample of event banks (ORA = 0) that experience an operational risk event at 𝑡 − 1 but do improve their
(ORMQ) from 𝑡 − 1 to 𝑡. See Appendix C for variable definitions. All t −statistics are calculated using two-way
clustered standard errors (by firm and year). All continuous variables are winsorised at the utmost 1% tails of their
respective distributions to adjust for the effects of extreme observations. Numbers inside parentheses are
t −statistics. ***, **, and * indicate significance levels at 1%, 5%, and 10%, respectively.
95
Table 9 (continued):
Panel E: The Effect of Changes in Operational Control Quality on Changes in the Cost of Equity
Capital (H2b)
Variable
ΔRP_ AVG(t-1,t)
RISK_REDUCERS(t-1,t) (1 < 0) –1.063***
(–4.54)
NON_REMEDIATORS(t-1,t) (2 > 0) 3.564***
(5.41)
REMEDIATORS(t-1,t) (3 = ?) –0.444*
(–1.92)
ΔSIZE(t-1,t) –0.133
(–0.89)
ΔBM(t-1,t) 1.219**
(2.39)
ΔICWA(t-1,t) –2.697***
(–3.16)
ΔCGQ(t-1,t) –0.070*
(–1.90)
ΔBETA(t-1,t) 0.903***
(2.65)
ΔIDIO_RISK(t-1,t) 5.033
(1.00)
ΔTIER_RATIO(t-1,t) –0.505***
(–10.72)
ΔASSET_RISK(t-1,t) 0.008
(0.22)
CONSTANT 0.206
(0.75)
N 1,570
Adjusted R2 0.105
F-Test 𝑯𝟎: 𝜶𝟐 > 𝜶𝟏 𝑯𝟎: 𝜶𝟐 > 𝜶𝟑
Prob > F (1-tailed) 0.000 0.000
This panel documents the results for the effect of the changes in operational control quality (ΔORMQ(t-1,t)) on the
changes in the cost of equity capital (ΔRP_ AVG(t-1,t)). BASELINE represents the subsample of non-event banks
(ORA = 1) that do not improve their (ORMQ). RISK REDUCERS represents the subsample of non-event banks
(ORA = 1) that despite not experiencing an operational risk event at 𝑡 − 1 improve their (ORMQ) from 𝑡 − 1 to 𝑡.
NON_REMEDIATORS represents the subsample of event banks (ORA = 0) that despite experiencing an operational
96
risk event at 𝑡 − 1 do not improve their (ORMQ) from 𝑡 − 1 to 𝑡. Lastly, REMEDIATORS represents the subsample
of event banks (ORA = 0) that experience an operational risk event at 𝑡 − 1 but do improve their (ORMQ) from
𝑡 − 1 to 𝑡. See Appendix C for variable definitions. All t −statistics are calculated using two-way clustered
standard errors (by firm and year). All continuous variables are winsorised at the utmost 1% tails of their respective
distributions to adjust for the effects of extreme observations. Numbers inside parentheses are t −statistics. ***, **,
and * indicate significance levels at 1%, 5%, and 10%, respectively.
97
Table 10: Operational Risk Event Types Analysis
Panel A: Relation between Operational Risk Event Types and Operational Efficiency
Variable EFFt
Coeff. 𝑡 −statistics
ORA_INT_FRAUD t+1 (𝛼1) 0.107*** 6.44
ORA_EXT_FRAUD t+1 (𝛼2) 0.102*** 3.40
ORA_CLIENTS t+1 (𝛼3) 0.095*** 6.86
ORA_REST t+1 (𝛼4) 0.047** 2.34
SIZEt 0.051 1.64
LOG(AGE)t –0.061*** –3.13
ICWAt 0.025*** 2.74
BIG4t 0.029** 2.48
CGQt 0.003*** 3.72
NONPERF_LOANS /LOANSt –0.091 –0.86
MERGERt 0.008* 1.91
FOREIGNt –0.008 –0.42
LOSSt –0.033*** –3.64
RESTRUCTUREt –0.011 –0.86
TRADING_ASSETS /ASSETSt 0.013 0.05
CONSTANT –0.057 –0.17
Time FE Yes
Firm FE Yes
N 2,086
Adjusted R2 0.803
F-Test 𝑯𝟎: 𝜶𝟏 ≥ 𝜶𝟐 𝑯𝟎: 𝜶𝟏 ≥ 𝜶𝟑 𝑯𝟎: 𝜶𝟏 ≥ 𝜶𝟒
Prob > F (1-tailed) 0.440 0.274 0.008
This table presents the results for the regression of operational efficiency on the ORA indicator variable partitioned
into four categories: (1) ORA_INT_FRAUD, (2) ORA_EXT_FRAUD, (3) ORA_CLIENTS, and (4) ORA_REST. See
Appendix C for variable definitions. All continuous variables are winsorised at the utmost 1% tails of their
respective distributions to adjust for the effects of extreme observations. All 𝑡 −statistics are calculated using two-
way clustered standard errors (by firm and year). Numbers inside parentheses are 𝑡 −statistics. ***, **, and *
indicate significance levels at the 1%, 5%, and 10%, respectively.
98
Table 10 (continued):
Panel B: Relation between Operational Risk Event Types and Cost of Debt Capital
Variable SPREADt+1
Coeff. 𝑡 −statistics
ORA_INT_FRAUDt+1 (𝛼1) –1.152*** –4.20
ORA_EXT_FRAUDt +1 (𝛼2) –1.075*** –3.94
ORA_CLIENTSt +1 (𝛼3) –0.718** –2.28
ORA_RESTt+1 (𝛼4) –0.529 –1.32
ISSUE_AMOUNTt+1 –0.249*** –3.90
BOND_LIFEt+1 0.005 0.59
CALLABLEt+1 0.350 1.61
RATINGt+1 0.108** 1.97
SIZEt+1 –1.038 –1.46
ASSET_RISKt+1 0.016* 1.77
TIER_RATIOt+1 –0.060 –0.77
DEPOSITS/ASSETSt+1 0.005 0.28
ROAt+1 –0.943*** –2.69
ICWAt+1 –1.066* –1.80
CGQt+1 –0.079** –2.49
CONSTANT 11.558* 1.96
Time FE YES
Firm FE YES
N 690
Adjusted R2 0.763
This table presents the results for the regression of cost of debt capital on the ORA indicator variable partitioned
into four categories: (1) ORA_INT_FRAUD, (2) ORA_EXT_FRAUD, (3) ORA_CLIENTS, and (4) ORA_REST. See
Appendix C for variable definitions. All continuous variables are winsorised at the utmost 1% tails of their
respective distributions to adjust for the effects of extreme observations. All 𝑡 −statistics are calculated using two-
way clustered standard errors (by firm and year). Numbers inside parentheses are 𝑡 −statistics. ***, **, and *
indicate significance levels at 1%, 5%, and 10%, respectively.
F-Test 𝑯𝟎: 𝜶𝟏 ≥ 𝜶𝟐 𝑯𝟎: 𝜶𝟏 ≥ 𝜶𝟑 𝑯𝟎: 𝜶𝟏 ≥ 𝜶𝟒
Prob > F (1-tailed) 0.421 0.136 0.147
99
Table 10 (continued):
Panel C: Relation between Operational Risk Event Types and Cost of Equity Capital
Variable RP_AVGt+1
Coeff. 𝑡 −statistics
ORA_INT_FRAUDt+1 (𝛼1) –4.450*** –3.58
ORA_EXT_FRAUDt+1 (𝛼2) –4.262*** –5.27
ORA_CLIENTSt +1 (𝛼3) –1.374* –1.87
ORA_RESTt+1 (𝛼4) –2.990*** –2.66
SIZEt+1 –0.770* –1.79
BMt+1 1.568* 1.93
ICWAt+1 –5.403*** –3.33
CGQt+1 –0.076 –1.23
BETAt+1 0.948*** 2.59
IDIO_RISKt+1 10.305*** 3.69
TIER_RATIOt+1 –0.669*** –3.08
ASSET_RISK t+1 0.121*** 4.11
CONSTANT 34.853** 2.12
Time FE YES
Firm FE YES
N 1,834
Adjusted R2 0.690
This table presents the results for the regression of banks’ cost of equity capital on the ORA indicator variable
partitioned into four categories: (1) ORA_INT_FRAUD, (2) ORA_EXT_FRAUD, (3) ORA_CLIENTS, and (4)
ORA_REST. See Appendix C for variable definitions. All continuous variables are winsorised at the utmost 1% tails
of their respective distributions to adjust for the effects of extreme observations. All 𝑡 −statistics are calculated
using two-way clustered standard errors (by firm and year). Numbers inside parentheses are 𝑡 −statistics. ***, **,
and * indicate significance levels at 1%, 5%, and 10%, respectively.
F-Test 𝑯𝟎: 𝜶𝟏 ≥ 𝜶𝟐 𝑯𝟎: 𝜶𝟏 ≥ 𝜶𝟑 𝑯𝟎: 𝜶𝟏 ≥ 𝜶𝟒
Prob > F (1-tailed) 0.453 0.024 0.180