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Corporate Governance, Regulatory Enforcement Actions and
Reputational Loss in the Banking Industry
Huong Ngoc Truong
Submitted in fulfilment of requirements for the degree of
Doctor of Philosophy
School of Economics and Finance
Queensland University of Technology
2019
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Abstract
The 2007-2009 financial downturn has prompted major U.S banking
regulators to be more vigilant and aggressive in issuing enforcement actions (EAs)
against banking firms. The surge in the number of banks targeted by regulatory
EAs seems somewhat surprising, given substantial improvements in corporate
governance over time. I find that in almost all instances, EAs are costly to the
institutions involved. Not only do affected entities have to spend money and
resources correcting the wrongdoings identified by the EA, they are often required
to also pay restitution to the aggrieved parties and/or pay fines. In addition to
inflicting direct financial losses upon a bank, EAs have an indirect impact on a
bank via reputational risk. That is, the disclosure of fraudulent activity or improper
business practices at a bank damage the bank’s reputation, thereby increasing
the cost of doing business. In my thesis, I aim to address the following three
research questions. First, I ask whether well-governed banks are less likely to be
the target of regulatory enforcement actions. Second, I ask whether banks will
suffer from reputational loss following the announcements of regulatory
enforcement action. Third, I ask whether the magnitude of reputational loss around
the announcement of regulatory enforcement actions is more or less severe in
well-governed banks.
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My thesis assesses the relevance of corporate governance on the likelihood
of bank misconduct over the period 2000-2014, where regulatory enforcement
actions are used to identify whether or not a bank has engaged in misconduct.
The subsequent negative publicity of enforcement actions trigger reputational
losses. Given that reputation is a key asset of banks whose activities are primarily
built on trust (Macey, 2013; Fiordelisi et al., 2014), managing potential reputational
risk is perhaps most important to the banking sector than any other industries.
Reputational risk has become a major concern for regulators, as evidenced by
the inclusion of reputational risk in published documents on the Enhancements
to the Basel II Framework (Basel Committee on Banking Supervisions, 2009),
bringing increased attention and awareness to this type of risk.
Despite its importance, few studies examine reputational loss in banking
firms and investigates its determinants (Perry & de Fontnouvelle, 2005; Plunus,
Gillet, & Hübner, 2012; Fiordelisi, Soana, & Schwizer, 2013; Fiordelisi et al., 2014;
Cumming, Leung, & Rui, 2015). While past studies limit the determinants of banks’
reputational loss to event and bank-specific financial characteristics, my study
adds to the literature by extending the determinants of bank reputational loss to
include internal governance structure.
Results from multivariate probit regressions show a non-linear relation
between board heterogeneity and likelihood of misconduct. Specifically, banks with
a larger and more diverse board have lower probability of misconduct (lower
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propensity of getting enforcement actions). However, as the board size, board
busyness and board diversity exceed a certain limit, misconduct becomes more
likely. These findings are consistent with the arguments that the effectiveness of
board monitoring mechanism is non-linear (Wang & Hsu, 2013; Harjoto et al.,
2015). These results remain robust across various sub-samples. I find no evidence
that governance matters to non-severe misconduct cases.
Following Karpoff et al. (2008), Gillet et al. (2010), and Fiordelisi et al.
(2013), I adopt the residual method to estimate the magnitude of reputational
loss for U.S. banks receiving regulatory enforcement actions. Results from event
study show that the average reputational loss is significantly negative by up to
0.74 percent (in relative to equity loss) for three event windows [-5,5], [-10,10],
and [-10,5]. The legal fines are trivial relative to total equity loss.
My analysis also reveals a non-linear relation between board characteristics
(board size, board heterogeneity in terms of gender and directors’ age) and the
magnitude of bank reputational loss following the announcements of enforcement
actions. That is, banking firms with larger and more diverse board have lower
reputational loss, but as the board size and diversity go beyond a certain limit,
the magnitude of bank reputation loss increases. These findings support prior
studies of a trade-off between board heterogeneity and firm outcomes. The results
are robust for the [-3,3] and [-5,5] windows. Overall, I document that “good”
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internal governance helps in reducing the propensity of bank misconduct and in
limiting subsequent reputational loss.
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Keywords
• Banking industry
• Boards
• Corporate governance
• Enforcement actions
• Event study methodology
• Ethical behaviour
• Fines
• Fraud
• Independent directors
• Misconduct
• Reputational loss
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Table of Contents Abstract .................................................................................................................................... ii
Keywords ................................................................................................................................... vi
Table of Contents .................................................................................................................... vii
List of Tables .............................................................................................................................. x
Acknowledgements .................................................................................................................... 12
Statement of Original Authorship ......................................................................................... 14
CHAPTER 1 .................................................................................................................................. 15
INTRODUCTION .......................................................................................................................... 15
1.1 Motivations ............................................................................................................................... 15
1.2 Research aims ......................................................................................................................... 19
1.3 Contributions ......................................................................................................................... 20
1.4 Key findings ............................................................................................................................. 22
1.5 Thesis structure ...................................................................................................................... 26
CHAPTER 2 .................................................................................................................................. 27
BACKGROUND ............................................................................................................................. 27
2.1 Introduction ............................................................................................................................ 27
2.2 Definition of Corporate Reputation ....................................................................................... 27
2.3 Theories of Corporate Reputation ........................................................................................ 29
2.3.1 Institutional theory .................................................................................................... 29
2.3.2 Signalling/Impression theory ..................................................................................... 31
2.3.3 Agenda-setting theory ............................................................................................... 33
2.3.4 Identity theory ........................................................................................................... 34
2.3.5 Stakeholder theory .................................................................................................... 35
2.3.6 Resource-based theory .............................................................................................. 35
2.4 Estimating reputational loss .................................................................................................. 36
2.5 Summary ................................................................................................................................. 43
CHAPTER 3 ................................................................................................................................. 44
LITERATURE REVIEW ................................................................................................................. 44
3.1 Introduction ........................................................................................................................... 44
3.2 Studies on likelihood of corporate misconduct ................................................................... 44
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3.3 Studies on reputational loss .................................................................................................. 54
3.3.1 Reputational loss magnitude .................................................................................... 54
3.3.2 Determinants of reputational loss ............................................................................ 59
3.4 Summary .................................................................................................................................. 71
CHAPTER 4 .................................................................................................................................. 72
HYPOTHESES DEVELOPMENT ................................................................................................. 72
4.1 Introduction ............................................................................................................................ 72
4.2 The likelihood of bank misconduct ........................................................................................ 72
4.2.1 Board size ....................................................................................................................73
4.2.2 Board independence ................................................................................................. 76
4.2.3 Board busyness .......................................................................................................... 78
4.2.4 Board diversity ............................................................................................................ 81
4.2.5 CEO duality ................................................................................................................. 85
4.3 Bank reputational loss hypotheses ....................................................................................... 88
4.4 Summary .................................................................................................................................. 91
CHAPTER 5 ................................................................................................................................. 93
DATA AND METHODOLOGY ................................................................................................... 93
5.1 Introduction ........................................................................................................................... 93
5.2 Sample selection and data sources ...................................................................................... 93
5.2.1 Controlling for confounding effects ......................................................................... 98
5.3 Methodology ......................................................................................................................... 102
5.3.1 Econometric models ................................................................................................. 102
5.3.2 Measurement of bank reputational loss ................................................................. 104
5.3.3 Measurement of corporate governance ................................................................. 106
5.3.4 Enforcement action-related variables ..................................................................... 108
5.3.5 Measurement of bank-specific characteristics ....................................................... 110
5.4 Descriptive statistics, correlation matrix and sample profile ............................................. 118
5.4.1 Descriptive statistics ................................................................................................. 118
5.4.2 Correlation matrix ..................................................................................................... 123
5.4.3 Monetary vs. non-monetary enforcement actions ................................................. 126
5.4.4 Severe vs. non-severe enforcement actions ........................................................... 128
5.4.5 Technical vs. non-technical enforcement actions ................................................... 130
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5.4.6 Enforcement actions by severity – technicality matrix .......................................... 130
5.4.7 Enforcement actions by primary regulators ........................................................... 132
5.5 Summary ................................................................................................................................ 134
CHAPTER 6 ................................................................................................................................ 135
EMPIRICAL RESULTS ................................................................................................................ 135
6.1 Introduction .......................................................................................................................... 135
6.2 Likelihood of regulatory enforcement actions ................................................................... 135
6.2.1 Univariate tests ......................................................................................................... 135
6.2.2 Results from probit regressions............................................................................... 138
6.2.3 Results from probit regressions with squared terms ............................................. 149
6.3 Estimation of bank reputational loss ................................................................................... 158
6.3.1 Event study results for the full sample .................................................................... 158
6.3.2 Event study results split by degree of severity ....................................................... 162
6.3.3 Event study results split by degree of technicality ................................................. 165
6.4 Determinants of bank reputational loss .............................................................................. 169
6.4.1 Results from OLS regressions .................................................................................. 169
6.4.2 Results from OLS regressions with squared terms ................................................ 176
6.4.3 Alternative Event Windows ...................................................................................... 182
6.5 Summary ................................................................................................................................ 193
CHAPTER 7 ................................................................................................................................ 196
CONCLUSION ............................................................................................................................ 196
7.1 Summary and conclusion ..................................................................................................... 196
7.2 Limitations and avenues for future research ...................................................................... 198
REFERENCES .............................................................................................................................. 201
APPENDICES ............................................................................................................................... 214
A.1 Federal financial regulators and their supervised entities ................................................. 214
A.2 Definitions of different types of enforcement actions ....................................................... 216
B.1 Upside and downside of reputational risk .......................................................................... 217
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List of Tables
Table 3.1 Magnitude of reputational loss in prior literature ...............................................................57
Table 4.1 List of hypotheses .....................................................................................................................................90
Table 5.1 Sample construction .............................................................................................................................. 101
Table 5.2 Variables description ............................................................................................................................. 116
Table 5.3 Summary statistics .................................................................................................................................. 121
Table 5.4 Correlation matrix ................................................................................................................................... 125
Table 5.5 Number of enforcement actions by types each year .................................................... 127
Table 5.6 Number of enforcement actions by degree of severity each year ...................... 129
Table 5.7 Number of enforcement actions by degree of technicality each year .............. 131
Table 5.8 Number of enforcement actions by severity and technicality matrix .................. 131
Table 5.9 Number of enforcement actions by primary regulators each year ...................... 133
Table 6.1 Univariate test ........................................................................................................................................... 137
Table 6.2 Probit regressions of the likelihood of enforcement actions .................................... 142
Table 6.3 Probit regressions of the likelihood of enforcement actions (Severe vs. non-
severe) .................................................................................................................................................................................... 143
Table 6.4 Probit regressions of likelihood of enforcement actions (technical vs. non-
technical) .............................................................................................................................................................................. 147
Table 6.5 Probit regressions of the likelihood of enforcement actions (with squared terms)
.................................................................................................................................................................................................... 152
Table 6.6 Probit regressions of the likelihood of enforcement actions with squared terms
(severe vs. non-severe) ................................................................................................................................................ 153
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Table 6.7 Probit regressions of corporate governance on the likelihood of enforcement
actions with squared terms (technical vs. non-technical) ................................................................... 156
Table 6.8 Share price reaction (CAR) to announcements of enforcement actions (2000-
2014) ....................................................................................................................................................................................... 160
Table 6.9 Reputational loss (CAR_REP) due to enforcement actions (2000-2014) ............ 161
Table 6.10 Reputational loss (CAR_REP) by degree of severity of the enforcement actions
.................................................................................................................................................................................................... 163
Table 6.11 Reputational loss (CAR_REP) by degree of technicality of the enforcement
actions .................................................................................................................................................................................... 167
Table 6.12 Regressions of bank reputational loss ................................................................................... 173
Table 6.13 Regressions of bank reputational loss (with squared terms) ................................. 179
Table 6.14 Regressions of bank reputational loss using [-3,3] event window ...................... 185
Table 6.15 Regressions of bank reputational loss using [-5,5] event window ................... 187
Table 6.16 Regression of bank reputational loss (with squared terms) using [-3,3] event
window .................................................................................................................................................................................... 189
Table 6.17 Regressions of bank reputational loss (with squared terms) using [-5,5] event
window .................................................................................................................................................................................... 191
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Acknowledgements
It is a pleasure to thank the many people who made my thesis possible.
First and foremost, I would like to express my deepest gratitude to my supervisors,
Professor Janice How and Associate Professor Peter Verhoeven, for their patient
guidance, enthusiastic encouragement and constructive critiques extended to me
over my PhD journey. With their invaluable support and encouragement, my work
on my thesis has been successfully accomplished. I have learnt a lot from my
supervisors throughout the entire four years, with many challenging yet valuable
experiences in order to complete my thesis. I would like to thank Janice and
Peter for spending their valuable time on reviewing my thesis in spite of their
very busy schedule and for their important suggestions to improve my research.
I wish to express my sincere and grateful thanks to these people for their
constructive feedback and comments on my thesis: my PhD Panel members (Dr
John Chen, Associate Professor John Nowland and Associate Professor Belinda
Luke); and the discussant (Professor Michael Skully) and participants at the 2016
Financial Markets and Corporate Governance (FMCG) PhD Symposium. I would like
to take this opportunity to record my sincere thanks to all members in the QUT
Business School, who have helped and inspired me along the way of the research
life. I am also grateful for QUT for offering me the QUT Postgraduate Research
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Award scholarship, which has waived my tuition fee and allowed me to fully focus
on my studies and thesis.
I extend my deepest gratitude to my parents for unceasing support and
encouragement during the entire four years. Dad, thank you for your unconditional
assistance and patience. Mom, thank you for being there for me when I was
stressed out. I also want to thank my aunt for coming over Australia to assist
our family during busy times and thank my sisters for lending me support when
I miss home. To my beloved husband, Suichen Xu, thank you for your
immeasurable love and support during the entire period of my PhD journey and
to my 2-year-old son, Steven Xu. When I was stressed out, there is nothing better
than seeing my son’s smile. Thanks also to my parents-in-law who have always
provided support when our small family needed it.
To my dear friends, thank you for your constant encouragement during my
difficult time and for always being there when I needed you most. Specifically, I
would like to thank Thong Quoc Ho, Lan Le, Tam Bui, Zairihan Halim and
Kurniawan Meinanda, for lending an ear to me when I needed someone to talk.
“We must find time to stop and thank the people who make a difference in our lives”
John F. Kennedy
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Statement of Original Authorship
The work contained in my thesis has not been previously submitted to
meet requirements for an award at this or any other higher education institution.
To the best of my knowledge and belief, my thesis contains no material previously
published or written by another person except where due reference is made.
Signature:
Date: 26/06/2019
QUT Verified Signature
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CHAPTER 1
INTRODUCTION
“It takes 20 years to build a reputation and five minutes to ruin it. If you think
about that, you’ll do things differently.” Warren E. Buffet (CEO Berkshire Hathaway)
1.1 Motivations
The U.S. regulatory enforcement activity in the banking sector has intensified
in the post-GFC period. Primary federal and state banking regulators, including
the Federal Reserve Board (FRB), the Federal Deposit Insurance Corporation (FDIC)
and the Office of the Comptroller of the Currency (OCC), have registered record
numbers of enforcement actions against banks operating in the U.S., requiring
financial institutions to take corrective measures against their alleged misconduct.
Amongst those allegedly engaged in misconduct are many high-profile institutions,
including Bank of America Corporation, fined US$175.5 million in 2012 for
deficiencies in its internal controls and for unsafe and unsound banking practices,
and J.P Morgan & Co. fined US$275 million in 2014 for failing to effectively
oversee loan servicing, loss mitigation, foreclosures activities, and related
functions.
While there may be many reasons for misconduct, one possible cause is
short-termism (managerial myopia) of powerful CEOs (Khanna, Kim, & Lu, 2015).
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The recent surge in bank misconduct cases may seem somewhat surprising by
most accounts since bank governance appears to have improved markedly in
recent years. Data from Riskmetrics show that 80 percent of US bank boards are
classified as independent in 2012, up from around 50 percent in 2000. With
increasing levels of independence, one would expect bank boards to be more
effective in its oversight of managerial behaviour (Bonn & Fisher, 2005). However,
far from a declining trend, the number of enforcement actions has increased
from 12 to 37 over the same time period. This puts into question the effectiveness
of boards in preventing bank misconduct and regulatory enforcement actions.
Whether corporate governance has any economic effects on the
consequences of regulatory enforcement actions is an interesting question to
examine. The announcement of enforcement actions can quickly damage the
offending bank’s reputation in terms of professional and ethical conduct, especially
in today’s high- tech world where with just a few clicks of the mouse, bad news
spread quickly. Once a corporation’s reputation is tarnished, it can be a lengthy
and costly process to restore it. The Basel Committee on Banking Supervision1
(2009, p.19) defines reputational risk as “the risk arising from negative perception
on the part of customers, counterparties, shareholders, investors, debt-holders,
1 The Basel Committee on Banking Supervision is the primary international standard-setter ensuring the prudential regulation of banks and creates a forum for regular cooperation on banking supervisory matters. The Committee has developed Basel Frameworks detailing a series of recommendations on banking laws and regulations, which is widely accepted by the G-20 countries (including the US).
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market analysts, other relevant parties or regulators that can adversely affect a
bank’s ability to maintain existing, or establish new, business relationships and
continued access to sources of funding”. It is also known as the “risk of risks”,
which often comes right on the heels of other many risk types in the banking
industry –including market risk, credit risk, liquidity risk and operational risk (Ross,
2005). However, it differs in that it is intangible and more intractable due to
insufficient data, strong fat tail characteristics, and limited quantifying metrics
(Walter, 2007). Scholars (Perry and de Fontnouvelle, 2005; Gillet et al., 2010;
Plunus et al., 2012; Fiordelisi et al., 2013) relate bank reputational risk directly to
operational risk, defined by Basel Committee on Banking Supervision (2005, p.
140) as “the risk of losses resulting from inadequate or failed internal processes,
people and systems or from external events. This definition includes legal risk but
excludes strategic risk and reputational risk”.
Managing reputational risk is perhaps more important for the financial sector
(especially banking) than any other industry. This is because reputation is a key
asset of financial and banking firms whose activities are primarily built on trust
(Macey, 2013; Fiordelisi et al., 2014). However, the highly complex and opaque
nature of the banking business2 make it rather easy for banking firms to deceive
2 Banks conduct an array of activities off the balance sheet and have their assets’ structure changing at fast space, causing the problem of information asymmetry (Morgan, 2002). Higher opacity makes it more challenging to distinguish between the ‘fine and ‘crooked’ banks as they all make similar statements about what they are going to deal with your money and how reliable they are (Macey, 2013). Economists often dub this situation “the adverse selection problem”.
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their customers. Bank managers might also have more incentives to engage in
illegal and inappropriate activities (such as money laundering) for their own private
gains since they perceive such activities can be easily hidden and not easily
detected. Theory suggests that rational individuals will only invest in banks to
whom they trust as it is a major tool in financial markets in helping resolve
information asymmetries (Vanston, 2012). This trust can be earned and nurtured
through two primary channels – government regulation and corporate reputation.
The former works directly towards preventing, punishing, and deterring corporate
wrongdoings. However, their effectiveness is often questioned, especially during
the GFC turmoil, with corporate scandals shaken shareholders’ confidence and
trust in bank boards and management, raising further doubt about the
trustworthiness of managers and the prominence of corporate governance in
deterring corporate misbehaviour.
Despite the importance of managing reputational risk, only a small number
of studies have examined reputational losses of banks (Perry & de Fontnouvelle,
2005; Plunus, Gillet, & Hübner, 2012; Fiordelisi, Soana, & Schwizer, 2013; Fiordelisi
et al., 2014; Cumming, Leung, & Rui, 2015).3 These studies primarily use event
study methodology to estimate reputational loss — the difference between the
market value of equity lost following an operational loss event and the announced
3 Throughout my thesis, alternative terms for reputational loss include reputational damage, reputational cost, reputational consequence ditto, reputational sanction, and reputational penalty.
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operational loss amount. Most of these studies relate the magnitude of bank
reputational loss around regulatory enforcement to event characteristics and bank-
specific characteristics. Others examine the effects of external governance
mechanisms on reputation loss (Perry & de Fontnouvelle, 2005; Cumming et al.,
2015). I contribute to this literature by examining whether internal governance
controls explain reputational loss around regulatory enforcement action
announcements in the U.S banking industry. My findings provide additional
empirical evidence to a branch of literature stating that corporate governance
can also influence critical intangible outcomes, such as legitimacy and reputation
(Musteen, Datta, & Kemmerer, 2010; Larkin, Bernardi, & Bosco, 2012; Baselga-
Pascual, Trujillo-Ponce, Vähämaa, & Vähämaa, 2018).
1.2 Research aims
My thesis aims to answer the following three research questions. First, I
ask whether well-governed banks are less likely to be the target of regulatory
enforcement actions. Second, I ask whether banks suffer from reputational loss
following the announcements of regulatory enforcement action, and if so by how
much. I judge economic significance by the size of the loss in market value of
the bank subsequent to the announcement of enforcement actions. Third, I ask
whether the magnitude of reputational loss around the announcement of
regulatory enforcement actions is more or less severe in well-governed banks.
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The last question tests the association between corporate governance and
reputation loss.
1.3 Contributions
I contribute to the existing literature in several ways. I contribute to the
existing literature in several ways. First, I contribute to the literature on the
effectiveness of board governance in the banking sector. Studies that examine
the effects of board attributes on fraud have concentrated exclusively on non-
financial firms (Agrawal & Chadha, 2005; Farber, 2005). My study represents
empirically examines the relation between corporate governance and the
occurrence of corporate misconduct in the financial industry. In examining the
relation between governance quality and the likelihood of regulatory enforcement
actions, I address the broader question of what kind of boards are more effective
in their monitoring role than others. Previous studies have explored the impact of
corporate governance on firm outcomes, reporting inconsistent results. Taking
board diversity as an illustration, some studies have documented a significant
positive association with firm performance (Campbell & Minguez-Vera, 2008;
Herring, 2009; Adams &Ragunathan, 2015); no support for the performance-
enhancing role of board diversity (Carter et al., 2010); and a negative association
between board diversity and firm performance (Adams & Ferreira, 2009). I find
robust empirical evidence of relations between various corporate governance
variables and the likelihood of enforcement actions.
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Second, my study contributes to the debate on the relation between internal
governance and risk-taking in the banking industry (Beltratti & Stulz, 2012; Minton
et al., 2014). Enforcement actions issued by banking regulators are another
suitable risk measure relative to other more conventional bank risk measures. My
findings provide evidence as to the average effectiveness of bank governance
towards their ethical behaviour.
Third, my study contributes to the thin literature that assesses the market
reactions to and reputational loss around enforcement action announcements in
the financial services sector.
Last but not least, previous studies (Gillet et al., 2010; Fiordelisi et al.,
2013, 2014) limit the determinants of banks’ reputational loss to event
characteristics (Gillet et al., 2010), external governance mechanisms as proxied
by Gompers et al.'s G-index4 (Perry and de Fontnouvelle, 2005) and financial
characteristics, such as firm size and leverage (Fiordelisi et al., 2013). My study
extends this line of research by determining the effects of board governance on
the magnitude and probability of bank reputational loss following regulatory
enforcement actions. My study is in response to Fiordelisi et al.'s (2013) call for
4 The G-index is the governance index developed by Gompers et al. (2003), comprising of 24 antitakeover provisions. A higher value of the G-index indicates greater managerial entrenchment and weaker shareholder rights.
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research on factors impacting reputational loss, an often discussed but little-
researched topic.
1.4 Key findings
For a sample of 355 enforcement actions against 210 U.S. banks between
2000 and 2014, I summarize the following main findings. First, I find evidence
that banks with a larger board size (whose directors are diverse in their age) are
associated with a lower probability of regulatory enforcement actions. This finding
is consistent with the argument that more diverse board spend more time and
efforts to overseeing management (Anderson et al., 2004). Managers of those
banks, due to stringent supervision by the board, are less inclined to commit
wrongdoings. In contrast, powerful CEOs (whose CEOs also occupy the chair
position) are associated with a higher probability of severe regulatory enforcement
actions, providing evidence supporting Hypothesis 5. I also find that the likelihood
of technical misconduct is positively associated with more powerful CEOs but
negatively associated with a more diverse board in terms of directors’ tenure.
Further, the likelihood of non-technical enforcement actions is negatively related
to banks whose boards are large, busy and diverse in terms of directors’ age.
Second, I find a non-linear relation between board heterogeneity (board
size and diversity) and likelihood of corporate misconduct. Specifically, these
findings suggest that a larger and more diverse board (in terms of directors’
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tenure) appear to initially reduce the likelihood of misconduct, but as board size
and diversity (in age) exceed 18 and 0.74 respectively, the probability of
misconduct increases. These findings provide evidence supporting the trade-off
argument of board heterogeneity (de Andres & Vallelado, 2008; Wang & Hsu,
2013). That is, the benefits of acquired knowledge and experience domains
provided by a large pool of directors are offset by increased conflicts and
coordination problems among them, hindering the board’s monitoring effectiveness.
For the severe misconduct sub-sample, I find that the propensity of
misconduct is non-linearly associated with the proportion of busy directors and
board diversity in terms of directors’ age and tenure. These findings suggest
banks with directors holding multiple external board positions can initially mitigate
the likelihood of bank misconduct, but as the proportion of these busy directors
grows beyond 8 percent of board size, their monitoring role diminishes and
eventually results in a rise in the likelihood of misconduct. This non-linear relation
between board busyness and likelihood of regulatory enforcement actions provides
evidence supporting both Reputation Hypothesis and Busyness Hypothesis. While
Reputation Hypothesis posits that multiple directorships are indicative of better
director quality and capability in monitoring managerial behaviour and decisions
(Fama & Jensen, 1983b), Busyness Hypothesis argues that with directorships in
several firms, busy directors may not be able to allocate sufficient time and
efforts to effectively fulfil their monitoring responsibilities at every single firm
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(Morck et al., 1988; Core et al., 1999; Shivdasani & Yermack, 1999). Similarly,
variation in directors’ age and tenure initially reduces, but eventually increases
the likelihood of severe misconduct as variations exceed 0.14 and 0.9 respectively.
For the non-severe sub-sample, only board size exhibits a non-linear relation with
the likelihood of enforcement action. The cut-off point is 18 directors, consistent
with the results from the full sample.
I also find that board size and board busyness exhibit a non-linear relation
with the likelihood of technical misconduct cases. This suggests that board
diversity initially mitigates the likelihood of technical misconduct, but as board
size goes beyond 14 directors and the proportion of busy directors exceeds 7.7
percent, the likelihood of technical wrongdoing increases. There is no evidence
of such a non-linear relation between other governance proxies and non-technical
misconduct.
Third, I present evidence that U.S banks suffer substantially from informal
(reputational) penalties following the announcement of enforcement actions, similar
to the findings of earlier studies (Cummins, Lewis & Wei, 2006; Gillet et al., 2010).
Event study results for the whole sample show that reputational loss is significantly
negative by up to 0.74 percent (in relative to equity loss) for three event windows
[-5,5], [-10,10], and [-10,5]. Across all event windows (except [-1,1]), over fifty
percent of reputational loss are negative and statistically significant at the 10
percent level. In sum, my results provide evidence that the market reacts negatively
25
on average to the announcement of regulatory enforcement actions. The market
imposes a more significant reputational penalty (5 percentage point higher) on
violating banks that received severe and technical enforcement actions than those
receiving non-severe and non-technical actions.
Finally, I find evidence that larger boards are associated with less
reputational damage following the announcement of regulatory enforcement
actions. This finding is consistent with the argument that investors are confident
that larger boards have better problem-solving capabilities toward complex tasks
such as overcoming potential cost of regulatory enforcement actions. Specifically,
an increase of one director is associated with 1.5 percent less reputational loss.
When adding the squared terms of governance measures to the regressions, I
also observe a non-linear relation between board heterogeneity (board size and
board diversity) and bank reputational loss following the announcements of
regulatory enforcement actions. A larger and more diverse board (in terms of
gender and directors’ age) reduces reputational damage, but as the board size,
proportion of female directors and the average directors’ age grow beyond 13
directors, 18 percent, and 49 respectively, the magnitude of reputational damages
intensifies. These findings support prior studies of a trade-off between board
heterogeneity and firm outcomes. Even though a larger and more diverse board
reinforces monitoring of management, its effectiveness starts to diminish as board
heterogeneity grows beyond a certain limit (Adams and Ferreira, 2009; Harjoto et
26
al., 2015). This non-linear association between board heterogeneity and bank
reputational loss is robust for the [-3,3] and [-5,5] windows.
1.5 Thesis structure
The remainder of my thesis is organized as follows. Chapter 2 provides a
summary of important theories on corporate reputation and a review of
reputational loss measurement. Chapter 3 reviews the relevant empirical literature.
Chapter 4 provides hypotheses development. Chapter 5 describes data, variable
measurement, research methodology, and presents the summary statistics. Chapter
6 presents and discusses the empirical results. Finally, Chapter 7 concludes my
thesis.
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CHAPTER 2
BACKGROUND
2.1 Introduction
This chapter discusses the main corporate reputation theories, including
institutional theory, signalling theory, agenda-setting theory, identity theory,
stakeholder theory and resource-based theory. I also provide a review of
measures of reputational loss and empirical findings.
2.2 Definition of Corporate Reputation
Corporation reputation is an amalgamation of different stakeholders’
perceptions about the firm as a result of their indirect and direct experiences
with the firm relative to its rival counterparts (Gotsi & Wilson, 2001; Chun, 2005).
As such, when evaluating a firm’s reputation, it is best to involve both attribute-
and audience-specific assessment questions like “reputation for what?” and
“reputation to whom?” (Shapira, 2015).
Waddock (2000, p. 323) views firm reputation as “the organization’s
perceived capacity to meet its stakeholders’ expectation”. In this regard, not
meeting this expectation will erode a firm’s reputation while meeting and exceeding
it will help consolidate the firm’s reputation. This is despite the fact that reputation
is a hard to mimic (Dhalla & Carayannopoulos, 2006) since stakeholders often
28
use the firm’s past actions as the basis for their reputation assessment toward
the firm (Milgrom & Roberts, 1982; McGuire et al., 1988; Fombrun & Shanley,
1990). Direct observation of a firm’s abilities and intentions is nearly impossible.
Shapira (2015) suggests that reputation can be viewed as the cash value
of the confidence and trust invested in the firm by various stakeholder groups. It
is a valuable intangible asset that yields numerous benefits to firms, including
attracting investors and customers (Fombrun, 1996), enabling supra-competitive
prices to be charged (Fombrun and Shanley, 1990; Rindova et al., 2005), requiring
less investment on the commercialization of products and services (Fetscherin,
2015), achieving more favorable trading terms with suppliers (Fombrun & Shanley,
1990; Rindova et al., 2005), attracting and retaining the best employees
(Fetscherin, 2015) at lower wages (Karpoff, 2010), and so forth.
Scholars have developed six conceptual theories of corporate reputation,
namely agenda-setting theory, identity theory, stakeholder theory, resource-based
theory, institutional theory, and signalling/impression theory. These theories have
been widely used to examine key determinants of reputational outcomes as well
as the impact of corporate reputation on individuals, corporations, and industries.
For the purpose of this study, institutional theory and signalling/impression theory
are discussed in more details as these two frameworks are key to arguing the
link with corporate governance.
29
2.3 Theories of Corporate Reputation
2.3.1 Institutional theory
According to institutional theory, firms which adopt the prevailing
institutionalized rules considered as being proper, adequate, rational and
necessary, are sheltered from having their conduct questioned and become more
successful and legitimate (Meyer & Rowan, 1977; DiMaggio & Powell, 1983). In
this regard, institutionalized rules are portrayed as “many of the positions, policies,
programs, and procedures of modern organizations are enforced by public opinion,
by the views of important constituents, by knowledge legitimated through the
educational system, by social prestige, by the laws, and by the definitions of
negligence and prudence used by the courts” (Meyer & Rowan, 1977, p. 343).
Firms comply with these widely accepted norms because they do not want to be
discriminated against (van der Walt & Ingley, 2003). Such firms can use the
established legitimacy to attract a continued flow of social support to secure
their survival prospects (Meyer & Rowan, 1977; Pfeffer & Salancik, 1978; DiMaggio
& Powell, 1983).5 All of these actions ultimately bolster the firms’ reputation. As
5 Legitimacy is defined as “a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions” (Suchman, 1995, p.574). The link between legitimacy and reputation remains questionable among institutional theorists. According to King and Whetten (2008), firm reputation is an extended version of firm legitimacy. Within a particular social identity prototype, firms that conform to the prototype’s minimum standards are perceived as having legitimacy while firms that conform to the prototype’s ideal standards are seen as having reputation.
30
such, there is expected to be a positive link between adherence to institutionalized
rules and firm reputation.
In the context of corporate governance, the theory suggests that firms
which adopt certain governance choices conforming to institutional (socially
acceptable) norms are viewed favorably by stakeholders, leading to better firm
reputation (King & Whetten, 2008; Musteen et al., 2010). However, as to what
makes up good governance and which governance characteristics are the most
desirable remain debatable (Cadbury, 2002; Cascio, 2004; Sonnenfeld, 2004). In
response, a number of researchers use the prevailing agency logic, referring to
the act of maximizing shareholder value while limiting managerial opportunistic
behaviour (Daily et al., 2003; Fiss & Zajac, 2004; Davis, 2005; Aguilera & Jackson,
2010), to distinguish between firms with good and bad governance (Zajac &
Westphal, 2004).
To the extent that governance choices are linked to the fundamental agency
logic, these choices are likely to be more positively viewed and symbolize “good”
governance, regardless of whether or not these choices have a positive impact
on financial performance. For instance, even though it has been difficult to prove
a link between a CEO duality structure and financial performance (Dalton et al.,
1998), the dual structure apparently heightens CEOs’ opportunistic behaviour and
improper management, thereby symbolizing a poor governance choice (Bednar,
2012). In addition to the agency logic, Musteen et al. (2010) emphasize that the
31
general consensus among regulators and corporate watchdog groups can be used
as a reference for governance benchmarks.
2.3.2 Signalling/Impression theory
Signalling theory, as initially developed by Akerlof (1970) and Spence (1973),
refers to the interaction between two parties in the presence of information
asymmetries. The party with more information (the sender of signals) has its
own discretion to select whether and how to convey that information. The party
with less information (the receiver of signals) decides on how to infer the
transmitted information. A similar intuition is applied to corporate reputation. Due
to information asymmetry, external stakeholders have to count on a variety of
corporate signals in forming their rational assumptions about the firm’s current
state, abilities, and future intentions (Spence, 1973; Fombrun and Shanley, 1990)
and in shaping their judgments about the firm’s relative reputational merits
(Spence, 1973; Certo et al., 2001; Basdeo et al., 2006). In other words, reputation
can be seen as the outcome of a signalling process, where firms purposely use
visible signals as tools to improve their standing and reputation in the market.
The literature often views corporate signals as indications of firm quality,
and the signals can take either financial or non-financial forms, such as
accounting performance (Fombrun & Shanley, 1990), financial structure (Ross,
1977; Myers & Majluf, 1984), dividend policy (Bhattacharya, 1979), share
32
repurchases (Dittmar, 2000), board composition and attributes of board members
(Certo, 2003; Certo et al., 2001), and so forth. To have a significant effect,
corporate signals must meet the following two criteria: (i) they must be observable;
and (ii) they must be difficult or costly for low-quality individuals/firms to imitate
(Ross, 1977).6 Take outside directors as an illustration of a credible signal. First,
this information about the board is observable as it is publicly available on proxy
filings in the EDGAR database.7 Second, this signal is costly since outside directors
who hold positions on the board of low-quality firms run the risk of getting their
reputation tarnished in the managerial labor market.8
Some corporate signals can be formed when firms adhere to norms and
practices that are widely accepted in the environment in which they operate.
That is, signals can be created in combination with institutional logic. For example,
Miller and Triana (2009) note that recruiting female members on the board can
act as a credible signal of firm quality as it reflects the firm’s willingness to
6 Other examples to illustrate two criteria of signals are when firms repay their debts or distribute dividends back to shareholders. The signal sent from these actions can be easily observed through official announcements and financial statements; and only high-quality firms can commit to pay consecutive payments over the long term while low-quality firms attempting to mimic this signal will eventually experience financial distress. 7 All U.S. corporations, domestic and foreign, are required by law to file forms with U.S. Securities and Exchange Commission (SEC) electronically through EDGAR (the Electronic Data Gathering, Analysis, and Retrieval system). 8 Several scholars examine the potential costs of being affiliated with a financially distressed corporation. For instance, Gilson (1989) find that fifty-two percent of defaulted firms in his sample suffer senior management (board chair, president and CEO) turnover, and importantly, none of these departing managers are employed by other exchange-listed corporations for at least three years after departure.
33
conform to the diverse cultural norms that are commonly promoted. This in turn
helps firms avoid being discriminated against, and gain a better reputation in the
eyes of the firm’s stakeholders.
2.3.3 Agenda-setting theory
According to agenda-setting theory, the mass media play an important role
in setting the agenda of public discussion and directing public attention toward
specific individuals and issues (Wartick, 2002; Carroll & McCombs, 2003). The
media are crucial to the reputation-building process (Rindova et al., 2006) since
they control both the content of and technologies that spread information about
particular firms to a large public audiences. That is, information about issues and
events on the media helps stakeholders form their impression and opinions of
specific firms (Deephouse, 2000). Accordingly, the more frequently the firms are
covered by the media, the more public attention they will receive. Firms with more
favourable media coverage are more likely to be assessed positively by their
stakeholders. In addition, the more prominent the specific attributes of firms that
are emphasized by the media, the more likely stakeholders are going to affiliate
them to the firms (Carroll & McCombs, 2003; Carroll, 2011).
34
2.3.4 Identity theory
Identity theory suggests that firms over time develop coherent self-defining
organizational attributes that are most central, enduring and distinctive (Albert &
Whetten, 1985). By central, it is meant that identity is concerned with those
elements that are primary rather than peripheral. By distinctive, it means that
identity comprises of a set of primary characteristics that represent the similarities
and differences between the organization itself with its counterparts. By enduring,
it means that identity is focused on those primary features that are relatively
consistent over time and space, rather than those that are irregular or short-
lived. This identity may be reflected in values, beliefs, mission, the structures and
practices, organizational climate, and so forth, that are widely shared and taken-
for-granted within the organization (Ashforth & Mael, 1989). As such, organizational
members (especially top management) can develop some sense of “who we are
as an organization” and “what we do” (Gioia et al., 2000; Corley & Gioia, 2004).
By speaking or acting on the behalf of the organization, they are able to
communicate that identity to internal and external stakeholders (King & Whetten,
2008).
35
2.3.5 Stakeholder theory
Stakeholders are groups of individuals in the environment within which firms
operate, and who are perceived as the direct and indirect targets of actions or
communications firms should make to attract resources, or to build and sustain
their legitimacy (Fombrun, 2012). Stakeholder theorists have considered a multiple
stakeholder approach in defining corporate reputation. For instance, corporate
reputation is “a perceptual representation of a company’s past actions and future
prospects that describe the firm’s appeal to all of its key constituents” (Fombrun,
1996, p. 165). In other words, reputation is a collective and multidimensional
construct as a result of aggregating the opinions, perceptions, and attitudes of a
firm’s various stakeholders (Clarkson, 1995; Donaldson & Preston, 1995; Post &
Griffin, 1997). As such, a firm can have multiple reputations rather than a single
one. For this group of researchers, while “image” is regarded as merely outsiders’
perceptions, reputation takes into account the perceptions of both internal (e.g.,
employees, managers) and external stakeholders (e.g., customers, investors,
suppliers, or community members).
2.3.6 Resource-based theory
Resource based theory focuses on the outcome of having a good reputation and
is often applied at the post-action stage (Walker, 2010). In a resource-based perspective,
corporate reputation is conceptualized as a valuable and rare intangible firm asset due
36
to its unique, socially complex, and highly causally ambiguous features, and being
difficult to imitate or substitute (Dierickx and Cool, 1989; Barney, 1991; Fombrun, 1996;
Deephouse, 2000). As such, firms with favourable reputations gain a competitive
advantage over their competitors and enjoy a great many benefits, including reducing
the flexibility of industry rivals (Caves & Porter, 1977), attracting investors and
customers (Fombrun, 1996), accessing capital resources at a low cost (Beatty & Ritter,
1986), achieving more favorable trading terms with suppliers (Fombrun & Shanley,
1990; Rindova et al., 2005), enabling supra-competitive prices to be charged
(Fombrun and Shanley, 1990; Rindova et al., 2005) and enlarging customer loyalty
(Milgrom & Roberts, 1982), attracting productive employees (Gray & Balmer, 1998) and
retaining the best employees (Fetscherin, 2015) at lower wages (Karpoff, 2010),
and so forth. Eventually, the benefits identified by the resource-based view suggest
that reputation drives value creation and boosts firm profitability.
2.4 Estimating reputational loss
Quantifying a firm’s reputation is empirically challenging due to its multi-
dimensional construct which takes into account changes in perceptions of different
stakeholder groups (Tonello, 2007). Researchers overcome this problem by
measuring loss in reputation, using the event study methodology, when news
about a firm’s adverse behaviour (when a firm lies, cheats, and steals) is released
(Perry & de Fontnouvelle, 2005; Gillet et al., 2010). It is during such events that
37
stakeholders downgrade their expectations and beliefs about a firm’s quality, and
adjust their trading behaviour accordingly (Shapira, 2015). The loss arises since
stakeholders are likely to withdraw their support (to various extent) from the
offending firm and/or change the terms and conditions on which they are willing
to trade with the firm. For example, customers demand a lower price and investors
demand a higher return. In the extreme, they may switch to the firm’s competitors
and no longer desire to do business with the offending firm.
Specific cases of negative outcomes discussed in the literature include
lower future sales (Barber & Darrough, 1996; Karpoff et al., 1999; Murphy et al.,
2009), higher costs of capital and trade credits (Graham et al., 2008; Murphy et
al., 2009), increased costs of new monitoring and control practices, and greater
executive turnover and leadership disruption (Desai et al., 2006; Karpoff et al.,
2008a). In addition, the culpable firm may inevitably incur real losses when its
managers have to devote significant resources to regulatory investigation and be
away from the business operation. All these real negative consequences of
corporate misconduct on firm value directly constitute reputational loss.
Reputational loss can thus be thought of as the aggregate of diminished
trading opportunities when firms violate market norms. It is measured by the
present value of the negative outcomes on the firm’s costs and operations
following the revelation of corporate misconduct (Klein & Leffler, 1981; Shapiro,
1983; Jarrell & Peltzman, 1985).
38
Other terms, such as reputational penalty, reputational sanction, and
reputational risk9 are often used as synonyms of reputational loss (Karpoff &
Lott, 1993; Murphy et al., 2004; Cummins et al., 2006; Gillet et al., 2010; Johnson
et al., 2014; ).10 In the law (legal) literature, reputational sanction can also be
referred as “shaming sanction”, where shaming is defined as “the process by
which citizens publicly and self-consciously draw attention to the bad dispositions
or actions of an offender, as a way of punishing him for having those dispositions
or engaging in those actions” (Kahan & Posner, 1999, p. 368).11
The link between formal (legal) and informal (reputational) sanctions has
been the subject of intense debate in the law literature. To some authors, these
two sanctions are substitutes (Baker & Choi, 2013); to others, they are
complements (Baniak & Grajzl, 2013; Shapira, 2015). As substitutes, selecting one
type of sanction means requiring less of the other in attaining the desired level
of deterrence (Baker & Choi, 2013). However, as complements, supervisory
information such as legal sanctions plays a key role in producing information
9 Even though few studies (March and Shapira, 1987) define reputation risk as a series of possible reputational gains and losses experienced by a given firm, it merely refers to reputation losses in my thesis to be consistent with previous studies that examine reputational loss in banking industry. 10 Throughout my thesis, alternative terms for reputational loss include reputational damage, reputational effect, reputational cost, reputational consequence, reputational sanction, reputational penalty and loss to reputation. 11 Kahan and Posner (1999) discuss the role of shaming sanctions as a substitute punishment for imprisonment. While imprisonment takes away the offender’s physical liberty, ‘shaming’ sanction punishes the offender by targeting at his/her self-esteem and reputation.
39
that is useful to market participants in pricing firm securities and shaping
corresponding reputational sanctions (Jordan, Peek, & Rosengren, 2000; Posner,
2000; Baker & Malani, 2011; Baniak & Grajzl, 2013; Shapira, 2015). Shapira (2015)
suggests that the market, if left alone, tends to form inaccurate reputational
assessments towards culpable firms, which would lead to under-deterrence or
over-deterrence of corporate behaviour. To be specific, when unfavorable news
about the firm is revealed, market participants appear to react quickly,12 but often
encounter difficulties in interpreting the news properly due to insufficient
information. As such, it is likely that they might underreact to certain misbehaviour
while overreacting to others. For example, market players may disregard alerting
signals and continue trading with “crooked” firms (under-reaction) or stop
transacting with perfectly fine ones (over-reaction). However, in reality, the market
is rarely left alone, often operating in the shadow of the law. Information produced
by the legal system helps fill the knowledge gap about culpable firms, providing
market players with better information to update their initial reputational judgments
about the firms. For large firms, Shapira (2015) argues that the market already
acknowledges and reacts to corporate misconduct even prior to the intervention
of regulators. Consequently, the real role of the legal system is to generate
12 This is under the assumption that the market is at least semi-strong efficient, where security prices incorporate the announcement of all publicly available information unbiasedly and instantaneously (Khanna, 1996).
40
second opinions on how things happened, and thus helps the market adjust its
reputational assessments.
Estimating the magnitude of reputation loss remains a formidable
challenging task in research on corporate reputation. Previous studies measure
reputational loss from the perspective of various stakeholder groups, including
shareholders (Gillet et al., 2010; Fiordelisi et al., 2013, 2014), bondholders (Plunus
et al., 2012), and customers (Armour, Mayer, & Polo, 2017). In the stock and
bond markets, event study methodology is the primary method used to estimate
reputational damage. Of all trigger event types, operational loss events are often
selected when estimating the magnitude of reputational loss in financial and
banking firms.
The abnormal return (AR) on the announcement day of an operational loss
event (time 0) represents the total market loss, inclusive of both operational loss
and reputational loss (Perry & de Fontnouvelle, 2005; Gillet et al., 2010). To
isolate the reputational loss effect, AR at time 0 is adjusted for the amount of
operational loss incurred as follows:
i
iii CAPMARKET
OLOSSARREPAR_
_ 0,0, +=
where for firm i at time 0, ARi,0 is the abnormal return estimated from the single
factor model; OLOSSi is the dollar amount of the operational loss suffered; and
MARKET_CAPi is the firm’s market capitalization. When the magnitude of the
41
operational loss incurred is unknown on the event date, the loss amount known
at a later date can be used.
Because reputational loss may take some time to be impounded in security
prices, the cumulative AR_REP, denoted CAR_REP, is often computed. This is
obtained by aggregating the AR_REPs over the event window t1 to t2. Statistical
significance of the mean return is usually computed using Patell’s (1976) variance
adjustment for abnormal returns (Gillet et al., 2010) or Boehmer et al.’s (1991)
variance Z-statistics (Cummins et al., 2006; Fiordelisi et al., 2013, 2014).
Depending on the nature of the event, reputational loss can be measured
with a minor adjustment to mitigate an overestimation of the loss. Taking financial
misstatement events as an example, Karpoff et al. (2008) suggest subtracting not
only the legal costs (operational loss) but also a readjustment effect from the
loss in market value. This readjustment effect reflects “the portion of the observed
loss in share values that reflects an adjustment to the value the firm would have
had if its financial statements had never been cooked” (p. 596).
For bonds, the CAR is interpreted as entirely due to reputational loss since
“the purely mechanical loss due to operational loss events is first supported by
the shareholders, as the equity bears the first loss” (Plunus et al., 2012, p. 69).
That is to say, shareholders are first to bear the operational loss, leaving almost
no mechanical loss suffered by bondholders. As such, the bond’s CAR exclusively
42
represents the reputational loss, where the ARs are measured by the difference
between the actual bond returns and the bond market index returns.13
Some researchers take stock market reactions as their measure of
reputational loss. Perry and de Fontnouvelle (2005) estimate the following simple
cross-sectional linear regression relating the CAR for each event to the ratio of
operational loss to market value:
ε+=i
ii CAPMARKET
OLOSSpCAR_
*0, .
The regression model is absent an intercept because when there is no
announced operational loss (OLOSS=0), the average CAR should theoretically be
zero. In this equation, p indicates the strength of the market reaction to the
operational loss event. In the absence of reputational loss, p is equal to one. If
the market overreacts to the operational loss announcement, p exceeds unity.
Johnson et al. (2014) provide a novel approach by constructing three
measures of reputational loss for fraud firms from customers’ perspective. Their
first construct is the hazard rate, measured by the number of relationship
terminations per unit time t as the time interval approaches zero. A higher hazard
rate means that the relationship is more likely to be terminated, representing
more severe customer reputational sanctions. The second construct is the
13 Plunus et al. (2012) use the Barclays Capital U.S. Corporate Investment Grade index to obtain data on the bond index return.
43
percentage of sales of the fraud firm to the number of large customers, with
greater percentage declines associated with heavier reputational penalties. The
third construct is the percentage of cost of goods sold to large customers, with
declining values being interpreted as evidence of reputational sanctions imposed
by customers.
Researchers are increasingly considering the timing of market reactions in
their estimation of reputational loss. Gillet et al. (2010) identify the following three
time frames for each operational loss event: (i) the press cutting date when
operational loss information is mentioned by the press; (ii) the recognition date
when the firm reports the operational loss; and (iii) the settlement date when all
losses are materialized, i.e., the loss amount is known and definite. These three
events have been considered in the bond market by Plunus et al. (2012).
2.5 Summary
This chapter provided the theoretical background to my thesis. I detailed a
number of theories on corporate reputation, including institutional theory,
signalling theory, agenda-setting theory, identity theory and stakeholder theory. I
also discussed how reputation loss is estimated from the perspective of various
stakeholder groups, including shareholders, debtholders, and customers.
44
CHAPTER 3
LITERATURE REVIEW
3.1 Introduction
In this chapter, I provide a summary of the literature on corporate
misconduct. I review both the financial and the non-financial literature on
reputational loss, including a discussion of empirical reputational loss estimates
and the various factors (a set of event characteristics, corporate governance, and
firm-specific characteristics) that are correlated with reputational loss. Lastly, I
provide a review of past studies on the determinants of operational/reputational
loss, and a short review of the literature on bank governance.
3.2 Studies on likelihood of corporate misconduct
A significant body of the literature suggests a link exists between fraud
occurrence and corporate governance. Dechow et al. (1996) connect earnings
manipulation with characteristics of weak governance structures. Using a sample
of accounting enforcement actions issued by the SEC, they find evidence that
firms manipulating earnings are more likely to have: (i) boards of directors
dominated by management; (ii) a CEO who simultaneously serves as chairman of
the board; and (iii) a CEO who is the firm’s founder. In contrast, they find that
45
these firms are less likely to have: (i) an audit committee; or (ii) an outside
blockholders on the board.
Building on Fama and Jensen's (1983b) agency theory, Beasley (1996)
hypothesizes that the viability of the board as an internal control mechanism is
enhanced by the inclusion of outside directors. This is because the external
market of directors prices them according to their monitoring performance. In
other words, reputational concerns in the director labor market can incentivize
outside directors to represent the interests of shareholders, and thus become
better monitors. Consistent with this expectation, Beasley (1996) finds the inclusion
of outside board members increases the board’s monitoring effectiveness, reducing
financial statement fraud.
Uzun et al. (2004) examine U.S. firms accused of fraud over the 1978-2001
period, employing a long list of governance proxies. Their major finding is that if
the board (and audit committee) has a high percentage of independent outside
directors, corporate fraud is less likely to occur. Other board characteristics,
including board size, frequency of meetings, and CEO/chairman duality are not
significantly associated with the likelihood of corporate wrongdoing.
Besides the presence of outsider directors, Chen et al. (2006) extend the
determinants of the incidence and severity of fraud to other boardroom
characteristics, including the number of board meetings and tenure of the chair.
Frequent board meetings may be a sign of increased vigilance and oversight of
46
top management of the firm. At the same time, the frequency of board meetings
may increase in times of financial distress or in times of controversial decisions
that may involve illegal or questionable activities. Using a sample of enforcement
actions of the Chinese Securities Regulatory Commission (CSRC) between 1999
and 2003, Chen et al. (2006) find a positive association between the number of
board meetings and fraud occurrence. Their explanation links frequent board
meetings to the fact that directors know there are some questionable activities
that the firm has engaged in (or about to engage in) and this requires a lot of
debate, which results in more meetings. They further argue that the impacts of
the tenure of the chairman on corporate wrongdoing can go two ways. On the
one hand, a new chair may have limited knowledge of the business and so fraud
perpetrated by others may be easier to accomplish. On the other hand, longer
tenure may lead to entrenchment and over-confidence if the chairman feels (s)he
can get away with fraud. Chen et al. (2006) find evidence in support of the former
argument.
Johnson et al. (2009) provide evidence in support of a positive relation
between CEO tenure and the level of entrenchment, leading to a higher incidence
of corporate wrongdoings. They argue that CEOs with longer tenure may have a
greater ability to influence other executives and employees to commit fraud. This
is especially so for CEOs closer to retirement age or those serving on fewer
47
boards since (s)he may be less affected by reputational loss if caught committing
fraud.
Lee, Mande and Son (2010) investigate the effects of corporate governance
quality on the incidence of illegal option backdating.14 They find that firms with
a lower level of governance quality are less likely to backdate stock options given
greater oversight over managers. Specifically, firms with a larger proportion of
independent directors, larger board and more frequent board meeting are less
likely to legally backdate stock options. Additionally, CEO demographic information
such as age, tenure, and ownership also influences the likelihood of option
backdating.
Previous research associates the characteristics of audit committees with
the likelihood of corporate fraud. While Beasley (1996) finds that the presence of
an audit committee has no effect on financial statement fraud, Dechow et al.
(1996) and Beasley et al. (2000) report that audit committees help minimize fraud
in the US.
Another feature includes the presence of a financial expert on the
board/committees. Using data from Canada, Park and Shin (2004) provide
evidence that simply increasing the proportion of outside directors per se does
14 Option Backdating refers to the act of intentionally changing the original grant date of an option award to a date when the stock price of the firm was particularly low. In this way, managers can maximize their compensation.
48
not deter earnings management; it is only when outside directors have expertise
in accounting and finance that they are able to deter earnings management.
Similarly, Agrawal and Chadha (2005) find that for a firm whose board/audit
committee includes at least one outside directors with an accounting (or finance)
background, the probability of account restatement is about 0.31 (0.23) points
lower than that for a control firm without such a director. They argue that the
absence of accounting and finance expertise renders outside directors ineffective
in curbing accounting errors and fraud.
A series of studies link earnings manipulation to the compensation of
executives. CEO compensation usually consists of salary, bonuses, restricted
stocks, stock options, and long-term incentive plans (LTIP). Among these, certain
components, such as bonuses, restricted stocks, and stock options, have values
that depend on short-term firm performance related to earnings or stock price.
As such, strong equity incentive might entice management to focus too much on
meeting short-term stock price targets and maximizing their private benefits, while
paying less attention to risk management controls and disclosure levels (Eng &
Mak, 2003; Nagar, Nanda, & Wysocki, 2003). Previous empirical evidence also
shows that the likelihood of a misstated financial statement increases greatly
when the CEO has very sizable holdings of in-the-money stock options (Efendi,
Srivastava, & Swanson, 2007).
49
Firm size has also been identified to influence the occurrence of corporate
misconduct. Larger firms usually have better internal controls systems (O’Reilly et
al., 1998), and face greater pressure to comply with societal expectations (Demsetz
& Lehn, 1985) than smaller firms. Larger firms are thus expected to less likely
commit wrongdoings. Consistent with these arguments, Burns, Kedia and Lipson
(2010) find a negative association between firm size and financial misreporting
for a sample of 845 U.S. firms between 1997 and 2002.
Financial health indicators (e.g., profitability and capital structure) are also
thought to be related to the likelihood of corporate wrongdoings. Since cost of
bank misconduct tends to be smaller for poor performing firms than financially
healthy firms, management of poorly performed firms have stronger incentives to
engage in fraud to inflate earnings to alleviate the adverse impact of poor
performance on their job security and compensation (Kellogg & Kellogg, 1991;
Maksimovic & Titman, 1991). Consistent with these arguments, Persons (2005)
investigates financial statement fraud reported by Accounting and Auditing
Enforcement Releases (AAERs) and finds that fraud firms have lower profitability
than no-fraud firms.
On the contrary, others argue that more profitable firms are less
constrained, allowing firms to devote more resources to internal control
(Chernobai, Jorion, & Yu, 2011). However, at the same time, profitability might
expose the firm to greater operational risk due to the presence of moral hazard
50
(Chernobai et al., 2011). For instance, employees might be more inclined to
embezzle funds when money is “left on the table”. The authors, however, find no
association between profitability and the likelihood of operational loss events (the
coefficient is positive but insignificant across all models).
One of the most significant “red flag” fraud indicators is the presence of
rapid growth within the firm. Firms that are growing more rapidly are expected to
face greater pressure to maintain high growth rates (Carcello & Nagy, 2004a,
2004b). This pressure increases the likelihood that management engages in
practices to maintain the appearance of rapid firm growth. In addition, it is
potentially harder to monitor executives of firms with higher growth opportunities
(Johnson et al., 2009), providing some space for firm executives to undertake
opportunistic behaviour. Indeed, empirical evidence shows that firms with higher
growth opportunities have a higher likelihood of class action lawsuits (Masulis &
Mobbs, 2017) and enforcement cases (Chen, Cumming, Hou, & Lee, 2016).
Younger firms are also expected to have higher incidence of corporate
fraud for several reasons. First, young firms may lack the resources and
experiences to fulfil the requirement of public markets. Beasley (1996) suggests
that the longer a firm has traded in public markets, the more likely it has made
changes to comply with requirements of public markets. Second, younger firms
could still be in the process of developing internal control procedures (Chernobai
et al., 2011). Third, young firms face greater pressures to meet earnings
51
expectations since they often experience difficulties in assessing capital markets
and thus rely too heavily on earnings to fund growth (Weisskopf, 2011). Consistent
with these arguments, younger firms are found to have higher incidences of
misrepresentation (Carcello & Nagy, 2004a, 2004b), internal control weaknesses
(Doyle, Ge, & McVay, 2007) and suffer from greater operational risk events
(Chernobai et al., 2011) than more established ones.
A number of studies have focused on the incidences of operational loss in
financial institutions. Chernobai et al. (2011) argue that firms with weak external
governance controls, as proxied by a high G-index, experience operational risk
events more frequently. Their argument is based on the premise that managers
in firms with a high antitakeover index prefer a “quiet life” since they are shielded
from the discipline of the market for corporate control. As such, the managers
do not actively manage risk, leading to a higher incidence of operational risk
events. Based on a sample of 925 operational loss events in 176 U.S. financial
institutions between 1980 and 2005, Chernobai et al. (2011) show that every four
points increase in G-index (from 25th to 75th percentile) is associated with a 2.5-
fold increase in the frequency of operational risk events.15
15 For sub-categories of event types, a change of 4 point in G-index increase 2.6 times increase in the number of events for “clients, products and business practices” and 2.1 times for other events. The internal and external frauds do not seem to be affected by this external governance variable.
52
Chernobai et al. (2011) examine the impact of board governance
mechanisms on the frequency of operational loss events. They hypothesize that
the incidence of operational loss events is smaller in firms with more auditors
sitting on the board, greater board independence, larger boards, and more
frequent board meetings. They argue that: (i) a higher proportion of auditors on
the board ensures transparency and consistency in the risk management process,
thus mitigating the occurrence of operational loss events; (ii) greater board
independence improves monitoring and internal control functions, thus reducing
the likelihood of operational losses; and (iii) complex firms with larger boards are
exposed to greater operational risk events as they are more difficult to monitor
and control. Using Poisson regressions on a sample of 925 operational risk events
among 176 U.S. financial institutions between 1998 and 2005, they present
evidence showing that the occurrence of operational loss events is indeed
associated with these three board-related variables and the direction predicted.
Chernobai et al. (2011) argue that operational loss events are more
prevalent in financial firms offering higher CEO incentives with CEOs incentivized
to take excessive risk to improve firm performance. Consistent with their
expectation, they find firms offering greater option- and bonus-based
compensation (relative to salary) are more likely to suffer from operational risk
events. Further, firms with more internal control weaknesses such as younger
firms, firms with more business segments, and firms with elevated levels of credit
53
risk experience operational risk events more frequently. Their results hold across
different operational risk event types, consistent with the lack of internal control
as the common source of operational risk. Older firms with more effective risk
management practices are less likely to experience operational loss events. The
reverse is found for younger firms which are more likely to be in the process of
developing internal control procedures. More complex firms, which operate in
multiple segments and face greater challenges in monitoring and controlling firm
operation, are found to have a higher incidence of operational loss. So are firms
with higher credit risk, measured by equity volatility and cash-to-assets ratio, Tier-
1 capital, market-to-book ratio, and Merton’s (1974) distance to default.
Wang and Hsu (2013) extend the work of Chernobai et al. (2011) by covering
a longer time period from 1996 to 2010.16 The authors propose two opposing
arguments for a relation between board diversity and the occurrence of
operational loss events. One the one hand, firms with a diverse board can benefit
from an extensive pool of knowledge, organizational experience, and creativity in
problem solving. This allows the firms to effectively manage operational risk,
leading to a lower likelihood of operational losses. On the other hand, as board
heterogeneity increases, the board’s effectiveness is reduced due to greater
coordination obstacles and failure to reach final agreements. This leads to a
16 While Chernobai et al. (2011) cover a sample period from 1998 to 2005, Wang and Hsu (2013) cover a sample period from 1996 to 2010.
54
higher incidence of operational risk events. Their findings confirm the latter
argument. Additional evidence shows that firms with a higher number of
independent directors are less likely to experience operational risk events relating
to clients, products, and business practice (CPBP) and fraud.
3.3 Studies on reputational loss
3.3.1 Reputational loss magnitude
There is a large literature documenting the size of reputational loss. This
literature is summarized in Table 3.1. Across past studies, the estimated
reputational loss varies from zero to 93.5 percent of total equity loss. ‘Corporate
fraud’ incurs the highest reputational loss at 93.5 percent (Karpoff & Lott, 1993).
Tracking closely behind are ‘punitive damage awards’ at 84 percent (Karpoff &
Lott, 1999) and ‘product recalls’ at 78 percent (Jarrell & Peltzman, 1985).
Reputational loss is negligible for firms that commit ‘regulatory violations’,
‘environmental violations’, and ‘frauds affecting unrelated parties’. For these types
of misbehaviour, market discipline tends to come in the form of legal fines rather
than reputational penalties. In particular, firms that commit offences that are not
directly harmful to customers, suppliers, or shareholders (such as environmental
violations) do not seem to incur significant reputational losses possibly perhaps
because such fraudulent behaviour do not have a direct impact on the firm’s
future sales and operating costs.
55
Reputational losses vary significantly across industries. Using a sample of
banks and insurance companies between 1978 and 2003, Cummins et al. (2006)
note that the stock market reacts more adversely to operational loss incurred in
the insurance industry than in the banking industry. This finding is consistent with
their expectation that investors assign greater penalties to firms with a larger
surprise factor. Since fraud events are less expected in the insurance industry,
their occurrence results in more severe market penalties. Alternatively, the lower
reputational loss in banks may be explained by banks having improved operational
risk management strategies following the release of Basel II framework, as
compared to insurance companies.
Analyzing a sample of operational loss announcements in the European
financial sector, Biell and Muller (2013) find that operational loss is highest in
the investment banking industry than in the retail banking, insurance, and asset
management industries. The authors interpret this finding as showing that the
investment bank industry is more competitive and riskier, and thus more likely to
suffer from greater loss consequences than other industries.
The magnitude of reputational loss also varies across countries. Gillet et
al. (2010) analyze a sample of U.S. and European financial firms, and find that
reputational damage varies between these two markets. In particular, for all event
windows around the press cutting date, the reputational loss is negative and
statistically significant for U.S. firms, but positive and significant for European
56
firms. This finding suggests that while U.S. firms suffer from greater reputational
damage, European firms experience market reactions that are significantly lower
than the magnitude of the operational loss amount.
Contrasting findings are documented for the banking industry. Specifically,
using Boehmer et al.'s (1991) parametric test, Fiordelisi et al. (2014) find that
reputational damage is proportionately larger in European banks than in U.S.
banks. The authors propose two main explanations for this finding. First, investors
tend to impose additional penalties on firms with a larger operational loss. Since
European banks suffer a larger magnitude of operational loss than U.S. banks,
they thus experience greater reputational damage. Second, since the stock
markets are more efficient in U.S. than in Europe, its sanction mechanisms are
more effective. That is, the drop in stock price should only reflect the amount of
operational loss, but not the additional penalty to the bank (reputational loss).
Tanimura and Okamoto (2013) find that the stock market reactions to
financial misrepresentation are more severe in Japan than in the U.S., implying
that Japanese firms place greater importance on corporate reputation than their
U.S. counterparts, perhaps a cultural dimension of reputation. They explain that
it is conceivable that reputational damage is more severe in Japan where
businesses and customers place greater value on corporate brand names.
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Table 3.1 Magnitude of reputational loss in prior literature
Type of misconduct Literature Obs. Sample Period CountryAverage reputational
loss as a percentage of equity loss
Crimminal fraud Karpoff and Lott (1993) 132 1978-1987 U.S 93.51%Punitive damage awards Karpoff and Lott (1999) 249 1983-1995 U.S 84%Product recalls Jarrell and Peltzman (1985) 32 1974-1982 U.S 77.54%Financial missrepresentations Beneish (1999) 64 1987-1993 U.S 57.43%
Karpoff, Lee and Martin (2008b) 585 1978-2002 U.S 66.55%Tanimura and Okamoto (2013) 160 2000-2008 Japan 94.39%
Airplane crashes Mitchell and Maloney (1989) 56 1964-1987 U.S 67.22%Employment violations Hersch (1991) 260 1964-1986 U.S 66.67%Fraud of related parties Murphy et al. (2009) 75 1982-1996 U.S 45.41%
Armour et al. (2017) 26 2001-2011 U.K 83.93%Other product failures Prince and Rubin (2002) 44 1985-1995 U.S 46.12%Antitrust violations van den Broek et al. (2012) 66 1998-2008 Netherlands 33%Procurement fraud Karpoff et al. (1999) 396 1983-1995 U.S 25.45%Environmental violations Karpoff et al. (2005) 198 1980-2000 U.S 0.00%Unrelated party violations Murphy et al. (2009) 79 1982-1996 U.S 0.00%
Armour et al. (2017) 14 2001-2011 U.K 0.00%
58
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3.3.2 Determinants of reputational loss
Characteristics of loss events
The size of reputational loss also depends on whether the adverse
behaviour stems from a temporary or lasting mistake. The magnitude is expected
to be more severe if the firm’s bad behaviour are due to a deep-rooted
organizational flaw (e.g., a collapse of checks and balances), rather than a
temporary mistake (e.g., a dishonest low-level employee). For example, Mitchell
and Maloney (1989) find that market reactions vary according to how the press
(the Wall Street Journal) reports airline crashes. When the press announces that
the crash resulted from internal causes, such as maintenance problems, the
market reacted very negatively. However, the market does not react as negatively
when the press attributes the crash to external factors, such as unexpected
weather conditions.
Similar evidence is documented in the financial sector. Perry and de
Fontnouvelle (2005) find that firm value declines on a one-to-one basis with
losses caused by external events and by over twice the loss percentage in cases
involving internal fraud. The authors coin these findings as the “smoking gun”
effect. Losses caused by external factors are viewed as one-off events, whilst
losses due to internal fraud have a lingering effect, increasing the probability of
further costs in the future through, for instance, a loss in sales or a reduction
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in future cash flows. In fact, the announcement of internal fraud may signal to
investors that there are fundamental internal control problems at the firm,
exposing the firm to further reputational damages in the future.
Gillet et al. (2010) report greater reputational loss in fraud than in ‘clients,
products, and business practices (CPBP)’ events17 (-6 vs. -2.2 percent, respectively).
Their finding suggests internal fraud is likely to trigger reputational damage due
to a decrease in market demand (Karpoff & Lott, 1993), increased suspicion and
distrust on management trustworthiness and competence (Palmrose et al.,2004),
and signals of fundamental internal control problems (Perry & de Fontnouvelle,
2005).
In contrast, Plunus et al. (2012) examine the European and U.S. bond
markets and find the reputational effect is more negative for CPBP events than
for fraud events. The authors interpret this finding as that “debtholders dislike to
a greater extent operational events that translate some kind of involuntary
weakness of the financial institutions” (p. 71). CPBP events cause a degradation
in the firm’s intrinsic credit quality embedded in the yield spread and bond
valuation.
17 Gillet et al. (2010) pay specific attention to CPBP and internal fraud events since the former accounts for 72% of their sample and the later receives much attention in Perry and Fontnouvelle (2005).
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For a sample of eight Australian banks during the period 1990 and 2007,
Moosa and Silvapulle (2012) find no evidence that internal fraud events generate
a greater loss in market value than other types of opeational loss events. The
authors suggest that external fraud events are as likely as internal fraud events
in causing reputational loss since both events are due to management
incompetence.
Reputational loss is also explained by whether investors are aware of the
actual operational loss amount incurred. Gillet et al. (2010) argue and find that
investors tend to overreact to operational loss announcements for which they do
not know the actual amount. They explain that by not disclosing the magnitude
of the operational loss, the firm is perceived by the market as attempting to hide
the extent of the loss. Consequently, investors put downward pressure on the
share price as compensation for the adverse information hidden by the firm.
Using the parametric test statistics of Patell (1976) to assess the
significance of reputational losses surrounding the first press cutting, Gillet et al.
(2010) find that for loss events with known magnitude, most of the market equity
declines can be explained by the operational loss amount, leaving reputational
losses to be insignificant. However, when the market does not know the extent
of the operational loss, CAR(Rep) is significantly negative across all event windows.
Plunus et al. (2012) observe similar findings in the bond market, with
reputational loss being more negative for unknown losses than for known losses
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(-1.88% vs. -1.64%, respectively). These findings suggest that participants in the
bond market award transparency. However, for a sample of European financial
firms, Sturm (2013) find no evidence that reputational loss is discriminated on
the basis of investors’ knowledge of the actual loss size.
Research is inconclusive regarding the effects of the magnitude of
operational loss on reputational loss. While Gillet et al. (2010) find stock market
participants react more negatively to the announcement of small operational
losses than large operational losses, Fiordelisi et al. (2014) find bank reputational
damage is independent of the operational loss amount. In the bond market,
Plunus et al. (2012) find that the affected financial firms are penalized at a rate
that is increasing with the magnitude of the operational loss. Differences in
findings across these studies maybe due to ambiguity in what constitutes a “large”
operational loss. For example, while Gillet et al. (2010) define large operational
losses as losses higher than the median of 0.29 percent of market value, Plunus
et al. (2012) use 15 percent of market value as a cutoff, and Fiordelisi et al.
(2014) use a cut-off of USD $10 million.
A number of empirical studies have shown that for operational losses
involving related parties, total equity losses cannot be fully explained by the
magnitude of legal sanctions (Karpoff & Lott, 1993; Alexander, 1999). This leads
to the presumption that market-imposed reputational loss should be greater for
related-party operational losses than for third-party operational losses. Related
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parties are more likely than third parties to change the (favorable) terms of
contract with the firm when the odds of losses are against them, resulting in
substantial market value declines and reputational loss. A salient example of
misconduct affecting third parties is provided by Karpoff, Lott, & Wehrly (2005)
with the disposal of toxic chemicals into municipal storm sewers by an
electroplating firm. Although the fishermen downstream were severely affected by
the disposal, the firm’s customers had no direct incentive to lower demand for
the firm’s products since the disposal did not affect the quality of the product.
As such, the electroplating firm suffered no significant reputational loss from this
environmental violation.
Shapira (2015) revisits this idea and explains it in a different way, arguing
that “the process of translating bad news into reputational assessments requires
not just facts about what happened but also interpretations of how thing
happened” (p.8). Stated differently, public transmission of bad news relating to a
corporation does not lead stakeholders to immediately impose reputational
sanctions on the culpable corporation. Instead, other interpretations of corporation
misconduct are required, with stakeholders only penalizing firms whose bad news
adversely affects stakeholders’ future transactions with the firm.
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Firm-specific characteristics
The nature of the relationship between reputational loss and firm size has
received considerable attention in the literature, with opposing arguments
developed and empirical findings documented. On the one hand, it is argued that
reputational impact of operational loss events is smaller for large corporations
than for small ones. One argument is that large firms have more reputable brand
names and are more able to handle the reputational impacts of an allegation
(Murphy et al., 2004). Also, large firms tend to have more diversified (multiple)
business lines, with little spillover between them (Armour et al., 2017). As such,
market penalties, both in terms of legal and reputational sanctions of the affected
business line, are less likely to affect the overall value of the firm. Additionally,
the richer information environment of large firms renders their operational loss
announcement less informative to the market, thus lessening the magnitude of
reputational damage. Consistent with these arguments, Armour et al. (2017) find
reputational penalties by customers decreases with firm size. In the bond market,
Plunus et al. (2012) find evidence that suggest larger firms are more likely to
receive fewer reputational penalties if they can recognize losses themselves.
Prokop and Pakhchanyan (2013) provide a competing argument. They argue
that large financial firms with large transactions and highly complex operations
have greater exposure to operational risk than otherwise similar firms.
Consequently, large firms are more likely to experience reputational damage.
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Consistent with this view, Fiordelisi et al. (2013) find reputational loss increases
with bank size for a sample of U.S. and European banks.
Reputational damage is also related to the extent to which firm value
depends on future growth opportunities. Gillet et al. (2010) use the price-to-book
ratio (PTB) to proxy for growth prospects and find reputational loss is larger for
U.S. financial firms with higher growth prospects. This finding is consistent with
their argument that growth firms are more fragile and thus suffer greater
reputational damages.
Focusing exclusively on the U.S. and European banking sectors between
2003 and 2008, Fiordelisi et al. (2013) find reputational damage decreases with
the price to book (PTB) ratio, used as a proxy for the level of intangible assets.
All else constant, banks with more intangible assets (i.e., brand, patents or higher
management quality) can more easily counter the reputational impacts of
operational loss events by using these assets to improve bank profitability and
cover the loss.
The association between reputational damage and firm profitability remains
dubious. The operational risk literature offers two opposing arguments on this
association (Chernobai et al., 2011). On the one hand, profitable firms are less
financially constrained and can thus devote more time and effort to promoting
internal control system, mitigating the incidence of operational risk events. This
in turn reduces the likelihood of experiencing reputational loss. On the other
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hand, firm profitability may be positively associated with operational risk events
due to moral hazard problem. For instance, managers are more likely to overinvest
when excess money is “left on the table”. This overinvestment exposes the firm
to higher operational risk, leading to reputational damage. Most empirical studies
support the latter argument. Gillet et al. (2010) observe that reputational loss
increases with firm profits (as proxied by the return on assets (ROA)) in the
European market. Similarly, Fiordelisi et al. (2013) find a positive association
between bank profits, as proxied by net operating income before depreciation
and amortization, and reputational loss. They argue that profitable banks are
more likely to suffer from greater reputational loss since investors would be more
surprised to see an operational loss event happening in profitable banks.
The amount of equity capital invested in the institution is another
explanatory variable for reputational loss. Sturm (2013) finds that financial firms
with more liabilities suffer more severe reputational effects than firms with more
equity. He interprets this finding as indicating that financial distress intensifies
reputational damage. Plunus et al. (2012) provide similar evidence from the bond
market.
Fiordelisi et al. (2013) argue that poor capitalization could expose banks
to greater moral hazard problem since managers in poorly capitalized banks have
more incentives to take risk at the expense of shareholders in order to attract
additional bank capital. In this case, the problem of moral hazard arises because
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of asymmetric information and the prevalence of agency problem between
managers and shareholders. Investors punish this moral hazard behaviour by
imposing greater penalties on banks with poor capital investment. In contrast,
better-capitalized firms are expected to receive less reputational penalties since
their managers are less incentivized to take greater risk and instead adopt cost
reducing strategies. Analyzing a sample of U.S. banks between 2003 and
2008, they find that reputational penalties are smaller in well-capitalized banks
than in poorly-capitalized banks, consistent with their arguments.
Corporate governance
Prior studies provide evidence in support of a positive relation between
governance on the stock market reaction to corporate misconduct. Carcello et
al. (2011) posit that an audit committee that is independent and rich in expertise
can attenuate the negative stock price reaction to the restatement announcement,
either because the market perceives an audit committee with these characteristics
are more likely to thoroughly investigate and correct the non-GAAP accounting
or because a restatement from a firm with otherwise strong governance structure
is viewed as a less systemic failure. Their findings show the negative stock price
reactions to restatement announcements are mitigated by audit committee
independence, but only when the CEO is not involved in director selection process.
This is because when there is a direct involvement of the CEO in selecting board
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members, the director appointed by the CEO is less likely to monitor the CEO
due to entrenchment (Westphal and Zajac, 1995).
There is a line of research examining the role of reputation as an effective
buffer to negative firm outcomes, such as the announcement of negative earnings
surprises or the announcement of corporate crime. Pfarrer et al. (2010) show
that firms with high quality intangible assets, such as reputation or celebrity,
experience weaker market reactions to negative earnings surprises than firms
without these assets. This is consistent with psychology studies (Heider, 1958;
Kelley, 1973) which propose that beliefs about the ability to perform are built on
positive information (past successes) and that the beliefs are relatively resilient
to negative information which contradicts them (current failure). Whilst failure can
be attributed to many causes, ability can be demonstrated only through persistent
past performance (Skowronski & Carlston, 1987, 1989). As a result, negative
information is less diagnostic for forming and changing impressions about ability.
In other words, since high reputations provide positive analytical frames about
firms’ demonstrated ability to deliver value, negative information is more likely to
be disregarded when a positive and ability-related frame exists.
Building their arguments on the work of Pfarrer et al. (2010), Song and
Han (2015) posit that corporate crime announcements of firms with good
governance helps mitigate the negative market reaction. However, they find no
evidence for their proposition. They explain that investors are perhaps more
69
disappointed with firms that have strong governance mechanisms in place, which
should supposedly prevent agency problems such as corporate crimes from
occurring.
Kouwenberg and Phunnarungsi (2013) find that in Thailand, there is no
significant difference in the negative reactions to news on the violation of listing
rules between firms with high and low governance scores. They argue that when
there is a violation of listing rules in firms with high governance score, the stock
market may recognize good governance as just window-dressing, which may lead
to a more negative reaction by investors. Perhaps the insignificant results are due
to the opposite effects of both positive expectations (less negative) and
disappointed reactions (more negative) to the firm’s governance.
An important strand of the literature examines the association between
corporate governance and reputational loss. Perry and de Fontnouvelle (2005)
use a sample of 115 operational losses of firms worldwide during the period
1974-2004. They propose two alternative arguments on how the level of
shareholder rights could influence reputational loss. First, firms with strong
shareholder rights experience are argued to have smaller reputational damage
from operational loss because “investors are confident that they will have enough
control over management to mitigate any future consequences from the loss” (p.
23). The alternative argument is that firms with strong shareholder rights are
more likely to face greater reputational loss since investors do not expect to see
70
such loss in firms where they have greater control and influence. They use the
G-index of Gompers et al. (2003) to proxy shareholder rights, with a higher value
indicating stronger shareholder rights. To test the reputational loss and
governance relation, they run separate linear regressions for the high and low G-
index 18 subsamples of reputational loss and an interaction term between
operational loss and an internal fraud dummy variable. Their findings show that
the market reacts more than one-to-one to internal fraud announcements. This
market overreaction, which is interpreted as evidence of reputational damage, is
more severe when the operational loss occurs in firms with strong shareholder
rights.
The impact of corporate governance on the magnitude of reputational
damage has also been examined in the bond market. Plunus et al. (2012) find
that the G-index is significantly negatively associated with bond CARs on the
recognition date. This result suggests that the bond market penalty for operational
loss is heavier for firms with weaker shareholder rights, consistent with the first
argument by Perry and Fontnouvelle (2005).
18 High G-index firms are defined as firms having anti-takeover provisions ≥ 10. Low G-index firms are defined as firm having the number of anti-takeover scores <10.
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3.4 Summary
This chapter reviewed the various studies on the likelihood of operational
loss and reputational loss events. There is a large body of literature on corporate
misconduct, investigating its association with governance and other firm-specific
characteristics. Previous evidence shows that firms with better governance are
less likely to engage in corporate wrongdoing. Evidence regarding executive
compensation and propensity of corporate wrongdoing are more mixed.
The literature shows that the magnitude of reputation loss is greater for
operational losses arising from deep-rooted organizational flaws (e.g. internal fraud
events), when it affects related parties, and when investors do not know about
the actual loss amount. The nature of the relationship between reputational loss
and various firm characteristics (e.g., firm size, growth opportunities, profitability,
and capitalization) has also received considerable attention in the literature, with
opposing arguments and mixed empirical findings.
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CHAPTER 4
HYPOTHESES DEVELOPMENT
4.1 Introduction
This chapter develops the hypotheses that concern the relation between
corporate governance and the likelihood of formal enforcement actions, as well
as with reputational loss size. The governance mechanisms I examine are four
widely adopted board characteristics (board size, board independence, board
busyness, and board diversity) and CEO duality.
4.2 The likelihood of bank misconduct
The duties of the board of directors include advising, overseeing, monitoring,
and disciplining managers in the decision-making process (Anderson et al., 2004;
Adams & Ferreira, 2007). The board is also responsible for supervising the financial
reporting process, aiming to improve the quality and disclosure of financial
reporting (Carcello & Neal, 2000; Klein, 2002), and for overseeing procedures
implemented by senior management with regard to risk identification and
prioritization (Tonello, 2007). The board thus arguably plays a key role in
mitigating the agency problem arising from managerial opportunism (Jensen &
Meckling, 1976). When there are prevailing control failures, the board plays a key
73
role in ensuring managers’ open cooperation and information sharing in dealing
with control shortcomings and failures (Watson & Bauer, 2005).
The board plays a particularly important role in banks as it owns fiduciary
duties not only to individual shareholders but also to creditors, depositors and
regulators (Macey & O’Hara, 2003). Further, banking firms are thought to be
inherently more opaque than other types of firms because their assets change
at a fast pace and are thus harder to observe (Challe et al., 2013). This has led
Morgan (2002, p. 874) to describe banks as “black boxes” where “money goes
in, and money goes out, but the risks taken in the process of intermediation are
hard to observe from outside the banks”. The greater complexity and opacity of
activities in banking firms imply that a strong board is thus necessary to ensure
effective monitoring in banks (Levine, 2004; Adams & Mehran, 2012).
Although various theories have been applied to discover the characteristics
of a strong board, there is yet no common agreement in the literature in this
regard (Pathan & Skully, 2010). Of those characteristics that have been studied,
I consider four widely adopted board characteristics (board size, board
independence, board busyness, and board diversity), and CEO duality.
4.2.1 Board size
From a signalling theory standpoint, board size is viewed as an informational
signal of firm quality, which can lead to a better reputational assessment (Musteen
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et al., 2010). One argument is that larger boards are able to devote more time
and efforts to overseeing management and the financial reporting process
(Anderson et al., 2004). With more board members, the work load is distributed
more widely, thus enhancing the board’s effectiveness in monitoring and advising
the managers (Klein, 2002). Also, having a larger board members enriches the
knowledge pool to advise and consult on managers’ decisions, which in turn
increases firm value (Adams & Ferreira, 2007; Andres & Vallelado, 2008) and
reduces the occurrence of operational risk events (Wang & Hsu, 2013). Given
board members typically have close connections with critical external
constituencies, firms with a larger board can enjoy easier access to a wider range
of tangible (i.e., capital, raw materials) and intangible resources (i.e., firm-specific
or industry-specific knowledge) (Certo et al., 2001).
On the other hand, having a too large board may be undesirable. More
specifically, firms with a large board are likely to experience the free-rider problem
relating to management control, i.e., due to costly monitoring, each director may
free-ride as he/she holds the assumption that other directors will do the
monitoring (Hart, 1995). If all directors share this way of thinking, there will be
little or no monitoring at all, making it easier for the CEO to exert his/her power
over board decisions. Larger boards may also be less effective monitors, owing
to greater communication obstacles, increased control conflicts, and reduced
reflexibility and cohesiveness (Simons & Peterson, 2000; Baranchuk & Dybrig,
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2009). With more directors on the board, more time and efforts are needed to
arrange board meetings and to come to agreement on decisions. This slows down
the decision-making process. Consistent with this view, Yermack (1996) and Coles
et al. (2008) find an inverse relationship between firm value and board size.
Chernobai et al. (2011) find a higher likelihood of operational loss events in
financial firms with a larger board.
A large body of literature proposes an inverted U-shaped relation between
board size and firm outcomes. That is to say that there is a so-called optimum
board size where firm outcomes are maximized. According to Jensen (1993), a
firm should limit its board to seven or eight directors. Beyond this limit, the
effectiveness of the board as a monitoring device is impaired, which in turn
creates opportunities for the CEO to engage in self-interested actions. Similarly,
Liption and Lorsch (1992) are in favor of a board with eight or nine directors
because they believe that this size “will be most likely to allow directors to get
to know each other well, to have more effective discussions with all directors
contributing, and to reach a true consensus from their deliberations” (p. 68).
Moody’s report stipulates that banks should aim for a board size of between 10
and 12 members as this helps facilitate a more detailed discussion of key issues
and encourage more active participation of all directors (Watson & Bauer, 2005).
In sum, top bank management can benefit from a larger board’s greater
knowledge pool in terms of managing operational risk events (e.g., legal actions).
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At the same time, this larger board can reduce decision-making quality and hinder
the bank’s ability to determine the appropriate level of board monitoring over
internal compliance and control. Thus, similar to previous studies (Andres &
Vallelado, 2008; Wang & Hsu, 2013), I argue for a trade-off between the benefits
of additional knowledge and the drawbacks of poor decision-making quality as
the board size increases. I expect this trade-off to be reflected in a non-linear
(U-shaped) relationship between board size and the likelihood of regulatory
enforcement actions.
Hypothesis 1: There is a U-shaped association between board size and the
likelihood of regulatory enforcement actions.
In line with my hypothesis, Andres and Vallelado (2008) find that initially
adding a new board member is positively associated with bank performance
(measured by Tobin’s Q). However, as the number of board members exceeds 19,
firm performance starts to diminish. Wang and Hsu (2013) document that adding
a new director can reduce the occurrence of operational risk events but as the
number of directors reaches beyond 14, firms experience more operational risk
events.
4.2.2 Board independence
The board is composed of two types of directors: (i)
‘inside/executive/dependent’ directors who are senior managers, including the
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CEO; and (ii) ‘outside/non-executive/independent’ directors who are not strongly
affiliated with the firm, except as director. While inside directors have the expertise
and experience necessary for strategic decisions, outside directors add to the
board a breadth of expertise and objectivity that limit the level of managerial
entrenchment and expropriation of firm resources (Fama & Jensen, 1983; Walsh
& Seward, 1990; Byrd & Hickman, 1992). This is in line with the agency logic,
according to which shareholders’ wealth is maximized and managerial opportunistic
behaviour is minimized. In addition, outside directors also play an active role in
mitigating the problem of asymmetric information by ensuring that firm
stakeholders have good access to information (Ljubojevic & Ljubojevic, 2008),
thereby making it easier for stakeholders to shape their perceptions about firm
quality.
There are a number of reasons why outside directors are effective monitors.
The first reason lies in the managerial labor market. By maintaining and promoting
their reputation as effective monitors, outside directors are likely to obtain
additional directorships (Fama & Jensen, 1983; Beasley, 1996). This is especially
true for outside directors who recognize that failing to be active monitors exposes
them to substantial reputation damage (Vafeas, 1999). Second, since outside
directors do not have psychological and economic ties to the incumbent
management, they are more willing to disagree with and question managers on
important corporate issues (Carcello & Neal, 2000). Therefore, having additional
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independent directors on the board can reinforce the integrity of senior
management, and ensure sound financial and risk reporting (Anderson et al.,
2004; Barakat & Hussainey, 2013). Consistent with these arguments, empirical
evidence shows that firms with greater board independence are less likely to
commit financial fraud (Beasley, 1996), have fewer operational risk events
(Chernobai et al., 2011; Wang & Hsu, 2013), and accrue greater reputational
capital (Musteen et al., 2010).
Therefore, I posit that banks with higher board independence are less likely
to violate the law and engage in unsafe and unsound banking practices. Banks
with a more independent board are thus less likely to be subject to enforcement
actions by banking regulatory agencies:
Hypothesis 2: There is a negative association between board independence and
the likelihood of regulatory enforcement actions.
4.2.3 Board busyness
There are two competing hypotheses on the effects of multiple board
appointments held by an individual director. The first hypothesis, Reputation
Hypothesis, suggests that multiple directorships are indicative of director quality
and capability in monitoring managerial behaviour and decisions (Fama & Jensen,
1983b), proxying for the director’s reputational capital in the external labour
market (Shivdasani, 1993; Vafeas, 1999). Only competent directors with
outstanding managerial skills, talent, and/or stronger monitoring capabilities are
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highly sought-after and are expected to hold multiple outside board seats. For
this reason, these directors are less likely to misbehave or collude with managers
at the expense of shareholders since these misbehaving activities might expose
them to the risk of reputational damage in the director labour market, leading to
fewer directorships assigned to them in the future.
Firms can benefit from better board’s advising and monitoring functions
since outside directorships provide the directors with opportunities to develop
their expertise and skills, learn about different management styles and strategies
(Carpenter & Westphal, 2001; Perry & Peyer, 2005), and establish a professional
network (Loderer & Peyer, 2002). In the context of banks, Elyasiani and Zhang
(2015) argue that banking institutions due to their complexity and opacity, are
more likely to require greater level of advising and monitoring and thus benefit
more from a busy board, as compared to simple non-financial firms.
In line with the Reputation Hypothesis, prior empirical evidence indicates
that top managers of poorer performing firms are less likely to receive additional
directorships in other firms than are managers of better performing firms. For
instance, Gilson (1990) reports that directors who leave the board of financially
distressed firms obtain approximately one-third fewer directorships three years
after their departures. Similarly, Kaplan and Reishus (1990) find that top executives
of firms that reduce dividends are approximately 50 percent less likely to obtain
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future outside directorships than are top executives of firms that do not decrease
their dividends.
In contrast, the Busyness Hypothesis argues that with directorships in
several firms, busy directors may not be able to allocate sufficient time and effort
to effectively fulfil their monitoring responsibilities at every single firm (Morck et
al., 1988; Core et al., 1999; Shivdasani & Yermack, 1999). Since serving on
multiple boards overcommits an individual (Ferris et al., 2003), a busy board
compromises its monitoring effectiveness and efficiency, and ultimately, destroys
corporate value. In addition, the less vigilant monitoring as a result of
overcommitted directors is likely to trigger greater agency costs, such as increased
litigation exposure for the firm. A large body of the corporate finance literature
studied in non-financial firms provides evidence in support of this view. For
instance, firms with a busy board tend to have excessive CEO pay (Core et al.,
1999); have lower market-to-book ratios, lower profitability, and lower sensitivity
of CEO turnover to firm performance (Fich & Shivdasani, 2007); have a higher
propensity to commit financial statement fraud (Beasley, 1996; Crutchley, Jensen,
& Marshall, 2007); and have poorer reputation for ethical behaviour (Baselga-
Pascual, Trujillo-Ponce, Vähämaa, & Vähämaa, 2018).
Nevertheless, Elyasiani and Zhang (2015) argue that the aforementioned
shirking problems of busy directors are less relevant in banking firms than in
non-financial firms for several reasons. First, bank directors and officers are
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subject to more heightened scrutiny than their non-financial counterparts because
they own fiduciary duties beyond shareholders, which include depositors, creditors
and regulators (Macey & O’Hara, 2003). These directors are also exposed to
greater liability risk because courts can hold them to a higher standard of duty
of care, than directors of non-bank firms, especially in the case of bank failures
(Macey & O’Hara, 2003; Adams & Ferreira, 2012). In addition, bank regulators
impose more severe monetary penalties on bank directors for violating fiduciary
duties (Adams & Ferreira, 2012). Other stakeholders such as counterparties in
banks’ derivatives positions and buyers of bank guarantee services are very risk-
sensitive and thus have a strong preference for safe and reliable banks. These
counterparties tend to switch to other suppliers if the bank misbehaves. All these
reasons help alleviate the negative effects of board busyness on banking firms
and ensure bank directors to remain diligent in performing their monitoring roles.
If busy directors do not shirk their responsibilities, I expect that they will not fail
to monitor the banks’ compliance with safe and sound banking practices.
Hypothesis 3: There is a negative association between board busyness and the
likelihood of regulatory enforcement actions.
4.2.4 Board diversity
Board diversity refers to variations among board members along
demographic (observable) and/or cognitive (unobservable) dimensions (Erhardt et
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al., 2003; Brammer et al., 2007). Examples of demographic diversity are gender,
age, ethnicity, race, and occupational background. Cognitive diversity refers to
differences in perception, knowledge, values, affection, and personality attributes
(Richard, 2000; Hillman et al., 2002; Erhardt et al., 2003; Adams and Ferreira,
2009; Brammer et al., 2009; Coffey & Wang, 2012; Hafsi & Turgut, 2013;
Hagendorff & Keasey, 2012). Most of the existing studies tend to focus on
demographic aspects of board diversity due to lack of instruments for cognitive
diversity.
Diversity in the boardroom can have both positive and negative effects on
shareholder value in the market for corporate control. On the one hand, board
diversity brings various benefits to firms. First, board diversity can serve as a
signal of firm quality and good corporate governance. One argument is that
director heterogeneity brings valuable resources into the boardroom (Conyon &
Mallin, 1997; Burke, 2000). Homogeneity in top management teams is believed to
result in a narrow perspective while diverse top management teams take a broader
view (Singh et al., 2001; Carter et al., 2003). The broader range of experiences
and opinions that emerges causes decision makers to evaluate more alternatives
and more carefully explore the consequences of these alternatives. This results
in a deeper understanding of the complexities of the environment, leading to
more effective problem-solving (Carter et al., 2003; Ramirez, 2003). As with the
arguments for board size, board diversity enriches the knowledge domains,
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perspectives, and ideas to advise and consult on managers’ decisions, which in
turn increases firm value (Adams & Ferreira, 2007; Andres & Vallelado, 2008).
Another benefit of board diversity can be explained from signalling theory.
The characteristics and composition of the board often serve to signal to investors
about the robustness of governance mechanisms in place and the quality of the
firm (Fama & Jensen, 1983b; Beatty & Ritter, 1986), influencing firm reputation
(Pfeffer & Salancik, 1978; Certo, 2003). Board diversity signals firms’ adherence
to social laws and values, and ability to (i) recognize the needs and interests of
different groups of stakeholders; (ii) identify the best strategies that would align
the different interests; and (iii) manage potential conflicts among stakeholders
(Bilimoria & Wheeler, 2000; Miller & Triana, 2009; Harjoto et al., 2015). This in
turn helps firms gain favorable reputation from different stakeholder groups.
In addition, a diverse gender, racial, ethnicity, or occupational make-up is
more likely to raise critical questions “that add to, rather than simply echo, the
voice of management” (Selby, 2000, p. 239). This diverse environment is expected
to “discourage groupthink and create an additional check on management
prerogative” (Ramirez, 2003, p. 849). As board diversity improves, management is
less likely to subvert the interest of stakeholders and the firm is therefore
favorably viewed in the eyes of external constituencies (Brammer et al., 2009). In
contrast, members of a homogeneous board are less likely to want to “rock the
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board”, allowing CEOs much freedom to use tactics to achieve their own personal
goals (Westphal, 1998).
There are also significant economic benefits to selecting suitable board
candidates from the complete pool of available talents rather than discriminating
against particular demographic characteristics (Burke, 1997). Since each board
member typically has close connections with critical external constituencies such
as banks and suppliers, firms with a diverse board can enjoy easier access to a
wider range of tangible (e.g., capital and raw materials) and intangible (e.g., firm-
specific or industry-specific knowledge) resources (Certo et al., 2001). Excluding
certain groups from key decision-making roles may restrict the firm’s access to
these valuable resources (Burke, 2000; Westphal & Milton, 2000), which might
hinder board effectiveness in monitoring.
On the other hand, board heterogeneity may destroy group cohesion and
lead to a board whose members are less cooperative and experience increased
emotional conflict (Lau & Murnighan, 1998). The presence of different viewpoints
on heterogeneous boards may cause coordination problems since different
directors would have different perceptions and opinions on a particular issue
(Forbes & Milliken, 1999). Even though board diversity reinforces better monitoring
of management, too much monitoring causes conflicts, lowers overall performance,
and lengthens the decision making process (Adams & Ferreira, 2009; Harjoto et
al., 2015). As such, diversity on the board could diminish decision-making
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capabilities for complex and ambiguous tasks (Hagendorff & Keasey, 2012), and
reduce board effectiveness in monitoring management performance (Harjoto et
al., 2015). Applied to the context of banks, as conflicts among directors intensify
with board diversity, it could hinder the bank’s ability to determine an appropriate
level of monitoring over internal compliance and risk management. This in turn
would expose banks with a diverse board to more regulatory enforcement actions.
Similar to arguments for board size, I expect a trade-off between the
benefits of additional knowledge domains and the drawbacks of poor decision-
making quality as board diversity enhances. I expect this trade-off to be reflected
in a non-linear (U-shaped) relationship between board diversity and the likelihood
of regulatory enforcement actions.
Hypotheses 4: There is a non-linear association between board diversity and the
likelihood of regulatory enforcement actions.
4.2.5 CEO duality
CEO duality refers to the situation where the CEO also chairs the board
(Boyd, 1995; Rechner & Dalton, 1991). Arguments against the dual leadership
structure are principally based on agency theory, which suggests that CEO duality
results in greater managerial opportunism and agency costs due to the lack of
the separation of the board’s decision control from the CEO’s decision
management (Fama & Jensen, 1983a, 1983b). This reflects a clear conflict of
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interest where a CEO who is responsible for the overall performance of a firm is
also in charge of evaluating the effectiveness of that performance (Donaldson &
Davis, 1991). In this regard, the board’s assessment of firm performance is likely
to be subjective and biased in favor of the CEO, signalling weak board governance
controls (Boyd, 1995; Yermack, 1996). In addition, a CEO-chairperson has more
discretion to influence the decision-making process since the dual leadership
structure provides a wider power base and keeps the locus of control in the
hands of the CEO, weakening the relative power of other groups (Boyd, 1995).
This ultimately could challenge the effectiveness of board monitoring and
disciplining (Mallette & Fowler, 1992).
The arising agency problems and the board’s failure to perform its
monitoring responsibilities caused by CEO duality have been empirically found to
be correlated with numerous adverse outcomes. CEO duality is found to be
positively associated with enforcement actions issued by the SEC for alleged
violations of GAAP (Dechow et al., 1996), lower quality financial reporting (Abbott
et al., 2000; Carcello & Nagy, 2004a, 2004b; Persons, 2005), lower levels of
mandatory employee stock option disclosure (Bassett et al., 2007), and higher
audit fees due to the greater firm inherent risk perceived by audit firms (Tsui et
al., 2001). CEO duality is also associated with a lower likelihood of the CEO being
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fired by the board due to poor firm performance (Goyal & Park, 2002), and
greater levels of CEO performance-based remuneration (Lee, 2009).19
Not only can CEO duality hinder effective board monitoring, it can also
offset the benefits of having an independent board (Jensen, 1993; Mallette &
Fowler, 1992). First, board members are highly dependent on the CEO/chair who
usually sets the board agenda and provides directors with information needed to
make decisions. Second, the CEO/chairperson typically has control over the
selection and nomination of both internal and external directors, and is likely to
favor those who are more prone to follow her lead. Hence, the presence of CEO
duality impairs the ability of the board to act as an independent supervisor of
managerial activity. In line with this view, Bliss (2011) find that for a sample of
Australian firms, directors sitting on dual boards are less likely to demand a high-
quality audit. Similarly, Kamarudin et al. (2012) find that the effectiveness of an
independent audit committee to assure high-quality earnings in financial
statements is compromised when the CEO also occupies the chair position.
Given that CEO duality signals higher agency problems and reduced board’s
monitoring effectiveness, I posit the following hypothesis:
19 The association between CEO duality and financial reporting quality is however ambiguous. Several studies using US. (Beasley, 1996; Abbott et al., 2004; Uzun et al., 2004; Agrawal and Chadha, 2005; Owusu-Ansah and Ganguli, 2010;) and non-US. (Jouber and Fakhfakh, 2012) firms find no such association.
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Hypothesis 5: There is a positive association between CEO duality and the
likelihood of regulatory enforcement actions.
4.3 Bank reputational loss hypotheses
I expect the reputational penalty to be more pronounced in banks with
good governance. This is because the market would be more surprised to see
the occurrence of enforcement actions at these well-monitored banks, and thus
downgrade their beliefs about the effectiveness of the governance mechanisms in
curbing bank misbehaviour. As a consequence, a more negative market reaction
and greater reputational loss are expected to follow. This proposition is also
supported by the institutional and signalling theory perspectives. From the
institutional theory view, banks adopting commonly accepted governance choices
(e.g. increasing board size, board independence) are favorably viewed by outside
stakeholders, leading to better corporate reputation (King & Whetten, 2008;
Musteen et al., 2010). From the signalling theory point of view, a good governance
structure has signalling value that can help firms to bolster their reputation in
the eyes of constituents (Certo, 2003; Basdeo et al., 2006). All of these arguments
suggest that banks with “good” governance have more reputational capital to
lose in the event of regulatory enforcement actions than those with “bad”
governance. Hence, I expect a positive association between corporate governance
and the magnitude of reputational damage:
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Hypothesis 6: In the event of regulatory enforcement actions, banks with “good”
governance experience more severe reputational loss.
Alternatively, if investors perceive good corporate governance as a signal
of better problem-solving capabilities toward complex tasks (Carter et al., 2003;
Ramirez, 2003) such as overcoming potential negative consequences of regulatory
enforcement actions, the reputational effect following the enforcement action
announcement may be small. This is because investors are confident that well-
governed banks can effectively recover from the reputation damage crisis, and
restore their reputation to the state prior to bank misconduct. Given these
arguments, I posit the following competing hypothesis:
Hypothesis 7: In the event of regulatory enforcement actions, banks with “good”
governance experience less severe reputational loss.
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Table 4.1 List of hypotheses This table provides summarizes the seven hypotheses tested in my thesis.
Hypotheses Details
Hypothesis 1: There is a non-linear association between board size and the likelihood of regulatory enforcement actions.
Hypothesis 2: There is a negative association between board independence and the likelihood of regulatory enforcement actions.
Hypothesis 3: There is a negative association between board busyness and the likelihood of regulatory enforcement actions.
Hypothesis 4: There is a non-linear association between board diversity and the likelihood of regulatory enforcement actions.
Hypothesie 5: There is a positive association between CEO duality and the likelihood of regulatory enforcement actions.
Hypothesis 6: In the event of regulatory enforcement actions, banks with a good governance structure experience more severe reputational loss.
Hypothesis 7: In the event of regulatory enforcement actions, banks with a good governance structure experience less severe reputational loss.
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4.4 Summary
This chapter reviews the theoretical framework used to develop the seven
testable hypotheses. Five hypotheses are developed to test the relationship
between bank governance and the likelihood of regulatory enforcement action,
whilst two hypotheses are developed to test the relationship between corporate
governance and bank reputational loss. Bank governance measures of interest
include board size, board independence, board busyness, board diversity, and
CEO duality. Table 4.1 provides a summary of the hypotheses developed in this
section.
Regarding the likelihood of formal enforcement action, I expect that banks
with a strong board (i.e., independent board, more female directors sitting on the
board) have fewer incidences of enforcement actions than those with a weak
board. For board size and board diversity, due to the trade-off between the
benefits of additional knowledge and the drawbacks of poor decision-making
quality, I expect the association between governance and likelihood of enforcement
action to be non-linear (U-shaped).
Regarding bank reputational loss, there are two competing arguments. On
the one hand, if investors view a specific governance choice as better advisors
and monitors over bank managers and the internal compliance procedures, they
would be surprised to see enforcement actions issued against banks adopting
this favorable governance choice. This is supported by the institutional theory –
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i.e., banks adopting a widely accepted norm is more reputable and thus are
expected to have more reputational capital to lose in the event of enforcement
action. Consequently, a greater reputational loss is expected to follow. On the
other hand, investors might penalize these banks less since they are confident
that “good” corporate governance would help the offending banks to overcome
the negative effects of reputation crisis event and to restore to the state prior
to the crisis.
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CHAPTER 5
DATA AND METHODOLOGY
5.1 Introduction
In this chapter, I discuss the sample and research methods used to test
the hypotheses developed in Chapter 4. Section 5.2 provides a discussion of the
sample construction procedure and data sources. Section 5.3 describes the
measurement of bank reputational loss following regulatory enforcement actions,
the measurement of explanatory variables, and the econometric models used to
test the impact of corporate governance on the likelihood of receiving enforcement
actions and bank reputational loss. In Section 5.4, I discuss the descriptive
statistics and correlation matrix of the variables used in the analysis. A chapter
summary is provided in Section 5.5.
5.2 Sample selection and data sources
Information on enforcement actions against U.S. banks, including the name
of the violating banks, issue date, and type of enforcement actions, is extracted
from SNL Financial Database. This database covers all enforcement actions issued
by three federal banking regulators: the Federal Reserve Board (FRB); the Federal
Deposit Insurance Corporation (FDIC); and the Office of the Comptroller of the
Currency (OCC). Additional data relating to the targeted banks, such as CUSIP
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identifier, corresponding parent bank, and bank financial data are obtained from
the Regulated Depositories. The latter is a subset of SNL Financial database,
covering all available U.S. banks which are either historical/adjunct (i.e., being
closed by bank regulators or being acquired by another bank) or operating.
Market information such as stock price data and market capitalization is collected
from the CRSP database, which is also used to identify which banks having parent
banks are listed on the major U.S. stock exchanges (NASDAQ or NYSE).
Data on corporate governance are gathered from the RiskMetrics database.
For 156 banks not covered by the RiskMetrics,20 I collect the corporate governance
data from their proxy statements (Filing DEF 14A) filed with the SEC’s Electronic
Data Gathering, Analysis, and Retrieval (EDGAR) system. Governance variables hand
collected include board size, board independence, board diversity (director gender,
age and tenure) and CEO duality. Information on board independence and CEO
duality is retrieved from Sections Director Independence and Board Leadership
Structure, respectively. To classify director gender, I identify whether the director
has a male (e.g., David, Robert, or Michael) or female name (e.g., Virginia, Mary,
or Helen). For names which cannot be easily identified as belonging to a male
or female, I examine the directors’ qualification and/or past employment records
sections for keywords like “Mr.” or “he”, indicating a male director, and “Mrs.”,
20 There are 251 enforcement actions (out of the total of 355 as summarized in Table 5.1) that have missing governance data, attributing to 156 unique banks according to CUSIP identifier.
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“Ms.” and “she”, indicating a female director. Information on age and tenure
diversity is gathered from Section Proposal 1 – Election of directors.
Table 5.1 summarizes the sampling selection procedures. An initial sample
of 5,950 formal enforcement actions is drawn from SNL Financial database.21 A
merger between this dataset and the list of operating and acquired/adjunct U.S.
banks from Regulated Depositories reduces the sample to 5,586 enforcement
actions. To ensure the availability of stock return and price information, I narrow
my sample to enforcement actions against banks whose shares are traded on
major stock exchanges or those acting as subsidiaries of publicly traded banks.22
This sampling criterion reduces the sample by nearly half to 2,023 enforcement
actions. 23 This sample is then merged with CRSP/Compustat Merged (CCM)
database to obtain information on the bank’s listed exchange. To be included in
the sample, the bank or its parent must be listed on the NYSE or NASDAQ. These
filters result in a sample of 1,099 enforcement actions.
I then identify the parent bank holding company (BHC) of violating banks.
Of the 1,099 enforcement actions, 77 actions are against banks acting as BHCs
21 Informal enforcement actions are not included in the analysis of my thesis since their announcements are not disclosed to the public. 22 Enforcement actions against banks (or those with parent banks) listed on the pink sheets, grey market or over the counter (OTCQB and OTCQX) are excluded from the analysis due to it being tightly held and thus thinly traded in the market, which makes it harder to observe the stock market reactions to enforcement announcements. 23 In SNL Financial database, ownership_status is denoted as “listed” when the banks are listed on pink sheets, grey market, over the counter or major stock exchanges (NYSE and NASDAQ). ‘Non-listed’ or missing value is assigned to private non-listed banks.
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(at the time of announcement of enforcement action). For the remaining 1,022
enforcement actions that are against subsidiary banks, I use SNL’s Regulated
Depositories sub-database to identify the corresponding parent banks. One
disadvantage of this sub-database is that it only provides information on the
ultimate (the latest) parent bank rather than the parent bank over the years.
Hence, additional filtering is needed to ensure the proper selection of the parent
bank’s stock information. More specifically, for banks pertaining to these 1,022
actions, I check whether they are “inborn” subsidiaries or subsidiaries created
from a merger and acquisition.24 This is done by cross-checking with the Merger
and Acquisition dataset (a subset of the SNL Financial database) and manually
checking through the Bloomberg Snapshot website. For those actions targeting
“inborn” subsidiaries, no further classification is required. Enforcement actions
against “acquired” subsidiaries need further verification on whether the merger
and acquisition occurs earlier or later than the announcement of the enforcement
action.25 If the merger and acquisition took place after the enforcement action
issue date, the announcement of the enforcement action might not trigger any
24 Of these 1,022 actions, 350 of which are issued against “inborn” subsidiaries and 672 actions against merged/acquired subsidiaries. 25 Of the total 1,022 actions against merged/acquired banks, 16 of which have major_exchange_score equal to 2, indicating cases where one bank holding company takes over another bank holding company. For these 16 cases, if the issued_date is later than the acquisition_date, the stock information of the acquiring parent bank is used. Alternatively, if issued_date is earlier than the acquisition_date, the stock information of the violated bank itself is used.
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market reaction and thus reputational loss on the acquiring parent bank but on
the previous parent bank. For example, Main Street Bank was ordered by FDIC to
pay civil penalty on 9th Sept 2005, but the bank and its BHC (Main Street Banks,
Inc.) were acquired by BB&T Corporation on 1st June 2006. Clearly, the
enforcement action against Main Street Bank on 2005 would not have triggered
any reputational effect on BB&T Corporation.
Hence, for those actions with an acquisition date 26 later than the
enforcement date, I identify the bank’s previous parent bank using information
reported on the ultimate parent’s press release. Press release is an alternative
source of news and information that bank stakeholders can rely on to gain some
insight into what the bank is doing, including its merger and acquisition activity.
Take the case of Whitney Bank, which was reported to be acquired by Hancock
Holding Company on 4th June 2011. To identify the bank’s historical parent, I
examine Hancock’s news releases about its merger and acquisition deal in 2011.
On 4th June 2011, Hancock reported the completion of Whitney Holding
Corporation and its primary subsidiary (Whitney Bank). Thus, prior to the
acquisition by Hancock, Whitney Holding Corporation was the parent of Whitney
Bank. After assigning the proper parent bank for each enforcement action, an
26 The acquisition date is gathered from three main sources: (i) date_acquisition_acquired as detailed in Regulated Depositories Database (ii) completion_termination_date as detailed in Merger and Acquisition database (a subset of SNL Financial database), and (iii) manually checked from the Bloomberg and the bank’s website.
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additional 536 actions are dropped due to not satisfying the major stock exchange
listing criterion.
Applying the aforementioned criteria results in a sample of 563 enforcement
actions, which can be grouped into the following four categories: (i) actions
against bank holding companies; (ii) actions against “inborn’ subsidiaries; (iii)
actions against acquired subsidiaries for whom the issue date is later than the
acquisition date; and (iv) actions against acquired subsidiaries for whom the issue
date is prior to the acquisition date and their historical parent banks are listed
on major stock exchanges. Of these 563 enforcement actions, 135 are removed
from the sample due to duplicated event dates, typically due to an enforcement
action being targeted both at the BHC and its subsidiary. This criterion reduces
the sample to 428 events.
5.2.1 Controlling for confounding effects
Next, I consider those events with subsequent announcement dates
occurring within +/- 30 days from their previous enforcement actions. To illustrate,
United B&TC (a subsidiary of Farmers Capital Bank Corporation) received two
enforcement actions with event dates on 15th Sept 2010 and 22nd Sept 2010.
Since the time lapse between these two dates is close (within only seven days),
including both events is likely to result in confounding event problems. Twelve
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actions fall in this criterion and thus are dropped from the sample, resulting in
416 enforcement actions.
Of those 416 enforcement actions, I further eliminate those enforcement
actions associated with contaminating events which in prior studies have been
shown to have a significant effect on a firm’s stock prices (Morck & Yeung, 1992;
Cannella & Hambrick, 1993; McWilliams & Siegel, 1997). Instances of typical
contaminating events are earnings announcements (Brown and Warner, 1985);
merger and acquisition activities (Morck & Yeung, 1992); and capital events such
as stock splits, dividends or corporate reorganization (Cannella & Hambrick, 1993).
I identify contaminating events by searching for major announcements about each
bank in the period surrounding the bank’s formal action announcement
(particularly, within +/- 3 trading days of action announcement), 27 including
quarterly earnings announcements, merger and acquisition announcements, and
dividend distribution announcements.
I obtain a bank’s quarterly earnings announcement date (item RDQ) from
the Quarterly Compustat North America Fundamentals dataset. There are 16
earnings announcements that occurred within +/- 3 trading days of enforcement
action announcement. The merger and acquisition data, including M&A
announcement date, acquirer’s CUSIP and buyer’s CUSIP, are collected from the
27 For robustness check, I also use the cut-off of 5 and 10 trading days to define whether the event is contaminating.
100
SNL Merger and Acquisition dataset. There are only 2 M&A activities announced
within +/- 3 trading days of the enforcement action announcement. The CRSP
Stock Events – Distribution dataset is used to collect the announcement dates of
capital events and dividend announcements (item DCLRDT - declaration date).
This CRSP dataset consists of corporate announcements relating to ordinary
dividends, liquidating dividends, exchanges and reorganizations, subscription rights,
splits and stock dividends, notation of issuance, and general information
announcement for dropped issues. There are 32 capital events announced within
+/- 3 trading days of action announcements. The total confounding events
identified are 50 events (16+2+32), with 6 events identified twice. This results in
44 unique enforcement actions dropped from the aforementioned 416
enforcement actions, giving me a total of 372 unique enforcement actions.
An additional 17 actions are dropped due to unavailable stock price data
over at least 150 trading days.28 The final sample consists of 355 enforcement
actions against 210 unique banks according to CUSIP identifier29 over a 15-year
period from 2000 to 2014. A summary of sample selection criteria is provided in
Table 5.1.
28 Among these 17 enforcement actions, there are 11 enforcement action that do not have stock price information over the estimation, while the remaining 6 enforcement actions have stock price data for less than 150 trading days. 29 Or against 270 unique banks according to the SNL identifier. SNL identifier is the unique number permanently assigned by the SNL Financial database for each individual bank. The difference in the number of unique banks is due to the fact that while the SNL identifier refers to each individual bank, the CUSIP identifier refers to the parent bank.
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Table 5.1 Sample construction The table presents the sample selection criteria. The data are retrieved from three different sources: the SNL Financial Database, the Bloomberg Snapshot Site, and the Annual CRSP/Compustat Merged Database. The final sample consists of 355 enforcement actions against banks from 2000 to 2014.
Criteria: Descriptions Dropped Obs. No. of Obs.1. Initial sample of enforcement actions between 2000 and 2014 (ENF_ACTIONS ) 5,950
2. A list of unique firms from the Annual CRSP/Compustat Merged Database (CCM ) between 2000 and 2014
13,322
3. Bank_Status = "Listed" or Parent_Status = "Listed" (LISTED_BANKS ) (3,563) 2,023
4. Merge between LISTED_BANKS and CCM (840) 1,183
5. Banks or those whose parent banks are listed on major stock exchanges (MAJOR_EXCHG )
(84) 1,099
6. BHCs OR 'Inborn' Subsidiaries OR (Acquisition date < Issued date) OR (Acquisition date > Issued date AND the bank itself or its previous parent is listed)
(536) 563
7. Unique event date (135) 428
8. Differences in issued date greater than 30 days (12) 416
9. Other major corporate announcements falling within +/- 5 trading days of the bank's enforcement action announcement
(44) 372
10. Available stock price data for at least 150 trading days (17) 355
Final Sample 355
No. of unique banks (according to SNL identifier) 270
No. of unique banks (according to CUSIP identifier) 210
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5.3 Methodology
5.3.1 Econometric models
Likelihood of regulatory enforcement actions
To examine the impact of corporate governance on the likelihood of
regulatory enforcement actions, I run probit regressions of the following form:
tim
timl
tilj
tij YEARCHARBANKGOVERNANCEEA ,
2014
2000,
5
1,
7
1,0 _ εφδβα ++++= ∑∑∑
===
(5.1)
where subscripts i denotes individual banks, t time period, j alternative corporate
governance measures, and l bank-specific characteristics. α is the constant term,
β and δ are estimated parameters, and ε is the idiosyncratic error term. Year
dummies are included to account for omitted macroeconomic factors. The
dependent variable EA is a dummy that equals one if the bank received a formal
enforcement action, and zero otherwise. A positive (negative) coefficient for a
given covariate suggests a positive (negative) association between that variate
and the likelihood of regulatory enforcement actions.
The sample of banks subject to EAs are matched with control banks that:
(i) has not received enforcement actions within three years of that of the offending
bank; (ii) is from the same industry; and (iii) has total assets within 20% of that
of the wrongdoing bank.
Following previous literature (Farber, 2005; Biggerstaff, Cicero, & Puckett,
2014), industry is defined as the four-digit Standard Industrial Classification (SIC)
103
code. If no match is found, the industry definition is relaxed to a three-digit and
two-digit SIC code. If there is more than one match for each treated observation
(offending bank), I retain the observation with its assets closest to that of the
offending bank. Due to missing value of total assets, 14 treated observations
cannot be matched to a control bank are removed from my sample.
Determinants of bank reputational loss
To investigate the determinants of bank reputation loss, I run pooled
ordinary least squared regressions (OLS) of the following form:
tim
timl
tilk
tikj
tijti YEARCHARBANKCHAREAGOVERNANCEREPCAR ,
2014
2000,
7
1,
4
1,
7
1,0, ___ εφδββα +++++= ∑∑∑∑
====
(5.2)
where subscripts i denotes individual banks, t time period, j alternative corporate
governance measures, k enforcement action characteristics, and l bank-specific
characteristics. α is the constant term, β and δ are estimated parameters, and ε
is the idiosyncratic error term. Year dummies are included to account for omitted
macroeconomic factors. The dependent variable CAR_REP is reputational loss.
Since reputational loss is generally negative, a negative coefficient for a given
covariate suggests a positive association between that covariate and reputational
loss. Definitions of all test variables are summarized in Table 5.2.
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5.3.2 Measurement of bank reputational loss
The abnormal return (AR) for each event within the event period ),( 21 TT is
calculated by subtracting the CRSP value-weighted index return (Rmkt) from the
raw return of the bank’s equity (Ri), expressed as follows:
𝐴𝐴𝐴𝐴𝑖𝑖,𝑡𝑡 = 𝐴𝐴𝑖𝑖,𝑡𝑡 − 𝛼𝛼𝑖𝑖 − 𝛽𝛽𝑖𝑖𝐴𝐴𝑚𝑚𝑚𝑚𝑡𝑡 (5.3)
where α and β are the coefficients estimated over a period of at least 150 days
to a maximum of 250 days ending 31 days prior to the event day (day 0).
Following previous studies (Karpoff et al., 2008; Gillet et al., 2010; Fiordelisi
et al., 2013), reputational loss (CAR_REP) is the cumulative abnormal return
adjusted for legal fines (FINES) over the event period ),( 21 TT . Specifically, the legal
fines following the announcement of the enforcement action is scaled by market
capitalization (MKTCAP) and is added to the abnormal return on event day (day
0) before computing the cumulative abnormal return CAR over the event period:
0,
0,0,0,_
i
iii MKTCAP
FINESARREPAR += (5.4)
∑=
=2
1
21 ,, __T
Ttti
iTT REPARREPCAR (5.5)
Parametric t-statistics for the mean abnormal return is estimated from the
cross-sectional standard errors of abnormal returns, expressed as follows:
𝐶𝐶𝐴𝐴𝐴𝐴𝑇𝑇1,𝑇𝑇2������������ = 1𝑁𝑁∑ 𝐶𝐶𝐴𝐴𝐴𝐴𝑇𝑇1,𝑇𝑇2
𝑖𝑖𝑁𝑁𝑖𝑖=1 (5.6)
105
𝑡𝑡_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = 𝐶𝐶𝐶𝐶𝐶𝐶𝑇𝑇1,𝑇𝑇2�������������
𝑆𝑆𝑆𝑆(𝐶𝐶𝐶𝐶𝐶𝐶𝑇𝑇1,𝑇𝑇2)≈ 𝑁𝑁(0,1) (5.7)
Since the assumption of normal distribution for the abnormal returns (ARs)
should not be taken for granted, I also perform non-parametric tests. The two
non-parametric tests include the Wilcoxon signed-ranks test and generalized sign
test, as discussed below.
The Wilcoxon signed-ranks test for median abnormal returns for each event
is computed as follows:
∑=
+=N
itit ARrankW
1, )( (5.8)
where +)( ,tiARrank is the positive rank of the absolute value of abnormal returns
tiAR , at time point t for bank i. When N is large, W asymptotically follows a
normal distribution with the following mean and variance:
𝑀𝑀𝑡𝑡𝑀𝑀𝑀𝑀 =𝑁𝑁(𝑁𝑁 + 1)
4
𝑉𝑉𝑀𝑀𝑉𝑉𝑉𝑉𝑀𝑀𝑀𝑀𝑉𝑉𝑡𝑡 = 𝑁𝑁(𝑁𝑁 + 1)(2𝑁𝑁 + 1)
12
The test statistic is then defined as:
𝑍𝑍𝑊𝑊𝑖𝑖𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊,𝑡𝑡 = 𝑊𝑊−𝑁𝑁(𝑁𝑁−1)/4�(𝑁𝑁(𝑁𝑁+1)(2𝑁𝑁+1)/12)
(5.9)
106
The sign test (Campbell, Lo, & Mackinlay, 1997) is computed as follows:
𝑡𝑡𝑉𝑉𝑠𝑠𝑀𝑀_𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 = �𝑁𝑁(−)
𝑁𝑁− 0.5� 𝑁𝑁
0.5
0.5 (5.10)
where )(−N is the number of events where the abnormal returns are negative and
N is the total number of events. The null hypothesis proposes that there is a
same probability that, in response to the announcement of enforcement actions,
the CAR of the bank’s shares will be negative or positive. The null hypothesis is
rejected when a significant number of negative CARs is recorded.
5.3.3 Measurement of corporate governance
Board size (BSIZE) is the natural logarithm of the total number of directors
sitting on the bank’s board. Independent board (INDEP_BOARD_PCT) is the
percentage of independent directors to the total number of directors sitting on
the board.
Following Ferris et al. (2003), I use three measures of multiple directorships.
The first, directorships per director is the average number of bank directorships
held by the directors of that bank (denoted as MEAN_DIR). The second measure
is the maximum number of directorships held by any one member of a bank’s
board (denoted as MAX_DIR). The third measure, BUSY_BOARD_PCT, is the
percentage of directors on a board who hold three or more directorships (item
OUTSIDE_PUBLIC_BOARDS from the RiskMetrics database).
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I measure board diversity along three dimensions: gender, age, and tenure.
Gender diversity is measured as the percentage of female directors on the board
(FEMALE_DIR) as in Adams and Ferreira (2009) and Hagendorff and Keasey (2012).
In addition, according to Miller and Triana (2009), only when female directors
hold powerful management and leadership positions, they are able to increase
their visibility to the public. The presence of powerful female directors can thereby
serve as effective signals affecting firm reputation. I use FEMALE_CEO as another
proxy of female directors in the analysis. FEMALE_CEO is a dummy variable, which
equals one if the CEO position is held by a female, and zero otherwise. Following
Hagendorff and Keasey (2012), I use the Pearson coefficient of variation to
measure age and tenure diversity, AGE_CV and TENURE_CV, repectively. It is
defined as the ratio of standard deviation to mean across the board, and thus
captures the dispersion of data (age and tenure) relative to the average. It is
particularly useful for comparing the variabilty of the same or different variables
across the sample when the means are very different.
Following existing literature (Adams et al., 2005; Pathan, 2009), I use CEO
duality (DUALITY) as a proxy for CEO power. DUALITY is a dummy variable, which
equals one if the CEO also holds the board chair position, and zero otherwise.
A CEO is defined as a chairman when the EMPLOYMENT_CHAIRMAN variable
reported in the RiskMetrics database is coded as 1 or “YES”. For banks whose
EMPLOYMENT_CHAIRMAN is missing, a chairman position is identified if TITLEANN
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contains such strings like ‘chairman’, ‘chairwoman’, ‘chair’, or its abbreviated form
like ‘chmn.’
5.3.4 Enforcement action-related variables
The SNL Financial database classifies enforcement actions into 15 types,
nine of which are enforcement actions against the entire bank rather than
individual managers.30 They are (i) ‘cease and desist’; (ii) ‘prompt corrective
action’; (iii) ‘formal agreement/consent order’; (iv) ‘call report infraction’; (v)
‘deposit insurance threat’; (vi) ‘formal memo of understanding’; (vii) ‘order requiring
restitution’; (viii) ‘other fines’; and (ix) ‘sanctions due to violation of Home
Mortgage Disclosure Act (HMDA)’. According to the database, the enforcement
actions that fall within the first three types are more severe compared to the
others due to their impact and significance for banking institutions. Hence, I
create a dummy variable for the level of severity of the enforcement actions
(SEVERE), which equals one if the actions are either one of the above three types,
and zero otherwise. In cases where banks receive both severe and non-severe
actions, SEVERE equals one.
30 The remaining six enforcement action types are targeted against individual managers, including cease and desist against a person, fine levied against a person, hearing notice or other action, other actions against a person, restitution by a person, and sanctions against personnel.
109
Following Nguyen, Hagendorff, and Eshraghi (2016), I also classify misconduct
cases into technical and non-technical types. Misconduct cases are classified as
technical (TECHNICAL) if the enforcement actions are related to violations of
requirements concerning asset quality, capital adequacy and liquidity, lending,
provisions, and reserves. Misconduct cases are classified as non-technical if the
enforcement actions have been caused by failures of a bank’s anti-money
laundering systems, internal control and audit systems, and risk management
systems. Non-technical misconduct cases also include breaches of the
requirements concerning the competency of the senior management team and
the board of directors as well as violations of various laws such as consumer
compliance programs, Equal Credit Opportunity Act (ECOA), and Federal Trade
Commission Act (FTCA). Bank misconducts that cannot be classified as either
technical or non-technical are classified as ‘other’. Examples in this third group
include violating Service members Civil Relief Act (SCRA), 12 C.F.R. Section 564.3
(Appraisals required), and custody requirements for retail repurchase agreements
of the Treasury regulations. The classification for enforcement actions according
to the technicality level is not mutually exclusive, that is, a misconduct can be
classified under multiple categories.
I also control for the primary regulator that supervises the violating bank. I
include two dummies: FRB which equals 1 if the violating bank is overseen by the
FRB and zero otherwise; and OCC which equals 1 if the violating bank is overseen
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by the OCC and zero otherwise. In addition, I define REPEATED as a dummy
variable, which equals one if the violating bank received more than one
enforcement action over the sample period; and zero otherwise. In this context,
the number of enforcement actions against both parent and subsidiary bank within
the same date is counted as only one offence since they are often interconnected.
5.3.5 Measurement of bank-specific characteristics
For regressions of likelihood of regulatory enforcement actions
Following previous literature, I include five bank-specific variables in my
regressions: banks size, ROA, price-to-book ratio, bank age and leverage. Bank
size (BANK_SIZE) is the natural logarithm of the book value of total assets (item
AT in the Compustat/Bank Fundamental database). Larger firms usually have
strong internal controls systems than smaller firms (O’Reilly et al., 1998), and
face greater pressure to comply with societal expectations (Demsetz & Lehn,
1985). Larger firms are thus less likely to commit wrongdoings. Consistent with
these arguments, Burns et al. (2010) find a negative association between firm
size and financial reporting fraud.
Financial health indicators (e.g., profitability and capital structure) are also
related to the likelihood of firm misbehaviour. Maksimovic and Titman (1991)
argue that the costs of bank misconduct tend to be lower for poor performing
firms than financially healthy ones. Kellogg and Kellogg (1991) argue that
management of poorly performing firms have stronger incentives to engage in
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fraud to inflate earnings since they are in fear of the adverse impact of poor
performance on their job security and compensation. In addition, more profitable
firms are less constrained, allowing them to devote more resources to internal
control (Chernobai et al., 2011). However, at the same time, profitability might
expose the firm to greater operational risk due to the presence of moral hazard
(Chernobai et al., 2011). For instance, employees might be more inclined to
embezzle funds when money is “left on the table”. I use LEVERAGE and ROA to
measure a bank’s financial health. LEVERAGE is computed as the ratio of total
liabilities to total assets (item LT divided by AT). ROA is the return-on-asset ratio,
measured as the adding back of depreciation and amortization to the bank’s net
income/loss, all divided by the book value of total assets (item (NI + DP) divided
by AT).
One of the most significant “red flag” fraud indicators is the presence of
rapid growth within the firm. Firms that are growing more rapidly are expected to
face greater pressure to maintain high growth rates (Carcello & Nagy, 2004a,
2004b). This pressure may increase the likelihood that management engages in
fraudulent practice to maintain the appearance of rapid firm growth. PTB is
measured by dividing a bank’s stock market value by its book value.
BANK_AGE is the natural log of the number of years that the bank has
been listed on a national stock exchange. Younger firms are expected to have
higher incidence of corporate fraud for various reasons. First, young firms may
112
lack of resources and experiences to fulfil the requirement of public markets.
Beasley (1996) suggests that the longer a firm has traded in public markets, the
more likely it has made changes to comply with requirements of public markets.
Second, younger firms have to face greater pressure to meet earning expectations,
resulting in a higher incidence of financial misrepresentation (Carcello & Nagy,
2004a, 2004b). Additionally, younger firms could be still in the process of
developing internal control procedures, and thus are exposed to greater
operational risk, e.g., receiving enforcement actions (Chernobai et al., 2011).
For regressions of bank reputation loss
The following eight bank-specific controls are included in my regressions
of bank reputational loss: bank size, bank complexity, leverage, ROA, price-to-
book ratio, equity capital, stock volatility and beta. These financial variables are
collected from the Compustat/Bank Fundamental database.
Large firms are more likely to suffer smaller reputational penalties since
they have more reputable brand names that helps handle the reputational impacts
of allegations and enforcement actions (Murphy et al., 2004). Larger firms also
have a richer information environment which makes operational loss
announcement less informative to the market (Armour et al., 2017). In contrast,
Prokop and Pakhchanyan (2013) and Fiordelisi et al. (2013) assert that large firms
with their large volumes of transactions and highly complexity operations are
113
exposed to greater operational risk than otherwise similar firms, triggering heavier
reputational penalties.
In addition to bank size, I also measure the complexity of a bank
(COMPLEXITY), proxied by the amount of trading and available for sale securities
(item TDST in the Compustat/Bank Fundamental database) as a proportion of
the bank’s total assets (item AT). The amount of trading and available for sale
securities has been identified by Basel II as one of three indicators of bank
complexity. Additional measures of bank complexity include the amount of over-
the-counter (OTC) derivatives and level 3 assets. Unfortunately, data on the
amount of OTC derivatives is not available in the Compustat/Bank Fundamental
database. Though there is a variable named AUL3, which refers to the level 3
assets, but the variable is stated as unobservable (all bank-year observations are
missing).
Prior studies suggest capital structure (LEVERAGE) as another determinant
of reputational damage in financial companies. Sturm (2013) investigates a sample
of European financial companies, and finds that the magnitude of reputational
loss increases with the level of firm liabilities, implying that financial distress
intensifies reputational damage.
Bank profitability is likely to impact on reputational loss as well. Fiordelisi
et al. (2013) find that bank reputational damage increases as profits increase, in
line with the argument that investors are more surprised to see an operational
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loss event happening at profitable banks and tend to penalize these banks more
heavily. I use return-on-asset (ROA) to proxy for bank profitability.
Growth prospects can have an impact on banks’ reputational damage. Gillet
et al. (2010) use PTB to proxy for growth opportunities, and argue that growth
firms are more fragile and thus suffer greater reputational consequences from
operational losses. Fiordelisi et al. (2013) use PTB to proxy for the level of
intangible assets and present that investors assign smaller reputational penalties
to banks with a higher level of intangible assets. All else constant, banks with
more intangible assets can more easily counter the reputational impacts of
operational loss events by using these assets to improve bank profitability and
cover the loss.
Bank equity (CAPITAL) is the bank’s total equity capital as a percentage of
total assets. Total equity capital is item ICAPT in the Compustat/Bank
Fundamental database. Fiordelisi et al. (2013) find bank reputational damage
decreases with the level of bank equity. They argue that investors seem to
penalize poorly capitalized banks more than well-capitalized banks for moral
hazard behaviour. This is because better-capitalized banks have less moral hazard
incentives than poorly capitalized banks, leading to smaller equity losses
experienced by well-capitalized banks.
Fiordelisi et al. (2013) argue that reputational loss suffered by riskier banks
is heavier than that suffered by safe banks which can better absorb the loss. I
115
include two measures of bank riskiness in my analysis: beta and standard
deviation of stock returns. Beta (BETA) is the bank’s risk associated with the
aggregate market returns, or so-called systematic risk, measured by the
covariance of bank stock returns to market returns over 250 trading days ending
21 days prior to the event day. The standard deviation of stock returns
(STOCK_VOL) captures the total risk, comprising both idiosyncratic and systematic
risk components that shareholders are exposed to. It is measured by the standard
deviation of stock returns over 150 days prior to the event day.31
31 The standard deviation of stock returns over 250 days prior to the event day is used for the robustness test.
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Table 5.2 Variables description
Variables Symbol Definition
Panel A: Dependent variables
Likelihood of enforcement actions EA A dummy variable, which equals one if a bank receives an enforcement action in a certain year in my sample period, and zero otherwise.
Bank reputational loss CAR_REP The cumulative abnormal return adjusted for legal fines over a three-day event window [-1,1].
Panel B: Enforcement-related variables
Severe dummy SEVERE A dummy variable, which equals one if the enforcement action is either one of the following three types: "cease and desist", "prompt corrective action", and "formal agreement/consent order", and zero otherwise.
Technical dummy TECHNICAL A dummy variable, which equals one if the enforcement action is related to violations of requirements concerning asset quality, capital adequacy and liquidity, lending, provisions, and reserves; and zero otherwise.
Federal Board Reserve dummy FRB A dummy variable, which equals one if the violating bank is overseen by FRB; and zero otherwise.Office of Comptroller of the Currency dummy
OCC A dummy variable, which equals one if the violating bank is overseen by OCC; and zero otherwise.
Repeated dummy REPEATED A dummy variable, which equals one if the violating bank received more than one enforcement action over the sample period; and zero otherwise.
Board size BSIZE The natual logarithm of the total number of directors sitting on the board. Proportion of independent directors INDEP_BOARD The proportion of independent directors sitting on the board. Average number of bank directorships MEAN_DIR The average number of bank directorships held by the directors of that bank.Maximum number of bank directorships MAX_DIR The maximum number of bank directorships held by any one member of a bank's board. Proportion of busy directors BUSY_BOARD The proportion of directors on the board who hold three or more directorships.Proportion of female directors FEMALE_DIR The proportion of female directors on the board.Female CEO FEMALE_CEO A dummy variable, which equals one if the CEO position is held by a female director; and zero
otherwise.
Panel C: Corporate governance proxies
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Table 5.2 (Continued)
Variables Symbol Definition
Age diversity AGE_CV The coefficient variation of the age of directors on the board, measured by the standard deviation of age of directors divided by the average age of directors.
Tenure diversity TENURE_CV The coefficient variation of the tenure of directors on the board, measured by the standard deviation of the tenure of directors divided by the average tenure of directors.
CEO duality DUALITY A dummy variable, which equals one if an executive holds both the CEO and board chair position; and zero otherwise.
Bank size BANK_SIZE The natural logarithm of book value of total assets.Bank complexity COMPLEXITY The amount of trading and available for sale securities as a proportion to the bank's total assets.Debt-to-asset ratio LEVERAGE The ratio of total debts to total assets. Return-on-asset ROA The adding depreciation and amortization back to net income, divided by total assets. Price-to-book ratio PTB The ratio of stock market price to the book value of bank equity.Bank age BANKAGE The natural logarithm of the number of years that the bank has been listed the national stock
exchange.
Invested capital CAPITAL The ratio of total equity invested in the bank to total assets. Stock return volatility STOCK_VOL The bank overall risk, measured by the standard deviation of daily stock returns over 150 days prior
to the event day.
Beta BETA The bank systematic risk, measured by the covariance of the bank stock returns to the stock market return.
Panel D: Bank-specific characteristics
Panel C (Continued):
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5.4 Descriptive statistics, correlation matrix and sample profile
5.4.1 Descriptive statistics
Table 5.3 provides summary statistics for the full sample. In Panel A, the
mean value of reputational loss is 0.5 percent, with a median value of 0.1 percent
of total market capitalization. Panel B reports that the average monetary penalty
is US$5.719 million. The minimum fine is zero as not all enforcement actions
trigger a monetary fine, whilst the maximum fine is US$350 million, imposed on
JPMorgan B&TC NA - a subsidiary of JPMorgan Chase & Co. in January 2014.
The average fine as a percentage of total market capitalization is just 0.036
percent.
Panel B also shows that on average, more than half of my sample banks
are subject to severe enforcement actions, with nearly 30 percent in technical
category. The number of enforcement actions that are issued by each of the
three regulators (FRB, OCC and FDIC) are similar at 30 percent. The majority (66
percent) of violating banks are repeat offenders, receiving at least two
enforcement actions over the sample period.
Panel C shows that the mean (median) board consists of 10.86 (11)
directors, which is smaller than 16 in the banking study of Cornett et al. (2009),
but close to the average board size (13) of Pathan (2009) for BHCs over the
period 1997-2004. On average, independent directors account for 78.4 percent
of the board, which is comparable to 76.5 percent reported by the recent banking
119
study of Nguyen et al. (2016). The average proportion of directors holding
directorships outside the bank is small, at only 1.3%. However, this represents
only a quarter (95 observations) of my sample banks due to data limitations of
RiskMetrics database.
The average bank board comprises one female director, which is similar to
that reported by Adams and Ferreira (2009) for a sample of U.S. firms during the
period 1996-2003. It is however smaller than the 13.7 percent reported by
Hagendorff and Keasey (2012) for publicly listed U.S. commercial banks between
1996 and 2004. Only 4.7 percent (2.5 percent) of sample banks have a female
CEO (chairman). About 41 percent of CEOs chair the board (DUALITY), which is
comparable to the average of 49 percent reported by Nguyen et al. (2016) for a
sample of 311 enforcement actions over 2000-2013 period.
My sample banks have boards which are heterogeneous in tenure but not
in age. The average age diversity score (AGE_CV) is 0.129, indicating a small
variation in directors’ age across the board. The average tenure diversity score
(TENURE_CV) is 0.738, which suggests a large variation in directors’ tenure. These
average values are close to the averages of 0.150 and 0.727 in the financial
study of Wang and Hsu (2013).
Panel D shows there is a large variation in bank size (BANK_SIZE), with
total assets ranging from US$190 million to US$2,573 billion. The mean (median)
of debt-to-asset ratio is 0.908 (0.909), which is comparable to the values reported
120
by Nguyen et al. (2016). There is large variation in the amount of trading and
available for sale securities, ranging from one percent to 21 percent of the banks’
total assets.
My sample banks have low return on asset (ROA), with a mean (median)
value of 0.001 (0.007). Price-to-book ratio (PTB) or so-called charter value has a
mean (median) of 1.142 (1.005), which is comparable to the mean (median) of
1.503 (1.312) reported by Nguyen et al. (2016). On average, total equity capital
accounts for 17.5 percent of the bank’s total assets. There is a large variation in
STOCK_VOL, varying from the smallest of 0.8 percent to the highest of 20.3
percent. BETA has a mean of 0.85, which is smaller than the 1.34 figure
documented in Fiordelisi et al. (2013) for a sample of European and U.S. banks
between 2003 and 2008.
121
Table 5.3 Summary statistics This table presents the descriptive statistics of reputational loss, enforcement-related variables, corporate governance proxies and bank-specific characteristics during the sample period from 2000 to 2014.
Variables Denoted as Mean Median Min Max SD Obs. Panel A: Reputation lossBank reputation loss CAR_REP 0.005 0.001 -0.358 0.542 0.070 355
Panel B: Enforcement action characteristicsMonetary penalty ($thounsands) 5,718.565 0.000 0.000 350,000.000 37,400.000 355Monetary penalty/Market capitalization (%) 0.036 0.000 0.000 5.084 0.285 355Severity level of enforcement action SEVERITY 0.538 1.000 0.000 1.000 0.499 355Technical dummy TECHNICAL 0.299 0.000 0.000 1.000 0.458 355Office of Comptroller of the Currency dummy OCC 0.296 0.000 0.000 1.000 0.457 355Federal Board Reserve dummy FRB 0.299 0.000 0.000 1.000 0.458 355Repeated offences dummy REPEATED 0.662 1.000 0.000 1.000 0.474 355
No. of board directors 10.863 11.000 5.000 25.000 3.290 315No. of independent directors 8.500 8.000 3.000 18.000 2.678 270No. of busy directors 0.192 0.000 0.000 3.000 0.559 104No. of female directors 1.048 1.000 0.000 6.000 1.089 315Board size BSIZE 2.436 2.485 1.792 3.258 0.273 315Proportion of independent directors INDEP_BOARD 0.784 0.800 0.500 1.000 0.111 270Average number of bank directorships MEAN_DIR 0.703 0.613 0.000 2.053 0.579 104Maximum number of bank directorships MAX_DIR 2.096 2.000 0.000 6.000 1.445 104Proportion of busy directors BUSY_BOARD 0.013 0.000 0.000 0.176 0.038 91Proportion of female directors FEMALE_DIR 0.092 0.091 0.000 0.750 0.095 315Female CEO FEMALE_CEO 0.047 0.000 0.000 1.000 0.212 318Age diversity AGE_CV 0.128 0.123 0.039 0.281 0.042 324Tenure diversity TENURE_CV 0.737 0.714 0.000 1.848 0.275 322CEO duality DUALITY 0.413 0.000 0.000 1.000 0.493 315
Panel C: Corporate governance proxies
122
Table 5.3 (Continued)
Variables Denoted as Mean Median Min Max SD Obs.
Total assets ($ millions) 140,168.405 2,137.148 190.305 2,573,126.000 457,038.880 341Market capitalization ($ millions) 13,800.000 185.998 2.358 269,000.000 44,200.000 355Bank size BANK_SIZE 8.377 7.668 5.529 14.633 2.292 341Bank complexity COMPLEXITY 0.010 0.000 0.000 0.209 0.036 327Leverage LEVERAGE 0.908 0.909 0.813 0.989 0.031 341ROA ROA 0.001 0.007 -0.067 0.025 0.018 341Price-to-book ratio PTB 1.142 1.005 0.147 3.336 0.700 334Invested capital CAPITAL 0.175 0.162 0.063 0.412 0.070 341Stock return volatility STOCK_VOL 0.039 0.029 0.008 0.203 0.031 355Beta BETA 0.850 0.835 -0.362 2.582 0.663 355
Panel D: Bank-specific characteristics
123
5.4.2 Correlation matrix
Table 5.4 presents the Pearson pair-wise correlations between the main
regression variables. There is no significant correlation between reputational loss
(CAR_REP) and the four enforcement action-related variables, SEVERE, TECHNICAL,
OCC and FRB. These correlation statistics suggest that the level of severity and
technicality of enforcement actions, and whether enforcement actions were issued
by OCC or FRB, are not significantly related to reputational loss.
Across all governance variables, reputational loss is negatively correlated
with board busyness (DIRECTORSHIP_MEAN and DIRECTORSHIP_MAX). Since a
higher level of board busyness is considered as “good” governance for banking
firms (Elyasiani & Zhang, 2015), these correlation statistics suggest that well-
governed banks suffer from more severe reputational loss. This provides
preliminary support for hypothesis 6. Reputational loss is, however, positively
correlated with board diversity (FEMALE_CEO), suggesting banks with a more
diverse board suffer less severe reputational loss. The magnitude however is small
(r= 0.11).
It is not surprising to see high correlations between three measures of
board busyness (DIRECTORSHIP_MEAN, DIRECTORSHIP_MAX and BUSY_BOARD),
and between bank size and various governance variables, all significant at the 5
percent level. Specifically, larger banks tend to have larger board size, have a
higher proportion of busy director and female directors sitting on board, and are
124
more likely to exhibit CEO duality. The highest correlation of 0.87 is between
DIRECTORSHIP_MEAN and DIRECTORSHIP_MAX, followed by the correlation between
bank size and the average number of directorships (r = 0.83). These high
correlations are as expected.
For the control variables, bank size is highly positively correlated with
complexity (r = 0.70) and systematic risks (r = 0.49), consistent with the
expectation that larger banks are more complex and exposed to higher level of
risks than smaller banks. Bank idiosyncratic risk is highly negatively correlated
with ROA and PTB, suggesting that banks with higher stock volatility tend to have
lower return-on-assets and growth opportunities. Because of multicollinearity bias,
I will avoid putting very highly correlated independent variable in the same
regressions specification.
125
Table 5.4 Correlation matrix This table reports Pearson pair-wise correlation matrix for the sample period between 2000 and 2014. Figures with * indicate statistical significance at 10% level.
CAR_
REP
SEVE
RE
TECH
NIC
AL
OCC
FRB
BSIZ
E
IND
EP_B
OA
RD
MEA
N_D
IR
MA
X_D
IR
BUSY
_BO
ARD
FEM
ALE
_DIR
FEM
ALE
_CEO
AGE
_CV
TEN
URE
_CV
DU
ALI
TY
BAN
K_SI
ZE
COM
PLEX
ITY
LEVE
RAGE
ROA
PTB
CAPI
TAL
STO
CK_V
OL
BETA
CAR_REP 1.00
SEVERE 0.00 1.00
TECHNICAL -0.04 0.25 * 1.00
OCC -0.09 0.11 * 0.12 * 1.00
FRB -0.02 0.27 * -0.17 * -0.30 * 1.00
BSIZE 0.04 -0.13 * -0.02 0.13 * -0.04 1.00
INDEP_BOARD -0.03 0.00 0.00 -0.05 0.10 -0.01 1.00
MEAN_DIR -0.22 * 0.04 0.11 0.36 * -0.03 0.42 * 0.04 1.00
MAX_DIR -0.20 * 0.07 0.08 0.36 * -0.02 0.30 * 0.07 0.87 * 1.00
BUSY_BOARD -0.04 0.05 0.16 0.26 * -0.05 0.10 -0.04 0.47 * 0.63 * 1.00
FEMALE_DIR -0.01 0.07 0.06 0.24 * -0.10 0.16 * 0.04 0.54 * 0.39 * 0.00 1.00
FEMALE_CEO 0.11 * 0.10 -0.03 -0.08 -0.02 -0.15 * -0.09 -0.06 0.00 -0.07 0.28 * 1.00
AGE_CV -0.01 0.07 0.06 -0.14 * -0.07 -0.31 * -0.17 * -0.35 * -0.24 * 0.04 -0.18 * 0.04 1.00
TENURE_CV 0.05 -0.03 0.08 -0.02 -0.07 0.16 * -0.04 0.03 0.05 0.31 * -0.08 -0.02 0.08 1.00
DUALITY -0.10 -0.11 0.03 0.17 * -0.13 * 0.02 -0.05 0.20 0.22 * 0.12 0.12 * -0.07 -0.12 * -0.02 1.00
BANK_SIZE 0.01 -0.09 -0.06 0.23 * 0.02 0.46 * 0.05 0.83 * 0.69 * 0.24 * 0.35 * -0.09 -0.47 * 0.02 0.28 * 1.00
COMPLEXITY -0.04 0.02 -0.01 0.14 * 0.02 0.20 * 0.04 0.59 * 0.47 * 0.10 0.27 * -0.07 -0.25 * 0.02 0.07 0.70 * 1.00
LEVERAGE 0.08 0.24 * 0.12 * -0.07 0.04 -0.17 * -0.18 * 0.18 0.12 -0.02 -0.05 0.16 * 0.18 * 0.08 -0.17 * -0.21 * -0.01 1.00
ROA -0.14 * -0.33 * -0.17 * 0.11 * -0.14 * 0.18 * 0.11 0.18 0.16 0.12 -0.04 -0.32 * -0.14 * -0.09 0.14 * 0.24 * 0.16 * -0.38 * 1.00
PTB -0.07 -0.35 * -0.27 * 0.00 -0.03 0.21 * -0.20 * 0.07 0.07 0.06 -0.01 -0.05 -0.15 * 0.01 0.22 * 0.27 * 0.13 * -0.02 0.49 * 1.00
CAPITAL -0.02 -0.08 -0.01 0.15 * -0.06 0.03 0.04 0.16 0.08 0.14 0.03 -0.14 * -0.06 0.10 0.06 0.19 * 0.13 * -0.35 * 0.21 * 0.04 1.00
STOCK_VOL 0.06 0.21 * 0.16 * -0.15 * 0.10 -0.14 * -0.08 -0.01 -0.01 0.04 -0.05 0.14 * 0.20 * 0.07 -0.17 * -0.27 * -0.16 * 0.34 * -0.52 * -0.43 * -0.10 1.00
BETA 0.18 * -0.11 * -0.12 * -0.03 0.06 0.31 * -0.01 0.06 0.06 -0.06 0.17 * 0.03 -0.25 * 0.07 0.13 0.52 * 0.20 * -0.24 * -0.09 0.04 0.08 -0.01 1.00
126
5.4.3 Monetary vs. non-monetary enforcement actions
Table 5.5 reports the sample distribution of 355 unique enforcement actions
by year and types of enforcement actions. Overall, the period prior to the 2008
global financial crisis (GFC) saw the least number of enforcement actions,
fluctuating between 6 and 20 actions per year. Compared to 2007 with only 12
actions, the number of enforcement actions increased by nearly half in 2008 (19),
fourfold in 2009 (46), and more than sixfold in 2010 (67). The four years from
2011 to 2014 experienced a decline in the number of enforcement actions, with
45, 33, 16 and 20 enforcement actions for each of the years, respectively.
Of the types of enforcement actions, ‘other fines’, ‘formal agreement/consent
order’, and ‘cease-and-desist’, are the most common, accounting for 32.4 percent
(=145/448), 24.1 percent (=108/448) and 26.7 percent (=119/448) of the actions,
respectively. Non-monetary penalties are marginally more common than monetary
penalties at 62.3 percent (=279/448).
127
Table 5.5 Number of enforcement actions by types each year The table reports the sample distribution of enforcement actions by the year and by enforcement action types.
Call Report Fraction
Cease and Desist Order
Deposit Insurance
Threat
Formal Agreement/
Consent Order
Formal Memo of
Understanding
Order Requiring
Restitution
Prompt Corrective
ActionOther Fines
Sanctions due to HMDA Violation
2000 0 2 0 10 0 0 0 0 0 12 11
2001 1 1 2 2 0 0 1 3 2 12 12
2002 0 2 1 0 0 0 0 3 1 7 6
2003 0 2 3 7 0 0 0 5 4 21 20
2004 0 5 32 3 0 0 0 6 2 48 16
2005 0 2 2 4 0 0 0 11 1 20 18
2006 0 3 2 2 0 0 0 7 0 14 14
2007 0 1 2 1 0 0 0 8 0 12 12
2008 0 4 0 5 0 0 0 12 0 21 19
2009 0 13 0 12 0 0 1 18 3 47 46
2010 0 17 1 40 0 0 0 15 4 77 67
2011 0 24 1 16 0 1 0 23 3 68 45
2012 0 15 0 8 0 0 0 10 4 37 33
2013 0 4 0 5 0 1 0 8 0 18 16
2014 0 13 0 4 0 1 0 16 0 34 20
Total 1 108 46 119 0 3 2 145 24 448 355
Year
Non-monetary EAs Monetary EAs
Total EAsTotal Unique
EAs
128
5.4.4 Severe vs. non-severe enforcement actions
Table 5.6 reports the number of enforcement actions by year and degree
of severity. Similar to the trend described in Table 5.5, GFC and post-GFC
periods experienced an increase in the number of severe and non-severe
enforcement actions. Over these two periods, the increasing rate of severe
actions was much higher than that of non-severe actions. Specifically, compared
to 2008, the number of severe enforcement actions was three, six and four
times higher in 2009, 2010 and 2011 respectively. The number of non-severe
actions were much lower during these three years when compared to 2008.
Overall, the total number of severe and non-severe enforcement actions over
my sample period are similar (229 vs. 219).
The differences in the number of enforcement actions, as summarized
in the last two columns of Table 5.6, suggest that a parent bank and/or its
subsidiary bank might receive more than one enforcement actions on the same
announcement date.
129
Table 5.6 Number of enforcement actions by degree of severity each year The table reports the sample distribution of enforcement actions by the year and by degree of severity.
Severe Non-severe
2000 12 0 12 11
2001 4 8 12 12
2002 2 5 7 6
2003 9 12 21 20
2004 8 40 48 16
2005 6 14 20 18
2006 5 9 14 14
2007 2 10 12 12
2008 9 12 21 19
2009 26 21 47 46
2010 57 20 77 67
2011 40 28 68 45
2012 23 14 37 33
2013 9 9 18 16
2014 17 17 34 20
Total 229 219 448 355
Total EAs Total Unique EAsYearDegree of severity
130
5.4.5 Technical vs. non-technical enforcement actions
Table 5.7 reports the number of enforcement actions by year and degree
of technicality. Overall, non-technical enforcement actions are the most
common at 198 as compared with 129 actions of technical category. There
were about 139 actions that cannot be classified as either technical or non-
technical. During the 2007-2009 GFC period, the number of non-technical
actions is about twice the number of technical actions. The number of technical
and non-technical actions are equally common in the pre- and post-GFC
periods.
The differences in the number of enforcement actions, as summarized
in the last two columns of Table 5.7, suggest that on the same announcement
date, a parent bank and/or its subsidiary bank might receive more than one
type of enforcement action classified according to the degree of technicality.
The total number of enforcement actions in Table 5.7 is different from that
reported in previous tables (466 vs. 448) because 18 enforcement actions are
classified under both technical and non-technical categories.
5.4.6 Enforcement actions by severity – technicality matrix
Table 5.8 summarizes the enforcement actions by severity and
technicality level. The non-severe/non-technical category has the largest
number of enforcement actions (100), followed by severe/technical category
(78) and severe/other category (71). The least number of enforcement actions
131
belong to non-severe/technical, and non-severe/other categories, both with 28
actions each.
Table 5.7 Number of enforcement actions by degree of technicality each year The table reports the sample distribution of enforcement actions by the year and by degree of technicality.
Table 5.8 Number of enforcement actions by severity and technicality matrix The table reports on the sample distribution of enforcement actions by severity and by technicality levels.
Technical Non-technical Other
2000 1 3 9 13 11
2001 3 4 5 12 12
2002 1 5 1 7 6
2003 8 9 6 23 20
2004 3 11 34 48 16
2005 3 13 5 21 18
2006 1 10 4 15 14
2007 0 9 3 12 12
2008 5 13 5 23 19
2009 16 27 12 55 46
2010 24 20 33 77 67
2011 32 25 14 71 45
2012 16 15 6 37 33
2013 3 13 2 18 16
2014 13 21 0 34 20
Total 129 198 139 466 355
Degree of technicalityTotal EAs Total Unique EAsYear
Types of EAs Severe Non-severe Total
Technical 78 28 106
Non-technical 50 100 150
Other 71 28 99
Total 199 156 355
132
5.4.7 Enforcement actions by primary regulators
Table 5.9 reports the number of enforcement actions by year and primary
regulators that issued those actions. FDIC issued the highest number of
enforcement actions over the sample period, with 164 actions compared to
151 and 133 actions issued by OCC and FRB respectively. The differences in
the number for enforcement actions, as summarized in the last two columns
of Table 5.9 suggest that a parent bank and/or its subsidiary bank might
receive enforcement actions issued by more than one regulator on the same
announcement date.
133
Table 5.9 Number of enforcement actions by primary regulators each year The table reports on the sample distribution of enforcement actions by the year and by primary regulators.
FRB FDIC OCC
2000 7 3 2 12 11
2001 1 6 5 12 12
2002 1 4 2 7 6
2003 7 10 4 21 20
2004 14 8 26 48 16
2005 2 8 10 20 18
2006 3 7 4 14 14
2007 6 4 2 12 12
2008 4 9 8 21 19
2009 14 27 6 47 46
2010 32 27 18 77 67
2011 26 19 23 68 45
2012 6 21 10 37 33
2013 4 7 7 18 16
2014 6 4 24 34 20
Total 133 164 151 448 355
YearPrimary regulators
Total EAs Total Unique EAs
134
5.5 Summary
This chapter details the sample selection criteria, data sources, variable
measurements and research methods applied in my analysis. With respect to
the likelihood analysis, I match my sample of offending banks to a
corresponding sample of non-offending banks on the basis of industry, and
assets range within 20 percent of that of the offending bank. If more than one
match is found, I select the non-offending bank with its assets closest to the
offending bank. Probit regressions are used to investigate the likelihood of
receiving regulatory enforcement actions.
Regarding the determinants of bank reputational loss analysis,
reputational loss due to regulatory enforcement action is measured as the
cumulative abnormal return adjusted for the legal fines return. Both parametric
and non-parametric tests are used to examine whether bank reputational loss
is significantly different from zero. In reputational loss analysis, pooled OLS is
my baseline regression model.
135
CHAPTER 6
EMPIRICAL RESULTS
6.1 Introduction
In this chapter, I present and discuss the results obtained from the
empirical models that test the stated hypotheses in Chapter 4. Section 6.2
presents the results of the impact of various governance measures on the
likelihood of regulatory enforcement actions, including the univariate tests, and
probit regressions. Bank governance measures used in the regressions include
board size, board independence, board busyness, board diversity, and CEO
duality. Section 6.3 presents event study results for the whole sample and sub-
samples (severe vs. non-severe, technical vs. non-technical). Section 6.4 reports
results obtained from regressions of various governance measures on bank
reputational loss. Section 6.5 concludes the chapter.
6.2 Likelihood of regulatory enforcement actions
6.2.1 Univariate tests
Table 6.1 provides a univariate analysis comparing governance quality
between violating and non-violating banks. Results of the univariate analysis
show that violating banks have smaller and more independent boards, busy
directors, and a lower proportion of female directors. About 42 percent of the
violating banks have the CEO holding the chair position, compared to 51
percent of control banks with the difference being statistically significant.
136
Overall, the results indicate violating banks are associated with better
governance mechanism than non-violating banks, although the differences are
economically small. I will explore this next in a multivariate framework.
137
Table 6.1 Univariate test The table summarizes the univariate statistics comparing corporate governance between the sample and control banks. Both the mean and median differences are reported. BOARD_SIZE is the natural logarithm of the number of directors sitting on the board. INDEP_BOARD is the proportion of independent directors sitting on the board. MEAN_DIR is the average number of bank directorships held by the directors of that bank. MAX_DIR is the maximum number of bank directorships held by any one member of a bank’s board. BUSY_BOARD is the proportion of directors on the board who hold three or more directorships. FEMALE_DIR is the proportion of female directors sitting on the board. FEMALE_CEO equals one if the CEO position is held by a female director; and zero otherwise. AGE_CV is measured as the standard deviation of age of directors divided by the average age of directors. TENURE_CV is measured as the standard deviation of tenure of directors divided by the average tenure of directors. DUALITY equals one if the bank CEO also holds the chairman position, and zero otherwise. *, **, *** indicate significance at the 10%, 5%, and 1% level, respectively.
Obs. Mean Median Obs. Mean Median t-statistics z-statistics
BSIZE 291 2.44 2.48 176 2.63 2.64 -8.300 *** -7.716 ***
INDEP_BOARD 249 0.79 0.80 176 0.76 0.79 -1.976 ** -1.650 *
MEAN_DIR 92 0.65 0.55 176 0.35 0.19 4.409 *** 3.830 ***
MAX_DIR 92 2.02 2.00 176 1.57 1.00 2.469 *** 2.723 ***
BUSY_BOARD 90 0.01 0.00 176 0.01 0.00 0.366 0.559
FEMALE_DIR 291 0.09 0.09 176 0.12 0.11 -2.718 *** -3.156 ***
FEMALE_CEO 294 0.05 0.00 240 0.04 0.00 0.513 0.509
AGE_CV 295 0.13 0.13 176 0.13 0.12 0.896 0.635
TENURE_CV 294 0.75 0.72 176 0.74 0.73 0.442 -0.071
DUALITY 292 0.42 0.00 236 0.51 1.00 -2.178 ** -2.173 **
Control BanksViolating Banks Difference
138
6.2.2 Results from probit regressions
Table 6.2 reports the multivariate probit regression results. The
dependent variable is enforcement action (EA), which equals one if the bank
receives a regulatory enforcement action, and zero otherwise. Governance
quality is captured by BSIZE, INDEP_BOARD, MEAN_DIR, MAX_DIR, BUSY_BOARD,
FEMALE_DIR, FEMALE_CEO, AGE_CV, TENURE_CV, and DUALITY. All governance
variables are lagged one period because I am interested in the role of
corporate governance in deterring enforcement actions. The coefficient of key
variables of interest, BSIZE is negative and statistically significant at the 1
percent level, implying a negative association between board size and the
likelihood of getting regulatory enforcement actions. These results are in line
with the arguments that larger boards devote more human capital to overseeing
management to ensure that their behaviour complying with regulations (Klein,
2002; Anderson et al., 2004), thus mitigating fraudulent behaviour. These results
are economically significant. The estimated coefficient suggests that a one
standard deviation increase in board size (BSIZE) result in a 15 percent32 drop
in the propensity of receiving regulatory enforcement actions (specification 1).
All other governance variables are statistically insignificant indicating that there
is no evidence that governance quality, as proxied by those variables, reduces
regulatory violation.
32 BSIZE has marginal coefficient in the likelihood regression of -0.544, and standard deviation of 0.273. The product is (-0.544)*0.273 = -15 percent (In specification 1).
139
The control variables have the expected signs. BANK_SIZE has a positive
and significant coefficient, suggesting that larger banks are more likely to be
targeted by banking regulators (i.e., FRB, OCC and FDIC) for their misbehaviour.
In terms of economic significance, a one standard deviation increase in bank
size (BANK_SIZE) is associated with a 37 percent33 increase in the likelihood
of receiving regulatory enforcement actions (specification 4). Results also show
that banks with higher leverage are more likely to commit corporate wrongdoing
due to pressure to meet up with requirements of debt covenants (Richardson
et al., 2002; Burns & Kedia, 2006; Efendi et al., 2007). A one standard deviation
increase in a bank’s leverage ratio (LEVERAGE) is associated with a 12 percent34
higher likelihood of regulatory enforcement actions (specification 2). Results
also demonstrate that high-growth banks as proxied by price to book ratio
(PTB) have a lower likelihood of misconduct. This suggests that managers of
banks with less growth opportunities are more likely to behave opportunistically.
From specification 1, a one standard deviation increase in the bank’s price to
book ratio decreases the likelihood of misconduct by 29 percent.35 Further,
bank age is negatively related to the likelihood of misconduct (p < 10 percent)
for specifications 6, 8 and 9, suggesting that younger banks are more likely
33 BANK_SIZE has marginal coefficient in the likelihood regression of 0.163, and standard deviation of 2.277. The product is 0.163*2.277 = 37 percent (In specification 4). 34 LEVERAGE has marginal coefficient in the likelihood regression of 3.960, and standard deviation of 0.031. The product is 3.960*0.031 = 12 percent (In specification 2). 35 PTB has marginal coefficient in the likelihood regression of -0.418, and standard deviation of 0.7. The product is (-0.418)*0.7 = - 29 percent (In specification 1).
140
to be the target of enforcement actions (Bonner, Palmrose, & Young, 1998;
O’Reilly et al., 1998).
I next split my sample into severe and non-severe categories. Results
for these two subsamples are summarized in Table 6.3. An enforcement action
is classified as severe if it falls in one of the following three categories: ‘cease
and desist’, ‘prompt corrective action’, and ‘formal agreement/consent order’.
Overall, my findings show that governance has more economic power in terms
of explaining the likelihood of severe cases than that of non-severe cases.
Panel A of Table 6.3 reports the results for severe cases. The coefficient on
board size is negative, consistent with the full sample results in Table 6.2. I
also find that banks with CEOs also taking the position of board chair are
more likely to engage in bank wrongdoing, supporting Hypothesis 5. The
estimated coefficient on DUALITY suggests that banks whose CEOs have dual
roles have a 21 percent36 higher probability of being subject to enforcement
actions. This finding is consistent with agency theory that concentration of
CEO power reduces the effectiveness of board monitoring and result in an
increase in opportunistic behaviour (Mallette & Fowler, 1992).
Panel B of Table 6.3 summarizes the results for non-severe cases. The
coefficients of most governance proxies are insignificant, providing no evidence
that corporate governance deters non-severe fraud cases. AGE_CV is negatively
36 DUALITY has marginal coefficient in the likelihood regression of 0.2146239, indicating that banks with CEOs that also chair the board have 21 percent higher likelihood of committing fraud.
141
(p < 10 percent) related to misconduct, suggesting that boards with a larger
variation of directors’ age have a lower likelihood of non-severe misconduct.
This finding provides evidence supporting the argument that a more diverse
board is a better monitor of management and internal compliance process,
and thus helps prevent bank engagement in unsafe and unsound banking
practices. In other words, banks with a diverse board are less likely to be
subject to enforcement actions charged by banking regulatory agencies. Bank
size (BANK_SIZE) and market to book ratio (PTB) are positively and negatively
related to the likelihood of regulatory enforcement actions respectively.
142
Table 6.2 Probit regressions of the likelihood of enforcement actions (full sample) This table presents the results of the probit estimates of Eq. (4.1):
tim
timl
tilj
tij YEARCHARBANKGOVERNANCEEA ,
20142000
1,
6
1,
7
1,0 _ εφδβα ++++= ∑∑∑
−
===
where
subscripts i denotes individual banks, t time period, j alternative corporate governance proxies, and l bank-specific characteristics. The dependent variable is EA, a dummy variable that equals one if the bank receives regulatory enforcement actions and zero otherwise. Please refer to Table 5.2 for a list of variables definition. YEAR is time dummies. α is the constant term. ε is the idiosyncratic error term. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at bank level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% level, respectively.
BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR FEMALE_CEO AGE_CV TENURE_CV DUALITY(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
GOVERNANCE -1.456*** 0.809 -0.072 -0.143 -4.672 -0.495 0.066 -4.245 -0.003 0.249(-2.98) (0.73) (-0.16) (-1.11) (-1.48) (-0.44) (0.12) (-1.53) (-0.01) (1.22)
BANK_SIZE 0.016 -0.034 0.401** 0.465*** 0.457*** -0.040 -0.116 -0.049 -0.013 -0.124(0.15) (-0.33) (2.56) (3.51) (4.06) (-0.40) (-1.30) (-0.48) (-0.13) (-1.32)
LEVERAGE 7.365* 10.345** -6.952 -7.591 -10.894** 8.890** 4.286 8.528** 7.912** 4.263(1.91) (2.56) (-1.44) (-1.58) (-2.11) (2.34) (1.06) (2.27) (2.17) (1.05)
ROA -1.886 -2.985 -18.521 -19.490 -14.680 -2.693 -5.121 -2.019 -3.261 -5.713(-0.18) (-0.28) (-1.45) (-1.48) (-1.16) (-0.25) (-0.62) (-0.19) (-0.30) (-0.72)
PTB -1.119*** -0.970*** -0.150 -0.121 -0.048 -1.018*** -0.777*** -0.928*** -0.956*** -0.808***(-5.71) (-4.36) (-0.81) (-0.66) (-0.24) (-4.93) (-3.79) (-4.80) (-4.92) (-3.81)
BANKAGE -0.370 -0.444 0.025 0.028 0.032 -0.502* -0.198 -0.533* -0.485* -0.272(-1.34) (-1.61) (0.08) (0.09) (0.10) (-1.79) (-0.87) (-1.95) (-1.80) (-1.20)
Constant -0.234 -7.311* 1.661 1.906 4.471 -4.739 -0.354 -3.847 -4.250 -0.169(-0.06) (-1.79) (0.39) (0.45) (1.00) (-1.28) (-0.09) (-1.05) (-1.19) (-0.04)
Year dummies YES YES YES YES YES YES YES YES YES YESObservations 431 382 257 257 255 431 495 434 434 490
X 2 85.688 67.586 33.201 31.970 45.636 82.035 57.691 79.446 78.142 57.592
Pseudo R 2 0.293 0.234 0.188 0.196 0.229 0.266 0.192 0.246 0.239 0.200
Variables
143
Table 6.3 Probit regressions of the likelihood of enforcement actions (Severe vs. non-severe) This table presents the results of the probit estimates of Eq. (4.1) for severe and non-severe subsamples
tim
timl
tilj
tij YEARCHARBANKGOVERNANCEEA ,
20142000
1,
6
1,
7
1,0 _ εφδβα ++++= ∑∑∑
−
===
where subscripts i denotes individual banks, t time period, j alternative
corporate governance proxies, and l bank-specific characteristics. The dependent variable is EA, a dummy variable that equals one if the bank receives regulatory enforcement actions and zero otherwise. An enforcement action is classified as severe if it falls in one of the following three types: "cease and desist", "prompt corrective action", and "formal agreement/consent order". Please refer to Table 5.2 for a list of variables definition. YEAR is time dummies. α is the constant term. ε is the idiosyncratic error term. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at bank level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Severe Enforcement Actions BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR FEMALE_CEO AGE_CV TENURE_CV DUALITY
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)GOVERNANCE -3.532*** 1.530 -0.271 -0.121 -5.605 0.240 0.473 -1.668 -0.547 0.566**
(-5.11) (1.09) (-0.50) (-0.74) (-1.18) (0.23) (1.02) (-0.47) (-1.07) (2.18)BANK_SIZE 0.149 -0.031 0.534*** 0.534*** 0.548*** -0.022 -0.109 -0.009 0.005 -0.128
(1.10) (-0.25) (3.26) (3.71) (4.24) (-0.17) (-0.99) (-0.07) (0.04) (-1.09)LEVERAGE 4.727 11.652** -5.665 -5.808 -9.276 9.671** 5.313 9.388** 8.920** 5.638
(1.04) (2.43) (-0.91) (-0.93) (-1.47) (2.18) (1.25) (2.08) (2.06) (1.33)ROA 3.038 -1.083 -16.872 -16.351 -8.203 -0.441 -13.120 -0.422 -0.543 -11.932
(0.26) (-0.09) (-1.02) (-0.99) (-0.51) (-0.04) (-1.11) (-0.03) (-0.04) (-1.02)PTB -1.857*** -1.258*** -0.411 -0.424 -0.362 -1.272*** -0.739** -1.217*** -1.286*** -0.813**
(-5.97) (-3.79) (-1.00) (-1.06) (-0.91) (-4.08) (-2.33) (-3.89) (-4.10) (-2.35)BANKAGE -0.669* -0.646* -0.057 -0.044 0.038 -0.753** -0.269 -0.777** -0.677* -0.378
(-1.83) (-1.80) (-0.15) (-0.12) (0.09) (-2.13) (-1.00) (-2.08) (-1.90) (-1.35)Constant 8.630* -7.843 0.097 0.363 2.683 -4.504 -1.138 -4.165 -3.860 -1.255
(1.68) (-1.61) (0.02) (0.07) (0.51) (-1.04) (-0.28) (-0.97) (-0.91) (-0.30)Year dummies YES YES YES YES YES YES YES YES YES YESObservations 226 206 120 120 119 226 254 226 225 252
X 2 102.575 62.863 38.710 38.236 43.844 74.121 46.752 69.309 68.264 49.181
Pseudo R 2 0.469 0.363 0.284 0.287 0.278 0.380 0.289 0.372 0.373 0.301
Variables
144
Table 6.3 (Continued)
Panel B: Non-severe Enforcement Actions
BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR FEMALE_CEO AGE_CV TENURE_CV DUALITY(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
GOVERNANCE -0.811 0.274 0.024 -0.181 -5.182 -1.089 -0.225 -6.986* 0.133 0.019(-1.42) (0.20) (0.05) (-1.13) (-1.52) (-0.65) (-0.30) (-1.84) (0.33) (0.08)
BANK_SIZE -0.109 -0.114 0.334 0.459*** 0.470*** -0.123 -0.194* -0.150 -0.078 -0.184(-0.82) (-0.88) (1.63) (2.61) (2.95) (-0.95) (-1.67) (-1.19) (-0.61) (-1.53)
LEVERAGE 6.821 9.084 -10.079 -11.528 -13.625* 7.560 1.933 6.300 5.474 1.843(1.21) (1.60) (-1.24) (-1.40) (-1.71) (1.36) (0.34) (1.21) (1.05) (0.33)
ROA -4.135 -4.701 -17.746 -20.292 -22.819 -3.916 1.744 -2.178 -5.070 1.862(-0.30) (-0.35) (-1.32) (-1.48) (-1.62) (-0.30) (0.19) (-0.16) (-0.38) (0.21)
PTB -0.822*** -0.758*** -0.026 0.044 0.121 -0.788*** -0.683*** -0.640*** -0.671*** -0.710***(-3.86) (-3.20) (-0.13) (0.22) (0.56) (-3.67) (-3.24) (-3.24) (-3.40) (-3.29)
BANKAGE -0.163 -0.253 0.094 0.090 0.055 -0.276 -0.061 -0.264 -0.259 -0.133(-0.54) (-0.85) (0.24) (0.22) (0.13) (-0.93) (-0.24) (-0.93) (-0.90) (-0.53)
Constant -1.421 -3.833 5.269 5.629 6.069 -4.009 0.961 -2.063 -2.708 1.191(-0.25) (-0.68) (0.74) (0.79) (0.91) (-0.75) (0.18) (-0.41) (-0.53) (0.22)
Year dummies YES YES YES YES YES YES YES YES YES YESObservations 204 177 134 134 135 204 240 207 208 237
X 2 32.618 26.617 24.358 19.473 32.950 33.104 30.461 33.977 28.305 30.595
Pseudo R 2 0.203 0.148 0.169 0.182 0.249 0.195 0.137 0.170 0.154 0.143
Variables
145
Following Nguyen et al. (2016), I also classify bank enforcement actions
into technical and non-technical categories. The former group includes
violations of requirements concerning asset quality, capital adequacy and
liquidity, lending, provisions, and reserves. The latter covers cases relating to
failures of a bank’s internal control and audit systems, risk management
systems, and anti-money laundering systems. Overall, the findings show that
governance has more economic power in explaining the likelihood of technical
actions than non-technical actions.
Panel A of Table 6.4 shows the result for technical sub-sample. The two
key variables of interest, TENURE_CV and DUALITY are statistically significant
at the 10 and 5 percent respectively. Based on the coefficient of TENURE_CV,
a one standard deviation increase of bank directors’ tenure diversity reduces
the likelihood of technical misconduct by 6.8 percent37 (specification 9). This
provides some evidence supporting the arguments that a diverse board in
terms of tenure is a better monitor of management and internal compliance
processes (Carter et al., 2003; Ramirez, 2003; Selby, 2000). When CEO also
occupies the chair position, the likelihood of technical misconduct increases
by as much as 30 percent. 38 This provides further evidence supporting
Hypothesis 5 that CEO duality signals higher agency problems and reduces
37 TENURE_CV has marginal coefficient in the likelihood regression of -0.248, and standard deviation of 0.275. The product is -0.248*0.275= -6.8 percent (In specification 9). 38 DUALITY has marginal coefficient in the likelihood regression of 0.2960, indicating that banks with CEOs that also chair the board have 30 percent higher likelihood of committing technical fraud (In specification 10).
146
board monitoring effectiveness leading to higher likelihood of committing
corporate wrongdoing. Consistent with previous results, all control variables
(except ROA) have their expected sign.
Panel B of Table 6.4 shows the results for non-technical enforcement
actions. BSIZE, BUSY_BOARD and AGE_CV are significantly negatively related to
likelihood of enforcement actions, suggesting banks whose boards are large,
busy and diverse (in terms of age) are less likely to engage in non-technical
unsafe and unsound banking practices. In terms of economic significance, a
one standard deviation increase in board size reduces the likelihood of non-
technical enforcement actions by 26 percent.39 A one standard deviation
increase in proportion of directors having positions on other board and in
directors’ age diversity leads to a 9 percent and 10 percent40 lower likelihood
of non-technical enforcement actions respectively.
39 BSIZE has marginal coefficient in the likelihood regression of -0.972, and standard deviation of 0.273. The product is -0.972*0.273= -26 percent (In specification 1). 40 BUSY_BOARD has marginal coefficient in the likelihood regression of -2.338, and standard deviation of 0.0375. The product is -2.338*0.0375= -8.76 percent (In specification 5). AGE_CV has marginal coefficient in the likelihood regression of -2.258, and standard deviation of 0.042. The product is -2.258*0.042= -9.5 percent (In specification 8).
147
Table 6.4 Probit regressions of likelihood of enforcement actions (technical vs. non-technical) This table presents the results of the probit estimates of Eq. (4.1) for technical and non-technical subsamples:
tim
timl
tilj
tij YEARCHARBANKGOVERNANCEEA ,
20142000
1,
6
1,
7
1,0 _ εφδβα ++++= ∑∑∑
−
===
where subscripts i denotes individual banks, t time period, j alternative
corporate governance proxies, and l bank-specific characteristics. The dependent variable is EA, a dummy variable that equals one if the bank receives regulatory enforcement actions and zero otherwise. An enforcement action is classified as technical if it is related to violations of requirements concerning asset quality, capital adequacy and liquidity, lending, provisions, and reserves. Please refer to Table 5.2 for a list of variables definition. YEAR is time dummies. α is the constant term. ε is the idiosyncratic error term. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at bank level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Technical Enforcement Actions
BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR FEMALE_CEO AGE_CV TENURE_CV DUALITY(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
GOVERNANCE 0.404 1.492 0.869 -0.115 6.334 0.169 -0.126 1.286 -1.172* 0.868**(0.45) (0.85) (1.31) (-0.54) (0.69) (0.09) (-0.25) (0.21) (-1.65) (2.46)
BANK_SIZE -0.096 -0.090 0.468* 0.726*** 0.818*** -0.096 -0.195* -0.035 -0.061 -0.254**(-0.61) (-0.51) (1.76) (2.59) (3.18) (-0.60) (-1.79) (-0.22) (-0.43) (-2.15)
LEVERAGE 14.946** 21.589*** -15.724 -10.087 -4.614 14.000* 9.380 13.657* 15.892** 11.955**(2.27) (2.62) (-0.94) (-0.62) (-0.26) (1.90) (1.60) (1.86) (2.01) (2.16)
ROA 0.975 4.957 -44.326 -40.869 -21.348 0.562 -2.077 1.213 -1.462 -4.162(0.06) (0.33) (-1.61) (-1.51) (-0.67) (0.04) (-0.19) (0.09) (-0.10) (-0.42)
PTB -1.627*** -1.906*** -0.013 -0.003 -0.679 -1.603*** -1.628*** -1.601*** -1.509*** -1.722***(-3.52) (-3.48) (-0.02) (-0.00) (-0.61) (-3.35) (-4.26) (-3.38) (-3.25) (-4.26)
BANKAGE -0.695* -1.019** -1.054* -0.852* -2.740** -0.627 0.320 -0.666 -0.812* 0.257(-1.87) (-2.28) (-1.89) (-1.69) (-2.57) (-1.58) (0.79) (-1.62) (-1.83) (0.67)
Constant -7.896 -12.542 12.805 5.116 5.755 -6.272 -4.607 -6.684 -8.903 -6.637(-1.23) (-1.63) (0.89) (0.37) (0.43) (-0.92) (-0.89) (-0.97) (-1.26) (-1.31)
Year dummies YES YES YES YES YES YES YES YES YES YESObservations 98 82 55 55 44 98 130 98 97 130
X 2 45.606 45.207 40.326 35.415 34.418 34.960 52.510 34.916 35.153 57.428
Pseudo R 2 0.429 0.469 0.411 0.394 0.497 0.427 0.355 0.419 0.432 0.388
Variables
148
Table 6.4 (Continued)
Panel B: Non-technical Enforcement Actions
BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR FEMALE_CEO AGE_CV TENURE_CV DUALITY(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
GOVERNANCE -2.503*** 0.762 -0.013 -0.092 -6.776* -0.874 -0.165 -5.733* -0.037 0.068(-4.29) (0.63) (-0.02) (-0.59) (-1.91) (-0.60) (-0.26) (-1.67) (-0.09) (0.30)
BANK_SIZE 0.083 -0.040 0.411** 0.452*** 0.426*** -0.026 -0.100 -0.074 -0.037 -0.091(0.71) (-0.37) (2.42) (3.15) (3.71) (-0.25) (-1.09) (-0.73) (-0.34) (-0.96)
LEVERAGE 7.248 10.201* -3.376 -4.164 -10.230 10.701** 2.809 12.020** 10.412** 2.473(1.29) (1.86) (-0.50) (-0.61) (-1.41) (2.00) (0.52) (2.16) (1.99) (0.47)
ROA -4.058 -4.980 -26.741 -28.025* -19.916 -3.405 -5.216 -0.736 -3.474 -5.559(-0.27) (-0.37) (-1.63) (-1.69) (-1.39) (-0.26) (-0.54) (-0.06) (-0.26) (-0.60)
PTB -1.072*** -0.891*** 0.055 0.079 0.097 -0.981*** -0.584*** -0.945*** -0.956*** -0.592***(-4.30) (-3.42) (0.25) (0.35) (0.39) (-3.76) (-2.59) (-3.70) (-3.74) (-2.58)
BANKAGE -0.028 -0.142 0.371 0.365 0.558 -0.234 -0.043 -0.294 -0.222 -0.113(-0.10) (-0.48) (0.88) (0.86) (1.20) (-0.79) (-0.18) (-1.01) (-0.76) (-0.48)
Constant 0.731 -8.062 -3.365 -2.805 2.194 -7.542 -0.115 -7.431 -7.271 0.296(0.13) (-1.51) (-0.55) (-0.46) (0.36) (-1.48) (-0.02) (-1.45) (-1.44) (0.06)
Year dummies YES YES YES YES YES YES YES YES YES YESObservations 231 215 150 150 148 231 266 228 230 263
X 2 65.933 39.534 37.297 37.925 39.387 47.795 26.591 53.886 47.651 26.850
Pseudo R 2 0.271 0.170 0.217 0.220 0.242 0.202 0.124 0.205 0.195 0.128
Variables
149
6.2.3 Results from probit regressions with squared terms
Since a large body of literature proposes a non-linear association
between corporate governance characteristics and firm outcomes (de Andres
& Vallelado, 2008; Wang & Hsu, 2013; Ararat, Aksu, & Cetin, 2015), I also add
the squared terms of corporate governance variables to the regressions. The
results for the full sample are reported in Table 6.5. As shown in specification
1, both board size and board size squared are significantly related to the
likelihood of bank misconduct, supporting Hypothesis 1. The positive coefficient
on the squared terms suggest that the effectiveness of the board monitoring
function is impeded as the number of directors increases beyond 18.41 Boards
with too many directors often have free rider problems, greater communication
obstacles, increased conflicts and ineffective decision-making process (Simons
& Peterson, 2000; Coles et al., 2008; Baranchuk & Dybrig, 2009).
The coefficients on FEMALE_DIR and AGE_CV are negative and statistically
significant at the 5 percent level, providing evidence in support of the argument
that board diversity helps mitigate the likelihood of engaging in unsafe and
unsound banking practices, leading to lower probability of being subject to
enforcement actions.42 However, I find no evidence that a non-linear relation
41 I use marginsplot function in Stata to visualize the marginal effects of BSIZE. The graph shows that BSIZE as increases beyond 2.890372, the probability of enforcement starts to go up. Since BSIZE is the natural log of the number of directors, anti-logging this number gives a value of 18 directors. 42 FEMALE_DIR and AGE_CV reduce the likelihood of enforcement actions by 19.3 and 36.4 percent respectively. FEMALE_DIR has marginal coefficient in the likelihood regression of -2.031, and standard deviation of 0.095. The product is -2.031*0.095= -19.3 percent (In specification 6).
150
exists for FEMALE_DIR and AGE_CV. Moreover, I find a non-linear relation
between TENURE_CV and the likelihood of misconduct. Banks with a more
diverse board in terms of directors’ tenure appear to initially reduce the
likelihood of misconduct. However, at higher level of variation in directors’
tenure, this reducing effect become weaker. Specifically, as TENURE_CV goes
beyond 0.74, the marginal effect from negative sign turns to positive sign,43
suggesting a non-linear relation between board diversity (in terms of directors’
tenure) and the likelihood of enforcement action. This finding is in line with
Hypothesis 4, supporting the trade-off argument of board diversity. That is, as
board diversity increases, the benefits of acquired knowledge and experience
domains provided by a large pool of directors are offset by increased conflicts
and coordination problems among them, hindering the board’s monitoring
effectiveness.
In Panel A of Table 6.6, I test the non-linear association between
governance quality and severe EAs. The coefficients of the three key variables
(BUSY_BOARD, AGE_CV, and TENURE_CV) and their corresponding squared
terms are statistically significant. Specification 5 shows that boards where
directors holding multiple external board positions have lower likelihood of
severe bank misconduct. However, this effect is lessened when boards with
directors having too many external positions (the proportion of busy directors
AGE_CV has marginal coefficient in the likelihood regression of -8.66137, and standard deviation of 0.0420. The product is -8.661*0.0420= -36.4 percent (In specification 7). 43 I use margins function in Stata to figure out this turning point.
151
in relative to board size exceeds 8 percent)44 as they cannot fulfill their
monitoring roles. Moreover, variations in AGE_CV and TENURE_CV are initially
negatively associated with likelihood of severe misconduct as the
communication problems overwhelm the benefits obtained from board diversity.
The cut-off points for AGE_CV and TENURE_CV are 0.14 and 0.9 respectively.45
Panel B shows most governance quality variables are not significantly
associated with likelihood of non-severe enforcement actions. All governance
variables and their squared terms are statistically insignificant, except BSIZE.
Specification 1 shows that bank with large board size helps reduce likelihood
of non-severe enforcement actions, but this reducing impact becomes lessened
as board size increases beyond 18 directors (the anti-logged value of
2.890372), consistent with the result in Table 6.5.
44 I use margins function in Stata to figure out this turning point. The marginal effect turns from a negative of -0.0254593 to a positive of 0.252864 and beyond when the proportion of busy directors reaches 0.0833 and over. 45 I use margins function in Stata to figure out these cut-off points. The negative marginal effect becomes positive when AGE_CV (TENURE_CV) goes beyond 0.1408 (0.897).
152
Table 6.5 Probit regressions of the likelihood of enforcement actions with squared terms (full sample) This table presents the results of the following regression:
tim
timl
tilj
tij YEARCHARBANKGOVERNANCEEA ,
20142000
1,
6
1,
7
1,0 _ εφδβα ++++= ∑∑∑
−
===
The dependent variable is enforcement action (EA), a dummy variable that equals one if the bank receives regulatory enforcement actions and zero otherwise. Please refer to Table 5.2 for a list of variables definition. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at the bank level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR AGE_CV TENURE_CV(1) (2) (3) (4) (5) (6) (7) (8)
GOVERNANCE -20.004*** -3.304 0.247 -0.161 3.246 -5.458* -22.997* -2.012*(-2.81) (-0.28) (0.32) (-0.74) (0.41) (-1.66) (-1.83) (-1.76)
GOVERNANCE 2 3.589*** 2.791 -0.194 0.004 -46.019 18.944 67.463 1.238*(2.60) (0.35) (-0.60) (0.11) (-1.21) (1.47) (1.53) (1.84)
BANK_SIZE 0.016 -0.034 0.403*** 0.464*** 0.458*** -0.034 -0.063 -0.024(0.15) (-0.33) (2.59) (3.52) (4.07) (-0.34) (-0.63) (-0.23)
LEVERAGE 7.879** 10.485** -6.792 -7.602 -10.332** 8.376** 8.646** 7.775**(2.03) (2.54) (-1.41) (-1.58) (-2.01) (2.19) (2.35) (2.11)
ROA 0.507 -3.525 -18.251 -19.515 -14.724 -4.296 -2.315 -4.262(0.05) (-0.34) (-1.43) (-1.49) (-1.17) (-0.40) (-0.21) (-0.40)
PTB -1.193*** -0.967*** -0.161 -0.122 -0.073 -1.006*** -0.922*** -0.938***(-6.00) (-4.37) (-0.87) (-0.67) (-0.37) (-4.81) (-4.80) (-4.93)
BANKAGE -0.367 -0.441 0.004 0.030 -0.003 -0.491* -0.534* -0.427(-1.28) (-1.60) (0.01) (0.10) (-0.01) (-1.73) (-1.94) (-1.60)
Constant 23.106** -5.936 1.529 1.919 3.984 -4.211 -2.604 -3.503(2.38) (-1.05) (0.36) (0.45) (0.89) (-1.14) (-0.73) (-0.95)
Year dummies YES YES YES YES YES YES YES YESObservations 431 382 257 257 255 431 434 434
X 2 113.601 67.924 34.044 32.136 52.657 85.985 90.102 82.151
Pseudo R 2 0.314 0.235 0.190 0.196 0.235 0.275 0.252 0.245
Variables
153
Table 6.6 Probit regressions of the likelihood of enforcement actions with squared terms (severe vs. non-severe) This table presents the results of the following regression:
tim
timl
tilj
tij YEARCHARBANKGOVERNANCEEA ,
20142000
1,
6
1,
7
1,0 _ εφδβα ++++= ∑∑∑
−
===
The dependent variable
is enforcement action (EA), a dummy variable that equals one if the bank receives regulatory enforcement actions and zero otherwise. An enforcement action is classified as severe if it falls in one of the following three types: "cease and desist", "prompt corrective action", and "formal agreement/consent order". Please refer to Table 5.2 for a list of variables definition. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at bank level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Severe Enforcement Actions
BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR AGE_CV TENURE_CV(1) (2) (3) (4) (5) (6) (7) (8)
GOVERNANCE -7.904 3.255 -0.961 -0.186 -33.127** -8.087 -43.173** -6.333***(-0.90) (0.25) (-0.84) (-0.62) (-2.54) (-1.57) (-2.24) (-3.10)
GOVERNANCE 2 0.867 -1.153 0.532 0.014 213.690*** 30.373 154.253** 3.532***(0.50) (-0.13) (0.83) (0.35) (2.67) (1.58) (2.19) (2.97)
BANK_SIZE 0.140 -0.031 0.497*** 0.529*** 0.554*** -0.015 -0.030 -0.024(1.06) (-0.24) (3.12) (3.78) (4.38) (-0.12) (-0.23) (-0.19)
LEVERAGE 4.861 11.619** -5.026 -5.586 -9.667 8.849** 10.754** 7.546*(1.06) (2.40) (-0.80) (-0.90) (-1.52) (2.00) (2.48) (1.73)
ROA 3.646 -0.855 -16.340 -16.140 -7.300 -2.357 -1.546 -1.406(0.30) (-0.07) (-1.01) (-0.98) (-0.45) (-0.20) (-0.12) (-0.11)
PTB -1.871*** -1.261*** -0.427 -0.430 -0.371 -1.275*** -1.271*** -1.310***(-5.83) (-3.77) (-1.11) (-1.09) (-0.90) (-4.01) (-4.00) (-4.21)
BANKAGE -0.649* -0.648* -0.043 -0.037 0.043 -0.756** -0.745** -0.531(-1.77) (-1.84) (-0.12) (-0.10) (0.11) (-2.05) (-2.01) (-1.59)
Constant 14.001 -8.452 -0.104 0.213 3.208 -3.536 -2.512 -0.607(1.20) (-1.38) (-0.02) (0.04) (0.60) (-0.81) (-0.61) (-0.14)
Year dummies YES YES YES YES YES YES YES YESObservations 226 206 120 120 119 226 226 225
X 2 101.338 64.030 40.118 38.981 73.249 64.708 77.652 82.498
Pseudo R 2 0.469 0.363 0.288 0.287 0.290 0.396 0.392 0.397
Variables
154
Table 6.6 (Continued)
Panel B: Non-severe Enforcement Actions BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR AGE_CV TENURE_CV
(1) (2) (3) (4) (5) (6) (7) (8)GOVERNANCE -21.729*** -2.835 0.800 0.041 28.675 -5.480 -11.902 -0.500
(-2.60) (-0.22) (0.83) (0.12) (1.27) (-1.56) (-0.66) (-0.43)
GOVERNANCE 2 4.012** 2.129 -0.429 -0.053 -280.639 17.571 17.759 0.388
(2.46) (0.25) (-1.11) (-0.76) (-1.37) (1.31) (0.29) (0.60)BANK_SIZE -0.097 -0.113 0.323 0.463*** 0.514*** -0.119 -0.155 -0.082
(-0.76) (-0.87) (1.59) (2.60) (3.15) (-0.95) (-1.24) (-0.63)LEVERAGE 7.873 9.245 -8.856 -10.837 -11.171 7.242 6.159 5.706
(1.38) (1.61) (-1.09) (-1.33) (-1.36) (1.30) (1.18) (1.09)ROA -0.494 -5.144 -16.528 -19.723 -21.475 -5.325 -2.244 -5.488
(-0.04) (-0.39) (-1.22) (-1.42) (-1.56) (-0.40) (-0.17) (-0.42)PTB -0.904*** -0.756*** -0.078 0.056 0.007 -0.792*** -0.637*** -0.662***
(-4.17) (-3.21) (-0.40) (0.26) (0.03) (-3.61) (-3.18) (-3.36)BANKAGE -0.177 -0.251 0.032 0.079 -0.099 -0.269 -0.267 -0.237
(-0.54) (-0.84) (0.08) (0.19) (-0.24) (-0.90) (-0.94) (-0.81)Constant 24.214** -2.909 4.409 4.842 3.815 -3.601 -1.577 -2.711
(2.13) (-0.41) (0.62) (0.70) (0.56) (-0.68) (-0.30) (-0.53)Year dummies YES YES YES YES YES YES YES YESObservations 204 177 134 134 135 204 207 208
X 2 60.410 27.273 25.732 19.389 38.352 41.538 34.185 28.978
Pseudo R 2 0.235 0.149 0.178 0.186 0.277 0.203 0.171 0.155
Variables
155
In Table 6.7 Panel A and B, I split my sample into technical and non-
technical enforcement actions. In Panel A, board size (BSIZE) and board
busyness (as proxied by MEAN_DIR and BUSY_BOARD) exhibit non-linear
relationships with the likelihood of technical misconduct. These findings confirm
that board diversity initially mitigates the likelihood of technical EA but as
board size goes beyond 14 directors, proportion of busy directors exceeds 7.7
percent and the average number of directorships exceeds 2,46 the probability
of technical EA increases. Panel B shows no evidence of a non-linear relation
between governance proxies and non-technical misconduct. The coefficient of
FEMALE_DIR is negative and statistically significant, with its economic
significance of -23.6 percent for a one standard deviation increase in female
director proportion.47
46 I use marginsplot function in Stata to visualize the marginal effects of BSIZE. The graph shows that BSIZE as increases beyond 2.639057, the probability of enforcement starts to go up. Since BSIZE is the natural log of the number of directors, anti-logging this number gives a value of 14 directors. When the proportion of busy directors increases beyond 7.7 percent, the marginal effect of BUSY_BOARD turn from -11.7304 to positive. When the average number of directorships exceeds 2, the marginal effect of MEAN_DIR turn from -15.8746 to positive. 47 FEMALE_DIR has a marginal significant coefficient in the likelihood regression of -2.484, and standard deviation of 0.095. The product is -2.484*0.095= -23.6 percent (In specification 6).
156
Table 6.7 Probit regressions of corporate governance on the likelihood of enforcement actions with squared terms (technical vs. non-technical) This table presents the results of the following regression:
tim
timl
tilj
tij YEARCHARBANKGOVERNANCEEA ,
20142000
1,
6
1,
7
1,0 _ εφδβα ++++= ∑∑∑
−
===
. The
dependent variable is enforcement action (EA), a dummy variable that equals one if the bank receives regulatory enforcement actions and zero otherwise. An enforcement action is classified as technical if it is related to violations of requirements concerning asset quality, capital adequacy and liquidity, lending, provisions, and reserves. Please refer to Table 5.2 for a list of variables definition. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at bank level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Technical Enforcement Actions BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR AGE_CV TENURE_CV
(1) (2) (3) (4) (5) (6) (7) (8)GOVERNANCE -55.583*** 10.859 -11.961*** -0.784 -62.842*** 3.631 -1.471 1.219
(-3.23) (0.75) (-3.15) (-1.52) (-3.54) (0.83) (-0.05) (0.34)GOVERNANCE 2 10.550*** -6.485 14.991*** 0.162 551.440*** -14.996 10.470 -0.026
(3.16) (-0.63) (3.22) (1.51) (3.92) (-0.96) (0.11) (-0.01)BANK_SIZE -0.009 -0.105 -0.310 0.569** 0.891*** -0.103 -0.037 -0.060
(-0.07) (-0.62) (-0.78) (2.12) (2.87) (-0.63) (-0.23) (-0.44)LEVERAGE 24.267*** 20.701** 0.712 -10.710 -8.468 14.834** 13.754* 15.908**
(2.89) (2.45) (0.04) (-0.67) (-0.44) (2.05) (1.85) (2.01)ROA 5.200 6.813 -59.826** -45.874* -36.360 0.942 1.094 -1.442
(0.35) (0.44) (-2.05) (-1.75) (-0.97) (0.06) (0.08) (-0.10)PTB -1.982*** -1.926*** 0.101 0.061 -0.318 -1.596*** -1.599*** -1.510***
(-3.43) (-3.40) (0.11) (0.08) (-0.28) (-3.34) (-3.38) (-3.28)BANKAGE -1.249*** -0.980** -1.293* -0.838* -3.817** -0.634 -0.669 -0.813*
(-2.87) (-2.14) (-1.85) (-1.66) (-2.13) (-1.60) (-1.63) (-1.84)Constant 58.924*** -14.940** 6.315 7.363 11.998 -6.981 -6.580 -8.937
(2.58) (-2.01) (0.42) (0.56) (0.82) (-1.04) (-0.94) (-1.23)Year dummies YES YES YES YES YES YES YES YESObservations 98 82 55 55 44 98 98 97X 2 57.835 44.545 59.452 37.940 1153.793 37.886 35.103 35.349Pseudo R 2 0.535 0.473 0.572 0.416 0.536 0.430 0.419 0.432
Variables
157
Table 6.7 (Continued)
Panel B: Non-technical Enforcement Actions BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR AGE_CV TENURE_CV
(1) (2) (3) (4) (5) (6) (7) (8)GOVERNANCE -11.785 -20.568 1.106 0.090 3.001 -6.318* -14.455 -1.396
(-1.48) (-1.44) (1.17) (0.27) (0.23) (-1.76) (-1.09) (-1.19)
GOVERNANCE 2 1.806 14.161 -0.756 -0.047 -48.539 20.638 31.518 0.861
(1.15) (1.48) (-1.55) (-0.60) (-0.89) (1.60) (0.71) (1.24)BANK_SIZE 0.079 -0.050 0.418** 0.448*** 0.437*** -0.021 -0.082 -0.040
(0.69) (-0.46) (2.53) (3.06) (3.77) (-0.21) (-0.82) (-0.37)LEVERAGE 7.046 10.681* -2.919 -3.928 -9.617 10.465* 11.356** 10.597**
(1.24) (1.87) (-0.43) (-0.57) (-1.32) (1.93) (2.05) (2.01)ROA -3.050 -6.603 -25.391 -27.182 -20.085 -5.041 -0.999 -4.183
(-0.21) (-0.50) (-1.52) (-1.61) (-1.40) (-0.38) (-0.08) (-0.32)PTB -1.066*** -0.910*** 0.038 0.074 0.066 -0.958*** -0.926*** -0.941***
(-4.26) (-3.45) (0.16) (0.33) (0.26) (-3.61) (-3.60) (-3.75)BANKAGE -0.038 -0.132 0.345 0.368 0.512 -0.236 -0.283 -0.180
(-0.13) (-0.45) (0.81) (0.85) (1.12) (-0.79) (-0.96) (-0.61)Constant 12.782 -0.417 -3.876 -3.007 1.553 -7.204 -6.302 -7.093
(1.12) (-0.06) (-0.63) (-0.50) (0.25) (-1.42) (-1.22) (-1.40)Year dummies YES YES YES YES YES YES YES YESObservations 231 215 150 150 148 231 228 230
X 2 74.701 40.402 40.649 39.495 40.545 52.195 54.996 49.715
Pseudo R 2 0.275 0.181 0.232 0.222 0.248 0.215 0.207 0.199
Variables
158
6.3 Estimation of bank reputational loss
6.3.1 Event study results for the full sample
Table 6.8 presents the results of both the parametric and non-parametric
tests that assess the statistical significance of the announcement effect of
enforcement action of operational loss over eight event windows. The mean
(CAR) is negative as expected, and statistically significant at the 10 percent
level for three of the event windows [-5,5], [-10,10] and [-10,5]. These results
provide some evidence that the market reacts negatively on average by up to
-0.78 percent to the announcement of enforcement actions. The median CAR
is significantly negative for five event windows, especially the larger windows.
The latter result suggests that news regarding regulatory enforcement action
might have leaked out well before its official announcement. The generalized
sign test shows that the majority of the CARs are negative and significant
across most event windows. Windows [-20,20] and [-10,5] have the highest
proportion (58 percent) of negative CARs. This result is consistent with previous
studies of a negative market reaction to regulatory enforcement actions
(Jordan et al., 2000; Karpoff et al., 2008b).
Table 6.9 reports the results of reputation loss after excluding legal fines
from equity loss. The mean CAR_REP is negative (by up to 0.74 percent) for
all event windows, but only statistically significant at the 10 percent level for
three event windows [-5,5], [-10,10] and [-10,5]. The median CAR_REP is negative
and statistically significant across most event windows. The fact that CAR and
159
CAR_REP values are similar in magnitude means that legal fines are negligible
in relative to market equity loss. Across most event windows, over fifty percent
of observations have a negative CAR_REP.
160
Table 6.8 Share price reaction (CAR) to announcements of enforcement actions (2000-2014) This table reports the results from an event study of 355 enforcement actions against 210 U.S. banks between 2000 and 2014. CAR is the cumulative abnormal return over the event window, where the abnormal return (AR) is the difference between the actual return of a security and the expected return from the single market model over a 250-trading-day estimation period. Superscripts *, **, *** indicate statistical significance at the 10%, 5% and 1% level, respectively.
Event Window No. of obs. Mean t-stat p-value Median Rank Test z-stat p-value % Neg Sign Test p-value
[-1,1] 355 0.51% 1.363 0.173 0.03% 0.262 0.793 49.58% 0.873
[-3,3] 355 -0.13% -0.286 0.775 -0.35% 1.525 0.127 54.93% 0.063 *
[-5,5] 355 -0.65% -1.801 0.071 * -0.67% 2.210 0.027 ** 56.62% 0.013 **
[-10,10] 355 -0.78% -1.844 0.069 * -0.97% 2.008 0.045 ** 55.77% 0.030 **
[-20,20] 355 -0.70% -0.561 0.575 -1.75% 1.855 0.064 * 57.75% 0.004 ***
[-15,1] 355 0.22% 0.257 0.797 -0.61% 1.068 0.285 54.93% 0.063 *
[-10,5] 355 -0.56% -1.729 0.086 * -1.22% 2.378 0.017 ** 57.75% 0.004 ***
[-5,10] 355 -0.87% -1.166 0.244 -0.74% 2.218 0.027 ** 55.77% 0.030 **
Mean CARs Median CARs % of Negative CARs
161
Table 6.9 Reputational loss (CAR_REP) due to enforcement actions (2000-2014) This table reports the results from an event study of 355 enforcement actions against 210 banks in the U.S. between 2000 and 2014. CAR_REP is the reputational loss, measured by the abnormal return minus legal fines. Superscripts *, **, *** indicate statistical significance at the 10%, 5% and 1% level, respectively.
Event Window No. of obs. Mean t-stat p-value Median Rank Test z-stat p-value % Neg Sign Test p-value
[-1,1] 355 0.54% 1.457 0.145 0.05% 0.418 0.676 49.01% 0.710
[-3,3] 355 -0.10% -0.210 0.834 -0.35% 1.397 0.162 54.93% 0.063 *
[-5,5] 355 -0.62% -1.840 0.073 * -0.67% 2.096 0.036 ** 56.34% 0.017 **
[-10,10] 355 -0.74% -1.804 0.075 * -0.88% 1.963 0.050 ** 55.49% 0.038 **
[-20,20] 355 -0.67% -0.532 0.595 -1.74% 1.819 0.069 * 57.46% 0.005 **
[-15,1] 355 0.25% 0.299 0.765 -0.57% 1.018 0.309 54.93% 0.063 *
[-10,5] 355 -0.53% -1.682 0.093 * -1.22% 2.327 0.020 ** 57.75% 0.004 ***
[-5,10] 355 -0.83% -1.117 0.264 -0.72% 2.137 0.033 ** 55.77% 0.030 **
Mean CAR_REP Median CAR_REP % of Negative CAR_REP
162
6.3.2 Event study results split by degree of severity
Table 6.10 reports the reputation loss for the sub-samples according to
the severity of EA. Panel A presents the results for severe enforcement actions.
The mean CAR_REP is negative and statistically significant for only the eleven-
day window [-5,5], with the magnitude of reputation loss being -1.59 percent.
The median CAR_REP is negative and significant across six event windows, with
the magnitude increasing with the length of the event window. The median
CAR_REP increases from -0.65 percent for the seven-day event window [-3,3],
to -1.29 percent for the eleven-day window [-5,5], and to -2.27 percent for the
forty-one-day window [-20,20]. The Sign test shows that the majority of
observations have a negative CAR_REP. In sum, the results from Panel A suggest
that the market imposes significant reputational penalty on violating banks that
received severe enforcement actions.
Panel B of Table 6.10 presents the results for non-severe enforcement
actions. Overall, the results show that none of the reputation loss are significant
across different event windows. This is consistent with my expectation that
investors impose heavier reputational penalty for severe cases than non-severe
cases.
163
Table 6.10 Reputational loss (CAR_REP) by degree of severity of the enforcement actions This table reports the results of an event study carried out on the data for 191 severe (Panel A) and 164 non-severe (Panel B) enforcement actions against U.S. banks between 2000 and 2014. An enforcement action is considered as severe if it is one of the following three types: “cease and desist”, “prompt corrective action” and “formal agreement/consent order”. An enforcement action is classified non-severe if it is in one of the following six types: “call report infraction”, “deposit insurance threat”, “formal memo of understanding”; “order requiring restitution”, “other fines”, and “sanctions due to violation of Home Mortgage Disclosure Act (HMDA)”. CAR_REP is the reputational loss taken as the abnormal return on event day (day 0) adjusted for legal fines. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% level, respectively.
Panel A: Severe Enforcement Actions
Event Window No. of obs. Mean t-stat p-value Median Rank Test z-stat p-value %Neg Sign Test p-value
[-1,1] 191 0.55% 0.883 0.377 0.04% 0.132 0.895 49.21% 0.828
[-3,3] 191 -0.45% -0.578 0.563 -0.65% 1.877 0.061 * 58.64% 0.017 **
[-5,5] 191 -1.59% -1.674 0.094 * -1.29% 2.311 0.021 ** 58.64% 0.017 **
[-10,10] 191 -1.85% -1.326 0.185 -2.08% 2.148 0.032 ** 56.54% 0.070 *
[-20,20] 191 -2.10% -1.164 0.244 -2.27% 1.786 0.074 * 59.16% 0.011 **
[-15,1] 191 -0.43% -0.379 0.705 -0.64% 1.005 0.315 54.97% 0.169
[-10,5] 191 -1.74% -1.458 0.145 -1.69% 2.480 0.013 ** 59.16% 0.011 **
[-5,10] 191 -1.70% -1.434 0.151 -1.22% 2.379 0.017 ** 58.64% 0.017 **
Mean CAR_REP Median CAR_REP % of Negative CAR_REP
164
Table 6.10 (Continued)
Panel B: Non-severe Enforcement Actions
Event Window No. of obs. Mean t-stat p-value Median Rank Test z-stat p-value %Neg Sign Test p-value
[-1,1] 164 0.53% 1.527 0.127 0.07% 0.530 0.596 48.78% 0.755
[-3,3] 164 0.31% 0.638 0.523 -0.09% 0.148 0.883 50.61% 0.876
[-5,5] 164 0.51% 0.798 0.425 -0.25% 0.478 0.633 53.66% 0.349
[-10,10] 164 0.55% 0.477 0.634 -0.31% 0.516 0.606 54.27% 0.274
[-20,20] 164 1.00% 0.583 0.560 -1.15% 0.672 0.502 55.49% 0.160
[-15,1] 164 1.05% 0.843 0.399 -0.47% 0.317 0.751 54.88% 0.212
[-10,5] 164 0.89% 0.962 0.336 -0.58% 0.560 0.576 56.10% 0.118
[-5,10] 164 0.18% 0.217 0.828 -0.22% 0.479 0.632 52.44% 0.532
Mean CAR_REP Median CAR_REP % of Negative CAR_REP
165
6.3.3 Event study results split by degree of technicality
Table 6.11 reports the results for sub-samples of enforcement actions
according to the degree of technicality. Panel A presents the results for
technical enforcement actions. The mean value of reputational loss is negative
across six event windows but only statistically significant at the 5 percent level
for the eleven-day window [-5,5]. This category of EA has the largest reputation
loss at -2.75 percent. This finding suggests that the market considers technical
enforcement actions as the most damaging to bank reputation. The median
CAR_REP is negative and significant across three windows, with the magnitude
of CAR_REP increasing with the length of the window. Specifically, median
CAR_REP increases from -0.77 percent for the seven-day event window [-3,3],
to -1.96 percent for the eleven-day window [-5,5], and to -2.33 percent for the
sixteen-day window [-5,10]. The Sign test shows that over 60 percent of
CAR_REPs are negative and significant at the 1 percent and 5 percent level
for the following event windows: [-3,3], [-5,5], [-10,5], and [-5,10]. Overall, these
results thus suggest that the market imposes significant reputational penalty
on violating banks that received enforcement actions because of technical
issues, including those violations on requirements of asset quality, capital
adequacy and liquidity, lending, provisions, and reserves.
Panel B presents the results for non-technical enforcement actions. The
average value of CAR_REPs is negative as expected but only significant at the
10 percent level for the twenty-one-day window [-10,10]. The magnitude of this
166
negative effect (-1.98 percent) is two times more than that of the whole sample
(-0.74 percent). The median CAR_REP is negative across all windows, but only
statistically significant for the [-10,10] and [-10,5] windows. The statistics from
the Sign test suggests that the majority of reputational loss are not significantly
negative.
Finally, Panel C presents results for “other” enforcement actions, i.e.
those that do not fall within the first two categories (technical and non-
technical types). The results from t-test, z-test and Sign test all suggest weak
evidence of negative reputational loss for banks that received “other”
enforcement actions.
167
Table 6.11 Reputational loss (CAR_REP) by degree of technicality of the enforcement actions This table reports the results of an event study carried out on the data for 106 technical (Panel A), 168 non-technical (Panel B), and other (Panel C) enforcement actions against U.S. banks between 2000 and 2014. Enforcement actions are classified as technical if they are related to violations of requirements concerning asset quality, capital adequacy and liquidity, lending, provisions, and reserves. Enforcement actions are classified as non-technical if they have been caused by failures of a bank’s anti-money laundering systems, internal control and audit systems, and risk management systems. Non-technical misconduct cases also include breaches of the requirements concerning the competency of the senior management team and the board of directors as well as violations of various laws such as consumer compliance programs, Equal Credit Opportunity Act (ECOA), and Federal Trade Commission Act (FTCA). CAR_REP is the reputational loss taken as the abnormal return on event day (day 0) adjusted for legal fines. Those bank misconducts that cannot be classified as either technical or non-technical are classified as “other”. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% level, respectively.
Panel A: Technical Enforcement Actions
Event Window No. of obs. Mean t-stat p-value Median Rank Test z-stat p-value %Neg Sign Test p-value
[-1,1] 106 0.13% 0.149 0.882 -0.31% 0.701 0.483 53.77% 0.437
[-3,3] 106 -1.10% -0.991 0.322 -0.77% 1.691 0.091 * 60.38% 0.033 **
[-5,5] 106 -2.75% -2.000 0.045 ** -1.96% 2.722 0.006 *** 65.09% 0.002 ***
[-10,10] 106 -0.66% -0.288 0.774 -1.82% 1.114 0.265 57.55% 0.120
[-20,20] 106 -0.73% -0.249 0.803 -2.18% 0.758 0.448 57.55% 0.120
[-15,1] 106 1.67% 0.753 0.451 -0.92% 0.433 0.665 56.60% 0.174
[-10,5] 106 -0.67% -0.344 0.731 -1.62% 1.537 0.124 60.38% 0.033 **
[-5,10] 106 -2.75% -1.534 0.125 -2.33% 2.599 0.009 *** 66.04% 0.001 ***
Mean CAR_REP Median CAR_REP % of Negative CAR_REP
168
Table 6.11 (Continued)
Panel B: Non-technical Enforcement Actions
Panel C: Other Enforcement Actions
Event Window No. of obs. Mean t-stat p-value Median Rank Test z-stat p-value %Neg Sign Test p-value
[-1,1] 168 -0.16% -0.413 0.679 0.15% 0.081 0.936 47.62% 0.537
[-3,3] 168 -0.56% -0.965 0.334 -0.13% 0.534 0.594 51.79% 0.643
[-5,5] 168 -0.84% -1.022 0.307 -0.25% 0.868 0.385 52.38% 0.537
[-10,10] 168 -1.98% -1.724 0.085 * -0.85% 1.706 0.088 * 55.95% 0.123
[-20,20] 168 -0.11% -0.069 0.945 -1.09% 0.640 0.522 53.57% 0.355
[-15,1] 168 -0.05% -0.056 0.955 -0.50% 0.545 0.586 52.38% 0.537
[-10,5] 168 -1.20% -1.312 0.190 -0.88% 1.680 0.093 * 57.14% 0.064 *
[-5,10] 168 -1.61% -1.635 0.102 -0.36% 1.327 0.184 52.38% 0.537
Mean CAR_REP Median CAR_REP % of Negative CAR_REP
Event Window No. of obs. Mean t-stat p-value Median Rank Test z-stat p-value %Neg Sign Test p-value
[-1,1] 107 0.67% 0.789 0.430 0.03% 0.252 0.801 49.53% 0.923
[-3,3] 107 -0.59% -0.573 0.566 -0.65% 1.563 0.118 58.88% 0.066 *
[-5,5] 107 -0.45% -0.402 0.688 -1.02% 1.051 0.293 57.94% 0.100
[-10,10] 107 -1.17% -0.753 0.452 -0.69% 1.184 0.236 53.27% 0.499
[-20,20] 107 -2.62% -1.199 0.231 -2.27% 1.983 0.047 ** 62.62% 0.009 ***
[-15,1] 107 -1.35% -1.069 0.285 -1.05% 1.221 0.222 58.88% 0.066 *
[-10,5] 107 -1.24% -0.951 0.342 -2.02% 1.458 0.145 57.94% 0.100
[-5,10] 107 -0.37% -0.277 0.782 -1.02% 0.898 0.369 56.07% 0.209
Mean CAR_REP Median CAR_REP % of Negative CAR_REP
169
6.4 Determinants of bank reputational loss
6.4.1 Results from OLS regressions
In this section, I run regression to study the factors that are correlated to
reputation loss. Table 6.12 provides the ordinary-least-square (OLS) regression
results, where bank reputational loss is the dependent variable. Reputational loss
is estimated over a three-day window [-1,1]. Since CAR_REP measures reputational
return, a negative coefficient on the independent variables suggests a positive
relationship between that variable and reputational loss.
Panel A presents OLS results of bank reputational loss, where I only include
one governance variable in each regression. Specification 1 shows that the
coefficient of BSIZE is positive and significant at the 10 percent level, suggesting
that a larger board is associated with less reputational damage. This finding is
consistent with Hypothesis 7 that investors are confident that banks with a “good”
governance structure (larger board) have better problem-solving capabilities toward
complex tasks such as overcoming potential costs of regulatory enforcement
action and thus tend to penalize these banks less. The estimated coefficient of
BSIZE is 0.022, implying that a one director increase in the board reduces bank
reputational loss by 1.5 percent of total market capitalization.48 This economic
48 BSIZE is calculated as ln(1+ number of directors). For an additional increase in board size by one director, the BSIZE becomes ln(1+1). The overall effect on reputation loss would be o.022 * ln(2) = 1.5 percent, where 0.022 is the regression coefficient of BSIZE.
170
significance is large given the mean reputational loss is just 0.5 percent (as
reported in Table 5.3).
The estimated coefficients of INDEP_BOARD, MAX_DIR and BUSY_BOARD are
all negative as expected although statistically insignificant. The various board
diversity measures (FEMALE_DIR, FEMALE_CHAIR, FEMALE_CEO, AGE_CV and
TENURE_CV) are also all statistically insignificant (specifications 6 to 10).
Regarding enforcement action characteristics, SEVERE is negative at the 5
percent significance level in specification 3 and 4, providing evidence of a larger
reputational loss of banks that receive severe enforcement actions. The estimated
coefficient of SEVERE suggests the reputational damage of severe enforcement
actions is 1.7 percentage point higher than that for the non-severe cases. There
is no evidence of differences in reputational loss between technical and non-
technical enforcement action types – TECHNICAL dummy is statistically insignificant
across all regressions. In seven regressions (specifications 1, 2, 6 to 10), the
interaction term SEVERE *TECHNICAL is negative and significant, i.e. banks with
enforcement actions that are classified as both severe and technical suffer an
approximate 5 percentage point higher reputational loss than the other cases.
OCC and FRB dummies are not statistically significant across all regressions,
indicating that reputational loss is indifferent among different banking regulators.
With regards to bank-specific characteristics, the coefficient of BANK_SIZE
is negative as expected, but statistically insignificant across all regressions except
171
Specification 1. STOCK_VOL and BETA are positive and significant in most
specifications, implying that riskier banks are associated with lower reputational
damage. These results contradict Fiordelisi et al. (2013) who argue that a risky
bank absorbs the loss worse than a safe bank and thus suffers larger reputational
damage. There is no evidence that LEVERAGE, ROA, PTB, and CAPITAL are
significant determinants of bank reputational loss on the heels of regulatory
enforcement action announcements.
Panel B reports the results where I include multiple governance measures
in the regression models. Due to limited data on busy directors, I exclude
MEAN_DIR, MAX_DIR and BUSY_BOARD from these regressions.49 When including
multiple governance measures in the regressions, I find no further evidence that
reputational loss is related to governance.
In sum, the results provided in Table 6.12 provide some evidence that the market
discriminates between well- and poorly-governed banks when imposing reputational
penalty. Banks with better governance structures tend to suffer lower reputational
damage following the announcement of enforcement actions relative to those with
poorer governance structures. These findings are consistent with the argument
that investors are confident that banks with a “good” governance structure (larger
board) have better problem-solving capabilities toward complex tasks such as
49 When including MEAN_DIR, MAX_DIR and BUSY_BOARD into regression models, the number of observations drops to 84.
172
overcoming potential costs of regulatory enforcement action and thus tend to
penalize these banks less.
173
Table 6.12 Regressions of bank reputational loss (full sample) This table presents the results for the following regression:
tim
timl
tilk
tikj
tijti YEARCHARBANKCHAREAGOVERNANCEREPCAR ,
20142000
1,
7
1,
4
1,
7
1,0, ___ εφδββα +++++= ∑∑∑∑
−
====
The dependent variable is reputational loss
estimated over a three-day event window [-1,1]. Please refer to Table 5.2 for a list of variables definition. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at firm level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Regression results of each governance measures on bank reputational loss
BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR FEMALE_CEO AGE_CV TENURE_CV DUALITY
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
GOVERNANCE 0.022* -0.016 -0.010 -0.003 -0.109 -0.009 0.019 0.025 -0.001 -0.012
(1.72) (-0.42) (-0.66) (-0.56) (-0.68) (-0.15) (0.48) (0.24) (-0.08) (-1.16)SEVERE 0.010 0.012 -0.017** -0.017** -0.016 0.010 0.009 0.010 0.008 0.012
(1.06) (1.05) (-2.00) (-2.04) (-1.63) (1.04) (0.89) (1.15) (0.88) (1.18)TECHNICAL 0.011 0.015 -0.008 -0.009 -0.007 0.013 0.013 0.013 0.014 0.016
(0.86) (1.06) (-0.60) (-0.65) (-0.40) (1.07) (1.01) (1.09) (1.10) (1.26)SEVERE*TECHNICAL -0.040* -0.046* 0.015 0.016 0.019 -0.042** -0.040** -0.040** -0.043** -0.043**
(-1.94) (-1.95) (0.65) (0.67) (0.69) (-2.07) (-1.99) (-2.08) (-2.13) (-2.12)OCC -0.006 -0.003 -0.005 -0.005 -0.000 -0.004 -0.005 -0.007 -0.006 -0.007
(-0.54) (-0.23) (-0.54) (-0.52) (-0.00) (-0.41) (-0.42) (-0.69) (-0.56) (-0.65)FRB -0.012 -0.009 0.008 0.008 0.012 -0.012 -0.010 -0.011 -0.010 -0.012
(-0.93) (-0.66) (0.81) (0.84) (1.05) (-0.90) (-0.75) (-0.94) (-0.81) (-0.99)BANK_SIZE -0.004* -0.004 -0.002 -0.003 -0.006 -0.003 -0.003 -0.002 -0.002 -0.002
(-1.84) (-1.59) (-0.43) (-0.72) (-1.55) (-1.02) (-1.37) (-1.08) (-1.24) (-0.99)COMPLEXITY -0.205* -0.205* 0.266** 0.259** 0.2370* -0.234** -0.156 -0.161 -0.146 -0.197
(-1.91) (-1.91) (2.44) (2.28) (1.85) (-2.21) (-1.46) (-1.50) (-1.41) (-1.65)
LEVERAGE 0.344 0.419 0.113 0.106 0.093 0.331 0.334 0.350 0.366 0.297
(1.42) (1.49) (0.48) (0.43) (0.31) (1.36) (1.41) (1.47) (1.50) (1.22)ROA -0.598 -0.643 -0.501 -0.488 -0.383 -0.564 -0.461 -0.557 -0.582 -0.604
(-1.43) (-1.48) (-0.84) (-0.81) (-0.54) (-1.37) (-1.02) (-1.38) (-1.42) (-1.46)
Variables
174
Table 6.12 (Continued)
BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR FEMALE_CEO AGE_CV TENURE_CV DUALITY
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
PTB -0.010 -0.010 0.013 0.013 0.010 -0.011 -0.009 -0.010 -0.012 -0.006
(-1.03) (-0.92) (0.86) (0.83) (0.60) (-1.11) (-0.89) (-1.08) (-1.28) (-0.61)CAPITAL 0.066 0.121 0.065 0.060 0.075 0.054 0.064 0.052 0.063 0.064
(0.90) (1.52) (0.66) (0.57) (0.66) (0.75) (0.88) (0.77) (0.91) (0.90)STOCK_VOL -0.228 -0.240 1.145** 1.158** 1.287** -0.218 -0.224 -0.220 -0.260 -0.200
(-1.41) (-1.42) (2.13) (2.17) (2.14) (-1.37) (-1.43) (-1.41) (-1.57) (-1.30)BETA 0.030** 0.034*** -0.003 -0.003 -0.002 0.031** 0.031** 0.029*** 0.030*** 0.030**
(2.43) (2.61) (-0.32) (-0.35) (-0.14) (2.47) (2.59) (2.62) (2.62) (2.49)Constant -0.383* -0.399 -0.179 -0.162 -0.128 -0.326 -0.324 -0.353 -0.360 -0.291
(-1.67) (-1.53) (-0.77) (-0.66) (-0.44) (-1.46) (-1.47) (-1.59) (-1.59) (-1.29)Year dummies YES YES YES YES YES YES YES YES YES YES
Observations 264 231 93 93 81 264 267 272 271 264
Adjusted R 2 0.070 0.078 0.182 0.181 0.171 0.067 0.066 0.068 0.072 0.070
Variables
175
Table 6.12 (Continued)
Panel B: Regression results of all governance measures (except board busyness) on bank reputational loss
Variables (1) (2) (3) (4)
BSIZE 0.022 0.025 0.024 0.022
-0.91 -1.06 -0.96 -0.96
INDEP_BOARD -0.026 -0.019 -0.011 -0.012
(-0.58) (-0.42) (-0.22) (-0.24)
FEMALE_DIR -0.016 -0.024 -0.014
(-0.24) (-0.35) (-0.20)
FEMALE_CEO 0.029 0.027 0.023
-0.76 -0.7 -0.58
AGE_CV 0.076 0.069
-0.51 -0.47
TENURE_CV -0.007 -0.009
(-0.47) (-0.62)
DUALITY -0.012
(-0.89)
Constant -0.402 -0.419 -0.460* -0.406
(-1.54) (-1.57) (-1.69) (-1.46)
EA characteristics YES YES YES YES
Bank-specific controls YES YES YES YES
Year dummies YES YES YES YES
Observations 231 231 226 223
Adjusted R 2 0.077 0.074 0.063 0.065
176
6.4.2 Results from OLS regressions with squared terms
Table 6.13 presents the full sample results for governance with squared
terms. Only continuous governance variables are considered for these non-linear
regressions. Panel A reports the results where I include one governance variable
and its squared term in each regression.
As reported in specification 1, the negative and significant coefficient of
the squared term of BSIZE show that there is a point at which adding a new
director increases bank reputational loss. For the banks in the sample, this value
is around 13 directors. 50 The positive relation between board size and
reputational loss is in line with Hypothesis 7, arguing that banks with favorable
governance structures have better problem-solving capabilities toward complex
tasks. Investors are confident that these banks can effectively recover from the
reputation damage crisis, and are able to restore their reputation to the state
prior to bank misconduct. The non-linear result provides evidence suggest that
problem-solving capabilities grows as board size increases, but as the board
grows beyond a certain size, the effectiveness of these capabilities starts to
diminish.
50 The coefficient for BSIZE and its squared term is 0.371 and -0.072, respectively. The cut-off point is computed as -0.371/(2*(-0.072)) = 2.5764. Since board size is measured as the natural logarithm of the number of directors sitting on board, this cut-off point should be anti-logged, giving a value of 13.15.
177
My analysis also reveals a non-linear relation between board heterogeneity
and bank reputational loss. Specifically, more female directors sitting on a bank’s
board is negatively associated with reputational loss (i.e., the coefficient of
FEMALE_DIR is 0.171, p < 0.10). However, the magnitude of bank reputational loss
increases if the proportion of female directors sitting on board grows beyond 18
percent. 51 Similar results are observed for board heterogeneity in terms of
directors’ age (AGE_CV). The coefficient of AGE_CV2 is -2.414, p < 0.10 whilst the
coefficient of AGE_CV is 0.748, p < 0.10. This suggests that banks with a diverse
board in terms of the age of the directors experience a lower reputational loss,
but as age diversity increases beyond 0.16 (or the average directors’ age is above
49), 52 bank reputational damage starts intensifying. These findings are in support
of Hypothesis 7, that is, board diversity reinforces better problem-solving
capabilities toward complex tasks such as overcoming potential negative
consequences of regulatory enforcement actions. However, as board diversity
increases beyond a certain limit, the quality of the board’s problem-solving
capabilities reduces, leading to more severe reputational damage. Results for
51 The coefficient for FEMALE_DIR and its squared term is 0.171 and -0.487, respectively. The cut-off point is computed as -0.171/(2*(-0.487))= 0.1756. 52 The coefficient for AGE_CV and its squared term is 0.748 and -2.414, respectively. The cut-off point is computed as -0.748/(2*(-2.414)) = 0.1549. Since age diversity is measured as the standard deviation of age of directors divided by the average age of directors, I divide the average standard deviation of my sample banks by 0.1549 to get the average value of directors’ age of 49 (7.902601/0.16).
178
enforcement-related variables and bank-specific controls have similar signs as
reported in Table 6.12.
Panel B reports the results where multiple governance variables and their
squared terms are included in the regression models. The non-linear relationship
between board size and bank reputational loss remain robust when I include other
governance and their corresponding squared terms in regressions. The cut-off
point for these four regressions is approximately 12 directors,53 suggesting that
as the number of directors expand beyond 12 directors, the negative relation
between board size and reputational loss becomes less negative.
In sum, the findings tabulated in Table 6.13 suggest the relationship between
board heterogeneity and bank reputational loss is non-linear.
53 The cut-off point is measured as [– b_BSIZE/(2*b_BSIZE2)], where b_BSIZE is the coefficient of BSIZE and b_BSIZE2 is the coefficient of BSIZE2. For example, in specification 4, the cut-off point is computed as -0.547/(2*(-0.111) = 2.4640 . Anti-logging this figure will give a value of 11.75.
179
Table 6.13 Regressions of bank reputational loss with squared terms (full sample) This table presents the results of Eq. (4.2) and added squared term of corporate governance variables:
tim
timl
tilk
tikj
tijj
tijti YEARCHARBANKCHAREAGOVERNANCEGOVERNANCEREPCAR ,
20142000
1,
7
1,
4
1,
27
1,
7
1,0, __)(_ εφδβγβα ++++++= ∑∑∑∑∑
−
=====
where subscripts i
denotes individual banks, t time period, j alternative corporate governance proxies, k enforcement action characteristics, and l bank-specific characteristics. The dependent variable is reputational loss estimated over a three-day event window [-1,1]. Please refer to Table 5.2 for a list of variables definition. YEAR is time dummies. α is the constant term. ε is the idiosyncratic error term. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at firm level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Regressions results of each governance measure and its squared term on bank reputational loss
BSIZE INDEP_BOARD BUSY_BOARD MEAN_DIR MAX_DIR FEMALE_DIR AGE_CV TENURE_CV
(1) (2) (3) (4) (5) (6) (7) (8)
GOVERNANCE 0.371** -0.248 -0.117 -0.036 -0.007 0.171* 0.748* -0.023
(2.15) (-0.54) (-0.28) (-1.00) (-0.68) (1.79) (1.74) (-0.55)
GOVERNANCE2 -0.072** 0.153 0.067 0.015 0.001 -0.487*** -2.414* 0.013
(-2.07) (0.49) (0.02) (1.00) (0.57) (-2.92) (-1.79) (0.61)
SEVERE 0.008 0.012 -0.016 -0.018** -0.017* 0.009 0.010 0.008
(0.87) (1.09) (-1.62) (-2.05) (-1.97) (0.97) (1.15) (0.88)
TECHNICAL 0.014 0.015 -0.007 -0.008 -0.008 0.013 0.013 0.013
(1.06) (1.08) (-0.39) (-0.54) (-0.58) (1.04) (1.03) (1.08)
SEVERE*TECHNICAL -0.040* -0.046* 0.019 0.013 0.014 -0.040* -0.040** -0.042**
(-1.97) (-1.96) (0.67) (0.60) (0.60) (-1.97) (-2.04) (-2.12)
OCC -0.005 -0.003 0.000 -0.004 -0.004 -0.003 -0.005 -0.005
(-0.43) (-0.23) (0.00) (-0.38) (-0.40) (-0.27) (-0.50) (-0.51)
FRB -0.011 -0.010 0.012 0.008 0.008 -0.011 -0.009 -0.010
(-0.85) (-0.68) (1.06) (0.87) (0.84) (-0.84) (-0.77) (-0.82)
BANK_SIZE -0.003 -0.004 -0.006 -0.001 -0.002 -0.004 -0.002 -0.002
(-1.64) (-1.61) (-1.56) (-0.26) (-0.66) (-1.37) (-0.85) (-1.26)
COMPLEXITY -0.238 -0.203 0.290* 0.261** 0.259** -0.216 -0.179 -0.143
(-2.19) (-1.91) (1.95) (2.29) (2.25) (-2.03) (-1.62) (-1.36)
LEVERAGE 0.337 0.426 0.093 0.104 0.115 0.344 0.337 0.370
(1.41) (1.52) (0.31) (0.45) (0.46) (1.42) (1.42) (1.51)
ROA -0.691 -0.636 -0.385 -0.611 -0.528 -0.490 -0.538 -0.585
(-1.63) (-1.46) (-0.52) (-0.96) (-0.85) (-1.18) (-1.34) (-1.42)
Variables
180
Table 6.13 (Continued)
BSIZE INDEP_BOARD BUSY_BOARD MEAN_DIR MAX_DIR FEMALE_DIR AGE_CV TENURE_CV
(1) (2) (3) (4) (5) (6) (7) (8)
PTB -0.008 -0.011 0.010 0.016 0.014 -0.011 -0.010 -0.011
(-0.79) (-0.97) (0.59) (1.00) (0.90) (-1.13) (-1.07) (-1.24)
CAPITAL 0.076 0.119 0.076 0.062 0.060 0.044 0.052 0.061
(1.02) (1.50) (0.62) (0.64) (0.57) (0.62) (0.77) (0.88)
STOCK_VOL -0.224 -0.244 1.284* 1.051* 1.132** -0.205 -0.194 -0.260
(-1.41) (-1.44) (1.94) (1.86) (2.06) (-1.31) (-1.22) (-1.56)
BETA 0.029** 0.035*** -0.001 -0.000 -0.002 0.030** 0.029*** 0.031***
(2.37) (2.64) (-0.13) (-0.02) (-0.25) (2.43) (2.63) (2.60)
Constant -0.796** -0.317 -0.128 -0.173 -0.172 -0.333 -0.385* -0.357
(-2.31) (-1.00) (-0.43) (-0.76) (-0.70) (-1.50) (-1.72) (-1.57)
Year dummies YES YES YES YES YES YES YES YES
Observations 264 231 81 93 93 264 272 271
Adjusted R 2 0.077 0.075 0.176 0.17 0.169 0.081 0.07 0.068
Variables
181
Table 6.13 (Continued)
Panel B: Regression of all governance measures (except board busyness) and their squared terms on bank reputational loss
Variables (1) (2) (3) (4)
BSIZE 0.617*** 0.589** 0.561** 0.547**
(2.78) (2.60) (2.45) (2.36)
BSIZE 2 -0.125*** -0.120** -0.115** -0.111**
(-2.70) (-2.56) (-2.42) (-2.32)
INDEP_BOARD -0.479 -0.490 -0.545 -0.470
(-0.99) (-1.02) (-1.14) (-0.97)
INDEP_BOARD 2 0.301 0.306 0.350 0.297
(0.94) (0.96) (1.10) (0.92)
FEMALE_DIR 0.160 0.168 0.171
(1.46) (1.44) (1.47)
FEMALE_DIR 2 -0.423** -0.436** -0.455**
(-2.30) (-2.15) (-2.26)
AGE_CV 0.706 0.732
(1.29) (1.31)
AGE_CV 2 -1.953 -2.037
(-1.21) (-1.23)
TENURE_CV -0.057
(-1.20)
TENURE_CV 2 0.028
(1.12)
Constant -0.946** -0.901** -0.913** -0.929**
(-2.07) (-2.02) (-2.05) (-2.01)
EA characteristics YES YES YES YES
Bank-specific controls YES YES YES YES
Year dummies YES YES YES YES
Observations 231 231 228 226
Adjusted R 2 0.090 0.097 0.094 0.086
182
6.4.3 Alternative Event Windows
In order to address concerns about potential leakages prior to the public
announcement of enforcement actions, I also use two alternative event windows,
[-3,3] and [-5,5], to capture the market reaction. As shown in Panel A of Table
6.14 and Table 6.15, the coefficient of BSIZE remains positive and significant at
the 10 percent level, suggesting that a larger board is associated with less
reputational damage. This finding is consistent with Hypothesis 7 that investors
are confident that banks with a “good” governance structure (larger board) have
better problem-solving capabilities toward complex tasks such as overcoming
potential costs of regulatory enforcement action and thus tend to penalize these
banks less. In terms of economic significance, a one director increase in the
board reduces bank reputational loss by 1.9 percent of total market
capitalization.54 I find no further evidence of bank reputational loss is affected by
other governance variables.
In Panel B of Table 6.15, I find evidence of a negative association between
directors’ age diversity and bank reputational loss while including multiple
governance variables in regression models. This is different from the results for
short window [-1,1], as reported in Panel B of Table 6.12.
54 BSIZE is calculated as ln(1+ number of directors). For an additional increase in board size by one director, the BSIZE becomes ln(1+1). The overall effect on reputation loss would be o.028* ln(2) = 1.9 percent, where 0.028 is the regression coefficient of BSIZE.
183
Consistent with previous regression analyses, I also find evidence that
reputational loss is significantly and non-linearly related with board size, the
proportion of female directors on board, and directors’ age diversity. The results
are summarized in Table 6.16 and Table 6.17 for windows [-3,3] and [-5,5],
respectively. In Panel A, I find evidence that having larger board helps alleviate
reputational loss (the coefficient of BSIZE is 0.259, p < 0.10), but as the number
of directors grows beyond 15 (i.e., the coefficient of BSIZE2 is -0.048, p < 0.10),55
reputation loss starts to increase. There is no evidence of a non-linear association
between board size and bank reputational loss for the [-5,5] window.
In addition, I find that more female directors sitting on a bank’s board is
negatively associated with reputational loss (i.e., the coefficient of FEMALE_DIR is
0.054, p < 0.10). However, the magnitude of bank reputational loss increases if
the proportion of female directors grows beyond 43 percent (the coefficient of
FEMALE_DIR2 is -0.064, p < 0.10). 56 Similar results are observed for board
heterogeneity in terms of directors’ age (AGE_CV). The coefficient of AGE_CV2 is
-1.874, p < 0.10, whilst the coefficient of AGE_CV is 0.643, p < 0.10. This suggests
that banks with a diverse board in terms of the ages of the directors on average
55 The coefficient for BSIZE and its squared term is 0.259 and -0.048, respectively. The cut-off point is computed as -0.259/(2*(-0.048)) = 2.6979. Since board size is measured as the natural logarithm of the number of directors sitting on board, this cut-off point should be anti-logged, giving a value of 14.85. 56 The coefficient for FEMALE_DIR and its squared term is 0.054 and -0.064, respectively. The cut-off point is computed as -0.054/(2*(-0.064))= 0.4288.
184
experience a lower reputational loss, but as age diversity increases beyond 0.17
(or when the average directors’ age is above 46),57 bank reputational damage
starts intensifying. These findings are in support of Hypothesis 7 at first, that is,
board diversity reinforces better problem-solving capabilities toward complex tasks
such as overcoming potential negative consequences of regulatory enforcement
actions and being able to restore corporate reputation to the state prior to bank
misconduct. But as board diversity increases beyond a certain limit, the
effectiveness of these capabilities starts to diminish. The non-linear relationship
between board diversity (in terms of gender and directors’ age and bank
reputational loss remains robust for the [-5,5] window as shown in Panel A of
Table 6.17.
As shown in Panel B of Table 6.16 and Table 6.17, only the non-linear
association between board size and bank reputational loss remains robust when
including multiple governance measures in regression models.
57 The coefficient for AGE_CV and its squared term is 0.643 and -1.874, respectively. The cut-off point is computed as -0.643/(2*(-1.874)) = 0.1716. Since age diversity is measured as the standard deviation of age of directors divided by the average age of directors, I divide the average standard deviation of my sample banks by 0.1716 to get the average value of directors’ age of 46 (7.902601/0.1716).
185
Table 6.14 Regressions of bank reputational loss using [-3,3] event window This table presents the results for the following regression:
tim
timl
tilk
tikj
tijti YEARCHARBANKCHAREAGOVERNANCEREPCAR ,
20142000
1,
7
1,
4
1,
7
1,0, ___ εφδββα +++++= ∑∑∑∑
−
====
.The dependent variable is reputational loss
estimated over a three-day event window [-3,3]. Please refer to Table 5.2 for a list of variables definition. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at firm level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Regression results of each governance measures on bank reputational loss BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR FEMALE_CEO AGE_CV TENURE_CV DUALITY
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
GOVERNANCE 0.028* 0.025 -0.016 -0.004 -0.251 0.030 0.011 0.080 0.002 -0.014
(1.75) (0.42) (-0.56) (-0.47) (-0.91) (0.57) (0.34) (0.56) (0.14) (-0.94)
SEVERE 0.006 0.006 -0.022* -0.022* -0.026 0.005 0.004 0.005 0.002 0.008
(0.55) (0.50) (-1.71) (-1.70) (-1.32) (0.43) (0.39) (0.52) (0.23) (0.71)
TECHNICAL -0.000 0.009 -0.033 -0.034 -0.042 0.001 0.002 0.003 0.003 0.006
(-0.03) (0.46) (-1.40) (-1.47) (-1.43) (0.08) (0.12) (0.17) (0.16) (0.37)
SEVERE*TECHNICAL -0.022 -0.030 0.031 0.033 0.049 -0.024 -0.025* -0.026* -0.026* -0.029*
(-0.86) (-1.04) (0.76) (0.76) (1.00) (-0.95) (-1.71) (-1.76) (-1.76) (-1.74)
OCC -0.008 -0.001 -0.009 -0.008 -0.001 -0.007 -0.005 -0.008 -0.007 -0.008
(-0.56) (-0.07) (-0.62) (-0.54) (-0.05) (-0.50) (-0.37) (-0.64) (-0.55) (-0.54)
FRB -0.002 -0.001 0.019 0.020 0.030 -0.001 -0.001 -0.001 0.001 -0.002
(-0.11) (-0.07) (1.14) (1.16) (1.45) (-0.05) (-0.04) (-0.07) (0.04) (-0.13)
Constant -0.282 -0.224 0.302 0.329 0.477 -0.214 -0.222 -0.243 -0.241 -0.151
(-1.01) (-0.67) (0.70) (0.74) (0.97) (-0.75) (-0.80) (-0.85) (-0.85) (-0.54)
Bank-specific controls YES YES YES YES YES YES YES YES YES YES
Year dummies YES YES YES YES YES YES YES YES YES YES
Observations 264 231 93 93 81 264 267 272 271 264
Adjusted R 2 0.030 0.011 0.014 0.014 0.012 0.026 0.028 0.025 0.029 0.030
Variables
186
Table 6.14 (Continued)
Panel B: Regression results of all governance measures (except board busyness) on bank reputational loss
Variables (1) (2) (3) (4)
BSIZE 0.029 0.030 0.032 0.038
(0.91) (0.93) (0.99) (1.21)
INDEP_BOARD 0.025 0.028 0.037 0.037
(0.41) (0.44) (0.58) (0.57)
FEMALE_DIR 0.045 0.039 0.052
(0.71) (0.60) (0.79)
FEMALE_CEO 0.017 0.014 0.012
(0.42) (0.36) (0.30)
AGE_CV 0.215 0.200
(1.17) (1.09)
TENURE_CV -0.007 -0.009
(-0.34) (-0.46)
DUALITY -0.002
(-0.15)
Constant -0.307 -0.297 -0.388 -0.335
(-0.93) (-0.88) (-1.12) (-0.93)
EA characteristics YES YES YES YES
Bank-specific controls YES YES YES YES
Year dummies YES YES YES YES
Observations 231 231 226 223
Adjusted R 2 0.011 0.006 0.005 0.006
187
Table 6.15 Regressions of bank reputational loss using [-5,5] event window This table presents the results for the following regression:
tim
timl
tilk
tikj
tijti YEARCHARBANKCHAREAGOVERNANCEREPCAR ,
20142000
1,
7
1,
4
1,
7
1,0, ___ εφδββα +++++= ∑∑∑∑
−
====
.The dependent variable is reputational loss
estimated over a three-day event window [-5,5]. Please refer to Table 5.2 for a list of variables definition. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at firm level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Regression results of each governance measures on bank reputational loss
BSIZE INDEP_BOARD MEAN_DIR MAX_DIR BUSY_BOARD FEMALE_DIR FEMALE_CEO AGE_CV TENURE_CV DUALITY
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
GOVERNANCE 0.025* -0.036 -0.011 -0.009 -0.474 -0.002 -0.014 0.276 0.010 -0.008
(1.69) (-0.44) (-0.27) (-0.83) (-1.09) (-0.03) (-0.32) (1.46) (0.41) (-0.49)
SEVERE 0.009 0.010 -0.012 -0.012 -0.022 0.008 0.010 0.006 0.002 0.011
(0.50) (0.54) (-0.51) (-0.54) (-0.81) (0.47) (0.56) (0.39) (0.15) (0.59)
TECHNICAL 0.006 0.016 -0.014 -0.020 -0.038 0.008 0.008 0.008 0.007 0.011
(0.28) (0.70) (-0.53) (-0.71) (-1.10) (0.41) (0.43) (0.41) (0.40) (0.57)
SEVERE*TECHNICAL -0.059* -0.067* -0.026 -0.019 0.008 -0.062** -0.064** -0.064** -0.063** -0.066**
(-1.95) (-1.94) (-0.49) (-0.33) (0.13) (-2.04) (-2.14) (-2.20) (-2.16) (-2.14)
OCC -0.002 0.006 -0.007 -0.005 0.014 -0.001 -0.000 -0.004 -0.002 -0.001
(-0.12) (0.24) (-0.26) (-0.19) (0.39) (-0.05) (-0.02) (-0.22) (-0.12) (-0.07)
FRB 0.002 0.004 0.040* 0.041* 0.058** 0.002 0.003 0.005 0.008 0.003
(0.08) (0.16) (1.72) (1.74) (2.02) (0.09) (0.11) (0.22) (0.39) (0.12)
Constant 0.009 0.054 0.150 0.218 0.488 0.071 0.052 -0.015 0.013 0.079
(0.03) (0.13) (0.24) (0.36) (0.78) (0.22) (0.16) (-0.05) (0.04) (0.24)
Bank-specific controls YES YES YES YES YES YES YES YES YES YES
Year dummies YES YES YES YES YES YES YES YES YES YES
Observations 264 231 93 93 81 264 267 272 271 264
Adjusted R 2 0.096 0.075 0.222 0.229 0.248 0.094 0.093 0.103 0.104 0.090
Variables
188
Table 6.15 (Continued)
Panel B: Regression results of all governance measures (except board busyness) on bank reputational loss
Variables (1) (2) (3) (4)
BSIZE 0.027 0.025 0.029 0.031
(0.62) (0.56) (0.64) (0.67)
INDEP_BOARD -0.036 -0.040 -0.032 -0.030
(-0.44) (-0.48) (-0.39) (-0.36)
FEMALE_DIR 0.020 0.012 0.020
(0.26) (0.15) (0.25)
FEMALE_CEO -0.015 -0.017 -0.019
(-0.31) (-0.33) (-0.37)
AGE_CV 0.429* 0.419*
(1.79) (1.76)
TENURE_CV -0.001 -0.003
(-0.04) (-0.10)
DUALITY -0.002
(-0.10)
Constant -0.023 -0.010 -0.161 -0.141
(-0.06) (-0.02) (-0.39) (-0.32)
EA characteristics YES YES YES YES
Bank-specific controls YES YES YES YES
Year dummies YES YES YES YES
Observations 231 231 226 223
Adjusted R 2 0.073 0.064 0.080 0.072
189
Table 6.16 Regression of bank reputational loss (with squared terms) using [-3,3] event window This table presents the results of Eq. (4.2) and added squared term of corporate governance variables:
tim
timl
tilk
tikj
tijj
tijti YEARCHARBANKCHAREAGOVERNANCEGOVERNANCEREPCAR ,
20142000
1,
7
1,
4
1,
27
1,
7
1,0, __)(_ εφδβγβα ++++++= ∑∑∑∑∑
−
=====
where subscripts i
denotes individual banks, t time period, j alternative corporate governance proxies, k enforcement action characteristics, and l bank-specific characteristics. The dependent variable is reputational loss estimated over a three-day event window [-3,3]. Please refer to Table 5.2 for a list of variables definition. YEAR is time dummies. α is the constant term. ε is the idiosyncratic error term. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at firm level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Regressions results of each governance measure and its squared term on bank reputational loss
Variables BSIZE INDEP_BOARD BUSY_BOARD MEAN_DIR MAX_DIR FEMALE_DIR AGE_CV TENURE_CV
(1) (2) (3) (4) (5) (6) (7) (8)
GOVERNANCE 0.259* 0.345 0.507 -0.008 -0.001 0.054* 0.643* -0.070
(1.89) (0.46) (0.50) (-0.13) (-0.04) (1.78) (1.73) (-1.37)
GOVERNANCE 2 -0.048* -0.212 -6.162 -0.005 -0.001 -0.064* -1.874* 0.043
(-1.81) (-0.42) (-0.79) (-0.18) (-0.26) (-1.71) (-1.86) (1.32)
SEVERE 0.005 0.005 -0.025 -0.022 -0.022 0.005 0.005 0.003
(0.47) (0.45) (-1.19) (-1.37) (-1.39) (0.42) (0.49) (0.25)
TECHNICAL 0.001 0.008 -0.042 -0.033 -0.035 0.001 0.002 0.002
(0.08) (0.43) (-1.43) (-1.40) (-1.41) (0.08) (0.12) (0.14)
SEVERE*TECHNICAL -0.022* -0.029* 0.054 0.032 0.035 -0.024 -0.025* -0.025*
(-1.87) (-1.70) (1.05) (0.79) (0.78) (-0.93) (-1.71) (-1.82)
OCC -0.007 -0.001 -0.006 -0.010 -0.009 -0.007 -0.006 -0.005
(-0.52) (-0.07) (-0.30) (-0.64) (-0.54) (-0.48) (-0.52) (-0.40)
FRB -0.001 -0.001 0.027 0.019 0.020 -0.001 0.000 0.001
(-0.09) (-0.05) (1.36) (1.12) (1.13) (-0.04) (0.03) (0.06)
Constant -0.552 -0.341 0.539 0.308 0.343 -0.215 -0.273 -0.233
(-1.18) (-0.85) (1.10) (0.71) (0.76) (-0.76) (-0.95) (-0.82)
Bank-specific controls YES YES YES YES YES YES YES YES
Year dummies YES YES YES YES YES YES YES YES
Observations 264 231 81 93 93 264 272 271
Adjusted R 2 0.029 0.007 0.014 0.129 0.129 0.022 0.024 0.030
190
Table 6.16 (Continued)
Panel B: Regressions of all governance measures (except board busyness) and their squared terms on bank reputational loss
Variables (1) (2) (3) (4)
BSIZE 0.297* 0.283* 0.254* 0.238*
(2.25) (1.85) (1.74) (1.68)
BSIZE 2 -0.056* -0.054* -0.047* -0.043
(-1.95) (-1.81) (-1.69) (-1.63)
INDEP_BOARD 0.294 0.247 0.206 0.369
(0.41) (0.34) (0.29) (0.51)
INDEP_BOARD 2 -0.178 -0.147 -0.114 -0.229
(-0.37) (-0.31) (-0.24) (-0.48)
FEMALE_DIR 0.058 0.081 0.080
(0.44) (0.57) (0.56)
FEMALE_DIR 2 -0.017 -0.069 -0.088
(-0.07) (-0.26) (-0.33)
AGE_CV 0.580 0.653
(0.80) (0.89)
AGE_CV 2 -1.242 -1.510
(-0.53) (-0.63)
TENURE_CV -0.121*
(-1.84)
TENURE_CV 2 0.064
(1.61)
Constant -0.724 -0.674 -0.707 -0.755
(-1.11) (-1.03) (-1.08) (-1.13)
EA characteristics YES YES YES YES
Bank-specific controls YES YES YES YES
Year dummies YES YES YES YES
Observations 231 231 228 226
Adjusted R 2 0.061 0.072 0.081 0.072
191
Table 6.17 Regressions of bank reputational loss (with squared terms) using [-5,5] event window This table presents the results of Eq. (4.2) and added squared term of corporate governance variables:
tim
timl
tilk
tikj
tijj
tijti YEARCHARBANKCHAREAGOVERNANCEGOVERNANCEREPCAR ,
20142000
1,
7
1,
4
1,
27
1,
7
1,0, __)(_ εφδβγβα ++++++= ∑∑∑∑∑
−
=====
, where subscripts i
denotes individual banks, t time period, j alternative corporate governance proxies, k enforcement action characteristics, and l bank-specific characteristics. The dependent variable is reputational loss estimated over a three-day event window [-5,5]. Please refer to Table 5.2 for a list of variables definition. YEAR is time dummies. α is the constant term. ε is the idiosyncratic error term. The reported t-statistics in parentheses are robust to heteroscedasticity and clustered at firm level. Superscripts *, **, *** indicate statistical significance at 10%, 5% and 1% levels, respectively.
Panel A: Regressions results of each governance measure and its squared term on bank reputational loss Variables BSIZE INDEP_BOARD BUSY_BOARD MEAN_DIR MAX_DIR FEMALE_DIR AGE_CV TENURE_CV
(1) (2) (3) (4) (5) (6) (7) (8)
GOVERNANCE 0.213 0.021 0.376 0.008 -0.002 0.041* 0.909* -0.034
(0.47) (0.02) (0.29) (0.12) (-0.07) (1.67) (1.76) (-0.47)
GOVERNANCE 2 -0.039 -0.037 -6.916 -0.012 -0.002 -0.116* -2.105* 0.026
(-0.42) (-0.05) (-0.68) (-0.36) (-0.40) (-1.91) (-1.92) (0.56)
SEVERE 0.008 0.010 -0.020 -0.011 -0.013 0.008 0.006 0.003
(0.47) (0.54) (-0.72) (-0.48) (-0.54) (0.46) (0.37) (0.16)
TECHNICAL 0.007 0.016 -0.038 -0.015 -0.023 0.008 0.007 0.007
(0.35) (0.68) (-1.09) (-0.54) (-0.72) (0.41) (0.36) (0.39)
SEVERE*TECHNICAL -0.059* -0.067* 0.013 -0.025 -0.014 -0.061** -0.062** -0.062**
(-1.95) (-1.95) (0.19) (-0.45) (-0.23) (-2.01) (-2.15) (-2.13)
OCC -0.002 0.006 0.008 -0.008 -0.007 -0.000 -0.002 -0.001
(-0.09) (0.24) (0.22) (-0.30) (-0.24) (-0.02) (-0.12) (-0.06)
FRB 0.002 0.004 0.056* 0.040* 0.041* 0.002 0.007 0.009
(0.10) (0.17) (1.90) (1.69) (1.70) (0.10) (0.30) (0.39)
Constant -0.210 0.033 0.558 0.165 0.248 0.069 -0.048 0.018
(-0.35) (0.07) (0.93) (0.27) (0.43) (0.21) (-0.15) (0.06)
Bank-specific controls YES YES YES YES YES YES YES YES
Year dummies YES YES YES YES YES YES YES YES
Observations 264 231 81 93 93 264 272 271
Adjusted R 2 0.093 0.070 0.241 0.210 0.219 0.090 0.102 0.101
192
Table 6.17 (Continued)
Panel B: Regressions of all governance measures (except board busyness) and their squared terms on bank reputational loss
Variables (1) (2) (3) (4)
BSIZE 0.355* 0.349* 0.309 0.315
(1.79) (1.66) (1.50) (1.41)
BSIZE 2 -0.069* -0.068* -0.059 -0.059
(-1.76) (-1.67) (-1.62) (-1.51)
INDEP_BOARD -0.046 -0.052 -0.074 0.099
(-0.04) (-0.05) (-0.07) (0.09)
INDEP_BOARD 2 0.007 0.011 0.031 -0.092
(0.01) (0.01) (0.04) (-0.13)
FEMALE_DIR 0.028 0.077 0.054
(0.18) (0.44) (0.31)
FEMALE_DIR 2 -0.017 -0.069 -0.088
(-0.07) (-0.26) (-0.33)
AGE_CV 1.037 1.123
(1.22) (1.30)
AGE_CV 2 -2.058 -2.400
(-0.75) (-0.85)
TENURE_CV -0.095
(-1.02)
TENURE_CV 2 0.052
(0.94)
Constant -0.410 -0.398 -0.477 -0.577
(-0.43) (-0.42) (-0.51) (-0.61)
EA characteristics YES YES YES YES
Bank-specific controls YES YES YES YES
Year dummies YES YES YES YES
Observations 231 231 228 226
Adjusted R 2 0.066 0.057 0.066 0.069
193
6.5 Summary
This chapter presented the results obtained from univariate tests, probit
regressions, event study and OLS regressions. Both linear and non-linear relations
are examined. In linear regressions, I find evidence that banks with a larger board
size (whose directors are diverse in their age) are less likely to be the target of
severe (non-severe) enforcement actions. These findings are consistent with the
arguments that the more diverse a bank board, the more time and efforts are
devoted to overseeing management (Anderson et al., 2004). Managers of those
banks, due to stringent supervision by the board, are less inclined to commit
wrongdoings. In contrast, powerful CEOs (who occupy the chair position) are more
likely to be subject to severe enforcement actions, providing evidence supporting
Hypothesis 5. I also find that the likelihood of technical misconduct increases
with more powerful CEOs but reduces when the board is more diverse in terms
of directors’ tenure. Further, non-technical enforcement actions are less likely in
banks whose boards are large, busy and diverse in terms of directors’ age.
In non-linear regressions, the results show that the likelihood of regulatory
enforcement actions is significantly negatively associated with board size and
variation in directors’ tenure, and positively associated with their squared terms.
I take this to imply that the effectiveness of board monitoring function is impeded
as board size and diversity in directors’ tenure keep increasing. None of the other
governance variables are non-linearly and significantly associated with the
194
likelihood of regulatory enforcement actions. When splitting the sample according
to level of severity of enforcement actions, I find evidence consistent with the
argument that the likelihood of severe misconduct is negatively and non-linearly
associated with board busyness and board diversity (in terms of directors’ age
and tenure), consistent with Hypothesis 4. By contrast, I find little evidence that
internal governance matters in deterring non-severe enforcement actions.
When splitting the sample into technical and non-technical enforcement
actions, board size and board busyness (as proxied by average number of
directorships, and the proportion of directors having board positions outside the
bank), exhibit a non-linear positive relation with the propensity of severe
enforcement actions.
Results from event study methodology show that the average reputational
loss is significantly negative at 0.74 percent for three event windows [-5,5],[-10,10],
and [-10,5]. Across most event windows, over fifty percent of CAR_REPs are
negative. I find that CAR and CAR_REP values are very similar in magnitude,
consistent with the fact that legal fines account for just a small proportion of
equity loss.
My analysis also reveals a non-linear relation between board characteristics
(board size, board heterogeneity in terms of gender and directors’ age) and bank
reputational damage. A larger and more diverse board is negatively related to
reputational damage, but as board size and diversity goes beyond a certain limit,
195
the magnitude of bank reputation loss increases. The results are robust for the
[-3,3] and [-5,5] windows.
In sum, my results provide evidence that “good” governance is negatively
and non-linearly related to the propensity of receiving regulatory enforcement
actions and subsequent reputational loss. This evidence is consistent with the
argument that good governance can deter poor management behaviour.
196
CHAPTER 7
CONCLUSION
7.1 Summary and conclusion
Despite the extensive research on reputational loss in the U.S. non-financial
industries, there is scant literature on the same in the financial services (especially
banking) sector. In this study, I fill this gap in the literature by examining
reputational penalties from public announcements issued by three major U.S.
banking regulators against unsafe and unsound banking practices over the period
2000-2014. I have set three key aims for my thesis: (i) to examine whether well-
governed banks are less likely to commit misconduct; (ii) to test whether banks
suffer from reputational loss following the announcements of regulatory
enforcement action, and (ii) to examine whether the magnitude of reputational
loss is more or less severe in well-governed banks.
My sample consists of 355 enforcement actions issued against 210 unique
listed U.S. banks between 2000 and 2014. I use regulatory enforcement actions
to identify whether banks have engaged in misconduct and measure reputational
loss as the difference between the abnormal return surrounding enforcement
actions adjusted for return of legal fines. The governance proxies examined in
this study include four widely adopted board characteristics (board size, board
independence, board busyness and board diversity) and CEO duality. There are
197
seven hypotheses developed (five for the likelihood analysis and two for the
determinant of reputational loss analysis).
Results from multivariate probit regressions show a non-linear relation
between “good” governance and the occurrence of misconduct. Specifically, a
larger and more diverse board have lower probability of committing regulatory
misconduct (lower propensity of getting enforcement actions) at first, but as the
board size, its busyness and diversity exceed a certain limit, the likelihood of
committing misconduct started to increase. These findings are consistent with the
argument that the effectiveness of board monitoring mechanism is hindered as
board size and its diversity go beyond a certain limit (Wang & Hsu, 2013; Harjoto
et al., 2015). The results remain robust for severe, technical and non-technical
sub-samples. I find no evidence that governance can deter non-severe misconduct
cases.
Results from event study show that reputational loss is significant at 0.74
percent of market capitalization when estimated for three event windows [-5,5], [-
10,10], and [-10,5]. By contrast, legal fines are economically trivial relative to
reputational loss. My analysis also reveals a U-shaped relation between board
characteristics (board size, board heterogeneity in terms of gender and directors’
age) and bank reputational damage following the announcements of enforcement
actions. These findings are in supportive of Hypothesis 7 at first, that is, board
heterogeneity reinforces better problem-solving capabilities toward complex tasks
198
such as overcoming potential negative consequences of regulatory enforcement
actions (Carter et al., 2003; Ramirez, 2003). But as board heterogeneity increases
beyond a certain limit, the quality of the board’s problem-solving capabilities
starts to diminish, leading to more severe reputational losses. The results remain
robust for the [-3,3] and [-5,5] windows. Overall, my results provide evidence that
“good” governance is negatively and non-linearly related to the propensity of
committing misconduct and subsequent reputational loss.
7.2 Limitations and avenues for future research
Although the research has reached its aims, there are a number of
limitations. One concern is the sample size of busy directors, which is relatively
small due to a lot of missing data in the RiskMetrics database. One way to
handle this data missing issue is to manually check historical employment of
each bank’ director, as described on the bank’s proxy statements, and cross-
check this information (the director’s current status) with the bank’s (or firm’s)
website.
Another concern with my analysis is the problem of partial observability,
that is, I can only observe detected bank misconduct (when a banking regulator
issue an enforcement action against a bank), but not the population of all cases
of misconduct committed. In other words, there are still possibilities that a bank
199
has engaged in undetected misconduct even in the absence of enforcement
actions. This would result in a potential misclassification of the bank.
Another weakness of my thesis is the potential endogeneity (reverse
causality/simultaneity) between corporate governance and reputational loss due
to corporate misconduct. That is, strong corporate governance might reduce bank
reputational loss, but banks that experience higher reputational loss might demand
a stronger corporate governance structure. Whilst lagged corporate governance
variables are commonly used to account for endogeneity concerns in
observational data (Brown & Caylor, 2006; Bellemare, Masaki, & Pepinsky, 2015),
more advanced econometric methods recommend generalized method of moments
(GMM) (Wintoki, Linck, & Netter, 2012) and structural equation modelling (SEM)
(Coles, Lemmon, & Felix Meschke, 2012). I will leave this for future research.
There are three possible avenues for future research on reputational loss
in the banking industry. First, future research may fruitfully investigate the link
between corporate governance and reputational damage using an international
sample of banking firms. One interesting research question is whether banks
domiciled in common law countries (e.g. the U.S.) whose shareholder protection
and corporate governance regulations/laws are thought to be stronger, experience
greater/lesser reputational damages than banks domiciled in civil law countries
(i.e., France), where the focus is on a wide variety of stakeholders.
200
Second, while my thesis exclusively assesses the relevance of corporate
governance in determining reputational loss in banking firms, future research may
investigate the effects monitoring by institutional investors on reputational losses.
Third, my thesis mainly measures bank reputational loss by observing the
stock market reaction to the announcements of enforcement actions issued by
three main banking regulators (the FRB, FDIC and OCC). Reputational loss following
enforcement actions of other regulators (such as the SEC), may also be worth
investigating since previous studies suggest that reputational loss varies according
to types of misconduct (Jarrell & Peltzman, 1985; Karpoff et al., 2008; Karpoff &
Lott, 1993; Murphy et al., 2009). I will leave these suggestions for future research.
201
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APPENDICES
A.1 Federal financial regulators and their supervised entities
Regulatory Agency Institutions regulated Emergency/Systemic Risk Powers
Other Notable Authority
Federal Reserve Board (FRB)
Bank holding companies and certain subsidiaries, financial holding companies, securities holding companies, savings and loan holdingcompanies, state banks that are members of the Federal Reserve System, U.S. branches of foreign banks, foreign branches of U.S. banks, and any firm designated as systemically significant by the Financial Stability Oversight Council (FSOC).
Officers, directors employees and certain other categories of individuals associatated with the above banks, companies and organizations (referred to as "institution-affiliated parties").
Payment, clearing, and settlement systems designated as systemicallysignificant by the FSOC, unless regulated by SEC or CFTC.
Lender of last resort to member banks (through discount window lending).
In “unusual and exigent circumstances,” the Fed may extend credit beyondmember banks, to provide liquidity to the financial system, but not to aidfailing financial firms.
May initiate resolution process to shut down firms that pose a grave threat to financial stability (requires concurrence of two-thirds of the FSOC).
The FDIC and the Treasury Secretary have similar powers.
Numerous market-level regulatory authorities, such as checking services, lending markets, and other banking-related activities.
Office of the Comptroller of the Currency (OCC)
Nation banks and their subsidiaries, federally chartered thrift institutions and their subsidiaries, federal branches and agencies of foreign banks.
Institution-affiliated parties associated with the above banks, companies and organizations.
Federal Deposit Insurance Corporation (FDIC)
Federally insured depository institutions, including state banks and thrifts that are not members of the Federal Reserve System, insured branches of foreign banks
Institution-affiliated associated with the above banks, companies and organizations
After making a determination of systemic risk, the FDIC may invoke broad authority to use the deposit insurance funds to provide an array of assistance to depository institutions, including debt guarantees.
Operates a deposit insurance fund for federally and state chartered banks and thrifts.
National Credit Union Administration (NCUA)
Federally chartered or insured credit unions.
Serves as a liquidity lender to credit unions experiencing liquidity shortfalls through the Central Liquidity Facility.
Operates a deposit insurance fund for credit unions, known as the National Credit Union Share Insurance Fund (NCUSIF).
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Reproduced from Murphy, E.V. (2015).
Regulatory Agency Institutions regulated Emergency/Systemic Risk Powers
Other Notable Authority
Securities and Exchange Commission (SEC)
Securities exchanges, brokers, and dealers; clearing agencies; mutualfunds; investment advisers (including hedge funds with assets over $150 million).
Nationally recognized statistical ratingorganizations.
Security-based swap (SBS) dealers, major SBS participants, and SBSexecution facilities.
Corporations selling securities to the public must register and make financial disclosures.
May unilaterally close markets or suspend trading strategies for limited periods
Authorized to set financial accounting standards in which all publicly tradedfirms must use.
Commodity Futures Trading Commission (CFTC)
Futures exchanges, brokers, commodity pool operators, and commodity trading advisors.
Swap dealers, major swap participants, and swap execution facilities.
May suspend trading, order liquidation of positions during market emergencies.
Federal Housing Finance Agency (FHFA)
Fannie Mae, Freddie Mac, and the Federal Home Loan Banks
Acting as conservator (since Sept. 2008) for Fannie Mae and Freddie Mac.
Bureau of Consumer Financial Protection
Nonbank mortgage-related firms, private student lenders, payday lenders, and larger “consumer financial entities” to be determined by the Bureau Consumer businesses of banks with over $10 billion in assets.
Does not supervise insurers, SEC and CFTC registrants, auto dealers, sellers of nonfinancial goods, real estate brokers and agents, and banks withassets less than $10 billion.
Writes rules to carry out the federal consumer financial protection laws.
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A.2 Definitions of different types of enforcement actions
The primary enforcement actions examined in my thesis include the following:
Types of enforcement Definition
Cease-and-desist orders (temporary and permanent)
Cease and desist orders are typically the most severe and can be issued either with or without consent. When cease and desist orders are issued without consent, they are done so after issuance of a Notice of Charges and an administrative hearing. If actions proceed to this level, the Notice of Charges, hearing and agency decision are also available to the public.
Written agreeement
A written agreement is enforceable just like a cease and desist order, but is a contract signed by both the institution/individual and the supervisor. A written agreement usually contains the same types of provisions found in a cease and desist order, but does not include a Notice of Charges-type recitation of facts. Supervisors who use written agreements refer to them by a variety of names, including "written agreements," "formal agreements" or "supervisory agreements."
Civil money penalties
Civil money penalties are not corrective in nature, but instead simply assess a fine for various types of infractions. The amount of the penalty that can be assessed by a regulator will typically be higher if the individual's or institution's conduct was knowing or reckless, caused a loss to a financial institution or resulted in gain to the wrongdoer.
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B.1 Upside and downside of reputational risk
Upside of reputational risk
Downside of reputational risk
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Source: Fombrun, C. J., Gardberg, N. A., & Barnett, M. L. (2000). Opportunity platforms and
safety nets: Corporate citizenship and reputational risk. Business and Society Review, 105(1), 85–
106.
