Bank Risk-Taking and Misconduct

Ieva Sakalauskaite∗

September 18, 2017

Abstract

This paper studies misconduct in banks. First, it introduces a novel dataset on

conduct failures in a sample of 30 financial institutions that resulted in disciplinary

actions during 2000-2016 to show that misconduct is not a recent phenomenon and that

its intensity varies over the business cycle. Furthermore, misconduct related to bank

underwriting activities increases together with bank leverage, the effects being exac-

erbated by high CEO bonuses and total pay. To explain the observed dynamics, the

paper suggests a theoretical model in which misconduct results from incentive schemes

designed to induce risk-taking by bank managers. When bank shareholders rely on

performance pay to encourage risk-taking, increasing risk requires more aggressive pay

structures and results in stronger incentives for managers to engage in other activities

that boost short-term performance. This results in the observed positive relationship

between bank misconduct, changes in investment opportunities, and incentive struc-

tures.

∗University of Amsterdam and Tinbergen Institute. Email: i.sakalauskaite@uva.nl. I am gratefulto Gerard Caprio, Jiang Liangliang, Charles Kahn, Florian Peters, Rachel Pownall, Razvan Vlahu,Andrew Winton and especially Enrico Perotti for their useful comments and discussions. The paper hasalso benefited from my stay as a visiting researcher at the Bank of Lithuania and the feedback receivedthere as well as from participants at the University of Amsterdam Brownbag seminar, TI PhD lunchseminar, Lithuanian Research Day, 14th Corporate Finance Day at the KU Leuven, 25th InternationalRome Conference on Money, Banking and Finance and the 29th Australasian Finance and BankingConference.

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1. Introduction

Recently, concerns have been voiced that misconduct might be a feature rather than a

bug in the financial industry.1 Since 2010, major financial institutions have paid nearly

300 billion US dollars for conduct failures that occurred mostly during the mortgage

boom.2 The surge in bank misconduct is not unique to the recent years: the dot-com

bust was also followed by significant disciplinary actions against investment banks. As

roughly 7% of US financial advisers have misconduct records, it appears to be prevalent

beyond the well-publicised cases.3

While the costs of misconduct in banks can be substantial,4 the drivers behind it

are not well understood. Empirical analysis on the causes of bank conduct failures has

been limited due to lack of data on such events, focusing mostly on the outcomes of

disciplinary actions.5 Recent theoretical literature suggests that the rise in misconduct

cases could be the consequence of performance-based bank compensation schemes that

result from competition for talent.6 On the other hand, it has been also assigned to

bank shareholders willingly trading-off reputation for instant gains when returns from

such behaviour are sufficiently high, thus peaking at the height of the business cycle.7

This paper contributes to the understanding of bank conduct failures in two ways.

First, it provides stylised evidence on misconduct in 30 major financial institutions that

resulted in disciplinary actions during 2000-2016. Besides to documenting its prevalence,

I analyse the relationship between bank misconduct intensity and bank characteristics,

compensation schemes and changes in economic conditions. To my knowledge, this is

the first attempt to study bank conduct failures over an extended time period and focus

on the dates at which they occurred rather than the resulting disciplinary actions.

Second, to explain the dynamics of bank misconduct observed in the data, the paper

builds a theoretical model which incorporates both agency conflicts within banks and

changes in shareholder preferences over the cycle. There, increasing attractiveness of

risky projects to bank shareholders shifts managers’ compensation towards performance

1Luigi Zingales in his presidential address to the AFA (2015).2Estimate by the CCP Research foundation.3Egan et al. (2016) use data on the universe of US financial advisers to arrive at this estimate.4Lower confidence in banks reduces investment in stock markets and can encourage people to withdrawdeposits (Sapienza and Zingales, 2012). The BoE (2015) refers to the possibility that depressed SMEborrowing from UK banks can be partly attributed to their mistrust in financial institutions. Bankconduct costs erode their capital levels, reducing lending capacity (Mark Carney in his speech given atthe Lord Mayor’s Banquet for Bankers and Merchants of the City of London stated that ”$150 billionof fines levied on global banks translates into more than $3 trillion of reduced lending capacity to thereal economy) and increasing financial system fragility (the ESRB report on misconduct risk in thebanking sector (June 2015) suggests it might pose systemic risks to the financial sector).

5See, for example, Koster and Pelster (2017).6See Benabou and Tirole, 2014, and Thanassoulis, 2013.7Griffin et al., 2014.

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pay in order to induce risk-taking, which results in stronger incentives to engage in

other activities that boost short-term returns. The proposed mechanism can therefore

help account both for the relationship between managers’ pay and misconduct, and the

observed cyclicality of bank conduct failures.

To study misconduct in banks empirically, I collect information on the alleged start-

ing dates of bank misconduct cases by reading information on both regulatory actions

and private lawsuits against major financial institutions that have resulted in disci-

plinary costs higher than 1m USD. Misconduct cases are then assigned to broad classes

of underwriting fraud, customer abuse, attempts to manipulate markets, compliance

failures or other instances. The resulting data on the number of cases starting each year

and their subsequent costs shows that bank conduct failures have been quite prevalent.

It can be also observed that while the number and resulting cost of underwriting fraud

or large customer disadvantaging cases are cyclical, other classes of misconduct are less

so, somewhat increasing over time.

I further use the monetary value of misconduct starting each year as a measure of

misconduct intensity to examine how changes in the business cycle, bank characteristics

and pay structures affect different types of unlawful activities and which factors amplify

the cyclicality of misconduct. The results suggest that while some classes of bank

misconduct do not appear to be explained by the business cycle or bank characteristics,

asset quality misrepresentations react to the business cycle, and such pro-cyclicality

is related to bank incentive schemes. Asset quality misrepresentations are at highest

when de-trended GDP reaches its peak, the effect being strongest when in banks with

on average high CEO bonuses and when their total pay are highest. Furthermore, it

can be observed that increases in bank leverage are associated with more underwriting

fraud, the relationship between leverage and misconduct intensity being strongest in

banks with the most aggressive compensation structures, as well.

Motivated by the empirical evidence, I introduce a theoretical model which relates

bank risk-taking, incentive schemes and conduct failures using a set-up where bank

shareholders hire managers to supervise investment projects. While safe projects yield

certain short- and long- term returns, risky projects have high short-term payoffs but

carry the risk of very low payoffs in the long run, in which case managers’ compensation

is zero. As the project type chosen by managers is unobservable, bank shareholders have

to offer sufficiently high short-term compensation relative to long-term pay to prevent

them from deviating to safe projects.

To model bank misconduct, the setting allows managers to take actions that can

increase the probability of observing high short-term returns at a cost to the bank’s

counterparties, such actions being socially costly. This behaviour increases managers’

short-term compensation, but runs the risk of being detected by regulators, resulting in

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managers losing their employment and bank shareholders suffering monetary conduct

costs.

In this setup, the intensity of misconduct varies with the risk of investments preferred

by bank shareholders. Namely, when the attractiveness of risky projects increases,

managers’ compensation shifts towards performance pay to induce risk-taking. When

the probability of detection by regulators is not sufficiently high to deter misconduct,

this results in stronger incentives to engage in such activities and boost the short-term

returns and thus compensation. On the other hand, at times when the risky projects

are unprofitable, shareholders can delay compensation, thus reducing the intensity of

misconduct.

The model further illustrates how the intensity of misconduct depends on the costs

that regulators can impose on bank shareholders. When conduct costs are sufficiently

high, they reduce the attractiveness of the risky projects if the compensation schemes to

induce risk-taking also result in conduct failures. In such cases, high financial penalties

to shareholders can result in safe projects being implemented at high risk levels. On the

other hand, when regulatory costs to bank shareholders are sufficiently low, they result

in the alignment of incentives for misconduct between bank managers and shareholders.

In such cases, since misconduct increases the returns of the risky projects, the latter

might be implemented even when safe projects have a higher net present value.

Consistently with empirical evidence on the effects of bank leverage on bank mis-

conduct related to underwriting activities, such effects are strengthened in the presence

of bank debt. First, leverage increases the attractiveness of risk-taking and thus the

region in which risk-taking and misconduct can be observed. The effects are stronger

when leverage further reduces the scope for costly monetary penalties from regulators.

Overall, the findings of the paper have interesting policy implications. The evidence

that bank conduct failures are not a recent phenomenon implies that continued regu-

latory scrutiny is required to prevent such cases, especially in economic upturns. The

evidence on the relationship between bank CEO compensation and misconduct intensity

provides some support to the recent regulatory changes. Furthermore, the results of the

theoretical model suggest that in cases when bank shareholders can prevent misconduct

only through changing compensation schemes, high detection probability and sanctions

for managers might be more effective in deterring misconduct than the imposition of

fines on bank owners who can only prevent it by choosing safer projects. However,

when detection is not sufficiently high and financial penalties to shareholders are low,

the incentives of bank managers and shareholders might become aligned and the op-

portunities for misconduct could encourage excessive risk-taking through increasing its

short-term profitability.

The remainder of the paper is structured as follows: Section 2 provides the literature

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review. Section 3 presents the empirical evidence, Section 4 introduces the theoretical

model and Section 5 concludes.

2. Related literature

This paper relates to literature on agency conflicts in firms, bank risk-taking, as well as

studies on fraud and misconduct.

The view that bank shareholders might accept value-destroying behaviour by em-

ployees when the trade-offs involved in preventing it grow excessively costly is not new.

In an article closely related to the narrative of this paper, Thanassoulis (2013) models

an economy in which banks design contracts so as to attract skilled managers and pre-

vent the implementation of low-value short-term projects. There, banks might choose

to allow myopic investments if competition for talent raises reservation wages and if

future discounting by managers makes deferring a proportion of total pay too costly.

Similarly, firms might be forced to tolerate deteriorating work ethics when competition

for talent intensifies and contracts shift to performance-pay for measurable tasks rather

than the less-measurable pro-social behaviour (Benabou and Tirole, 2014).

In the model of this paper, banks might accept misconduct by managers not because

of competition for talent and the monetary costs of postponing managers’ pay, but rather

due to the weaker risk-taking incentives that deferring compensation generates. While

deferred pay can prevent short-termism, it could also lead managers to choose projects

with certain future success that are not risky enough from bank owners’ perspective.

Empirical evidence on bank remuneration schemes and risk outcomes provides some

support to the narrative of shareholder-driven bank risk-taking affecting bankers’ pay:

Cheng et al. (2015) have demonstrated that banks with inherently more aggressive

investment strategies pay higher wages to compensate bankers for unemployment risk.

Similarly, Livne et al. (2013) suggest that banks with more short-term investments tend

to pay higher bonuses. This is also consistent with evidence in Philippon and Reshef

(2012) that the periods of bank deregulations in the US led to both higher banking-

sector wages and unemployment risk.8 Meanwhile, the literature on the incentives

for shareholders to prefer excessive risks as a result of deposit insurance schemes or

government bailouts is ample (see, for example, Dewatripont and Tirole (1993) on the

effects of the public safety net on bank risk-taking, and Gropp et al. (2014) for recent

8On the other hand, several papers have recently provided arguments that the high risks taken bybanks before the recent crisis have been excessive even from shareholders’ perspective: for example,Bannier et al. (2012) and Acharya et al. (2016) attribute excessive risk-taking to competition forskilled managers and the inability of banks not to compete for talent. Evidence by Acharya et al.(2014) points to excessive risks taken by non-executives is U.S. banks which possibly derives fromcompetition for talent and the resulting pay schemes.

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empirical evidence).

The suggested link between misconduct and agency conflicts within firms comple-

ments the existing literature in which fraud results from shareholders’ deliberate de-

cisions to trade-off instant gains for reputation. The principle that asymmetric infor-

mation about agents’ preferences might encourage the uncommitted types to build up

reputation in early stages of the game to be destroyed later on (Kreps and Wilson

(1982), Milgrom and Roberts (1982)) has been used recently to explain securitisation

fraud during the mortgage boom. For instance, Griffin et al. (2014) allow firms to build

time-bomb securities that fail only in some cases, and show that incentive problems

worsen at times of low risk, and when the share of committed types in the population

increases. Bongaerts (2015) models firm incentives when they have some private infor-

mation about the state of the economy, and thus the value of good reputation, in the

future. If financial firms can foresee a future bust, we would observe more asset quality

misrepresentations before crises. An alternative mechanism is proposed by Chen et al.

(2014) who model the intertemporal decision of bank shareholders to allow managers

to act at a disadvantage to their clients. There, talented managers can signal their skill

by engaging in misconduct, resulting in higher future income at a cost of the firm losing

its good reputation.

Empirical evidence on the extent to which misconduct in banks is driven by share-

holder versus manager preferences is limited. A recent study on the drivers of miscon-

duct in banks has shown that the quality of boards can prevent compliance failures in

banks, increase their detection, and alleviate shareholder losses, suggesting that mis-

conduct could be initiated by bank employees rather than shareholders (Nguyen et al.,

2016). When it comes to disciplinary actions imposed against bank shareholders and

managers, Koster and Pelster (2017) analyse stock reactions of 68 listed banks to an-

nouncements of financial penalties imposed in 2007-2014 and find that bank after-tax

profitability is not significantly affected by such expenses, and that stock reactions to

case resolutions are positive in general. This evidence might suggest that misconduct

in banks might not be excessively costly to bank shareholders. Similarly, Griffin et

al. (2017) show that senior bankers in major banks that engaged in fraudulent RMBS

underwriting do not experience worse labour market outcomes, which could mean that

such actions had been approved by senior-level management. On the other hand, Egan

et al. (2016) use data on the universe of US financial advisers and show that around

50% of them lose jobs after cases of misconduct.

Similarly, while the increased incentives for securitisation fraud when investor sen-

timent is high has been demonstrated by Wang et al. (2010), the empirical analysis in

Griffin et al. (2014) provides little evidence of high-reputation banks being less likely

to miss-state the quality of MBS underwritten. The power of reputation concerns to

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prevent fraud in major U.S. corporations has also been found to be limited by Dyck et

al. (2010). They further demonstrate that most of securities fraud in large US corpora-

tions is detected by employees, media and industry supervisors rather than regulators

or auditors.

3. Empirical Evidence

In this section, I first introduce the dataset on conduct failures in the sample of 30 major

financial institutions. It provides an overview of the dynamics of the different types of

bank conduct failures and their resulting costs. I then move to examine whether and

how the value of conduct failures starting each year depends on bank characteristics,

incentive schemes of top management, and changes in economic conditions.

3.1. Data

Sources and Sample

To examine the dynamics of bank misconduct over time and across banks, I construct

a dataset on misconduct in major financial institutions which focuses on the dates at

which misconduct started rather than the timing of disciplinary events. Besides to

actions by regulatory authorities, I use information from private lawsuits, arbitrations,

and class actions.

The banks in the sample are the largest US banks and a subset of the Globally

Systemically Important Banks (G-SIB’s). The 30 banks in the sample are JPMorgan

Chase, Bank of America, Citigroup, Wells Fargo, Goldman Sachs, Morgan Stanley,

U.S. Bancorp, Bank of New York Mellon, PNC Financial Services, Capital One, State

Street Corporation, TD Bank, N.A., BB&T, SunTrust Bank, American Express Com-

pany, Fifth Third Bank, Charles Schwab Corporation, HSBC, RBS, UBS, Credit Suisse,

Deutsche Bank, Barclays, Lloyds Bank, Standard Chartered, Santander, BNP Paribas,

ING, Credit Agricole and Societe Generale. The choice of banks in the sample is de-

termined by the availability of information: I limit attention to Anglo-Saxon banks, or

banks with substantial presence in the US, because of their higher coverage in English

language.

Data on misconduct comes from bank annual reports or SEC 10-K filings, regula-

tors’ websites (SEC, FSA, FINRA, FRB, OCC, OTC, NYSE) and Internet searches. In

all cases, I read the related documents to determine the alleged starting date of miscon-

duct, its nature, and the size of resulting penalties and restitutions. Only cases when

misconduct results in costs larger than 1m US dollars and for which the alleged starting

date is available are used. While in some cases such as one-time events the starting

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date of misconduct is clear, for cases that run for longer periods of time, the starting

date might be hard to ascertain. In such instances, I use the beginning of the offence

period indicated by regulators, or the beginning of class action periods in class action

litigations. This would in general result in a somewhat upward-bias in the starting

dates.

I further classify misconduct into broad categories, as the events that result in con-

duct costs vary in their nature and could result from different incentives that banks

face. Namely, I differentiate between compliance failures; cases that involve asset qual-

ity misrepresentations related to banks underwriting or issuing securities; attempts to

manipulate markets or asset prices and collusion; cases in which banks are disciplined for

abusing customers; cases related to breaching sanctions, money laundering and assisting

tax evasion; and other instances.

I look at misconduct cases that were resolved and resulted in disciplinary actions,

court decisions or settlements during the period 2000-2016. The time period is chosen

based on the availability of data, as the period before 2000 is in general not well cov-

ered online. The time period selected for analysing disciplinary actions also affects the

sample of years for which I have reliable information on bank misconduct. From Figure

1 which presents the dynamics of misconduct starts, investigations and disciplinary ac-

tions starting each year, it can be observed that peaks in bank misconduct are followed

by peaks in investigations and finally conduct costs with a time lag. Figure 2 presents

the resulting cumulative distribution of the time differences between alleged starting

dates of misconduct and the resulting conduct costs (for cases that took less than 15

years to be resolved): most cases are resolved within 6 years since their alleged start;

90% of the cases were resolved within 10 years. The figure also implies that of the

actions started before 1995, 30% would have been resolved before 2000 and would not

be included in the data, while around 50% of the cases started after 2010 would have

not been resolved by the end of 2016. Because of such time lag between misconduct

starting date and resulting disciplinary actions, the sample is broadly representative of

events that occurred in 1998-2010, where some of the cases starting late in the sample

period have possibly not been fully resolved yet. This results in a total of 763 actions

over the 14 years for which the approximate starting date is known.

Measuring Misconduct

In this paper, I use two measures of the intensity of bank conduct failures: total conduct

costs resulting from misconduct that started in a given year and the number of miscon-

duct cases that start in a given year. In regression analysis, I use the former measure as

it has the advantage of providing a monetary value for misconduct starting each year,

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which might be more informative about the severity and extent of bank actions than

the number of cases.

The second measure of misconduct, the number of activities starting in a given year,

will be used to complement the data on misconduct measured in monetary value. While

it overcomes some of the concerns related to using conduct costs as it is not affected by

bank financial positions nor their bargaining power, it might fail to accurately reflect

the intensity of misconduct by assigning equal weights to events that differ in scale.9

In order to avoid over-weighting events in which multiple parties take actions against

banks, an attempt has been made to collapse such cases into one, the starting date

of misconduct taken as the earliest one cited among the multiple regulators or private

lawsuits.10

Descriptive Statistics

Table 1 presents the descriptive statistics of the number of alleged misconduct starts

and their associated costs starting in 1998. In columns (1)-(3), the total number of

cases, the number of cases if grouped to avoid counting events resulting from multiple

actions, and the total associated costs are presented, respectively.

It appears that misconduct has been quite prevalent during the last couple of

decades. We can also observe that its intensity varies over time, peaking in 2001 and

2007. This dynamic does not result from the lags between misconduct and disciplinary

actions: even if misconduct implemented in 1995-1996 might have been resolved before

2000 and some actions starting in late 2000’s might not have been resolved by 2016, in

the absence of pro-cyclicality, we would not observe a decrease in the number of cases

in mid-2000’s.

Looking at resulting total costs (column 3), a somewhat similar pattern can be

observed to the number of cases, the monetary value of misconduct dropping in mid-

2000’s. We can also see that the recent boom has resulted in higher costs to the financial

institutions, which could result from their increased size or more severe conduct failures.

Another difference from the dynamics in the number of cases relates to misconduct costs

peaking in 2005 rather than 2007. It can be explained by the huge settlements related

to mortgage backed security quality misrepresentations that allegedly started already

in 2005.

To examine whether the observed cyclicality of misconduct could be resulting from

changes in regulators’ and public authorities’ behaviour, I further split the sample ac-

9For example, both Libor and silver price manipulations by the Deutsche Bank would each count as asingle event, although the former might have been more wide-spread and affected a larger market.

10While such grouping of cases helps to avoid potential overweighting for some cases, it also under-weights instances with multiple injured parties which might be more widespread or severe.

9

cording to whether the action against the bank was initiated by public authorities or

private plaintiffs (columns 4-6 and 7-9, respectively). The observed fluctuations do not

seem to be driven by changes in regulators’ behaviour: private individuals and compa-

nies have also sued banks more extensively for their conduct during the early 2000’s

and before the recent crisis.

In Table 2, I report the number of cases by type of misconduct, splitting it into

cases related to underwriting, compliance, customer abuse, sanctions, and market ma-

nipulation, grouped so that events resulting in multiple actions are not over-weighted.

As expected, misconduct related to underwriting is the most pro-cyclical, while com-

pliance failures and abusing bank clients also vary over time. Meanwhile the incidence

of cases related to breaching sanctions and market manipulation are less cyclical. The

number of cases in most of the groups has been increasing over time, which could result

from growing bank sizes, stricter regulation, or more transparent coverage of such cases

lately.

Table 3 presents the monetary value of cases by type of misconduct, also splitting it

into cases related to underwriting, compliance, customer abuse, sanctions and collusion

or market manipulation. Misconduct related to underwriting constitutes a major part of

bank conduct costs, followed by market manipulations and customer abuse. The pattern

of the monetary value of compliance costs is by and large consistent with dynamics in

the number of cases.

3.2. Regression Analysis

In this section, I examine which factors are related to the intensity of bank misconduct.

Following the literature on misconduct in financial institutions, I focus on changes in

the business cycle and incentive schemes, as well as bank characteristics such as leverage

to account for risk-taking incentives, size and profitability. The general model that is

estimated takes the following form:

Misconducti,t = α+β1Cyclec,t+β2Compensationi,t−1+β3Leveragei,t+γControlsi,t+ui+δt+εi,t.

Misconducti,t is the natural logarithm of conduct costs in bank i, year t.11 In the

baseline regression, I use the total cost resulting from all types of compliance failures,

which is later on split into different classes. The independent variable Cyclec,t is added

to the model in line with the literature on changes in shareholder incentives to en-

gage in underwriting fraud or securities fraud over the cycle. The measure used is the

annual deviation in GDP growth from its trend in bank location country c, retrieved

from OECD Short-term indicators database. Three variables are chosen to account for

11I winsorize 1% of the most extreme annual observations to avoid extreme observations driving theresults.

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compensation schemes Compensationi,t−1:12 the ratio of CEO bonus to total pay, the

natural logarithm of total CEO pay, and the average ratio of bank CEO bonuses to total

pay during the sample period for each bank. This data is available from Execucomp

only on the subsample of US banks.

Leveragei,t is the ratio of bank’s total liabilities to total assets and controls for

changes in bank capital structure and the resulting risk-taking incentives by sharehold-

ers. I further add Controlsi,t which include total bank assets and return on assets to

control for bank characteristics and performance and the number of regulator-initiated

investigations started the previous year to control for the strictness of regulators as this

might affect the incentives of bankers to engage in misconduct (the grouped number of

cases is used, again). Finally, fixed bank effects ui and year effects δt are added to the

model. Due to the small sample size, I use the joint test for the significance of fixed

bank effects to determine whether they should be added to the model estimated. This

results in fixed bank effects being used in regressions on the full sample of banks. The

descriptive statistics of the variables used in data analysis are presented in Table 4.

I start the analysis by looking at total bank misconduct, measured in terms of total

costs resulting in conduct costs starting each year (Table 5). The findings confirm the

dynamics observed in Table 1, the value of bank misconduct varying together with the

business cycle. Also, as expected, larger banks pay higher fines. However, other bank

characteristics such as leverage or returns on assets do not seem to be systematically

related to conduct failures.

In columns (3)-(9), I turn to investigate the relationship between bank conduct

failures and compensation schemes. First, although CEO bonus-to-total compensation

the preceding year and over the sample period are positively related to conduct failures,

this relationship is not statistically significant. Meanwhile, total CEO compensation

appears to be positively related to bank conduct failures, even after controlling for

bank size. From columns (7)-(9), banks with on average high ratio of CEO bonuses

in their total pay behave more procyclically, such banks having high value of conduct

failures when the business cycle reaches its peak.

As the total value of bank misconduct incorporates all types of bank conduct failures

that might be affected by various factors, I further turn to examine how the different

types of bank misconduct are related to the chosen variables.

In Table 6, the model is estimated for a major class of misconduct, namely cases

related to bank underwriting activities. There, similarly to Table 5, strong procyclicality

can be observed. Interestingly, it appears that more leveraged banks are more likely to

12I use the values in the preceding year to avoid reverse causality where misconduct results in high CEOcompensation.

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engage in such activities.13

When we turn to the effects of compensation schemes, they are related to under-

writing fraud positively albeit not statistically significantly. Similarly to the results in

Table 5, banks with on average higher share of bonuses in CEO compensation react

to the business cycle more, the effects of compensation being positive at the peak of

the cycle. In columns (9)-(11), I further investigate whether the effects of bank com-

pensation schemes are related to bank leverage. Interestingly, I find that the effects of

bank compensation schemes have a positive effect on conduct failures related to bank

underwriting activities when banks are highly leveraged, even after controlling for their

size and developments in economic conditions.

In Table 7, the other major class of bank conduct failures related to disadvantaging

clients is analysed.14 There, pro-cyclicality can also be observed. However, higher bank

leverage appears to be negatively related to such cases. It also seems that total bank

CEO compensation rather than its structure matters in explaining misconduct cases,

banks with higher CEO pay having a higher value of conduct failures starting, the effect

being stronger when bank leverage increases. The differences in drivers of misconduct

related to underwriting activities and disadvantaging customers can be explained by

the different business lines involved, the latter class to a large extent concerning retail

banking that could be subject to different dynamics.

Overall, while the results presented in this section somewhat support the view that

bank conduct failures react to the business cycle and compensation schemes, this does

not hold for all types of conduct failures. Although the effects of banker compensa-

tion schemes are in general positive but not statistically significant, it appears that

their effects are stronger when the economic conditions are good, or that compensation

schemes tend to exacerbate the effects of investment opportunities. Meanwhile, it has

to be acknowledged that the sample size used for this analysis is small and the data

on misconduct is noisy. The classes of misconduct used to distinguish between cases of

different nature are still broad, thus summarising events that might result from different

incentives. The imperfect information on exact starting dates of conduct failures might

also create noise, especially when such cases last for extended time periods, which could

be the case in both of misconduct classes analysed above.

13 Since the effects of leverage remain statistically significant when fixed bank effects are added to theregressions (not reported here), it does not seem to result from banks with significant investmentbanking business lines being both more leveraged and experiencing more investment-banking relatedconduct failures.

14 I also look at other classes of bank misconduct: those related to individual instances of misconductinitiated by bank employees, cases of market manipulation, or compliance failures detected by regula-tors. The results suggest that in explaining those, compensation schemes, business cycle and leveragedo not appear to play a role (results reported in Appendix 1).

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4. The model

4.1. Model Setup

As the empirical evidence on bank misconduct suggests there exists a relationship be-

tween the business cycle, bank leverage, conduct failures related to bank underwriting

activities and bank compensation schemes, I introduce a simple agency model to ex-

plain the observed dynamics. The model has three periods (t = 0, 1, 2) and there are

two types of risk-neutral agents: bank shareholders (he) and managers (she). At t=0,

bank shareholders hire managers to supervise projects and managers make the choice

over which projects to invest in and whether to engage in misconduct. At t=1, the

short-term returns of projects are realised. At t=2, the long-term risk of projects is

realised, and misconduct results in disciplinary action by the regulator if detected.

Bank Shareholders and Projects

Bank shareholders have funds that can be invested in a risky or safe project. In the

baseline model, the bank is fully equity-financed and the funds that are invested are

normalised to 0.

The projects generate payoffs in two periods, t = 1 and t = 2. The safe project pays

R with probability pL and S otherwise at t = 1, and S with certainty at t = 2. The

risky project pays R with probability pH at t = 1 and S otherwise, but generates S only

with probability x at t = 2 (and 0 otherwise), the t = 2 returns being discounted by

the time value of money in the economy, δ. As R > S and pH > pL, the risky project

yields more at t = 1, but carries long-term risk.15 In the model, the risk of observing

a very low long-run return, 1 − x, determines whether the risky project yields a higher

return than the safe one.16

To implement the projects, bank shareholders have to hire managers. Bank share-

holders cannot observe the type of projects chosen by the managers, but can observe

project returns. Banks can commit to a remuneration scheme that is dependent on ob-

served returns, and cannot promise any payments to managers when bank returns are

0. Shareholder objective is to maximise bank profits which depend on project returns

at t = 1 and t = 2, manager compensation expenses and conduct costs described below.

15 As noted by Acharya et al. (2016), the feature of earning a carry in the short run while entailinglong-term risks is present in many financial products (mortgage backed securities, credit default swaps,insurance instruments).

16Relating the two investment projects to the evidence presented in the preceding section, the returns ofthe safe projects S could be seen as returns on the safe assets such as government bonds, whereas riskyprojects with returns R and risk 1−x reflect dynamics in other investment opportunities such as sharesof technology companies or mortgage-backed securities. The changes in the two latter parameters overtime would then correspond to dynamics in risk and return over the cycle, risky projects chosen whenthe expected payoffs are sufficiently high.

13

Managers

Bank managers live for three periods and have the objective to maximise their ex-

pected lifetime income. At t = 0, managers are offered employment contracts by bank

shareholders that specify their compensation at t = 1 and t = 2. The cost of project

supervision is the same for all managers and is normalised to 0. Managers have a life-

time reservation wage w and discount their t = 2 income using the time value of money

in the economy, δ.

Misconduct

Besides to project supervision, managers can engage in misconduct, thus increasing the

probability of generating high return R at t = 1 by ∆. Instances of such behaviour

are selling unsuitable products to consumers to increase commission wages, engaging in

insider trading to boost trading profits, collusion or underwriting fraud. It is assumed

that misconduct is socially costly: the cost of misconduct to bank customers is η∆(R−S)

where η > 1, and so misconduct is not just redistribution of income from bank customers

to shareholders.17

In this paper, it is assumed that bank misconduct is initiated by bank managers.

If managers decide to engage in misconduct, it is detected at t = 2 with probability λ

that is set exogenously. If detected, managers who engage in misconduct lose their job

and so their remuneration at t = 2. This form of sanction mimics the real world where

misbehaving employees lose their reputation and are barred by regulators or fired by the

banks themselves (Egan et al. (2016) show that around half of financial advisers lose

jobs after misconduct detection). I assume that in this case, her compensation at t = 2

is appropriated by the regulator rather than being retained by bank shareholders.18 The

assumption of misconduct costs being borne not with certainty and only at t = 2 rather

than immediately at t = 1 is motivated by the observed time lag between bankers’

activities and resulting disciplinary actions, as well as legal uncertainty.

If managers decide to engage in misconduct and it is detected at t = 2, this also

results in regulatory fines and/or restitution paid by shareholders, C. The assumption

of misconduct resulting in costs to bank shareholders is consistent with the observed

regulatory actions in which financial costs are mostly borne by financial institutions

rather than individuals. In the baseline model, contrary to bank managers who only

17This assumption could be motivated by risk-aversion of bank clients, the losses related to legal pro-cesses, or the reduction in trust in the financial sector in general that is related to bank misconduct.

18It could also be viewed as a claw-back or financial penalty where a banker’s pay is seized by regulatorsif improper past actions are detected. While in this case, only t = 2 income is clawed back and t = 1income resulting from the realisation of return R would be more reminiscent of such regulations, itcan be argued that an agent’s t = 1 income is consumed and she has limited liability.

14

suffer costs when the long-term risk of the project does not materialise, bank share-

holders are assumed to be subject to C in all states of the economy. Although risky

project returns are modelled as yielding 0 in the low state, the model considers the bank

suffering costs in those states, as well, which could be an outcome of reputation losses of

delayed judicial process which results in costs after t = 2. This assumption also reflects

the observation that banks are subject to conduct costs even after realisations of low

returns once they recover. In an extension, I consider the case where bank shareholders

are subject to conduct costs only when returns S are realised at t = 2.

In the baseline model, it is further assumed that misconduct has a negative NPV

to bank shareholders: the increased probability of observing a high return R through

misconduct is outweighed by regulatory costs if detected:

Assumption 1: ∆(R− S) < δλC .

In practice, there is no clear consensus over whether conduct failures in banks are

encouraged by shareholders or resorted to by managers themselves. While some argue

that bank shareholders allow such cases and realise positive returns from misconduct,

at least some cases appear to have resulted from manager incentives to boost their

short-term profits at a cost to bank owners.19

Finally, in the model bank shareholders can only prevent misconduct by changing

compensation schemes that alter the incentives of managers to engage in misconduct.

Therefore, they cannot prevent or detect fraud by changing internal controls or gover-

nance. This assumption is consistent with the findings by Dyck et al. (2010) who have

shown that the traditional corporate governance bodies are not the main detectors of

fraud in corporations, while somewhat in contrast with the findings of Nguyen et al.

(2016) who show that the quality of bank boards matter, but are mostly concerned

about bank compliance failures detected by bank regulators.

Time line

t=0: The risk and returns of the risky and safe projects are observed. Bank shareholders

offer contracts to managers, specifying their remuneration at t = 1 and t = 2. Managers

who get employed choose between implementing the risky and safe project, and choose

whether to engage in misconduct.

t=1: The short-term returns of the project chosen by manager are realised. Bank

managers receive their t = 1 compensation as specified in the contract.

19 For example, the currency exchange manipulations in Barclays and UBS were carried by employeesafter the banks had reached non-prosecution agreements with regulators, breaching them and resultingin high fines. Similarly, a Barclays trader Daniel James Plunkett attempted to fix gold prices the dayafter his bank was fined for rigging Libor rates, resulting in a 44m USD fine to the bank shareholders,possibly outweighing the potential gains.

15

t=2: Long-term risk is realised, and the manager receives her t = 2 compensation. If

misconduct was implemented at t = 0, it gets detected with probability λ, resulting in

the manager losing her t = 2 compensation and costs C to bank shareholders.

4.2. Baseline Results

I start by solving the model in which bank managers cannot engage in misconduct. In

this case, bank shareholders compare the returns of the risky and safe project net of

manager compensation costs, and design the remuneration scheme so that the profit-

maximising project is chosen by bank managers.

The bank cannot commit to pay anything to the manager if the risky project is

implemented and returns are zero at t = 2. Therefore, if manager pay was not dependent

on observing R or S at t = 1 and t = 2 wages were higher than zero, she would

always choose to implement the safe project as it increases the probability of receiving

compensation at t = 2.

I rule out the case of bank shareholders inducing risk-taking just by promising the

banker’s reservation wage at t = 1 and making her indifferent between the risky and safe

projects by assuming that she chooses the safe one if indifferent. This assumption could

also be rationalised if bank managers are required to stay in the bank for supervision

at t = 1 and t = 2, and if paid the reservation wage at t = 1, they would prefer not to

work with infinitely small effort costs.

If bank shareholders prefer the risky project to be implemented, they have to offer

higher compensation in cases when R is observed. Namely, denoting bankers’ remu-

neration at t = 1 after R is observed as α and t = 2 compensation as β, managers

would choose to implement the risky project when her participation constraint (PC)

and incentive compatibility constraint (ICC) are satisfied:

pHα+ δxβ ≥ w (PC)

pHα+ δxβ > pLα+ δβ (ICC)

From the two conditions, the minimal variable pay that ensures risky project choice

at t = 0 is α > (1−x)wpH−xpL , with β set so as the PC is satisfied, being β < w(pH−pL)

δ(pH−xpL) . As

∂α

∂x=

−w(pH − pL)

(pH − xpL)2, (1)

to induce risk-taking, α, the variable pay conditional on observing R, has to increase

when project risk increases (and x diminishes) to compensate the manager for the risk

of having to forgo β at t = 2.

16

If the manager’s PC is just satisfied, the shareholder prefers the risky project to be

implemented whenever

pHR+ (1 − pH)S + δxS − w > pLR+ (1 − pL)S + δS − w. (2)

This condition coincides with the socially optimal choice given that the bank is fully

equity financed in this case and will simply choose the project with higher return. The

condition further reduces to the threshold value of x, the probability of observing returns

S at t = 2, or maximum risk that shareholders are willing to bear:

x ≥ δS − (pH − pL)(R− S)

δS≡ x. (3)

Bank misconduct

Bank managers have incentives to engage in misconduct at t = 0 when their gains

from increasing the likelihood of observing high return R by ∆ outweigh the costs of

regulatory actions. Such incentives depend on compensation schemes, which in turn

depend on whether bank shareholders want to encourage risk-taking.

When the safe project is implemented, the manager prefers to engage in misconduct

when

(pL + ∆)α+ δ(1 − λ)β > pLα+ δβ. (4)

Condition (4) is satisfied when α is sufficiently high: α > δλβ∆ . When risky projects

are profitable and shareholders favour risk-taking, conditional pay creates incentives for

misconduct by managers when

(pH + ∆)α+ δx(1 − λ)β > pHα+ δxβ, (5)

the manager preferring to engage in misconduct when α > δλxβ∆ . Comparing this with

the condition for α when the safe project is implemented in (4), managers have stronger

incentives to engage in misconduct when the risky project is implemented. This results

from the manager suffering the associated costs only when the long-term risk is not

realised, very high project risk 1 − x implying she almost never gets sanctioned for

misconduct.

By Assumption 1, bank misconduct is costly to bank shareholders and they can only

prevent it through changing manager compensation schemes. When bank shareholders

prefer the safe project to be implemented, α is not required to induce risk taking, the

contract can be easily set to satisfy both (5) and the manager’s PC. On the other hand,

when bank shareholders prefer the risky project to be implemented, they need to set

α > (1−x)wpH−xpL to induce risk-taking and α < δλxβ

∆ to prevent misconduct. Combining the

17

two conditions leads to Proposition 1:

Proposition 1: when x < ∆∆+λ(ph−pL) ≡ xM , bank shareholders cannot both in-

centivise risk-taking and prevent misconduct.

When long-term risks associated with the risky project increase (x decreases), higher

α is required to compensate managers for the long-run risk, which in turn encourages

behaviour to increase the likelihood of receiving α beyond pH . As expected, threshold

x increases in ∆, the efficiency of misconduct, and decreases in λ, the frequency of

detection: increasing gains from misconduct make preventing it harder, while increasing

the associated costs to managers allow bank shareholders to prevent misconduct and

implement the risky project at relatively low levels of x.

Risk, Regulation and Bank Project Choice

In the presence of misconduct risk, the choice by bank shareholders between imple-

menting the risky and safe project depends on the returns of the projects as well as the

costs of bank conduct failures. When x > xM and misconduct can be prevented, bank

shareholders choose the socially optimal project and invest in the risky project when

its returns are higher, or x > x.

When x < xM , bank shareholders cannot prevent misconduct if the risky project is

implemented, reducing their corresponding returns. Their choice between the risky and

safe project then depends on λC, excessively high costs making the risky project less

attractive.

To illustrate how bank shareholders decision depends on bank regulation, I rewrite

the threshold risk level for misconduct xM , to be a condition on λ: when λ > ∆(1−x)x(pH−pL) ≡

λ, the probability of detection is sufficiently high to discourage bank managers from

misconduct, thus eliminating the associated costs to shareholders.

In cases when λ < λ, the intensity of detection can reduce misconduct only through

discouraging risky project implementation. When bank shareholders cannot both induce

risk-taking and prevent misconduct, they prefer risky project implementation when

(ph + ∆ − pL)(R− S) > δ(1 − x)S + δλC, (6)

or risky project returns are sufficiently high to compensate for higher conduct costs

imposed by regulators.

From (6), the risky project is chosen when λ < (pH+∆−pL)(R−S)−δ(1−x)SδC ≡ λ. As λ

decreases in C, for bank shareholders, conduct costs and the probability of being subject

to disciplinary actions are substitutes in deterring risky project implementation, where

even low values of λ that do not prevent managers from conduct failures can have

strong effects on the profitability of risky projects. In cases when λ < λ < λ, or bank

18

supervision is too strict to make the risky projects viable, but not frequent enough to

deter misconduct by managers, bank shareholders prefer the safe projects.20

The resulting relationship between project risk, detection intensity and the choice

between risky and safe projects is presented in Figure 3.

4.3. Extensions

Constraining costs to bank shareholders

In this extension, I consider the case in which bank shareholders only suffer costs C

when returns realised at t = 2 are S. This assumption might be more realistic in

capturing bank conduct costs when regulators are reluctant to impose fines on poorly

capitalised banks, or when banks default in the case of low returns.

This assumption alters only bank costs from misconduct, and thus the effect of

misconduct on the preferred riskiness of bank projects: bank misconduct is costly to

shareholders when

∆(R− S) < δxλC. (7)

Contrary to the case in which conduct costs imposed by regulators are not con-

strained, combining (7) with the condition for λ under which regulators cannot prevent

bank misconduct shows21 that in cases when λ < λ, misconduct is costly to bank share-

holders only when C > S, or conduct costs exceed their income at t = 2. This result

suggests that in cases when financial costs imposed by regulators are constrained by

the funds that banks hold, bank shareholders find misconduct attractive at times when

bank managers do.

The alignment of the incentives of shareholders and managers stems from the similar

trade-offs involved when choosing between the risky and safe investment and deciding on

whether to engage in misconduct. Namely, when misconduct is attractive to managers

in cases of low x, it is also attractive to bank shareholders. If bank shareholders are

willing to trade-off a higher chance of losing S at t = 2 for a gain of (R−S) by choosing

the risky project, they might be willing to trade-off corresponding conduct costs C for

a gain from misconduct.

The finding has implications for bank shareholder preference of risky versus safe

projects. When λ < λ, misconduct increases the returns from risky project implemen-

tation, making shareholders prefer the high-risk projects that were previously unattrac-

tive. As a result, in the case when the conduct costs to bank shareholders are limited,

20Appendix 2 shows that such cases when λ < λ < λ and bank shareholders would prefer the riskyproject in the absence of misconduct costs but choose safe project implementation under the risk ofmisconduct do exist for sufficiently high C.

21See Appendix 2.

19

bank regulators can prevent misconduct only through sufficiently high detection prob-

ability that makes misconduct unattractive to managers. Otherwise, the ability for

managers to engage in misconduct increases both risk-taking and misconduct being

observed. Figure 4 depicts the relationship between detection probability λ, bank risk-

taking and misconduct in this case.

Bank leverage

In the baseline model, it is assumed that the bank is fully equity-funded. In this

extension, I consider the case in which the bank takes on debt. I assume that each

project now costs one unit to invest, and bank funds D of it with insured deposits that

are due in at t = 2. It is further assumed that S − β > D, or the bank does not have

sufficient returns to repay their depositors at t = 2 only in the case when the risky

project is chosen and returns are 0.

First, in the absence of bank misconduct, bank shareholders prefer the risky project

when

pHR+ (1 − pH)S + δx(S −D) > pLR+ (1 − pL)S + δ(S −D), (8)

which shifts down the threshold x below which the safe project is preferred to x′D =δ(S−D)−(pH−pL)(R−S)

δ(S−D) where∂x′D∂D < 0. The result is akin to traditional risk-shifting

where leverage increases the attractiveness of the risky project as part of the cost from

implementing it is now borne by creditors.

Although leverage does not change the incentives of bank managers to engage in

misconduct, it does change the effects of bank misconduct on projects preferred by

bank shareholders when λ < λ. Namely, leverage increases the attractiveness of the

risky projects in the presence of misconduct, as well.

In the case when the costs that regulators can impose on bank shareholders are con-

strained by bank returns at t = 2, the effects of leverage on misconduct and risk-taking

are therefore straightforward: as leverage pushes the threshold x down, it increases the

region in which risky projects are implemented. As at low values of x very high de-

tection probability by regulators is required to prevent manager misconduct, leverage

results in both more risk-taking and conduct failures.

When bank conduct costs are not constrained by bank income, leverage changes the

effects of such costs on bank project choice, as now the risky project is chosen when

(ph + ∆ − pL)(R− S) > δ(1 − x)(S −D) + δλC. (9)

The addition of bank debt to the model has several implications. In the baseline

setup, misconduct could discourage the implementation of risky projects even when

20

they had higher returns when detection was not sufficiently high to deter manager

misbehaviour, but excessively costly to shareholders. In this case, the negative effects

of misconduct on the attractiveness of the risky project are fully compensated through

the effect of leverage when (1− x)D = λC −∆(R−S), resulting the choice of the risky

project and more misconduct observed. When leverage increases even further, it results

in excessive risk-taking as compared to the baseline model, also extending the region of

conduct failures.

Finally, outstanding debt can constrain the financial penalties C that regulators are

willing impose on shareholders. When leverage results in sufficiently high bank fragility

and constraints on regulators, it could make the interests of shareholders and managers

aligned, reinforcing the positive relationship between bank misconduct and risk-taking

as observed in Figure 4.

5. Discussion and Conclusion

Recent record settlements between large financial firms and regulators have resulted

in increased interest in bank conduct failures. The cases of misconduct have sparked

changes in financial regulation, putting constraints on remuneration schemes in banks

(for example, CRD IV introduces deferral of variable pay and its maximum ratio) and

increasing the responsibilities of senior management (for instance, the Senior Managers

and Certification Regimes introduced by the Financial Conduct Authority). The large

conduct costs and their implications to bank capital levels and (systemic) risk have also

resulted in concerns from financial stability supervisors.

This paper attempts to provide a systematic view on misconduct in banks that has

resulted in conduct costs over a period of 17 years in order to improve our understanding

of the drivers behind it. It appears that misconduct in general is quite persistent and

might have been increasing lately. There is also evidence that some classes of misconduct

might be pro-cyclical, the effects being exacerbated by compensation schemes. Although

the data suffers shortcomings due to a small sample and noise and should be interpreted

with caution, this provides some support to the recent regulatory changes, and suggests

that bank supervision intensity should be pro-cyclical.

The paper further contributes to the literature by suggesting a theoretical model

which attempts to link misconduct in banks to their risk-taking incentives and resulting

compensation schemes. It suggests that when bank shareholders do not have sufficient

tools to prevent misconduct effectively, frequent detection by regulators might be more

effective in preventing conduct failures than changes in incentive schemes as the latter

can result in profitable investment opportunities being given up. On the other hand,

when detection is not sufficient to prevent misconduct and financial penalties are limited

21

by bank capacity to pay, misconduct can be preferred by bank shareholders and induce

excessive risk-taking.

22

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25

Figures and Tables

Figure 1: Bank Misconduct Starts, Investigation Starts and Fines

Notes: This figure plots the total number of misconduct cases and their investigations startingeach year as well as the total number of fines imposed. The events are not grouped so that casesresulting in multiple actions receive more weight.

Figure 2: Share of cases resolved since the date of alleged start

Notes: This table presents the cumulative distribution of the time lag between alleged miscon-duct starts and the dates at which they resulted in conduct costs to banks.

26

Figure 3: Misconduct, detection and project type

Notes: This figure presents the regions of bank risk-taking and misconduct that result fromcombinations of project risk and detection intensity in cases when misconduct is costly to bankshareholders.

27

Figure 4: Misconduct and project type under low conduct costs

Notes: This figure presents the regions of bank risk-taking and misconduct that result fromcombinations of project risk and detection intensity in cases when bank shareholders are subjectto conduct costs only when long-term risk at t = 2 is not realised and the maximum amount ofconduct costs is constrained by S, the t = 2 project return.

28

Table 1

Notes: This table presents the number of total misconduct cases with resulting conduct costsof at least 1m USD that start in each given year. In columns 1, 4 and 7, the total number ofcases initiated by both private parties and regulators, only regulators, and only private partiesare presented, respectively. In columns 2, 5 and 8, the respective number of cases are groupedin order to avoid double-counting when they result in multiple actions by both private partiesand regulators (column 2), multiple actions by regulators (column 5) and multiple actions byprivate parties (column 8). In columns 3, 6 and 9, the total costs resulting from actions startingeach year are presented.

Whole Sample Actions by Regulators Private ActionsYear No.

CasesNo.cases,grouped

TotalCost

No.Cases

No.cases,grouped

TotalCost

No.Cases

No.cases,grouped

TotalCost

(1) (2) (3) (4) (5) (6) (7) (8) (9)1998 34 31 1855 10 10 442 24 22 14121999 63 51 2510 37 35 1542 26 18 9682000 53 41 10739 25 19 4831 28 22 59072001 33 30 2745 24 22 1724 9 9 10202002 36 33 12452 32 31 11152 4 4 13002003 35 28 9667 22 21 8910 13 10 7572004 44 34 1616 28 23 709 16 12 9072005 80 43 57343 56 34 50699 24 15 66442006 79 47 24992 42 34 23856 37 18 11362007 100 70 12399 66 52 8197 34 27 42012008 88 73 34825 56 49 30734 32 30 40902009 63 53 7673 40 36 6568 23 21 11052010 55 51 5980 43 39 5878 12 12 101

29

Table 2

Notes: This table presents the total number of misconduct cases with resulting costs of at least1m USD starting each year, grouped by type of misconduct. The cases are also grouped so asto avoid counting cases that resulted in multiple actions by regulators and/or private parties.

Year Total Underwriting Compliance Manipulation Large Abuse Small Cases Sanctions1998 31 13 4 2 8 1 01999 51 19 12 4 6 2 22000 41 19 7 1 9 1 12001 30 2 7 0 14 1 12002 33 2 13 3 4 1 52003 28 2 4 3 14 0 32004 34 6 7 3 12 2 22005 43 13 3 7 7 3 62006 47 13 7 3 17 2 02007 70 3 18 10 21 9 42008 73 1 26 9 19 8 42009 53 0 15 8 19 3 02010 51 14 12 3 11 5 1

Table 3

Notes: This table presents the total cost of misconduct cases with resulting costs of at least 1mUSD starting each year, grouped by type of misconduct.

Year Total Underwriting Compliance Manipulation Large Abuse Small Cases Sanctions1998 1855 1114 4 137 499 7 01999 2510 1489 37 203 177 75 3232000 10739 7463 53 160 1778 1 7802001 2745 1211 41 0 817 19 3402002 12452 794 153 264 762 0 91882003 9667 7427 6 40 1196 0 7912004 1616 635 18 121 767 32 382005 57343 49552 28 6083 766 1 3442006 24992 20864 99 1767 1695 18 02007 12399 2566 367 4676 4331 372 242008 34825 18 189 4787 26312 839 1342009 7673 315 473 380 6126 8 02010 5980 103 61 5342 326 63 3

30

Table 4

Notes: This table presents the descriptive statistics of variables used in data analysis for the sample period of1998-2010. All variables are expressed in their values at 2000 prices. ln(total assets) is the natural logarithm ofthe value of bank’s assets in million USD, ROA is the ratio of bank’s net income to total assets (in %), ln(totalrevenue) is the natural logarithm of the total revenue of a bank in million USD, leverage is the ratio of bank’s totalliabilities to total assets (in %), all retrieved from Compustat Global or North America databases. The variableCEO bonus/total compensation is the ratio of the bank’s CEO bonus to total compensation in a given year,while ln(totalCEOcompensation) is the natural logarithm of (1+total CEO compensation in millions USD),both available from Execucomp. Detrended GDP is the annual figure of de-trended GDP index in the countryin which a bank is headquartered available from OECD short-term indicators database. The variables usedfor measuring the intensity of misconduct are winsorized natural logarithms of (1+the real value of misconductstarting each year in million USD). The bank-year level statistics for the number of misconduct cases reportedare grouped to avoid over-weighting misconduct that results in actions from multiple parties.

Obs. mean median sd min maxBank balance sheet & CEO compensationln(total assets) 416 13.01 13.19 1.17 9.44 15.22ROA (%) 339 0.93 0.85 0.76 -1.76 5.69ln(total revenue) 416 10.32 10.45 0.96 8.15 12leverage (%) 416 93.32 93.54 3 83.15 98.55CEO bonus/total compensation 207 0.18 0.12 0.21 0 0.97ln(1+total CEO compensation) 207 9.43 9.68 1.31 0 12.57

Business Cycledetrended GDP 416 100.08 99.98 1.34 96.86 103.56

Misconductln(1+total misconduct costs) 416 2.60 1.65 2.80 0 8.86ln(1+underwriting costs) 416 0.97 0 2.17 0 8.20ln(1+systematic abuse costs) 416 1.10 0 2.03 0 6.90ln(1+individual cases costs) 416 0.15 0 0.67 0 3.90ln(1+compliance failures costs) 416 0.44 0 0.94 0 4.01ln(1+market manipulation costs) 416 0.72 0 1.82 0 7.03total number of cases 416 1.41 1 1.79 0 10number of underwriting cases 416 0.26 0 0.64 0 4number of large abuse cases 416 0.39 0 0.76 0 6number of individual cases 416 0.09 0 0.36 0 3number of compliance cases 416 0.32 0 0.70 0 5number of manipulation cases 416 0.13 0 0.39 0 2

31

Table

5

Note

s:T

his

tab

leu

ses

the

sam

ple

of

30

ban

ks

over

1998-2

010

inco

lum

ns

1an

d2

an

da

sam

ple

of

16

ban

ks

inco

lum

ns

3-9

.T

he

dep

end

ent

vari

ab

leis

the

natu

ral

logari

thm

of

the

valu

eof

all

mis

con

du

ctca

ses

start

ing

ina

giv

enyea

r.CYCLEt

isth

ed

e-tr

end

edG

DP

ind

exin

the

cou

ntr

yin

wh

ich

the

ban

kis

hea

dqu

art

ered

.CEO

bonus/totalcomp. t−1

isth

eaver

age

rati

oof

CE

Ob

onu

ses

toto

tal

pay

inth

ep

rece

din

gyea

r,ln(C

EO

compensation) t−1

isth

en

atu

ral

logari

thm

of

tota

lC

EO

com

pen

sati

on

inth

ep

rece

din

gyea

r,an

davg.C

EO

bonus/totalcomp.

isth

eaver

age

rati

oof

CE

Ob

onu

sto

tota

lC

EO

rem

un

erati

on

over

the

sam

ple

per

iod

).leverage

t

isb

an

kle

ver

age

mea

sure

das

the

rati

oof

tota

lb

an

kliab

ilit

ies

toto

tal

bank

ass

ets.

Contr

ols

ln(assets)

an

dln(reven

ue)

are

natu

ral

logari

thm

sof

tota

lb

an

kass

ets

an

dto

tal

reven

ue

an

dROA

isth

era

tio

of

ab

an

k’s

net

inco

me

toto

tal

ass

ets.

reg.

investig. t−1

isth

enu

mb

erof

inves

tigati

on

sin

itia

ted

by

regu

lato

rsagain

sta

giv

enb

an

kin

the

pre

ced

ing

yea

r(g

rou

ped

soth

at

case

sare

not

over

-cou

nte

din

case

sof

mu

ltip

lere

gu

lato

rs)

that

resu

lted

ind

isci

pli

nary

act

ion

sagain

stb

an

ks

du

rin

g2000-2

016.

Rob

ust

stan

dard

erro

rsare

inp

are

nth

eses

,∗

(p<

0.1

0),∗∗

(p<

0.0

5),∗∗∗

(p<

0.0

1).

Dep

.var

-ln

(1+

tota

lm

isco

nd

uct

cost

s)(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)

ln(a

sset

s)t

1.8

18∗∗∗

1.4

78∗∗∗

1.7

63∗∗∗

1.7

05∗∗∗

1.7

01∗∗∗

1.7

05∗∗∗

1.7

64∗∗∗

1.6

76∗∗∗

1.6

79∗∗∗

(0.4

46)

(0.3

00)

(0.1

48)

(0.1

48)

(0.1

32)

(0.1

48)

(0.1

46)

(0.1

48)

(0.1

36)

RO

At

-0.3

65

-0.3

08

0.0

15

-0.0

49

0.0

08

-0.0

49

0.0

22

-0.0

52

-0.0

06

(0.2

28)

(0.1

81)

(0.1

77)

(0.1

97)

(0.1

79)

(0.1

97)

(0.1

77)

(0.1

97)

(0.1

75)

lever

age t

0.0

86

0.1

33∗

0.0

13

-0.0

05

0.0

07

-0.0

05

0.0

22

0.0

01

0.0

04

(0.0

86)

(0.0

76)

(0.0

36)

(0.0

41)

(0.0

39)

(0.0

41)

(0.0

39)

(0.0

40)

(0.0

41)

reg.

inves

tig. t−1

-0.1

78

-0.1

27

-0.0

33

-0.0

07

-0.0

51

-0.0

07

-0.0

65

-0.0

20

-0.0

12

(0.1

71)

(0.1

64)

(0.2

11)

(0.2

15)

(0.2

11)

(0.2

15)

(0.2

09)

(0.2

03)

(0.2

15)

CY

CL

Et

0.3

01∗∗∗

0.2

27∗∗

0.2

31∗∗

0.2

40∗∗

0.2

31∗∗

0.1

28

-0.9

50

-0.1

30

(0.0

77)

(0.1

04)

(0.0

99)

(0.1

02)

(0.0

99)

(0.1

14)

(0.7

77)

(0.1

63)

CE

Ob

onu

s/to

tal

com

p. t−1

0.1

37

-58.3

55

(0.5

43)

(38.0

59)

ln(C

EO

com

pen

sati

on

) t−1

0.2

23∗

0.2

23∗

-11.9

64

(0.1

20)

(0.1

20)

(8.4

02)

avg.C

EO

bonu

s/to

tal

com

p.

1.7

54

-192.8

56∗∗

(1.1

28)

(87.5

93)

CY

CL

Et

xC

EO

bonu

s/to

tal

com

p. t−1

0.5

82

(0.3

78)

CY

CL

Et

xln

(CE

Oco

mp

ensa

tion

) t−1

0.1

22

(0.0

85)

CY

CL

Et

xavg.C

EO

bonu

s/to

tal

com

p.

1.9

45∗∗

(0.8

75)

Fix

edyea

reff

ects

Yes

No

No

No

No

No

No

No

No

Fix

edb

an

keff

ects

Yes

Yes

No

No

No

No

No

No

No

No.

Ob

s.339

339

206

206

208

206

206

206

208

Ad

j.R

20.1

59

0.1

31

0.5

09

0.5

16

0.5

11

0.5

16

0.5

10

0.5

17

0.5

17

32

Table

6

Note

s:T

his

tab

leu

ses

the

sam

ple

of

30

ban

ks

over

1998-2

010

inco

lum

ns

1an

d2

an

da

sam

ple

of

16

ban

ks

inco

lum

ns

3-1

1.

Th

ed

epen

den

tvari

ab

leis

the

natu

ral

logari

thm

of

the

valu

eof

mis

con

du

ctca

ses

rela

ted

tob

an

ku

nd

erw

riti

ng

act

ivit

ies

start

ing

ina

giv

enyea

r.E

xam

ple

sof

such

even

tsare

the

un

der

wri

tin

gof

tech

nolo

gy

firm

sd

uri

ng

the

dot-

com

boom

,h

elp

ing

frau

du

lent

firm

ssu

chas

Worl

dC

om

an

dE

nro

nto

rais

efi

nan

ce,

an

dis

suin

gse

curi

ties

back

edby

low

-qu

ality

mort

gages

or

get

tin

gth

ose

insu

red

.CYCLEt

isth

ed

e-tr

end

edG

DP

ind

exin

the

cou

ntr

yin

wh

ich

the

ban

kis

hea

dqu

art

ered

.CEO

bonus/totalcomp. t−1

isth

eaver

age

rati

oof

CE

Ob

onu

ses

toto

tal

pay

inth

ep

rece

din

gyea

r,ln(C

EO

compensation) t−1

isth

en

atu

ral

logari

thm

of

tota

lC

EO

com

pen

sati

on

inth

ep

rece

din

gyea

r,an

davg.C

EO

bonus/totalcomp.

isth

eaver

age

rati

oof

CE

Ob

onu

sto

tota

lC

EO

rem

un

erati

on

over

the

sam

ple

per

iod

).leverage

tis

ban

kle

ver

age

mea

sure

das

the

rati

oof

tota

lb

an

kliab

ilit

ies

toto

tal

ban

kass

ets.

Contr

ols

ln(assets)

an

dln(reven

ue)

are

natu

ral

logari

thm

sof

tota

lb

an

kass

ets

an

dto

tal

reven

ue

an

dROA

isth

era

tio

of

ab

an

k’s

net

inco

me

toto

tal

ass

ets.

reg.

investig. t−1

isth

enu

mb

erof

inves

tigati

on

sin

itia

ted

by

regu

lato

rsagain

sta

giv

enb

an

kin

the

pre

ced

ing

yea

r(g

rou

ped

soth

at

case

sare

not

over

-cou

nte

din

case

sof

mu

ltip

lere

gu

lato

rs)

that

resu

lted

ind

isci

plin

ary

act

ion

sagain

stb

anks

du

rin

g2000-2

016.

Rob

ust

stan

dard

erro

rsare

inp

are

nth

eses

,∗

(p<

0.1

0),∗∗

(p<

0.0

5),

∗∗∗

(p<

0.0

1).

Dep

.var

-ln

(1+

un

der

wri

tin

gco

sts)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

ln(a

sset

s)t

0.6

86∗

0.4

38∗∗

1.0

89∗∗∗

1.0

62∗∗∗

1.1

07∗∗∗

1.0

90∗∗∗

0.9

91∗∗∗

1.0

72∗∗∗

1.0

89∗∗∗

0.9

97∗∗∗

1.0

22∗∗∗

(0.3

80)

(0.2

00)

(0.2

05)

(0.2

08)

(0.2

01)

(0.2

03)

(0.2

09)

(0.2

03)

(0.2

07)

(0.2

13)

(0.2

02)

RO

At

-0.0

12

0.2

30

0.1

27

0.1

03

0.1

40

0.1

36

0.0

96

0.1

17

0.1

58

0.1

06

0.1

22

(0.2

06)

(0.2

44)

(0.2

56)

(0.2

69)

(0.2

69)

(0.2

60)

(0.2

73)

(0.2

68)

(0.2

62)

(0.2

60)

(0.2

65)

lever

age t

0.1

19

0.1

42∗

0.1

19∗∗

0.1

24∗∗

0.1

63∗∗

0.1

31∗∗

0.1

37∗∗∗

0.1

58∗∗

0.0

83∗

-0.8

34∗∗

-0.0

52

(0.0

74)

(0.0

73)

(0.0

50)

(0.0

44)

(0.0

58)

(0.0

53)

(0.0

43)

(0.0

59)

(0.0

45)

(0.2

85)

(0.1

00)

reg.

inves

tig. t−1

-0.3

03

-0.1

97

-0.1

92

-0.1

86

-0.2

20

-0.2

35

-0.2

18

-0.1

55

-0.2

11

-0.1

54

-0.2

09

(0.2

27)

(0.2

31)

(0.3

07)

(0.3

14)

(0.3

06)

(0.3

06)

(0.2

68)

(0.2

82)

(0.3

07)

(0.3

05)

(0.2

84)

CY

CL

Et

0.3

51∗∗∗

0.4

74∗∗

0.4

90∗∗∗

0.4

96∗∗∗

0.3

42∗∗

-2.3

36∗∗

-0.1

17

0.4

72∗∗

0.5

00∗∗∗

0.4

87∗∗∗

(0.1

20)

(0.1

62)

(0.1

57)

(0.1

58)

(0.1

21)

(1.0

10)

(0.2

86)

(0.1

66)

(0.1

48)

(0.1

57)

CE

Ob

onu

s/to

tal

com

p. t−1

0.6

18

-77.8

47

-24.0

12

(0.5

94)

(45.9

18)

(13.8

46)

ln(C

EO

com

pen

sati

on

) t−1

0.1

36

-29.0

23∗∗

-8.9

05∗∗∗

(0.0

81)

(10.9

67)

(2.7

49)

avg.C

EO

bonu

s/to

tal

com

p.

-0.4

74

-322.7

21∗

-104.2

28∗∗∗

(1.4

28)

(177.8

31)

(33.7

95)

CY

CL

Et

xC

EO

bonu

s/to

tal

com

p. t−1

0.7

81

(0.4

56)

CY

CL

Et

xln

(CE

Oco

mp

ensa

tion

) t−1

0.2

93∗∗

(0.1

11)

CY

CL

Et

xavg.C

EO

bonu

s/to

tal

com

p.

3.2

21∗

(1.7

88)

lever

age t

xC

EO

bonu

s/to

tal

com

p. t−1

0.2

68∗

(0.1

51)

lever

age t

xln

(CE

Oco

mp

ensa

tion

) t−1

0.1

00∗∗∗

(0.0

30)

lever

age t

xavg.C

EO

bonu

s/to

tal

com

p.

1.1

55∗∗∗

(0.3

82)

Fix

edyea

reff

ects

Yes

No

No

No

No

No

No

No

No

No

No

Fix

edb

an

keff

ects

Yes

Yes

No

No

No

No

No

No

No

No

No

No.

Ob

s.339

339

206

206

208

206

206

208

206

206

208

Ad

j.R

20.1

96

0.0

99

0.3

55

0.3

57

0.3

60

0.3

60

0.3

77

0.3

85

0.3

56

0.3

71

0.3

78

33

Table

7

Note

s:T

his

tab

leu

ses

the

sam

ple

of

30

ban

ks

over

1998-2

010

inco

lum

ns

1an

d2

an

da

sam

ple

of

16

ban

ks

inco

lum

ns

3-1

1.

Th

ed

epen

den

tvari

ab

leis

the

natu

ral

logari

thm

of

the

valu

eof

mis

con

du

ctca

ses

rela

ted

tob

an

kd

isad

vanta

gin

git

scl

ients

syst

emati

cally

start

ing

ina

giv

enyea

r.E

xam

ple

sof

such

case

sare

over

charg

ing

for

pro

du

cts,

sellin

gu

nsu

itab

lese

rvic

esor

pro

du

cts,

pre

dato

ryle

nd

ing

or

fore

closu

reab

use

sth

at

are

not

lim

ited

toa

sin

gle

even

tor

asi

ngle

cou

nte

r-p

art

y.CYCLEt

isth

ed

e-tr

end

edG

DP

ind

exin

the

cou

ntr

yin

wh

ich

the

ban

kis

hea

dqu

art

ered

.CEO

bonus/totalcomp. t−1

isth

eaver

age

rati

oof

CE

Ob

onu

ses

toto

tal

pay

inth

ep

rece

din

gyea

r,ln(C

EO

compensation) t−1

isth

en

atu

ral

logari

thm

of

tota

lC

EO

com

pen

sati

on

inth

ep

rece

din

gyea

r,an

davg.C

EO

bonus/totalcomp.

isth

eaver

age

rati

oof

CE

Ob

onu

sto

tota

lC

EO

rem

un

erati

on

over

the

sam

ple

per

iod

).leverage

tis

ban

kle

ver

age

mea

sure

das

the

rati

oof

tota

lb

an

kliab

ilit

ies

toto

tal

ban

kass

ets.

Contr

ols

ln(assets)

an

dln(reven

ue)

are

natu

ral

logari

thm

sof

tota

lb

an

kass

ets

an

dto

tal

reven

ue

an

dROA

isth

era

tio

of

ab

an

k’s

net

inco

me

toto

tal

ass

ets.

reg.

investig. t−1

isth

enu

mb

erof

inves

tigati

on

sin

itia

ted

by

regu

lato

rsagain

sta

giv

enb

an

kin

the

pre

ced

ing

yea

r(g

rou

ped

soth

at

case

sare

not

over

-cou

nte

din

case

sof

mu

ltip

lere

gu

lato

rs)

that

resu

lted

ind

isci

plin

ary

act

ion

sagain

stb

an

ks

du

rin

g2000-2

016.

Rob

ust

stan

dard

erro

rsare

inp

are

nth

eses

,∗

(p<

0.1

0),∗∗

(p<

0.0

5),∗∗∗

(p<

0.0

1).

Dep

.var

-ln

(1+

cost

of

dis

ad

v.

cust

om

.)(1

)(2

)(3

)(4

)(5

)(6

)(7

)(8

)(9

)(1

0)

(11)

ln(a

sset

s)t

0.4

63

0.6

92∗∗∗

0.8

32∗∗∗

0.7

41∗∗∗

0.8

96∗∗∗

0.8

32∗∗∗

0.7

22∗∗∗

0.9

01∗∗∗

0.8

31∗∗∗

0.7

13∗∗∗

0.9

17∗∗∗

(0.4

87)

(0.2

08)

(0.1

59)

(0.1

61)

(0.1

72)

(0.1

59)

(0.1

63)

(0.1

76)

(0.1

53)

(0.1

68)

(0.1

90)

RO

At

-0.4

40

-0.4

18

-0.2

08

-0.3

14

-0.2

09

-0.2

04

-0.3

16

-0.2

06

-0.2

52

-0.3

13

-0.2

05

(0.2

65)

(0.2

71)

(0.2

18)

(0.2

63)

(0.2

28)

(0.2

18)

(0.2

63)

(0.2

26)

(0.2

26)

(0.2

54)

(0.2

33)

lever

age t

0.0

75

0.0

46

-0.0

15

-0.0

59

-0.0

15

-0.0

11

-0.0

56

-0.0

15

0.0

35

-0.4

78∗∗

0.0

36

(0.0

86)

(0.0

80)

(0.0

64)

(0.0

61)

(0.0

56)

(0.0

61)

(0.0

62)

(0.0

55)

(0.0

74)

(0.1

84)

(0.0

71)

reg.

inves

tig. t−1

-0.0

39

0.0

84

-0.0

41

0.0

07

-0.0

17

-0.0

58

-0.0

02

-0.0

26

-0.0

15

0.0

21

-0.0

20

(0.2

77)

(0.2

62)

(0.3

62)

(0.3

47)

(0.3

55)

(0.3

68)

(0.3

50)

(0.3

60)

(0.3

55)

(0.3

48)

(0.3

56)

CY

CL

Et

0.1

38

0.0

47

0.0

37

0.0

25

-0.0

06

-0.7

46

0.1

05

0.0

51

0.0

41

0.0

27

(0.0

89)

(0.1

24)

(0.1

20)

(0.1

15)

(0.1

58)

(1.1

77)

(0.2

38)

(0.1

25)

(0.1

15)

(0.1

16)

CE

Ob

onu

s/to

tal

com

p. t−1

-0.4

57

-32.2

05

34.1

88

(0.8

57)

(72.0

86)

(24.5

58)

ln(C

EO

com

pen

sati

on

) t−1

0.3

06∗

-7.7

72

-3.6

44

(0.1

53)

(12.6

62)

(2.1

24)

avg.C

EO

bonu

s/to

tal

com

p.

-1.9

55

39.7

48

22.7

72

(1.1

70)

(127.4

78)

(46.9

21)

CY

CL

Et

xC

EO

bonu

s/to

tal

com

p. t−1

0.3

16

(0.7

18)

CY

CL

Et

xln

(CE

Oco

mp

ensa

tion

) t−1

0.0

81

(0.1

27)

CY

CL

Et

xavg.C

EO

bonu

s/to

tal

com

p.

-0.4

17

(1.2

75)

lever

age t

xC

EO

bonu

s/to

tal

com

p. t−1

-0.3

77

(0.2

66)

lever

age t

xln

(CE

Oco

mp

ensa

tion

) t−1

0.0

44∗

(0.0

24)

lever

age t

xavg.C

EO

bonu

s/to

tal

com

p.

-0.2

75

(0.5

28)

Fix

edyea

reff

ects

Yes

No

No

No

No

No

No

No

No

No

No

Fix

edb

an

keff

ects

Yes

Yes

No

No

No

No

No

No

No

No

No

No.

Ob

s.339

339

206

206

208

206

206

208

206

206

208

Ad

j.R

20.0

75

0.0

46

0.1

58

0.1

77

0.1

60

0.1

55

0.1

75

0.1

56

0.1

61

0.1

76

0.1

57

34

Appendix

1

Table

A1

Note

s:T

his

tab

leu

ses

the

sam

ple

of

30

ban

ks

over

1998-2

010

inco

lum

ns

1an

d2

an

da

sam

ple

of

16

ban

ks

inco

lum

ns

3-1

0.

Th

ed

epen

den

tvari

ab

leis

the

natu

ral

logari

thm

of

the

valu

eof

case

sre

late

dto

ban

kco

mp

liance

failu

res

resu

ltin

gin

act

ion

sfr

om

regu

lato

rsst

art

ing

ina

giv

enyea

r.CYCLEt

isth

ed

e-tr

end

edG

DP

ind

exin

the

cou

ntr

yin

wh

ich

the

ban

kis

hea

dqu

art

ered

.CEO

bonus/totalcomp. t−1

isth

eaver

age

rati

oof

CE

Ob

onu

ses

toto

tal

pay

inth

ep

rece

din

gyea

r,ln(C

EO

compensation) t−1

isth

en

atu

ral

logari

thm

of

tota

lC

EO

com

pen

sati

on

inth

ep

rece

din

gyea

r,an

davg.C

EO

bonus/totalcomp.

isth

eaver

age

rati

oof

CE

Ob

onu

sto

tota

lC

EO

rem

un

erati

on

over

the

sam

ple

per

iod

).leverage

tis

ban

kle

ver

age

mea

sure

das

the

rati

oof

tota

lb

an

kliab

ilit

ies

toto

tal

ban

kass

ets.

Contr

ols

ln(assets)

an

dln(reven

ue)

are

natu

ral

logari

thm

sof

tota

lb

an

kass

ets

an

dto

tal

reven

ue

an

dROA

isth

era

tio

of

ab

an

k’s

net

inco

me

toto

tal

ass

ets.

reg.

investig. t−1

isth

enu

mb

erof

inves

tigati

on

sin

itia

ted

by

regu

lato

rsagain

sta

giv

enb

an

kin

the

pre

ced

ing

yea

r(g

rou

ped

soth

at

case

sare

not

over

-cou

nte

din

case

sof

mu

ltip

lere

gu

lato

rs)

that

resu

lted

ind

isci

plin

ary

act

ion

sagain

stb

an

ks

du

rin

g2000-2

016.

Rob

ust

stan

dard

erro

rsare

inp

are

nth

eses

,∗

(p<

0.1

0),∗∗

(p<

0.0

5),∗∗∗

(p<

0.0

1).

Dep

.var

-ln

(1+

cost

of

com

plian

cefa

ilu

res)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

ln(a

sset

s)t

0.5

37∗∗

0.4

43∗∗∗

0.2

43

0.2

58

0.2

47

0.2

92

0.2

93

0.2

30

0.2

80

0.3

43

(0.1

99)

(0.1

34)

(0.2

17)

(0.2

08)

(0.2

01)

(0.2

11)

(0.2

03)

(0.2

08)

(0.2

12)

(0.1

96)

RO

At

0.0

37

-0.0

36

-0.0

09

-0.0

07

-0.0

09

-0.0

13

-0.0

02

0.0

05

-0.0

13

-0.0

19

(0.0

92)

(0.0

75)

(0.0

68)

(0.0

66)

(0.0

68)

(0.0

65)

(0.0

71)

(0.0

65)

(0.0

65)

(0.0

66)

lever

age t

-0.0

65

-0.0

27

-0.0

78

-0.0

80

-0.0

78

-0.0

79

-0.0

83

-0.0

96

0.1

61

0.1

35∗∗

(0.0

58)

(0.0

56)

(0.0

60)

(0.0

61)

(0.0

61)

(0.0

60)

(0.0

62)

(0.0

61)

(0.1

17)

(0.0

62)

reg.

inves

tig. t−1

0.0

36

-0.0

44

-0.1

78

-0.1

80

-0.1

76

-0.1

80

-0.1

83

-0.1

84

-0.1

92

-0.1

94

(0.1

10)

(0.1

09)

(0.1

25)

(0.1

30)

(0.1

35)

(0.1

27)

(0.1

23)

(0.1

26)

(0.1

32)

(0.1

29)

CY

CL

Et

0.0

13

-0.0

28

-0.0

31

-0.0

21

0.5

50∗

0.0

40

-0.0

27

-0.0

33

-0.0

27

(0.0

48)

(0.0

60)

(0.0

61)

(0.0

78)

(0.2

66)

(0.0

69)

(0.0

59)

(0.0

59)

(0.0

60)

CE

Ob

onu

s/to

tal

com

p. t−1

-0.2

12

3.4

90

-10.2

59

(0.4

50)

(36.0

54)

(11.5

48)

ln(C

EO

com

pen

sati

on

) t−1

-0.0

42

5.9

34∗∗

2.1

92∗∗

(0.0

59)

(2.5

24)

(0.8

98)

CY

CL

Et

xC

EO

bonu

s/to

tal

com

p. t−1

-0.0

37

(0.3

60)

CY

CL

Et

xln

(CE

Oco

mp

ensa

tion

) t−1

-0.0

60∗∗

(0.0

25)

CY

CL

Et

xavg.C

EO

bonu

s/to

tal

com

p.

-0.3

84

(0.5

31)

lever

age t

xC

EO

bonu

s/to

tal

com

p. t−1

0.1

09

(0.1

26)

lever

age t

xln

(CE

Oco

mp

ensa

tion

) t−1

-0.0

25∗∗

(0.0

10)

lever

age t

xavg.C

EO

bonu

s/to

tal

com

p.

-1.0

67∗∗∗

(0.2

64)

Fix

edyea

reff

ects

Yes

No

No

No

No

No

No

No

No

No

Fix

edb

an

keff

ects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No.

Ob

s.339

339

206

206

206

206

208

206

206

208

Ad

j.R

20.1

32

0.0

51

0.0

39

0.0

39

0.0

34

0.0

41

0.0

45

0.0

37

0.0

42

0.0

87

35

Appendix

1

Table

A2

Note

s:T

his

tab

leu

ses

the

sam

ple

of

30

ban

ks

over

1998-2

010

inco

lum

ns

1an

d2

an

da

sam

ple

of

16

ban

ks

inco

lum

ns

3-1

0.

Th

ed

epen

den

tvari

ab

leis

the

natu

ral

logari

thm

of

the

valu

eof

mis

con

du

ctca

ses

rela

ted

toa

ban

katt

emp

tin

gto

man

ipu

late

pri

ces

of

ass

ets

or

serv

ices

start

ing

ina

giv

enyea

r.E

xam

ple

sof

such

case

sare

man

ipu

lati

on

of

ben

chm

ark

inte

rest

rate

sor

curr

ency

pri

ces

as

wel

las

collu

sion

inp

rice

sof

ban

kse

rvic

es.CYCLEt

isth

ed

e-tr

end

edG

DP

ind

exin

the

cou

ntr

yin

wh

ich

the

ban

kis

hea

dqu

art

ered

.CEO

bonus/totalcomp. t−1

isth

eaver

age

rati

oof

CE

Ob

onuse

sto

tota

lp

ay

inth

ep

rece

din

gyea

r,ln(C

EO

compensation) t−1

isth

en

atu

ral

logari

thm

of

tota

lC

EO

com

pen

sati

on

inth

ep

rece

din

gyea

r,an

davg.C

EO

bonus/totalcomp.

isth

eaver

age

rati

oof

CE

Ob

onu

sto

tota

lC

EO

rem

un

erati

on

over

the

sam

ple

per

iod

).leverage

tis

bank

lever

age

mea

sure

das

the

rati

oof

tota

lb

an

kliab

ilit

ies

toto

tal

ban

kass

ets.

Contr

ols

ln(assets)

an

dln(reven

ue)

are

natu

ral

logari

thm

sof

tota

lb

an

kass

ets

an

dto

tal

reven

ue

an

dROA

isth

era

tio

of

ab

an

k’s

net

inco

me

toto

tal

ass

ets.

reg.

investig. t−1

isth

enu

mb

erof

inves

tigati

on

sin

itia

ted

by

regu

lato

rsagain

sta

giv

enb

an

kin

the

pre

ced

ing

yea

r(g

rou

ped

soth

at

case

sare

not

over

-cou

nte

din

case

sof

mu

ltip

lere

gu

lato

rs)

that

resu

lted

ind

isci

plin

ary

act

ion

sagain

stb

an

ks

du

rin

g2000-2

016.

Rob

ust

stan

dard

erro

rsare

inp

are

nth

eses

,∗

(p<

0.1

0),∗∗

(p<

0.0

5),∗∗∗

(p<

0.0

1).

Dep

.var

-ln

(tota

lco

stof

mark

etm

anip

ula

tion)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

ln(a

sset

s)t

0.7

41

0.9

61∗∗

0.5

94

0.5

60

0.5

33

0.6

06

0.5

39

0.5

76

0.6

14

0.5

73

(0.5

61)

(0.3

49)

(0.3

90)

(0.3

58)

(0.3

94)

(0.3

70)

(0.3

56)

(0.3

97)

(0.3

72)

(0.3

47)

RO

At

-0.2

37

-0.2

60

-0.1

46

-0.1

33

-0.1

47

-0.1

40

-0.1

61

-0.1

28

-0.1

47

-0.1

62

(0.2

49)

(0.2

34)

(0.2

62)

(0.2

49)

(0.2

63)

(0.2

52)

(0.2

58)

(0.2

61)

(0.2

53)

(0.2

60)

lever

age t

0.0

87

0.1

03

0.0

28

0.0

38

0.0

36

0.0

40

0.0

30

0.0

03

0.6

33

0.1

13

(0.0

85)

(0.0

70)

(0.0

52)

(0.0

54)

(0.0

49)

(0.0

57)

(0.0

50)

(0.0

55)

(0.4

32)

(0.0

96)

reg.

inves

tig. t−1

0.0

65

0.1

18

0.1

92

0.1

68

0.1

60

0.1

68

0.1

93

0.1

83

0.1

38

0.1

81

(0.1

43)

(0.1

40)

(0.1

27)

(0.1

13)

(0.1

25)

(0.1

07)

(0.1

27)

(0.1

26)

(0.0

99)

(0.1

24)

CY

CL

Et

0.2

38∗∗∗

0.0

69∗

0.0

80∗

-0.0

40

0.8

69

0.0

48

0.0

69∗

0.0

76∗

0.0

88∗∗

(0.0

72)

(0.0

36)

(0.0

38)

(0.0

75)

(0.5

86)

(0.0

75)

(0.0

37)

(0.0

43)

(0.0

40)

CE

Ob

onu

s/to

tal

com

p. t−1

0.1

98

-65.2

04

-13.4

31

(0.3

38)

(39.3

73)

(13.6

03)

ln(C

EO

com

pen

sati

on

) t−1

-0.1

58

7.9

47

5.3

50

(0.1

80)

(5.5

76)

(3.6

81)

CY

CL

Et

xC

EO

bonu

s/to

tal

com

p. t−1

0.6

51

(0.3

91)

CY

CL

Et

xln

(CE

Oco

mp

ensa

tion

) t−1

-0.0

81

(0.0

57)

CY

CL

Et

xavg.C

EO

bonu

s/to

tal

com

p.

0.1

95

(0.3

38)

lever

age t

xC

EO

bonu

s/to

tal

com

p. t−1

0.1

48

(0.1

46)

lever

age t

xln

(CE

Oco

mp

ensa

tion

) t−1

-0.0

61

(0.0

42)

lever

age t

xavg.C

EO

bonu

s/to

tal

com

p.

-0.3

91

(0.4

92)

Fix

edyea

reff

ects

Yes

No

No

No

No

No

No

No

No

No

Fix

edb

an

keff

ects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

No.

Ob

s.339

339

206

206

206

206

208

206

206

208

Ad

j.R

20.1

31

0.1

18

0.0

44

0.0

59

0.0

58

0.0

60

0.0

40

0.0

42

0.0

77

0.0

43

36

Appendix

1

Table

A3

Note

s:T

his

tab

leu

ses

the

sam

ple

of

30

ban

ks

over

1998-2

010

inco

lum

ns

1an

d2

an

da

sam

ple

of

16

ban

ks

inco

lum

ns

3-1

1.

Th

ed

epen

den

tvari

ab

leis

the

natu

ral

logari

thm

of

the

valu

eof

case

sin

wh

ich

asi

ngle

ban

kem

plo

yee

engaged

inm

isco

nd

uct

,or

ab

an

kw

as

fou

nd

toh

ave

dis

ad

vanta

ged

asi

ngle

cou

nte

rp

art

y.E

xam

ple

sof

such

case

sare

rogu

etr

ad

ers

takin

gex

cess

ive

risk

s,b

an

km

an

ager

sst

ealin

gcl

ient

mon

ey,

or

banks

losi

ng

cou

rtca

ses

again

stin

div

idu

als

or

com

pan

ies

claim

ing

they

have

not

rece

ived

ad

equ

ate

ad

vic

eor

serv

ice.

CYCLEt

isth

ed

e-tr

end

edG

DP

ind

exin

the

cou

ntr

yin

wh

ich

the

ban

kis

hea

dqu

art

ered

.CEO

bonus/totalcomp. t−1

isth

eaver

age

rati

oof

CE

Ob

onu

ses

toto

tal

pay

inth

ep

rece

din

gyea

r,ln(C

EO

compensation) t−1

isth

en

atu

ral

logari

thm

of

tota

lC

EO

com

pen

sati

on

inth

ep

rece

din

gyea

r,an

davg.C

EO

bonus/totalcomp.

isth

eaver

age

rati

oof

CE

Ob

onu

sto

tota

lC

EO

rem

un

erati

on

over

the

sam

ple

per

iod

).leverage

tis

ban

kle

ver

age

mea

sure

das

the

rati

oof

tota

lb

an

kliab

ilit

ies

toto

tal

ban

kass

ets.

Contr

ols

ln(assets)

an

dln(reven

ue)

are

natu

ral

logari

thm

sof

tota

lb

an

kass

ets

an

dto

tal

reven

ue

an

dROA

isth

era

tio

of

ab

an

k’s

net

inco

me

toto

tal

ass

ets.

reg.

investig. t−1

isth

enu

mb

erof

inves

tigati

on

sin

itia

ted

by

regu

lato

rsagain

sta

giv

enb

ank

inth

ep

rece

din

gyea

r(g

rou

ped

soth

at

case

sare

not

over

-cou

nte

din

case

sof

mu

ltip

lere

gu

lato

rs)

that

resu

lted

ind

isci

plin

ary

act

ion

sagain

stb

an

ks

du

rin

g2000-2

016.

Rob

ust

stan

dard

erro

rsare

inp

are

nth

eses

,∗

(p<

0.1

0),∗∗

(p<

0.0

5),∗∗∗

(p<

0.0

1).

Dep

.var

-ln

(tota

lco

stof

ind

ivid

ual

mis

con

du

ct)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

ln(a

sset

s)t

0.0

78

0.0

85∗

0.0

80

0.0

66

0.0

59

0.0

80

0.0

56

0.0

56

0.0

80

0.0

58

0.0

47

(0.0

53)

(0.0

47)

(0.0

46)

(0.0

39)

(0.0

43)

(0.0

46)

(0.0

35)

(0.0

44)

(0.0

46)

(0.0

39)

(0.0

47)

RO

At

0.0

40

-0.0

42

0.0

20

0.0

04

0.0

18

0.0

21

0.0

03

0.0

17

0.0

22

0.0

04

0.0

16

(0.0

69)

(0.0

64)

(0.0

38)

(0.0

41)

(0.0

39)

(0.0

38)

(0.0

42)

(0.0

36)

(0.0

41)

(0.0

41)

(0.0

36)

lever

age t

0.0

16

0.0

06

0.0

18

0.0

12

0.0

06

0.0

19

0.0

14

0.0

06

0.0

15

-0.1

07

-0.0

24

(0.0

18)

(0.0

12)

(0.0

12)

(0.0

13)

(0.0

14)

(0.0

13)

(0.0

13)

(0.0

14)

(0.0

13)

(0.1

25)

(0.0

19)

reg.

inves

tig. t−1

0.0

94

0.0

66

0.0

52

0.0

59

0.0

53

0.0

46

0.0

54

0.0

57

0.0

51

0.0

63

0.0

55

(0.0

65)

(0.0

62)

(0.0

99)

(0.0

97)

(0.0

97)

(0.0

99)

(0.0

98)

(0.1

02)

(0.0

98)

(0.0

97)

(0.0

98)

CY

CL

Et

0.0

51

0.0

27

0.0

27

0.0

27

0.0

10

-0.3

58

-0.0

14

0.0

27

0.0

28

0.0

26

(0.0

30)

(0.0

32)

(0.0

34)

(0.0

33)

(0.0

42)

(0.3

99)

(0.0

60)

(0.0

31)

(0.0

35)

(0.0

33)

CE

Ob

onu

s/to

tal

com

p. t−1

-0.0

22

-10.4

76

-1.5

02

(0.1

23)

(14.3

84)

(4.9

17)

ln(C

EO

com

pen

sati

on

) t−1

0.0

51

-3.9

24

-1.0

72

(0.0

33)

(4.1

65)

(1.1

81)

avg.C

EO

bonu

s/to

tal

com

p.

0.6

35∗∗

-21.2

49

-13.6

16

(0.2

88)

(41.4

13)

(12.1

82)

CY

CL

Et

xC

EO

bonu

s/to

tal

com

p. t−1

0.1

04

(0.1

44)

CY

CL

Et

xln

(CE

Oco

mp

ensa

tion

) t−1

0.0

40

(0.0

42)

CY

CL

Et

xavg.C

EO

bonu

s/to

tal

com

p.

0.2

19

(0.4

14)

lever

age t

xC

EO

bonu

s/to

tal

com

p. t−1

0.0

16

(0.0

54)

lever

age t

xln

(CE

Oco

mp

ensa

tion

) t−1

0.0

12

(0.0

13)

lever

age t

xavg.C

EO

bonu

s/to

tal

com

p.

0.1

59

(0.1

38)

Fix

edyea

reff

ects

Yes

No

No

No

No

No

No

No

No

No

No

Fix

edb

an

keff

ects

No

No

No

No

No

No

No

No

No

No

No

No.

Ob

s.339

339

206

206

208

206

206

208

206

206

208

Ad

j.R

20.0

67

0.0

35

0.0

20

0.0

28

0.0

25

0.0

17

0.0

30

0.0

22

0.0

15

0.0

27

0.0

26

37

Appendix 2

Proofs

λ < λ < λ and shareholder project choice

As λ = ∆(1−x)x(pH−pL) and λ = (pH+∆−pL)(R−S)−δ(1−x)S

δC , detection risk by regulators is not

sufficiently high to deter misconduct, but the resulting conduct costs are too high to

shareholders and they prefer the risky project to be implemented when the following

holds:(pH + ∆ − pL)(R− S) − δ(1 − x)S

δC<

∆(1 − x)

x(pH − pL), (10)

which can be reduced to a condition

[(pH − pL)(R− S) − δ(1 − x)S](pH − pL)x < ∆(δC(1 − x) − (R− S)x(pH − pL). (11)

At the same time, the risky project is preferred to the safe one when in the absence of

misconduct when (pH−pL)(R−S) > δ(1−x)S. Combining the two, we get a condition

for C under which bank shareholders cannot prevent misconduct and choose the safe

project when a risky one would have been preferred otherwise:

C >x(pH − pL)(R− S)

δ(1 − x)(12)

Constrained conduct costs and the alignment of incentives between bank

shareholders and managers

In this case, λ = ∆(1−x)x(pH−pL) , while λ′ = (pH+∆−pL)(R−S)−δ(1−x)S

δxC . Therefore, situations in

which safe projects are preferred to the risky ones and misconduct cannot be prevented

are possible when

[(pH − pL)(R− S)− δ(1− x)S](pH − pL)x < ∆(δxC(1− x)− (R− S)x(pH − pL) (13)

At the same time, the risky project is preferred to the safe one when in the absence of

misconduct when (pH −pL)(R−S) > δ(1−x)S. Combining the two, we get a condition

for C under which bank shareholders cannot prevent misconduct and choose the safe

project when a risky one would have been preferred otherwise:

(pH − pL)(R− S) < δ(1 − x)C (14)

However, this implies that the condition only holds when C > S, the total of bank’s

return at t = 2.

38