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Essays on the Financial Crisis, Financial Regulation,and BanksDanielle ZanzalariClemson University, dzanzal@g.clemson.edu

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ESSAYS ON THE FINANCIAL CRISIS, FINANCIAL REGULATION, ANDBANKS

A ThesisPresented to

the Graduate School ofClemson University

In Partial Fulfillmentof the Requirements for the Degree

Doctor of PhilosophyEconomics

byDanielle Zanzalari

May 2015

Accepted by:Dr. Howard Bodenhorn, Committee Chair

Dr. Robert FleckDr. Scott Baier

Dr. Gerald Dwyer

ABSTRACT

The conventional wisdom was that the Capital Purchase Program (CPP), under

the Troubled Asset Relief Program (TARP), would benefit large banks over small

sized banks, magnifying the claim that banks were “Too Big to Fail”. In my first

chapter, I examine whether investors react differently to important news regarding

the CPP, depending on the asset size of banks. Using returns to common stock share-

holders, I analyze the CPP announcement, bank capital infusions, and repayments

and find that large banks, regardless of whether or not they entered into the CPP

program and received capital, had large and significant returns at the time of the

program’s announcement (approximately 10% and 7%, respectively). Compared to

returns for investors of small banks, investors of large banks received a 7.5% higher

return, indicating that investors of large banks initially thought that the TARP-

CPP disproportionately helped larger banks. This “Too Big to Fail” reaction is also

prevalent when examining systematic risk (market risk), as the betas of large banks

increased the year after banks received CPP capital, indicating that the capital was

not enough to cover possible capital shortfalls or the loaned government capital would

lead banks to be riskier in the future.

My second chapter investigates the empirical relationship between a bank’s in-

volvement in various nontraditional activities and the likelihood that a bank fails or

receives TARP capital. Using a probit analysis, I estimate that banks are more likely

to receive TARP capital if they have lower tier one capital ratios, lower ROAs, more

real estate loans, more standby letters of credit and commercial letters of credit. My

probit estimates of bank failure from 2007-2011 also suggest that these balance sheet

variables and off balance sheet variables affect bank failures similarly. Previous stud-

ies that independently analyze bank failure or entry into TARP only include balance

ii

sheet variables. However, due to the increasing amount of securitized assets used for

collateral during the financial crisis coupled with the increasing amount of bank fail-

ures and government bank bailouts, it is imperative to account for off balance sheet

items in assessing the determinants of bank failures and bank bailouts in the probit

analysis. The striking similarity in probit estimates for entry into TARP and bank

failure suggest that TARP may have helped banks that otherwise would have failed.

iii

ACKNOWLEDGMENTS

My dissertation has greatly benefited from the comments, advice and encourage-

ment of numerous people. First and foremost, I would like to thank Dr. Howard

Bodenhorn, my advisor, for his constant feedback. Without his positivity and en-

couragement, my dissertation may never have been completed. Additionally, I would

like to thank my committee members, Dr. Robert Fleck, Dr. Scott Baier, and Dr.

Gerald Dwyer, for their guidance, comments and suggestions. My research has also

benefited from the suggestions of students and faculty in the Clemson University Pub-

lic Economics workshop and Macroeconomics workshop. Many of the suggestions are

incorporated into the following papers and have greatly improved them.

Next I would like to thank my family for their constant love and support. Without

my parents, who have pushed me to attend college ever since I was young, this would

not be possible. Their willingness to answer the phone at all hours of the day has

been imperative to completing my dissertation. I would also like to thank Alex Fiore

for his kindness, encouragement and patience, particularly in my final year.

Lastly, I would like to thank the graduate students at Clemson University, espe-

cially those that entered the Ph.D. program with me in 2010-2011. I cherish the long

hours we spent in Sirrine Hall on problem sets, studying for exams and discussing

research. To Alex Fiore, Andy Swanson, Adam Millsap, Anne Anders, Yukun Sun,

Jacob Burgdorf, and Bobby Chat–your encouragement, laughter and willingness to

work hard has inspired me over the last five years. I could not have done this without

your friendship. All remaining errors are my own.

iv

Contents

Title page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

1 Does Bank Size Matter? Investor Reactions to TARP . . . . . . . 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Background on the TARP-CPP program . . . . . . . . . . . . . . . . 3

1.3 Related Studies on the CPP and TARP . . . . . . . . . . . . . . . . . 5

1.4 Empirical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4.1 Main Questions . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4.2 Event Study Estimation . . . . . . . . . . . . . . . . . . . . . 8

1.5 Data collected for the Event Study . . . . . . . . . . . . . . . . . . . 12

1.6 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

1.6.1 Abnormal Returns . . . . . . . . . . . . . . . . . . . . . . . . 16

1.6.2 How Market Risk Changes the Year After TARP-CPP Events 20

1.7 Sample Matching to Test TARP Selection Issues . . . . . . . . . . . . 24

1.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2 The Effect of Nontraditional Banking on Entry into TARP and

Bank Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

v

2.2 The Rise of Nontraditional Banking . . . . . . . . . . . . . . . . . . . 42

2.2.1 Background and TARP . . . . . . . . . . . . . . . . . . . . . . 44

2.2.2 Bank failures . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.3 Methodology and Data . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.4 Balance sheet bank characteristics . . . . . . . . . . . . . . . . . . . . 48

2.4.1 Off-balance sheet items . . . . . . . . . . . . . . . . . . . . . . 51

2.4.2 Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.5.1 Summary Statistics on TARP and failed banks . . . . . . . . . 53

2.5.2 Bank entry into TARP . . . . . . . . . . . . . . . . . . . . . . 55

2.5.3 Bank failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.6 Bank Failure Robustness Check . . . . . . . . . . . . . . . . . . . . . 58

2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

vi

List of Tables

1.1 Mean and Standard Deviations for Bank Variables . . . . . . . . . . . 33

1.2 Abnormal Returns During Three TARP Events . . . . . . . . . . . . 34

1.3 A: Market Model Results, Announcement . . . . . . . . . . . . . . . . 36

1.3 B: Market Model Results, Capital Infusion . . . . . . . . . . . . . . . 37

1.3 C: Market Model Results, Capital Repayment . . . . . . . . . . . . . 38

1.4 Sample Selection Test: Propensity Score Matching . . . . . . . . . . . 39

2.1 Bank Failures from December 2007-December 2011 . . . . . . . . . . 64

2.2 Correlation Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

2.3 Summary Statistics for TARP Banks . . . . . . . . . . . . . . . . . . 66

2.4 Summary Statistics on Failed and Undercapitalized Banks . . . . . . 67

2.5 A: Probit analysis: TARP . . . . . . . . . . . . . . . . . . . . . . . . 68

2.5 B: Probit analysis: TARP (excluding required 8) . . . . . . . . . . . . 69

2.5 C: TARP Probit Marginal Estimates . . . . . . . . . . . . . . . . . . 70

2.5 D: TARP Probit Marginal Estimates (excluding required 8) . . . . . 71

2.6 A: Failed and Undercapitalized Banks Probit Analysis (2007-2011) . . 72

2.6 B: Failed and Undercapitalized Banks Probit Marginal Estimates . . 73

2.7 Robustness Check: Cox Proportional Hazard Ratios . . . . . . . . . . 74

3.1 Private Bank and U.S. Government Key Events, 2007-2009 . . . . . . 75

3.2 Abnormal Returns During Three TARP Events . . . . . . . . . . . . 85

3.3 A: Market Model Results, Announcement . . . . . . . . . . . . . . . . 87

3.3 B: Market Model Results, Capital Infusion . . . . . . . . . . . . . . . 88

3.3 C: Market Model Results, Capital Repayment . . . . . . . . . . . . . 89

3.4 CAPM Abnormal Returns During Three TARP Events . . . . . . . . 92

3.5 A: CAPM Results, Announcement . . . . . . . . . . . . . . . . . . . . 94

vii

3.5 B: CAPM Results, Capital Infusion . . . . . . . . . . . . . . . . . . . 95

3.5 C: CAPM Results, Capital Repayment . . . . . . . . . . . . . . . . . 96

3.6 CAPM Abnormal Returns During Three TARP Events . . . . . . . . 97

3.7 A: CAPM Results, Announcement . . . . . . . . . . . . . . . . . . . . 99

3.7 B: CAPM Results, Capital Infusion . . . . . . . . . . . . . . . . . . . 100

3.7 C: CAPM Results, Capital Repayment . . . . . . . . . . . . . . . . . 101

3.8 Variable Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

3.9 Failed Banks Probit Marginal Estimates (Individual Year 2009, 2010,

2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

viii

List of Figures

1.1 Snapshot of the Economy, 2007-2009 . . . . . . . . . . . . . . . . . . 29

1.2 Capital Infusions, 2008-2009 . . . . . . . . . . . . . . . . . . . . . . . 30

1.3 Capital Repayments, 2009-2013 . . . . . . . . . . . . . . . . . . . . . 31

1.4 Beta Time Series for Required Banks, 2009-2013 . . . . . . . . . . . . 32

2.1 Bank Failures and Undercapitalized Mergers, 2007-2011 . . . . . . . . 63

ix

1 Does Bank Size Matter? Investor Reactions to

TARP

1.1 Introduction

During the financial crisis from 2007-2009, banks were in need of capital due to an

increasing amount of foreclosures on mortgages and derivatives tied to mortgages.1

To recapitalize banks and revitalize lending markets, the U.S. government created

the Troubled Asset Relief Program (TARP) which assisted not only banks, but also

mortgage lenders, automobile companies, and insurance companies. The Capital

Purchase Program (CPP) was the largest of TARP’s six bank programs and loaned

approximately $205 billion to 707 financial institutions.2 The CPP was designed to

“bolster the capital position of viable institutions of all sizes and to build confidence

in these institutions and the financial system as a whole” (U.S. Treasury). Although

the CPP provided more capital to banks, government commitment of public funds

caused many American citizens to be concerned about moral hazard. Moral hazard,

as Wheelock (2012) describes, is created by treating a bank as “Too Big to Fail”

and extending unlimited protection to depositors and creditors, giving a bank not

only a funding advantage, but an incentive to take on more risk.3 In this paper I

examine whether investors react differently to key CPP events, depending on the

asset size of banks, to determine whether investors viewed the CPP as a program

1The number of foreclosure filings in 2005 was just under one million. In 2007 there were 2,203,295filings, which increased to 3,957,643 filings by the beginning of 2009.

2This is approximately 83% of the total amount of TARP disbursements to financial institutionsthrough five distinct bank programs and 37% of the total TARP disbursements.

3Too Big To Fail is a concern that still remains. An article published, February 24, 2014 states,“We shouldn’t be surprised that markets continue to doubt that these institutions’ investors andcounterparties will be fully accountable for their losses. To make matters worse, market doubtscan lead to funding advantages that encourage the biggest financial companies to get even bigger– and the industry even more concentrated. In this way, too big to fail can become self-fulfilling.”(Millstein and Delfin, 2014).

1

that disproportionately helped larger banks.

Using an event study analysis, I find that in the five days surrounding the CPP

announcement there is a 6% abnormal return for banks participating in the CPP com-

pared to a 1.8% abnormal return for banks that did not participate in the CPP. 4 This

result is magnified when looking at the abnormal returns of large banks that entered

the CPP program and large banks that did not. Large banks that participated in the

CPP had an abnormal return of 10.17% at the program’s announcement compared

to 7.61% for large banks that did not participate. Small banks that participated in

the CPP had only a 1.59% abnormal return. Additionally, this paper tests whether

one type of risk, systematic risk, changes after the CPP and whether systematic risk

changes differentially depending on the size of the bank. I find that systematic risk

increases in the year after banks receive CPP capital for large banks, but decreases in

small banks. This result indicates that investors may have believed the loaned CPP

capital would lead banks to make riskier investments.

Event study analysis on the CPP is not new. Elyasiani et al. (2014) and Farruggio

et al. (2013) examine abnormal returns and systematic risk changes to the TARP

announcement and find similar results to my analysis. However, my paper is the first

to examine whether investor reactions to the CPP are different, depending on the asset

size of banks. Estimating the differences between small and large bank performance

has been researched with mixed results. Godbillon-Camus and Godlewski (2006)

show that small banks can better manage risk compared to large banks, whereas

Elyasiani and Mehdian (1990) show that small banks are more efficient prior to the

1980s deregulation period, but after deregulation small and large banks are equally

efficient. Bertay et al. (2012) examine the difference between small and large bank

performance and show that as banks get bigger in size, the increased risk outweighs the

4Abnormal returns are calculated by estimating predicted returns less actual returns.

2

return on assets. My research contributes to both the CPP event study and bank size

literature. Understanding how investors reacted to the CPP can help confirm whether

or not the CPP was thought to only help large banks. Also, investor reactions to the

CPP can give insight as to whether systematic risk changes over time relative to the

asset size of banks.

1.2 Background on the TARP-CPP program

In early 2008, mortgage foreclosures were increasing, housing prices were falling

and banks involved in securitization, credit derivative swaps and subprime mortgages

found it more difficult to obtain credit. Further, due to contagion effects, banks that

were not involved with mortgage-related financial products also found it increasingly

difficult to obtain credit.5 In an effort to stabilize the economy and provide needed

credit to the market, President Bush signed the Emergency Economic Stabilization

Act on October 3, 2008, which authorized the Department of Treasury to buy $700

billion in troubled assets. The Troubled Asset Relief Program (TARP) was designed

to provide capital to the financial sector specifically for “residential or commercial

mortgages and any securities, obligations, or other instruments that are based on or

related to such mortgages” (Emergency Economic Stabilization Act of 2008).

The TARP is composed of six programs designed to stabilize the financial sector.

The largest, and perhaps most significant, program was the Capital Purchase Program

which loaned capital directly to financial institutions. In exchange for the capital

5 As housing failures were increasing, many banks were in need of capital, but it was difficult tofind banks willing to lend. In the months prior to TARP’s announcement, Bear Stearns, CountrywideFinancial Corporation, IndyMac Bank, Lehman Brothers and Washington Mutual failed, whileFannie Mae and Freddie Mac were placed into government conservationship. Conservationship isthe legal process for entities that are not eligible for bankruptcy proceedings; it establishes controland oversight of the company (St. Louis Federal Reserve Bank). Therefore, banks that had extracapital may have been reluctant to lend due to the increasing amount of bank failures and assistedmergers.

3

infusions, the Treasury received preferred stock that paid quarterly dividends of 5%

for the first five years and 9% thereafter until the loans were paid back. The Treasury

also received warrants to purchase common shares while banks still held CPP capital

to “enable taxpayers to reap additional returns on their investments” (U.S. Treasury).

Capital was infused to 707 institutions in 48 states. Of these, 25 banks received over

$1 billion in TARP funding, 56 banks received over $100 million to $1 billion in

TARP funding, 610 banks received between $1 million and $100 million and 15 banks

received between $301,000 and $1 million. Banks with the largest capital injections

received the most media attention, but TARP intervened in banks of all sizes.6 The

TARP-CPP was designed by the Treasury to address capital issues in banks, while the

Federal Reserve lowered the federal funds target rate during the 2007-2009 financial

crisis and created new programs to address any liquidity concerns for banks. A

timeline indicating key financial crisis events, such as new U.S. Treasury or Federal

Reserve programs, can be found in the Appendix.

Additionally, a snapshot of the uncertainty in the economy during 2007-2009 is

graphed in Figure 1.1. The uncertainty index is a measure that takes into account the

number of times the top 10 U.S. newspapers discuss economic uncertainty, tax code

expirations, and forecaster disagreement about inflation.7 The solid, black line repre-

sents the spread between financial commercial paper and T-bills. Commercial paper

is a short-term debt instrument that is not backed by collateral, so only high-quality

banks will issue this debt. Examining Figure 1.1 shows that the spread between what

6Citigroup Inc., Bank of American Corporation, J.P. Morgan Chase & Company and Wells Fargo& Company had the largest bailout of $25 billion each, while The Freeport State Bank in Harper,Kansas had the lowest bailout of $301,000.

7Tax code expirations set to expire in the next few years are “a source of uncertainty becauseCongress often waits till the last hour before deciding whether to extend them, undermining stability”(Baker al. 2013). Further, forecaster disagreement represents the dispersion of individual forecastsfrom the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters. Forecasterdisagreement proxies for uncertainty.

4

would be considered a safe, private market debt instrument, commercial paper, and

the spread between the risk-free government asset, Treasury Bills, is widening before

the CPP announcement. At the end of 2007, this spread hits almost 2%, however after

the CPP announcement the spread between commercial paper and T-bills decreases,

mostly due to investors finding commercial paper to be a safer debt instrument. The

uncertainty index shows that uncertainty in the economy peaked right at the time

of the CPP announcement, but slowly falls after the CPP announcement, indicating

that CPP provided some certainty to financial markets.

1.3 Related Studies on the CPP and TARP

A handful of event studies examine investor reactions to TARP related events.

Farruggio et al. (2013) empirically look at the first announcement of TARP and find

that announcements and capital repayments promote positive wealth effects and a

decrease in bank risk. They examine systematic risk, systemic risk and idiosyncratic

risk of banks and find that systemic risk, or the financial industry risk, increases as a

result of the TARP announcement and capital infusions.8 I examine one type of risk,

systematic risk, after the CPP based on the asset size of banks. I do not estimate

systemic risk, because, as Wheelock (2012) points out, systemic risk is mitigated when

the government treats large banks as “Too Big to Fail” (TBTF) and commits public

funds to ensure payment of large bank debts. TBTF, however, creates moral hazard.

Therefore, measuring systematic risk allows me to understand how the aggregate risk

from the market changes as a result of government policy.

A second event study that relates to my research examines large seasoned eq-

8Idiosyncratic risk is individual bank risk, systematic risk is non-diversifiable risk or market risk,and systemic risk is risk inherent to the financial industry as a whole.

5

uity offerings (SEOs) by U.S. financial institutions from 2000 to 2009 and compares

investor reactions to TARP capital injections. Elyasiani al. (2014) find that over

2000-2009, investors negatively react to the news of private market SEOs by finan-

cial institutions, but positively to TARP injections.9 Alternatively, King (2009) uses

credit default swap spreads and stock prices for 52 large, international and national

banks and finds that the U.S. rescue package created larger positive stock returns

for banks within a fifty day window compared to European rescue packages. This

result is not surprising considering Veronesi and Zingales (2010) estimated that the

first banks that took TARP money had an increase in enterprise value of $131 billion

immediately after the announcement.10

Additional studies also examine how different measures of risk change after TARP.

Nijskens and Wagner (2011) examine credit default swaps and collateralized loan

obligations and find that systematic risk increases, represented by beta. Duchin

and Sosyura (2012) find that after receiving TARP capital, banks increased credit

issuance to riskier firms.11 Coffey et al. (2009) find that covered interest rate par-

ity deviations,12 decreased after the TARP announcement indicating that arbitrage

transactions in international capital markets decreased due to less counterparty credit

risk. Black and Hazelwood(2012) find that average risk of loan originations increased

for large TARP banks relative to non-TARP banks through 2009, while small TARP

banks decreased average risk relative to non-TARP banks. Their results mirror my

9They also find that firms with more leverage received capital via TARP, but their capital ade-quacy did not increase after TARP injections due to increased lending activity after receiving TARPfunds. Elyasiani et al. further breaks down risk into alternative categories and they find that TARPbanks had an increase in credit risk, but no increase in liquidity risk.

10Enterprise value for banks is found by looking at the change in the value of liquid credit defaultswap (CDS) rates minus the change in the CDS rates of GE Capital plus abnormal variation inmarket value of common equity, preferred equity, derivative liabilities and reduction in FDIC depositguarantee.

11They measure borrowers’ cash flow volatility, interest coverage and asset tangibility to determinethe riskiness of firms.

12When a forward contract is used to cover exchange rate risk.

6

own as they examine banks based on asset size and find similar changes in the riskiness

of banks13

Although I do not model the decision for the Treasury to accept banks into the

CPP, several studies investigate which banks received TARP infusions and find that

larger, riskier and more politically connected banks were more likely to receive fund-

ing. First, Bayazitova and Shivdasani (2012) look at the bank applications for the

CPP program and find that larger banks and those with more systemic risk were

approved for the TARP injections.14 Additionally, they find that banks that applied

for, but declined the CPP funds, typically were strong banks, as measured by capital

ratios and asset quality. They posit that banks that found the new executive com-

pensation regulations, which were passed by Congress in early 2009, too restrictive

exited the CPP earlier. These results are consistent with the spike in capital repay-

ments in early 2009 in Figure 1.3. Figure 1.3 shows how much TARP-CPP was paid

back from 2009-2013. The largest amount paid back occurred in early 2009, around

the same time Congress set new executive compensation regulations.15 Duchin and

Sosyura (2012) find that “politically connected” firms, which are defined as firms

who have “directors [that] hold current or former positions at the Treasury, the firms

banking regulator, or Congress,” were more likely than banks without connections

to be accepted for TARP funding. Politically connected firms underperformed banks

with unconnected counterparts after controlling for firm size, financial conditions, and

other factors declared by regulators as CPP decision criteria.16

13The large TARP bank group in their sample includes 15 banks compared to 10 large non-TARPbanks. Overall, their sample size is 81 banks, compared to 438 for my analysis. This is due to theirusage of the Survey of Terms and Business Lending which stratifies a sample of commercial banks.Therefore, my sample size is larger, but still yield similar results.

14They define systemic risk to be bank size and outstanding derivative contracts which measurethe potential magnitude of counterparty risk that a bank might impose on the financial system.

15It is also noted that the first round of stress testing was completed in early 2009. Satisfactoryresults allowed banks the ability to pay back TARP capital.

16They find that firms with connections to a House member or a Finance Sub-Committee member

7

1.4 Empirical Model

1.4.1 Main Questions

My paper uses the event study framework to examine whether there are differences

in abnormal returns and systematic risk, based on a bank’s asset size. Further, I use

sample selection techniques to correct for any sample bias that may occur due to

larger banks participating in the CPP. The three main questions my research seeks

to answer are:

1. Are there different effects in investor reactions to the CPP depending on the

asset size of banks?

2. Does government loaned capital make banks even riskier than before?

3. Do large TARP banks get riskier than small banks?

1.4.2 Event Study Estimation

I use a market model to examine investor reactions related to the TARP announce-

ment, capital infusions and capital repayments, which measures the expected return

on an asset, or a portfolio of assets, relative to a market index. The simple market

model is:

E(Rit) : R̂it = α̂i + β̂i(E(Rmt)) (1.1)

AR = Rit − E(Rit) (1.2)

increased your chances of getting accepted for TARP funds by 7.7%. Duchin and Sosyura alsoexclude the nation’s biggest banks because the eight biggest TARP-CPP funds were given to banksthat were of nationwide importance and thus would have received money regardless of politicalconnections.

8

CAAR =1

N

N∑i=1

t=T2∑t=T1

ARit (1.3)

where,

E(Rit) = The expected return of asset i

E(Rmt) = The expected return of the market portfolio

αi = The model’s intercept (a measure of risk-adjusted daily performance)

βi = cov(Ri, Rm)/var(Rm)

AR= abnormal return

CAAR= cumulative average abnormal return

The market model shows that the expected return on a security is equal to βi times

the expected return on a market portfolio plus a risk-adjusted “excess” performance

measure. βi can be interpreted as the amount of non-diversifiable risk inherent in

the security relative to the risk of the market portfolio.17 αi should be equal to zero

in an efficient market, because there should be no “excess” return for the risk born

by the investor. αi < 0 if the investment returned less than the risk born by the

investor and αi > 0 occurs when the investment returned more than the risk born by

the investor.18

Equation (1.1) is estimated by using real returns on the S&P 500 market index

and bank common stock data. Abnormal returns, are found by estimating over a

pre-event window period and subtracting the predicted return value from the actual

17In general, investors face two kinds of risks, diversifiable and non-diversifiable risk. Non-diversifiable risk is also called systematic risk. Diversifiable risk can be eliminated by increasingthe size of the portfolio and variations in asset investments, whereas systematic risk is associatedwith movements in the general market. This can also be called market risk and this cannot beeliminated through portfolio diversification.

18Refer to Jensen’s alpha in Jensen(1968). In the Appendix, I alternatively use the capital assetpricing model and report tables using that specification.

9

return in Equation 1.2.19 A pre-event window, or control period, allows for a com-

parison of common equity volatility in the year preceding the event to determine

the abnormality of returns during the event. Specifically, I estimate returns over a

250 day pre-event and post-event window20 and use five-days surrounding an event

to calculate cumulative abnormal returns.21 Therefore, abnormal returns are calcu-

lated as AR = Rit − E(Rit) and cumulative average abnormal returns are calculated

CAAR = 1N

∑Ni=1

∑t=T2t=T1ARit. Where t=T1 to T2 is the five-day event window

surrounding CPP event.

If investors view the CPP announcement, infusion or capital repayment positively,

perhaps because the banking sector is more capitalized, then abnormal returns will

be higher for all banks surrounding the event. If investors think that the CPP will

only benefit large banks and not the entire banking sector, than investors of large

banks will react positively, but investors of small banks may see no abnormal returns

or small abnormal returns. Additionally, there may be differential effects not only

between banks of different asset sizes, but also between banks that participated in

the CPP and banks that did not.

After identifying the pre-event window, the event window and post-event window

I can calculate abnormal returns and identify systematic risk. However, to examine

systematic risk over time, I expand the model in Equation (1.1) to account for changes

in αi and βi surrounding the CPP event (the event window) and in the post-event

19All returns are logged which is standard in the literature and also used in another TARP event-study by Farruggio et al. (2013). Logging returns is acceptable because they are time additive, alsocalled time consistent.

20There are 250 trading days in a year.21Event study pre-estimation periods vary in the literature from 90 days (-3 days prior to an

event to -93 days) and even 120 days. Event window periods can also be reported differently byeither using cumulative abnormal returns in the (-1,1), (-1,0) and (0,+1) periods. Post-event windowperiods vary between 250 and 120 days or up to 5 years if it is a long-run event study. Elyasiani etal. (2014) use a 250 day pre- and post- event window, while Farruggio et al. (2013) use a 120 daypre- and post-event window on their event study analysis related to TARP. Representing the datawith different event and pre-estimation windows does not drastically change my results.

10

window (the year after the CPP event). βi is unlikely to stay constant over a two

year period, so estimating the change in βi can indicate whether systematic risk is

increasing or decreasing. For example, if βi increases over the post-event period

then the non-diversifiable risk inherent in the security is increasing relative to the

market portfolio after the CPP event, perhaps indicating that the CPP capital was

not enough to cover capital shortfalls or loaned government capital may lead banks

to be riskier in the future. The augmented Markowitz model has varying systematic

risk and excess performance components:

Rit = αi0 + βi0Rmt + αi1Eventit + βi1(Rmt · Eventit) (1.4)

+αi2PostEventit + βi2(Rmt · PostEventit) + εit

where,

Rit = Return on day t on the i-th bank’s common stock

Rmt = Return on day t on the “market”, i.e. the systematic risk factor, measured

by the S&P 500 index

Eventit= a dummy variable equal to 1 for all trading days [-2, +2] within the event

window

PostEventit= a dummy variable equal to 1 for all trading days [+3, +253] after the

event window

αi0 = measure of “excess performance”

βi0 = measure of non-diversifiable risk, or systematic risk

αi1 = change in alpha during the event window

βi1 = change in systematic risk during the event window

αi2 = change in alpha pre-event to post-event

βi2 = change in systematic risk pre-event to post-event

11

εit = a disturbance term with a zero mean

In this augmented market model, βi0 represents the systematic risk inherent in

a bank in the year prior to the CPP event. βi1 represents the change in systematic

risk during the event window, while βi2 represents the change in systematic risk from

the pre-event period (the year prior to the event) to the post event period (the year

after the event). If bank investors view the CPP announcement, capital infusion

or capital repayment events positively, because a bank could apply for capital or is

better capitalized, than post-event alpha values would rise and post-event beta values

would decrease. In other words, “excess performance” would increase after a CPP

event and market risk would decrease. On the other hand, if bank investors view

the CPP events negatively because their bank is undercapitalized, and still needs

government capital, then excess performance measures (post-event alpha, αi2) would

fall and systematic risk (post-event beta, βi2) would rise.22

1.5 Data collected for the Event Study

The original dataset was collected from the U.S. Treasury Department TARP

Transaction Report on December 27, 2013. The report included 737 capital infusions

to 707 banks and included variables such as original investment amount, original

investment type, outstanding investment, total cash back, capital repayment dates

and any warrant proceedings. Bank capital infusions occurred between October 28,

2008 and December 29, 2009; whereas, TARP was officially announced on October

14, 2008. The timing of capital infusions to banks is graphed in Figure 1.2. The

largest amount of capital was given out in October 2008 to the nation’s largest banks.

The eight biggest banks by asset size received over half the amount of money that

was allotted to the program. The cumulated volume of capital infusions to banks

22A similar model appears in Elyasiani et al. (2014).

12

in my sample is $193 billion which is approximately 94% of the total amount of

capital assistance under the TARP Capital Purchase Program ($204,894,726,320).

The volume of capital repayments totals to $187 billion or approximately 97% of the

total amount of capital assistance to banks in my sample. To date not all of the

TARP funds have been paid back.23

Since my research examines investor reactions to the TARP-CPP, I used stock

price data for each bank, which I collected from the Center for Research in Security

Prices (CRSP). Additionally, I obtained data on the Fama-French factors from Ken-

neth French’s data library and any bank characteristic data from the Uniform Bank

Performance Report. The S&P 500 is the benchmark index that I use to estimate

market returns, but I have crosschecked my results using the CRSP value-weighted

index, NASDAQ, NYSE Composite 100, and DJIA. The results are consistent re-

gardless of the market index used.24 Despite the allocation of CPP capital to 707

banks, I only include banks that have common stock data. After dropping banks

that are not publically traded, I am left with 219 banks that received CPP capital.

As a comparison group, I construct a random sample of publically traded banks that

do not receive TARP capital. My non-TARP sample includes 219 banks.

Means of key variables are represented in Table 1.1 for banks that entered into the

TARP-CPP and the comparison banks that did not participate in the TARP-CPP.

All numbers are in millions and from quarter 3 2008, which is the quarter before

the TARP-CPP announcement. Specifically, quarter 3 2008 data was reported on

September 30, 2008, two weeks before the CPP was announced. I also partition

banks into three classifications: large, mid-size and small based upon their asset size.

23As of September 10, 2014 the outstanding TARP-CPP loan amount was $633,688,601.88.24I also report estimates using the CRSP value-weighted index in the Appendix. Farruggio et al.

(2013) use the S&P 500 market index, while Elyasiani et al. (2014) use the CRSP value-weightedindex. These estimates are similar to the estimates found using the S&P 500.

13

A bank is classified to be a large bank if assets exceed $3 billion, mid-size if assets are

equal to or between $1 billion and $3 billion and small if assets are less than $1 billion.

This results in 111 banks being classified as large banks–74 banks which received CPP

capital compared to only 37 large banks that did not receive CPP capital. There are

65 mid-size banks that received CPP capital and 62 banks that did not. Only 72

small banks received CPP capital, compared to 120 banks that did not receive CPP

capital.

Banks that receive CPP capital have greater total assets than banks that did

not enter into the TARP-CPP. The required eight banks are the eight banks who

were required to take TARP money: Citigroup, State Street, Bank of America, J.P.

Morgan Chase, Wells Fargo, Morgan Stanley, Goldman Sachs, and Bank of New

York.25 These 8 banks had on average $517 billion in total assets in quarter 3,

compared to $20 billion for other large banks and only $708 million for small banks

that received CPP capital. I excluded the required eight banks from the “large” bank

sample, because they were required to take TARP money, whereas other banks had to

apply. Therefore, these banks should not be treated the same. For large banks that

did not receive CPP capital, total assets on average were $45 billion. However, if I

included the required eight banks in the large TARP bank sample this would increase

average asset size of large banks that received TARP-CPP money to $94 billion.

I also report additional bank characteristics that I will use in Table 1.4 to ad-

dress the issue of sample selection. I include non-performing loans to total loans,

where non-performing loans are loans that are 90 days past due. Long-term debt

to total assets, as well as return on assets which is a ratio of net income to assets

25Merrill Lynch was included in the original meetings on October 13 regarding the TARP program.However, I do not count Merrill Lynch as a separate company because I do not observe them havingan independent stock ticker throughout the entire sample due to their merger with Bank of Americashortly after the CPP announcement on October 14, 2008.

14

are also included. Credit exposure on the off-balance sheet examines current credit

exposure across all off-balance sheet contracts covered by the risk-based capital stan-

dards divided by Tier 1 capital. Tier 1 capital includes equity capital and sometimes

non-cumulative preferred stock. This ratio can measure a bank’s financial strength

considering mortgage-backed securities and many derivative products were put on the

off-balance sheet of banks during the financial crisis. Lastly, the liquidity ratio is the

ratio of cash over total assets.

Two of the largest differences between bank characteristics in Table 1.1 are seen

through my ratio of long term debt to total assets and credit exposure on the off-

balance sheet to Tier 1 capital. The required eight banks and large banks have more

long term debt to total assets than small banks. The required eight banks have a

ratio of 13.64, large TARP banks have a ratio of 7.39 and large non-TARP banks

have a ratio of 3.46. Therefore, this ratio is larger for large TARP banks compared

to non-TARP large banks. TARP banks represent banks that received CPP capital

and non-TARP banks indicate banks that did not receive CPP capital. Small banks’

long term debt ratios were 1.25 for TARP banks and 0.71 for non-TARP banks.

Similarly, there is greater credit exposure on the off-balance sheet relative to Tier 1

capital for the 8 banks that were required to take CPP capital. This ratio is 0.66

for the required eight banks, compared to 0.06 for large TARP banks and 0.04 for

large non-TARP banks. A particularly interesting bank characteristic is the liquidity

ratio on the required eight bank sample compared to all other bank size groups. The

eight required banks had a higher average liquidity ratio while other bank groupings

held a ratio of cash to total assets of 0.02. Since it does not appear that banks

received TARP-CPP capital based on the liquidity ratio of banks, this indicates that

the Treasury’s TARP programs were looking to address capital issues, not liquidity

issues. Numerous Federal Reserve programs and policies addressed any liquidity

15

problems from 2007-2009.26

1.6 Main Results

1.6.1 Abnormal Returns

Cumulative average abnormal return results are reported for all three CPP events

in Table 1.2A-C, where CAAR= 1N

∑Ni=1

∑t=T2t=T1ARit and AR= Rit − E(Rit). In

the first column, I report the results using the Newey-West model, which corrects

for possible serial correlation and heteroskedasticity between stock returns, and find

that large banks that received CPP capital have an abnormal return of about 15%

during the CPP announcement compared to a 2% abnormal return for small TARP

banks. Large banks that did not receive CPP capital realized a return of about

11% indicating that investors of large banks may have believed that the CPP would

better capitalize the banking industry. There are also positive abnormal returns to

investors of mid-size banks, regardless of whether the bank would eventually receive

CPP capital. However, small banks that did not receive CPP capital realized a slight

negative return.

Correcting for serial correlation is not universal in event study literature, but the

presence of serial correlation would invalidate standard hypothesis tests and interval

estimates in OLS. Therefore, I test for serial correlation using the Durbin-Watson

statistic and the Ljung-Box test for white noise. I find that I cannot reject the

null of no autocorrelation of one-lag.27 The Newey-West model corrects for both

heteroskedasticity and serial correlation and allows for changes in variance over time.

Using OLS assumes that the error term has constant variance, but with time series

26A timeline of important Federal Reserve events is in the Appendix.27The Durbin-Watson test considers the possibility of autoregressive one-lag errors, whereas the

Ljung-Box test examines whether the errors are “white noise” or autocorrelated. The results ofthese tests are reported in the Appendix.

16

variables, the error term is likely to vary with increased observations. Not correcting

for heteroskedasticity would not bias my estimates in an OLS regression, but can bias

the standard errors and therefore inferences from the data.28

In addition to the Newey-West specification, I report a second specification which

includes the Fama-French factors. Fama and French (1995) found that the cross-

sectional variation in average security returns is better explained by examining more

than a market beta, so I also estimate abnormal returns by controlling for the

Fama and French non-market risk factors, small-minus-big (SMB) and high-minus-

low(HML). SMB is the the difference between the return on a portfolio of small

stocks and the return on a portfolio of large stocks. HML is the difference between

the return on a portfolio of high-book-to-market stocks and the return on a portfo-

lio of low-book-to-market stocks. Specification (2) uses the Newey-West model but

includes the SMB and HML Fama-French factors.

Examining the results from specification (2) in Table 1.2A, I find that banks that

received TARP-CPP capital have a higher abnormal return during the announcement

of 6.01% in the five-day window compared to 1.82% for banks that did not enter into

the TARP-CPP. Investor reactions differ depending on whether they were invested

in a large, mid-size or small bank. Investors of the required eight banks received an

abnormal return of 10.67% in the five-days surrounding the announcement compared

to 10.17% return for other large banks, 5.60% abnormal return for mid-size banks and

1.59% abnormal return for small banks that would later receive CPP capital. Large

banks that did not participate in the CPP saw a 7.61% abnormal return and investors

of mid-size banks that did not receive CPP capital had a 4.87%. Investors of banks

that did not enter into TARP-CPP may have saw the TARP-CPP announcement on

October 14, 2008 as a positive signal that the overall banking industry will be better

28Elyasiani et al. (2014) also use this estimator in their TARP-event study.

17

capitalized. Alternatively, these investors may have also thought that their banks

would eventually enter into the TARP-CPP program.

However, at the time of the announcement, investors were not aware of which

banks would receive CPP capital except the nations nine largest banks. The TARP-

CPP was announced by the U.S. Treasury Department on October 14, 2008. In the

announcement, it was mentioned that the nine largest banks intended to take $125

billion in emergency capital injections, but at the time, the U.S. Treasury Department

made no distinction as to which banks they were. This announcement came after

the “secret” meeting29 on October 13, 2008 at 3:00 p.m. where nine of the biggest

banks’ CEOs, Hank Pauslon, Ben Bernanke, Sheila Bair and Timothy Geithner met to

discuss the mandatory issuance of TARP funding. These banks were chosen because

regulators believed it was imperative to include the country’s largest banks to bolster

confidence in the banking sector.30 After the announcement of the CPP, other banks

were allowed to apply for money31 so at the time, investors did not fully know which

banks would be receiving money. Interestingly, investors of small banks that did not

eventually receive CPP capital received a small, negative abnormal return surrounding

the CPP announcement. Investors may have believed that the CPP would only aid

big banks or that it may have put small banks at a disadvantage.

The announcement date is the only date that is similar across all banks regardless

of size or whether a bank received TARP or not. Since banks that did not participate

in the TARP-CPP are not observed being infused with CPP capital by the U.S.

Treasury, I assign a random capital infusion and capital repayment date to each

non-TARP-CPP bank to allow for comparison across TARP banks and non-TARP

29Details of this meeting were openly discussed weeks after.30None of these banks were, at that time, subjected to a formal evaluation.31The complete Treasury Press release on the CPP announcement is included in the Appendix.

18

banks.32 Figure 1.2 shows the amount of money, in millions, that banks received

over the capital infusion period from 2008-2009. The eight required banks received

over half of the capital for the program and they received the capital mostly in

October 2008. When examining abnormal returns surrounding the capital infusion

in Table 1.2B, all banks experience a negative cumulative abnormal return. A bank

that received TARP-CPP capital may have a negative return surrounding the capital

infusion, for example, if investors thought the bank was sound and did not need extra

capital.33

Banks that received TARP-CPP capital had a cumulative abnormal return of

approximately -2.45% based on the Newey-West Fama-French factor model compared

to a -1.98% return for non-participating banks. More interestingly, the required

eight banks had a negative cumulative abnormal return of -7.10%, while the other

large TARP banks had a cumulative abnormal return of -7.62% compared to only

-3.38% for small TARP banks. Large banks that did not receive CPP capital only

saw a -4.27% abnormal return. Investors of banks that received CPP capital either

reacted negatively to the capital infusion event because the government received senior

preferred shares, ranking the government shares above senior and common stock in

bankruptcy proceedings,34 or because the bank has capital issues and they needed

U.S. Treasury loans.

Investors reacted to the repayment of CPP capital with a 1.75% abnormal return

for the eight banks that were required to take TARP, a 0.73% abnormal return for

large banks that received CPP capital and a 3.52% abnormal return for small banks

32A non-TARP bank is randomly assigned a date for CPP capital infusion based upon the rangeof dates that TARP banks received money. A similar process is used to assign a capital repaymentdate.

33Jamie Dimon, CEO of JP Morgan Chase, has often claimed, JP Morgan Chase never needed themoney, but was asked to take it for the sake of weaker banks (Dealbook, 2009). The negative returnsurrounding JP Morgan Chase’s capital infusion on October 28, 2008 supports this hypothesis.

34Further, these shares came with a hefty dividend.

19

that received CPP capital.35 Figure 1.3 records when banks repaid back capital from

2009 to the end of 2013. Early in 2009, there were restrictions put on bank CEO

pay for banks that received TARP capital, so the large jump in capital repayment in

June 2009 corresponds to Congressional action and the first time banks were allowed

to pay back capital after passing stress tests. Therefore, investors may have reacted

positively to the capital repayment for the eight required banks, because they were

less likely to lose their management team to compensation restrictions. Interestingly,

the larger cumulative average abnormal returns during this event window are for small

sized banks that paid back TARP-CPP capital. Investors of banks that did enter into

the TARP-CPP may have seen the TARP-CPP repayment as a positive signal that

the bank is better capitalized. Further, investors no longer had to come second to

the government in bankruptcy proceedings and the high dividend payments to the

government would cease. However, the only significant result is that all TARP banks

received a 1.83% abnormal return at the CPP capital repayment and mid-sized CPP

banks had a 1.42% abnormal return.36

1.6.2 How Market Risk Changes the Year After TARP-CPP Events

The market model in Equation (1.2) is estimated and the results are reported in

Table 1.3A-C for banks that participated in the TARP-CPP and banks that did not.

Excess return, or performance, for investors of banks that entered into the CPP and

banks that did not participate are similar before and after all three events. However,

during the five day event window surrounding the CPP announcement investors of

35Other CPP event studies such as Elyasiani et al. (2014) and Farruggio et al. (2013) do notexamine the CPP capital repayment due to data unavailability.

36This is likely due to the fact that many banks divided their capital repayments into multiple,smaller payments. To avoid examining the same bank twice over a similar range of data, I onlyexamine the first time a bank repaid capital back to the government. Additionally, some banksreceived more than one capital infusion, but in no more than two segments. I also examined onlythe first capital infusion.

20

large TARP banks saw a higher change in excess return, αi1, of 0.0248 compared to a

change in excess return of 0.0011 for small TARP banks. Further, there is a positive

excess return in the five-days surrounding the CPP announcement for all banks except

small banks that did not participate in the CPP program. αi should be equal to zero

in an efficient market, because there should be no “excess” return for the risk born by

the investor. So, a positive αi1 indicates that there is additional return to an investor

relative to the risk that is inherent in the stock. Investors of large banks, regardless of

whether these banks received CPP capital or not, saw higher excess returns compared

to small banks. αi0 represents the “excess performance” of a stock in the year before

the event, αi1 represents the change in “excess performance” during the five-day event

window and αi2 shows the change in alpha pre-event to post-event.

However, during the event window surrounding the infusion of CPP capital into

banks in Table 1.3B, investors of all banks had a negative excess return. During

the event window for TARP banks, αi1 was -0.46% compared to -0.34% for banks

that did not receive CPP capital. These investor reactions show that investors were

not receiving a return that was sufficient for the risk they were bearing. Investors

may have believed their bank was well-capitalized and now there is a risk of pos-

sible bankruptcy or that the government could influence bank operations now with

preferred stock. Table 1.3C shows small and non-significant excess return for banks

during the capital repayment.

Systematic risk, or market risk, before the CPP announcement was higher for

banks that entered into the CPP than for banks that did not participate in the CPP.

This result can be seen in Table 1.3A when looking at βi0 and is driven by large TARP

banks. βi0 represents the systematic risk in the year before the event, βi1 represents

the change in systematic risk during the five-day event window, βi2 shows the change

in beta pre-event to post-event. Since the market has a normalized risk of 1.0, if a

21

stock has a risk greater than 1.0, as measured by beta, it has more price volatility

than the overall market and is hence considered to be more risky. Stocks with a beta

of 1.0 fluctuate in price at the same rate as the market, while stocks with a beta

less than 1.0 fluctuate less than the market. Therefore, TARP banks had greater

systematic risk than non-TARP banks before the CPP announcement.

When examining βi1 during the CPP announcement, systematic risk decreased

for the eight required banks and large banks that received CPP capital and those

that did not. This is interesting because investors of large banks did not know that

their banks would eventually get money. Investors only knew that banks would have

the opportunity to apply and receive government loaned capital. However, βi2, is of

greater interest because it examines how systematic risk changes the year after TARP.

I find that large TARP banks have a significant decrease in risk after the event.

The more interesting CPP events to study are the capital infusion and capital

repayment. Examining how large bank stocks move relative to the market in the year

after banks pay back CPP capital can indicate whether investors believe the capital

led banks to be riskier in the future. In the year after banks received CPP capital,

systematic risk, or βi2, increased for the eight required banks and large TARP and

non-TARP banks (+0.1208, +0.1033 and +0.6056, respectively). However, only the

large TARP bank increase was significant. This means that large TARP bank stocks

had more price volatility than the overall market and were riskier than the year

prior to the bank receiving CPP capital. Interestingly, small bank stocks had less

price volatility than the overall market after receiving CPP capital. The dissimilar

change in beta the year after receiving capital for TARP banks and non-TARP banks

indicates that investors perceived TARP-banks to be riskier after receiving capital.

Investors may have thought that the CPP capital was not enough to cover possible

capital shortfalls or that the loaned capital would lead to a “moral hazard” issue–

22

banks will make riskier investments now that they have additional capital and know

that the government would aid them in rough times.

In Table 1.3C, systematic risk decreased during the capital repayment event win-

dow (represented by βi1) for both TARP and non-TARP banks. Systematic risk also

decreased in the year after a bank repaid back CPP capital for the eight required

banks, large TARP banks, large non-TARP banks and mid-size non-TARP banks.

This result supports the idea that the CPP was successful at mitigating large bank

risk after banks repaid the capital. My previous results indicate that this is not true

before banks repay back the money, but after they repay it systematic risk falls. Ad-

ditionally, banks would have not been permitted to pay back the CPP capital if they

were not sound. If investors thought that banks were better capitalized after repaying

CPP capital, systematic risk should go down the year after the event. Alternatively,

mid-size and small TARP banks saw a slight increase in systematic risk in the year

after repaying CPP capital (+0.079 and +0.1277, respectively). It is important to

note that βi2 represents the change in β from the year before the capital repayment

to the year post the repayment, so the positive βi2 means that mid- and small-sized

banks still have a beta <1.0

Further, Figure 1.4 shows the evolution of betas from 2000-2014 for the eight

banks that were required to take CPP capital. Overall, banks follow a similar trend

from 2000 to 2014 with low betas in 2005-2006 and high betas in 2008-2009. My

results from the market model mirror Figure 1.4, as they show that betas were high

during the financial crisis and the immediate year after, but slowly decreased over

time. Particularly, Wells Fargo had a beta of approximately 0.50 in year 2000. Their

beta increased dramatically to about 2.5 in 2009. This means that Wells Fargo’s

stock returns varied 2.5 times more than the overall market return. However, Wells

Fargo’s beta decreases to about 1.0 in 2014, which indicates that Wells Fargo’s stock

23

returns now move proportionally with the market. The black and purple dotted lines

represent the betas of Citigroup and Bank of America, which were the two banks

most in need of CPP capital.37 By the end of 2014, Citigroup and Bank of America’s

betas are near 1.2 which is similar to pre-crisis beta levels. So, despite the betas

decreasing after the financial crisis, they have not decreased to levels less than before.

1.7 Sample Matching to Test TARP Selection Issues

To address the potential sample selection issue that banks that received TARP

capital were larger than banks that did not, TARP banks are matched to non-TARP

banks based upon similarities in financial characteristics. A match is declared if there

are two banks, one in each sample, for which the value of a financial characteristic

is identical. Since identical financial characteristics between banks are difficult to

find in data, Rosenbaum and Rubin (1983) show that matching is best performed

conditioning on the probability of being in TARP on the financial characteristic.

This probability is known as the propensity score of the financial characteristic.

Since I do not know the probability a bank receives TARP money, I must calculate

the propensity score for bank i (i = 1, ..., N), which is the conditional probability of

being assigned to treatment (TARP=1) vs. control (TARP=0) given a vector of

observed covariates, such as non-performing loans to total loans, long-term debt to

total assets, ROA, credit-exposure on off-balance sheet contracts to Tier 1 capital

37Citigroup and Bank of America both received $25 billion of CPP capital in 2008. However, theyalso received an additional $20 billion in the TARP Targeted Investment Program, which providedcapital to only two “systematically significant” institutions (U.S. Department of Treasury). Addi-tionally, these banks greatly benefited from concurrent programs from the FDIC, Federal Reserveand Treasury during the financial crisis.

24

and the liquidity ratio. This is represented by Equation (1.5).

TARPBank = π0 + π1..N(Covariates) + εi (1.5)

CumulativeReturnsit = ρ0 + ρ1NonPerformingLoans+ ρ2LongTermDebti,t−1(1.6)

+ρ3ROAi,t−1 + ρ4CreditExposureOffBalanceSheeti,t−1

+ρ5LiquidityRatioi,t−1 + εit

where,

TARP Bank = a dummy variable for banks that received TARP capital

Non-Performing Loans = loans 90 days past due/ total loans

Long Term Debt = long term debt/total assets

ROA = net income/ total assets38

Credit Exposure Off-Balance Sheet = current credit exposure on off-balance sheet

contracts/ Tier 1 capital

Liquidity ratio = cash/total assets

Covariates = non-performing loans to total loans, long-term debt to total assets,

ROA, credit-exposure on off-balance sheet contracts to Tier 1 capital and the liquid-

ity ratio

εit= a disturbance term with zero mean

Empirically this involves a three part process. First I must assign propensity

38Elyasiani et al. (2014) use a similar measure for ROA, which is typically proxied by either netincome/total assets or retained earnings/total assets in the literature. Using either specificationdoes not drastically change my results.

25

scores in Equation (1.5) and create a matching equation based upon the propensity

scores. Then, I create a matched sample and estimate the average treatment effect

of returns using Equation (1.6). Table 1.4 represents the probit regression used to

estimate propensity scores on the basis of which the matching is subsequently done.

The probability of a bank participating in the CPP increases with long-term debt,

ROA and credit exposure. Even though non-performing loans and the liquidity ratio

have weak predictive ability in the selection into the CPP, including them can still be

helpful to minimize bias in estimating casual effects in propensity score matching. As

De Silva (2012) states, the ultimate goal is not to predict selection into treatment, but

to balance covariates and get closer to the observationally identical non-participant.39

Table 1.42 reports the estimates of Equation (1.6) with nearest neighbor matching.

In every case using propensity score matching yields estimates similar to what is found

in Table 1.2A-C. I report the average treatment effect that being in TARP has on

cumulative abnormal returns surrounding the CPP announcement, capital infusion

and capital repayment as well as the average treatment of the untreated (or non-TARP

bank group) and the average treatment effect. I find that the difference in cumulative

returns for the announcement between banks that received TARP and banks that

did not was 4.19% in Table 1.2A, whereas after matching banks with similar financial

characteristics I find that the difference is 3.62%. For the infusion, the Newey-West

regression in Table 1.2B yields a difference of approximately 0.47% between TARP

banks and non-TARP banks, whereas nearest neighbor matching results in Table 1.42

yield 0.87%, which is slightly larger. Therefore, even after correcting for bias between

TARP banks and non-TARP banks the cumulative return results still hold.

39Examining the common support regions in a histogram indicate that characteristics in the CPPbank group also appear in non-CPP banks.

26

1.8 Conclusion

The conventional wisdom was that the Capital Purchase Program (CPP), under

the Troubled Asset Relief Program (TARP), would benefit large banks over small sized

banks, magnifying the claim that banks were Too Big to Fail. In this paper, I use an

event study analysis to examine whether investors react differently to key CPP events

to determine whether investors viewed the CPP as a program that disproportionately

helped larger banks. Event study analysis on investor reactions to the CPP is not new

in the literature, however my paper is the first to examine whether investor reactions

to the CPP are different, depending on the asset size of banks. The literature on

small and large bank efficiency shows that banks of different asset sizes perform and

operate differently, so investor reactions to the CPP may vary based on the asset size

of banks.

I find that the reactions by investors to the CPP were different depending on

the asset size of banks. The eight banks that were required to accept CPP capital

had a 10.67% cumulative average abnormal return at the program’s announcement

compared to a 10.17% abnormal return for other large banks that received CPP

capital and a 7.61% abnormal return for large banks that did not participate in the

program. Investors of large banks clearly saw the CPP announcement as a positive

signal that the overall banking industry would be better capitalized or that their banks

would eventually enter into the CPP program and receive capital. However, small

banks that later entered into the CPP had an abnormal return of 1.59%, whereas

small banks that did not participate in the CPP had a negative abnormal return.

Even after correcting for any potential sample bias (such as larger banks were more

likely to receive CPP capital) by propensity score matching, I still find that banks

that participated in the CPP received a higher abnormal return during the program’s

27

announcement and those returns were greater for bigger banks.

In the year after banks received CPP capital, systematic risk or market risk,

increased for the eight banks that were required to enter the program. Additionally,

systematic risk also increased for large TARP and non-TARP banks. This indicates

that large bank stocks had more price volatility than the overall market and were

riskier compared to the year before a bank received capital. Interestingly, small

bank stocks had less price volatility than the overall market after receiving loaned

government capital. The dissimilar change in beta the year after receiving capital,

for large banks and small banks, indicates that investors perceived large banks to be

riskier after receiving CPP capital. Investors may have thought that the capital was

not enough to cover possible capital shortfalls or that the loaned capital would lead

to a “moral hazard” issue– banks will make riskier investments now that they have

additional capital and know that the government will aid them in rough times.

28

Figure 1.1: Snapshot of the Economy, 2007-2009

29

Figure 1.2: Capital Infusions, 2008-2009

30

Figure 1.3: Capital Repayments, 2009-2013

31

Figure 1.4: Beta Time Series for Required Banks, 2009-2013

32

Tab

le1.

1:M

ean

and

Sta

ndar

dD

evia

tion

sfo

rB

ank

Var

iable

s

All

Ban

ks

Larg

eB

an

ks

Mid

-Siz

eB

an

ks

Sm

all

Ban

ks

Req

uir

ed8

Tarp

Non

-Tarp

Tarp

Non

-Tarp

Tarp

Non

-Tarp

Tarp

Non

-Tarp

Tota

lA

sset

s517,0

00

43,0

00

7,8

38

20,8

00

45,8

00

1,4

97

1,5

56

708

625

(679,0

00)

(210,0

00)

(65,0

00)

(37,9

00)

(165,0

00)

(910)

(619)

(708)

(747)

Non

-per

form

ing

loan

s/0.0

088

0.0

021

0.0

018

0.0

027

0.0

022

0.0

130

0.0

019

0.0

013

0.0

017

tota

llo

an

s(0

.0087)

(0.0

044)

(0.0

037)

(0.0

052)

(0.0

032)

(0.0

022)

(0.0

039)

(0.0

027)

(0.0

038)

Lon

g-t

erm

deb

t/13.6

453

4.2

681

1.0

249

7.3

960

3.4

656

0.5

786

0.3

166

1.2

595

0.7

125

tota

lass

ets

(12.6

079)

(8.8

189)

(5.5

910)

(10.9

483)

(8.2

058)

(2.4

922)

(2.4

025)

(3.7

976)

(5.6

880)

RO

A-0

.0046

0.0

028

0.0

003

0.0

051

0.0

030

0.0

028

0.0

007

0.0

015

-0.0

058

(0.0

906)

(0.0

244)

(0.0

003)

(0.0

099)

(0.0

088)

(0.0

062)

(0.0

145)

(0.0

063)

(0.0

123)

Cre

dit

exp

osu

re/

0.6

677

0.0

706

0.0

081

0.0

645

0.0

426

0.0

020

0.0

009

0.0

240

0.0

024

Tie

r1

cap

ital

(0.7

015)

(0.2

629)

(0.0

616)

(0.1

691)

(0.1

505)

(0.0

060)

(0.0

032)

(0.0

111)

(0.0

162)

Liq

uid

ity

rati

o0.0

462

0.0

231

0.0

215

0.0

201

0.0

238

0.0

243

0.0

201

0.0

206

0.0

216

(0.0

593)

(0.0

224)

(0.0

130)

(0.0

117)

(0.0

186)

(0.0

184)

(0.0

117)

(0.0

181)

(0.0

116)

Siz

eof

Tarp

Infu

sion

15,6

00

1,1

60

-693

-30.4

-10.3

-(1

0,4

00)

(4,2

90)

(141)

(22)

(7.5

)N

8219

219

74

37

65

62

72

120

Tota

lass

ets

an

dsi

zeof

cap

ital

infu

sion

are

inm

illion

s.S

tan

dard

dev

iati

on

sare

inp

are

nth

eses

.L

arg

eT

AR

Pb

an

ks

does

not

incl

ud

eth

e8

ban

ks

that

wer

ere

qu

ired

tota

ke

TA

RP

.If

Iin

clu

de

those

ban

ks

inth

ela

rge

TA

RP

cate

gory

,th

em

ean

tota

lass

ets

for

larg

e,T

AR

Pb

an

ks

wou

ldb

e94

billion

.C

red

itex

posu

reon

the

off

-bala

nce

shee

tex

am

ines

curr

ent

cred

itex

posu

reacr

oss

all

off

-bala

nce

shee

tco

ntr

act

sco

ver

edby

the

risk

-base

dca

pit

al

stan

dard

sd

ivid

edby

Tie

r1

cap

ital.

Tie

r1

cap

ital

incl

ud

eseq

uit

yca

pit

al

an

dso

met

imes

non

-cu

mu

lati

ve

pre

ferr

edst

ock

.T

his

rati

oca

nm

easu

rea

ban

k’s

fin

an

cial

stre

ngth

con

sid

erin

gm

ort

gage-

back

edse

curi

ties

an

dm

any

der

ivati

ve

pro

du

cts

wer

ep

ut

off

-bala

nce

shee

td

uri

ng

the

fin

an

cial

cris

is.

Th

eliqu

idit

yra

tio

isth

era

tio

of

cash

over

tota

lass

ets.

33

Table 1.2: Abnormal Returns During Three TARP Events

(1) (2)

Banks N Newey-West Newey-West+FF

A. Announcement

TARP 219 0.0897∗∗∗ 0.0601∗∗∗

(0.0095) (0.0089)

Non-TARP 219 0.0364∗∗∗ 0.0182∗∗

(0.0087) (0.0082)

Required 8 8 0.1204∗∗∗ 0.1067∗∗

(0.0381) (0.0380)

Large TARP 74 0.1549∗∗∗ 0.1017∗∗∗

(0.0159) (0.0155)

Large Non-TARP 37 0.1176∗∗∗ 0.0761∗∗∗

(0.0187) (0.0188)

Mid-Size TARP 65 0.0869∗∗∗ 0.0560∗∗∗

(0.0165) (0.0155)

Mid-Size Non-TARP 62 0.0763∗∗∗ 0.0487∗∗∗

(0.0153) (0.0144)

Small TARP 72 0.0217 0.0159∗∗∗

(0.0144) (0.0145)

Small Non-TARP 120 -0.0092 -0.0153∗∗∗

(0.0109) (0.0107)

B. Infusion

TARP 219 -0.0370∗∗∗ -0.0245∗∗∗

(0.0082) (0.0083)

Non-TARP 219 -0.0208∗∗∗ -0.0198∗∗∗

(0.0069) (0.0069)

Required 8 8 -0.1045∗∗∗ -0.0710∗

(0.0358) (0.0356)

Large TARP 74 -0.0323∗∗∗ -0.0762

(0.0091) (0.0095)

Large Non-TARP 37 -0.0430∗∗∗ -0.0427∗∗∗

(0.0145) (0.0147)

Mid-Size TARP 65 -0.0324∗∗ -0.0277∗∗

(0.0140) (0.0138)

Mid-Size Non-TARP 62 -0.0314∗∗ -0.0298∗∗

(0.0127) (0.0121)

Continued on next page

34

Table 1.2 – Continued from previous page

Banks N Newey-West Newey-West+FF

Small TARP 72 -0.0384∗ -0.0338∗

(0.0189) (0.0191)

Small Non-TARP 120 -0.0084 -0.0076

(0.0096) (0.0098)

C. Capital Repayment

TARP 186 0.0166∗ 0.0183∗∗

(0.0088) (0.0089)

Non-TARP 212 -0.0012 -0.0002

(0.0009) (0.0052)

Required 8 8 0.0090 0.0175

(0.0145) (0.0176)

Large TARP 70 0.0048 0.0073

(0.0055) (0.0055)

Large Non-TARP 36 0.0039 0.0012

(0.0092) (0.0097)

Mid-Size TARP 50 0.0104 0.0142∗

(0.0077) (0.0075)

Mid-Size Non-TARP 61 0.0006 0.0034

(0.0087) (0.0089)

Small TARP 58 0.0372 0.0352

(0.0267) (0.0270)

Small Non-TARP 115 -0.0038 -0.0028

(0.0078) (0.0078)

The cumulative average abnormal returns, CAAR = 1N

∑Ni=1

∑t=T2t=T1 ARit,

surrounding the three TARP-CPP events: announcement, capital infusion

and capital repayment are reported. Cumulative abnormal returns are

calculated∑N

i=1 ARit, where AR = Rit − E(Rit). Expected returns for each

bank are estimated over a 250 day pre-event window. Standard errors

are parentheses. The cumulative average abnormal return for the

[-2, +2] event window is reported. * p < 0.10, ** p < 0.05, *** p < 0.01

35

Tab

le1.

3:A

:M

arke

tM

odel

Res

ult

s,A

nnou

nce

men

t

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.6

993∗∗∗

-0.0

441

-0.0

078

-0.0

009∗∗∗

0.0

129∗∗∗

-0.0

014∗∗∗

0.0

064∗∗∗

0.0

102∗∗∗

(0.0

139)

(0.0

410)

(0.0

207)

(0.0

001)

(0.0

026)

(0.0

002)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.5

324∗∗∗

-0.0

964∗∗

0.0

344

-0.0

008∗∗∗

0.0

050∗∗

-0.0

012∗∗∗

0.0

054∗∗∗

0.0

037∗∗∗

(0.0

220)

(0.0

473)

(0.0

606)

(0.0

001)

(0.0

025)

(0.0

003)

(0.0

003)

(0.0

007)

Req

uir

ed8

1.3

460∗∗∗

0.1

305

0.0

336

-0.0

004

0.0

184

-0.0

005

-0.0

045∗∗∗

0.0

223∗∗∗

(0.0

567)

(0.3

050)

(0.0

900)

(0.0

004)

(0.0

016)

(0.0

011)

(0.0

011)

(0.0

012)

Larg

eT

AR

P1.2

320∗∗∗

-0.3

689∗∗∗

-0.0

863∗∗∗

-0.0

004∗∗∗

0.0

248∗∗∗

-0.0

029∗∗∗

0.0

105∗∗∗

0.0

189∗∗∗

(0.0

208)

(0.0

485)

(0.0

282)

(0.0

001)

(0.0

039)

(0.0

004)

(0.0

003)

(0.0

003)

Larg

eN

on

-TA

RP

1.1

833∗∗∗

-0.4

476∗∗∗

0.1

880

0.0

000

0.0

216∗∗∗

-0.0

023∗∗∗

0.0

107∗∗∗

0.0

086∗∗∗

(0.0

927)

(0.1

430)

(0.0

298)

(0.0

002)

(0.0

057)

(0.0

005)

(0.0

007)

(0.0

003)

Mid

-Siz

eT

AR

P0.6

714∗∗∗

0.0

165

0.0

753∗∗

-0.0

008∗∗∗

0.0

119∗∗∗

-0.0

019∗∗∗

0.0

097∗∗∗

0.0

068∗∗∗

(0.0

218)

(0.0

684)

(0.0

337)

(0.0

001)

(0.0

045)

(0.0

004)

(0.0

004)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.8

400∗∗∗

-0.0

674

-0.0

584

-0.0

006∗∗∗

0.0

106∗∗

-0.0

022∗∗∗

0.0

010∗∗∗

0.0

054∗∗∗

(0.0

244)

(0.0

653)

(0.0

359)

(0.0

002)

(0.0

043)

(0.0

004)

(0.0

004)

(0.0

004)

Sm

all

TA

RP

0.1

051∗∗∗

0.2

174∗∗∗

-0.0

051

-0.0

015∗∗∗

0.0

011

0.0

004

0.0

003

0.0

029∗∗∗

(0.0

239)

(0.0

822)

(0.0

346)

(0.0

002)

(0.0

045)

(0.0

005)

(0.0

004)

(0.0

004)

Sm

all

Non

-TA

RP

0.1

716∗∗∗

-0.0

001

0.0

273

-0.0

012∗∗∗

-0.0

031

-0.0

003

0.0

013∗∗∗

0.0

015∗∗∗

(0.0

204)

(0.0

594)

(0.0

299)

(0.0

001)

(0.0

003)

(0.0

004)

(0.0

004)

(0.0

003)

Mark

etm

od

elre

sult

sfo

rth

eT

AR

P-C

PP

an

nou

nce

men

tare

rep

ort

ed.

Th

ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(1.4

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·E

vent it)+

αi2PostEvent it+βi2

(Rm

t·P

ostEvent it)+

ε it.

Th

eT

AR

P-C

PP

an

nou

nce

men

tocc

urr

edon

Oct

ob

er14,

2008.βi0

rep

rese

nts

mark

etri

skb

efore

the

an

nou

nce

men

t,βi1

rep

rese

nts

the

chan

ge

inm

ark

etri

skd

uri

ng

the

five-

day

even

tw

ind

ow

surr

ou

nd

ing

the

an

nou

nce

men

tan

dβi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inp

ost

-even

tm

ark

etri

sk[3

+,

253+

]d

ays

aft

erth

ean

nou

nce

-m

ent.αi0

rep

rese

nts

exce

ssp

erfo

rman

ceth

eyea

rb

efore

the

an

nou

nce

men

t,αi1

rep

rese

nts

the

chan

ge

inex

cess

per

form

an

ced

uri

ng

the

five-

day

even

tw

ind

ow

surr

ou

nd

ing

the

an

nou

nce

men

tan

dαi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inp

ost

-even

tex

cess

per

form

an

ce[3

+,

253+

]d

ays

aft

er.∗

p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

36

Tab

le1.

3:B

:M

arke

tM

odel

Res

ult

s,C

apit

alIn

fusi

on

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.6

467∗∗∗

0.0

082

0.2

127∗∗∗

-0.0

009∗∗∗

-0.0

046∗∗∗

-0.0

014∗∗∗

0.0

058∗∗∗

0.0

091∗∗∗

(0.0

118)

(0.0

713)

(0.0

310)

(0.0

001)

(0.0

019)

(0.0

002)

(0.0

003)

(0.0

003)

Non

-TA

RP

0.5

145∗∗∗

-0.1

326

0.0

949

-0.0

010∗∗∗

-0.0

034

-0.0

005∗∗

0.0

053∗∗∗

0.0

035∗∗∗

(0.0

189)

(0.1

488)

(0.1

533)

(0.0

001)

(0.0

023)

(0.0

002)

(0.0

003)

(0.0

009)

Req

uir

ed8

1.3

530∗∗∗

-0.3

315∗

0.1

208

-0.0

002

-0.0

089

-0.0

001

-0.0

051∗∗∗

0.0

212∗∗∗

(0.0

774)

(0.1

724)

(0.1

026)

(0.0

005)

(0.0

070)

(0.0

011)

(0.0

011)

(0.0

012)

Larg

eT

AR

P1.1

338∗∗∗

-0.2

693∗∗∗

0.1

033∗

-0.0

017

-0.0

016

-0.0

030∗∗∗

0.0

100∗∗∗

0.0

184∗∗∗

(0.0

160)

(0.0

657)

(0.0

541)

(0.0

002)

(0.0

003)

(0.0

004)

(0.0

004)

(0.0

006)

Larg

eN

on

-TA

RP

1.0

890∗∗∗

-0.4

711

0.6

056

-0.0

002

-0.0

058∗

-0.0

018∗∗∗

0.0

097∗∗∗

0.0

075∗

(0.0

756)

(0.3

883)

(0.7

785)

(0.0

002)

(0.0

035)

(0.0

006)

(0.0

010)

(0.0

039)

Mid

-Siz

eT

AR

P0.6

814∗∗∗

-0.2

111

0.1

581∗∗∗

-0.0

008∗∗∗

-0.0

065∗∗

-0.0

022∗∗∗

0.0

093∗∗∗

0.0

061∗∗∗

(0.0

195)

(0.1

391)

(0.0

408)

(0.0

002)

(0.0

030)

(0.0

004)

(0.0

004)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.8

068∗∗∗

0.0

274

-0.0

649

-0.0

008

-0.0

060

-0.0

016∗∗∗

0.0

099∗∗∗

0.0

052∗∗∗

(0.0

189)

(0.1

792)

(0.0

414)

(0.0

002)

(0.0

028)

(0.0

004)

(0.0

004)

(0.0

004)

Sm

all

TA

RP

0.1

330∗∗∗

-0.0

710

-0.0

181

-0.0

017∗∗∗

-0.0

077∗

0.0

012∗∗∗

0.0

003

0.0

025∗∗∗

(0.0

210)

(0.1

865)

(0.0

406)

(0.0

002)

(0.0

043)

(0.0

004)

(0.0

004)

(0.0

004)

Sm

all

Non

-TA

RP

0.1

795∗∗∗

-0.0

535

0.0

459

-0.0

013∗∗∗

-0.0

012

0.0

003

0.0

015∗∗∗

0.0

015∗∗∗

(0.0

158)

(0.2

189)

(0.0

387)

(0.0

001)

(0.0

039)

(0.0

004)

(0.0

004)

(0.0

004)

Mark

etm

od

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ted

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ati

on

(1.4

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·Event it)+

αi2PostEvent it+βi2

(Rm

t·PostEvent it)+

ε it.

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0.0

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37

Tab

le1.

3:C

:M

arke

tM

odel

Res

ult

s,C

apit

alR

epay

men

t

Ban

ks

βi0

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BH

ML

TA

RP

0.7

156∗∗∗

-0.2

645∗∗

0.0

106

-0.0

007∗∗∗

0.0

045∗∗

0.0

009∗∗∗

0.0

065∗∗∗

0.0

098∗∗∗

(0.0

067)

(0.1

149)

(0.0

191)

(0.0

001)

(0.0

018)

(0.0

002)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.5

434∗∗∗

-0.2

725∗

-0.1

046∗∗∗

-0.0

008∗∗∗

-0.0

004

0.0

003

0.0

059∗∗∗

0.0

039∗∗∗

(0.0

347)

(0.1

350)

(0.0

341)

(0.0

001)

(0.0

013)

(0.0

002)

(0.0

001)

(0.0

004)

Req

uir

ed8

1.4

297∗∗∗

-0.2

436

-0.5

520∗∗∗

-0.0

012

0.0

041

-0.0

004

-0.0

036∗∗∗

0.0

209∗∗∗

(0.0

553)

(0.1

911)

(0.0

783)

(0.0

005)

(0.0

026)

(0.0

013)

(0.0

010)

(0.0

012)

Larg

eT

AR

P1.1

523∗∗∗

-0.4

314∗∗∗

-0.3

163∗∗∗

-0.0

006∗∗∗

0.0

021∗

0.0

003

0.0

103∗∗∗

0.0

171∗∗∗

(0.0

122)

(0.1

193)

(0.0

189)

(0.0

002)

(0.0

011)

(0.0

002)

(0.0

003)

(0.0

003)

Larg

eN

on

-TA

RP

1.1

230∗∗∗

-0.2

985

-0.2

740∗

-0.0

004∗∗

0.0

007

0.0

001

0.0

095∗∗∗

0.0

090∗∗∗

(0.1

692)

(0.2

727)

(0.1

447)

(0.0

002)

(0.0

016)

(0.0

003)

(0.0

004)

(0.0

018)

Mid

-Siz

eT

AR

P0.7

594∗∗∗

-0.2

121

0.0

790∗∗

-0.0

008∗∗∗

0.0

034

0.0

014∗∗∗

0.0

105∗∗∗

0.0

068∗∗∗

(0.0

174)

(0.1

460)

(0.0

366)

(0.0

001)

(0.0

015)

(0.0

005)

(0.0

003)

(0.0

003)

Mid

-Siz

eN

on

-TA

RP

0.7

623∗∗∗

-0.1

943

0.0

145∗∗∗

-0.0

009∗∗∗

0.0

000

0.0

004

0.0

094∗∗∗

0.0

054∗∗∗

(0.0

152)

(0.1

455)

(0.0

315)

(0.0

001)

(0.0

021)

(0.0

003)

(0.0

003)

(0.0

003)

Sm

all

TA

RP

0.1

368∗∗∗

-0.2

606

0.1

277∗∗∗

-0.0

076∗∗∗

0.0

096∗

0.0

013∗∗∗

0.0

010∗∗∗

0.0

022∗∗∗

(0.0

178)

(0.2

868)

(0.0

419)

(0.0

002)

(0.0

056)

(0.0

003)

(0.0

004)

(0.0

003)

Sm

all

Non

-TA

RP

0.2

082∗∗∗

-0.2

397

-0.0

459

-0.0

008∗∗∗

-0.0

004

0.0

004

0.0

022∗∗∗

0.0

016∗∗∗

(0.0

094)

(0.1

645)

(0.0

282)

(0.0

001)

(0.0

020)

(0.0

003)

(0.0

002)

(0.0

002)

Mark

etm

od

elre

sult

sfo

rth

eT

AR

P-C

PP

cap

ital

rep

aym

ent

are

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ort

ed.

Th

ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(1.4

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·E

vent it)+

αi2PostEvent it+βi2

(Rm

t·P

ostEvent it)+

ε it.

Th

eT

AR

P-C

PP

cap

ital

rep

aym

ents

oc-

curr

edin

the

sam

ple

bet

wee

nM

arc

h2008

toD

ecem

ber

2013.βi0

rep

rese

nts

mark

etri

skb

efore

ab

an

kre

pays

cap

ital,βi1

rep

rese

nts

the

chan

ge

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ng

the

five-

day

even

tw

ind

ow

aro

un

dth

ed

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ab

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kre

pays

cap

ital

an

dβi2

rep

rese

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al

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ge

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tm

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+,

253+

]d

ays

aft

era

ban

kre

pays

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ital.αi0

rep

rese

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ssp

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rman

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eyea

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efore

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pays

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ital,αi1

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dth

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pays

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ital

an

dαi2

rep

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per

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an

ce[3

+,

253+

]d

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kre

pays

cap

ital.∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

38

Table 1.4: Sample Selection Test: PropensityScore Matching

1.41 ProbitEstimates Coefficient Std. ErrorNon-performing loans -11.0316 12.0798

Long-term debt 0.0302∗∗∗ 0.0078

ROA 18.2928∗∗∗ 4.9471

Credit exposure 1.1961∗∗ 0.5623

Liquidity ratio -0.7471 2.8478

Pseudo-R2 0.05751.42 Impact onCumulative Returns ATT ATU ATECPP Announcement 0.0592 0.0234 0.0362

CPP Infusion -0.0206 -0.2966 0.0087

CPP Capital Repayment 0.0085 -0.0020 0.0088

Table 1.41 reports the probit regression used to estimate propen-sity scores. A match is declared if there are two banks, one in eachsample, for which the value of a financial characteristic is identical.Rosenbaum and Rubin(1983) show that matching is best performedconditioning on the probability of being selected into a group (inmy case, the TARP-CPP) on group characteristics. Table 1.42 re-ports the cumulative abnormal returns for the treated group (banksthat entered into TARP), the untreated group (banks that did notreceive TARP capital) and the average treatment effect.

39

2 The Effect of Nontraditional Banking on Entry

into TARP and Bank Failure

2.1 Introduction

Starting in the early 2000s, there was a shift in banking activities away from tra-

ditional banking, where loans are made with insured deposits, towards securitized

banking, where loans previously held on balance sheets are sold in capital markets.40

At the start of the financial crisis in 2007, there was over $2.5 trillion in U.S. asset-

backed securities outstanding.41 At the same time, there was an increase in bank

failures and government-assisted bank bailouts. There were 373 banks that failed

during the financial crisis and the years immediately after, while 707 banks or bank-

holding companies received government loans through the Troubled Asset Relief Pro-

gram (TARP).42 This paper investigates the empirical relationship between a banks

involvement in various nontraditional activities and the likelihood that a bank fails

or receives TARP funding. I find that banks are more likely to enter into TARP if

they have more standby letters of credit and commercial letters of credit.43 Banks

that also have less capital adequacy, less return on assets (ROA) and more real estate

loans are also likely to enter into TARP. Similarly, banks that failed during 2007-2011

had less capital adequacy, less ROA, more real estate loans and more standby letters

of credit.

40Securitized banking is also called nontraditional banking.41This number is taken from Gorton and Metrick (2011). They report that by April 2011, there

were $11 trillion of asset-backed securities sold, which was more than all outstanding U.S. Treasuriescombined.

42Underneath TARP, the Capital Purchase Program (CPP) provided the largest government bankloan program ever.

43These off-balance sheet items obligate a bank to provide funding to a securitization structureto ensure that investors receive timely payment on securities or in the event of market disrup-tions(Mandel et. al 2012).

40

The striking similarity of entry into TARP and bank failure based on balance

sheet and off-balance sheet bank characteristics has not been previously discussed in

the literature. Further, among the numerous studies that either examine bank failure

or entry into TARP during the recent crisis, emphasis on nontraditional activities

is missing. In particular, studies by Li (2013) and Duchin and Sosyura (2012) esti-

mate the likelihood a bank enters into TARP based on observable bank and political

characteristics, but do not mention off-balance sheet bank characteristics. Both pa-

pers use balance sheet bank characteristics to proxy for the CAMELS ratings that

bank regulators determine during on-site examinations.44 They find that ROA, tier

one capital ratio and asset quality (measured by a computed troubled assets ratio)

are statistically significant in determining whether a bank receives TARP funding.

Further, Duchin and Sosyura (2012) collect data on each bank’s connection to politi-

cians and determine a small, positive effect on the entry into TARP. I find similar

results to both papers, but by including standby letters of credit and commercial

letters of credit among other off-balance sheet items, I am able to explore the effect

nontraditional banking had on entry into TARP.

Other literature such as Cole and Gunther (1995 and 1998), Gasbarro et. al (2002),

and Wilson and Wheelock (1995) examine bank failures and the determinants of bank

failures also using bank characteristics to proxy CAMELS ratings. Collectively, these

papers do not find statistically significant results for all proxies of CAMELS, but they

do find that a bank is more likely to fail if it has lower capital adequacy, asset quality

and profitability. Also similar to the bank failure literature, I find that a bank with

lower capital adequacy, asset quality and profitability is more likely to fail during

2007-2011. Due to the rise of nontraditional banking, it is imperative to not only

include balance sheet bank characteristics, but also off-balance sheet characteristics

44 CAMELS ratings are not public knowledge.

41

when examining bank failures and entry into TARP.

2.2 The Rise of Nontraditional Banking

In the traditional banking system, banks make loans and accept deposits that are

insured by the government. However, non-retail deposits, which are deposits held in

a bank or firm on somebody’s behalf are not insurable. The rise of nontraditional

banking started in the early 1990s when banks provided “safe” ways for non-retail

depositors (sovereign wealth funds, mutual funds and cash-rich companies) to implic-

itly insure deposits via collateral (Gorton and Metrick 2012a). Further, Pozsar (2011)

claims that, on the demand side, these institutional cash pools desire liquidity and

wanted to allocate cash to safe asset classes, but there was not enough U.S. Treasuries

for pools to hold due to large amounts of Treasuries being held by foreigners.45 As a

result, banks substituted the government guaranteed assets for short-term debt prod-

ucts, like repurchase agreements and asset-backed commercial paper which provided

collateral for the risk, and offered them to non-retail depositors.46

The financial assets that serve as collateral are found on the off-balance sheet

and are securitized or pooled together by a special purpose vehicle to form a security

that has a claim against the cash flows of the underlying assets.47 Collateral for

asset-backed commercial paper was typically mortgages and credit-card receivables,

but due to the housing boom in the early 2000s, frequently mortgages or mortgage-

45This alludes to the fact that buying Treasuries, drives up the price and drives down the yield.46A repurchase agreement, or repo, is a short term deposit in a bank, where the bank promises

to pay back the repo rate. In other words, repo agreements are when an investors buys some assetfrom a bank for $X and the bank agrees to repurchase it for $Y at some time in the future. Therepo rate, or percentage (Y-X)/X, is analogous to the interest rate on a bank deposit. For example,if an asset has a market value of $100 and a bank sells it for $70 with an agreement to repurchaseit at $77 then the repo rate is 10%. If a bank defaults on the the promise to repurchase the asset(collateral) then the investor keeps the asset collateral (Gorton and Metrick 2012a).

47 A detailed analysis of how securitization works and why bank’s choose to securitize can befound in the Appendix.

42

backed securities were used as collateral (Gordon and Metrick 2012b).

One problem with securitized banking was that average haircuts for collateral

began steadily rising when borrowers started to default on their mortgages in 2007.

Haircuts are the percentage difference between market value of an asset used for

collateral and the amount of the deposit.48 Gorton and Metrick (2012a) show that

the repo-haircut index drastically increased in 2007 from about 0% in January to 10%

by December 2007, increasing to nearly 45% by January 2009. Increasing haircuts can

create bank insolvency, because if a bank has $100 million in collateral and needs $100

million in cash, a bank is unable to obtain that amount with a high haircut. By mid

2008, the weighted-average haircut was near 25%, so in this example a bank would be

short $25 million dollars in July 2008.49 In order to make up for the cash shortage of

$25 million, banks sought to issue new securities. Usually unable to make up the cash

selling new securities, banks then were forced to sell assets. The increasing sale of

assets, drove down asset prices further which created a downward cycle. In addition,

several asset classes were also no longer being accepted as collateral, which further

exacerbated the problem (Covitz et al. 2011 and Gordon and Metrick 2012b).

Due to lack of collateral for the asset-backed commercial paper, investors moved

away from asset-backed commercial paper to unsecured money market mutual funds.50

As Gorton and Metrick (2012b) point out, the size of the asset-backed commercial

paper market fell by $350 billion, or approximately 30%, in the second half of 2007.

Money market mutual funds were considered safer assets, until the Lehman Brothers

48More specifically, a haircut is the percentage, or (Z-X/X), of the difference in the deposit of arepo transaction, X, and the underlying asset, Z. In the previous example, if an asset has a marketvalue of $100 and a bank sells it for $70 with an agreement to repurchase it at $77 then the haircutis 30%. If a bank defaults on the promise to repurchase the asset (collateral) then the investor keepsthe asset (collateral) (Gorton and Metrick 2012a).

49If the repo market size is $100 million, with haircuts near 0%, banks can achieve the desiredamount of cash needed. In traditional banking, when a bank needs cash it must raise deposit ratesto attract customers, whereas in nontraditional banking banks must raise the repo rates.

50These funds are privately managed and invested in short term, globally diversified assets.

43

bankruptcy in September. This caused the Reserve Primary Fund, which was a large

money market mutual fund that primarily held Lehman Brothers debt,51 to “break

the buck” (McCabe 2010).52 Money market fund investors pulled their investments

after this event, forcing money market mutual funds to sell assets, which caused a

decline in the prices of money market instruments. This also prompted a cycle of

capital losses which further encouraged redemptions. This withdrew liquidity from

the market and eventually these “runs” on the market became so frequent that fed-

eral monetary support for the banking industry ensued, providing short term money

market guarantees and eventual bank bailouts via the Troubled Asset Relief Program

(Gorton and Metrick 2012b).

2.2.1 Background and TARP

The Troubled Asset Relief Program (TARP) was one of the largest programs

implemented by the U.S. government to address the financial crisis. This program

authorized the Treasury to buy $700 billion in troubled assets in order to stabilize the

economy and provide needed credit.53 On October 14, 2008 the Treasury announced

its first, and largest, of six TARP bank programs, the Capital Purchase Program

(CPP). Under this program, the Treasury loaned over $200 billion dollars directly to

financial institutions. In return for their capital, the Treasury Department received

preferred stock with a 5% dividend for the first five years and 9% thereafter. Addi-

tionally, they received warrants to purchase common stock up to 15% of the initial

capital investments in order to benefit from the equity appreciation of the financial

institution.

51This debt was valued at $785 million.52Breaking the buck means that the share price falls below $1.53More specifically, TARP was designed to provide capital to the financial sector specifically for

“residential or commercial mortgages and any securities, obligations, or other instruments that arebased on or related to such mortgages” (Emergency Economic Stabilization Act of 2008).

44

Under the CPP, financial institutions could apply for capital between 1% and 3%

of their risk-weighted assets. However when the CPP was announced, the Treasury

Secretary, Henry Paulson Jr., stated that the nation’s largest banks would receive

money, but other financial institutions would be able to apply in the ensuing weeks.54

Domestically controlled banks, bank holding companies, savings associations, and

savings and loan companies were eligible to apply for TARP funding by the dead-

line of November 14, 200855 to their primary federal banking regulator.56 After the

federal banking regulator received the application, they would send their recommen-

dation to the Office of Financial Stability at the Treasury Department. The Treasury

Department made final decisions as to which banks would receive capital.

I construct my TARP dataset from the U.S. Treasury Department TARP Trans-

action Report, Summary of Deposit data and commercial bank Call Reports. Since I

am interested in evaluating on- and off-balance sheet bank characteristics, as it relates

to bank entry into TARP and commercial bank failures, I have to match the TARP

banks sample to commercial banks. In order to do that, I match the Summary of De-

posit data which includes all commercial banks and their top bank holding company

to the TARP bank sample. Most banks that received TARP-CPP were members of

a bank-holding company that held either one commercial bank or held multiple com-

mercial banks. There were 318 TARP banks that were members of a bank-holding

company that held multi-commercial banks. Like Li (2013), I assume that if a bank

holding company received TARP capital, they distributed it equally to all commercial

54The largest institutions that did not have to apply for money, but were required to take it,were JPMorgan Chase, State Street Corp. Bank of New York Mellon, Citigroup, Bank of America,Morgan Stanley, Goldman Sachs, Wells Fargo, and Merrill Lynch. Since Merrill Lynch was acquiredby Bank of America, I will hereafter refer to only the eight banks that were required to take TARPcapital.

55Analysis on the timing of capital infusions and investor reactions can be found in Zanzalari(2015).

56This includes the Federal Reserve, the Federal Deposit Insurance Corporation (FDIC), the Officeof the Comptroller of Currency (OCC) or the Office of Thrift Supervision (OTS).

45

banks within their bank holding company.57 After matching the TARP bank sample

to the Summary of Deposit data, this enables me to use Call Report data to obtain

on- and off-balance sheet characteristics. Call Report data is obtained from 2007-2011

consisting of quarterly observations of approximately 6,500 banks.

2.2.2 Bank failures

Data on participating TARP banks and their corresponding commercial banks are

merged with data on the FDIC failed bank list and the Federal Reserve of Chicago

merged bank list. Using the FDIC definition of failure, a failure is when the govern-

ment intervenes in a merger or a pay off. Based on this definition, 373 banks failed

during 2007-2011 with some failures more geographically concentrate than others.

Figure 2.1 shows a timeline of bank failures from 2007-2011. Bank failures increase

dramatically during 2009-2010, or during the financial crisis.58 There were 2 bank

failures in 2007, 36 bank failures in 2008, 137 bank failures in 2009, 123 bank fail-

ures in 2010, and 75 in 2011. I focus on bank failures that happened during the

official recession period from December 2007-June 2009 and those that occurred in

the immediate period after the financial crisis in July 2009-December 2011.

Banks that failed with government assistance are represented by the solid line in

Figure 2.1 which indicates that the financial institution ceases to exist and disposition

was arranged by the FDIC, Resolution Trust Corporation (RTC), National Credit

Union Association (NCUA) or another regulatory agency.59 This means that assets

57Data on how a bank exactly uses their funds is not disclosed. Numerous papers study the effectsof TARP on bank activity. Specifically, Li(2013) finds evidence that bank loan supply increases forbanks with below median Tier 1 capital ratios; Duchin and Sosyura (2012) find that banks shift riskwithin the same asset class after TARP thus going undetected by regulatory capital ratios; Blackand Hazelwood (2011) find that the risk of loan originations increases at large TARP banks anddecreases at small TARP banks.

58The recession officially started in December 2007 and ended in June 2009, although bank failurescontinued after the official end date of the recession.

59RTC was formed by Congress is 1989 to respond to Savings & Loan insolvencies (Federal Reserve

46

from the ceased financial institution may be distributed to the regulatory agency or

to another financial institution. The dotted line in Figure 2.1 shows undercapitalized

mergers which are mergers that occurred without government assistance with the

acquired bank having a tier one ratio <0.04.60 Banks with a Tier 1 ratio of <0.04

are classified by the FDIC as ‘undercapitalized’, and thus, the FDIC would begin to

set up a plan with the bank for re-capitalization. If a bank fails to meet the required

Tier 1 capital levels, the FDIC may start the process of finding a government-assisted

buyer. Therefore, if a bank with a Tier 1 capital ratio of <0.04 merges with another

bank, and no longer exists as an institution, I call this a undercapitalized merger.

As Koetter et al. (2007) mention, most studies that analyze bank takeovers do not

distinguish between healthy and troubled banks due to the scarcity of failures without

supervisory authorities. Particularly, there were only 15 undercapitalized mergers

that occurred between 2007-2011.61 Therefore, I include undercapitalized mergers

with banks that failed with government assistance in a separate specification to see

if bank characteristics have different effects on both private undercapitalized mergers

and government failures.

Additionally, Table 2.1 shows the importance of the real estate during the 2007-

2009 recession and the years after as the states with most bank failures also had

a large proportion of delinquent homes. Georgia, Illinois, Florida and California

were the states with the most bank failures during 2007-2011, while the 90+ day

delinquency rates across the United States were among the highest in Florida and

Nevada. Georgia, Florida, Illinois and Nevada were states with a high number of

Bank of Chicago).60I do not model the decision of why some banks are acquired and others do not. Wheelock and

Wilson (2000) find that the lower a bank’s equity/asset ratio and return on assets, the more likely itwas to be acquired in 1984. However, banks with high ratios of real estate owned to total assets wereless likely to be acquired, whereas small banks and banks that were members of holding companieswere more likely to be acquired.

61Undercapitalized mergers during this time period are <2% of total bank mergers.

47

bank failures and were among the top ten states with the highest delinquency rates

in the United States during 2007-2011. Since I do not have individual mortgage

delinquencies per bank, I will use real estate loans as an explanatory variable for

bank failure.

2.3 Methodology and Data

To understand how bank entry into TARP and bank failures during 2007-2011 are

associated with on-balance sheet and off-balance sheet characteristics, I estimate the

three following probit regressions:

P (TARP = 1|X) = Φ(α+ β1Bankcharacteristics+ β2PoliticalandSizeControls) (2.1)

P (FAIL = 1|X) = Φ(α+ β1Bankcharacteristics+ β2PoliticalandSizeControls) (2.2)

P (UNCMERGE = 1|X) = Φ(α+ β1Bankcharacteristics+ β2PoliticalandSizeControls) (2.3)

where TARP is an indicator variable equaling one if a bank received TARP capital,

FAIL is an indicator variable equaling one if a bank fails with government assistance

from December 2007-December 2011 and UNCMERGE is an indicator variable equal-

ing one if a bank fails with government assistance or is an undercapitalized merger

during December 2007- December 2011.62 Equations (2.1)-(2.3) include the same

bank characteristics and political/size controls.

2.4 Balance sheet bank characteristics

Previous studies that investigate entry into TARP, such as Duchin and Sosyura

(2012) and Li (2013), use bank characteristics that proxy for the CAMELS rating

system to measure bank performance. The CAMELS rating system is used by bank

62I also estimate probit regressions for bank failures that specifically occur in 2009 and 2010.These year-specific probit regressions are reported in the Appendix in Table 3.10.

48

regulators during onsite examinations, but the results are not publically available.

The six factors that banks are evaluated on are: Capital adequacy, Asset quality,

Management, Earnings, Liquidity and Sensitivity to market risk. Duchin and Sosyura

(2012) find that their measures of capital adequacy and earnings are significant across

various specifications. They use Tier 1 capital ratio and ROA to measure the C and

E of ‘CAMELS’. Li (2013) only found the troubled asset ratio to be significant across

variable specifications. Li defines the troubled asset ratio as loans past due 90 days

or more plus non-accrual loans and other real estate owned all divided by a bank’s

capital and loan loss reserves.63

Earlier studies on bank failures also have mixed results on the predictability of

the CAMELS ratings on bank failures. Gasbarro et. al (2002) uses a unique dataset

from the Bank of Indonesia to measure the CAMEL ratings of 52 Indonesian banks

using a fixed effects and random effects model and find only earnings to be the

critical variable in explaining the ratings. Similarly, Cole and Gunther (1998) measure

CAMEL ratings as it relates to bank failures in 1986-1988 and in 1988-1990 and

they find that only capital adequacy, asset quality and profitability are statistically

significant in predicting bank failures.64 Further, they find that the information

content of CAMEL ratings decays rapidly, so the ability to identify failures is best

when using Call Report data no more than one to two quarter prior to the forecast

period.

To account for balance sheet variables that are significant in previous bank failure

studies and in more recent entry into TARP studies, I use the following on- balance

sheet measures:

63A detailed analysis of bank characteristics are defined in the Appendix.64This result is similar to Martin (1977), Gajewski (1989), Demirguc-Kunt (1989) and Cole and

Gunther (1995). Also, in 1997 U.S. regulators added the S to CAMEL to capture banks’ sensitivityto global competitive markets. Since most of the literature measured CAMEL ratings for the early1990s and 2000s, and S is difficult to proxy for, many authors do not include S in their ratings.

49

Tier 1 ratio = Tier 1 capital/ risk weighted assets

Troubled assets ratio= (loans past due 90 days or more + nonaccrual loans + other

real estate owned)/(Tier 1 capital + loan loss reserve)

ROA= return on assets; net income/ total assets

Real estate loans= real estate loans/ total loans

Non-performing loans= loans 90 days past due or more /total loans

The tier 1 ratio represents a bank’s ability to absorb losses on assets of different

classes. The troubled asset ratio was used by Li (2013) and approximates a bank’s

asset quality. If a bank has more tier one capital to risk-weighted assets, I would

expect them to be less likely to fail. Likewise, the Treasury department wanted to

disburse TARP funds to banks that were capital-constrained, but otherwise strong

banks. In the announcement of the TARP Capital Purchase Program, Treasury

Secretary Henry Paulson Jr. stated that, “While many banks have suffered significant

losses during this period of market turmoil, many others have plenty of capital to

get through this period, but are not positioned to lend as widely as is necessary

to support our economy. Our goal is to see a wide array of healthy institutions sell

preferred shares to the Treasury, and raise additional private capital, so that they can

make more loans to businesses and consumers across the nation.” Paulson’s statement

reinforces that the Treasury wanted to lend to healthy banks, thus I hypothesize that

banks that received TARP capital will be banks with higher Tier 1 capital ratios.

Return on assets measures the net income a bank receives relative to their total

assets, which is a common proxy used to measure earnings in previous papers. Real

estate loans to total loans measures the exposure to the real estate market. I look

at real estate loans which can tell me the extent of how much or how little failed

50

and non-failed banks were involved in the financial crisis. If a bank was involved in

a large number of real estate loans I would expect them to fail more often. Lastly,

non-performing loans measure the amount of loans that are past due by 90 days or

more divided by the total amount of loans a bank makes.

2.4.1 Off-balance sheet items

In addition to balance sheet bank characteristics, I include off balance sheet vari-

ables due to the increasing nature of nontraditional banking during the financial crisis.

Since banks were the driving force behind asset-backed commercial paper, and this

component is away from the traditional location of the balance sheet (Mandel et. al

2011), it is imperative to include off balance sheet items on the estimation of bank en-

try in TARP and on bank failure during 2007-2011. Hassan (1993) also indicates that

both balance sheet and off-balance sheet variables should be included in evaluating

risk since they generally have a low correlation which makes multicollinearity a non-

issue. The correlation matrix of variables can be found in Table 2.2. All off-balance

sheet items reported are as a percentage of total assets.65 The off-balance sheet items

that I examine are: standby letters of credit, assets securitized or sold with recourse,

commercial letters of credit, amount of recourse exposure, and all other off-balance

sheet items.

Standby letters of credit are binding commitments for banks to pay a beneficiary.

Particularly, these are often used as backups for other capital market financing, like

commercial paper (Berger and Roman 2015). Commercial letters of credit are the

outstanding or unused amount of commercial or travelers’ letters of credit not issued

for money. Each of these variables indicates a future draw on a bank’s assets. Standby

65This is one way to normalize off-balance sheet items to allow for easy comparison to balancesheet variables. Berger and Roman (2015) use gross total assets which equals total assets plus loanand lease losses and allocated transfer risk reserve in order to account for the value of assets financed.

51

letters of credit are a guarantee from a bank to a third party, whereas commercial

letters of credit are a commitment to facilitate in commerce by a bank and a third

party. More specifically, commercial letters of credit are drawn upon in the normal

course of business, whereas standby letters of credit are not, unless the account party

defaults. The two primary areas of risk relative to standby letters of credit are credit

risk and funding risk. Credit risk is the risk of default on the account party and

funding risk is the inability of a bank to fund credit from normal sources.66

Assets securitized or sold with recourse are any assets bundled together in tranches

and resold, or assets with seller-provided credit enhancements. The amount of re-

course exposure is similar in that it is the maximum amount of credit exposure

arriving from recourse. All other off-balance sheet items include commitments to

purchase and sell securities, commitments to purchase property being acquired for

lease to others, contracts on equities and other off-balance sheet liabilities.

2.4.2 Controls

Beyond the balance sheet and off-balance sheet variables listed above, I use bank

characteristics of size, age and number of branches as controls. I divide banks into

different groups based on quintiles of asset size in Quarter 3 2008. The smallest

quintile is for banks with assets less than or equal to $51,687,000 and is referred to as

extra-small. Small are banks with assets >$51,687,000 in assets up to $100,271,000.

Medium are banks with assets >$100,271,000, but less than or equal to $180,418,000,

whereas large have assets >180,418,000 and up to $390,222,000. The largest bank

category, which is called extra large, is omitted in the analysis. The nation’s largest

banks are what most people think about when considering banks, TARP and the

66More detail on variables on the off-balance sheet can be in found in the Appendix.

52

financial crisis, so comparing to this base group makes sense.67

Further, I control for the number of branches that a bank has. The Treasury was

worried about systemic problems that would pose a risk to the economy. Therefore,

the Treasury may have been more likely to give capital to banks that had multiple

branches.68 Lastly, I control for regulatory connections like Li (2013) and Duchin

and Sosyura (2012).69 Since the Federal Reserve is the primary regulatory body

for state-chartered banks and bank-holding companies, it is important to control for

the potential influence the Federal Reserve would have on bank entry into TARP.70

Further, the FDIC may be less likely to let a bank fail if the bank has been around

for a long time.

2.5 Results

2.5.1 Summary Statistics on TARP and failed banks

Table 2.3 reports the mean values and standard deviations for TARP and non-

TARP banks in Quarter 3, 2008. Since banks had to apply for TARP before November

14, 2008, Quarter 3, 2008 data will be the most recent that bank regulators would

67These quintiles change slightly when omitting the eight banks that were “forced” to take TARP.Those quintiles are reported below Table 2.5B.

68Ng and Roychowhury (2014) use a different control variable, bank-holding company, since mostof the recipients of TARP-CPP capital were members of a bank-holding company. There were318 TARP banks that were held by bank-holding companies that held multiple commercial banks.However, the number of branches of a bank is more accurate in capturing how wide-spread a bankcrisis could be.

69Both papers also control for whether a bank executive served as a Director of the Fed. Li (2013)additionally controls for campaign contributions to local Representatives from financial, insuranceand real estate agencies.

70The FDIC has supervisory authority over state-chartered banks, but most of them are held bybank-holding companies whose applications must be submitted to the Federal Reserve. The OCCregulates national banks, which follow federal banking laws and are required to be members of theFederal Reserve System. The OTS, which is no longer exists after the financial crisis, supervisedsavings institutions and thrifts which used to only give out residential mortgages and accept deposits.Differences between the OTS and other federal regulators diminished over time (Duchin and Sosyura2012).

53

use in making recommendations about TARP. TARP banks had total assets of $11.1

billion compared to $0.58 billion for non-TARP banks, but only $3.32 billion if the

eight banks (and their subsidiaries) are excluded.71 TARP banks have a slightly higher

mean troubled asset ratio, amount of recourse exposure, real estate loans and all other

off-balance sheet items to total assets compared to non-TARP banks. Also, TARP

banks have a greater number of standby letters of credit, assets securitized or sold with

recourse and commercial letters of credit. Excluding the required eight banks, standby

letters of credit fall a bit to 107% of total assets, but are still significantly larger than

banks that do not receive TARP capital. Assets securitized or sold with recourse

falls dramatically (from 139% of total assets to 90% of total assets) after excluding

the required eight banks, indicating that the nation’s biggest banks were largely

involved in securitizing assets with recourse.72 Lastly, twelve TARP banks failed

with government assistance, but none privately merged that were undercapitalized.

Table 2.4 reports the summary statistics for all failed banks. Column 1 reports

mean values and standard deviations for banks that failed with government assistance

and column 2 includes undercapitalized mergers into the sample of banks that failed

with government assistance. Mean values are reported for the quarter before a bank

fails.73 For almost all explanatory variables, there is a big discrepancy between banks

that failed and banks that did not. Banks that failed with government assistance had

a mean value of $610 million in assets compared to $1.44 billion for banks that did

not fail; tier 1 ratios are almost 1% compared to 12% for non-failed banks; troubled

asset ratio is at 253% compared to 8% for non-failed banks; real estate loans are 84%

71Zanzalari (2015) addresses sample selection issues by using propensity score matching techniqueson TARP and non-TARP bank samples.

72The number of banks in the total TARP sample to the TARP sample excluding the big eight de-creases from 743 to 712, indicating that the eight bank-holding companies held multiple commercialbanks.

73This is done in order to compare banks that entered into TARP with banks that fail in thequarter before each one occurs.

54

of total loans for banks that failed compared to 67% of banks that did not; ROA, on

average, was negative 12% in the quarter before a bank fails compared to nearly 10%

for a bank that does not.74 Surprisingly, off-balance sheet items tend to be smaller

for banks that failed. Comparing summary statistics to those of TARP banks in

Table 2.3, TARP banks had high real estate loans compared to non-TARP banks,

just as failed banks had high real estate loans compared to banks that did not fail in

2007-2011.

2.5.2 Bank entry into TARP

There is not a publically available list of banks that applied to TARP and did not

receive money. Therefore, estimating bank entry into TARP assumes that all banks

had applied for TARP funding and thus probit estimates may be biased. Since it is

difficult to predict the direction of the bias, Li (2013) divides banks into two groups,

well-capitalized and less well-capitalized, because conceptually, banks that were over-

qualified would have been approved for TARP funds if they applied and banks that

were unqualified would have been rejected.75 Examining subsamples of banks allows

the problem of unobservable TARP applicants to be less severe. Following Li (2013),

I divide banks into three different specifications for Tables 2.5A-D. The full TARP

sample is reported in columns (1) and (2), less well-capitalized in columns (3) and

(4) and well-capitalized in columns (5) and (6). Since there were 596 of 743 TARP

banks with a below median tier 1 ratio, I will focus more on the subsample of less

well-capitalized banks. Column (2), (4) and (6) include all control variables.

74Some of the discrepancy between failed banks and non-failed banks may be a result of hownon-failed bank numbers are reported. Since more failures occurred in 2009 and 2010, the failedbank means will be reported mostly from those quarters. However, I report the means of bankcharacteristics for the non-fail sample over the entire 2007-2011 period.

75Li (2013) sorts banks based on whether they have a tier 1 ratio greater than the median andless than the median.

55

Table 2.5A reports the probit estimates for the entire TARP sample. The negative

coefficient on tier 1 ratio in column (4) indicates that banks with higher capital

adequacy were less likely to receive TARP capital. Further, banks are less likely to

get TARP capital if they have more troubled assets or non-performing loans. For

example, for every one unit change in the troubled asset ratio, the probability of

entering into TARP (versus not entering into TARP) decreases by 0.60 for the overall

sample and by 0.80 for less well-capitalized banks. Interestingly, banks with higher

real estate loans are more likely to get into TARP. When looking at off-balance

sheet bank characteristics, banks with more standby letters of credit and commercial

letters of credit are more likely to receive TARP funding. Taken together, these two

measures show that banks are more likely to enter into TARP if they are involved in

nontraditional activities.76

Political and regulatory controls also are significant in column (4). Banks are

more likely to receive TARP funding if it there are more bank branches.77 Further, a

bank is also more likely to receive TARP funding if they are regulated by the Federal

Reserve. Li (2013) and Duchin and Sosyura (2012) also find a positive and significant

estimate for the relationship between the Federal Reserve and a bank’s entry into

TARP. Lastly in Table 2.5A, banks are less likely to get TARP capital if they are

extra small, small, medium or large relative to being in the extra large grouping.78

Table 2.5B reports the probit estimates for the TARP sample, excluding the eight

76Further, in the full TARP sample specification a bank was more likely to enter into TARP ifa bank has greater assets securitized or sold with recourse, the subsample of less well-capitalizedbanks makes this effect disappear.

77Mandel et al. (2011) states that securitization activity is related to whether a bank is in abank-holding company or is tied to multiple banks. However, I find that standby letters of creditand whether a bank is in a bank-holding company has a correlation coefficient of 0.11.

78Recall that the size controls are broken into quintiles. The smallest quintile is for banks withassets less than or equal to $51,687,000 and is referred to as extra-small. Small are banks with assets>$51,687,000 in assets up to $100,271,000. Medium are banks with assets >$100,271,000, but lessthan or equal to $180,418,000, whereas large have assets >180,418,000 and up to $390,222,000. Thelargest bank category, which is called extra large, is omitted in the analysis.

56

banks that were required to take TARP funding. I find similar results to those stated

in Table 2.5A. Further, I report the marginal effects at the mean Table 2.5C-D. Using

the estimates that exclude the required eight banks in Table 2.5D, column (4) shows

that if a bank with an average amount of troubled assets has 0.01 more troubled

assets, then the bank is 0.181 times less likely to get into TARP. I also find that for

one additional dollar of standby letters of credit to total assets, banks with an average

amount of standby letters of credit to total assets are 0.041 times more likely to enter

into TARP. Some of my control variables are categorical, so the marginal estimates on

a control variable indicate how the probability of a bank entering into TARP changes

when the control variables change from 0 to 1, holding all other variables at their

means. For example, the marginal estimate of 0.020 on the Federal Reserve regulator

variable means that when the variable Federal Reserve regulator changes from 0 to

1, a bank is 0.020 times more likely to enter into TARP.

2.5.3 Bank failure

Since balance sheet variables and off balance sheet variables are significant in

both probit regressions for entry into TARP in the full sample and the sample that

excludes the eight required banks, I am interested in examining whether the failed

bank sample also follows a similar pattern. Examining Table 2.6A, indicates that

banks with lower tier one ratios are more likely to fail during 2007-2011. Banks with

lower ROA, and more real estate loans are also more likely to fail during this time

period. Columns (2) and (4) in Table 2.6A-B include quarter fixed effects, since bank

failures occur during different quarters between 2007-2011.

When examining off-balance sheet items on bank failures from 2007-2011, I find

that the more standby letters of credit a bank has the more likely they are to fail. This

result can be seen in either bank failures that occurred with government assistance

57

or the second specification which includes undercapitalized mergers. Specifically, the

marginal analysis in Table 2.6B column (2) indicates that for a one unit increase in

standby letters of credit to total assets for a bank, with a mean level of standby letters

of credit to total assets, leads to a 0.0001 greater chance (0.01%) of a bank failing.

Surprisingly, the more recourse exposure or other off-balance sheet items a bank has

the less likely it is to fail. Political and regulatory controls are also significant in

Tables 2.6A-B. Banks are less likely to fail if they are small, relative to the extra

large bank sample. Banks are more likely to fail if they are regulated by the Federal

Reserve, while they are also less likely to fail if they the bank has been established

for awhile.

Comparing the estimates of balance sheet variables in the failed bank probit anal-

ysis in Table 2.6A to the entry into TARP probit analysis in Table 2.5B, banks are

more likely to fail or receive TARP capital if they have lower tier one ratios, lower

ROAs, more real estate loans, and less non-performing loans. I also find similarities

between the probit analysis on entry into TARP and bank failures when examining

off-balance sheet variables. Banks are more likely to enter into TARP with greater

standby letters of credit, but the more standby letters of credit a bank has the more

likely a bank is also to fail. The relationship between certain balance sheet and off-

balance sheet items on entry into TARP and bank failure, provides evidence that

TARP may have helped banks that otherwise would have failed.

2.6 Bank Failure Robustness Check

Since the probit analysis on bank failure does not incorporate bank characteristics

over time, I use a Cox proportional hazard regression to test whether certain bank,

size and regulatory characteristics have an effect on bank failure. Wheelock and

58

Wilson (1995 and 2000) state that a Cox proportional model allows the estimation to

incorporate changes in bank characteristics over time, whereas discrete choice models

only allow estimation on the probability that a bank fails within a specified time

interval.79 Using data from Kansas banks in 1910-1926, Wilson and Wheelock (1995)

find that weakly capitalized banks and those holding few reserves were more likely

to fail. More recently, Ng and Roychowhury (2014) use a Cox proportional model to

examine the effects of pre-crisis loan loss reserves on bank failures and find that the

higher level of total capital, the less likely a bank is to fail. Further, a higher level of

tier one capital is associated with a lower likelihood of bank failure80

The Cox proportional model incorporates information about the time before an

event occurs (time-to-failure). The probability of failure, which is called the hazard

rate, can be estimated by:

H(t) = H0(t) ∗ exp(β1X1 + β2X2 + β3X3 + ...+ βNXN) (2.4)

where,

X1..XN = Tier 1 ratio, troubled assets ratio, ROA, real estate loans, non-performing

loans, standby letters of credit, assets securitized or sold with recourse, commercial

letters of credit, amount of recourse exposure, all other off-balance sheet items; con-

trols= number of branches, age of institution, Federal Reserve regulator, bank size

controls; H0(t) = baseline hazard at time(t).

The hazard rate is the instantaneous rate a bank fails at time t, conditional on the

bank surviving until time t. The Cox proportional hazard model is a semi-parametric

model, because the baseline hazard function, H0(t), does not have to be specified.

79In discrete choice models, like the probit, bank characteristics are necessarily assumed to beconstant.

80Cole and Wu (2009) split their 1980-1992 Call Report data into small banks and large banksand find that smaller banks with higher levels of non-performing loans are more likely to fail.

59

Since the hazard function is not restricted to a specific form, this model is flexible

and allows for a different parameter to be used for each survival time. The hazard

ratio can be interpreted similar to a probit or logit regression and thus is reported in

Table 2.7. The hazard ratio can be obtained by dividing both sides of the previous

equation by H0 and taking the logarithm. The hazard ratio is estimated by:

ln(H(t)

H0

) = β1X1 + β2X2 + β3X3 + ...+ βNXN (2.5)

A hazard ratio of 1.0 means that the hazard rate of the failed and non-failed bank

group is equal. A hazard ratio other than one indicates a difference in hazard rates

between groups. More specifically, the coefficient on the explanatory bank character-

istic and regulatory variables represents a proportional change in the hazard rate for

a one-unit change in the explanatory variable.

Results from Table 2.7 show that banks with a higher Tier 1 ratio and more real

estate loans were less likely to fail with government assistance from December 2007-

December 2011. More specifically, banks with a 0.01 unit increase of real estate loans

to total loans were 1.0155 more likely to fail with government assistance than those

that were not. Also, banks with more branches, or banks that were smaller than the

large bank grouping, were less likely to fail than those that were not. Further, this

analysis shows that banks with a 0.01 unit increase in the tier one ratio are 0.5689

times less likely fail during 2007-2011. In the probit analysis in Table 2.6B, I find

that a bank with a tier one ratio equal to the mean level that has a one unit increase

in tier one is less likely to fail by 0.084.

There were a few minor differences compared to Tables 2.6A-B. For example,

banks with a 0.01 unit increase of ROA (net income/total assets) were 1.0037 times

60

as likely to fail than those that were not, whereas the marginal effect for a bank with

a mean level of ROA on bank failure is -0.6%. A mild difference is also found with

standby letters of credit, although the hazard ratio is close to 1.0, which indicates that

they are not statistically different than 1.0. Hazard ratios including undercapitalized

mergers are also very similar to those for government assisted failures. Hazard ratio

results are directionally the same as the probit analysis for real estate loans, non-

performing loans and amount of recourse exposure. Overall the Cox proportional

hazard model yields results that mirror those in Tables 2.6A-B.81

2.7 Conclusion

The overwhelming increase of asset-backed commercial paper and securitized col-

lateral during the financial crisis in 2007-2009, coupled with the increasing number

of bank failures and government bank bailouts suggest that a bank’s involvement in

securitized banking may change the likelihood that a bank fails or enters into TARP.

In this paper, I estimate this potential relationship using a probit model for entry

into TARP and bank failures during 2007-2011. I find that a bank is more likely to

receive TARP funding if it has more standby letters of credit and commercial letters

of credit. Similarly, a bank is more likely to fail if it has more standby letters of

credit. My paper is the first to report this striking similarity in bank failures and

entry into TARP as most literature does not include bank characteristics related to

nontraditional banking activities.

However, the literature does include balance sheet bank characteristics when es-

timating entry into TARP or bank failure. I find that banks that enter into TARP

81I have included TARP as an explanatory variable, like Ng and Roychowhury (2014), and foundTARP to be < 1.0 and significant, indicating that banks that enter into TARP are less likely to failthan those that do not. To allow for comparison to the explanatory variables in the failed bank andTARP logistic regressions, I left TARP as an explanatory variable out.

61

have a lower tier one ratio, lower ROAs and more real estate loans, which is consistent

with the result that Li (2013) and Duchin and Sosyura (2012) find. I also find that

banks are more likely to fail during 2007-2011 with lower tier one ratios, lower ROA

and more real estate loans. The significance of these balance sheet characteristics

are also found in Cole and Gunther (1998). There is also a striking similarity when

examining balance sheet bank characteristics in the two independent probit analyses

on bank failures and entry into TARP. The relationship between some balance sheet

and off-balance sheet items on bank failure is directionally similar to those on entry

into TARP, which suggests that the Troubled Asset Relief Program (TARP) may

have helped banks that otherwise would have failed.

62

Figure 2.1: Bank Failures and Undercapitalized Mergers, 2007-2011

63

Table 2.1: Bank Failures from December 2007-December2011

State Number of Bank 90+ Day Delinquency Rank Among StatesFailures Rate on Delinquency Rate

GA 59 8.0% 6IL 42 9.2% 4FL 33 17.4% 1CA 27 7.0% 13MN 15 4.6% 38WA 14 6.2% 23AZ 10 7.1% 12MO 9 4.6% 40NV 8 13.4% 2MI 8 6.5% 20

Bank mergers and failures that occurred with government assistance duringDecember 2007-December 2011 are included. CoreLogic Nationals Foreclosuredata from December 2011 is used for 90+ Day Delinquency Rate. Other no-table states missing from top bank failure list: NJ was ranked 3 and MD ranked5 for 90+ day delinquency rates.

64

Tab

le2.

2:C

orre

lati

onM

atri

x

Tie

r1

Tro

uble

dR

OA

Real

Est

ate

Non-p

erf

orm

ing

Sta

ndby

Ass

ets

Com

merc

ial

Am

ount

of

All

oth

er

rati

oA

sset

Rati

oL

oans

(%)

Loans

(%)

Lett

ers

of

Securi

tized

or

Lett

ers

of

Recours

eO

ff-B

ala

nce

Cre

dit

(%)

Sold

wit

hC

redit

(%)

Exp

osu

re(%

)Sheet

Recours

e(%

)It

em

s(%

)

Tie

r1

Rati

o(%

)1.0

000

Tro

uble

dA

ssets

Rati

o(%

)-0

.0693

1.0

000

RO

A(%

)0.1

905

-0.0

437

1.0

000

Real

Est

ate

Loans

(%)

-0.1

441

0.0

572

-0.1

206

1.0

000

Non-P

erf

orm

ing

Loans

(%)

0.1

137

-0.0

471

0.0

515

-0.2

429

1.0

000

Sta

ndby

Lett

ers

of

Cre

dit

(%)

-0.0

642

-0.0

094

-0.0

048

-0.0

481

-0.0

368

1.0

000

Ass

ets

Securi

tized

or

Sold

0.0

304

-0.0

013

0.0

233

-0.0

221

0.0

083

0.0

032

1.0

000

wit

hR

ecours

e(%

)

Com

merc

ial

Lett

ers

of

Cre

dit

(%)

-0.0

054

-0.0

046

-0.0

015

-0.0

387

-0.0

011

0.1

002

-0.0

017

1.0

000

Am

ount

of

Recours

eE

xp

osu

re(%

)0.0

210

0.0

005

0.0

199

-0.0

041

0.0

004

0.0

044

0.1

651

0.0

016

1.0

000

All

oth

er

Off

-Bala

nce

Sheet

-0.0

232

-0.0

029

0.0

081

-0.0

493

-0.0

017

0.1

358

0.0

004

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65

Table 2.3: Summary Statistics for TARP Banks

TARP Banks TARP Banks Non-TARP All Banks(excl. big 8) Banks

Panel A: Balance Sheet and Off-Balance Sheet

Assets ($ billions) 11.1 3.32 0.58 1.61(9.69) (1.63) (9) (3.16)

Tier 1 Ratio (%) 9.46 9.10 12.2 11.9(8.00) (6.52) (10.2) (9.99)

Troubled Assets Ratio (%) 16.5 16.8 15.2 15.3(14.7) (14.8) (24.7) (23.9)

ROA (%) 8.68 8.22 10.4 10.2(13.49) (12.96) (22.11) (21.4)

Real Estate Loans (%) 73.6 75.0 68.7 69.2(17.6) (14.5) (19.9) (19.7)

Non-Performing Loans (%) 14.6 13.3 24.8 23.7(24.0) (22.2) (33.3) (32.6)

Standby Letters of Credit (%) 112 107 40.5 47.5(196) (183) (88.8) (106)

Assets Securitized or Sold 139 90 94.2 99with Recourse (%) (925) (677) (872) (878)

Commercial Letters of Credit (%) 11.5 8.35 5.3 5.9(91.8) (36.5) (27.8) (3.90)

Amount of Recourse Exposure (%) 14.9 12.9 13 13.2(79.8) (67.6) (160) (154)

All other Off-Balance Sheet 129 76.1 117 117.9Items (%) (1189) (648) (7065) (6722)

No. of branches 54.05 36.98 8.08 13.72(317) (168) (60.43) (125)

Age of institution 63.51 62.65 68.34 67.73(53.04) (51.84) (44.42) (45.62)

Panel B: Failure and Regulatory

No. of Banks 743 712 6886 7629

Regulator Federal Reserve 127 124 640 767

Failure with Gov’t Assistance 12 12 350 362

Private Merger 130 130 489 619

Undercapitalized Merger 0 0 11 11

All balance sheet and off-balance sheet mean values are reported from Quarter 3, 2008 Call Reports. Assetsare in billions. Standard deviations are reported in parentheses. All failures and mergers reported are for thosethat occur starting in December 2007-December 2011. Banks that have a Tier 1 ratio of < 0.04 and merge, areclassified by the FDIC as ‘undercapitalized’ and thus the FDIC would begin to set up a plan with the bank forre-capitalization.

66

Table 2.4: Summary Statistics on Failed and UndercapitalizedBanks

Bank Undercapitalized Non-FailFailures Mergers Banks

2007-2011 2007-2011 2007-2011

Panel A: Summary Statistics for Banks

Assets ($ billions) 0.61 0.59 1.44(1.65) (1.62) (2.9)

Tier 1 Ratio (%) 1.14 1.12 12.5(2.82) (3.05) (11)

Troubled Assets Ratio (%) 253 249 8.23(3065) (301) (12.8)

ROA (%) -12.9 -12.8 9.98(14.8) (14.8) (2.08)

Real Estate Loans (%) 84.0 83.9 67.4(13.0) (13.0) (19.8)

Non-Performing Loans (%) 5.86 5.89 29.8(12.1) (12) (35.7)

Standby Letters of Credit (%) 31.8 31.3 47(61.6) (60.6) (109)

Assets Securitized or 37.4 35.9 95.9Sold with Recourse (%) (302) (296) (914)

Commercial Letters of Credit (%) 3.70 3.55 6.10(22.1) (21.7) (44.3)

Amount of Recourse Exposure (%) 2.72 2.62 13.4(14.9) (14.6) (162)

All other Off-Balance Sheet 9.33 8.97 157Items (%) (72.6) (71.2) (8879)

No. of branches 9.07 8.91 13.41(22.6) (22.3) (125)

Age of institution 28.5 28.3 71.7(36.6) (36.25) (44.6)

Panel B: Number of Banks

No. of Banks 372 387 6580

% of TARP banks 3.2 3.1 11.1

Regulator Federal Reserve 44 45 641

All data on failed banks are from the quarter before a bank fails. Average values andstandard deviations are reported. Failed banks during the recession include banks thatfailed and received government assistance from December 2007-December 2011. I alsoinclude undercapitalized mergers, which are mergers with a bank having a Tier One ratioof <0.04 at the time of the merger.

67

Table 2.5: A: Probit analysis: TARP

Variables All Banks Less Well-Capitalized Well-Capitalized

(1) (2) (3) (4) (5) (6)

Tier 1 Ratio -12.68∗∗∗ -6.59∗∗∗ -10.16∗∗∗ -3.84 -4.73∗∗∗ -1.41(0.95) (1.14) (2.59) (2.89) (1.37) (1.72)

Troubled Assets Ratio -0.54∗∗∗ -0.60∗∗∗ -0.81∗∗∗ -0.80∗∗∗ 0.27 -0.003(0.11) (0.13) (0.13) (0.15) (0.23) (0.27)

ROA -0.78∗∗ -2.13∗∗∗ -1.48∗∗∗ -3.12∗∗∗ -0.09 -0.88(0.31) (0.40) (0.43) (0.51) (0.45) (0.64)

Real Estate Loans 0.74∗∗∗ 0.48∗∗ 1.17∗∗∗ 0.74∗∗∗ 0.14 -0.98(0.14) (0.17) (0.18) (0.21) (0.22) (0.31)

Non-Performing Loans -0.39∗∗∗ -0.22∗∗ -0.48∗∗∗ -0.25∗∗ -0.22 -0.19(0.08) (0.09) (0.11) (0.12) (0.14) (0.18)

Standby Letters of Credit 0.23∗∗∗ 0.14∗∗∗ 0.29∗∗∗ 0.18∗∗∗ 0.15∗∗∗ 0.09∗∗∗

(0.02) (0.02) (0.02) (0.03) (0.03) (0.03)

Assets Securitized or 0.009∗∗∗ 0.009∗∗ 0.005 0.004 0.006∗∗ 0.018∗∗

Sold with Recourse (0.003) (0.004) (0.004) (0.005) (0.006) (0.007)

Commercial Letters of Credit 0.13∗∗ 0.04 0.28∗∗∗ 0.14 0.05 -0.04(0.06) (0.07) (0.09) (0.10) (0.07) (0.13)

Amount of Recourse Exposure -0.003 -0.02 0.007 -0.01 -0.004 -0.03(0.017) (0.02) (0.024) (0.03) (0.020) (0.04)

All other Off-Balance Sheet 0.006 0.004 0.009 0.007 -0.008 -0.01Items (0.060) (0.005) (0.009) (0.007) (0.017) (0.02)

Constant -0.53∗∗∗ 0.04 -0.91∗∗∗ -0.31 -1.35 0.66∗

(0.15) (0.18) (0.26) (0.30) (0.25) (0.35)

Controls:No. of branches 0.0005∗∗ 0.0003∗∗ 0.0113∗∗

(0.0002) (0.0001) (0.0045)

Age of institution -0.0028∗∗∗ -0.0020∗∗∗ -0.0054∗∗∗

(0.0006) (0.0006) (0.0012)

Regulator Federal Reserve 0.0802 0.0787 0.0918(0.0687) (0.0794) (0.1447)

Extra Small -1.24∗∗∗ -1.2730∗∗∗ -0.9275∗∗∗

(0.14) (0.1917) (0.2182)

Small -1.04∗∗∗ -0.9643∗∗∗ -0.8922∗∗∗

(0.09) (0.1122) (0.1812)

Medium -0.88∗∗∗ -0.7848∗∗∗ -0.8052∗∗∗

(0.07) (0.0901) (0.1661)

Large -0.51∗∗∗ -0.4814∗∗∗ -0.3328∗∗

(0.06) (0.0718) (0.1426)

Observations 6771 5256 3632 3148 3138 3138Pseudo R-squared 0.12 0.18 0.09 0.15 0.06 0.17

Probit estimates corresponding to the entire TARP sample are reported. Extra small refers to banks with assets in Quarter 3, 2008 inthe lowest quintile (assets<=$51,687,000); small refers to banks with assets <=$100,271,000 and >$51,687,000; medium refers to bankswith assets <=$180,418,000 and >$100,271,000; large refers to banks with assets <=$389,741,000 and >$180,271,000. The top quintile,or extra large group has assets <=$390,222,000

68

Table 2.5: B: Probit analysis: TARP (excluding required 8)

Variables All Banks Less Well-Capitalized Well-Capitalized

(1) (2) (3) (4) (5) (6)

Tier 1 Ratio -13.19∗∗∗ -7.14∗∗∗ -8.44∗∗∗ -2.78 -4.91∗∗∗ -1.88(0.98) (1.18) (2.64) (2.93) (1.42) (1.77)

Troubled Assets Ratio -0.59∗∗∗ -0.64∗∗∗ -0.85∗∗∗ -0.83∗∗∗ 0.21 -0.02(0.11) (0.13) (0.14) (0.15) (0.23) (0.27)

ROA -1.11∗∗∗ -2.39∗∗∗ -1.93∗∗∗ -3.55∗∗∗ -0.34 -0.97∗∗

(0.31) (0.41) (0.44) (0.53) (0.48) (0.64)

Real Estate Loans 0.95∗∗∗ 0.66∗∗∗ 1.26∗∗∗ 0.84∗∗∗ 0.52∗∗ 0.40(0.15) (0.18) (0.19) (0.22) (0.25) (0.33)

Non-Performing Loans -0.50∗∗∗ -0.28∗∗∗ -0.53∗∗∗ -0.28∗∗ -0.42∗∗∗ -0.29(0.09) (0.10) (0.11) (0.12) (0.15) (0.19)

Standby Letters of Credit 0.23∗∗∗ 0.14∗∗∗ 0.30∗∗∗ 0.19∗∗∗ 0.15∗∗∗ 0.10∗∗∗

(0.02) (0.02) (0.03) (0.03) (0.03) (0.03)

Assets Securitized or 0.004 0.005 -0.01 -0.01 0.006 0.017∗∗

Sold with Recourse (0.004) (0.004) (0.01) (0.01) (0.003) (0.007)

Commercial Letters of Credit 0.14∗∗ 0.05 0.29∗∗∗ 0.15 0.006 -0.03(0.06) (0.07) (0.09) (0.10) (0.130) (0.13)

Amount of Recourse Exposure -0.002 -0.02 0.001 -0.01 -0.005 -0.04(0.023) (0.03) (0.025) (0.03) (0.03) (0.04)

All other Off-Balance Sheet 0.01 0.003 0.01 0.007 -0.007 -0.013Items (0.01) (0.008) (0.01) (0.009) (0.017) (0.02)

Constant -0.60∗∗∗ -0.07 -1.07∗∗∗ -0.45 -1.56 -0.92(0.15) (0.18) (0.26) (0.30) (0.27) (0.37)

Controls:No. of branches 0.0004∗ 0.0003 0.0110∗∗

(0.0002) (0.0002) (0.0048)

Age of institution -0.0026∗∗∗ -0.0017∗∗∗ -0.0048∗∗∗

(0.0006) (0.0006) (0.0012)

Regulator Federal Reserve 0.093 0.086 0.110(0.069) (0.079) (0.145)

Extra Small -1.15∗∗∗ -1.25∗∗∗ -0.78∗∗∗

(0.14) (0.19) (0.22)

Small -0.97∗∗∗ -0.93∗∗∗ -0.76∗∗∗

(0.09) (0.11) (0.19)

Medium -0.84∗∗∗ -0.78∗∗∗ -0.75∗∗∗

(0.07) (0.09) (0.17)

Large -0.44∗∗∗ -0.45∗∗∗ -0.24(0.07) (0.07) (0.14)

Observations 6748 5243 3617 3138 3131 2105Pseudo R-squared 0.13 0.18 0.09 0.15 0.07 0.16

Probit estimates excluding the eight banks that were required to take TARP (and their subsidiaries) are reported. Extra small refers tobanks with assets in Quarter 3, 2008 in the lowest quintile (assets<=$51,664,000); small refers to banks with assets <=$99,883,000 and>$51,664,000; medium refers to banks with assets <=$179,361,000 and >$99,883,000; large refers to banks with assets <=$386,889,000and >$179,361,000. The top quintile, or extra large group has assets <=$386,889,000

69

Table 2.5: C: TARP Probit Marginal Estimates

Variables All Banks Less Well-Capitalized Well-Capitalized

(1) (2) (3) (4) (5) (6)

Tier 1 Ratio -1.790∗∗∗ -1.032∗∗∗ -2.266∗∗∗ -0.855 -0.352∗∗∗ -0.103(0.122) (0.177) (0.576) (0.644) (0.098) (0.126)

Troubled Assets Ratio -0.076∗∗∗ -0.094∗∗∗ -0.181∗∗∗ -0.179∗∗∗ 0.020 -0.0002(0.015) (0.020) (0.029) (0.033) (0.017) (0.0198)

ROA -0.111∗∗ -0.334∗∗∗ -0.330∗∗∗ -0.694∗∗∗ -0.007 -0.064∗∗

(0.044) (0.062) (0.094) (0.113) (0.034) (0.047)

Real Estate Loans 0.105∗∗∗ 0.075∗∗ 0.260∗∗∗ 0.165∗∗∗ 0.011 0.0072(0.019) (0.027) (0.040) (0.047) (0.016) (0.0225)

Non-Performing Loans -0.056∗∗∗ -0.035∗∗ -0.107∗∗∗ -0.056∗∗ -0.016 -0.0139(0.012) (0.016) (0.024) (0.027) (0.010) (0.0131)

Standby Letters of Credit 0.032∗∗∗ 0.022∗∗∗ 0.066∗∗∗ 0.041∗∗∗ 0.011∗∗∗ 0.0064∗∗∗

(0.003) (0.003) (0.005) (0.006) (0.002) (0.0024)

Assets Securitized or 0.0012∗∗∗ 0.0013∗∗ 0.001 0.0009 0.0005∗∗∗ 0.0013∗∗

Sold with Recourse (0.0004) (0.0006) (0.001) (0.0011) (0.0002) (0.0005)

Commercial Letters of Credit 0.018∗∗ 0.006 0.063∗∗∗ 0.031 0.004 -0.0031(0.009) (0.010) (0.021) (0.021) (0.005) (0.0093)

Amount of Recourse Exposure -0.0004 -0.003 0.002 -0.003 -0.0003 -0.0026(0.0024) (0.004) (0.005) (0.006) (0.0029) (0.0011)

All other Off-Balance Sheet 0.0008 0.0007 0.002 (0.0016) -0.0006 -0.0010Items (0.0008) (0.0009) (0.002) (0.0017) (0.0012) (0.0014)

Controls:No. of branches 0.0001∗∗ 0.0001∗∗ 0.0008∗∗

(0.0000) (0.0000) (0.0003)

Age of institution -0.0004 -0.0004∗∗∗ 0.0004∗∗∗

(0.0009) (0.0001) (0.0001)

Regulator Federal Reserve 0.013 0.0181 0.0072(0.012) (0.0188) (0.0121)

Extra Small -0.105∗∗∗ -0.1514∗∗∗ -0.043∗∗∗

(0.006) (0.0010) (0.007)

Small -0.106∗∗∗ -0.1451∗∗∗ -0.046∗∗∗

(0.007) (0.0109) (0.008)

Medium -0.099∗∗∗ -0.1332∗∗∗ -0.042∗∗∗

(0.007) (0.0116) (0.0073)

Large -0.067∗∗∗ -0.0942∗∗∗ -0.0205∗∗∗

(0.007) (0.0124) (0.0076)

Observations 6769 6769 3632 3148 3138 3138

Marginal effects for the probit analysis are reported at the mean for the full TARP sample, probit estimates are reported in Table 2.5A.Extra small refers to banks with assets in Quarter 3, 2008 in the lowest quintile (assets<=$51,687,000); small refers to banks with assets<=$100,271,000 and >$51,687,000; medium refers to banks with assets <=$180,418,000 and >$100,271,000; large refers to banks withassets <=$389,741,000 and >$180,271,000. The top quintile, or extra large group has assets <=$390,222,000

70

Table 2.5: D: TARP Probit Marginal Estimates (excluding required 8)

Variables All Banks Less Well-Capitalized Well-Capitalized

(1) (2) (3) (4) (5) (6)

Tier 1 Ratio -1.739∗∗∗ -1.069∗∗∗ -1.837∗∗∗ -0.608 -0.321∗∗∗ -0.133(0.117) (0.174) (0.573) (0.641) (0.090) (0.126)

Troubled Assets Ratio -0.079∗∗∗ -0.095∗∗∗ -0.185∗∗∗ -0.181∗∗∗ 0.013 -0.001(0.015) (0.019) (0.029) (0.033) (0.015) (0.002)

ROA -0.147∗∗∗ -0.358∗∗∗ -0.420∗∗∗ -0.776∗∗∗ -0.022 -0.068(0.041) (0.061) (0.095) (0.114) (0.031) (0.046)

Real Estate Loans 0.126∗∗∗ 0.098∗∗∗ 0.274∗∗∗ 0.184∗∗∗ 0.034∗∗ 0.028(0.019) (0.027) (0.040) (0.047) (0.016) (0.023)

Non-Performing Loans -0.067∗∗∗ -0.042∗∗∗ -0.116∗∗∗ -0.061∗∗ -0.027∗∗∗ -0.020(0.011) (0.015) (0.024) (0.027) (0.010) (0.013)

Standby Letters of Credit 0.031∗∗∗ 0.022∗∗∗ 0.066∗∗∗ 0.041∗∗∗ 0.010∗∗∗ 0.007∗∗∗

(0.002) (0.003) (0.005) (0.006) (0.002) (0.002)

Assets Securitized or 0.0005 0.0007 -0.002 -0.002 0.0004∗ 0.0001Sold with Recourse (0.0005) (0.0007) (0.002) (0.002) (0.0002) (0.0005)

Commercial Letters of Credit 0.018∗∗ 0.007 0.064∗∗∗ 0.033∗ 0.0004 -0.002(0.008) (0.010) (0.020) (0.021) (0.0085) (0.009)

Amount of Recourse Exposure -0.0003 -0.004 0.003 -0.004 -0.0003 -0.0026(0.0026) (0.004) (0.006) (0.006) (0.002) (0.0031)

All other Off-Balance Sheet 0.0007 0.0004 0.002 0.0001 -0.0004 -0.0009Items (0.0009) (0.0010) (0.002) (0.002) (0.0011) (0.0013)

Controls:No. of branches 0.0001∗∗ 0.0001 0.0008∗∗

(0.0000) (0.0001) (0.0003)

Age of institution -0.0004∗∗∗ -0.0004∗∗∗ -0.0003∗∗∗

(0.0000) (0.0001) (0.0001)

Regulator Federal Reserve 0.015∗∗ 0.020∗ 0.008(0.011) (0.019) (0.012)

Extra Small -0.096∗∗∗ -0.147∗∗∗ -0.037∗∗∗

(0.006) (0.010) (0.008)

Small -0.097∗∗∗ -0.139∗∗∗ -0.040∗∗∗

(0.007) (0.011) (0.008)

Medium -0.091∗∗∗ -0.129∗∗∗ -0.039∗∗∗

(0.007) (0.011) (0.007)

Large -0.056∗∗∗ -0.087∗∗∗ -0.015(0.007) (0.012) (0.009)

Observations 6748 5423 3616 3616 3130 3130

Marginal effects for the probit analysis are reported at the mean for the TARP sample excluding the eight banks (and their subsidiaries)from the sample. Probit estimates are reported in Table 2.5B. Extra small refers to banks with assets in Quarter 3, 2008 in the lowestquintile (assets<=$51,664,000); small refers to banks with assets <=$99,883,000 and >$51,664,000; medium refers to banks with assets<=$179,361,000 and >$99,883,000; large refers to banks with assets <=$386,889,000 and >$179,361,000. The top quintile, or extra largegroup has assets <=$386,889,000

71

Table 2.6: A: Failed and Undercapitalized Banks Probit Analysis (2007-2011)

Variables Bank Failures Bank Failures &Undercapitalized Mergers

(1) (2) (3) (4)

Tier 1 Ratio -17.82∗∗∗ -25.18∗∗∗ -18.05∗∗∗ -25.60∗∗∗

(0.37) (0.62) (0.36) (0.61)

Troubled Assets Ratio 0.015∗∗∗ 0.025∗∗∗ 0.016∗∗∗ 0.024∗∗∗

(0.002) (0.003) (0.002) (0.003)

ROA -1.34∗∗∗ -1.78∗∗∗ -1.36∗∗∗ -1.74∗∗∗

(0.06) (0.13) (0.06) (0.13)

Real Estate Loans (%) 1.87∗∗∗ 2.02∗∗∗ 1.85∗∗∗ 2.01∗∗∗

(0.07) (0.11) (0.06) (0.10)

Non-Performing Loans (%) -0.29∗∗∗ -0.45∗∗∗ -0.30∗∗∗ -0.46∗∗∗

(0.04) (0.06) (0.04) (0.06)

Standby Letters of Credit (%) 0.068∗∗∗ 0.03∗∗ 0.066∗∗∗ 0.027∗

(0.009) (0.01) (0.009) (0.014)

Assets Securitized or -0.017∗∗∗ -0.004 -0.019∗∗∗ -0.005Sold with Recourse (%) (0.003) (0.004) (0.003) (0.004)

Commercial Letters of Credit 0.021 -0.068 0.011 -0.082(0.003) (0.052) (0.034) (0.053)

Amount of Recourse Exposure -0.006 -0.035∗∗ -0.003 -0.029∗∗

(0.011) (0.015) (0.011) (0.014)

All other Off-Balance Sheet -0.05∗∗∗ -0.029∗∗∗ -0.051∗∗∗ -0.030∗∗∗

Items (0.01) (0.008) (0.007) (0.008)

Constant -1.64∗∗∗ -2.64∗∗∗ -1.59∗∗∗ -2.54∗∗∗

(0.06) (0.21) (0.06) (0.20)

Controls:Number of branches 0.0001 -0.0001

(0.00001) (0.0001)

Age of institution -0.0095∗∗∗ -0.0096∗∗∗

(0.0003) (0.0003)

Regulator Federal Reserve 0.11∗∗∗ 0.11∗∗∗

(0.03) (0.03)

Extra small -0.46∗∗∗ -0.41∗∗∗

(0.06) (0.06)

Small -0.59∗∗∗ -0.54∗∗∗

(0.05) (0.04)

Medium -0.41∗ -0.41∗∗∗

(0.04) (0.03)

Large -0.17 -0.16∗∗∗

(0.03) (0.03)

Quarter Fixed Effects No Yes No Yes

Observations 112759 86115 112735 86115Pseudo R-squared 0.23 0.39 0.23 0.25

Probit estimates are reported.Extra small refers to banks with assets in Quarter 3, 2008 in the lowestquintile (assets<=$51,687,000); small refers to banks with assets <=$100,271,000 and >$51,687,000;medium refers to banks with assets <=$180,418,000 and >$100,271,000; large refers to bankswith assets <=$389,741,000 and >$180,271,000. The top quintile, or extra large group has assets<=$390,222,000

72

Table 2.6: B: Failed and Undercapitalized Banks Probit Marginal Esti-mates

Variables Bank Failures Bank Failures &Undercapitalized Mergers

(1) (2) (3) (4)

Tier 1 Ratio -0.1912∗∗∗ -0.0843∗∗∗ -0.5086∗∗∗ -0.0905∗∗∗

(0.0133) (0.0074) (0.0136) (0.0077)

Troubled Assets Ratio 0.0004∗∗∗ 0.0001∗∗∗ 0.0004∗∗∗ 0.0001∗∗∗

(0.0000) (0.0000) (0.0001) (0.0000)∗∗

ROA -0.0370∗∗∗ -0.0060∗∗∗ -0.0384∗∗∗ -0.0061∗∗∗

(0.0020) (0.0006) (0.0020) (0.0006)

Real Estate Loans 0.0514∗∗∗ 0.0068∗∗∗ 0.0520∗∗∗ 0.0071∗∗∗

(0.0018) (0.0006) (0.0020) (0.0007)

Non-Performing Loans -0.0080∗∗∗ -0.0015∗∗∗ -0.0083∗∗∗ -0.0016∗∗∗

(0.0011) (0.0002) (0.0011) (0.0002)

Standby Letters of Credit 0.0018∗∗∗ 0.0001∗∗ 0.0018∗∗∗ 0.0001∗∗

(0.0002) (0.0000) (0.0003) (0.0000)

Assets Securitized or -0.0005 1e−5 -0.0005∗∗∗ 2e−5

Sold with Recourse (0.0001) (1e−5) (0.0001) (1e−5)

Commercial Letters of Credit 0.0006 -0.0002 0.0003 -0.0003(0.0009) (0.0001) (0.0010) (0.0002)

Amount of Recourse Exposure -0.0002 -0.0001∗∗ -0.0007 -0.0001∗∗

(0.0003) (0.0000) (0.0003) (0.0000)

All other Off-Balance Sheet -0.0014∗∗∗ -0.0001∗∗∗ -0.0014∗∗∗ -0.0001∗∗∗

Items (0.0002) (0.0000) (0.0002) (0.0000)

Controls:No. of branches 4e−7 5e−7

(0.0000) (0.0000)

Age of institution -3e−5∗∗∗ -3e−5∗∗∗

(0.0000) (0.0000)

Regulator Federal Reserve 0.0004∗∗∗ 0.0005∗∗∗

(0.0001) (0.0001)

Extra small -0.0010∗∗∗ -0.0009∗∗∗

(0.0001) (0.0001)

Small -0.0012∗∗∗ -0.0012∗∗∗

(0.0001) (0.0001)

Medium -0.0010∗∗∗ -0.0011∗∗∗

(0.0001) (0.0001)

Large -0.0005 0.0005∗∗∗

(0.0001) (0.0001)

Quarter Fixed Effects No Yes No Yes

Observations 112735 86115 112735 86115

Marginal effects for the probit analysis are reported at the mean. Extra small refers to banks withassets in Quarter 3, 2008 in the lowest quintile (assets<=$51,687,000); small refers to banks withassets <=$100,271,000 and >$51,687,000; medium refers to banks with assets <=$180,418,000 and>$100,271,000; large refers to banks with assets <=$389,741,000 and >$180,271,000. The top quintile,or extra large group has assets <=$390,222,000

73

Table 2.7: Robustness Check: Cox Proportional Hazard Ra-tios

Variables Gov’t Assistance Bank Failures &Undercapitalized Mergers

Tier 1 Ratio 0.5689∗∗∗ 0.5704∗∗∗

(0.0119) (0.0117)

Troubled Assets Ratio 1.0000∗ 1.0001∗

(7e−5) (7e−5)

ROA 1.0037 1.0042(0.0052) (0.0051)

Real Estate Loans 1.0155∗∗ 1.0153∗∗

(0.0072) (0.0070)

Non-Performing Loans 0.9620∗∗∗ 0.9618∗∗∗

(0.0067) (0.0066)

Standby Letters of Credit 0.9917∗∗∗ 0.9917∗∗∗

(0.0010) (0.0010)

Assets Securitized or 1.0000 1.0000Sold with Recourse (1e−5) ((1e−5)

Commercial Letters of Credit 1.0026 1.0022(0.0026) (0.0027)

Amount of Recourse Exposure 0.9972 0.9970(0.0030) (0.0031)

All other Off-Balance Sheet 0.9996 0.9996Items (0.0007) (0.0007)

Controls:No. branches 1.0018∗∗∗ 1.0018∗∗∗

(0.0005) (0.0005)

Age of institution 0.9827∗∗∗ 0.9824∗∗∗

(0.0022) (0.0021)

Regulator Federal Reserve 0.7453 0.7516(0.1535) (0.1528)

Extra small 0.1819∗∗∗ 0.2050∗∗∗

(0.0708) (0.0764)

Small 0.3365∗∗∗ 0.3529∗∗∗

(0.0984) (0.1001)

Medium 0.4579∗∗∗ 0.4629∗∗∗

(0.0963) (0.0962)

Large 0.4630∗∗∗ 0.4801∗∗∗

(0.0745) (0.0760)

Quarter Fixed Effects Yes Yes

Observations 86115 86115

Extra small refers to banks with assets in Quarter 3, 2008 in the lowest quin-tile (assets<=$51,687,000); small refers to banks with assets <=$100,271,000and >$51,687,000; medium refers to banks with assets <=$180,418,000and >$100,271,000; large refers to banks with assets <=$389,741,000 and>$180,271,000. The top quintile, or extra large group has assets <=$390,222,000

74

3 Appendix

Timeline of Key Events to the Financial Crisis

Represented below is a sample of key events that occurred by financial institutions,

the Federal Reserve and the U.S. government from the beginning of 2007 to 2009. A

more complete list with detailed descriptions can be found at the St. Louis Federal

Reserve Timeline of the Financial Crisis.

Table 3.1: Private Bank and U.S. Government Key Events, 2007-2009

Date Event

2007

February 27 Freddie Mac announces it will no longer buy the most risky subprime mortgages and

mortgage-backed securities

June 7 The FOMC maintains the federal funds target rate at 5.25%

July 24 Countrywide Financial warns of ”difficult conditions”

August 6 American Home Mortgage Investment Corporation files for Chapter 11 bankruptcy

August 7 The FOMC maintains the federal funds target rate at 5.25%

September 18 The FOMC reduces the federal funds target rate to 4.75%

October 10 U.S. Treasury announces the HOPE NOW initiative to provide counselor support to investors,

services, mortgage participants and homeowners

October 31 The FOMC reduces the federal funds target rate to 4.50%

December 11 The FOMC reduces the federal funds target rate to 4.25%

December 12 The Federal Reserve announces the creation of a Term Auction Facility (TAF) to auction

fixed amount of funds to depository institutions

2008

January 22 The FOMC reduces the federal funds target rate to 3.5%

January 30 The FOMC reduces the federal funds target rate to 3.0%

February 13 President Bush signs into law the Economic Stimulus Act of 2008

March 7 The Federal Reserve Board announces $50 billion in TAF auctions this month and extends

the TAF for at least 6 months

March 11 The Federal Reserve Board announces the Term Securities Lending Facility (TSLF) which

lends $200 billion of Treasury securities for 28-days against federal agency debt and MBS

March 18 The FOMC reduces the federal funds target rate to 2.25%

March 24 The NY Fed will finance $29 billion of JP Morgan Chase’s acquisition of Bear Stearns

April 30 The FOMC reduces the federal funds target rate to 2%

May 2 The Federal Reserve Board expands TAF auctions from $50 billion to $75 billion

Continued on next page

75

Table 3.1 – Continued from previous page

Date Event

June 5 Bank of America’s offer to buy Countrywide Financial is approved by the Federal Reserve

Board

June 25 The FOMC maintains the federal funds target rate at 2%

July 13 The U.S. Treasury increases credit lines to Fannie Mae and Freddie Mac

July 30 President Bush signs into law the Housing and Recovery Act. This establishes a new Federal

Housing Finance Agency and the Treasury can also purchase GSE obligations

August 5 The FOMC maintains the federal funds target rate at 2%

September 7 The Federal Housing Finance Agency places Fannie Mae and Freddie Mac into conservation-

ship. This allows the FHFA to take control over Fannie Mae and Freddie Mac while the

Treasury gets preferred stock.

September 15 Bank of America announces it will buy Merrill Lynch for $50 billion, gets approved in De-

cember; Lehman Brothers files for Chapter 11 bankruptcy protection

September 16 The FOMC votes to maintain the federal funds target at 2%; The NY Fed lends $85 billion

to AIG

September 17 The U.S. Treasury announces a Supplementary Financing Program consisting of a series of

Treasury bill issues to provide cash for Federal Reserve initiatives; the SEC temporarily bans

short selling in financial company stocks

September 21 Goldman Sachs and Morgan Stanley restructure to become bank holding companies

September 25 Washington Mutual bank is closed, banking operations are bought by JP Morgan Chase

September 29 The FDIC announces Citigroup will purchase Wachovia backed by a FDIC $312 billion loss-

sharing arrangement with Citigroup. The U.S. House of Representatives rejects U.S. Treasury

legislation to purchase troubled assets from financial institutions

October 3 President Bush signs into law the Emergency Economic Stabilization Act. This law estab-

lished the $700 billion Troubled Asset Relief Program (TARP); Wells Fargo announces a

proposal to buy Wachovia without FDIC assistance, this is approved 9 days later

October 6 The Federal Reserve will start paying interest on required and excess reserves

October 7 The FDIC increases deposit insurance coverage to $250,000 per depositor

October 8 The NY Fed borrows $37.8 billion in securities from AIG in return for cash collateral;

The FOMC reduces the federal funds target rate to 1.50%

October 14 U.S. Treasury announces that TARP will purchase $250 billion in capital in financial insti-

tutions under the authority of the Emergency Economic Stabilization Act

October 28 U.S. Treasury purchases the first $125 billion of preferred stock from 9 U.S. banks under the

Capital Purchase Program

October 29 The FOMC reduces the federal funds target rate to 1.00%

Continued on next page

76

Table 3.1 – Continued from previous page

Date Event

November 10 The Federal Reserve Board approves American Express to become a bank holding company;

The U.S. Treasury will purchase $40 billion of AIG preferred shares under TARP while the

NY Fed will lend over $50 billion to AIG

November 20 Fannie Mae and Freddie Mac suspend mortgage foreclosures until January 2009

December 3 The SEC approves a measure to ensure fairer and more transparent credit ratings

December 16 The FOMC reduces the federal funds target rate to 0-0.25%

December 19 The U.S. Treasury loan General Motors $13.4 billion and Chrysler $4.0 billion from TARP

2009

January 5 The NY Fed begins purchasing MBS guaranteed by Fannie Mae, Freddie Mac and Ginnie

Mae

January 16 The U.S. Treasury and FDIC enter into a loan-loss sharing arrangement with Bank of America

on $118 billion in loans and securities; Citigroup is guaranteed $306 billion through a similar

mechanism

February 17 President Obama signs into law the American Recovery and Reinvestment Act of 2009. This

act increases government spending and tax cuts

February 18 The Homeowner Affordability and Stability Plan is created to refinance home mortgages

owned or guaranteed by Fannie Mae and Freddie Mac that are 80% of underlying home

value; U.S. Treasury takes preferred stock of $200 billion in Fannie Mae and Freddie Mac

February 23 The U.S. Treasury, FDIC, OCC, OTS and the Federal Reserve Board issue a statement that

the U.S. government stands behind the banking system. They also promise enough capital

and liquidity

February 25 The U.S. Treasury, FDIC, OCC, OTS and the Federal Reserve Board announce “stress tests”

on U.S. bank-holding companies with assets over $100 billion

March 2 The U.S. Treasury and the Federal Reserve Board announce they will restructure AIG

March 3 The U.S. Treasury and the Federal Reserve Board launch the Term Asset-Backed Securities

Loan Facility (TALF). TALF lends up to $200 billion to owners of AAA-rated asset-backed

securities backed by auto loans, credit loans, student loans and small business loans

March 18 The FOMC maintains the federal funds target rate of 0-0.25%

March 19 The U.S. Treasury announces a new program to provide $5 billion to the auto industry;

IndyMac Federal bank sale by the FDIC is complete. They had total assets of $23.5 billion,

loss to FDIC of $10.7 billion

May 20 Helping Families Save Their Homes Act of 2009 is signed which raises FDIC deposit insurance

guarantees to $250,000 per depositor

June 26 The U.S. Treasury announces that banks under CPP can repurchase warrants following a

multi-step process to determine fair market value

Note: This table contains only major events related to TARP. Source: St. Louis Federal Reserve.

77

October 14, 2008 Treasury Press Release on the CPP

Washington - Treasury today announced a voluntary Capital Purchase Program to

encourage U.S. financial institutions to build capital to increase the flow of financing

to U.S. businesses and consumers and to support the U.S. economy.

Under the program, Treasury will purchase up to $250 billion of senior preferred

shares on standardized terms as described in the program’s term sheet. The program

will be available to qualifying U.S. controlled banks, savings associations, and certain

bank and savings and loan holding companies engaged only in financial activities

that elect to participate before 5:00 pm (EDT) on November 14, 2008. Treasury will

determine eligibility and allocations for interested parties after consultation with the

appropriate federal banking agency.

The minimum subscription amount available to a participating institution is 1

percent of risk-weighted assets. The maximum subscription amount is the lesser of

$25 billion or 3 percent of risk-weighted assets. Treasury will fund the senior preferred

shares purchased under the program by year-end 2008. Institutions interested in

participating in the program should contact their primary federal regulator for specific

enrollment details.

The senior preferred shares will qualify as Tier 1 capital and will rank senior to

common stock and pari passu, which is at an equal level in the capital structure,

with existing preferred shares, other than preferred shares which by their terms rank

junior to any other existing preferred shares. The senior preferred shares will pay

a cumulative dividend rate of 5 percent per annum for the first five years and will

reset to a rate of 9 percent per annum after year five. The senior preferred shares

will be non-voting, other than class voting rights on matters that could adversely

affect the shares. The senior preferred shares will be callable at par after three

years. Prior to the end of three years, the senior preferred may be redeemed with

78

the proceeds from a qualifying equity offering of any Tier 1 perpetual preferred or

common stock. Treasury may also transfer the senior preferred shares to a third party

at any time. In conjunction with the purchase of senior preferred shares, Treasury

will receive warrants to purchase common stock with an aggregate market price equal

to 15 percent of the senior preferred investment. The exercise price on the warrants

will be the market price of the participating institution’s common stock at the time

of issuance, calculated on a 20-trading day trailing average.

Companies participating in the program must adopt the Treasury Department’s

standards for executive compensation and corporate governance, for the period during

which Treasury holds equity issued under this program. These standards generally

apply to the chief executive officer, chief financial officer, plus the next three most

highly compensated executive officers.

The financial institution must meet certain standards, including: (1) ensuring

that incentive compensation for senior executives does not encourage unnecessary

and excessive risks that threaten the value of the financial institution; (2) required

clawback of any bonus or incentive compensation paid to a senior executive based

on statements of earnings, gains or other criteria that are later proven to be materi-

ally inaccurate; (3) prohibition on the financial institution from making any golden

parachute payment to a senior executive based on the Internal Revenue Code pro-

vision; and (4) agreement not to deduct for tax purposes executive compensation in

excess of $500,000 for each senior executive. Treasury has issued interim final rules

for these executive compensation standards.

Nine large financial institutions already have agreed to participate in this pro-

gram, moving quickly and collectively to signal the importance of the program for

the system. These healthy institutions have voluntarily agreed to participate on the

same terms that will be available to small and medium-sized banks and thrifts across

79

the nation.

Testing for Autocorrelation

Given that my data is a time series from 2007 to 2013, the estimates in Equation

(1.1) may be subject to autocorrelation. In order to test whether the model follows

an autoregressive order of lag 1 (AR 1) I test whether εt = ρ · εt−1 + µt such that the

current value of residuals are related to last period’s residuals and a random error.

To test for the presence of AR (1) I compute a Durbin Watson statistic

DW =

∑Tt=2(ε̂t − ˆεt−1)2)∑T

t=1((ε̂t)2)(3.1)

where residuals are saved from estimating Equation (1.1). The null hypothesis is

that there is no first-order autocorrelation. Estimating the Durbin-Watson d statistic

which equals 2(1 − ρ̂) determines whether we reject the null or not. When calcu-

lating the distribution of the d-statistic, empirical upper and lower bounds must be

established. For a test of the null hypothesis of no autocorrelation versus the alter-

native hypothesis that there is autocorrelation of AR(1), the lower bound of the DW

d-statistic is 1.758 and the upper bound is 1.779 with one regressor (excluding the

intercept) and over 200 observations at the 5% level.

If the d statistic is less then the lower bound of the DW d statistic we reject the

null hypothesis that there is no autocorrelation. If the DW d statistic is above the

upper bound of the DW d statistic we cannot reject the null hypothesis that there is

no autocorrelation. If the DW d statistic is between the upper and lower bounds the

test is inconclusive. The DW d statistic calculated from estimating Equation (1.1)

is 1.716 which is slightly below the lower bound. The Durbin Watson test indicates

that we can reject the null hypothesis that there is no autocorrelation.

80

Additionally, I examine whether the error terms follow a white noise process using

the Ljung-Box test. This test relies on regressors being a realization from a white-

noise process. The null hypothesis is that the data is independently distributed and

the alternative hypothesis is that the data is not independently distributed. The test

statistic is given by:

Q = n(n+ 2)m∑j=1

1 · ρ̂2(j)

n− j(3.2)

where m is the number of autocorrelations calculated, in my model m = 1 and n is

the given times series length. ρ̂ is the estimated autocorrelation of the series at a lag

of j. The degrees of freedom is equal to h = m − p − q taken from the ARMA(p,q)

model. Assuming a lag of 1, p=1, and q=0. Therefore, h=0.

If the Q statistic that is estimated is greater than a chi-square distribution at a

given significance level than the null hypothesis is rejected. Such that:

Q > χ21−α,h (3.3)

At a 5% significance level the chi-square distribution reveals an upper statistic of

3.841 and a lower bound of 0.004. Since my estimated Q statistic is 27, I reject the

null hypothesis that the data is independently distributed.

Correcting for Autocorrelation and Heteroskedasticity

The Newey West method devised by Newey and West (1987) suggest that only impor-

tant covariates should be estimated, rather than an estimation of all the covariances

of possible correlated terms over time. This simplication suggests that observations

are more correlated with each other as they get closer together. So, the first step in

estimating Newey-West standard errors is choosing a lag, l=1, for my data, which is

81

the standard lag period when using stock prices. Testing the Durbin-Watson statis-

tic or Ljung-Box test statistic can also verify the lag term. Therefore the estimated

standard errors are calculated by:

ese(β̂i) =T∑t=1

t+L∑t′=t−L

xtxt′ · etet′∑Ts=1 x

2s

(3.4)

Using this correction will yield the same coefficients, but more accurate standard er-

rors as OLS estimation. However, tests on autocorrelation and white noise indicate

that I should correct for autocorrelation and heteroskedasticity. These issues are cor-

rected using the Newey West method in my tables.

Data Construction

CRSP uses the bid-ask midpoint price that is closer to the close price at 4p.m when

reporting data. When CRSP uses this bid-ask midpoint, it represents the stock price

as a negative price in the data. I correct for that using absolute value. An alternate

source for daily stock data is Compustat, which reports the trade based closing price

for the day. Compustat has accessible data until 2007 on WRDS, so I compared the

CRSP stock prices with the Compustat stock prices prior to 2007 and find that the

differences in reporting data is small and negligible. This should not be an issue,

because CRSP data is consistently used for all stock price data.

Further, there are a few days where there are missing price data for some tickers.

This occurs in CRSP, Compustat and also cross-checking on a third-site, Yahoo. For

example, if ticker OVBC is missing stock price data on June 11, 2008 it will be rep-

resented by . in the CRSP. This is due to stock splits, mergers, no trades that day or

a stop put in by the SEC. Instead of dropping the entire bank from my sample, I just

record the previous day price in the missing blank. Since I examine daily returns,

82

this means that there will be no price change from one day to the next and the return

would be 0. Doing this would only bias my results downward, thus not causing much

of an issue.

83

CRSP Value-Weighted Index

The following tables use the CRSP-value weighted index rather than the S&P

500. Comparing cumulative abnormal returns in Appendix Table 3.2 to Table 1.2

and market model results in Appendix Table 3.3A-C and Table 1.3A-C yields similar

results. Results using other market indices such as the NYSE 100 Composite In-

dex, NASDAQ and Dow Jones Industrial Average are not reported, but yield similar

results. They are available upon request.

84

Table 3.2: Abnormal Returns During Three TARP Events

(1) (2)

Banks N Newey-West Newey-West+FF

A. Announcement

TARP 219 0.0851∗∗∗ 0.0574∗∗∗

(0.0094) (0.0089)

Non-TARP 219 0.0333∗∗∗ 0.0162∗∗

(0.0087) (0.0082)

Required 8 8 0.1118∗∗∗ 0.1009∗∗

(0.0378) (0.0378)

Large TARP 74 0.1477∗∗∗ 0.0977∗∗∗

(0.0159) (0.0155)

Large Non-TARP 37 0.1113∗∗∗ 0.0723∗∗∗

(0.0186) (0.0187)

Mid-Size TARP 65 0.0826∗∗∗ 0.0534∗∗∗

(0.0164) (0.0155)

Mid-Size Non-TARP 62 0.0717∗∗∗ 0.0457∗∗∗

(0.0152) (0.0144)

Small TARP 72 0.0201 0.0147

(0.0144) (0.0145)

Small Non-TARP 120 -0.0107 -0.0163

(0.0109) (0.0107)

B. Infusion

TARP 219 -0.0387∗∗∗ -0.0254∗∗∗

(0.0082) (0.0083)

Non-TARP 219 -0.0220∗∗∗ -0.0206∗∗∗

(0.0069) (0.0069)

Required 8 8 -0.1190∗∗ -0.0805∗

(0.0369) (0.0389)

Large TARP 74 -0.0337∗∗∗ -0.0081

(0.0090) (0.0095)

Large Non-TARP 37 -0.0446∗∗∗ -0.0436∗∗∗

(0.0147) (0.0147)

Mid-Size TARP 65 -0.0346∗∗ -0.0285∗∗

(0.0140) (0.0138)

Mid-Size Non-TARP 62 -0.0334∗∗ -0.0312∗∗

(0.0127) (0.0121)

Continued on next page

85

Table 3.2 – Continued from previous page

Banks N Newey-West Newey-West+FF

Small TARP 72 -0.0386∗ -0.0341∗

(0.0189) (0.0191)

Small Non-TARP 120 -0.0091 -0.0080

(0.0096) (0.0098)

C. Capital Repayment

TARP 186 0.0176∗ 0.0191∗∗

(0.0089) (0.0089)

Non-TARP 212 -0.0008 0.0004

(0.0052) (0.0051)

Required 8 8 0.0197 0.0254

(0.0157) (0.0186)

Large TARP 70 0.0054 0.0079

(0.0057) (0.0057)

Large Non-TARP 36 0.0060 0.0064

(0.0085) (0.0071)

Mid-Size TARP 50 0.0113 0.0145∗

(0.0077) (0.0076)

Mid-Size Non-TARP 61 0.0011 0.0035

(0.0087) (0.0089)

Small TARP 58 0.0374 0.0355

(0.0269) (0.0270)

Small Non-TARP 115 -0.0039 -0.0030

(0.0079) (0.0079)

The cumulative average abnormal returns, CAAR = 1N

∑Ni=1

∑t=T2t=T1 ARit,

surrounding the three TARP-CPP events: announcement, capital infusion

and capital repayment are reported. Cumulative abnormal returns are

calculated∑N

i=1 ARit, where AR = Rit − E(Rit). Expected returns for each

bank are estimated over a 250 day pre-event window. Standard errors

are parentheses. The cumulative average abnormal return for the

[-2, +2] event window is reported. The CRSP value-weighted index is used

for market returns. * p < 0.10, ** p < 0.05, *** p < 0.01

86

Tab

le3.

3:A

:M

arke

tM

odel

Res

ult

s,A

nnou

nce

men

t

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.7

067∗∗∗

-0.0

651

-0.0

390∗

-0.0

010∗∗∗

0.0

127∗∗∗

-0.0

017∗∗∗

0.0

055∗∗∗

0.0

105∗∗∗

(0.0

139)

(0.0

415)

(0.0

206)

(0.0

001)

(0.0

026)

(0.0

002)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.5

255∗∗∗

-0.0

882∗∗

-0.0

447∗

-0.0

009∗∗∗

0.0

044∗∗

-0.0

013∗∗∗

0.0

046∗∗∗

0.0

046∗∗∗

(0.0

167)

(0.0

423)

(0.0

235)

(0.0

001)

(0.0

025)

(0.0

003)

(0.0

003)

(0.0

002)

Req

uir

ed8

1.3

650∗∗∗

0.0

894

-0.0

123

-0.0

005

0.0

179

-0.0

011

-0.0

062∗∗∗

0.0

228∗∗∗

(0.0

576)

(0.3

111)

(0.0

912)

(0.0

004)

(0.0

162)

(0.0

011)

(0.0

011)

(0.0

012)

Larg

eT

AR

P1.2

250∗∗∗

-0.3

804∗∗∗

-0.1

300∗∗∗

-0.0

006∗∗∗

0.0

244∗∗∗

-0.0

032∗∗∗

0.0

090∗∗∗

0.0

196∗∗∗

(0.0

210)

(0.0

495)

(0.0

285)

(0.0

001)

(0.0

039)

(0.0

004)

(0.0

003)

(0.0

003)

Larg

eN

on

-TA

RP

1.1

070∗∗∗

-0.3

428∗∗∗

-0.1

588∗∗

0.0

002

0.0

192∗∗∗

-0.0

023∗∗∗

0.0

089∗∗∗

0.0

126∗∗∗

(0.0

927)

(0.1

430)

(0.0

298)

(0.0

002)

(0.0

054)

(0.0

007)

(0.0

007)

(0.0

005)

Mid

-Siz

eT

AR

P0.6

765∗∗∗

0.0

013

0.0

406

-0.0

009∗∗∗

0.0

115∗∗

-0.0

021∗∗∗

0.0

088∗∗∗

0.0

072∗∗∗

(0.0

218)

(0.0

684)

(0.0

337)

(0.0

001)

(0.0

045)

(0.0

004)

(0.0

004)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.8

439∗∗∗

-0.0

806

-0.0

919∗∗

-0.0

008∗∗∗

0.0

102∗∗

-0.0

025∗∗∗

0.0

089∗∗∗

0.0

058∗∗∗

(0.0

245)

(0.0

653)

(0.0

360)

(0.0

002)

(0.0

043)

(0.0

004)

(0.0

004)

(0.0

004)

Sm

all

TA

RP

0.1

260∗∗∗

0.1

838∗∗

-0.0

187

-0.0

015∗∗∗

0.0

010

0.0

003

0.0

002

0.0

028∗∗∗

(0.0

239)

(0.0

836)

(0.0

347)

(0.0

002)

(0.0

045)

(0.0

005)

(0.0

004)

(0.0

004)

Sm

all

Non

-TA

RP

0.1

822∗∗∗

-0.0

145

0.0

160

-0.0

012∗∗∗

-0.0

031

-0.0

004

0.0

011∗∗∗

0.0

015∗∗∗

(0.0

204)

(0.0

603)

(0.0

300)

(0.0

001)

(0.0

034)

(0.0

004)

(0.0

004)

(0.0

003)

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nce

men

tan

dαi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inp

ost

-even

tex

cess

per

form

an

ce[3

+,

253+

]d

ays

aft

er.

Th

eC

RS

Pvalu

e-w

eighte

din

dex

isu

sed

for

mark

etre

turn

s.∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

87

Tab

le3.

3:B

:M

arke

tM

odel

Res

ult

s,C

apit

alIn

fusi

on

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.6

381∗∗∗

-0.0

219

0.1

555∗∗∗

-0.0

010∗∗∗

-0.0

048∗∗∗

-0.0

015∗∗∗

0.0

049∗∗∗

0.0

097∗∗∗

(0.0

118)

(0.0

703)

(0.0

232)

(0.0

001)

(0.0

019)

(0.0

002)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.4

960∗∗∗

-0.1

481

-0.0

861∗∗∗

-0.0

010∗∗∗

-0.0

034

-0.0

004

0.0

049∗∗∗

0.0

046∗∗∗

(0.0

115)

(0.1

435)

(0.0

262)

(0.0

001)

(0.0

024)

(0.0

003)

(0.0

002)

(0.0

002)

Req

uir

ed8

1.3

670∗∗∗

-0.3

045∗

0.0

593

-0.0

004

-0.0

109

-0.0

006

-0.0

068∗∗∗

0.0

219∗∗∗

(0.0

817)

(0.1

764)

(0.1

052)

(0.0

004)

(0.0

069)

(0.0

011)

(0.0

011)

(0.0

012)

Larg

eT

AR

P1.1

090∗∗∗

-0.3

012∗∗∗

-0.0

057

-0.0

004

-0.0

016

-0.0

032∗∗∗

0.0

086∗∗∗

0.0

196∗∗∗

(0.0

154)

(0.0

665)

(0.0

302)

(0.0

002)

(0.0

021)

(0.0

004)

(0.0

004)

(0.0

004)

Larg

eN

on

-TA

RP

0.9

914∗∗∗

-0.4

730

-0.3

312∗∗∗

-0.0

004

-0.0

056∗

-0.0

008

0.0

095∗∗∗

0.0

126∗∗∗

(0.0

226)

(0.3

232)

(0.0

449)

(0.0

002)

(0.0

034)

(0.0

006)

(0.0

005)

(0.0

004)

Mid

-Siz

eT

AR

P0.6

738∗∗∗

-0.2

304∗

0.1

126∗∗∗

-0.0

009∗∗∗

-0.0

065∗∗

-0.0

023∗∗∗

0.0

085∗∗∗

0.0

066∗∗∗

(0.0

196)

(0.1

343)

(0.0

395)

(0.0

002)

(0.0

030)

(0.0

004)

(0.0

004)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.7

958∗∗∗

0.0

079

-0.1

105

-0.0

009

-0.0

063

-0.0

015∗∗∗

0.0

090∗∗∗

0.0

057∗∗∗

(0.0

189)

(0.1

777)

(0.0

405)

(0.0

002)

(0.0

028)

(0.0

004)

(0.0

004)

(0.0

004)

Sm

all

TA

RP

0.1

447∗∗∗

-0.0

946

-0.0

262

-0.0

017∗∗∗

-0.0

078∗

0.0

012∗∗∗

0.0

002

0.0

025∗∗∗

(0.0

213)

(0.1

831)

(0.0

403)

(0.0

002)

(0.0

043)

(0.0

004)

(0.0

004)

(0.0

004)

Sm

all

Non

-TA

RP

0.1

840∗∗∗

-0.0

632

0.0

354

-0.0

014∗∗∗

-0.0

012

0.0

003

0.0

013∗∗∗

0.0

015∗∗∗

(0.0

161)

(0.2

137)

(0.0

383)

(0.0

001)

(0.0

038)

(0.0

004)

(0.0

004)

(0.0

004)

Mark

etm

od

elre

sult

sfo

rth

eT

AR

P-C

PP

cap

ital

infu

sion

are

rep

ort

ed.

Th

ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(1.4

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·Event it)+

αi2PostEvent it+βi2

(Rm

t·PostEvent it)+

ε it.

Th

eT

AR

P-C

PP

cap

ital

infu

sion

socc

urr

edin

the

sam

ple

bet

wee

nO

ctob

er28,

2008

an

dD

ecem

ber

2009.βi0

rep

rese

nts

mark

etri

skb

efore

ab

an

kre

ceiv

esca

pit

al,βi1

rep

rese

nts

the

chan

ge

inm

ark

etri

skd

uri

ng

the

five-

day

even

tw

ind

ow

aro

un

dth

ed

ate

ab

an

kre

ceiv

esca

pit

al

an

dβi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inp

ost

-even

tm

ark

etri

sk[3

+,

253+

]d

ays

aft

era

ban

kre

ceiv

esca

pit

al.αi0

rep

rese

nts

exce

ssp

erfo

rman

ceth

eyea

rb

efore

ab

an

kre

ceiv

esca

pit

al,αi1

rep

rese

nts

the

chan

ge

inex

cess

per

form

an

ced

uri

ng

the

five-

day

even

tw

ind

ow

aro

un

dth

ed

ate

ab

an

kre

ceiv

esca

pit

al

an

dαi2

rep

rese

nts

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ad

dit

ion

al

chan

ge

inex

cess

per

form

an

ce[3

+,

253+

]d

ays

aft

era

ban

kre

ceiv

esca

pit

al.

Th

eC

RS

Pvalu

e-w

eighte

din

dex

isu

sed

for

mark

etre

turn

s.∗p<

0.1

0,

∗∗p<

0.0

5,∗∗∗p<

0.0

1

88

Tab

le3.

3:C

:M

arke

tM

odel

Res

ult

s,C

apit

alR

epay

men

t

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.7

030∗∗∗

-0.2

640∗

0.0

068

-0.0

008∗∗∗

0.0

046∗∗

0.0

010∗∗∗

0.0

056∗∗∗

0.0

100∗∗∗

(0.0

097)

(0.1

382)

(0.0

186)

(0.0

001)

(0.0

018)

(0.0

002)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.5

034∗∗∗

-0.2

560∗

-0.0

809∗∗∗

-0.0

008∗∗∗

-0.0

004

0.0

004∗∗

0.0

052∗∗∗

0.0

043∗∗∗

(0.0

097)

(0.1

454)

(0.0

120)

(0.0

001)

(0.0

013)

(0.0

002)

(0.0

002)

(0.0

002)

Req

uir

ed8

1.4

220∗∗∗

-0.2

674

-0.5

612∗∗∗

-0.0

005

0.0

053∗

-0.0

001

-0.0

053∗∗∗

0.0

213∗∗∗

(0.0

573)

(0.1

884)

(0.0

826)

(0.0

005)

(0.0

027)

(0.0

013)

(0.0

010)

(0.0

012)

Larg

eT

AR

P1.1

220∗∗∗

-0.4

217∗∗∗

-0.3

250∗∗∗

-0.0

008∗∗∗

0.0

023∗∗

0.0

005∗

0.0

090∗∗∗

0.0

177∗∗∗

(0.0

132)

(0.1

185)

(0.0

189)

(0.0

002)

(0.0

011)

(0.0

002)

(0.0

003)

(0.0

003)

Larg

eN

on

-TA

RP

0.9

300∗∗∗

-0.1

123

-0.1

206∗∗∗

-0.0

006∗∗∗

0.0

008

0.0

003

0.0

084∗∗∗

0.0

112∗∗∗

(0.0

184)

(0.2

505)

(0.0

289)

(0.0

002)

(0.0

016)

(0.0

003)

(0.0

004)

(0.0

004)

Mid

-Siz

eT

AR

P0.7

434∗∗∗

-0.1

834

0.0

782∗∗

-0.0

010∗∗∗

0.0

036∗∗

0.0

016∗∗∗

0.0

095∗∗∗

0.0

071∗∗∗

(0.0

173)

(0.1

433)

(0.0

351)

(0.0

001)

(0.0

015)

(0.0

005)

(0.0

003)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.7

459∗∗∗

-0.1

983

0.0

131

-0.0

011∗∗∗

0.0

001

0.0

005

0.0

084∗∗∗

0.0

057∗∗∗

(0.0

152)

(0.1

492)

(0.0

314)

(0.0

001)

(0.0

021)

(0.0

003)

(0.0

003)

(0.0

003)

Sm

all

TA

RP

0.1

456∗∗∗

-0.3

205

0.1

222∗∗∗

-0.0

008∗∗∗

0.0

098∗

0.0

013∗∗∗

0.0

008∗∗∗

0.0

021∗∗∗

(0.0

180)

(0.3

179)

(0.0

405)

(0.0

002)

(0.0

057)

(0.0

003)

(0.0

004)

(0.0

004)

Sm

all

Non

-TA

RP

0.2

122∗∗∗

-0.2

504

-0.0

521∗

-0.0

008∗∗∗

-0.0

003

0.0

004

0.0

020∗∗∗

0.0

016∗∗∗

(0.0

014)

(0.2

340)

(0.0

287)

(0.0

001)

(0.0

020)

(0.0

003)

(0.0

003)

(0.0

003)

Mark

etm

od

elre

sult

sfo

rth

eT

AR

P-C

PP

cap

ital

rep

aym

ent

are

rep

ort

ed.

Th

ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(1.4

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·E

vent it)+

αi2PostEvent it+βi2

(Rm

t·P

ostEvent it)+

ε it.

Th

eT

AR

P-C

PP

cap

ital

rep

aym

ents

oc-

curr

edin

the

sam

ple

bet

wee

nM

arc

h2008

toD

ecem

ber

2013.βi0

rep

rese

nts

mark

etri

skb

efore

ab

ank

rep

ays

cap

ital,βi1

rep

rese

nts

the

chan

ge

inm

ark

etri

skd

uri

ng

the

five-

day

even

tw

ind

ow

aro

un

dth

ed

ate

ab

an

kre

pays

cap

ital

an

dβi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inp

ost

-even

tm

ark

etri

sk[3

+,

253+

]d

ays

aft

era

ban

kre

pays

cap

ital.αi0

rep

rese

nts

exce

ssp

erfo

rman

ceth

eyea

rb

efore

ab

an

kre

pays

cap

ital,αi1

rep

rese

nts

the

chan

ge

inex

cess

per

form

an

ced

uri

ng

the

even

tw

ind

ow

aro

un

dth

ed

ate

ab

an

kre

pays

cap

ital

an

dαi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inex

cess

per

form

an

ce[3

+,

253+

]d

ays

aft

era

ban

kre

pays

cap

ital.

The

CR

SP

valu

e-w

eighte

din

dex

isu

sed

for

mark

etre

turn

s.∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗

p<

0.0

1

89

Capital Asset Pricing Model

Similar to Elyasiani et. al (2014), I use a market model as my main specification.

However, it has been suggested to consider the capital asset pricing model (CAPM) as

a specification. In the market model, investors are risk averse and choose a portfolio

to minimize the variance of the return and maximize expected return (Fama and

French 2004). Sharpe (1964) and Lintner (1965) add two assumptions to the market

model which include: investors agree on the joint distribution of asset returns and

investors all borrow and lend at the risk-free rate.

The capital asset pricing model is:

E(Rit) : R̂it = Rf + β̂i(E(Rmt)−Rf ) (3.5)

where,

E(Rit) = The expected return of asset i

Rf = The risk free rate of return

E(Rmt) = The expected return of the market portfolio

αi = The model’s intercept (a measure of risk-adjusted daily performance)

βi = cov(Ri, Rm)/var(Rm)

Compared to the Capital-Asset Pricing model (CAPM), the market model allows

the intercept to vary across assets. CAPM places a parameter restriction of alpha,

such that αi=(1 − βi)Rf . Another criticism of CAPM is that empirical results over

the last fifty years show that the assumption of unrestricted risk-free borrowing and

lending is unrealistic. However, the interpretation of the market model and CAPM

are similar; the CAPM beta measures the systematic or nondiversifiable risk. In the

90

CAPM model, alpha can be interpreted as the excess return above the risk-free rate

that an investor receives. Below cumulative average abnormal returns and CAPM

results are estimated first using the S&P 500 (in Tables 3.4, 3.5A-C), then using the

CRSP-value weighted index (in Tables 3.6, 3.7A-C) as the market index. The choice

of which market index to use has little effect on the parameter estimates.

91

Table 3.4: CAPM Abnormal Returns During Three TARP Events

(1) (2)

Banks N Newey-West Newey-West+FF

A. Announcement

TARP 219 0.0914∗∗∗ 0.0660∗∗∗

(0.0091) (0.0087)

Non-TARP 219 0.0461∗∗∗ 0.0291∗∗∗

(0.0083) (0.0080)

Required 8 8 0.0957∗∗∗ 0.0921∗∗

(0.0373) (0.0371)

Large TARP 74 0.1383∗∗∗ 0.0933∗∗∗

(0.0160) (0.0157)

Large Non-TARP 37 0.1089∗∗∗ 0.0726∗∗∗

(0.0180) (0.0183)

Mid-Size TARP 65 0.0920∗∗∗ 0.0639∗∗∗

(0.0161) (0.0153)

Mid-Size Non-TARP 62 0.0788∗∗∗ 0.0526∗∗∗

(0.0145) (0.0140)

Small TARP 72 0.0422∗∗∗ 0.0368

(0.0145) (0.0145)

Small Non-TARP 120 0.0098 0.0035

(0.0110) (0.0107)

B. Infusion

TARP 219 -0.0304∗∗∗ -0.0151∗

(0.0082) (0.0083)

Non-TARP 219 -0.0093 -0.0076

(0.0070) (0.0070)

Required 8 8 -0.1235∗∗ -0.0814∗

(0.0374) (0.0395)

Large TARP 74 -0.0426∗∗∗ -0.0122

(0.0088) (0.0094)

Large Non-TARP 37 -0.0461∗∗∗ -0.0426∗∗∗

(0.0154) (0.0151)

Mid-Size TARP 65 -0.0230∗∗ -0.0179∗∗

(0.0136) (0.0136)

Mid-Size Non-TARP 62 -0.0267∗∗ -0.0249∗∗

(0.0129) (0.0124)

Continued on next page

92

Table 3.4 – Continued from previous page

Banks N Newey-West Newey-West+FF

Small TARP 72 -0.0140 -0.0082∗

(0.0189) (0.0192)

Small Non-TARP 120 0.0110 0.0122

(0.0095) (0.0097)

C. Capital Repayment

TARP 186 0.0160∗ 0.0186∗∗

(0.0089) (0.0090)

Non-TARP 212 -0.0005 0.0005

(0.0053) (0.0055)

Required 8 8 -0.0002 0.0139

(0.0131) (0.0172)

Large TARP 70 0.0033 0.0074

(0.0056) (0.0055)

Large Non-TARP 36 0.0002 -0.0027

(0.0113) (0.0129)

Mid-Size TARP 50 0.0107 0.0149∗

(0.0078) (0.0076)

Mid-Size Non-TARP 61 0.0009 0.0042

(0.0088) (0.0090)

Small TARP 58 0.0380 0.0361

(0.0268) (0.0272)

Small Non-TARP 115 -0.0016 -0.0057

(0.0080) (0.0080)

The cumulative average abnormal returns, CAAR = 1N

∑Ni=1

∑t=T2t=T1 ARit,

surrounding the three TARP-CPP events: announcement, capital infusion

and capital repayment are reported. Cumulative abnormal returns are

calculated∑N

i=1 ARit, where AR = Rit − E(Rit). Expected returns for each

bank are estimated over a 250 day pre-event window. Standard errors

are parentheses. The cumulative average abnormal return for the [-2, +2]

event window is reported. The S&P 500 is used for market returns.

* p < 0.10, ** p < 0.05, *** p < 0.01

93

Tab

le3.

5:A

:C

AP

MR

esult

s,A

nnou

nce

men

t

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.7

281∗∗∗

-0.0

724∗

-0.0

343∗

-0.0

046∗∗∗

0.0

152∗∗∗

0.0

020∗∗∗

0.0

065∗∗∗

0.0

101∗∗∗

(0.0

126)

(0.0

405)

(0.0

199)

(0.0

002)

(0.0

026)

(0.0

003)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.5

894∗∗∗

-0.0

153∗∗∗

-0.0

156

-0.0

063∗∗∗

0.0

083∗∗∗

0.0

040∗∗∗

0.0

056∗∗∗

0.0

035∗∗∗

(0.0

203)

(0.0

464)

(0.0

606)

(0.0

003)

(0.0

025)

(0.0

003)

(0.0

003)

(0.0

007)

Req

uir

ed8

1.2

951∗∗∗

0.1

813

0.0

787

0.0

035∗∗∗

0.0

164

-0.0

042∗∗∗

-0.0

047∗∗∗

0.0

226∗∗∗

(0.0

508)

(0.3

034)

(0.0

858)

(0.0

009)

(0.0

173)

(0.0

013)

(0.0

011)

(0.0

012)

Larg

eT

AR

P1.2

073∗∗∗

-0.3

459∗∗∗

-0.0

698∗∗∗

0.0

024∗∗∗

0.0

215∗∗∗

-0.0

055∗∗∗

0.0

105∗∗∗

0.0

191∗∗∗

(0.0

185)

(0.0

476)

(0.0

266)

(0.0

003)

(0.0

039)

(0.0

004)

(0.0

003)

(0.0

003)

Larg

eN

on

-TA

RP

1.1

853∗∗∗

-0.4

515∗∗∗

0.1

852

0.0

002∗∗

0.0

182∗∗∗

-0.0

046∗∗∗

0.0

107∗∗∗

0.0

086∗∗∗

(0.0

853)

(0.1

372)

(0.0

299)

(0.0

012)

(0.0

054)

(0.0

010)

(0.0

007)

(0.0

033)

Mid

-Siz

eT

AR

P0.7

098∗∗∗

-0.0

217

0.0

404

-0.0

047∗∗∗

0.0

146∗∗∗

0.0

019∗∗∗

0.0

099∗∗∗

0.0

067∗∗∗

(0.0

200)

(0.0

678)

(0.0

327)

(0.0

003)

(0.0

045)

(0.0

005)

(0.0

004)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.8

698∗∗∗

-0.0

975

-0.0

847∗∗

-0.0

024∗∗∗

0.0

115∗∗∗

-0.0

006

0.0

101∗∗∗

0.0

053∗∗∗

(0.0

245)

(0.0

653)

(0.0

360)

(0.0

002)

(0.0

043)

(0.0

004)

(0.0

004)

(0.0

004)

Sm

all

TA

RP

0.1

867∗∗∗

0.1

370∗

-0.0

758∗∗

-0.0

124∗∗∗

0.0

093∗∗

0.0

107∗∗∗

0.0

006

0.0

027∗∗∗

(0.0

218)

(0.0

816)

(0.0

334)

(0.0

004)

(0.0

045)

(0.0

006)

(0.0

004)

(0.0

004)

Sm

all

Non

-TA

RP

0.2

589∗∗∗

-0.0

869

-0.0

494

-0.0

011∗∗∗

0.0

036

-0.0

091

0.0

017∗∗∗

0.0

012∗∗∗

(0.0

187)

(0.0

588)

(0.0

289)

(0.0

003)

(0.0

034)

(0.0

005)

(0.0

004)

(0.0

003)

Cap

ital

ass

etp

rici

ng

mod

elre

sult

sfo

rth

eT

AR

P-C

PP

an

nou

nce

men

tare

rep

ort

ed.

Th

ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(3.5

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·E

vent it)+

αi2PostEvent it+βi2

(Rm

t·P

ostEvent it)+

ε it,

wh

ere

each

αi=

(1−βi)R

f.

Th

eT

AR

P-

CP

Pan

nou

nce

men

tocc

urr

edon

Oct

ob

er14,

2008.βi0

rep

rese

nts

mark

etri

skb

efore

the

an

nou

nce

men

t,βi1

rep

rese

nts

the

chan

ge

inm

ark

etri

skd

uri

ng

the

five-

day

even

tw

ind

ow

surr

ou

nd

ing

the

an

nou

nce

men

tan

dβi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inp

ost

-even

tm

ark

etri

sk[3

+,

253+

]d

ays

aft

erth

ean

nou

nce

men

t.αi0

rep

rese

nts

exce

ssre

turn

the

yea

rb

efore

the

an

nou

nce

men

t,αi1

rep

rese

nts

the

chan

ge

inex

cess

retu

rnd

uri

ng

the

five-

day

even

tw

ind

ow

surr

ou

nd

ing

the

an

nou

nce

men

tan

dαi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inp

ost

-even

tex

cess

retu

rn[3

+,

253+

]d

ays

aft

er.

Th

eS

&P

500

isu

sed

for

mark

etre

turn

s.∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

94

Tab

le3.

5:B

:C

AP

MR

esult

s,C

apit

alIn

fusi

on

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.6

801∗∗∗

-0.0

173

0.1

864∗∗∗

-0.0

047∗∗∗

-0.0

009

0.0

024∗∗∗

0.0

060∗∗∗

0.0

090∗∗∗

(0.0

111)

(0.0

711)

(0.0

309)

(0.0

002)

(0.0

019)

(0.0

003)

(0.0

002)

(0.0

003)

Non

-TA

RP

0.5

692∗∗∗

-0.1

726

0.0

592

-0.0

058∗∗∗

0.0

010

-0.0

042∗∗∗

0.0

056∗∗∗

0.0

030∗∗∗

(0.0

170)

(0.1

492)

(0.1

538)

(0.0

002)

(0.0

024)

(0.0

003)

(0.0

003)

(0.0

009)

Req

uir

ed8

1.3

204∗∗∗

-0.3

013∗

0.0

150

0.0

040∗∗∗

-0.0

131∗

-0.0

041∗

-0.0

053∗∗∗

0.0

214∗∗∗

(0.0

758)

(0.1

717)

(0.1

006)

(0.0

013)

(0.0

071)

(0.0

011)

(0.0

012)

(0.0

012)

Larg

eT

AR

P1.1

316∗∗∗

-0.2

696∗∗∗

0.1

028

0.0

015∗∗∗

-0.0

033

-0.0

046∗∗∗

0.0

100∗∗∗

0.0

185∗∗∗

(0.0

148)

(0.0

654)

(0.0

642)

(0.0

002)

(0.0

020)

(0.0

004)

(0.0

004)

(0.0

006)

Larg

eN

on

-TA

RP

1.0

836∗∗∗

-0.4

760

0.6

096

0.0

007

-0.0

069∗

-0.0

024∗∗∗

0.0

098∗∗∗

0.0

076∗

(0.0

668)

(0.3

890)

(0.7

828)

(0.0

008)

(0.0

036)

(0.0

009)

(0.0

011)

(0.0

004)

Mid

-Siz

eT

AR

P0.7

146∗∗∗

-0.2

369∗

0.1

325∗∗∗

-0.0

042∗∗∗

-0.0

032

0.0

012∗∗

0.0

095∗∗∗

0.0

059∗∗∗

(0.0

184)

(0.1

386)

(0.0

404)

(0.0

003)

(0.0

031)

(0.0

005)

(0.0

004)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.8

303∗∗∗

0.0

134

-0.0

808∗∗

-0.0

027∗∗∗

-0.0

043

0.0

003

0.0

101∗∗∗

0.0

050∗∗∗

(0.0

175)

(0.1

794)

(0.0

410)

(0.0

003)

(0.0

028)

(0.0

005)

(0.0

004)

(0.0

004)

Sm

all

TA

RP

0.2

083∗∗∗

-0.1

210

-0.0

670∗

-0.0

111∗∗∗

0.0

013

0.0

102∗∗∗

0.0

007

0.0

019∗∗∗

(0.0

198)

(0.1

871)

(0.0

404)

(0.0

003)

(0.0

043)

(0.0

005)

(0.0

004)

(0.0

004)

Sm

all

Non

-TA

RP

0.2

680∗∗∗

-0.1

189

-0.0

113

-0.0

096∗∗∗

0.0

065∗

0.0

083∗∗∗

0.0

020∗∗∗

0.0

007∗∗

(0.0

147)

(0.2

195)

(0.0

385)

(0.0

002)

(0.0

039)

(0.0

004)

(0.0

004)

(0.0

004)

Cap

ital

ass

etp

rici

ng

mod

elre

sult

sfo

rth

eT

AR

P-C

PP

cap

ital

infu

sion

are

rep

ort

ed.

Th

ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(3.5

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·E

vent it)+

αi2PostEvent it+βi2

(Rm

t·P

ostEvent it)+

ε it,

wh

ere

each

αi=

(1−βi)R

f.

Th

eT

AR

P-

CP

Pca

pit

al

infu

sion

socc

urr

edin

the

sam

ple

bet

wee

nO

ctob

er28,

2008

an

dD

ecem

ber

2009.βi0

rep

rese

nts

mark

etri

skb

efore

ab

an

kre

ceiv

esca

pit

al,βi1

rep

rese

nts

the

chan

ge

inm

ark

etri

skd

uri

ng

the

five-

day

even

tw

ind

ow

aro

un

dth

ed

ate

ab

an

kre

ceiv

esca

pit

al

an

dβi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inp

ost

-even

tm

ark

etri

sk[3

+,

253+

]d

ays

aft

era

ban

kre

ceiv

esca

pit

al.αi0

rep

rese

nts

exce

ssre

turn

the

yea

rb

efore

ab

an

kre

ceiv

esca

pit

al,αi1

rep

rese

nts

the

chan

ge

inex

cess

retu

rnd

uri

ng

the

five-

day

even

tw

ind

ow

aro

un

dth

ed

ate

ab

an

kre

ceiv

esca

pit

al

an

dαi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inex

cess

retu

rn[3

+,

253+

]d

ays

aft

era

ban

kre

ceiv

esca

pit

al.

Th

eS

&P

500

isu

sed

for

mark

etre

turn

s.∗

p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

95

Tab

le3.

5:C

:C

AP

MR

esult

s,C

apit

alR

epay

men

t

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.7

720∗∗∗

-0.3

091∗

-0.0

367∗∗

-0.0

021∗∗∗

0.0

057∗∗∗

0.0

022∗∗∗

0.0

066∗∗∗

0.0

092∗∗∗

(0.0

080)

(0.1

293)

(0.0

185)

(0.0

001)

(0.0

018)

(0.0

002)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.6

345∗∗∗

-0.3

445∗∗

-0.1

826∗∗∗

-0.0

029∗∗∗

0.0

015

0.0

029∗∗∗

0.0

060∗∗∗

0.0

043∗∗∗

(0.0

278)

(0.1

513)

(0.0

292)

(0.0

002)

(0.0

013)

(0.0

003)

(0.0

002)

(0.0

002)

Req

uir

ed8

1.3

584∗∗∗

-0.1

682

-0.1

951∗∗∗

0.0

031∗∗∗

0.0

011

-0.0

036∗∗∗

-0.0

039∗∗∗

0.0

216∗∗∗

(0.0

478)

(0.1

901)

(0.0

778)

(0.0

007)

(0.0

027)

(0.0

013)

(0.0

010)

(0.0

012)

Larg

eT

AR

P1.1

190∗∗∗

-0.4

088∗∗∗

-0.2

890∗∗∗

0.0

002∗∗∗

0.0

012

-0.0

005∗∗

0.0

103∗∗∗

0.0

175∗∗∗

(0.0

104)

(0.1

193)

(0.0

182)

(0.0

003)

(0.0

011)

(0.0

002)

(0.0

003)

(0.0

003)

Larg

eN

on

-TA

RP

1.0

977∗∗∗

-0.2

761

-0.2

514∗∗

0.0

001

0.0

002

-0.0

004

0.0

096∗∗∗

0.0

093∗∗∗

(0.1

372)

(0.2

611)

(0.1

169)

(0.0

008)

(0.0

018)

(0.0

009)

(0.0

004)

(0.0

015)

Mid

-Siz

eT

AR

P0.8

044∗∗∗

-0.2

536∗

0.0

382

-0.0

019∗∗∗

0.0

044∗∗∗

0.0

025∗∗∗

0.0

106∗∗∗

0.0

063∗∗∗

(0.0

143)

(0.1

454)

(0.0

343)

(0.0

002)

(0.0

015)

(0.0

005)

(0.0

003)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.8

077∗∗∗

-0.2

340

-0.0

252

-0.0

020∗∗∗

0.0

010

0.0

014∗∗∗

0.0

095∗∗∗

0.0

050∗∗∗

(0.0

126)

(0.1

459)

(0.0

304)

(0.0

002)

(0.0

021)

(0.0

003)

(0.0

003)

(0.0

003)

Sm

all

TA

RP

0.3

157∗∗∗

-0.4

261

-0.0

302

-0.0

044∗∗∗

0.0

130∗∗

0.0

048∗∗∗

0.0

011∗∗∗

0.0

003

(0.0

148)

(0.2

848)

(0.0

413)

(0.0

002)

(0.0

056)

(0.0

003)

(0.0

004)

(0.0

003)

Sm

all

Non

-TA

RP

0.3

668∗∗∗

-0.3

640

-0.1

796∗∗∗

-0.0

048∗∗∗

0.0

033∗

0.0

042∗∗∗

0.0

026∗∗∗

-0.0

001

(0.0

118)

(0.2

506)

(0.0

283)

(0.0

001)

(0.0

020)

(0.0

003)

(0.0

003)

(0.0

003)

Cap

ital

ass

etp

rici

ng

mod

elre

sult

sfo

rth

eT

AR

P-C

PP

cap

ital

rep

aym

ent

are

rep

ort

ed.

Th

ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(3.5

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·Event it)+

αi2PostEvent it+βi2

(Rm

t·PostEvent it)+

ε it,

wh

ere

eachαi=

(1−βi)R

f.

Th

eT

AR

P-C

PP

cap

ital

rep

aym

ents

occ

urr

edin

the

sam

ple

bet

wee

nM

arc

h2008

toD

ecem

ber

2013.βi0

rep

rese

nts

mark

etri

skb

efore

ab

an

kre

pays

cap

ital,βi1

rep

rese

nts

the

chan

ge

inm

ark

etri

skd

uri

ng

the

five-

day

even

tw

ind

ow

aro

un

dth

ed

ate

ab

an

kre

pays

cap

ital

an

dβi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inp

ost

-even

tm

ark

etri

sk[3

+,

253+

]d

ays

aft

era

ban

kre

pays

cap

ital.αi0

rep

rese

nts

exce

ssre

turn

the

yea

rb

efore

ab

an

kre

pays

cap

ital,

αi1

rep

rese

nts

the

chan

ge

inex

cess

retu

rnd

uri

ng

the

even

tw

ind

ow

aro

un

dth

ed

ate

ab

an

kre

pays

cap

ital

an

dαi2

rep

rese

nts

the

ad

dit

ion

al

chan

ge

inex

cess

retu

rn[3

+,

253+

]d

ays

aft

era

ban

kre

pays

cap

ital.

The

S&

P500

isu

sed

for

mark

etre

turn

s.∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

96

Table 3.6: CAPM Abnormal Returns During Three TARP Events

(1) (2)

Banks N Newey-West Newey-West+FF

A. Announcement

TARP 219 0.0858∗∗∗ 0.0630∗∗∗

(0.0090) (0.0087)

Non-TARP 219 0.0420∗∗∗ 0.0267∗∗∗

(0.0083) (0.0080)

Required 8 8 0.0859∗∗∗ 0.0860∗∗

(0.0368) (0.0367)

Large TARP 74 0.1299∗∗∗ 0.0896∗∗∗

(0.0160) (0.0158)

Large Non-TARP 37 0.1013∗∗∗ 0.0688∗∗∗

(0.0180) (0.0183)

Mid-Size TARP 65 0.0865∗∗∗ 0.0609∗∗∗

(0.0160) (0.0153)

Mid-Size Non-TARP 62 0.0731∗∗∗ 0.0493∗∗∗

(0.0145) (0.0141)

Small TARP 72 0.0398∗∗∗ 0.0349

(0.0145) (0.0145)

Small Non-TARP 120 0.0077 0.0021

(0.0109) (0.0107)

B. Infusion

TARP 219 -0.0324∗∗∗ -0.0158∗

(0.0082) (0.0083)

Non-TARP 219 -0.0108 -0.0083

(0.0070) (0.0070)

Required 8 8 -0.1385∗∗ -0.0920∗

(0.0389) (0.0410)

Large TARP 74 -0.0443∗∗∗ -0.0117

(0.0087) (0.0094)

Large Non-TARP 37 -0.0479∗∗∗ -0.0431∗∗∗

(0.0155) (0.0151)

Mid-Size TARP 65 -0.0257∗∗ -0.0184∗∗

(0.0136) (0.0136)

Mid-Size Non-TARP 62 -0.0290∗∗ -0.0259∗∗

(0.0129) (0.0124)

Continued on next page

97

Table 3.6 – Continued from previous page

Banks N Newey-West Newey-West+FF

Small TARP 72 -0.0146 -0.0090∗

(0.0189) (0.0192)

Small Non-TARP 120 0.0110 0.0116

(0.0095) (0.0096)

C. Capital Repayment

TARP 186 0.0170∗ 0.0194∗∗

(0.0089) (0.0090)

Non-TARP 212 0.0006 0.0020

(0.0052) (0.0052)

Required 8 8 0.0106 0.0220

(0.0142) (0.0181)

Large TARP 70 0.0039 0.0082

(0.0057) (0.0057)

Large Non-TARP 36 0.0062 0.0068

(0.0087) (0.0072)

Mid-Size TARP 50 0.0117 0.0152∗

(0.0078) (0.0077)

Mid-Size Non-TARP 61 0.0015 0.0042

(0.0088) (0.0090)

Small TARP 58 0.0382 0.0364

(0.0269) (0.0271)

Small Non-TARP 115 -0.0017 -0.0008

(0.0080) (0.0080)

The cumulative average abnormal returns, CAAR = 1N

∑Ni=1

∑t=T2t=T1 ARit,

surrounding the three TARP-CPP events: announcement, capital infusion

and capital repayment are reported. Cumulative abnormal returns are

calculated∑N

i=1 ARit, where AR = Rit − E(Rit). Expected returns for each

bank are estimated over a 250 day pre-event window. Standard errors

are parentheses. The cumulative average abnormal return for the

[-2, +2] event window is reported. The CRSP value-weighted index is used

for market returns. * p < 0.10, ** p < 0.05, *** p < 0.01

98

Tab

le3.

7:A

:C

AP

MR

esult

s,A

nnou

nce

men

t

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.7

445∗∗∗

-0.1

003∗∗

-0.0

737∗∗∗

-0.0

045∗∗∗

0.0

148∗∗∗

0.0

016∗∗∗

0.0

056∗∗∗

0.0

104∗∗∗

(0.0

127)

(0.0

413)

(0.0

199)

(0.0

002)

(0.0

026)

(0.0

003)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.5

917∗∗∗

-0.1

532∗∗∗

-0.1

037

-0.0

064∗∗∗

0.0

078∗∗∗

0.0

038∗∗∗

0.0

048∗∗∗

0.0

044∗∗∗

(0.0

152)

(0.0

419)

(0.0

225)

(0.0

002)

(0.0

025)

(0.0

003)

(0.0

003)

(0.0

002)

Req

uir

ed8

1.3

254∗∗∗

0.1

350

0.0

258

0.0

037∗∗∗

0.0

155

-0.0

050∗∗∗

-0.0

064∗∗∗

0.0

230∗∗∗

(0.0

525)

(0.3

110)

(0.0

876)

0.0

009)

(0.0

170)

(0.0

013)

(0.0

011)

(0.0

012)

Larg

eT

AR

P1.2

147∗∗∗

-0.3

666∗∗∗

-0.1

232∗∗∗

0.0

022∗∗∗

0.0

211∗∗∗

-0.0

059∗∗∗

0.0

090∗∗∗

0.0

197∗∗∗

(0.0

185)

(0.0

476)

(0.0

266)

(0.0

003)

(0.0

039)

(0.0

004)

(0.0

003)

(0.0

003)

Larg

eN

on

-TA

RP

1.1

262∗∗∗

-0.3

606∗∗∗

-0.1

739

0.0

014∗∗

0.0

168∗∗∗

-0.0

039∗∗∗

0.0

090∗∗∗

0.0

012∗∗∗

(0.0

487)

(0.0

839)

(0.0

631)

(0.0

006)

(0.0

054)

(0.0

009)

(0.0

007)

(0.0

033)

Mid

-Siz

eT

AR

P0.7

247∗∗∗

-0.0

445

-0.0

031

-0.0

047∗∗∗

0.0

141∗∗∗

0.0

014∗∗∗

0.0

089∗∗∗

0.0

071∗∗∗

(0.0

203)

(0.0

685)

(0.0

326)

(0.0

003)

(0.0

045)

(0.0

005)

(0.0

004)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.8

847∗∗∗

-0.1

187∗

-0.1

280∗∗∗

-0.0

024∗∗∗

0.0

110∗∗

-0.0

010∗

0.0

090∗∗∗

0.0

057∗∗∗

(0.0

230)

(0.0

650)

(0.0

351)

(0.0

004)

(0.0

043)

(0.0

055)

(0.0

004)

(0.0

004)

Sm

all

TA

RP

0.2

123∗∗∗

0.0

993

-0.0

953∗∗∗

-0.0

121∗∗∗

0.0

089∗∗

0.0

103∗∗∗

0.0

005

0.0

026∗∗∗

(0.0

220)

(0.0

833)

(0.0

335)

(0.0

004)

(0.0

045)

(0.0

006)

(0.0

004)

(0.0

004)

Sm

all

Non

-TA

RP

0.2

761∗∗∗

-0.1

077∗

-0.0

682∗∗

-0.0

109∗∗∗

0.0

034

0.0

088∗∗∗

0.0

014∗∗∗

0.0

012∗∗∗

(0.0

188)

(0.0

601)

(0.0

290)

(0.0

003)

(0.0

034)

(0.0

005)

(0.0

004)

(0.0

003)

Cap

ital

ass

etpri

cin

gm

od

elre

sult

sfo

rth

eT

AR

P-C

PP

an

nou

nce

men

tare

rep

ort

ed.

Th

ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(3.5

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·E

vent it)+

αi2PostEvent it+βi2

(Rm

t·P

ostEvent it)+

ε it,

wh

ere

each

αi=

(1−βi)R

f.

Th

eT

AR

P-

CP

Pan

nou

nce

men

tocc

urr

edon

Oct

ob

er14,

2008.βi0

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rese

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etri

skb

efore

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an

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cem

ent,βi1

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rese

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the

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ge

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uri

ng

the

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day

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tw

ind

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ou

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ing

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an

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nce

men

tan

dβi2

rep

rese

nts

the

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dit

ion

al

chan

ge

inp

ost

-even

tm

ark

etri

sk[3

+,

253+

]d

ays

aft

erth

ean

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nce

men

t.αi0

rep

rese

nts

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ssre

turn

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yea

rb

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an

nou

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men

t,αi1

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rese

nts

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cess

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rnd

uri

ng

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five-

day

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tw

ind

ow

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ou

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ing

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nce

men

tan

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-even

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ays

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er.

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eC

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dex

isu

sed

for

mark

etre

turn

s.∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

99

Tab

le3.

7:B

:C

AP

MR

esult

s,C

apit

alIn

fusi

on

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.6

762∗∗∗

-0.0

525

0.1

247∗∗∗

-0.0

050∗∗∗

-0.0

010

0.0

024∗∗∗

0.0

051∗∗∗

0.0

095∗∗∗

(0.0

111)

(0.0

702)

(0.0

230)

(0.0

002)

(0.0

019)

(0.0

030)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.5

561∗∗∗

-0.1

933

-0.1

266∗∗∗

-0.0

061∗∗∗

0.0

012

0.0

044∗∗∗

0.0

052∗∗∗

0.0

042∗∗∗

(0.0

170)

(0.1

492)

(0.1

538)

(0.0

002)

(0.0

024)

(0.0

003)

(0.0

003)

(0.0

009)

Req

uir

ed8

1.3

243∗∗∗

-0.2

791

0.0

806

0.0

040∗∗∗

-0.0

151∗∗

-0.0

048∗∗∗

-0.0

070∗∗∗

0.0

220∗∗∗

(0.0

811)

(0.1

767)

(0.1

039)

(0.0

013)

(0.0

069)

(0.0

017)

(0.0

011)

(0.0

012)

Larg

eT

AR

P1.1

136∗∗∗

-0.3

078∗∗∗

-0.0

120

0.0

009∗∗∗

-0.0

030

-0.0

044∗∗∗

0.0

087∗∗∗

0.0

197∗∗∗

(0.0

148)

(0.0

654)

(0.0

642)

(0.0

002)

(0.0

020)

(0.0

004)

(0.0

004)

(0.0

006)

Larg

eN

on

-TA

RP

1.0

000∗∗∗

-0.4

864

-0.3

350∗∗∗

-0.0

005

-0.0

059∗

-0.0

008

0.0

096∗∗∗

0.0

125∗∗∗

(0.0

202)

(0.3

224)

(0.0

441)

(0.0

034)

(0.0

007)

(0.0

005)

(0.0

004)

(0.0

003)

Mid

-Siz

eT

AR

P0.7

120∗∗∗

-0.2

616∗

0.0

820∗∗

-0.0

044∗∗∗

-0.0

031

0.0

012∗∗

0.0

086∗∗∗

0.0

064∗∗∗

(0.0

186)

(0.1

339)

(0.0

392)

(0.0

003)

(0.0

030)

(0.0

005)

(0.0

004)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.8

239∗∗∗

-0.0

096

-0.1

309∗∗∗

-0.0

030∗∗∗

-0.0

044

0.0

004

0.0

091∗∗∗

0.0

055∗∗∗

(0.0

177)

(0.1

782)

(0.0

402)

(0.0

003)

(0.0

028)

(0.0

005)

(0.0

004)

(0.0

004)

Sm

all

TA

RP

0.2

220∗∗∗

-0.1

487

-0.0

822∗∗

-0.0

110∗∗∗

0.0

011

0.0

101∗∗∗

0.0

005

0.0

019∗∗∗

(0.0

202)

(0.1

837)

(0.0

401)

(0.0

003)

(0.0

043)

(0.0

005)

(0.0

004)

(0.0

004)

Sm

all

Non

-TA

RP

0.2

751∗∗∗

-0.1

329

-0.0

263

-0.0

096∗∗∗

0.0

064

0.0

083∗∗∗

0.0

016∗∗∗

0.0

008∗∗

(0.0

147)

(0.2

195)

(0.0

385)

(0.0

002)

(0.0

039)

(0.0

004)

(0.0

004)

(0.0

004)

Cap

ital

ass

etp

rici

ng

mod

elre

sult

sfo

rth

eT

AR

P-C

PP

cap

ital

infu

sion

are

rep

ort

ed.

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ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(3.5

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·E

vent it)+

αi2PostEvent it+βi2

(Rm

t·P

ostEvent it)+

ε it,

wh

ere

each

αi=

(1−βi)R

f.

Th

eT

AR

P-

CP

Pca

pit

al

infu

sion

socc

urr

edin

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ple

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ctob

er28,

2008

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ecem

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2009.βi0

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rese

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ceiv

esca

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+,

253+

]d

ays

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eC

RS

Pvalu

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dex

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sed

for

mark

etre

turn

s.∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

100

Tab

le3.

7:C

:C

AP

MR

esult

s,C

apit

alR

epay

men

t

Ban

ks

βi0

βi1

βi2

αi0

αi1

αi2

SM

BH

ML

TA

RP

0.7

651∗∗∗

-0.3

131∗∗

-0.0

439∗∗

-0.0

023∗∗∗

0.0

060∗∗∗

0.0

024∗∗∗

0.0

056∗∗∗

0.0

094∗∗∗

(0.0

081)

(0.1

377)

(0.0

180)

(0.0

001)

(0.0

018)

(0.0

002)

(0.0

002)

(0.0

002)

Non

-TA

RP

0.6

058∗∗∗

-0.3

354∗∗

-0.1

668∗∗∗

-0.0

033∗∗∗

0.0

018

0.0

027∗∗∗

0.0

053∗∗∗

0.0

033∗∗∗

(0.0

081)

(0.1

446)

(0.0

194)

(0.0

001)

(0.0

013)

(0.0

002)

(0.0

002)

(0.0

002)

Req

uir

ed8

1.3

544∗∗∗

-0.1

964

-0.5

082∗∗∗

0.0

026∗∗∗

0.0

023

-0.0

032∗∗

-0.0

056∗∗∗

0.0

220∗∗∗

(0.0

495)

(0.1

876)

(0.0

826)

(0.0

007)

(0.0

027)

(0.0

013)

(0.0

010)

(0.0

012)

Larg

eT

AR

P1.0

984∗∗∗

-0.4

052∗∗∗

-0.3

059∗∗∗

-0.0

002∗∗∗

0.0

016

-0.0

001∗∗

0.0

090∗∗∗

0.0

180∗∗∗

(0.0

106)

(0.1

187)

(0.0

184)

(0.0

002)

(0.0

011)

(0.0

002)

(0.0

003)

(0.0

003)

Larg

eN

on

-TA

RP

0.9

477∗∗∗

-0.1

215

-0.1

354∗∗∗

-0.0

009∗∗∗

0.0

011

0.0

006∗

0.0

084∗∗∗

0.0

111∗∗∗

(0.0

152)

(0.2

511)

(0.0

272)

(0.0

002)

(0.0

016)

(0.0

003)

(0.0

004)

(0.0

003)

Mid

-Siz

eT

AR

P0.7

951∗∗∗

-0.2

295

0.0

021

-0.0

330∗∗∗

0.0

047∗∗∗

0.0

027∗∗∗

0.0

095∗∗∗

0.0

066∗∗∗

(0.0

143)

(0.1

428)

(0.0

330)

(0.0

002)

(0.0

015)

(0.0

005)

(0.0

003)

(0.0

004)

Mid

-Siz

eN

on

-TA

RP

0.7

977∗∗∗

-0.2

423

-0.0

311

-0.0

023∗∗∗

0.0

012

0.0

017∗∗∗

0.0

085∗∗∗

0.0

052∗∗∗

(0.0

128)

(0.1

499)

(0.0

303)

(0.0

002)

(0.0

021)

(0.0

003)

(0.0

003)

(0.0

003)

Sm

all

TA

RP

0.3

251∗∗∗

-0.4

846

-0.0

321

-0.0

045∗∗∗

0.0

132∗∗

0.0

048∗∗∗

0.0

007∗∗∗

0.0

003

(0.0

151)

(0.3

147)

(0.0

399)

(0.0

002)

(0.0

056)

(0.0

003)

(0.0

004)

(0.0

003)

Sm

all

Non

-TA

RP

0.3

725∗∗∗

-0.3

740

-0.1

844∗∗∗

-0.0

049∗∗∗

0.0

034∗

0.0

043∗∗∗

0.0

021∗∗∗

0.0

000

(0.0

120)

(0.2

322)

(0.0

279)

(0.0

002)

(0.0

020)

(0.0

003)

(0.0

003)

(0.0

003)

Cap

ital

ass

etp

rici

ng

mod

elre

sult

sfo

rth

eT

AR

P-C

PP

cap

ital

rep

aym

ent

are

rep

ort

ed.

Th

ees

tim

ate

sare

calc

ula

ted

usi

ng

Equ

ati

on

(3.5

),R

it=αi0

+βi0R

mt+αi1Event it+βi1

(Rm

t·Event it)+

αi2PostEvent it+βi2

(Rm

t·PostEvent it)+

ε it,

wh

ere

eachαi=

(1−βi)R

f.

Th

eT

AR

P-C

PP

cap

ital

rep

aym

ents

occ

urr

edin

the

sam

ple

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wee

nM

arc

h2008

toD

ecem

ber

2013.βi0

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rese

nts

mark

etri

skb

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an

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pays

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ital,βi1

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rese

nts

the

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ge

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ark

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uri

ng

the

five-

day

even

tw

ind

ow

aro

un

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ed

ate

ab

an

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pays

cap

ital

an

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rese

nts

the

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ion

al

chan

ge

inp

ost

-even

tm

ark

etri

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+,

253+

]d

ays

aft

era

ban

kre

pays

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ital.αi0

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ssre

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pays

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ital.

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Pvalu

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sed

for

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s.∗p<

0.1

0,∗∗p<

0.0

5,

∗∗∗p<

0.0

1

101

Understanding Securitization

Pooling of loans together to form a security to sell to investors for a claim against

the cash flows of that asset is called securitization. Securitization is not new to

banking, but the usage of securitization of asset-backed securities in the market has

exploded in recent years. Gorton and Metrick (2011) provide extensive background

on securitization that I believe is useful here. Since the 1990s, pools of loans were

sold in capital markets held together by legal entities called special purpose vehicles.

These asset-backed securities are not only within the mortgage market, but more

commonly have used credit card receivables and automobile loans.

Special purpose vehicles are a separate, legal entity under business trust law.

SPVs or trusts are businesses in which no one actually works there. A bank decides

that it does not want to hold some loans on their balance sheet and decides to sell

them to a SPV. These mortgages, credit card receivables or loans are then bundled

up and issued by the SPV with a credit rating into different tranches, based on their

inherent perceived risk. These tranches can then be sold to investors. As Gorton

and Metrick (2011) point out, a trust just executes orders, but does not make any

managerial decisions. The bank gets a lump-sum payment for selling the mortgages,

while investors get the promise of future cash flows.

Gorton and Metrick (2011) further detail the special features that SPVs receive.

i. SPVs are tax neutral meaning that they are not subject to income tax at the

trust level, but they also do not get the tax advantage of having more on-balance

sheet debt.

ii. SPVs cannot go bankrupt. Instead, if they cannot contractually pay the cash

flows each month from asset-backed securities they go into an early amortization

event. This means that they pay down principal with any available funds they have,

so long funds are available.

102

iii. SPVs are bankruptcy remote from the sponsor bank. The contractual agree-

ment between an SPV and an investor states that the trustee and rating agency

ensure asset-backed securities meet qualifications for it be securitized, not the bank.

Due to these three features, SPVs are an attractive mechanism for which banks

can sell securities. However, asset-backed securities are also an attractive investment

for investors as there is no ability to produce private information about the payoffs of

the security, so the monitoring costs are low. Further, there is no active management

that can alter the risk of the investment (Schwarz 2003). The contractual cash flows

then are what investors should expect to receive.

Why do Banks Choose to Securitize Assets Off-Balance

Sheet?

Numerous competing hypotheses concerning risk-taking behavior of off-balance sheet

items have been discussed in older literature around the time when securitization

really began. Hassan (1991) offers two hypotheses: the diversification hypothesis and

the leverage hypothesis. The diversification hypothesis states that there is a negative

relationship between total bank risk and off-balance sheet items, while the leverage

hypothesis states that there is a positive relationship between total bank risk and

off-balance sheet items. The former hypothesis suggests that banks engage in selling

information services and warehousing assets which induces them to put some items on

and off the balance sheet. A bank chooses to use off-balance sheet items to enhance

its credit and accommodate the needs of customers. The latter hypothesis states

that fixed rate deposit insurance provides incentives for banks to obtain financial

leverage through off-balance sheet items that are not subject to capital requirements.

Therefore, riskier assets are more likely to be found on the off-balance sheet.

Similarly, Nachane and Ghosh (2002) offer two opposing theories for the reason

103

banks choose to engage in a large number of off-balance sheet activities. First, a

bank securitizes safe assets off the balance sheet and the risky assets on the balance

sheet due to a pooling problem. Second, the hedging and tax hypothesis states that

derivatives represented as an off-balance sheet item can increase the value of the firm

via bank fees and increase the probability that the banks pre-tax income is in the

progressive region of the tax schedule.

More recent literature agrees with the positive relationship or leverage hypothe-

sis between off-balance sheet items and risk. Notably, Thiemann (2011) believes that

there are three dimensions for why banks choose to use off-balance sheet items: credit

transformation, maturity transformation and liquidity transformation. They involve

transforming low quality credit into high quality credit via securitization, financing

long term assets with short term liabilities and creating liquid assets from what would

have been an illiquid asset on the balance sheet. Jin et. al (2011) indicates that risky

loans increase a banks probability of failing, so in order to look strong in the market

a bank is better off putting these loans as off-balance sheet items.

104

Variable names

Table 3.8: Variable Definitions

Name Definition

Tier one capital Common shareholder equity and disclosed reserves or retained earnings

that accrue to the shareholders’ benefit

Loans past due 90 days or more If the principal or interest on an asset is past due by 90 days or more,

but at least 90 days of interest payments have been capitalized,

refinanced or delayed by agreements

Nonaccrual loans If the principal or interest on an asset becomes due and is unpaid for 90

days or more, then it is placed in nonaccrual status until it meets criteria

to be restored to accrual status. Generally, a nonaccrual asset will be

restored to accrual when its principal and interest are paid, the banks

expects repayment of contractual principal and interest or when it

becomes secured and in the process of collection

Other real estate owned Real property held for reasons other than to conduct bank business.

Banks usually acquire ORE through foreclosure after a borrower defaults

on a loan secured by real estate

Loan loss reserve The allowance for loan and lease losses based on the credit risk within

the institution’s assets. This reserve reduces the book value of a

bank’s loans and leases to the amount an institution expects to receive

Real estate loans Real estate loans include loans that each reporting bank characterizes as

such

Return on assets (ROA) Indicates how profitable a company is relative to total assets

Standby letters of credit An irrevocable commitment by the issuing bank to make a payment to

a designated beneficiary. It obligates the bank to guarantee or stand as

surety for the benefit of the third party

Commercial letter of credit A commitment to facilitate trade or commerce and represent contingent

liabilities

Assets securitized or sold with recourse Assets that are sold by a bank and control is surrendered on the asset.

Control is transfered if there is legal isolation of the financial assets

from the seller, the ability of the investor to pledge or sell the assets,

or the absence of a right to repurchase the financial assets. In order to

be moved to the off-balance sheet, an asset transfer must occur. Assets

sold with recourse means that a bank transfer assets in a sale, but

retains an obligation to repurchase the assets or absorb losses due to

default in payment or deficiency in performance

Continued on next page

105

Table 3.8 – Continued from previous page

Variable Name Definition

All other off-balance sheet items This includes contracts on other commodities and equities, all other

off-balance sheet liabilities, participation in acceptances conveyed and

acquired securities borrowed, securities lent, commitments to purchase

and sell when-issued securities

Note: Variable definitions from FDIC, OCC, Federal Reserve and Call Reports.

106

Table 3.9: Failed Banks Probit Marginal Estimates(Individual Year 2009, 2010, 2011)

Variables 2009 2010

(1) (2)

Tier 1 Ratio -0.1574∗∗∗ -0.0129∗∗∗

(0.0226) (0.0046)

Troubled Assets Ratio 0.00003∗∗ 1e−5

(0.00001) (1e−5)

ROA -0.0096∗∗∗ -0.0002∗∗

(0.0017) (0.0003)

Real Estate Loans 0.0079∗∗∗ 0.0002∗∗

(0.0013) (0.0001)

Non-Performing Loans -0.0023∗∗∗ -0.0002∗∗

(0.0007) (0.0005)

Standby Letters of Credit 0.0003∗∗∗ 3e−5∗

(0.0001) (0.00001)

Assets Securitized or 5e−6 1e−7

Sold with Recourse (2e−5) (0.00000)

Commercial Letters of Credit -0.0011∗ -4e−6

(0.0006) (5e−5)

Amount of Recourse Exposure -0.0007∗∗∗ -0.0001∗∗

(0.0002) (0.0006)

All other Off-Balance Sheet -0.0001∗ -9e−7

Items (0.0000) (0.00000)

Controls:No. of branches 1e−8 4e−8

(0.0000) (0.0000)

Age of institution 3e−5 1e−6∗∗∗

(1e−5) (0.0000)

Regulator Federal Reserve 0.0005 5e−5

(0.0003) (0.0002)

Extra small -0.0013∗∗∗ -9e−5∗∗∗

(0.0003) (0.00000)

Small -0.0015∗∗∗ -5e−5∗

(0.0003) (0.00000)

Medium -0.0013∗∗∗ -4e−5∗∗

(0.0002) (0.00003)

Large -0.0005∗∗∗ -4e−5

(0.0002) (0.00003)

Quarter Fixed Effects Yes Yes

Observations 20767 112735

Extra small refers to banks with assets in Quarter 3, 2008 in the low-est quintile (assets<=$51,687,000); small refers to banks with assets<=$100,271,000 and >$51,687,000; medium refers to banks with assets<=$180,418,000 and >$100,271,000; large refers to banks with assets<=$389,741,000 and >$180,271,000. The top quintile, or extra largegroup has assets <=$390,222,000. There were 121 banks that failed in2009 and 102 failures in 2010 with government assistance.

107

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