BANKRUPTCY PREDICTION MODELS FOR BANKS USING CAMEL FACTORS

Estimating a predictive Logistic Model using CAMEL Factors financial ratios; data from U.S banks during 2008-2010

Name: Macedo Sebastiao, Jose Maria

Program: MSc Finance

Snr: 2011415

Supervisor: Prof. Harald Benink

Date: 6 December 2019

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Contents Contents ........................................................................................................................................................ 1

Abstract................................................................................................................................................. 2

1. Introduction ...................................................................................................................................... 3

1.1 Problem statement ............................................................................................................................. 4

2. Literature Review ............................................................................................................................. 7

2.1 The Role of Banks ................................................................................................................................ 8

2.2 History of Bankruptcy Prediction Studies (1930 -1965) .................................................................... 11

2.3. History of Bankruptcy Prediction Studies (1965 -Present) .............................................................. 12

2.3.1 Market-Based Indicator vs Accounting Ratios ........................................................................... 14

2.3.2 Banking Sector ........................................................................................................................... 20

2.3.3 CAMEL Model ............................................................................................................................. 22

2.4. Models Limitations ........................................................................................................................... 28

3. Methodology .................................................................................................................................. 32

3.1 Data Sample ...................................................................................................................................... 32

3.2 Logit Model ....................................................................................................................................... 39

3.2.1 Estimating the Logit Model ........................................................................................................ 40

3.3 Model Validation Tests ..................................................................................................................... 41

3.3.1 Variables Selection ..................................................................................................................... 41

3.3.2 Model Accuracy Test .................................................................................................................. 42

3.3.3 Model Significance ..................................................................................................................... 43

3.3.4 Hypothesis .................................................................................................................................. 44

4. Empirical Results ............................................................................................................................ 45

4.1 Descriptive Statistics ......................................................................................................................... 45

4.2 Model Significance Results ............................................................................................................... 51

4.2.1 Backward Stepwise .................................................................................................................... 51

4.2.2. Multinomial Logit Regression ................................................................................................... 54

4.2.3 Macro-Economic Factor ............................................................................................................. 64

4.3 Hypothesis Results ............................................................................................................................ 67

5. Conclusion ...................................................................................................................................... 71

References .......................................................................................................................................... 73

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Abstract

Banks play a crucial role in the economy and it is worth evaluating their financial health, but

more importantly finding out how to prevent them from going bankrupt. In this study, 3 different

Logit Regression prediction models were estimated using only financial ratios that best represented

the five CAMEL Model factors to predict bank failure up to three years in advance. The Model

measures banks Capital Adequacy, Assets Quality, Management Efficiency, Earning Capacity and

Liquidity.

Data was collected from three years prior to failure on a quarterly basis from banks that went

bankrupt after September 2008, during 2009 and 2010 and matched with banks that did not go

bankrupt in that same period. Three different Logit models were estimated; 3-year, 2-year and 1-

year prior failure.

The main idea of this study was to see how accurate these three models are and which of the five

CAMEL factors are significant when it comes to predicting bank failure. The results showed that

the models can predict failure with 72.5% accuracy rate 3-prior failure, 86.1% accuracy rate 2-

year prior failure and 97.3% accuracy rate 1-year prior failure. From the five CAMEL factors, only

3 (Capital Adequacy, Earning Capacity and Liquidity) seemed to have been significant for bank-

ruptcy prediction.

Keywords: CAMEL Model, bankruptcy prediction, Logistic Regression

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1. Introduction

Banks act as intermediate financial institutions that carry out business activities relying on

public funds and trust (Hilman, 2014). The public trust in a bank will only grow when banks are

in good financial condition, therefore it is important to constantly examine the financial health of

banks. The financial health of banks is in the interest of different parties involved such as

stakeholders, owners, manager, costumers and the bank’s supervisory authority. Banks are

important institutes for the world economy for various reasons. Banks act as financial

intermediations by taking money from depositors and giving out loans to borrowers. They are in

some sort the pillars, the most important financial institution in the economy. Beyond all the

important intermediation function that banks deal with, what matters is the financial condition of

the banks. Tied to bank performances there are several domino’s effects that lead to economic

growth or failure to do so depending on how banks perform.

After the banking crisis in 2008, researchers became more interested in how to predict the failure

of banks and at the same time figuring out how to avoid these failures. Predicting bank failure is

not an easy task as it becomes difficult and time costly to make a model containing every single

important factor related to bank failure. Measuring banks operational and financial difficulties is

a subject which has been particularly susceptible to financial ratios analysis (Altman, 1968). One

good model that uses financial ratios to evaluate the financial health of banks is the Camel model

approach. Among different methods to evaluate bank performance, the CAMEL model is a very

useful method to evaluate bank operational and overall financial conditions. Camels rating is a

supervisory rating system originally developed in the U.S. to classify a bank's overall condition.

It was initially created for US banks, but now it is a financial tool that is used by different coun-

tries around the world. This model is based on financial ratios that help in evaluating banks per-

formance. It is a management tool that measures Capital Adequacy, Assets Quality, the effi-

ciency of Management, quality of Earnings and Liquidity of financial institutions (Maheshwara

Reddy & Prasad, 2011). The choice of these five CAMEL factors is based on the idea that each

factor represents a major element in a bank’s financial statement (Kouser & Saba, 2012).

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Apart from the Camel ratios, non- camel ratios are also used to help predict bank failure. The

two non-camel ratios or external factors as they are called are Gross Domestic Product (GDP)

and Inflations. As Camel ratios are tools to evaluate banks internal performances, these two ex-

ternal factors influence banks overall performance in such a way that banks can rarely control

their effect. In the following chapters, these two external factors are discussed in detail.

1.1 Problem statement

Measuring bank financial health can be done in two different ways, the first one is called the

‘’on-site’’ and the other is the ‘’off-site’’ examination. Off-site supervision is fundamental in

monitoring the conduct of business activities of licensees. It entails reviewing and analyzing of

the audited financial statements. Analyzing a bank’s health using financial statement can be done

with the Camel model approach.

The purpose of this paper is to create a simple bankruptcy predictive model which is highly

accurate in predicting failure from up to three years in advance. The methodology being used is

the Logistic Regression Model. In the upcoming chapter, a broader explanation for choosing this

Logistic Model is discussed. The idea is to use the five CAMEL factor as a frame to come up

with financial ratios that are relevant when analyzing a bank’s overall performance. The financial

ratios which best represent these five CAMEL factors are selected to create the prediction model.

In order to conduct this study, the data from U.S banks that went bankrupt starting during

September 2008, during 2009 and during 2010 were selected. The Lehman Brothers went

bankrupt in September 2008; therefore, September 2008 is considered as the starting point of the

banking crisis in this paper. The reason why 2 years and 3 months was chosen (from Sept 2008

till the end of 2010) is from the fact that during that period a huge number of banks went

bankrupt in the U.S. Furthermore, the availability of the data compared to other countries is

large. Data was collected from the Federal Deposit Insurance Corporation (FDIC). The Federal

Deposit Insurance Corporation is a United States government corporation providing deposit

insurance to depositors in U.S. commercial banks and savings institutions. FDIC is an

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independent federal agency insuring deposits in U.S. banks and thrifts in the event of bank

failures.

The research question which is intended to be answered throughout this paper is as follows;

How accurate can the bankruptcy predictive model be when utilizing only the five CAMEL

factor ratios?

To answer this question, first four sub-questions were answered first to determine how accurate

this predictive model is. These sub-questions are explained in detail in the Methodology chapter.

What is important to mention is that this study does not try to figure out which external factors

initiated/ caused the banking crisis in 2008. But this paper is more about understanding banks

internal performance, it is about understanding which factors internally influences banks

probability of going bankrupt in contrast to other authors, which focus more on the crisis than the

banks self. For example, Sanders (2008), who tried to explain the role that the subprime crisis had

on the financial crisis, and Longstaff (2010), who tried to explain the effect subprime credit crisis

and contagion had on the financial market. These are some example of papers whose focus were

on understanding the effect that the subprime crisis had on the financial institution. Same as these

two papers, the banking crisis triggered a lot of researchers into finding out why and which affect

the financial crisis had on the market.

Although many studies have created a predictive model for banks using CAMEL Factors during

the financial crisis in 2008, there are still some differences between those papers and this paper.

Most of these papers use some ratios that represent the CAMEL model, but the idea behind their

study is either comparing different methods or investigating the financial crisis itself. They don’t

focus much on examining banks internal financial health, rather they focus more on the causes that

originated the crisis. In the next chapter, under section 2.3.3, a whole analysis is found about what

makes this paper different in relation to other papers that also use CAMEL factors.

Apart from that, most studies use some financial ratios combined with crisis-related factors which

according to them are relevant to investigate the cause of the crisis. But in this paper, only financial

ratios that best represent the five CAMEL factors were used, excluding Non-Camel factors. Also,

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some macro-economic factors were used which is rarely seen in other studies in the bankruptcy

prediction study field. It is to find out if these five CAMEL factors together with other relevant

macro-economic factors can create a good predictive model as done by Gonzalez-Hermosillo

(1999).

As bank plays an important role in the economy of a country it is worth evaluating their financial

condition. More important, finding out which factors are relevant for preventing banks to fail.

After the banking crisis in 2008 more researchers became interested in how to predict bank fail-

ure and use these analyses to help prevent other banks from having a crisis. With this paper, the

aim was not to “invent the wheel again” in the field of the bankruptcy prediction model. This in-

tention of this paper was to contribute to the existing literature surrounding the study field of

bank failure and predictive models using the CAMEL model. The findings from this research

bring extra information to the existing literature about bankruptcy prediction model.

The paper proceeds as follow; In the next chapter (Literature), the role of banks and the reason to

study them in presented. Also, some of the most relevant research papers in the bankruptcy pre-

diction and CAMEL Model field is analyzed. The third chapter (Methodology) explain where

and which data set is was used to conduct this study. The fourth chapter, the results from the ta-

bles and graphics are explained and the economic intuition behind the results. In the last chapter

(Conclusion), a summary of the results and future recommendations about this paper is pre-

sented.

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2. Literature Review

In this chapter different aspects related to the existing literature about the history of bankruptcy

prediction model, methodologies and CAMEL model is explained. This Literature chapter is

divided into four different sections.

The first section (2.1) starts with a brief explanation of the financial intermediate role of banks.

Furthermore, the relevance of studying the financial health of banks is touched upon. In the second

section (2.2), the history and origins of the bankruptcy prediction theory and models are briefly

explained. This section consists of the theory between the 1930s till 1965s. The third section (2.3)

continues with the history of the bankruptcy prediction models, with respect to the year 1965 till

present. This section is divided into three sections, (2.3.1) which covers the difference between the

two most popular bankruptcy methodology (market-based vs accounting-based), (2.3.2) which

focuses on the predicting model used in the banking sector, (2.3.3) the introduction and analysis

of the CAMEL Model and what makes this study different compared to previous studies related to

CAMEL Model. The fourth section (2.4) discusses some of the limitations and advantages of the

methodologies used for the bankruptcy prediction field. This last section gives a better

understanding of why the Logit methodology is chosen in this study.

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2.1 The Role of Banks

The banking system plays an important role in the modern economic world, therefore develop-

ments in the financial sector and economic growth are in some sort dependent on each other. The

role of banks to act as financial intermediations is very important to the economy, consequently

placing the banking sector in a very high/ important position of the entire economy.

The objective of commercial banks, like many other corporations, is to generate wealth for share-

holders. What differentiates commercial banks from other particular firms is the fact that they act

as a financial institution that accepts deposits from costumers and use it to make loans for individ-

uals and business.

Commercial banks collect the savings of individuals and lend them out to business- people and

manufacturers. Therefore, commercial banks act as financial intermediation between individuals

with an excess of liquidity (depositor) and agent in need of liquidity (borrowers).

Deposits from these individuals are the principal liability for commercial banks, as they can with-

draw their money on short notice. Banks use the deposit money and lend it out to other parties;

therefore, loans are considered to be the principal assets of the banks. This intermediation role of

banks creates commerce, it creates new capital/ new information and thus helps the growth process

of a country.

The profitability levels of banks differ because each bank is affected by their own features, industry

characteristics and contextual properties. Many studies regarding determinants of banks profita-

bility contain various banks own specific factors and some macroeconomics factors (e.g. Infla-

tions, interest rates and GDP).

The way the profitability of commercial banks is measured is by their average return on assets.

This can be expressed as a function of internal and external factors. The internal factors are the

ones that include the bank’s own specific variables. In other words, how banks perform internally,

and this can be measured using financial ratios. The external factors are environmental variables

that can have an impact on the profitability of banks.

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The financial health of banks not only depend on its quality of investment or in other words capable

of creating earnings, but it also depends on different other factors; for example, their capital ade-

quacy, quality of management, liquidity and external factors to mention a few.

Because banks play an important role in the economy it is worth evaluating their performance. In

recent years, researchers have become more interested in models that in some sort predict bank-

ruptcy. Precise bankruptcy forecasting models are interesting topics for not only academics but

also practitioners and regulators, as the latter uses these forecasting models to monitor banks fi-

nancial health (Shumway, 2001). As mentioned in Martin (1977) paper, large depositor and other

uninsured creditors are constantly interested in the risk of loss if banks are not going well. Also,

regulatory agencies are interested in anticipating failure from banks in order for them to intervene

in the situation.

One of the important reasons why banks fail is when it’s net worth becomes negative. Or if the

bank is unable to continue operating without incurring losses that would result in negative net

worth. According to Karel & Prakash (1987), in the literature, the word “bankruptcy” has been

used for firms that are experiencing financial troubles. Some authors have used the term ‘’fail

interchangeably’’ to describe bankrupt. It is worth mentioning that the precise moment that bank-

ruptcy begins is difficult to determine. In the overall literature, there are different ways which

authors define failure/ bankruptcy used for their prediction model.

Many definitions found, define failure as an actual process of filing for bankruptcy and liquidation.

Others define failure as the stressful condition under which a firm is momentarily living, where

they are unable to meet their financial obligations. Some other studies don’t even mention what

they mean when they use the term “failure” or “bankruptcy”.

Although there are different ways of interpreting the term “failure” or “bankruptcy” in the financial

world, according to the Business Dictionary the definition of bankruptcy. Bankruptcy is defined

as follows;

“Legal procedure for liquidating a business (or property owned by an individual) which cannot

fully pay its debts out of its current assets. Bankruptcy can be brought upon itself by an insolvent

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debtor (called 'voluntary bankruptcy') or it can be forced on court orders issued on creditors'

petition (called 'involuntary bankruptcy'). Two major objectives of bankruptcy are (1) fair settle-

ment of the legal claims of the creditors through an equitable distribution of debtor's assets, and

(2) to provide the debtor with an opportunity for a fresh start”

To keep things simple and prevail in confusion, bankruptcy, in this paper is referred to a bank

failing to stay in the financial market. The outcome from the model used in this study is a binary

outcome which means that it lays between 1 for failed banks and 0 otherwise. Bankruptcy in this

paper was narrowed down into the probability of banks going bankrupt.

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2.2 History of Bankruptcy Prediction Studies (1930 -1965)

We must go back to the early 1930s when we talk about literature referring to bankruptcy predic-

tion models. In the early 1930’s the first studies trying to understand and create a model that could

prevent/ warn people about possible bankruptcy started. In those days the use of ratio analysis as

a bankruptcy prediction tool was first implemented. This method of using ratio analysis is better

known as univariate studies. This univariate study only focuses on the results of individual ratios.

This Ratio analysis method opened a new window in the research field, especially in the bank-

ruptcy prediction field. This study was considered as the first stepping-stone for future models in

the bankruptcy prediction field.

One of the first publications were done by the BRB, (Bureau of Business Research, 1930) in which

they applied the ratio analysis for failing industrial firms. In this study, researchers compared 24

ratios of the 29 chosen firms to the mean results, to determine the similar characteristics that the

failing firms had with each other. Back in the 1930’s they applied the ratio analysis and compared

the result of failed companies and non-failed companies. To be concrete, papers like (FitzPatrick,

1932) was one of the first published papers in the field of bankruptcy prediction. The author used

the matched paired technique to present data for 20 failed and 20 non-failed firms and he applied

the ratio analysis method (13 ratios) and discussed the results as indicators of bankruptcy. What

he discovered was that the non-failed firm displayed better ratios results compared to “mean” ratios

trends. On the other hand, the failed firms did not display goof result compared to the “mean” trend

ratios.

Smith and Winakor (1935) were other examples of the univariate study. The authors did a follow-

up study on the first publication done by BRB (1930). Smith and Winakor (1935) used the ratio

analysis for 183 failed firms from different industries. From their study, it was found that different

ratios were good in predicting failure, especially the Working Capital to Total Assets which was a

better predictor of failure than the Current Assets and Cash to Total Assets.

Another example of the first published studies in the field of bankruptcy prediction was done by

Merwin (1942) in which the author focused on small manufacturing firms. One of the findings

from the author study was that the failing firm's ratios where displaying weakness signs as early

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as 4 to 5 years before going bankrupt. From the ratios that were used by the author is his study,

three of these ratios were considered as significant predictors of failure (Net Working Capital/

Total Assets, Current Ratio and Net Worth/ Total Debt).

One of the last published studies in the field of bankruptcy prediction was Jackendoff (1962). In

this study, the author compared different ratios for failed and non-failed firms. One of the conclu-

sions was that the Current ratio and Net Working Capital/ Total Assets are higher for profitable

firms than for non-profitable firms. Furthermore, the author concluded from the results that the

Debt/ Worth ratio is lower for profitable firms.

All of these above-mentioned studies (Bureau of Business Research, 1930) (Smith & Winakor,

1935) (Merwin, 1942) (Jackendoff, 1962) concluded that the ratio Working Capital/ Total Assets

was a good predictor of failure. These early published studies from the beginning of the 1930s till

65’s laid the first stones that would later help create new prediction models in the field of

bankruptcy.

The ratio analysis was the first method used to predict firms failure and help investors detect the

future failure. This ratio analysis, as mentioned at the beginning of this section, opened new

windows for future researchers to create better and more complex methods that are till today useful

in this particular research field. In the following sections from this Literature chapter, different

methods are presented, from Beaver’s (1966) Univariate method till more complex approaches

like the Neural Network.

2.3. History of Bankruptcy Prediction Studies (1965 -Present)

As discussed in section 2.2 of this chapter, it all started with the ratio analysis approach to predict

firm’s failure. Ratio analysis was the beginning of a new phase for this type of studies, but what

made an impact was Beaver (1966) Univariate study.

As mentioned in Beaver’s (1966) paper, the author referred his study as not being one of the last

endeavors in this bankruptcy field but as being one of the first. The author continues and stated the

following; “ It is designed to be a benchmark for future investigations into alternative predictors

of failure, into competing forms of presenting accounting data, and into other uses for accounting

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data”. Beaver’s study is based on analyzing 30 ratios obtained from the firm own financial

statement. Beaver’s study was about predicting the failure status of firms, based solely upon

information obtained from financial ratios.

His study is somehow similar to the early ratio analysis, where he compares 79 firms and non-

failed firms divided into 38 different industries using a period between 1954 – 1964. The method

he used to get the 79 firms was a paired-sample design, where for each failed firm there was a non-

failed firm from the same industry and asset size selected.

Indeed, Beaver study has some similarities with the early ratio analysis studies. However, Beaver

added an extra touch to his study, and he tested the individual predictive ability of each of the 30

used ratios. In his study he mentioned that in all previous studies, the ratio analysis can demonstrate

that there is a difference between failed a non-failed firm, but it cannot indicate how large this

difference is.

Beaver (1966) concluded that the analysis he did has been a Univariate analysis, which examined

the predictive analysis of the ratios one by one. In his own words “the immediate purpose of this

study was not to find the best predictor of failure but rather to investigate the predictive ability of

financial ratios”. For future recommendations, the author indicated the possibility to instead of

examining the ratios one at the time, to create a multi-ratio analysis which might lead to a better

predictor ability than single ratio analysis.

Altman (1968) was the first Multivariate study published. The focus of the paper is to attempt an

assesement of the issue of the quality of ratio analysis as an analytical technique. The author

mentioned some limitations of the previously used ratio analysis and after carefully analyzing these

limitations he decided to use a Multiple Discriminant Analysis (MDA). He used the Multiple

Discriminant Analysis (MDA) to create a five- factor bankruptcy prediction model. This model

was called the “Z-score”. As the author explained in his paper, the MDA is a statistical technique

used to classify an observation into one of several a priori groupings dependent upon the

observation's characteristics. The main purpose of this model is to make predictions in situations

where the dependent variable is in qualitative form.

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Altman (1968) concluded that if ratios where analyzed within a multivariate framework, it would

take on a better statistical significance predictive ability than the standard single ratio analysis. His

conclusion was based on the theory that standard single ratio analysis was no longer an important

analytical technique in the field of bankruptcy predictive model due to the relatively

unsophisticated way it has been presented.

2.3.1 Market-Based Indicator vs Accounting Ratios Since Altman (1968) Multiple Discriminant Analysis (MDA), there has been an increase in bank-

ruptcy prediction models. During the late 1960’s till the late 70’s most of the studies followed the

Altman (1968) MDA models. Studies like Daniel (1968), Meyer & Pifer (1970) where one of the

first published studies to follow Altman (1968) Multiple Discriminant Analysis (MDA) approach

and made a Linear Probability model.

One of the first studies in the early 70’s which followed Beaver was Deaking (1972), which per-

formed two method intending to propose an alternative model for bankruptcy prediction. His idea

was to create a model that would predict failure as early as possible to help reduce the losses that

creditors and stockholder would suffer during the firm failure. The author first replicated Beaver

Univariate method using the same ratio, then he wanted to examine the linear combination of these

14 ratios. According to the author, the purpose of discriminant analysis is to find linear combina-

tion of ratios which best discriminates between the groups which are being classified. He con-

cluded from his study that the discriminant analysis can predict business failure from accounting

data up to three years in advance with a high accuracy rate.

But in the early 70’s there was a new bankruptcy prediction methodology being introduced. This

new methodology was different to previous models, where Market-Based indicators were being

used instead of financial ratios. The first prediction model using Market based indicators were

Merton (1974) study, where the author proposed a bankruptcy prediction model based on the Black

Scholes option pricing model for calculating stock values. The purpose of this study was to the

relative risk that a leveraged firm has. According to the author self, the purpose of his paper was

to “present a theory which might be called a theory of risk structure of interest rates”.

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But this so-called Merton Model can only be used under certain assumptions which limits the

model. One of the assumptions is that the Bond should be a Zero-coupon bond. Another assump-

tion is that the risk-free rate and volatility of assets needs to be constant over time. This is supported

by Saunders and Allen (2002) who criticized the model by saying that the model is dependent on

assumptions about the stock market. They also highlighted the assumption that there is no differ-

ence between the type of debts, neither can it differentiate from the assets value nor volatility.

Although the methodology of using Financial Ratios from Balance sheet to create a bankruptcy

prediction model is better known in the literature, there has been a few studies who used the mar-

ket-based indicator method. This paper will not explain in dept everything about this market-based

method, but instead a few popular studies who implemented the market-based indicators and com-

parison about the advantages and disadvantage of each two method (financial ratios vs market-

based indicator) will be presented.

In the modern literature about Market-based indicator, there are two popular bankruptcy prediction

model, namely Shumway (2001) estimating hazard model and Hillegeist et al. (2004) estimating

Black-Scholes pricing model. Shumway (2001) developed a bankruptcy model that used three

market-based indicator factors to identify failure. The author created a hazard model using both,

market-based indicators and accounting ratios variables. The author concluded that his model out-

performs other models in out of sample forecast. His idea behind the hazard model was that pre-

vious studies who used accounting ratios are mis-specified. The author also finds that half of the

accounting ratios model used previously perform poorly and that they neglect market-based indi-

cators. He continues by saying that by only combining three market-based indicators (market size,

past stock returns and idiosyncratic standard deviation of its stock returns) together with two ac-

counting ratios variables he could estimate a prediction model that is quite accurate in out of sam-

ple test. Shumway (2001) hazard model was inspired by two previous marked-based indicator

studies, Queen and Roll (1987) and Theodossiou (1993).

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Queen and Roll (1987) examined whether ot not firm mortality could be predicted using publicly

available market data. The variables they used where firm size, price, return, volatility and beta

and market capitalization. There are several reasons to use market-based indicators instead of fi-

nancial ratios. According to Queen and Roll (1987), previous empirical bankruptcy prediction

studies suffered from two handicaps, one of theme being the limited numbers of banks that

actually go bankrutp and the second one is the relative insensitivity of accounting data. According

to Theodossiou (1993) which also inspired the Shumway (2001) study, the older models which

use accounting data are static in nature and they ignore valuable information from past condition

of the firms.

Queen and Roll (1987) continued arguing that the fewer the actual bankrupt firms for analysis is,

the fewer is the predictive power of the model. When it comes to the relative insensitivity of

accounting data, the authors argued that two alternative accounting methods can lead to at least

one measurement error and this can affect the predictive power of the model. The other argument

towards using accounting data relates to timing. To avoid these handicaps, the authors decided to

solely use market-based indicators to estimate a predictive model for firm mortality. The authors

added that despite these handicaps, using accounting data does not necessarily mean that the model

is irrelevant or meaningless for prediction.

The other popular study in market-based indicator modern literature is Hillegeist et al. (2004) who

compared Altman (1968) and Ohlson (1980) model against their market-based model. They con-

cluded that using stock market information can provide an alternative and by far superior source

of information regarding a firm probability of bankrupt because it aggregates information from

other sources together with information extracted from accounting data. Some benefits introduced

by the authors regarding using the option pricing model are that they provide guidance about the

theoretical determinants of bankruptcy risk and they can supply the necessary structure to extract

bankruptcy-related information from market prices. The disadvantages of using this market-based

model is that it relies on assumptions in which many of the cases do not hold in practice. Another

possible disadvantage is that stock market may not efficiently consider all publicly information

about probability of bankrupt into stock prices. This is consistent with Sloan (1996) findings where

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it was found that the market does not accurately reflect all possible information that can be obtained

from firms financial statements. Agarwal and Taffler (2008) argue that the superiority of the mar-

ket-based model provided by Hillegeist et al. (2004) only reflect the poor performance of their

comparator models and not particularly reflect the strong performances of its market-based model.

Agarwal and Taffler (2008) is one of the most known studies in the recent period which examine

the difference between prediction model that use accounting ratios and market-based indicator as

primary source of data. In this paper the authors highlighted some of the most important advantages

and disadvantages from each two methodology.

As already known, accounting-based ratios models are typically constructed by using information

available on firms’ financial statements estimated on sample of failed and non-failed firms. This,

according to the authors, are likely to make the model to be sample specific. Another disadvantage

of accounting-based model which is consistent with Queen and Roll (1987) second handicap

argument is the relative insensitivity of accounting data. Agarwal and Taffler (2008) argue that the

accounting statements on which these models are based cast doubt on their validity. They came up

with four reasons to support this; (1) accounting statements represent only past performance of a

firm and this may decrease its ability to predict the future, (2) the true asset values may be different

from the recorded book value because of the conservatism and historical cost accounting, (3) ac-

counting numbers are subject to manipulation by management, (4) since the accounting statements

are prepared on a going concern basis, they are by design, of limited utility in predicting bank-

ruptcy, consistent with Hillegeist et al. (2004)

The market-based models that use the Black Scholes model (1973) and Merton (1974) model

claims to provide a more appealing alternative to the above-mentioned criticism. There are several

studies in recent period which used these Black Scholes and Merton (1974) models as reference to

their studies, e.g. Bharath and Shumway (2004); Hillegeist et al. (2004); Reisz and Perlich (2007)

to mentioned a few.

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Agarwal and Taffler (2008) argue that the market-based methodology can counter most of the

criticisms of accounting-based models. They highlighted five reasons; (1) the market-based model

can provide a sound theoretical model for firm bankruptcy, (2) inefficient markets, stock prices

will reflect all the available information that is contained in accounting financial statements and

will also contain all available information not obtained in the financial statements, (3) market-

based indicators are unlikely to be influenced by firm accounting systems, (4) market prices reflect

future expected cash flows, and hence are more appropriate for predicting future outcomes, (5) the

output of market-based model are not time or sample dependent.

Although the market-based model seems in some sort of way to solve the criticisms of accounting-

based models, this methodology has some criticism of its own. Consistent with Saunders and Allen

(2002) conclusion about the Merton (1974) model, for it requires a number of assumptions which

are already mentioned above. It also required measures of asset values and volatility which are

almost unobservable.

The CAMEL ratio is a good estimator of banks overall solvency and is exactly what was intended

to do in this paper. In section 2.3.3, the role and importance of the CAMEL ratio were explained.

There are several papers in the literature that favors the use of the accounting-based method and

in addition to that they inspired the use of accounting method in this paper. Cole and White (2012)

concluded from their results that the traditional proxies for CAMEL ratios are a good estimator of

bank failure in 2009, but also, they were important in predicting failure for the crisis between 1985-

1992.

After analyzing their empirical results, Agarwal and Taffler (2008) consistent with Hillegeist et al.

(2004) findings concluded that both, market-based and accounting-based model carry unique

information about firm failure. Although market-based model seems to be more attractive, their

lack of superior performance empirically due to all the assumptions are not a surprise.

The author also highlighted some important conclusions in favors of the accounting-based model

which will be used to support the idea around this paper. First, it is important to mention that firm

failure is generally not a sudden event, it is not normal that firms in good financial health file for

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bankruptcy. Usually, firm failure is the culmination of the poor financial health of previous years

that will be largely captured by the firm own financial statement. Second, the double-entry system

of accounting guaranty that window dressing the accounting or change in accounting policies will

have minimal effect on a measure that combines different facets of accounting information

simultaneously. The third and final conclusion the author wrote was that loan covenants are

generally based on accounting numbers and that this information is more likely to be found the

accounting-based models.

Together with Agarwal and Taffler (2008) conclusions in favors of accounting ratio, there are

some additional reasons in this paper to exclude market-based indicator which are also consistent

with (Betz et al. (2013) idea of not considering the market-based indicators. They argued that

market-based indicators tend to have shorter horizon (Bongini et al., 2002 and Milne, 2014) and

in this paper, the idea is to create a model that can predict failure up to three years in advance. In

addition, rather than using only listed banks, this paper uses a broad sample of banks. Bongini et

al., 2002 had this problem where they had to cut off data because some of the firms where not

listed or rated, this is exactly what was intended to prevent in this paper.

It is then concluded that the accounting-based models despite all the criticism are not dominated

empirically by the market-based models. In fact, the accounting-based models produce much more

significant economic benefits contrary to the market-based models.

After carefully examining the advantages and disadvantages between the market-based and

accounting-based models, this paper favored the use of the accounting-based methodology for the

above-mentioned reasons. Therefore, from this part on the history of bankruptcy prediction model

is solely based on methodologies and prediction models that were estimated using accounting

financial ratios.

Going back to the late 1970s, Hanweck (1977) introduced the first published study which was not

the Multivariate Discriminant Analysis or Linear Discriminant analysis. Hanweck (1977)

introduced a new model called “Probit analysis”, where he used 6 factors to create the Probit

model. In the same year, another bankruptcy prediction model was introduced by (Martin, 1977).

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The so-called “Logistic Analysis Model”, or Logit. This paper introduced the first application of

logit analysis to the banks early warning problems, where the author used 25 financial ratios to

perform his test.

Another study who used the Logit Analysis model was Ohlson (1980). In his study, the author

highlighted one fundamental difference from his paper and previous studies in the field of

bankruptcy prediction models. According to the author, the data used for his study was not

obtained from Moody’s Manual, instead was obtained from a 10-K financial statement as reported

at that time. The author stated that there is an important advantage of taking information from the

firm own 10-K financial statement. He continued; the 10-K financial statement gives a better view

of at what point in time they were released to the public, and so people could, therefore, check

whether the firm entered bankruptcy prior to or after the date of release. According to the author,

previous studies have not explicitly considered this timing issue.

From 1968 starting with Altman Multiple Discriminant Analysis (MDA) till 1990, most of the

studies in the bankruptcy prediction field used the MDA, Logit and Probit model to create a good,

more accurate predictive model. In 1990, a more complex and sophisticated model was

introduced, the Neural Network (NN). This Neural Network model was first introduced in the

bankruptcy prediction field by Odom and Sharda (1990). But it was in the year 1992 where Tam

and Kiang (1992) used this model as a tool to predict banks failure.

2.3.2 Banking Sector

First Beaver (1966) introduced the first Univariate method, then Altman (1968) introduced the

Multiple Discriminant Analysis (MDA), and from that point, several other models with the purpose

of predicting bankruptcy/failure of firms has been introduced. All the way from simple approaches

like Ratio Analysis till more complex approaches like the Neural Network (NN) has been used in

the field of bankruptcy prediction models.

What makes this field of study interesting is that these models where used from time to time for

firms in different industries. For example, Altman (1968) used firms from the Manufacturing

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industry. In a later study done by Altman (1973), he used firms from the Railroad industry.

Santomero and Vinso (1977) used firms situated in the banking industry. Sharma and Mahajan

(1980) applied the MDA for retail firms. Scaggs and Crawford (1986) tried to apply Altman

Multiple Discriminant Analysis using firms in the airline industry. Wertheim and Lynn (1993)

decided to apply the Logit model for firms in the Hospital sector and Gardiner, Oswald and Jahera

(1996) decided to apply the MDA model for the Hospital sector. These are just a few examples

done in the past. Another reason which makes this field of study interesting is that these were

originally applied for firms located in the U.S. But there are also studies who use data from firms

in another country. For example, in the paper of Taffler (1984), the author addressed different

studies that used data from UK firms. Castagna and Matolcsy (1981) used the MDA to create a

predictor model from failed firms in Australia. These are just a few examples from studies that did

not use data from U.S based firms.

In the history of studies in the field of bankruptcy prediction models, different models, ratio, data

set has been used to create an accurate model. From general firm, manufacturing firms, till firms

located in the hospital industry, there is a variety of options in this research field. As mentioned in

the Introduction chapter, the focus of this paper is on the banking sector.

In this section of the Literature chapter, the focus is on bankruptcy predictor models in the banking

industry. This is a crucial section, where some of the most relevant papers about bankruptcy

models which use banks as a primary source of data are presented. Also, in the next section, the

CAMEL Model is introduced and explanations about its function are presented and how it is being

used by other authors in all these periods of times.

In the literature, there are some studies that focused specifically on the banking sector. For

example, one of the first ones was Meyer and Pifer (1970) which used a Linear Probability model.

Two other examples which were already mentioned in this 3rd section are; (Hanweck, 1977) with

his Probit Analysis and (Martin, 1977) with his Logit Model. According to Martin (1977), (Secrist,

1938) was one of the first studies about bank failure. Secrist (1938) used a simple tabular and

graphic comparison of one or two balance-sheet ratios at a time, with groups consisting of banks

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that failed at different time periods and banks that did not fail. Stuhr and Van Wicklen (1974) and

Sinkey (1977) were also one of the first papers that focused on banks fro their studies.

Another paper who followed Martin (1977) Logit model for the banking sector was Ohlson (1980).

Another study on the banking sector was conducted by Rose and Kolari (1985) who decided to use

the Multiple Discriminant Analysis (MDA) for their study.

Papers like Bell (1997) which decided to perform two tests (NN and Logit) and compare the results

are some of the interesting papers in the bank sector. The focus of his paper was to compare these

two methodologies and compare their abilities to predict commercial banks failure. His conclusion

from the results where that both methodologies yield somehow similar accuracy, but with Neural

Network performing what better. According to the author, neither of the two models dominated

the other one in terms of predictive ability.

Espahbodi (1991) study was also an interesting paper that compared two methodologies for firms

in the bank sector. In his paper, he compared the Logit model with the Multiple Discriminant

Analysis. The author also addressed some limitations of the MDA which will be covered in the

4th section of the Literature chapter.

Like the above-mentioned examples, there are numerous other papers that focused on firms in the

banking sector. It is almost impossible to study all these papers as it is not the main focus of this

paper. In section 2.2 and 2.3 of the Literature chapter, the most relevant papers and history of the

bankruptcy prediction models where highlighted.

2.3.3 CAMEL Model

Till now all the popular statistical models and authors where mentioned in sections 2.2 and 2.3 of

the Literature chapter, but what about the CAMEL Rating Model. Some of the relevant papers on

banking sector were introduced, but now the focus is narrowed down into the CAMEL Rating

Model. In this section, some important papers about CAMEL Rating are introduced, but most

important an analysis of why this study is different from those other studies will be presented.

The role of financial intermediations shows that banks play is crucial for economic growth and

well-being of an entire economy. This put the banking sector on the very top of the financial

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sectors. Therefore, failure in the banking sector can lead to disastrous consequences for an entire

country and this is the reason why it is so important to study banks financial health. The probability

for bank failure is a function of its own internal factors. These internal factors are related to banks

own solvency. Here is where the CAMEL Model comes into action as it is used to measures banks

solvency.

The Uniform Financial Rating System, informally known as the CAMEL rating system, was

introduced by U.S. regulators in November 1979 to assess the health of individual banks. The idea

behind this rating model is to do an onsite examination on banks financial health, with the purpose

of assigning a score on a scale of 1 (strong performance) to 5 (unsatisfactory performance). The

CAMEL Rating Model examines various aspects of the bank, such as its financial condition, its

compliance with the law, regulatory policies and the quality of its internal management system

control. This kind of model is very important for shareholders, depositors and creditors as it reflects

the bank’s financial health.

The CAMEL Rating Model was initially created for US banks only, but now it is a financial rating

model that is recognized internationally and is used for different banks around the world. This

model is an innovative tool in analyzing banks financial performance Sangmi and Nazir (2010). A

study conducted by Siva & Natarajan (2011) found that CAMEL scanning helps the bank to

diagnose its financial health and alert the bank to take preventive steps for its sustainability. So,

this is a useful financial tool that can be used to evaluate banks financial health all around the

world.

This CAMEL Model is based on factors that help in evaluating banks performance. It is a

management tool that measures Capital Adequacy, Assets Quality, the efficiency of Management,

quality of Earnings and Liquidity of financial institutions Maheshwara Reddy and Prasad (2011).

In fact, the CAMEL-motivated proxy variables for bank condition demonstrate that most of these

factors are significantly related to the probability of failure as much as four years before a bank

fails (Thomson, 1991)

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The five CAMEL factors are the following;

o Capital Adequacy: It is important to have an adequate level of Capital Adequacy to

ensure that banks have enough capital to boost their business, while simultaneously having

enough capital to absorb any financial trouble that might lead to bankruptcy. So, Capital

Adequacy is important to measure banks financial health, to see if banks have enough cap-

ital to overcome unexpected capital losses in the future.

o Asset Quality: Another bank-specific variable that also affects banks profitability is

assets. Asset Quality is useful to measure the bank's degree of financial strength because it

refers to the quality of the loans, thus if the loans have a high or low probability of repay-

ment. Poor asset quality leads to bank failure in most of the cases. Asset quality is com-

monly used by banks to decide what numbers of their assets are at financial risk and how

much allowance for potential losses they must have to make. (Zaheer, 2016).

o Management Efficiency: Management efficiency of banks are evaluated in terms of

asset quality, earnings and profitability, liquidity, capital adequacy (Zaheer, 2016). This

factor is vital in evaluating management efficiency and effectiveness. Misra and Aspal

(2013) define management efficiency as an ability to plan and respond to changing envi-

ronment, leadership and administrative capability of the bank.

o Earnings Capacity: Generating earning is essential if banks want to keep performing

well. Earning capacity factor is important when evaluating banks performance because it

is key in determining a bank’s ability to maintain quality and earn consistently. This spe-

cific CAMEL factor explains the profitability of the bank and explains its sustainability

and future growth opportunities.

o Liquidity: Liquidity is an important factor in which the abilities of banks is reflected

when it comes to meeting their financial obligations. In this context, a good liquidity posi-

tion by banks is referred to when banks can generate enough funds, either by decreasing

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liabilities or by converting its assets quickly into cash. There should be an adequate level

of liquidity compared to banks present and future obligations and availability of assets that

can be readily converted to cash without undue loss.

Most previous studies that examine banks solvency during the period of crisis, used proxies of the

CAMEL model. Same as these studies, in this paper, the CAMEL ratios are being selected from

the fact that they have demonstrated to be crucial when evaluating banks overall performance.

After all, the data used to come up with the ratios comes from banks own financial statement.

When evaluating banks financial performances, it is a fact that there are many relevant factors out

there. But what makes this paper different from other papers is that here only CAMEL ratios are

being used, excluding other Non-CAMEL variables. The focus of this study is on the five CAMEL

factors, namely Capital Adequacy, Asset Quality, Management Capability, Earnings and

Liquidity, which is used to construct a predictor model.

Same as other studies in the past, this study will also use CAMEL factor financial ratios to create

the bankruptcy prediction model. The reason behind this is that if the CAMEL factors are

according to the general literature, a good estimator for analyzing bank overall performance, this

means that the ratios that represent the five factors might also be a good predictor for bankruptcy

failure. This is consistent with (Betz et al. (2013) who also uses the CAMEL factors as an approach

to extract information from banks financial statement. After all, financial ratios that best represent

the five CAMEL factors will be used to estimate the Logit model. Of course, most of the financial

ratios used in previous studies of bankruptcy prediction field are the same financial ratio’s that

represent the CAMEL factors, but not all of them. In this study, a good balance of ratio for each

of these five factors is used to create a bankruptcy prediction model.

Studies by Thomson (1992) and Cole and Gunther (1998) who compare the on-site vs off-site

method of evaluating banks and see whether the ability of offsite monitoring system predicts

bankruptcy provides a valuable supplement to on-site exams, found that 4 of the 5 CAMEL factors

are significant in predicting bankruptcy. These 4 out of 5 factors (Capital Adequacy, Assets

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Quality, Earnings and Liquidity) were also found significant in previous studies; (Bovenzi,

McFadden and Marino, 1983), (Korobow and Stuhr, 1983) according to Cole and Gunther (1998).

What makes the study of Cole and Gunther (1998) different in contracts to other papers is that the

authors try to compare two different methods of evaluating bank performance using 4 of the 5

CAMEL factors instead of creating a single model.

According to a study done by Betz et all. (2013) for the European Central Bank (ECB), they state

that all the studies that they reviewed do report a high accuracy in predicting bankruptcy for U.S

banks using the CAMEL factors. Some of these papers are (Cole & White, 2012), (Jin,

Kanagaretnam, & Lobo, 2011) and (DeYoung & Torna, 2013). Betz et all. (2013) added on that

these mentioned papers combine some CAMEL factors with other external factors. The use of

these external factors is to complement the CAMEL factor variables. Their focus is not on the

banks themselves, rather they focus more on the causes that originated the crisis.

Jin, Kanagaretnam and Lobo (2011) examine the ability of some selected accounting and audit

quality variables to predict banks that failed during the financial crisis of 2008. The predictor

variables they used where balance sheet strength, loan characteristics, financial reporting

discretion, and auditor type and auditor industry specialization. According to the authors their

study where the first to study document the impact of audit quality on bank failure. They only use

some financial ratios that represent the CAMEL model and combine it with other predictor

variables. This is different from what it is intended to do in this study.

Cole and White (2012) were also one of the studies which did combine outside predictor variables

with some CAMEL factors. The author using the Logit model tried to find reasoning as to which

factors caused the financial crisis. Indeed, the authors use some CAMEL ratios, but their main

focus was on obtaining an answer to what caused the crisis rather than creating a bankruptcy

predictive model. The intention of this study is clearly not finding out what caused the crisis.

Another difference is the fact that they use predictor variables that specifically has to do with real

estate investments, so they specify to create a model that focuses on the crisis of 2008 rather than

creating a model that prevents future failure.

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DeYoung and Torna (2013) focused on examining whether the income from nontraditional

banking activities contributed to the failure of U.S banks during the financial crisis in 2008. They

don’t specifically use the CAMEL ratios, rather they obtained data from only the banks with

CAMEL rating 4 (marginal performance) and 5(unsatisfactory performance).

Analysis

The CAMEL Model from its creation till present this approach is very well known when it comes

to analyzing banks overall financial performances. This is also one of the reasons behind this study.

There are a lot of research papers who studies banks financial performances. One way or other

these studies are connected to the CAMEL Rating Model, either by using only a few factors from

the model or combining it with other predictor variables. This model is not only used for studies

using U.S banks as a dataset, but it is was also being used for studies using non- U.S banks.

An already mentioned example is the study conducted by Betz et all. (2013) for the European

Central Bank (ECB) that uses data from European banks, which is not so frequent according to the

authors since bank failure is rare in Europa. Kenneth and Adeniyi (2014) use CAMEL factor to

create a Multiple Discriminant Analysis (MDA) model. So this model although it was created for

examining U.S banks, it is being used for banks worldwide. As mentioned in the Introduction

chapter, the idea behind this study is not to investigate the causes of the banking crisis 2008, nor

coming up with alternative variables that might cause the crisis, rather the focus is on investigating

banks overall financial health.

After carefully analyzing some relevant papers that use the CAMEL approach, it is concluded that

most of these published studies use some ratios that represent the CAMEL model, but their focus

is on either comparing different methods using financial ratios or investigating the financial crisis

itself. They don’t focus much on examining banks internal financial health.

For example, one recent study done by Aubuchon and Wheelock (2010) examine the

characteristics of bank failure during 2007-2010 and investigates whether the geographic

distribution of failures reflected differences in local economic conditions. They use some financial

ratios, but their principal objective is to examine the effect that regional economics characteristics

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have on failed banks during the crisis and they don’t investigate the characteristics of the bank

themselves.

Jordan et al. (2010) is another recent example that focused more on investigating the crisis in 2008

by adding an extra component to the study and combining it with some CAMEL ratios. They use

some CAMEL factors, not all, but their idea is to construct an MDA model combining financial

ratios with real estate loans and influence as extra predicting variables.

These were just a few examples, but if the idea of this paper was to study the general literature,

many more papers which does the same would have been presented in this section. After 2008

there has been an increased interest in investigating the causes of the financial crisis 2008. There

are a lot of research papers combining different theories and variables to come up with answers to

why the crisis occurred. Their focus is more on solving, understanding the crisis rather than

investigating banks performances.

2.4. Models Limitations

In the first three first sections, the role of banks, history of bankruptcy models and the CAMEL

Model has been discussed, but what about the limitations of each of the statistical models? This

4th section, the limitations and advantages of each of the 4 most used and important models (MDA,

Logit, Probit and NN) is discussed. This is to help understand why choosing the Logit Model to

conduct this study.

Like the above-mentioned examples, numerous other papers focused on firms in the bank sector.

It is almost impossible to study all these papers as it is not the main focus of this paper. In the 2nd

and 3rd section of the Literature chapter, the most relevant papers and history of the bankruptcy

prediction models where highlighted. Till now all the popular models and authors were mentioned

in these two sections, but what about the limitations of each of these models? This section, 4th

section, the limitations and advantages of each of the 4 most used and important models (MDA,

Logit, Probit and NN) is discussed.

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In the late 1970s, new methods on bankruptcy prediction were coming. One of the main reasons

was that the (Multiple) discriminant analysis done by Beaver (1966) and Altman (1968) among

others, was violating some restrictive assumptions.

Studies done by Eisenbeis (1977) addressed some of these problems of the Multiple Discriminant

Analysis (MDA) that limited the usefulness of their result and Premachandra et al. (2009) which

addressed some of the pitfalls from Eisenbeis (1977) studies. Some of the problems with the MDA

according to these authors are (1) propensity of equal variance-covariance matrices across the

respective groups; (2) the financial ratios entering in the model are multivariate normally

distributed; (3) the prior probability of the distress and costs of misclassifications are specified.

Also, the MDA does assume multivariate normality and equal covariance matrices. This was

supported by Taran (2012) that concluded that one of the most crucial assumptions being violated

was that financial statement data must be normally distributed and assume that the variance-

covariance matrix of failed and non-failed banks need to be equal.

Not everything is negative with the MDA, some benefit of this methodology is that it can determine

the importance of the factors being used, also this methodology is useful in explaining the results.

And regarding the assumptions that are being violated by the MDA, there a many statistical

software application that can solve correct these assumptions.

Bankruptcy prediction models like Logit were introduced as a solution for all these assumptions

that MDA violates. The Logit model does not assume multivariate normality and equal covariance

matrices as the MDA does. But according to Aziz and Dar (2006), both the Multiple Discriminant

Analysis and the Logit Model are being frequently sued in the bankruptcy prediction research field.

Apart from the Logit model, there is the Probit model and the Ordinary Least Square (OLS), which

at first may seem to be equal. Logit model assumes that the outcome, probability of failure,

depends on a set of independent variables. By using the Logit model, the predicted outcomes are

limited to lies between 1 and 0 in this case and this outcome is the probability of an event happening

or not. Because in this study binary dependent variable is used, it becomes difficult to use the

Ordinary Least Square regression, as the OLS tend to have values that are not necessarily between

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1 and 0. According to theory, the reason to choose Logit Regression rather than Ordinary Least

Squares (OLS) is because of all the undesirable properties that the OLS regression must deal with

when the dependent variable is binary (Amemiya, 1981). It is much more practical to use Logit

instead of Ordinary Least Square (OLS).

A good reason in favor of Logit model compared to Probit model is that Logit model has the

statistical property of not assuming multivariate normality among the independent variables,

contrary to the Probit model that does assume a normal distribution of the data. According to

Espahbodi (1991), one of the major differences between the Logit and Probit model is that Logit

is based on a cumulative logistic probability function, while Probit is based on a cumulative normal

probability function. Since the normality assumptions are generally not met in these types of

studies, the Logit model is preferred as it is more similar in form to the cumulative normal function.

Especially when analyzing data related to banking this can be an advantage as the data is generally

not normally distributed. According to Maddala (1983), a good reason to use Logit instead of

Probit is the unequal frequency of the failed and non-failed samples because logit is not sensitive

to the uneven sam­pling frequency problem.

As mentioned in the 3rd section in this Literature chapter, the Logit model has been used as a bank

failure prediction model in many studies. This model is proven to give accurate prediction and it

is a very useful tool for analyzing bankruptcies. According to Jagtinali et al. (2003), this logistic

regression model can beat much more sophisticated and complex models. Another reason why the

logistic regression model is preferred compared to other sophisticated predicting models is its

easiness to use in statistical software.

The logit model was proved to have better predictive ability than other methods regarding this

study, for example, the discriminant analysis and other one-period methods. This statistical method

is one of the most used methods until recent studies regarding the field of bankruptcy prediction

models. Recent modern studies were done by Estrella et al. (2000), Arena (2008) and Andersen

(2008) confirm that the logit model performs well when it comes to predicting banks failure.

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According to Mihalovič (2016), one of the most prevalent models in the bankruptcy prediction

field that uses artificial intelligence is the Neural Network (NN). In this paper little attention is

given to this methodology as it is a very sophisticated and time-consuming method to apply in this

type of research field. The Neural Network is a biologically inspired analytical method that can

run extremely sophisticated non-linear functions. Bell (1997), the author compared the Logit

model with the Neural Network. The author defined the NN as a methodology inspired biologically

and it is useful for modelling a wide variety of classification, clustering and pattern recognition

problems. According to the author, one advantage of this NN model is that it has the ability to

mathematically represent the inherent process non-linearities through the specification of intricate

network architecture with many interconnections. Ahn and Kim (2009) emphasized that there are

some difficulties in using the Neural Network. These difficulties arise from the fact that the NN

uses many parameters to be set by heuristics and therefore, the NN model is exposed to overfitting

and finally, the NN model might lead to poor predictive ability.

For numerous reasons mentioned in this section of the Literature chapter, this Logit model is the

adequate model to create a bankruptcy prediction model. Some of these reasons already mentioned

are that other methodologies like the Multiple Discriminant Analysis or Probit model have some

disadvantages compared to the Logistic Model. How the Logit model is constructed using the

CAMEL factors will be presented in the following chapter. Also, in the next chapter, a broader

explanation is given about the ratios being selected is given and how well they represent the five

CAMEL factors.

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3. Methodology

This chapter provides important information about the source from where the data came from

and how the sample for this study was selected. Furthermore, a few tables and figure are presented

to present the data selected. For example, a table containing the number of failed banks and non-

failed banks is presented. The second table contains all the financial ratios that were used to create

the predictive bankruptcy model.

The Methodology chapter is divided as follow; first, the data sample, followed by the tables

containing financial ratios formulas and finally the Logistic Regression Model specification is

explained. With regards to the Logistic Regression model, which is the last section of this chapter,

an explanation is given on how the regression model was constructed using the data and which

tests were performed to test the accuracy of the model.

3.1 Data Sample

As already mentioned, the focus is to create a bankruptcy prediction model for firms in the banking

sector. The data needed to conduct this research was gathered from the Federal Deposit Insurance

Corporation (FDIC) database, under the section of Statistics Depository Institution (SDI). A lot of

studies have been collecting data from the FDIC database, because of its wide range availability

of data, e.g. (Espahbodi, 1991). It is important to mention that FDIC only provides data for U.S

commercial banks and savings institutions excluding investment banks.

The intention was to collect data to create a model that can predict failure for up to three years in

advance. It was decided to do approximately the same data collection approach as Cole and White

(2012) and Jordan, et al. (2010) did, where they collected data from previous years to predict bank

failure from a later year. According to the authors, collecting data from previous years give us the

ability to ascertain early indicators of financial troubles from banks, as well as late indicators.

Another approach that Cole and White (2012) used that is implemented in this study is similar to

this one where they also used Logit regression together with CAMEL ratios to predict failure of

banks.

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Therefore, data were collected from the banks that according to FDCI failed in the U.S during the

two and one quarter-year lap period starting from September 2008 till the end of 2010. The reason

to begin from September 2008, stems from the fact that the Lehman Brothers went bankrupt in

that month, indicating that it is the starting point. The reasoning behind choosing the timeframe

from Sept 2008 till the end of 2010 stems from the fact that the banking crisis started in 2008, so

most of the banks where being affected during 2008, but most important this effect was better seen

in the years beyond 2008. (See Figure 1). According to the FDIC database, a total of 322 banks in

the U.S went bankrupt during that period. Excluding the banks that went bankrupt before Septem-

ber 2008 (13), the total then becomes 309 banks.

What was different from Cole and White (2012) study is that they collected data from 2008-2010

to predict failure in 2011, having only one data set and having only one prediction model. But in

this paper three prediction model was estimated, therefore data were collected up to three years in

advance for banks that failed after Sept 2008, during 2009 and 2010 respectively. So, data was

collected on quarterly bases from the banks that went bankrupt during that period (Jordan, et al.,

2010) up to three years in advance, starting from 2005, 2006 and 2007 respectively. For example,

for the banks that went bankrupt during 2009, the data was collected up to three-year prior 2009,

in this case from 2006, 2007 and 2008. The same entails for banks that went bankrupt during 2010,

data were collected from 2007 (3-year prior 2010), 2008 (2-year before 2010) and 2009 (1-year

prior 2010). Thus, at the end, one big data set was constructed which was divided into three sub-

samples. The first sub-sample contained only data from 1-year prior failure period, the second and

third sub-sample contained data from 2 and 3-year prior failure period. (See illustration below).

Because three different prediction models were estimated per each year prior failure, it was im-

portant to arrange the data properly and effectively. For example, to see how accurate the predic-

tion model can be a 1-year prior failure, the sub-sample containing only data 1-year prior failure

period for the banks that went bankrupt after September 2008, during 2009 and 2010 was being

used, in this case (2007, 2008 and 2009). The same was done for two and three years prior to

failure. Thus, each sub-sample were used individually to estimate the three prediction models. This

method was implemented by Espahbodi (1991), where he created two sub-samples for 1 and 2

years prior to failure.

Page | 34

2008 2007 (1Y Prior)

2006 (2Y Prior) 2005 (3Y Prior)

2009

2008 (1Y Prior) 2007 (2Y Prior) 2006 (3Y Prior)

2010

2009 (1Y Prior) 2008 (2Y Prior) 2007 (3Y Prior)

o 1st Sub-sample 1-year prior failure [2007 – 2008 – 2009]

o 2nd Sub-sample 2-year prior failure [2006 – 2007 – 2008]

o 3rd Sub-sample 3-year prior failure [2005 – 2006 – 2007]

A total of 309 banks went bankrupt between September 2008 – 2010. However for this particular

study a sample of 100 randomly selected banks was used. This randomly selection process was

done using the Random function formula in Excel. The sample was divided into weighted

percentage, 5 banks from 2008, 45 banks from 2009 and 50 banks from 2010. See calculation1 and

Table #1.

Aside from collecting data from banks that failed, the data was also collected from the non-failed

banks. In no particular order, a total of 100 non-failed banks, were being selected to match the

number of failed banks. This was done by using the same Random function in Excel Consistent

with Jordan, et al., 2010, where they randomly selected and matched the amount of both failed and

1 Calculation: 2008: 12/309*100 = 5 2009: 140/309*100 = 45 2010: 157/309*100 = 50

Page | 35

non-failed banks. This method of collecting data from two groups (Failed & Non-failed firm) was

being applied way back in the ’60s and ’70s according to Martin (1977). The author stated that

majority of studies followed this approach, where a group of actually failed firms is identified from

individual case studies, and these banks one or more years prior to failure are matched with a group

of firms that did not fail.

According to Martin (1977), this method of collecting data from two sample groups has some

benefits for the study. This method takes the real-world classification into failed and non-failed

firms as a dependent variable and attempts to explain the classification as a function of several

independent variables. These independent variables are mostly financial ratios being calculated

from banks financial statements, a method which is implemented in this study. Another advantage

of using actual banks that went bankrupt is that you don’t rely on subjective judgement whether a

bank is in financial trouble or not. It is known that different agencies often use different techniques

to categorize whether a bank is in a problem or not. A prediction model based on subjective

judgement according to Martin (1977) has a higher risk of having unknown errors.

Table 1. Data Sample

Year Failed Banks Non-Failed Banks

2008* 5 5

2009 45 45

2010 50 50

Total 100 100

* Only banks that failed after September 2008

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Figure 1. Number of Failed banks from 2000 - 2014

From this Graph it is observed that before the banking crisis 2008, there weren’t so many banks

that went bankrupt in the U.S according to the FDIC data base. As soon as the crisis began to rise

during 2008, it can be observed that there was an increase in U.S banks that went bankrupt. In

2009 an increase of more than 550% in the number of banks compared to 2008 is observed. This

number continues to increase during 2010. After 2010, it is observed that the number of U.S banks

that went bankrupt starts to drop till late 2014. Therefore, that peak in bankrupt banks is used to

estimate the prediction model.

7 411

3 4 0 0 3

25

140

157

92

51

2418

0

20

40

60

80

100

120

140

160

180

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Failed Bank

Failed Bank

Page | 37

Table 2. Financial Ratios

CAMEL Factors Financial Ratios

(C)apital Adequacy 1. Equity / Total Assets 2. Equity / Total Liabilities 3. Tier 1 Risk-based Capital Ratio

(A)sset Quality 4. Return on Assets

(M)anagement Efficiency 5. Net Int Income / Numb of Employees 6. Efficiency Ratio

(E)arnings Capacity 7. Net Operating Income / Total Assets 8. Return on Equity 9. Interest Expense/ Interest Income

(L)iquidity 10. Liquid Assets to Deposit 11. Net Loans / Total Assets 12. Domestic Deposit / Total Assets

Macro-economic Factors

GDP 1. Gross Domestic Product

Inflation 2. Real Interest Rate

As can be seen from the table above (See Table 2) there are two different groups of independent

variables that are being used in this study. The first group consist of the financial ratios that

represent each of the CAMEL factors. All the figures necessary to calculate these ratios can be

found on the FDIC database which is available on a quarterly basis. The data available is from the

banks that failed as well for the non-failed banks. These chosen financial ratios are designed to

measure bank’s overall financial condition in areas such as capital adequacy, banks assets quality,

earnings and profitability, efficiency, loans quality and liquidity of banks assets Sinkey J. (1975).

It is expected that banks that went bankrupt, in general, perform poorer in these aspects compared

to the non-failed banks. It is also expected to observe an increase in the difference between the

ratios for the failed banks the closer it gets to failure period. That is, the model 2-year prior failure

is expected to have more significant variables compared to the 3-year prior failure model. The

same for 1-year prior failure model, to perform better than the 2-year prior failure model.

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The other group representing 2 macro-economic factors (GDP & Inflation) that believe have some

positive influence on banks overall performance. Consistent with Gonzalez-Hermosillo, (1999),

where he concluded that both micro (banks’ balance sheet data) and macro are important in

determining banks failure. He added that by introducing macro-economic variables to the model

solely based on microeconomic variables improved significantly the predictive power of the

model. This was also the case in Hernandez and Wilson (2013). This method is also being

implemented by (Betz et al. (2013) where they combined CAMEL ratios with country-specific

macro-financial indicators. Another study suggests that banks performance is affected by bank-

specific independent variables, however, next to this, it is expected to be sensitive to

macroeconomics variables (Alper & Anbar, 2011). In general literature, we generally find four

macroeconomic variables that affect a bank’s performance. These macro-economic variables are

Gross Domestic Product (GDP), Inflation, Real Interest rate and Political instability (Ongore &

Kusa, 2013). However, in this study, only GDP and Inflation was used.

Gross Domestic Product

Gross Domestic Product (GDP) affects the demands of banks assets (Ongore & Kusa, 2013). Ac-

cording to this same author during a period of declining GDP growth, the demand for loans/credit

tend to fall which may negatively affect a bank’s performance. On the other hand, increasing GDP

growth may positively affect banks performance. The reason behind this is that a positive GDP

growth reflects a growing economy, leading to higher demand in loans/credit.

According to the general literature regarding the growth and financial sector profitability, it is

expected that GDP growth has a positive relationship with banks performance (Alper & Anbar,

2011). This is supported by the findings of Bikker et al. (2001) and Kunt et al. (2000)

Inflation

According to Alper and Anbar (2011), inflation measures the overall percentage change in Con-

sumer Price Index (CPI) for all goods and services. This macroeconomic variable affects the real

value of cost and revenues. According to Perry (1992), if banks can anticipate inflation, they can

Page | 39

adjust the interest rate to increase revenues in a proportion of the cost. On the other hand, if infla-

tion is unanticipated, it becomes difficult for banks to make the proper adjustment on their interest

rate. This may lead to an increase in cost in proportion to revenues. Most studies find a positive

relationship between inflation and bank performance (Molyneux and Thornton 1992, Hassan and

Bashir, 2003).

3.2 Logit Model

As mentioned in the first chapter, the methodology used in this study is the Logistic Regression

model which was also used by two famous studies Martin (1977) and Ohlson (1980). For numerous

reasons already mentioned in the Literature section 2.4, this Logit model is the adequate model to

create a bankruptcy prediction model. Some of these reasons are that other methodologies like the

Multiple Discriminant Analysis or Probit model have some disadvantages compared to the

Logistic Model.

Some of these disadvantages as already mentioned are that for example, MDA violates some

assumptions, it assumes multivariate normality and equal covariance matrices. On the other hand,

the Logit model does not assume multivariate normality or equal covariance matrices. If

comparing the Logit with Probit model, although they seem to be similar, the Probit tends to

assume a normal distribution of the data while Logit model has the statistical property of not

assuming multivariate normality among the independent variables. For these reasons and other

reasons already mentioned in the Literature chapter, the Logit model is the chosen methodology

that will be used in this study.

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3.2.1 Estimating the Logit Model

The basic regression equation is;

{𝛶 = ∝ +𝛽𝓍}

But in this study because of the multiple independent variables, the Logit regression equation

becomes as follow;

𝜰 = 𝐶𝑜𝑛𝑠 + 𝛽1𝓍𝐶, 𝑡 + 𝛽2𝓍𝐴, 𝑡 + 𝛽3𝓍𝑀, 𝑡 + 𝛽4𝓍𝐸, 𝑡 + 𝛽5𝓍𝐿, 𝑡 + 𝛽6𝓍𝐺𝑃𝐷, 𝑡 + 𝛽7𝓍𝐼𝑛𝑓𝑙, 𝑡

𝛶 = Binary outcome between 0 and 1.

1 refers to “Failed Bank”

0 refers to “Non-Failed Banks”

∝ = Constant

Internal Factors

𝓍C= All the ratios representing Capital Adequacy

𝓍A= All the ratios representing Assets Quality

𝓍M = All the ratios representing Management Efficiency

𝓍E = All the ratios representing Earnings Capacity

𝓍L= All the ratios representing Liquidity

t = Represent quarterly time period

Macro-economic Factors

𝓍GPD = GDP (Gross Domestic Product)

𝓍Infl = Inflation

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3.3 Model Validation Tests

This section is divided into 4 sections. In this section, it is explained how to come up with the

Logit model and which are the following steps toward estimating the bankruptcy prediction model.

Section 1 containing a brief explanation about how the financial ratios are obtained to create the

model. Section 2 explaining how to test the accuracy of the model. Section 3 explaining how to

test if the model and the variables are statistically significant. Section 4, the last section is about

the hypotheses being tested in this study.

3.3.1 Variables Selection

As seen in table #2, there are multiples ratios for each CAMEL factor. The CAMEL factors as

mentioned above are; Capital Adequacy, Assets Quality, Management Efficiency, Earning Capac-

ity and Liquidity. The macro-economic factors are Gross Domestic Products (GDP) and Inflation.

The dependent variable consists of a binary outcome, meaning the output is restricted to be be-

tween 0 for non-failed banks and 1 for the U.S banks that went bankrupt between 2008 and 2010.

According to the literature, different strategies can be applied to choose the most significant ratios

for each variable. Int this study, two of these strategies were applied. The first strategy was to

choose a ratio based on its significant levels/ relevancy on previous studies conducted in this field

of study. For example, Beaver (1966) used 3 criteria to choose the 30 ratios he used, which was

also used by other researchers. These 3 criteria are; (1) the popularity of the ratios in previous

studies, (2) the ratio needed to perform well in previous studies and (3) the ratio must be defined

in terms of a cash flow concept.

The second strategy was stepwise regression. The function of the Stepwise regression is to build

up statistical models by adding or removing explanatory variables. In this study the Backward

stepwise method was applied, where variables were removed one at the time based on their con-

tribution to the overall fir of the model Espahbodi (1991).

A combination of both strategies was implemented to come on with the most significant variables.

First, the most used/popular variables in the literature per each CAMEL factors were selected.

Page | 42

Second, with the help of the stepwise regression, some variables were removed from the model to

have a model consisting only of significant variables. The idea was not to overfit the model by

using a lot of variables, non-significant variables, but creating a simple highly accurate model with

good predictive power variables.

3.3.2 Model Accuracy Test

The main goal of the Logit regression is to find the best fitting model to predict the relationship

between the dependent and a set of independent variables. Testing the Logit model for Type I and

Type II errors is one the most used approach to test the accuracy of the model in predicting failure.

Examples of papers that use this approach are; Altman (1968), Ohlson (1980) and Espahbodi

(1991). A model can be inaccurate in two different ways, it can make a mistake of incorrectly

predict a bankrupt firm to survive (Type I) or it can predict a non-bankrupt firm to fail (Type II).

See the graphical illustration of Type I and Type II Error.

Model

Bankrupt Non-Bankrupt

Actual Bankrupt

Non-Bankrupt

Correct Prediction Type I

Type II Correct Prediction

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3.3.3 Model Significance

To find out how good the model is when it comes to predicting bankruptcy, some test must have

been performed first. This has to do with the statistical significance of the variables and with the

model in general.

o First, the backward stepwise regression method was applied to select the most significant

variables in the model. The remaining significant variables that fulfil the model were used

to estimate the Logit models.

o Second, the chi-square test was performed to test the overall significance of the model.

This approach of testing the significance of the model was used by Espahbodi (1991) and

was also used in this study.

o Third, the statistical significance level of each independent variable was analyzed, to see

which variables had a more statistical influence on the dependent variable.

o The fourth and last test was estimating the accuracy of the model in general. This was done

by using a classification table, where can be observed how accurate the model is in general.

This classification table also gave how much of Type I and Type II error are present in the

model.

In this study, the accuracy of the Logit model was tested to see how well it can predict bankruptcy

up to three years in advance. Therefore, all the four mentioned tests were performed 3 times using

the three different sub-sample data set. In that way, it was possible to observed how significant

and accurate the model is 1,2- and 3-year prior failure. Same as Espahbodi (1991) did, where he

constructed a logit prediction model for 1- and 2-year prior failure.

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3.3.4 Hypothesis

In order to reach the objective of this paper, some hypotheses were tested first.

These are the following hypotheses;

o H1: Test whether the five components of the CAMEL model are statistically significant

estimators when predicting bankruptcy for all 3-year prior failure. This was basically done

by observing if the ratios that represent each of the CAMEL components are statistically

significant or not.

o H2: Test if the model accurately predict failure three year before bankrupt.

o H3: Test if the model accurately predict failure two year before bankrupt.

o H4: Test if the model accurately predict failure one year before bankrupt.

In the following chapter (4), each test was performed, and the results were presented with the help

of tables and graphics obtained from the statistical package SPSS. In this chapter, the hypotheses

were tested and answered. After testing all these four hypotheses, a conclusion was made in the

last chapter (5), which showed whether or not this logit model can accurately predict failure up to

three years in advance.

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4. Empirical Results

After collecting data on a quarterly basis for 3 year from 100 banks that went bankrupt and

matching it with 100 active banks, the data set now consist of 8002 observations per variable per

year. So, in total the whole data set consist of 28.8003 observations. In this chapter all the

mentioned tests in section 3.3.3 were executed and empirical results were provided with tables and

explanations. This chapter is divided into 3 sections, the first one (4.1) containing the descriptive

statistics results. In the second section (4.2) the models are presented as well as the overall accuracy

of the models. In the third and last section (4.3) the hypothesis is answered with the help of the

results. To make a definite conclusion about the research question, all these three sections are

essential.

4.1 Descriptive Statistics

As we can be seen in table 2, section 3.1, there are 12 initial variables. Some of these variables

were removed as we proceeded with the tests. The next tables presented represent the mean and

standard deviation from each variable for the banks that failed and the non-failed banks as well.

In this section, the mean values of the failed group of banks are being compared to the group of

Non-Failed banks. This was done by analyzing each factor of the CAMEL Model separately.

o Capital Adequacy: [Equity to Total Assets - Equity to Total Liability -Tier 1 Risk]

Having a good Capital Adequacy means that banks have enough capital to boost their business and

enough capital to overcome capital losses. Looking at table 3 it is observed that from the failed

group the mean values are higher compared to the non-failed group, although the differences are

minimal. This illustrates that failed groups 3-year prior to failure who have fewer assets compared

to equity, are being financed less with liability compared to equity and have higher Tier 1 risk

2 1 year consist of 4 quarters * 200 banks (100 Failed & 100 N-Failed) = N 800 per variable 3 There are 12 variables per each 3 years (3*12=36). And a total of 800 observation per variables per year. [ 36*800 = 28.800 Total observations]

Page | 46

ratio, meaning they are financially more protected against risky transactions performed. This indi-

cates that looking only at the Capital Adequacy it is difficult to say which group of banks will fail

in the upcoming year. Observing table 4 & 5 which shows the mean value 2 and 1-year prior

failure, these ratios become smaller compared to the non-failed group of banks. So, the data shows

that from two years in advance it is possible to observe which banks are starting to have trouble

when it comes to managing their capital.

o Asset Quality: [Return on Asset]

Poor Asset Quality leads to banks having a higher chance of failing. When looking at table 3 the

failed group of banks have a lower RoA mean value. This indicates that they are less profitable

relative to their assets, or they aren’t managing their assets well enough to generate earnings com-

pared to the non-failed group. In table 4 & 5 we see that the RoA mean value keeps declining and

even becomes negative for the failed group, while for the non-failed group even though the RoA

also decreases it remains positive. The negative RoA indicates that from 2 years before failure,

both groups of banks struggle to generate returns, but this effect seems to be even worse for the

Failed group.

o Management Efficiency: [Interest Income per Employee – Efficiency Ratio]

These two ratios measure how efficient a company is when it comes to utilizing its resources to

generate income. It illustrates how well or poorly a company uses its assets and liability internally.

From the 3 tables, it is observed that the failed group have lower mean value income per employee

for 3, 2 and 1-year prior failure. But they have a higher mean value efficiency ratio for all the 3

years prior failure. Thus, although they generate less income per employee, they are better in using

their assets and liability to generate income compared to the non-failed group. Analyzing only the

mean values of Management Efficiency of each group, it seems that the Failed group should not

fail. Or it indicates that this Management factor is not good enough when it comes to predicting

failure.

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o Earning Capacity: [Operating Income to Tot Assets – Return on Equity – Int Exp to

Int Income]

If a bank wants to keep performing well and be financially stable, they need to generate enough

earnings/ income. The first two ratios have a smaller mean value for all 3-years prior failure, and

the last ratios have a higher mean value for all 3 years compared to the non-failed group. This

means that for all the 3 years prior failure it costs the failed group more to generate earning which

in some way, is understandable. This is consistent with the Asset Quality factor. There is a

difference when it comes to generating earnings/ returns between the two groups. Again, the Failed

groups seem to be having more troubles when it comes to generating earnings, which is a crucial

aspect that keeps banks alive.

o Liquidity: [Liquid Assets to Deposit – Net Loan to Tot Assets – Deposit to Tot Deposit]

Good Liquidity position means that banks can convert assets quickly into cash to meet present and

future obligations. The last two variables have higher mean values for all the 3-years prior failure.

This means that for the failed group, most of their assets were in the form of Loans and Deposits.

From one angle this is good because loans are one of the most profitable assets of commercial

banks. But from the other angle, in a period of crisis, banks need to convert these assets quickly

into cash but converting loans into quick cash is almost impossible, leading these banks to have a

higher probability of failure. On the other hand, the first variable has a higher mean value 3- year

prior failure as can be seen in table 3 but it becomes smaller compared to non-failed group 2 and

1-year prior failure. This indicates that the failed group has more liquidity 3-year before going

bankrupt than the non-failed banks. But this changes as they get closer to failure time, the failed

group become less liquid and therefore having a higher probability of failure.

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Table 3. Descriptive Statistic, 3-year prior failure

Failed Banks Non-Failed Banks

N Mean St. Deviation Mean St. Deviation

Failed or Non-Failed 800 1.0000 .00000 .0000 .00000

Equity to Tot Assets 800 .1131 .08657 .1004 .03802

Equity to Tot Liability 800 .1827 .65762 .1128 .05420

Tier 1 Risk 800 .1677 .56908 .1229 .05405

Return on Assets 800 .0073 .15587 .0105 .01081

Interest Income to Employees

800 37.4163 30.51992 87.9837 368.25906

Efficiency Ratio 800 1.1388 3.80795 .6116 .16487

Operating Income to Tot Assets

800 -.0005 .02430 .0104 .01065

Return of Equity 800 .0061 .27281 .1129 .10300

Interest Expenses to Interest Income

800 2.5701 29.57637 .4521 .11594

Liquid Assets to Deposit

800 1.4563 12.94607 .3638 .59047

Net Loan to Tot Assets 800 .7597 .11895 .6929 .14573

Domestic Deposit to Tot Assets

800 .7884 .11474 .7244 .16501

Valid N (listwise) 9.600 This table presents the descriptive statistic (mean and St. dev) for the sub-sample 3-year prior failure for both Failed and Non-failed groups. Each of the 12 financial ratios has a total of 800 observations and in total there are 9.600 observations. This table shows how the banks performed internally for 3-year before actually going bankrupt. And this can be compared to the banks that did not fail.

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Table 4. Descriptive Statistic, 2-year prior failure

Failed Banks Non-Failed Banks

N Mean St. Deviation Mean St. Deviation

Failed or Non-Failed 800 1.0000 .00000 .0000 .00000

Equity to Tot Assets 800 .0895 .04964 .0976 .03503

Equity to Tot Liability 800 .1069 .09988 1.3626 1.80306

Tier 1 Risk 800 .1043 .05175 .1181 .04690

Return on Assets 800 -.0133 .05603 .0058 .01679

Interest Income to Employees

800 34.0479 19.61736 83.5876 349.37786

Efficiency Ratio 800 .9916 .89077 .6585 .30270

Operating Income to Tot Assets

800 .0060 .26903 .0068 .01522

Return of Equity 800 -.1616 .95447 .0634 .19910

Interest Expenses to Interest Income

800 .5277 .12216 .4226 .12364

Liquid Assets to Deposit

800 .4377 2.15566 3.2167 38.55135

Net Loan to Tot Assets 800 .7574 .10153 .7029 .14251

Domestic Deposit to Tot Assets

800 .8153 .10166 .7189 .16595

Valid N (listwise) 9.600 This table presents the descriptive statistic (mean and St. dev) for the sub-sample 2-year prior failure for both Failed and Non-failed groups. Each of the 12 financial ratios has a total of 800 observations and in total there are 9.600 observations. This table shows how the banks performed internally for 2-years before actually going bankrupt. And this can be compared to the banks that did not fail. The ROA and ROE are negative for the Column “Failed banks”, indicating that 2-year before going bankrupt, failed banks indicate a poor earnings return.

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Table 5. Descriptive Statistic, 1-year prior failure

Failed Banks Non-Failed Banks

N Mean St. Deviation Mean St. Deviation

Failed or Non-Failed 800 1.0000 .00000 .0000 .00000

Equity to Tot Assets 800 .0495 .03040 .0964 .03571

Equity to Tot Liability 800 .0594 .07950 1.3739 2.36189

Tier 1 Risk 800 .0620 .03968 .1204 .04805

Return on Assets 800 -.0627 .07537 .0021 .02336

Interest Income to Employees

800 26.8419 19.38350 84.1878 309.40868

Efficiency Ratio 800 2.4649 10.41133 .6636 .33317

Operating Income to Tot Assets

800 .1559 .65561 1.4870 1.00253

Return of Equity 800 -1.6855 7.41568 .0195 .27969

Interest Expenses to Interest Income

800 .5478 .17727 .3414 .12186

Liquid Assets to Deposit

800 .2461 .23986 .4210 .99337

Net Loan to Tot Assets 800 .7140 .09857 .6872 .14390

Domestic Deposit to Tot Assets

800 .8518 .09042 .7266 .16142

Valid N (listwise) 9.600 This table presents the descriptive statistic (mean and St. dev) for the sub-sample 1-year prior failure for both Failed and Non-failed groups. Each of the 12 financial ratios has a total of 800 observations and in total there are 9.600 observations. This table shows how the banks performed internally for 1-year before actually going bankrupt. And this can be compared to the banks that did not fail. Apart from the negative ROA and ROE, it is observed that most of the ratios are lower in amount for the Failed group compared to the Non-failed group.

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4.2 Model Significance Results

After analyzing the descriptive statistic, the focus now is on estimating the Logit prediction model

and see how significant and accurate the models are for all 3 years prior to failure. As mentioned

in section 3.3.3, there are several steps to be taken before getting to the prediction model.

The first step is to eliminate non-significant variables from the model, this is done by using the

Backward Stepwise regression method. The remaining variables were used to run the other statis-

tical tests. The second step is to check the overall significance of the model. The third step is to

analyze each variable separately and check how they influence the dependent variable. The last

step is to check the overall accuracy of each of the three models.

4.2.1 Backward Stepwise

First, a normal linear regression test was performed with all the 12 variables, the results are pre-

sented in Table 6, “Whole Sample” column. An R-square of 0.690 is observed from the table,

which indicates that 69% of the variance in the dependent variable is being explained by the inde-

pendent variables. Looking at the adjusted R-Square of 0.685 which is almost the same as the R-

square, it indicates that the model fits the data well. From the same column all the variables are

statistically significant except for the following four; Interest Income to Employee, Efficiency Ra-

tio consistent with Mayes and Stremmel (2013), Return on Equity consistent with Betz et al.

(2013) and Liquid Assets to Deposit consistent with Ploeg (2010). All these four variables have a

p-value of larger than 0.05 (p > 0.05). Therefore, they should be excluded from the model.

Column “Stepwise regression” presents the result from the Backward Stepwise regression which

only considers the significant variables and excludes the non-significant ones. This column con-

sists of only 8 significant variables, the same as in the column “Whole Sample”. These 8 significant

variables are; Equity to Tot Assets, Equity to Tot Liability, Tier 1 Risk, Return on Assets, Operating

Income to Tot Assets, Interest Expenses to Interest Income, Net Loan to Tot Assets, Domestic De-

posit to Tot Assets.

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Although the R-squared from the stepwise regression is 0.001 smaller than previous R-square from

normal linear regression, the adjusted R-square did improve, but only with 0.1%. The two models

(whole model and Stepwise Model) fits the data almost with the same percentage, with the only

difference that the Stepwise model does not include non-significant variables. Table 7, Model

Summary, shows the increase in R-square and adjusted R-square as the model includes significant

variables one at the time until remaining with the 8 significant variables. The remaining 8 variables

and how they represent each of the CAMEL factors are present in section 4.3.

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Table 6. Coefficients, Whole Sample vs Stepwise Linear Regression

Whole Sample Stepwise Regression

Beta Sig. Beta Sig.

(Constant) .015 .895 .073 .473

Equity to Tot Assets -1.582 .001* -1.594 .001*

Equity to Tot Liability

-.043 .000* -.044 .000*

Tier 1 Risk -.819 .025* -.863 .018*

Return on Assets -.696 .000* -.697 .000*

Interest Income to Employees

-1.544E-5 .739

Efficiency Ratio .001 .268

Operating Income to Tot Assets

-.197 .000* -.198 .000*

Return of Equity .000 .899

Interest Expenses to Interest Income

.707 .000* .707 .000*

Liquid Assets to Deposit

.017 .283

Net Loan to Tot Assets

.313 .000* .305 .001*

Domestic Deposit to Tot Assets

.387 .000* .339 .000*

R Square .690 .689

Adjusted R Square .685 .686 * significant at 5% level. Column “Whole Sample” is the result of a Linear Regression including all the 12 variables. The results indicate that 4 of the 12 variables are not significant at 5%. The 4 variables are; Interest Income to Employee, Efficiency Ratio, Return on Equity and Liquid Assets to Deposit. The column “Stepwise Regression” shows the results after performing the Backward Stepwise regression in order to eliminate the non-significant variables from the model. After performing the Stepwise regression, the model is left with the same 8 significant variables at 5% level as in the Column “Whole Sample”. The remaining 8 variables will be used to estimate all 3 Logistic prediction models. From now on, the 4 non-significant variables will not appear in any of the following prediction models.

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Table 7. Model Summary Stepwise Regression

Model R R Square Adjusted R Square

1 .618a .382 .382

2 .743b .552 .551

3 .801c .642 .640

4 .816d .665 .663

5 .823e .677 .675

6 .826f .682 .680

7 .827g .684 .682

8 .830h .689 .686

This table shows an increase in R-square & Adjusted R-square value as the Stepwise regression adds on significant variables to the model. This table also indicates that the R-squares values will not increase by adding more than 8 variables, therefore the other variables are considered non-significant according to the results provided.

4.2.2. Multinomial Logit Regression

In this section three different model were estimated for all 3-years prior failure. From now on, the

8 remaining variables after performing the Stepwise regression in section 4.2.1 are used to estimate

the Logit prediction model for all 3-years prior to failure. First, the overall significance of the

models is presented and then the economic interpretation of the financial ratios is explained. This

process is repeated for all 3 Logistic models.

Logit Regression Model, (3-year prior Failure):

Table 8 present the results of the Logit regression 3-year prior failure for 2 different models, one

with 8 variables and the other with 6 variables. The reason to create two models (Model I & Model

II) was from the fact that in Model I, all the variables are statistically significant at 5% level, except

for Tier 1 Risk and Return on Assets with a p-value larger than 0.05), 0.684 and 0.571 respectively.

Therefore, Model II was created and presents the same Logit model, but this time excluding the

two non-significant variables, thus containing only significant variables. After all the idea is to

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create a Logit prediction model with significant variables. This process of creating two models

(Model I & Model II) was also repeated for the other two sub-sample 2 and 1-year prior to failure.

Because only Model II is important to analyze, table 9 only shows the results of how significant

and accurate Model II is. Table 9 shows a statistically significant Chi-square of 245.692 with (p-

value .000< 0.05) for Model II. This can be interpreted as follow; the 6 variables included in Model

II statistically significantly improve the model compared to the intercept alone, thus the model

without any variables. When looking at the “Goodness-of-Fit”, this is additional information re-

garding the overall fit of the model, the Pearson Chi-square shows p-value of .098 > 0.05 for Model

II. This means that the model fit the data well.

The Overall Classification accuracy for Model II is 72.5%. So, the sub-sample data set of 3-year

prior failure can statistically predict bank failure 3 years in advance with an accuracy rate of 72.5%.

Economic Interpretation:

The Logit model is a binary prediction model, which means that the outcome lays between 1

(Failed) and 0 (Non-Failed). As expected, the beta sign of Equity to Total Assets is negative con-

sistent with Betz et al. (2013). This indicates that banks that are financed with more equity com-

pared to liability have a lower probability of going bankrupt. A lower Equity to Assets ratio indi-

cates that banks have a higher level of leverage which makes the bank less flexible to confront

sudden economics shocks Wheelock and Wilson (2000). The Equity to Total Liability beta sign is

positive and means that the higher the proportion of equity compared to liability the higher the

probability for a bank to go bankrupt. This result was not expected as it contradicts the first variable

Equity to Total Assets. High equity to liability ratio should decrease a bank probability of failure,

a larger amount of equity compared to liability should protect banks against assets breakdown

Ploeg (2010). Operating Income to Total Assets has a negative sign as expected, this indicates that

a higher level of earnings decreases the probability of failure. Interest Expenses to Interest In-

come has a positive beta sign consistent with Ploeg (2010). This suggests that banks with less

ability to generate income from interest become riskier. Also, a higher level of this ratio implies

lower profitability of interest for banks which increases the probability of failure for banks. The

last two variables (Net Loan to Total Assets & Domestic Deposit to Total Assets) have a positive

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sign as expected consistent with Wheelock and Wilson (2000) and Arena (2008). It has to do with

the Liquidity position of banks, as explained above when banks are facing difficult periods it be-

comes difficult for banks to convert their loans and deposit into quick cash. The Net Loan to Total

Assets suggests that if most of the bank’s assets are in the form of loans, the bank become less

liquid and more likely to have a higher probability of failure Ploeg (2010). Banks that are in a poor

liquidity position have a higher probability of failure as the Logit model sign indicates.

Table 8. Logit Regression, 3-year prior failure

Model I Model II

Beta Sig. Beta Sig.

Intercept -11.352 .000* -11.360 .000*

Equity to Tot Assets -33.624 .033* -33.922 .032*

Equity to Tot Liability

29.422 .008* 29.866 .007*

Tier 1 Risk .280 .684 --

Return on Assets 1.710 .571 --

Operating Income to Tot Assets

-36.333 .000* -33.981 .000*

Interest Expenses to Interest Income

5.193 .000* 5.205 .000*

Net Loan to Tot Assets

5.339 .000* 5.322 .000*

Domestic Deposit to Tot Assets

6.770 .000* 6.808 .000*

* significant at 5% level. This table presents the result after performing the Logistic Regression for the sub-sample 3-year prior to failure. This table shows the coefficient/ betas and significant levels for each of the variables used. The column “Model I” consist of the 8 remaining variables after excluding the non-significant variables. Tier 1 Risk and Return on Assets are not significant at 5% level therefore they are also being excluded from the model. Column “Model II” represent the Logit Regression results containing only significant variables. So, the Logit model 3-year prior failure according to these results only contains 6 prediction variables.

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Table 9. Overall information, 3-year prior failure

Model II. Fitting Information

Chi-Square df Sig.

Intercept Only -- -- --

Final 245.692 6 .000*

Goodness-of-Fit

Pearson 793.172 743 .098

Classification

Predicted

Observed Non-Failed Failed Percent Correct

Non-Failed 291 109 72.8%

Failed 111 289 72.3%

Overall Percentage

50.2% 49.8% 72.5%

This table gives indicates the significance of the model. Here only Model II from table 8 is being tested as this contains only significant variables. The Chi-square on the second row indicates that Model II improves the model compared to the intercept only. From the Pearson chi-square which tests how well the data fits the model, it can be concluded that the data does fit the model. The Classification section indicates the accuracy prediction rate of the Model. This model can predict failure with a 72,5% accuracy 3-year before banks go bankrupt.

The following is the Logit Regression Model 3-year prior failure using the beta’s values obtained

from table 8.

𝜰 = -11.360∝ + -33.922x1 + 29.888x2 + -33.981x3 + 5.205x4 + 5.322x5 + 6.808x6

∝ = Constant

𝓍1 = Equity to Total Assets

𝓍2 = Equity to Total Liability

𝓍3 = Operating Income to Total Assets

𝓍4 = Interest Expenses to Interest Income

𝓍5 = Net loan to Total Assets

∝6 = Domestic Deposit to Total Assets

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Logit Regression Model, (2-year prior Failure):

The same procedure was done for the sub-sample data set 2-year prior to failure. Table 10 shows

the betas and significant levels of each variable. Model I show that there are 4 non-significant

variables which are; Equity to Total Asset, Tier 1 Risk, Return on Asset and Operating Income to

Total Assets. Again Model II, represent the model without any non-significant variables. Table 11

shows that Model II has a Chi-Square of 604.280 with p-value (.000 < 0.05), and a Pearson Chi-

Square p-value (1.000 > 0.05), meaning that the 4 variables have improved the model compared

to the intercept alone and that the model fit the data well. The Overall Classification Accuracy

indicates that 2-year prior failure the Model II can predict bank failure with an 86.1% accuracy.

Although this model consists of fewer variables, it has higher predictive accuracy than the previous

model 3-year prior failure.

Economic Interpretation:

This model consists of fewer explanatory variables than the previous model but has higher predic-

tive accuracy. The Equity to Total Liability in this model has a negative sign consistent with Arena

(2008), opposite to the positive sign in the previous model which was not expected. In this model,

the equity to total liabilities suggests that being less leverage lowers banks probability of failure.

According to Sundararajan et al. (2002) banks that are financed more with equity are less leverage

and needs to borrow less to finance their assets, therefore having lower interest expenses and higher

interest/ net income. Being less leverage and more profitable lower the probability of failure for

banks, consistent with the negative Equity to Total Liability. Banks that are financed with less debt

are less risky and therefore their probability of failing decreases. Here the positive beta sign of In-

terest Expenses to Interest Income is consistent with the previous model but has a higher beta value

(9.100 > 5.205). So, the same as in the previous model, banks with poor ability to generate income

from interest have a higher probability of failing. But from this model, the same level of ratio

results in a much higher probability of failure the closer banks get to actual bankruptcy period.

This is consistent with Sinkey J. (1975), which concluded that the closest banks are to actual failure

period the poorer their overall financial performances become and the overall degree of signifi-

cance of most explanatory variables increases. Looking at the last two variables which shows

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banks Liquidity position, they have positive betas signs, but the Net Loan to Total Assets have a

lower beta value compared to the previous model. Although it still increases banks probability of

failure, it does not increase as fast as the previous model 3-year prior failure. Same as the varia-

ble Interest Expenses to Interest Income the variable Domestic Deposit to Total Assets here has a

higher beta value compared to the previous model. Although there is a difference in beta value

between the two models, this model still suggests that poorer liquidity positions of banks increase

the probability of failure.

Table 10. Logit Regression, 2-year prior failure

Model I Model II

Beta Sig. Beta Sig.

Intercept -11.930 .000* -12.597 .000*

Equity to Tot Assets 2.037 .637 --

Equity to Tot Liability

-5.252 .000 -5.317 .000*

Tier 1 Risk -5.434 .127 --

Return on Assets -5.390 .198 --

Operating Income to Tot Assets

3.581 .127 --

Interest Expenses to Interest Income

9.053 .000* 9.100 .000*

Net Loan to Tot Assets

3.255 .001* 3.696 .000*

Domestic Deposit to Tot Assets

9.086 .000* 9.018 .000*

* significant at 5% level. This table presents the result after performing the Logistic Regression for the sub-sample 2-year prior to failure. This table shows the coefficient/ betas and significant levels for each of the variables used. Column “Model I” consists of the 8 remaining variables after excluding the non-significant variables. Equity to Tot assets, Tier 1 Risk, Return on Assets and Operating Income to Total Assets are not significant at 5% level therefore are also being excluded from the model. Column “Model II” represent the Logit Regression results containing only significant variables. So, the Logit model 2-year prior failure according to these results only contains 4 prediction variables.

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Table 11. Overall information, 2-year prior failure

Model II. Fitting Information

Chi-Square df Sig.

Intercept Only -- -- --

Final 604.280 4 .000*

Goodness-of-Fit

Pearson 581.908 759 1.000

Classification

Predicted

Observed Non-Failed Failed Percent Correct

Non-Failed 322 78 80.5%

Failed 33 367 91.8%

Overall Percentage

44.4% 55.6% 86.1%

This table indicates the significance of the model. Here only Model II from table 10 is being tested as this contains only significant variables. The Chi-square on the second row indicates that Model II improves the model compared to the intercept only. From the Pearson chi-square which test how well the data fits the model, it can be concluded that the data does fit the model. The Classification section indicates the accuracy prediction rate of the Model. This model can predict failure with a 86.1% accuracy 2-year before banks go bankrupt.

The following is the Logit Regression Model 2-year prior failure;

* 𝜰 = -12.5979∝ + -5.317x1 + 9.100x2 + 3.696x3 + 9.018x4

∝ = Constant

𝓍1 = Equity to Total Liability

𝓍2 = Interest Expenses to Interest Income

𝓍3 = Net loan to Total Assets

𝓍4 = Domestic Deposit to Total Assets

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Logit Regression Model, (1-year prior Failure):

For the last sub-sample data set 1-year prior failure, only one of the eight variables are not signif-

icant at 5% level, Equity to Total Assets with p-value (0.493 > 0.05). For Model II, the Chi-square

is statistically significant with a p-value less than 0.05. So, the model did improve compared to the

intercept only. Looking at the Pearson chi-square p-value, this indicates that the model, same as

the previous models do fit the data. The Overall Classification table indicates that 1-year prior

failure the model predicts failure with an accurate rate of 97.3%.

Economic Interpretation:

From the three models, this model 1-year prior failure consists of more explanatory variables and

as expected have a higher prediction accuracy rate. Looking at the Equity to Total Liability it is

observed that it has the same expected negative sign and a higher beta value than the model 2-year

prior failure. Tier 1 risk is some sort the money banks have stored to keep it functioning during

risky transactions and difficult times, this variable was not present in the two previous models.

Here it has a negative sign consistent with Betz et al. (2013), it suggests that the higher the ratio

the well economically prepared the bank is, therefore reducing its probability of failure. The neg-

ative sign of Return on Assets suggests that higher returns for banks reduce its probability of failure

which was expected. Although Betz et al. (2013) had a positive sign for return on assets, the author

stated that in the literature RoA must always be negative, this is also confirmed by other authors

like Cole and White (2012) and Arena (2008). For the remaining variables, the results are con-

sistent with what was expected, only the variable Net Loans to Total Assets gives a different result.

In the two previous models, this variable had a negative beta sign, indicating that having a higher

level of assets tied up in loans becomes difficult for banks to convert these loans quickly into cash

and therefore putting banks into a poor liquidity position. But in this last model, the results suggest

that having a higher level of loans reduces the probability of failure. A possible explanation could

be that loans are one of the most profitable sources of income/assets for banks and it is expected

that banks with higher loans to assets ratio will have higher interest income, therefore, reducing its

probability of failure. Comparing the two first models (3 and 2-year prior failure) with the last one

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(1-year prior failure) it is concluded that the closer to failure period the more significant the ex-

planatory variables becomes when it comes to predicting bankruptcy, this is consistent with Sinkey

J. (1975) findings.

Table 12. Logit Regression, 1-year prior failure

Model I Model II

Beta Sig. Beta Sig.

Intercept -2.682 .386 -3.251 .276

Equity to Tot Assets -11.654 .493 --

Equity to Tot Liability

-9.367 .000* -9.343 .000*

Tier 1 Risk -24.072 .019* -28.459 .001*

Return on Assets -30.927 .002* -30.807 .003*

Operating Income to Tot Assets

-3.019 .000* -3.078 .000*

Interest Expenses to Interest Income

14.306 .000* 14.754 .000*

Net Loan to Tot Assets

5.339 .003* -7.024 .000*

Domestic Deposit to Tot Assets

10.783 .000* 11.265 .000*

* significant at 5% level. This table presents the result after performing the Logistic Regression for the sub-sample 1-year prior to failure. This table shows the coefficient/ betas and significant levels for each of the variables used. The column “Model I” consists of the 8 remaining variables after excluding the non-significant variables. Only Equity to Totals Assets is not significant at 5% level therefore are also being excluded from the model. Column “Model II” represent the Logit Regression results which contains only significant variables. So, the Logit model 1-year prior failure according to these results only contains 7 prediction variables.

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Table 13. Overall information, 1-year prior failure

Model II. Fitting Information

Chi-Square df Sig.

Intercept Only -- -- --

Final 997.026 7 .000*

Goodness-of-Fit

Pearson 264.609 757 1.000

Classification

Predicted

Observed Non-Failed Failed Percent Correct

Non-Failed 388 12 97.0%

Failed 10 390 97.5%

Overall Percentage

49.8% 50.2% 97.3%

This table indicates the significance of the model. Here only Model II from table 12 is being tested as this contains only significant variables. The Chi-square on the second row indicates that Model II improves the model compared to the intercept only. From the Pearson chi-square which tests how well the data fits the model, it can be concluded that the data does fit the model. The Classification section indicates the accuracy prediction rate of the Model. This model can predict failure with a 97.3% accuracy 1-year before banks go bankrupt.

The following is the Logit Regression Model 1-year prior failure;

* 𝜰 = -3.2510∝ + -9.343x1 + -28.459x2 + -30.807x3 + -3.078x4 + 14.754x5 + -7.024x6 +

11.265x7

∝ = Constant

𝓍1 = Equity to Total Liability

𝓍2 = Tier 1 Risk

𝓍3 = Return on Assets

𝓍4 = Operating Income to Total Assets

𝓍5 = Interest Expenses to Interest Income

∝6 = Net Loan to Total Assets

∝7 = Domestic Deposit to Total Assets

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4.2.3 Macro-Economic Factor

After estimating the Logit models for every 3 years, it was decided to test whether macro-economic

factors could increase the prediction accuracy of the models as done by Gonzalez-Hermosillo

(1999) Hernandez Tinoco and Wilson (2013). Both concluded from their results that by introduc-

ing macro-economic variables to a model solely based on microeconomic variables, could signif-

icantly improve the predictive accuracy of the model.

All three Models II from section 4.2.2 were used in combination with the two macro-economic

factors (Inflation and GDP Growth) mentioned in section 3.1. Table 14 shows that the two macro-

economic factors are statistically insignificant at 5% level for all 3 years. Therefore, no further test

could have been performed. Adding macro-economic factors to a simple model gave Gonzalez-

Hermosillo (1999) Hernandez Tinoco and Wilson (2013) a good result. Applying the same method

in this study gave an opposite result, the two macro-economic factors were statistically insignifi-

cant for all 3-years prior failure. For future recommendation, maybe using other macro-economic

factors or adding more than 2 macro-economic factors to the financial ratio model, would result in

a significant and more accurate model for all 3-years prior to failure. But with the obtained results

from this study, the variables weren’t statistically significant enough to estimate a prediction

model.

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Table 14. CAMEL Ratios + Macro-economic factors

3-year 2- year 1-year

Beta Sig. Beta Sig. Beta Sig.

Intercept 11.040 .751 Intercept 12.633 .812 Intercept 3.251 .924

Inflation 6.653 1.000 Inflation 21.046 1.000 Inflation 13.768 1.000

GDP Growth

5.648 1.000 GDP Growth

-8.536 1.000 GDP Growth

-10.559 1.000

Equity to Tot Assets

34.650 .030* Equity to Tot Liability

5.137 .000* Equity to Tot Liability

9.341 .000*

Equity to Tot Liability

-30.174 .007* Interest Expenses to Interest Income

-9.088 .000* Tier 1 Risk 28.457 .001*

Operating Income to Tot Assets

34.350 .000* Net Loan to Tot Assets

-3.718 .000* Return on Assets

30.803 .003*

Interest Expenses to Interest Income

-4.752 .000* Domestic Deposit to Tot Assets

-9.027 .000* Operating Income to Tot Assets

3.078 .000*

Net Loan to Tot Assets

-5.302 .000* Interest Expenses to Interest Income

-14.753 .000*

Domestic Deposit to Tot Assets

-6.778 .000* Net Loan to Tot Assets

7.023 .000*

Domestic Deposit to Tot Assets

-11.264 .000*

* significant at 5% level. This table is the result of adding two macro-economic variables (Inflation & GDP Growth) to the three estimated Logistic prediction models. Column “3-Year” represent the results 3-year prior failure. The same counts for the other two Columns. The idea behind this table was to prove that if adding macro-economic variables to the existing model, this could improve the prediction accuracy rate of the model. But the result shows that both Inflation and GDP are not significant for none of the 3-years prior failure. Therefore, no prediction model was estimated containing either of these two macro-economic variables.

Table 15. CAMEL Ratios Summary

CAMEL Factors Financial Ratios

Original Variables

Stepwise Regression

3-year prior failure

2-year prior failure

1-year prior failure

(C)apital Adequacy

1. Equity to Total Assets 2. Equity to Total Liabilities 3. Tier 1 Risk-based Capital Ratio

1. Equity to Total Assets 2. Equity to Total Liabilities 3. Tier 1 Risk-based Capital Ratio

1. Equity to Total Assets 2. Equity to Total Liabilities 3. --

1. -- 2. Equity to Total Liabilities 3. --

1. -- 2. Equity to Total Liabilities 3. Tier 1 Risk-based Capital Ratio

(A)sset Quality 4. Return on Assets

4. Return on Assets

4. -- 4. -- 4. Return on Assets

(M)anagement Efficiency

5. Net Int Income to Numb of Employees 6. Efficiency Ratio

5. XX 6. XX

5. XX 6. XX

5. XX 6. XX

5. XX 6. XX

(E)arnings Capacity

7. Net Operating Income to Total Assets 8. Return on Equity 9. Interest Expense to Interest Income

7. Net Operating Income to Total Assets 8. XX 9. Interest Expense to Interest Income

7. Net Operating Income to Total Assets 8. XX 9. Interest Expense to Interest Income

7. -- 8. XX 9. Interest Expense to Interest Income

7. Net Operating Income to Total Assets 8. XX 9. Interest Expense to Interest Income

(L)iquidity 10. Liquid Assets to Deposit 11. Net Loans to Total Assets 12. Domestic Deposit to Total Assets

10. XX 11. Net Loans to Total Assets 12. Domestic Deposit / Total Assets

10. XX 11. Net Loans to Total Assets 12. Domestic Deposit / Total Assets

10. XX 11. Net Loans to Total Assets 12. Domestic Deposit / Total Assets

10. XX 11. Net Loans to Total Assets 12. Domestic Deposit / Total Assets

Total Variables 12 8 6 4 7

“XX” -> refers to the 4 initially excluded variables after performing the Stepwise Regression in table 6. This table is a summary about how well the financial ratios represent each of the five CAMEL Factors after performing the regression tests. The column “Original Variables” is the same as in table 2, which indicates how the financial ratios represent the CAMEL Factors before performing any test. Columns “Stepwise Regression” shows the 8 significant variables after excluding the non-significant ones after performing the stepwise regression. The 4 excluded variables are marked with an “XX”. Columns “3-year, 2-year and 1-year prior failure” shows the remaining financial ratios that represent each CAMEL factor 3, 2 and 1-year prior bankrupt.

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4.3 Hypothesis Results

The idea behind this study was to create a bankruptcy prediction model that could predict failure

up to 3 years in advance. What is important to mention is that the prediction model would be solely

estimated using the CAMEL factors. So, in other words, how important each of the 5 factors is

when it comes to analyzing banks overall financial performance and predicting failure. After all,

the CAMEL model analyzes from banks financial condition to their internal management system.

Table 15 shows how well each of the 5 CAMEL factors is being represented by the significant

variables compared to the initial 12 variables. Compared to the initial set of variables, each of the

5 factors have been reduced in ratios. This is important for answering the first hypothesis;

H1: whether the five components of the CAMEL model are statistically significant estimators

when predicting bankruptcy.

As the table shows, Capital Adequacy is being represented by 2, 1 and 2 ratios, as shown in the

last three columns on table 15. Betz et al. (2013) stated that Capital Adequacy acts as a barrier

against financial difficulties and it protects banks from solvency, thus reducing the probability of

failure. Mayes and Stremmel (2013) also stated something similar, that this factor acts as a cushion

to absorb economics losses and shocks. It is then expected that banks that have a higher level of

capital adequacy should have a lower probability of failure. Looking at the three Logit models, it

is concluded that this factor (Capital Adequacy) is a significant estimator for bankruptcy predic-

tion, for the simple reason that most of the ratios that represent this factor have the expected neg-

ative sign and are consistent with the literature about reducing the probability of failure.

For Assets Quality factor, this factor is being represented by the initial ratio only for the model 1-

year prior failure. Thus, this factor is only significant for predicting failure for one of the three

models estimated. Poor management of assets is positively associated with bank failure Betz et al.

(2013). Poor asset quality leads to bank failure in most of the cases and this study, this is not the

exception. Although with the obtained results, this factor doesn’t seem to have the expected sig-

nificant impact when predicting failure.

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When it comes to analyzing the probability of failure of banks, Management Efficiency doesn’t

seem to have any significant influence at all. The two initial ratios were excluded from the begin-

ning when performing the Backward Stepwise regression was performed. So, they weren’t part of

any Logit Model since the beginning. A possible explanation is that from all five CAMEL factors,

this Management factor is the most difficult to capture with financial ratios, supported by Whee-

lock and Wilson (2000). Mayes and Stremmel (2013) stated that efficient management reduces the

likelihood of making wrong decision and therefore reducing the probability of failure. The author

also stated that although good management reduces the probability of failure for banks, this rela-

tionship is difficult to capture with financial ratios. Maybe the financial ratios chosen in this study

to represent this Management factor weren’t the adequate ones. According to the same authors,

other studies uses asset quality or earnings factor ratios to approximately measure the management

efficiency of banks. Another approach used in other models to measure banks management effi-

ciency is the Data Envelopment Analysis (DEA). This DEA method examine production efficiency

by transforming a given number of inputs into a given number of outputs Tatom (2011). This

method has been used in other studies in the past, see Barr, Seiford and Siems (1993) and Kao and

Liu (2004).

The Earning Capacity is being represented by 2 of the 3 ratios for 1 and 3-year prior failure. Return

on Equity is part of the 4 initial excluded variables after the Stepwise regression was performed.

This specific CAMEL factor explains the profitability of the bank and explains its sustainability

and future growth opportunities. This factor is expected to decrease the probability of failure for

banks, consistent with Betz et al. (2013). Mayes and Stremmel (2013) stated that high level of

earning for banks should improve banks economic condition, therefore reducing the likelihood of

financial distress. Looking at the results, most of the financial ratio’s that represent the Earning

factor does have the expected sign and are also consistent with the literature.

The last factor, Liquidity, is being well represented with 2 of the 3 ratios each all 3-years prior

failure models. This was the most stable factor of all. This factor is important for measuring banks’

ability to meet their financial obligations, a good liquidity position means banks easily can convert

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assets into cash. According to Mayes and Stremmel (2013) having liquid assets should result in a

lower probability of failure. Because in this study non-liquid assets ratios were used (Loans to

Assets & Deposit to Assets), the probability of failure then should increase. This is observed in the

results.

Answering the first hypothesis, after analyzing the results the factors Capital Adequacy, Earning

Capacity and Liquidity are more significant when it comes to predicting bankruptcy. Having a

good level of Capital Adequacy and Liquidity position is a fundamental aspect for banks when it

comes to having enough capital on hand to boost the company, meet future obligation or overcome

losses. Therefore, banks that are weak in these aspects, banks that don’t have enough backup or

aren’t well prepared for economic shocks have a higher probability of failure. The same happens

for the factor Earning Capacity when banks cannot generate enough income to keep the business

running their probability of failure increases. Looking at all 3 logit models, it is concluded that

having enough capital, liquid assets on hand, being well prepared to confront difficult period and

having the ability to keep generating enough income during difficult period weights more when it

comes to predicting bank’s failure.

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Table 16. Overall Accuracy of the Models

Overall Prediction Accuracy

1-year prior failure 2-year prior failure 3-year prior failure

Prediction Accuracy 97.3% 86.1% 72.5%

H2, H3 and H4: whether the Logit regression can with high accuracy predict failure for 3, 2 and

1-year prior failure, was answered just by looking at the Overall Prediction Accuracy percentages

on table 16.

So, the three models, 3,2 and 1-year prior failure have an accurate prediction rate of 72.5, 86.1%

and 97.3% respectively. Consistent with Sinkey J. (1975), the closer to failure time, the higher the

accurate rate of the model becomes. Although it was expected that the models, especially Model

II (2-year prior failure), would consist of more variables. It is not rare to see prediction models

with a small number of variables and a high accuracy rate. Espahbodi (1991) created a binary

prediction model with 87.67% and 75.71% prediction accuracy rate for 1 and 2-year prior failure

respectively, containing only 4 of the 13 initial variables. Martin (1977) also created a prediction

model consisting only of 4 variables after excluding the non-significant ones. After all, it is not

about how many non-significant variables can fit the model, but about obtaining the optimal accu-

racy of the model.

So, going back to the three-last hypothesis it is concluded that all 3 prediction models can with

high accuracy rate predict failure up to three years in advance for banks that went bankrupt during

2008, 2009 and 2010 respectively

.

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5. Conclusion

Banks play a crucial role in the economy and it is worth evaluating their financial health, but

more importantly finding out how to prevent them from going bankrupt. After the banking crisis

in 2008, the interest of the researchers turned to how to predict bank failure. This study tried to do

the same. By performing a Logit Regression using only CAMEL factor ratios, this paper tried to

estimate a prediction model that could predict bank failure up to three years in advance.

Data was collected three years in advance on a quarterly basis from banks that went bankrupt after

September 2008, during 2009 and 2010 and matched with banks that did not go bankrupt in that

same period. The data was divided into three sub-sample data set that were used to estimate three

different Logit models 3, 2 and 1-year prior failure. Initially, there were 12 financial ratios that

best represent the five CAMEL factors which are; Capital Adequacy, Asset Quality, Management

Efficiency, Earning Capacity and Liquidity.

After performing Backward Stepwise regression to eliminate non-significant variables, the three

models were left with only 6, 4 and 7 financial ratios as table 15 shows. From the same table, it

was concluded that only 3 of the 5 CAMEL factors were being well represented and were essential

to estimate the models with high prediction rate. These 3 factors are Capital Adequacy, Earning

Capacity and Liquidity. The other 2 factors were not being well represented by the financial ratios.

The three estimated models can predict failure with an accuracy rate of 72.5% 3-year prior failure,

86.1% 2-year prior failure and 97.3% 1-year prior failure. The closer to failure time, the better the

model predicts failure consistent with the literature.

Coming back to the main question (section 1.1), about how accurate the CAMEL factors could be

when it comes to predicting bank failure. The obtained results showed consistency with previous

studies about the importance of the CAMEL Model, see Cole and Gunther (1998), Thomson

(1992) Cole and White (2012) Jin, Kanagaretnam and Lobo (2011) DeYoung and Torna (2013).

The CAMEL factors are good analytical tools when it comes to significantly predict failure with

high accuracy. From this study it can be concluded that the three estimated models could with a

high accuracy predict failure up to three years in advance, however not all 5 CAMEL factors seem

to have the same significant influence.

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Recommendation

Same as other studies, in this study, there were things that could have been done differently to

improve the results. Because of time constraint, it was difficult to collect data from all 309 banks

that went bankrupt during the period of September 2008 till the end of 2010. To have a wider

amount of data, it is recommended if possible, to gather information from all the banks that went

bankrupt during that period. In this study, 12 financial ratios were selected, but there are many

more financial ratios that can also use that represent the five CAMEL factors. Seeing that the two

ratios that represented the Management Efficiency factor weren’t significant in none of the 3 mod-

els, it is recommended to use other ratios or use a different approach to measure the management

efficiency of banks, (see section 4.3). Also, GDP and Inflation were the only two macro-economic

variables, maybe using other external factors instead of or adding more than 2 factors would have

improved the results.

Page | 73

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