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
Page | 23
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.
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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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