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Predicting Bankruptcy through Financial Statement AnalysisApplication of Altman Z-score Model and Logistic Regression Modeling to Analyze Publicly Held Banks in Bangladesh to Predict Bankruptcy
31st May,
2011
Prepared for:Mohammad SaifNoman KhanAssistant Professor,Institute of Business Administration,University of Dhaka.
Prepared by:Group 8Omaer Ahmad, ZR-09KawsarAhmad, ZR-50RafaatWasik Ahmed, ZR-53NasimUlHaque, ZR-54Rashed Al Ahmed Tarique, ZR- 61BBA 16th Batch,Institute of Business Administration,University of Dhaka.
Term Paper
Financial Markets and Instruments
Predicting Bankruptcy through Financial
Statement Analysis
- Application of Altman Z-score Model and Logistic
Regression Modellingto Analyze Publicly Held
Banks in Bangladesh to Predict Bankruptcy
Team Leader: Rashed Al Ahmad Tarique
Contact:
e-mail: [email protected]
Mobile: 01671507262
Mohammad SaifNoman Khan
Assistant Professor,
Institute of Business Administration,
University of Dhaka.
Dear Sir,
We, Group 8, present to you our term paper for the course Financial Markets and Institutions.
The title of our paper is “Predicting Bankruptcy through Financial Statement Analysis”. Along
with this paper we provide you with a number of articles, spreadsheets and other documents to
support our work.
In this paper, we have studied models widely used over the world to predict bankruptcy in
different sectors and have applied them to locally enlisted scheduled banks. This paper only
derives its ideas from other researchers and hence is an original analysis. No works have been
copied for its production. As we have not worked on the topic on any other course, this paper is
exclusively for the purposes of this course. We will not, therefore, use any of its content without
written permission from you.
We have tried our best to abide by your guidelines in the preparation of this paper. For further
inquiry about it, please feel free to contact us.
Yours Sincerely,
Omaer Ahmad, ZR-09 Kawsar Ahmad, ZR-50
RafaatWasik Ahmed, ZR-53 NasimUlHaque, ZR-54
Rashed Al Ahmad Tarique, ZR-61
BBA 16th Batch,
Institute of Business Administration,
University of Dhaka
31st May, 2011.
ContentsExecutive Summary.......................................................................................................................4
Introduction....................................................................................................................................5
Literature Review...........................................................................................................................6
Financial Ratios as Predictors Failure.......................................................................................6
Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy...............6
Step-Wise Multiple Discriminate Analysis..................................................................................7
On the Pricing of Corporate Debt: The Risk Structure of Interest Rates...................................7
Option-Based Bankruptcy Prediction.........................................................................................8
Bankruptcy Prediction for Credit Risk Using Neural Networks..................................................9
Bankruptcy Prediction of Turkish Commercial Banks Using Financial Ratios...........................9
Limitations & Scope.....................................................................................................................10
Methodology................................................................................................................................11
Altman z-score test..................................................................................................................11
Logistic Regression Model (Logit model).................................................................................13
Results.........................................................................................................................................15
Recommendations.......................................................................................................................20
Bibliography.................................................................................................................................20
Table 1: Banks for the study........................................................................................................11
Table 2: Ratios for the Logistic Regression Model......................................................................13
Table 3: Summarized z-scores for Banks....................................................................................15
Table 4:Summarized Bank Logit Score.......................................................................................18
Executive Summary
Banks are an integral part of everyday lives of people. Their importance is only increasing with
every passing day. They are among the fastest growing corporations in the country. Hence, it is
vital that such an arm of society does not come down and bring down with it the savings and
livelihoods of a large chunk of the population.
This report attempts to look at a number of tools which have been developed around the world
in various spheres of industry. These include William H. Beaver’s t-tests, Edward Altman’s z-
score, Fulman’s step-wise multiple discriminate analysis, Robert C. Merton’s Merton model,
Andreas Charitou and Lenos Trigeorgis’s Options based bankruptcy prediction model, Amir F.
Atiya’s Neural Network based bankruptcy prediction model and finally logistic regression model
by Birsen Eygi Erdogan. These bankruptcy prediction tools were developed as pre-warning
systems to identify problematic organizations and provide a method to warn stakeholders at
least a couple of years before to wake up and get their act together.
We used annual report data from the enlisted banks on Dhaka Stock Exchange and used ratio
analysis to get the variables that allowed us to run a couple of tests on them. These were the
Altman z-score model and the Logistics Regression Model. These allowed us to identify
corporations under threat.
Of the banks analyzed First Security Islami Bank and Uttara Bank Limited were found to have
problems that they need to address urgently. Another major finding of the report was that when
these models were being developed in the western world, they encountered fewer instances of
bankruptcy probability that here. This points out the excellent job that the Bangladesh Bank has
been doing.
Introduction
Around the world, globalization of the marketplace is taking place. This has resulted from a
deregulation of the markets. Banks now-a-days can delve into many spheres of business. Many
industry experts reckon that this was one of the major reasons behind the financial crisis that
rocked the world and from which we are still struggling to recover from. Numerous firms went
bankrupt, taking with them many peoples’ entire life’s savings.
Banks have become tied to every walk of life. It provides a liquidity to customers through a wide
variety of services. It provides credit to entrepreneurs and home-buyers. It provides a means for
building up a store of wealth for people through savings instruments for when they retire. So
when a bank goes down, not only is the equity holder, the actual owner of the bank, affected,
but everyone around them. Hence, it is crucial that early warning systems be in place so that
these vital organs of society be able to offer their services in continuum.
The regulatory body on banking in Bangladesh is Bangladesh Bank, the central bank. It has
received plaudits from various corners of the globe for its role in effectively managing the banks
in Bangladesh and not allowing them to fail. It has been vigilant in thinking on its feet and
changing with the times and implementing new and effective measures whenever the need
hasrisen for a change in regulations governing the banks to safeguard the money of the
customers.
The regulations set up by the central bank have been crucial to building the confidence of the
general public in the banking system. The strict guidelines set by the Bank have allowed the
banks to maintain their health and build themselves up. As a result, the banking industry is one
of the fastest growing in the country.
The purpose of this report is to test the fortitude of the banks towards staving off bankruptcy.
We will look at a number of tools used to identify signs of possible bankruptcy on the horizon.
We will apply these tools to analyze publicly available data on the banks and identify
weaknesses or cracks, if any, in the banking sector. Finally we will attempt to come up with
recommendations to plug the holes.
Literature Review
The study of bankruptcy extends to at least 1932.Paul J. FitzPatrick published a paper in The
Certified Public Accountant on the topic.He used data for 20 matched pairs of firms and
discussed accounting ratios as indicators of bankruptcy. This formed the basis for a much more
thorough paper by William Beaver in 1968.
Financial Ratios as Predictors Failure
William H. Beaver’s paper named “Financial Ratios as Predictors Failure”(Beaver 1966), was
the first truly credible paper on the topic. Financial ratios as a means of identifying the position
and health of a firm and its credit worthiness had already been in practice by 1966.He aimed to
identify the efficacy of these ratios as predictors of financial distress. For his study, he took
paired sample of both failed and firms still in business to comparemeansusing t-tests.He then
performed a dichotomousclassification test of likelihood ratios using the ratio of cash flow to
total debt. He gleaned from the study “Althoughratio analysis may provide useful information, it
be used with discretion :(1) Not all ratios predict equally well. The cash-power throughoutflowto
total-debt ratio has excellentdiscriminatory power in the five-yearperiod. However, the
predictivepower of the liquid assetratios is much weaker. (2) The ratios do not predict failed and
non-failed firms can be correctlyclassified to a greaterextentthan can failed firms.”
Financial Ratios, Discriminant Analysis and the Prediction of Corporate
Bankruptcy
Following Beaver’s work, Edward I. Altman from the Stern Business School of New York
University wrote a paper on assessing the analytical quality of ratio analysis. Titled “Financial
Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy” (Altman Sep.,1968).
Due to the relatively unsophisticated manner of traditional ratio analysis, it had lost its shine in
academic spheres.In order to assess whether financial ratios were still up to the job, he
combined a set of financial ratios in a discriminant analysis approach to the problem of
corporate bankruptcy prediction. The theory is that “ratios, if analyzed within a multivariate
framework, will take on greater statistical significance than the common technique of
sequential ratio comparisons.” The discriminant-ratio model proved to be extremely accurate
in predicting bankruptcy correctly in “94 per cent of the initial sample with 95 per cent of
all firms in the bankrupt and non-bankrupt groups assigned to their actual group
classification.” Furthermore, the discriminant function was accurate in several secondary
samples introduced to test the reliability of the model. The model's findings were that
bankruptcy can be accurately predicted up to two years prior to actual failure with the
accuracy diminishing rapidly after the second year. A limitation of the study was that the
firms examined were all publicly held manufacturing corporations for which comprehensive
financial data were obtainable, including market price quotations. Several practical and
theoretical applications of the model were suggested. Among them were business credit
evaluation, internal control procedures, and investment guidelines.
Step-Wise Multiple Discriminate Analysis
John G. Fulmer (Fulmer 1984) and his team used step-wise multiple discriminate analysis to
evaluate 40 financial ratios applied to a sample of 60 companies -30 failed and 30 successful.
This model was different as it focused on small firms. The average asset size of these firms was
$455,000.Fulmer reported a 98% accuracy rate in classifying the test companies one year prior
to failure and an 81% accuracy rate more than one year prior to bankruptcy.
Another paper that looked into the topic was by James A. Ohlson (A.Ohlson, 1980). The major
findings of the study can be summarized briefly. First, he identified four basic factors as being
statistically significant in affecting the probability of failure (within one year). These are: (i) the
size of the company; (ii) a measure(s) of the financial structure; (iii) a measure(s) of
performance; (iv) a measure(s) of current liquidity. He conducted the study due to concern that,
if one employs predictors derived from statements which were released after the date of
bankruptcy, then the evidence indicates that it will be easier to "predict" bankruptcy. However,
even if one allows for this factor, for the sample of firms used in this study, the prediction error-
rate is larger in comparison to the rate reported in the original Altman [1968] study.He
expressed that the previous models were relatively simple to apply and may be of use in
practical applications. He stated that a potential disadvantage was that the model does not
utilize any market transactions (price) data of the firms.
On the Pricing of Corporate Debt: The Risk Structure of Interest Rates
The Merton model is a model proposed by Robert C. Merton in 1974 for assessing the credit
risk of a company by characterizing the company's equity as a call option on its assets. Put-call
parity is then used to price the value of a put and this is treated as an analogous representation
of the firm's credit risk. (Merton May 1974) The model takes three company specific inputs: the
equity spot price, the equity volatility (which is transformed into asset volatility), and the
debt/share. The model also takes two inputs which should be calibrated to market quoted CDS
spreads: the default barrier, and the volatility of the default barrier. These inputs are used to
specify a diffusion process for the asset value. The entity is deemed to have defaulted when the
asset value drops below the barrier. The barrier itself is stochastic, which has the effect of
incorporating jump-to-default risk into the model. The Merton model evolves asset value
movements through a diffusion process and a fundamental purpose of the default barrier
volatility is to provide a jump-like process which can capture short term default probabilities.
Option-Based Bankruptcy Prediction
More recent approaches towards predicting bankruptcy include option valuation models for
bankruptcy prediction. Some of the pioneers of this technique are Andreas Charitou and Lenos
Trigeorgis. (Option-Based Bankruptcy Prediction, June 2000) Their study builds on and extends
option-pricing theory to explain financial distressbased on a sample of 420 distressed U.S. firms
for the period 1986-2001. Their results indicatethat the primary option variables, such as firm
volatility, play an important role in explainingdistress up to five years prior to bankruptcy filing.
When the model is extended to accountfor the probability of default on interest and debt
repayments due at intermediate times priorto debt maturity (due to voluntary equityholder
default or due to cash flow problems), anoption-motivated transformation of the cash flow
coverage is shown to have incrementalexplanatory power, while the primary option variables
remain statistically significant. The significant primary option variables include the face value of
debtowed at maturity (lnB), the current market value of the firm’s assets (lnV), and the standard
deviation (σ) of firm value changes (returns). The distance to default (d2d) and theprobability of
default at maturity (-d2) were also found to be significant predictor variables. Despite the
probability of intermediatedefault on due interest and debt repayments, the above primary
option variables maintaintheir sign and significance. The latter results indicate that the extended
option variablesbased on cash flow coverage have incremental explanatory power beyond the
primary optionvariables.
The latest models for bankruptcy prediction depend on neural network modeling. Artificial neural
networks are composed of interconnecting artificial neurons (programming constructs that mimic
the properties of biological neurons). Good performance (e.g. as measured by good predictive
ability, low generalization error), or performance mimicking animal or human error patterns, can
then be used as one source of evidence towards supporting the hypothesis that the abstraction
really captured something important from the point of view of information processing in the
brain. Another incentive for these abstractions is to reduce the amount of computation required
to simulate artificial neural networks, so as to allow one to experiment with larger networks and
train them on larger data sets.Mathematically, a neuron's network function is defined as a
composition of other functions , which can further be defined as a composition of other
functions. This can be conveniently represented as a network structure, with arrows depicting
the dependencies between variables. What has attracted the most interest in neural networks is
the possibility of learning. Given a specific task to solve, and a class of functions, , learning
means using a set of observationsto find which solves the task in some optimal sense.
Bankruptcy Prediction for Credit Risk Using Neural Networks
Amir F. Atiya (Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New
Results, JULY 2001) developed a model for predicting bankruptcy using Neural Networks. In the
article he reviewed the problem of bankruptcy prediction using NNs.He found NNs generally
superior to other techniques. Once that was established, the logical next step for the research
community is to improve further the performance of NNs for this application, perhaps through
better training methods, better architecture selection, or better inputs. It is this latter
improvement aspect that he addressed in the second half ofthis paper. He proposed novel
inputs extracted from the equity markets. His results showed new indicators which improve the
prediction considerably, especially for long horizon forecast. This can be explained by the
tendency ofthe equity markets to be highly predictive, not only of the healthof a firm, but also of
the health of the economy, which in turnaffects the creditworthiness of the firm.
Bankruptcy Prediction of Turkish Commercial Banks Using Financial Ratios
In his paper “Bankruptcy Prediction of Turkish Commercial Banks Using Financial Ratios”,
(Erdogan 2008) Birsen Eygi Erdogan uses data compiled from the years 1997 and 1999.
Logistic Regression was used to form a prediction model with financial ratios. 42 commercial
banks were included in this research. It was observed that 80% of failed banks could be
predicted two years a priori, and Logistic Regression can be used as a part of an “early warning”
system. In this research, in order to determine the statistically significant ratios many suggested
methods were used, such as Single Logistic Regression, Multivariate Variable Selection
Procedure, All Possible Regression, Forward and Backward Elimination methods. 20 financial
ratios were examined for each year for the 1997 -1999 periods. After using Factor Analysis, the
forward logistic regression and backward elimination methods were applied, and different
combinations of the ratios were tested. The selection of the final ratios was based on the
statistical significance (at 10% level) of the estimated parameters and the model classification
results.
Logistic Regression is a method coming from statistics whose objective is to obtain a functional
relationship between a transformation from a qualitative variable called logit and predictor
variables which can be either quantitative or qualitative. Where B(X) is a classification model,
the Logistic Regression model is described by the following formula:
Prob(X) = 1/(1+e(−B(X))
It is used to classify new individuals starting from rules in the following way:
“If Prob(x) < c then individual is classified as 0, otherwise it is classified as 1”.
“c” is the cut-off point. The cut-off point or level of probability that is used to categorize a bank
as “failed” is usually chosen as 0.5 in literature. In this research bankrupt banks were classified
as “0” and successful banks were classified as “1”. Cut off point was chosen as 0.5. Those
under 0.5 were classified as “0” and above 0.5 as “1”. In some studies it is noted that classifying
a “failed” bank as a “non-failed” bank can have more severe consequences than classifying a
“non-failed” bank as a “failed” bank.
It was observed that from predictions made by the study, 18 banks were predicted to fail over
the coming two years. Of these all were predicted successfully.
Limitations & Scope
In this report, we will analyze bankruptcy probability only among the enlisted banks on Dhaka
Stock Exchange. Information only available from the financial statements of these companies
would be used to find out the ratios used for the report. Therefore, only the methods that use
financial ratios to predict bankruptcy of firms.
The lack of cases of bank failure in the country (due to vigilance of the central bank) means the
sample for testing these models is very low. Therefore, this study will only apply the established
tools used elsewhere and not test them.
Methodology
The report will use the annual reports of the enlisted banks on Dhaka Stock Exchange. We will
apply the Altman z-score test and the Logistic Regression model. The banks that were
considered were:
Table 1: Banks for the study
ABBANK EBL JAMUNABANK PREMIERBAN SHAHJABANK
ALARABANK EXIMBANK MERCANBANK PRIMEBANK STANDBANKL
BANKASIA FIRSTSBANK MTBL PUBALIBANK TRUSTBANK
BRACBANK ISLAMIBANK NBL RUPALIBANK UCBL
CITYBANK ICBIBANK NCCBANK SOUTHEASTB UTTARABANK
DHAKABANK IFIC ONEBANKLTD SIBL
Altman z-score test
The Multi Discriminant Analysis narrows down multiple variables into a single dimension. This
single dimension is the z-score. The z-score model is defined as:
Z=V1X1+V2X2+…+VnXn
Where V1, V2.. are the discriminant coefficients and X1,X2 … are the actual values of the
financial ratios.
Of the 22 variables initially selected, five were identified to have the greatest significance in
predicting bankruptcy. These variables were:
X1= (Working Capital/Total Assets)
X2= (Retained Earnings/Total Assets)
X3= (EBT/Total Assets)
X4= (MV of Equity/Total Liabilities)
X5= (Interest Income/Total Assets)
Using MDA for the banking sector, the values obtained for the discriminants were:
V1=1.2
V2=1.4
V3=3.3
V4=0.6
V5=1.0
Using the spreadsheets titled “Altman z-score” we find the relevant z-scores for the banks
analyzed by using the following equation:
Z= 1.2*X1+1.4* X2+3.3* X3+0.6*X4+1.0* X5
According to Altman, the z-score boundaries that we should look for are:
Z≥3 The company will continue to thrive
1.8≥Z≥2.7 The company is in a grey area. The firm should be kept under strict management
scrutiny
Z≤1.8 The company is highly likely to go bankrupt in the next three years.
Logistic Regression Model (Logit model)
The ratios that were considered in the study included:
Table 2: Ratios for the Logistic Regression Model
The ratios that were found to be statistically significant are:
C2 = (Shareholders’ Equity + Total Income)/ (Deposits + Non-deposit
Funds)
C12 = Net Income (Loss)/ Average Total Assets
C14 = Net Income (Loss)/ Average Share-in Capital
C16 = Interest Income/ Interest Expenses
C17 = Non-Interest Income/ Non-Interest Expenses
C19 = Provision for Loan Losses/ Total Loans
Using these ratios, the following equation was developed using the logit analysis:
XB = -13,20738+ 626098xC2-2,169955xC12+ 9,429545E-02xC14+ 5,528393E-
02xC16+2,361215E-02xC17-1,704793xC19
Where XB refers to the expectation of bankruptcy and the coefficients are obtained through logit
analysis.
Results
The ratings found from the Altman z-score analysis for the banks are:
Table 3: Summarized z-scores for Banks
Bank(in Alphabetical Order)Z-
scorePrediction
ABBANK 3.0 Will survive
ALARABANK 2.5Needs careful
management
BANKASIA 4.3 Will survive
BRACBANK 4.2 Will survive
CITYBANK 4.9 Will survive
DHAKABANK 4.9 Will survive
EBL 5.6 Will survive
EXIMBANK 4.7 Will survive
FIRSTSBANK 2.3Needs careful
management
ISLAMIBANK 3.7 Will survive
ICBIBANK 7.8 Will survive
IFIC 5.6 Will survive
JAMUNABANK 3.6 Will survive
MERCANBANK 4.2 Will survive
MTBL 4.2 Will survive
NBL 7.7 Will survive
NCCBANK 7.2 Will survive
ONEBANKLTD 4.8 Will survive
PREMIERBAN 3.5 Will survive
PRIMEBANK 6.2 Will survive
PUBALIBANK 5.6 Will survive
RUPALIBANK 2.5Needs careful
management
SOUTHEASTB 4.8 Will survive
SIBL 3.7 Will survive
SHAHJABANK 5.2 Will survive
STANDBANKL 4.6 Will survive
TRUSTBANK 4.5 Will survive
UCBL 3.7 Will survive
UTTARABANK 1.8Needs careful
management
It can be observed that none of the banks in the study are predicted to run into financial distress
in the upcoming two years. However, Uttara Bank is on the brink and First Security Islami Bank
is following suit. This can be stated with an error of 5%.
The following table shows condensed results of the logistical regression analysis run on the
banks:
Table 4:Summarized Bank Logit Score
XB Prob(Y=1)Success
C=0.8
ABBANK 2.60350284 0.93109 1
ALARABANK 1.914949135 0.87157 1
BANKASIA 2.481710771 0.92285 1
BRACBANK 2.368667275 0.91441 1
CITYBANK 2.344648753 0.91251 1
DHAKABANK 2.287322983 0.90782 1
EBL 1.925777817 0.87278 1
EXIMBANK 1.649473182 0.83882 1
FIRSTSBANK -0.129509725 0.46767 0
ISLAMIBANK 1.905841949 0.87055 1
ICBIBANK 3.32887004 0.96541 1
IFIC 2.590530777 0.93025 1
JAMUNABANK 1.81528443 0.86000 1
MERCANBANK 1.948493876 0.87528 1
MTBL 1.743904699 0.85118 1
NBL 2.940085608 0.94979 1
NCCBANK 2.206194565 0.90080 1
ONEBANKLTD 2.3020672 0.90905 1
PREMIERBAN 2.977622628 0.95155 1
PRIMEBANK 3.880745967 0.97978 1
PUBALIBANK 1.083317134 0.74712 0
RUPALIBANK 1.499605496 0.81752 1
SOUTHEASTB 2.132822901 0.89405 1
SIBL 1.452824034 0.81043 1
SHAHJABANK 2.869738662 0.94633 1
STANDBANKL 2.015806192 0.88245 1
TRUSTBANK 2.39490092 0.91644 1
UCBL 2.427403216 0.91889 1
UTTARABANK 1.228150789 0.77349 0
From the above table the only bank found likely to fail in the next two years is First Security
Islami Bank if we use a cut-off point of 0.8, as prescribed for developing economies.
If we combine the findings of the two analysis we arrive at a number of conclusions. First, when
utilizing models developed in developed economies we observe that the probability of failure
among local banks are very low. This clearly refers to the great job done by the Central Bank in
managing the private banking sector in Banlgadesh. The second conclusion that can be derived
from the results is that a couple of banks – First Security Islami Bank and Uttara Bank - have
performed sorrily on both counts. Careful management of the firm’s asset quality, gapping
strategies, cash management and provisions made for bad loans, cost cutting in non-interest
earning fields will be needed to nurse these banking businesses.
Recommendations
Further study with data available outside the financial statements needs to be undertaken. All
the models described need to undergo tests. A more rigorous study needs to be undertaken to
find the validity of these models in predicting failures in the sector. This will help in the ultimate
goal which is to develop a thorough holistic model that will avert danger in the sector.
Bibliography
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Accounting Research, Vol. 18, No. 1, (Spring, 1980: 109-131.
Altman, Edward I. "Financial Ratios, Discriminant Analysis and the Prediction of Corporate
Bankruptcy." The Journal of Finance, Vol.23, No.4. , Sep.,1968: 589-609.
"Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results."
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Beaver, William H. "Financial Ratios as Predictors of Failure." Journal of Accounting Research,
1966: 71-111.
Erdogan, Birsen Eygi. "Bankruptcy Prediction of Turkish Commercial Banks Using Financial
Ratios." Applied Mathematical Sciences,Vol.2,no.60,2973-2982, 2008.
Fulmer, John G. Jr., Moon, James E., Gavin, Thomas A., Erwin, Michael J. "A Bankruptcy
Classification Model For Small Firms." Journal of Commercial Bank Lending, 1984: 25-37.
Merton, Robert C. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates." The
Journal of Finance, Vol. 29, No. 2, May 1974: 449-470.
Trigeorgis, Andreas Charitou and Lenos. "Option-Based Bankruptcy Prediction." European
Financial Management Journal, June 2000.