Predication of Company Bankruptcy using Altman-Z Score and Ohlson O-Score

This document discusses the prediction of company bankruptcy using two models: Altman-Z Score and Ohlson O-Score. It explores the background and significance of predicting bankruptcy, research questions, methodology, findings, literature review, and the applicability of the models. The document also presents the research design, strategy, sampling, data collection, and analysis methods. The expected results and conclusion are provided as well.

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Predication of company bankruptcy by
using two models: Altman-Z score and
Ohlson o-score

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Table of Contents
Introduction......................................................................................................................................3
Research Question.......................................................................................................................3
Research Methodology................................................................................................................3
Research Findings........................................................................................................................3
Literature Review............................................................................................................................3
The case of Organisational Bankruptcy.......................................................................................3
Reasons for Bankruptcy...............................................................................................................4
Altman’s Z-Score Model.............................................................................................................4
Applicability of the Altman’s Z-Score Model.............................................................................5
The Ohlson’s O-Score Model......................................................................................................6
Application of Ohlson’s o score based on Logit Analysis..........................................................6
Research Question/Hypotheses.......................................................................................................9
Proposed Research Methods............................................................................................................9
Research Paradigm......................................................................................................................9
Research Design..........................................................................................................................9
Research Strategy........................................................................................................................9
Sampling....................................................................................................................................10
Data Collection..........................................................................................................................10
Data Analysis.............................................................................................................................10

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Expected Results............................................................................................................................11
Conclusion.....................................................................................................................................11
References......................................................................................................................................12

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Introduction
Background
The chances for business organisations to go into bankruptcy tend to alarm the investors. The
investors thereby focus on evaluating the risks associated to the occurrence of bankruptcy and
thereby focus on using models like Ohlson’s O-Score or Altman’s Z-Score for deciding on future
courses of action regarding generation of investments to the firms facing financial and economic
risks.
Research Question
Q.1. What is company bankruptcy and why does it tend to occur?
Q.2. Is the Altman’s Z-Score Model more efficient in predicting the occurrence of company
bankruptcy compared to Ohlson’s O-Score model or vice-versa?
Research Methodology
Use of both secondary with that of quantitative research methodology would be employed for
understanding the significance of Altman’s Z-Score Model over Ohlson’s O-Score model and
vice-versa associated to predicting the occurrence of bankruptcy in firms.
Research Findings
The findings of the research activity tend to reflect that whether Ohlson’s O-Score Model is
more efficient and effective in predicting the occurrence of bankruptcy in organisations

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compared to Altman’s Z-Score Model. Both empirical and quantitative nature of findings from
data gained based on primary sources would be used for generating needed inferences.
Literature Review
The case of Organisational Bankruptcy
Karamzadeh (2012) identifies that bankruptcy is a legal status that is conferred to an individual
or to an organisation that fails to repay the debts of the diverse set of creditors. In most cases,
bankruptcy is identified as an order rendered by the court of law that is initiated by the debtor
itself. In the case of an organisation when it is estimated that the amount of debts the firm owes
to its creditors tends to surpass the value of the total amount of assets that the firm tends to
possess at the time of valuation the firm is stated to have become bankrupt. Bankruptcy appears
in organisations mainly in three different categories viz. financial, economic and legal. Lack of
working capital in case of a firm tends to cause financial bankruptcy where economic bankruptcy
occurs where the firm could not earn the benefit of trade gained by other firms in the like trade.
Legal bankruptcy emerges where the court identifies the firm to be incapable for meeting
financial and other obligations (Karamzadeh, 2013).
Reasons for Bankruptcy
Korol (2017) identifies three distinct natures of reasons are identified that contribute in the event
of bankruptcy for business organisations. Firstly, the existence of inept management in an
organisation is identified as a contributing factor to bankruptcy in that it tends to enhance the
level of operational, administrative and business costs for the firm compared to the revenues
gained. Another principal factor owing to bankruptcy is identified as sets of economic

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circumstances like recession, inflation and also monetary fluctuations. Finally, changes in
government regulations and legislations associated to emergence of hazardous circumstances
also tend to cause bankruptcy (Korol, 2017).
Altman’s Z-Score Model
In a research carried out by Lawrence, Pongsatat and Lawrence (2015) that Altman’s Z-Score
and also that of Ohlson’s O-Score are generally employed in the western economies for
predicting the case of bankruptcy in business institutions. The Altman’s Z-Score model is
identified as an effective model by researchers in that it has shifted from the univariate fashion to
the mode of employing multi-discriminant approach for effectively predicting bankruptcy. Later,
the model was revised by Altman in terms of incorporating a four variable based Z-Score model
which in turn enhanced the efficiency of the model for predicting the case of bankruptcy
(Lawrence et al., 2015).
Applicability of the Altman’s Z-Score Model
Mohammed (2017) reflected that the Altman’s Z-Score Model for predictability of bankruptcy
situations associated to business institutions was developed by Edward I. Altman during 1968.
Altman formulated the model based on carrying research on large scale data gained from diverse
American corporations. The Altman’s Model for predictability of bankruptcy situation is
essentially developed based on five different types of financial ratios that work together for the
generation of the Z-Score. The Z-Score ideally helps in evaluating the financial health of the
institution. The five different financial ratios with the model are reflected as under.

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(Mohammed, 2017)
If the Z-Score estimated in the model tends to be less than 1.21 the same indicates on the poor
financial health of the business institution which would thereby become bankrupt in the long-run.
On the other hand, if the Z-Score estimated is greater than the value of 2.9 the same indicates a
good or effective financial health of the firm or that which would not go bankrupt (Mohammed,
2017).
The Ohlson’s O-Score Model
Wulandari and Iradianty (2015) reflected that the Ohlson’s O-Score for prediction of
organisational bankruptcy was formulated by James Ohlson during the 1980s. The O-score was
formulated based on the use of logistic regression based analysis in the calculations. The
application of logistic regression based analysis is identified to be similar to that of discriminant
analysis in which the probability of occurrence of events associated to the dependent variable can
be rightly predicted based on the use of the independent variables. In the computation of the
probability based on the use of O-Score two categories of data are generated. It is observed that

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where the O-score estimated is more than 0.038, the category is treated to be failed and again
where the O-score estimated has a figure of less than 0.038 the category is identified as non-
failed (Iradianty & Wulandari, 2015).
Application of Ohlson’s o score based on Logit Analysis
Nair (2015) reflected that subsequently during the 1980 period, another management researcher
James Ohlson focused on the application of logit analysis for carrying out an in-depth study of
organisational bankruptcy. The Ohlson o-score model earns significance in the field of research
in that the same is simple to be applied and also can be effectively used along diverse situations
or circumstances. The ‘Logit’ model of Ohlson focuses on incorporating Multiple Logistic
Regression approach such that the same would help in predicting the situation of bankruptcy.
The Ohlson model for predicting of bankruptcy also adds to a specific advantage in that it
included the timing factor that would help in understanding that whether the organisation became
bankrupt prior of after the issue of the financials. Prior to the study carried out by Ohlson there is
no such evidence associated to the generation of significance to the time paradigm for predicting
the emergence of bankruptcy (Nair, 2015). The Ohlson’s Model is reflected as under.

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(Iradianty & Wulandari, 2015)
(Iradianty & Wulandari, 2015)
(Iradianty & Wulandari, 2015)

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Shyamni et. al. (2018) reflects that the use of the time element in the Ohlson model contributed
in enhancing the level of precision involved with the study. However, in his own words, Ohlson
highlighted the weakness of the logit model stating that the same fail to account the transaction
data of the firm associated to the greater market. Ohlson however reflected that in addition to the
advantage of using the time element in the model the logit model conferred other benefits
associated with the avoidance of issues that tended to emerge owing to the application of
multiple discriminant analysis. The practical applications of the logit model were also reflected
by Ohlson such that three different types of computational analysis were carried out based on the
use of the logit model. The first computation reflected a case where bankruptcy is predicted to
happen within a year’s time. The second set of analysis based on the use of the logit model
predicted that the case of bankruptcy for the firm is deemed to happen within a period of two
years time while finally the third model predicted that the firm would tend to go bankrupt within
a span of one to two years (Syamni et al., 2018).
Research Question/Hypotheses
The research activity would focus on the proving of the following research hypotheses outlined
as follows.
H0: Altman Z-score is more accurate than Ohlson O-score.
H1: Ohlson O-score is more accurate than the Altman Z-Score

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Proposed Research Methods
Research Paradigm
The application of positivism research philosophy is identified to be employed for carrying out
the research activity in that the same reflects on the use of scientific and quantitative analysis for
generation of needful inferences for addressing the research query. Herein, application of
numerical analysis tends to enhance the scientific approach undertaken for proving the research
hypotheses (Kivunja & Kuyini, 2017).
Research Design
The deductive research design is identified to be the best applicable research design that can be
used for carrying out the research activity. The application of deductive research activity ideally
helps in the narrowing down of research findings based on the study of potential literatures
associated to the research topic. It thereby contributes in the generation of specific research
inferences for addressing the research questions and hypotheses (Williams, 2007).
Research Strategy
The research strategy would effectively focus on gaining needed information associated to the
carrying out of secondary research activity. Qualitative and descriptive nature of information
would be gained based on the study of diverse secondary sources like journals associated to the
research issue. Secondary research is flexible in nature and helps in providing background
information to the issue at a cost-effective fashion (Johnston, 2014). Further secondary research
information would be gained based on earning datasets for different types of companies related

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to diverse sets of industries that have suffered from potential financial distress and also those that
have not failed from London Stock Exchange.
Sampling
Two different sets of 50 companies either falling in the category of ‘financially distress firms’
and ‘non-failed firms’ are to be chosen based on the use of stratified random sampling method.
The application of stratified random sampling is made in that the same helps in reducing the
chances of emergence of sampling errors that becomes evident in cases of use of random
sampling approach (Shi, 2015). The firms are chosen from the London Stock Exchange.
Data Collection
The data collection is carried out based on tabulating the data of ‘financially distress firms’ and
‘non-failed firms’ in excel sheet. Further information associated to the financial situation and
also financial statements are gained based on consulting of electronic library sources of the
London Stock Exchange. Again, the accounting reports of the different firms available over the
internet are consulted for further financial information. Data collection activity would also be
made from sources like Bloomberg and Datastram for the different companies.
Data Analysis
The data analysis is carried out based on subjecting the data of the firms to the Altman’s Z-Score
and Ohlson’s O-Score Model. Thereafter, descriptive statistics is applied on the score data
gained from the analysis at 0.05 significance level to understand the significance of the models
(Simpson, 2015).

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Expected Results
The results are expected to reflect that Ohlson’s O-Score Model serves to be more efficient
compared to Altman’s Z-Score Model in predicting the occurrence of bankruptcy for the
business institutions.
Conclusion
The study contributes in understanding the comparative efficiency of the Altman’s Z-Score
Model and Ohlson’s O-Score Model in predicting the occurrence of bankruptcy in different
organisations associated to different types of industries. The study however suffers from a
potential limitation in that it only focuses on carrying out research on those firms that are traded
over the London Stock Exchange (LSE). Thus future studies of the two models need to be
carried out on firms that are not yet traded on LSE.

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References
Iradianty, A. & Wulandari, A.P., 2015. The Effect of Bankruptcy Prediction Using Ohlson Score
Model Towards Stock Returns (Study in Textile and Garment Company Listed in IDX For Year
2010-2014). International Journal of Science and Research (IJSR) , 4(12), pp.1853-58.
Johnston, M.P., 2014. Secondary Data Analysis: A Method of which the Time Has Come.
Qualitative and Quantitative Methods in Libraries , 3, pp.619-26.
Karamzadeh, M.S., 2013. Application and Comparison of Altman and Ohlson Models to Predict
Bankruptcy of Companies. Research Journal of Applied Sciences, Engineering and Technology,
5(6), pp.2007-11.
Kivunja, C. & Kuyini, A.B., 2017. Understanding and Applying Research Paradigms in
Educational Contexts. International Journal of Higher Education, 6(5), pp.26-41.
Korol, T., 2017. EVALUATION OF THE FACTORS INFLUENCING BUSINESS
BANKRUPTCY RISK IN POLAND. Financial Internet Quarterly „e-Finanse, 13(2), pp.22-35.
Lawrence, J.R., Pongsatat, S. & Lawrence, H., 2015. The Use Of Ohlson's O-Score For
Bankruptcy Prediction In Thailand. The Journal of Applied Business Research, 31(6), pp.2069-
78.
Mohammed, S., 2017. Bankruptcy Prediction by Using the Altman Z-score Model in Oman: A
Case Study of Raysut Cement Company SAOG and its subsidiaries. Australasian Accounting,
Business and Finance Journal, 10(4), pp.70-80.

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Nair, J., 2015. EXAMINATION OF FINANCIAL DISTRESS IN INDIAN SUGAR SECTOR –
APPLICATION OF OHLSON’S ‘O SCORE MODEL. International Journal of Management
and Social Science Research Review, 1(14), pp.221-26.
Shi, F., 2015. Study on a Stratified Sampling Investigation Method for Resident Travel and the
Sampling Rate. Discrete Dynamics in Nature and Society, pp.1-7.
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for a Study. J C P H, 68(4), pp.311-17.
Syamni, G., Majid, M.S.A. & Siregar, W.V., 2018. A Literature Review on Ohlson (1995).
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