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Predicting corporate failure using accounting information : the Malaysian experience

Financial ratios have long been used as predictor of important events in the financial markets. Researchers have formulated business failure prediction models utilising financial ratios. However, relatively few failure prediction studies on Malaysian firms have been documented. The objective of t...

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Main Author: Muhamad Sori, Zulkarnain
Format: Thesis
Language:English
English
Published: 2000
Subjects:
Corporations - Finance
Business failures
Corporations - Accounting
Online Access:http://psasir.upm.edu.my/id/eprint/8824/1/FEP_2000_6%20IR.pdf
http://psasir.upm.edu.my/id/eprint/8824/
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spelling my.upm.eprints.88242024-03-05T02:12:05Z http://psasir.upm.edu.my/id/eprint/8824/ Predicting corporate failure using accounting information : the Malaysian experience Muhamad Sori, Zulkarnain Financial ratios have long been used as predictor of important events in the financial markets. Researchers have formulated business failure prediction models utilising financial ratios. However, relatively few failure prediction studies on Malaysian firms have been documented. The objective of this study is to develop a model that can discriminate between Malaysian failed and nonfailed firm. Also, this study investigates the distributional properties of the financial ratios of failed and non-failed listed firms. One-to-one sampling technique was utilised, where 33 failed and non-failed mixed industry sector firms, and 24 failed and non-failed industrial sector firms for the period from 1980 to 1996 were sampled. Using Kolgomorov-Smirnov test adjusted to Lillifors test, it was found that, only one financial ratio was normally distributed. Nine financial ratios were found to be lognormal in mixed industry sector and the number increased to 18 in the industrial sector In addition, 3 financial ratios were square root normal in mixed industry sector and 6 in industrial sector It is found that the log transformation technique was the most effective procedure and the square transformation technique was the least effective to transform non-normally distribution data to the family of lognormal distribution Finally, industry sector played an Important role in determining the normality level, where focused into specific industry sector gave better results than mixed industry sector However, it is found that the equality of variance covariance of the failed and non-failed firms was not observed However, the impact of this inconsistency was minimal on the classification accuracy After the assumptions of discriminant analysis were satisfied, stepwise multiple discriminant analysis was utilised to develop failure prediction models The mixed industry model correctly classified 86 2% and 91% of the original sample and holdout sample respectively The model was further validated using leaveone- out classification or U-method (86 2% correct classification) The results remain robust and the failed and non-failed classification accuracy was found to be significantly better than chance An alternative prediction model was developed based on accounting information, which outperformed the original model and correctly classified 88 1% of the original sample and 86 7% in U method The models for industrial sector were equally accurate for the mixed industry, which correctly classified more than 80% of the failed and non-failed firms and the original model outperformed the alternative model. The selected variables in the final model were a good proxy for the profit, cash flow, working capital and net worth variables. 2000-06 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/8824/1/FEP_2000_6%20IR.pdf Muhamad Sori, Zulkarnain (2000) Predicting corporate failure using accounting information : the Malaysian experience. Masters thesis, Universiti Putra Malaysia. Corporations - Finance Business failures Corporations - Accounting English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
topic Corporations - Finance
Business failures
Corporations - Accounting
spellingShingle Corporations - Finance
Business failures
Corporations - Accounting
Muhamad Sori, Zulkarnain
Predicting corporate failure using accounting information : the Malaysian experience
description Financial ratios have long been used as predictor of important events in the financial markets. Researchers have formulated business failure prediction models utilising financial ratios. However, relatively few failure prediction studies on Malaysian firms have been documented. The objective of this study is to develop a model that can discriminate between Malaysian failed and nonfailed firm. Also, this study investigates the distributional properties of the financial ratios of failed and non-failed listed firms. One-to-one sampling technique was utilised, where 33 failed and non-failed mixed industry sector firms, and 24 failed and non-failed industrial sector firms for the period from 1980 to 1996 were sampled. Using Kolgomorov-Smirnov test adjusted to Lillifors test, it was found that, only one financial ratio was normally distributed. Nine financial ratios were found to be lognormal in mixed industry sector and the number increased to 18 in the industrial sector In addition, 3 financial ratios were square root normal in mixed industry sector and 6 in industrial sector It is found that the log transformation technique was the most effective procedure and the square transformation technique was the least effective to transform non-normally distribution data to the family of lognormal distribution Finally, industry sector played an Important role in determining the normality level, where focused into specific industry sector gave better results than mixed industry sector However, it is found that the equality of variance covariance of the failed and non-failed firms was not observed However, the impact of this inconsistency was minimal on the classification accuracy After the assumptions of discriminant analysis were satisfied, stepwise multiple discriminant analysis was utilised to develop failure prediction models The mixed industry model correctly classified 86 2% and 91% of the original sample and holdout sample respectively The model was further validated using leaveone- out classification or U-method (86 2% correct classification) The results remain robust and the failed and non-failed classification accuracy was found to be significantly better than chance An alternative prediction model was developed based on accounting information, which outperformed the original model and correctly classified 88 1% of the original sample and 86 7% in U method The models for industrial sector were equally accurate for the mixed industry, which correctly classified more than 80% of the failed and non-failed firms and the original model outperformed the alternative model. The selected variables in the final model were a good proxy for the profit, cash flow, working capital and net worth variables.
format Thesis
author Muhamad Sori, Zulkarnain
author_facet Muhamad Sori, Zulkarnain
author_sort Muhamad Sori, Zulkarnain
title Predicting corporate failure using accounting information : the Malaysian experience
title_short Predicting corporate failure using accounting information : the Malaysian experience
title_full Predicting corporate failure using accounting information : the Malaysian experience
title_fullStr Predicting corporate failure using accounting information : the Malaysian experience
title_full_unstemmed Predicting corporate failure using accounting information : the Malaysian experience
title_sort predicting corporate failure using accounting information : the malaysian experience
publishDate 2000
url http://psasir.upm.edu.my/id/eprint/8824/1/FEP_2000_6%20IR.pdf
http://psasir.upm.edu.my/id/eprint/8824/
_version_ 1793156972059557888
score 13.211869

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