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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English English |
| Published: |
2000
|
| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/8824/1/FEP_2000_6%20IR.pdf http://psasir.upm.edu.my/id/eprint/8824/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|