Predicting on the financial distress by using the financial ratio / Nur Nazifa Murfiqah Busu
This study is descriptive in nature and the purpose of this study was to confirm whether financial ratio applicable in order to predict financial distress by using logistic regression as a model that predict corporate failures in Malaysia over a five year period covering the from 19 companies that w...
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/93095/1/93095.pdf |
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| Summary: | This study is descriptive in nature and the purpose of this study was to confirm whether financial ratio applicable in order to predict financial distress by using logistic regression as a model that predict corporate failures in Malaysia over a five year period covering the from 19 companies that were analyzed with an initial 14 financial ratios. This paper also determining whether all the PN 17 companies that are listed are financial failure. Secondary data was used and these were obtained through review of literature include journal and published financial report. E-views software was used to carry out the statistical analysis. This study choose the ratios based on the frequency used in previous studies. The result of study indicate there are three ratio out of 14 ratios chosen have significant relationship toward financial distress which is one ratio from profitability ratio (NI/SALES); one ratio (SALES/TA) from the liquidity ratio; and one ratio (TD/TA) from the leverage ratio. A classification results in using logistic regression showed high average accuracy rates of 78.87 percent for the analysis for each of the five years. Thus, this study shows that even with more advanced statistical tools more popularly used recently like multivariate discriminant analysis, logistic regression can still be effective and reliable as a statistical tool. Next, it was found that not all PN 17 companies are financial failure. This study was conducted using the recent data on public listed companies in Malaysia. Hence, this model is more relevant in predicting corporate failure in Malaysia. |
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