Distress risk and stock returns : Malaysia evidence
Predicting firms’ financial distress is important as accurate prediction would improve financial and investment decisions. The logit model and multiple discriminant analysis are commonly used predicting approaches among researchers, but both models encounter econometric problems that may affect the...
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my-uum-etd.111612024-06-10T02:36:40Z Distress risk and stock returns : Malaysia evidence 2023 Ahmad Harith Ashrofie, Hanafi Md Rus, Rohani School of Economics, Finance & Banking School of Economics, Finance and Banking HG Finance Predicting firms’ financial distress is important as accurate prediction would improve financial and investment decisions. The logit model and multiple discriminant analysis are commonly used predicting approaches among researchers, but both models encounter econometric problems that may affect the model's consistency and validity. In order to overcome the issues, researchers suggest hazard model to produce more consistent and valid prediction model. Thus, this study examines and compares the accuracy, consistency, and validity of the logit and hazard model in predicting financial distress. A model with high accuracy, consistency, and validity is used to measure distress risk, which represents one of the risk factors in estimating returns. Prior studies have employed a small sample size and short study period in predicting financial distress or estimating the relationship between distress risk and returns for the Malaysian market, where the results may not have adequately represented the entire market. To address this issue, this study utilises data from 1079 firms during the period of 1990 to 2020 to develop prediction models based on 18,314 firm-year observations. Meanwhile, in estimating return, this study uses 141,425 monthly observations. The results show that liquidity, activity, profitability, and leverage ratios are significant factors in predicting financial distress. Furthermore, the hazard model seems to generate higher accuracy and consistency relative to the logit model. In estimating returns, financial distress risk is consistently insignificant in all models while size and value show consistent significant results in all models. As the developed model could be used to complement the existing guidelines, these results are useful for policymakers such as Bursa Malaysia in improving the policies and guidelines related to Amended Practice Notes 17 (APN17). As for the creditors, the developed model is useful in making a lending decision since the model is helpful in measuring and monitoring firms’ distress levels. 2023 Thesis https://etd.uum.edu.my/11161/ https://etd.uum.edu.my/11161/1/depositpermission.pdf text eng staffonly https://etd.uum.edu.my/11161/2/s901258_01.pdf text eng public phd doctoral Universiti Utara Malaysia |
| institution |
Universiti Utara Malaysia |
| collection |
UUM ETD |
| language |
eng eng |
| advisor |
Md Rus, Rohani |
| topic |
HG Finance |
| spellingShingle |
HG Finance Ahmad Harith Ashrofie, Hanafi Distress risk and stock returns : Malaysia evidence |
| description |
Predicting firms’ financial distress is important as accurate prediction would improve financial and investment decisions. The logit model and multiple discriminant analysis are commonly used predicting approaches among researchers, but both models encounter econometric problems that may affect the model's consistency and validity. In order to overcome the issues, researchers suggest hazard model to produce more consistent and valid prediction model. Thus, this study examines and compares the accuracy, consistency, and validity of the logit and hazard model in predicting financial distress. A model with high accuracy, consistency, and validity is used to measure distress risk, which represents one of the risk factors in estimating returns. Prior studies have employed a small sample size and short study period in predicting financial distress or estimating the relationship between distress risk and returns for the Malaysian market, where the results may not have adequately represented the entire market. To address this issue, this study utilises data from 1079 firms during the period of 1990 to 2020 to develop prediction models based on 18,314 firm-year observations. Meanwhile, in estimating return, this study uses 141,425 monthly observations. The results show that liquidity, activity, profitability, and leverage ratios are significant factors in predicting financial distress. Furthermore, the hazard model seems to generate higher accuracy and consistency relative to the logit model. In estimating returns, financial distress risk is consistently insignificant in all models while size and value show consistent significant results in all models. As the developed model could be used to complement the existing guidelines, these results are useful for policymakers such as Bursa Malaysia in improving the policies and guidelines related to Amended Practice Notes 17 (APN17). As for the creditors, the developed model is useful in making a lending decision since the model is helpful in measuring and monitoring firms’ distress levels. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Ahmad Harith Ashrofie, Hanafi |
| author_facet |
Ahmad Harith Ashrofie, Hanafi |
| author_sort |
Ahmad Harith Ashrofie, Hanafi |
| title |
Distress risk and stock returns : Malaysia evidence |
| title_short |
Distress risk and stock returns : Malaysia evidence |
| title_full |
Distress risk and stock returns : Malaysia evidence |
| title_fullStr |
Distress risk and stock returns : Malaysia evidence |
| title_full_unstemmed |
Distress risk and stock returns : Malaysia evidence |
| title_sort |
distress risk and stock returns : malaysia evidence |
| granting_institution |
Universiti Utara Malaysia |
| granting_department |
School of Economics, Finance & Banking |
| publishDate |
2023 |
| url |
https://etd.uum.edu.my/11161/1/depositpermission.pdf https://etd.uum.edu.my/11161/2/s901258_01.pdf |
| _version_ |
1804888208471752704 |