Forecasting financial distress of PN17 companies in Malaysia using logistic regression
This study aims to predict financial distress among Malaysian companies included in Practice Note 17 (PN17). The analysis examines a sample of 35 companies classified as PN17 from 2014 to 2023. To create a balanced comparison, these companies are matched with 35 non-PN17 companies, resulting in a to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2025
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| Online Access: | http://journalarticle.ukm.my/25773/ http://journalarticle.ukm.my/25773/1/133-148%20-.pdf |
| _version_ | 1848816445594533888 |
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| author | Alia Nadira Rosle, Munira Ismail, Fatimah Abdul Razak, Zalina Mohd Ali, |
| author_facet | Alia Nadira Rosle, Munira Ismail, Fatimah Abdul Razak, Zalina Mohd Ali, |
| author_sort | Alia Nadira Rosle, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | This study aims to predict financial distress among Malaysian companies included in Practice Note 17 (PN17). The analysis examines a sample of 35 companies classified as PN17 from 2014 to 2023. To create a balanced comparison, these companies are matched with 35 non-PN17 companies, resulting in a total sample size of 70 firms. Logistic regression was employed in this study because the financial ratios do not need to be normally distributed. The findings indicate that the model is most effective in the near term, specifically during the year of financial difficulty and up to one year prior to the occurrence, when it achieves the highest level of prediction accuracy. Financial metrics, including working capital, retained earnings, and earnings before interest and taxes (EBIT) are crucial in assessing a company's probability of experiencing financial difficulties, underscoring the vital need of liquidity, profitability, and financial soundness. However, when the prediction timeframe extends beyond two years, the model's precision decreases, highlighting the limitations of using conventional financial ratios for long-term forecasts. This suggests that while logistic regression is a valuable method for predicting short-term distress, its effectiveness diminishes in early-stage forecasts, when distress indicators are less prominent. |
| first_indexed | 2025-11-15T01:05:59Z |
| format | Article |
| id | oai:generic.eprints.org:25773 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:05:59Z |
| publishDate | 2025 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:257732025-08-20T03:03:04Z http://journalarticle.ukm.my/25773/ Forecasting financial distress of PN17 companies in Malaysia using logistic regression Alia Nadira Rosle, Munira Ismail, Fatimah Abdul Razak, Zalina Mohd Ali, This study aims to predict financial distress among Malaysian companies included in Practice Note 17 (PN17). The analysis examines a sample of 35 companies classified as PN17 from 2014 to 2023. To create a balanced comparison, these companies are matched with 35 non-PN17 companies, resulting in a total sample size of 70 firms. Logistic regression was employed in this study because the financial ratios do not need to be normally distributed. The findings indicate that the model is most effective in the near term, specifically during the year of financial difficulty and up to one year prior to the occurrence, when it achieves the highest level of prediction accuracy. Financial metrics, including working capital, retained earnings, and earnings before interest and taxes (EBIT) are crucial in assessing a company's probability of experiencing financial difficulties, underscoring the vital need of liquidity, profitability, and financial soundness. However, when the prediction timeframe extends beyond two years, the model's precision decreases, highlighting the limitations of using conventional financial ratios for long-term forecasts. This suggests that while logistic regression is a valuable method for predicting short-term distress, its effectiveness diminishes in early-stage forecasts, when distress indicators are less prominent. Penerbit Universiti Kebangsaan Malaysia 2025-03 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/25773/1/133-148%20-.pdf Alia Nadira Rosle, and Munira Ismail, and Fatimah Abdul Razak, and Zalina Mohd Ali, (2025) Forecasting financial distress of PN17 companies in Malaysia using logistic regression. Journal of Quality Measurement and Analysis, 21 (1). pp. 133-148. ISSN 2600-8602 https://www.ukm.my/jqma/ |
| spellingShingle | Alia Nadira Rosle, Munira Ismail, Fatimah Abdul Razak, Zalina Mohd Ali, Forecasting financial distress of PN17 companies in Malaysia using logistic regression |
| title | Forecasting financial distress of PN17 companies in Malaysia using logistic regression |
| title_full | Forecasting financial distress of PN17 companies in Malaysia using logistic regression |
| title_fullStr | Forecasting financial distress of PN17 companies in Malaysia using logistic regression |
| title_full_unstemmed | Forecasting financial distress of PN17 companies in Malaysia using logistic regression |
| title_short | Forecasting financial distress of PN17 companies in Malaysia using logistic regression |
| title_sort | forecasting financial distress of pn17 companies in malaysia using logistic regression |
| url | http://journalarticle.ukm.my/25773/ http://journalarticle.ukm.my/25773/ http://journalarticle.ukm.my/25773/1/133-148%20-.pdf |