Financial distress prediction across firms
One of the most important events in a firm’s life is financial distress, which can propel sectors into financial and sustainable growth problems. Moreover, independent variables in the background of financial distress are accounting ratios, which are extracted from financial statements and macroecon...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Dorma Journals
2020
|
| Subjects: |
| _version_ | 1796865608855322624 |
|---|---|
| author | Rafatnia, Ali Akbar Ramakrishnan, Suresh Abdullah, Dewi Fariha Nodeh, Fazel Mohammadi Mohammad Farajnezhad, Mohammad Farajnezhad |
| author_facet | Rafatnia, Ali Akbar Ramakrishnan, Suresh Abdullah, Dewi Fariha Nodeh, Fazel Mohammadi Mohammad Farajnezhad, Mohammad Farajnezhad |
| author_sort | Rafatnia, Ali Akbar |
| collection | ePrints |
| description | One of the most important events in a firm’s life is financial distress, which can propel sectors into financial and sustainable growth problems. Moreover, independent variables in the background of financial distress are accounting ratios, which are extracted from financial statements and macroeconomic variables that are mostly beyond the control of a firm or sector. The current study analysed the information related to a sample of 300 public Iranian companies, during the periods of 2000-2007 and 2009-2016. Logistic regression and decision trees were applied to the prediction of financial distress. It was found that the profitability, liquidity, leverage, interest rate, cash flow, accruals, and GDP were statistically significant in distinguishing distressed from non-distressed firms across sectors. The obtained results showed that the predictive performance of a DT model was more successful than the other model. |
| first_indexed | 2024-03-05T20:59:36Z |
| format | Article |
| id | utm.eprints-93373 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T20:59:36Z |
| publishDate | 2020 |
| publisher | Dorma Journals |
| record_format | dspace |
| spelling | utm.eprints-933732021-11-30T08:21:42Z http://eprints.utm.my/93373/ Financial distress prediction across firms Rafatnia, Ali Akbar Ramakrishnan, Suresh Abdullah, Dewi Fariha Nodeh, Fazel Mohammadi Mohammad Farajnezhad, Mohammad Farajnezhad HG Finance One of the most important events in a firm’s life is financial distress, which can propel sectors into financial and sustainable growth problems. Moreover, independent variables in the background of financial distress are accounting ratios, which are extracted from financial statements and macroeconomic variables that are mostly beyond the control of a firm or sector. The current study analysed the information related to a sample of 300 public Iranian companies, during the periods of 2000-2007 and 2009-2016. Logistic regression and decision trees were applied to the prediction of financial distress. It was found that the profitability, liquidity, leverage, interest rate, cash flow, accruals, and GDP were statistically significant in distinguishing distressed from non-distressed firms across sectors. The obtained results showed that the predictive performance of a DT model was more successful than the other model. Dorma Journals 2020 Article PeerReviewed Rafatnia, Ali Akbar and Ramakrishnan, Suresh and Abdullah, Dewi Fariha and Nodeh, Fazel Mohammadi and Mohammad Farajnezhad, Mohammad Farajnezhad (2020) Financial distress prediction across firms. Journal of Environmental Treatment Techniques, 8 (2). pp. 646-651. ISSN 2309-1185 http://www.jett.dormaj.com/docs/Volume8/Issue%202/html/Financial%20Distress%20Prediction%20across%20Firms.html |
| spellingShingle | HG Finance Rafatnia, Ali Akbar Ramakrishnan, Suresh Abdullah, Dewi Fariha Nodeh, Fazel Mohammadi Mohammad Farajnezhad, Mohammad Farajnezhad Financial distress prediction across firms |
| title | Financial distress prediction across firms |
| title_full | Financial distress prediction across firms |
| title_fullStr | Financial distress prediction across firms |
| title_full_unstemmed | Financial distress prediction across firms |
| title_short | Financial distress prediction across firms |
| title_sort | financial distress prediction across firms |
| topic | HG Finance |
| work_keys_str_mv | AT rafatniaaliakbar financialdistresspredictionacrossfirms AT ramakrishnansuresh financialdistresspredictionacrossfirms AT abdullahdewifariha financialdistresspredictionacrossfirms AT nodehfazelmohammadi financialdistresspredictionacrossfirms AT mohammadfarajnezhadmohammadfarajnezhad financialdistresspredictionacrossfirms |