Applying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market
<strong>Objective:</strong> Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a hi...
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| Format: | Article |
| Language: | fas |
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University of Tehran
2018-11-01
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| Series: | تحقیقات مالی |
| Subjects: | |
| Online Access: | https://jfr.ut.ac.ir/article_68609_c1e394839433ecf33e259418cf729bfb.pdf |
| _version_ | 1818385435713339392 |
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| author | Saeid Fallahpour Reza Raei Eisa Norouzian |
| author_facet | Saeid Fallahpour Reza Raei Eisa Norouzian |
| author_sort | Saeid Fallahpour |
| collection | DOAJ |
| description | <strong>Objective:</strong> Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a high performance predictive model and to compare the results with other commonly used models in financial distress prediction
<strong>Methods:</strong> For this purpose, sequential floating forward selection that is considered as the generalized form of sequential forward selection method and as one of the wrapper methods, and sequential forward selection methodin combination with support vector machine were used. These models are combined models of feature selection and classifier. Logistic regression model which is a statistical classification models, has also been used in the present study.
<strong>Results:</strong> After reviewing the important financial ratios, 29 financial ratios that were mostly used in previous researches were chosen. Paired T-test results showed thatwith a 95% confidence level. The proposed model provides higher accuracy than other models used in this study.
<strong>Conclusion:</strong> Results showed that the proposed model of this research has significantly better performance in predicting financial distress than the sequential forward selection method and Logistic regression model in one year, two years and three years before financial distress. |
| first_indexed | 2024-12-14T03:38:07Z |
| format | Article |
| id | doaj.art-be5306c9bcde4585ac8e27d6b2a5f304 |
| institution | Directory Open Access Journal |
| issn | 1024-8153 2423-5377 |
| language | fas |
| last_indexed | 2024-12-14T03:38:07Z |
| publishDate | 2018-11-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | تحقیقات مالی |
| spelling | doaj.art-be5306c9bcde4585ac8e27d6b2a5f3042022-12-21T23:18:34ZfasUniversity of Tehranتحقیقات مالی1024-81532423-53772018-11-0120328930410.22059/frj.2018.113928.100586868609Applying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange MarketSaeid Fallahpour0Reza Raei1Eisa Norouzian2Assistant Prof., Department of Finance, Faculty of Management, University of Tehran, Tehran, IranProf., Department of Finance, Faculty of Management, University of Tehran, Tehran, IranMSc. Student, Department of Financial Engineering, Faculty of Management, University of Tehran, Tehran, Iran<strong>Objective:</strong> Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a high performance predictive model and to compare the results with other commonly used models in financial distress prediction <strong>Methods:</strong> For this purpose, sequential floating forward selection that is considered as the generalized form of sequential forward selection method and as one of the wrapper methods, and sequential forward selection methodin combination with support vector machine were used. These models are combined models of feature selection and classifier. Logistic regression model which is a statistical classification models, has also been used in the present study. <strong>Results:</strong> After reviewing the important financial ratios, 29 financial ratios that were mostly used in previous researches were chosen. Paired T-test results showed thatwith a 95% confidence level. The proposed model provides higher accuracy than other models used in this study. <strong>Conclusion:</strong> Results showed that the proposed model of this research has significantly better performance in predicting financial distress than the sequential forward selection method and Logistic regression model in one year, two years and three years before financial distress.https://jfr.ut.ac.ir/article_68609_c1e394839433ecf33e259418cf729bfb.pdffeature selectionsequential floating forward selectionwrapperfinancial distresshybrid models |
| spellingShingle | Saeid Fallahpour Reza Raei Eisa Norouzian Applying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market تحقیقات مالی feature selection sequential floating forward selection wrapper financial distress hybrid models |
| title | Applying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market |
| title_full | Applying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market |
| title_fullStr | Applying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market |
| title_full_unstemmed | Applying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market |
| title_short | Applying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market |
| title_sort | applying combined approach of sequential floating forward selection and support vector machine to predict financial distress of listed companies in tehran stock exchange market |
| topic | feature selection sequential floating forward selection wrapper financial distress hybrid models |
| url | https://jfr.ut.ac.ir/article_68609_c1e394839433ecf33e259418cf729bfb.pdf |
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