Use of Combined Approach of Support Vector Machine and Feature Selection for Financial Distress Prediction of Listed Companies in Tehran Stock Exchange Market

• Main Authors: Saeid Fallahpour, Eisa Norouzian Lakvan, Mohammad Hendijani Zadeh
• Format: Article
• Language: fas
• Published: University of Tehran 2017-04-01
• Series: تحقیقات مالی
• Subjects: genetic algorithm; wrapper; financial distress; filter; support vector machine
• Online Access: https://jfr.ut.ac.ir/article_52758_4543b5fdb0eb5e872f7e4aef76b54dd7.pdf

Financial distress prediction (FDP) is a great important subject that has always been interesting to researchers, financial institutions and banks. Tough many works have been done in this area, but use of combined approach of feature selection and classifier is an issue that has attracted researchers' attention just in recent years. In this paper, four well-known kinds of SVM that each of them has it's own kernel function including: linear, polynomial, radial and sigmoid have been introduced as the main classifiers of our proposed approach. These four methods have been integrated with genetic algorithm (GA) as a wrapper feature selection approach as well as three techniques of filtering feature selection approach called: principle component analysis (PCA), information gain and relief. Brought results indicated that genetic algorithm outperformed the other feature selection techniques in it's combination with SVM methods. Furthermore, implemented hypothesis test implied that there was no significance level among GA-SVM (linear), GA-SVM (radial), GA-SVM (polynomial) and GA-SVM (sigmoid) techniques with confidence level of %95.