Comparing Different Feature Selection Methods in Financial Distress Prediction of the Firms Listed in Tehran Stock Exchange

Subject Areas : Financial engineering

Mohammad Namazi ^{1 *} , Mostafa Kazemnezhad ² , M. Mahdi Nematollahi ³

1 - Professor of Accounting, Department of Accounting, Shiraz University, Shiraz, Iran.
2 - Phd Student, Department of Accounting, Shiraz University, Shiraz, Iran
3 - Phd Student, Department of Computer Engineering, Shahrood University of Technology, Shahrood, Iran

Received: 2016-03-07 Accepted : 2016-05-12 Published : 2016-12-21

Keywords: Feature Selection, bankruptcy, Financial Distress Prediction, Prediction Performance,

Abstract :

Research in financial distress and bankruptcy emphasize the design of more sophisticated classifiers, and less feature (variables) selection and their appropriate methods. In this regard, the purpose of this study is to compare performance of different feature selection methods in financial distress prediction of the companies listed on Tehran Stock Exchange (TSE). In this regard, we investigated and compared five feature selection methods, including t-test, stepwise regression, factor analysis, relief, wrapper subset selection and RFE-SVM feature selection. To obtain comparable experimental results (reliable comparison), three different classifiers (including neural networks, support vector machine and AdaBoost) were used in this study. In overall, the experimental results confirmed the usefulness of variable selection methods and significant difference among level (amount) of different methods performance. In other words, the application of the feature selection methods increases the mean of accuracy, and reduces the occurrence of type I and type II errors. Furthermore, the results indicated that wrapper subset selection method outperforms the other feature selection methods.