Apply SVM in Feature Selection to Improve the Predictive Ability of Financial Distress Model

• Main Authors: Yu-ting weng, 翁宇廷
• Other Authors: Tsung-Nan Chou
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/57384407971087531826

碩士 === 朝陽科技大學 === 財務金融系碩士班 === 100 === This paper uses logistic regression and neural network to examine whether the companies had the financial crises during and after the periods of Financial Tsunami by collecting data from the listed companies in Taiwan. We choose 26 independent variables initially and then use recursive feature elimination based on support vector machine to select the important variables from the original 26 variables. After selecting the important variables, we use logistic regression and neural network again to determine the correct rates of prediction. Our empirical results indicate neural network methods are better than logistic regression, no matter which the sample period is. The recursive feature elimination based on support vector machine is a good feature selection. The correct rates increase, when the sample periods close to the timing of financial crises or the sample periods lengthen.