Financial ratio selection for business failure prediction using soft set theory

This paper presents a novel parameter reduction method guided by soft set theory (NSS) to select financial ratios for business failure prediction (BFP). The proposed method integrates statistical logistic regression into soft set decision theory, hence takes advantages of two approaches. The procedure is applied to real data sets from Chinese listed firms. From the financial analysis statement category set and the financial ratio set considered by the previous literatures, our proposed method selects nine significant financial ratios. Among them, four ratios are newly recognized as important variables for BFP. For comparison, principal component analysis, traditional soft set theory, and rough set theory are reduction methods included in the study. The predictive ability of the selected ratios by each reduction method along with the ratios commonly used in the prior literature is evaluated by three forecasting tools support vector machine, neural network, and logistic regression. The results demonstrate superior forecasting performance of the proposed method in terms of accuracy and stability.