Prediction of corporate financial distress: an application of the America banking industry

Abstract

Financial distress prediction is an important and widely researched issue because of its potential significant influence on bank lending decisions and profitability. Since the 1970s, many mathematical and statistical researchers have proposed prediction models on such issues. Given the recent vigorous growth of artificial intelligence (AI) and data mining techniques, many researchers have begun to apply those techniques to the problem of bankruptcy prediction. Among these techniques, the support vector machine (SVM) has been applied successfully and obtained good performance with other AI and statistical method comparisons. Particle swarm optimization (PSO) has been increasingly employed in conjunction with AI techniques and has provided reliable optimization capability. However, researches addressing PSO and SVM integration are scarce, although there is great potential for useful applications in this field. This paper proposes an adaptive inertia weight (AIW) method for improving PSO performance and integrates SVM in two aspects: feature subset selection and parameter optimization. The experiments collected 54 listed companies as initial samples from American bank datasets. The proposed adaptive PSO-SVM approach could be a more suitable methodology for predicting potential financial distress. This approach also proves its capability to handle scalable and non-scalable function problems.