Predicting bankruptcy is one of the most important problems to parties such as bankers, managers, government policy makers, and investors. It provides information for interested parties to minimize their predictable losses from bankruptcy.
There has been substantial research into the bankruptcy prediction. Many researchers used the statistical method in the problem until the early 1980s. Since the late 1980s, Artificial Intelligence (AI) has been employed in bankruptcy prediction. And many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN’s superior performance, it has some problems such as overfitting and poor explanatory power.
To overcome these limitations, this paper suggests a relatively new machine learning technique, support vector machine (SVM), to bankruptcy prediction. SVM is simple enough to be analyzed mathematically, and leads to high performances in practical applications. The objective of this paper is to examine the feasibility of SVM in bankruptcy prediction by comparing it with ANN, logistic regression, and multivariate discriminant analysis. The experimental results show that SVM provides a promising alternative to bankruptcy prediction.
