Abstract:
The Self-Organizing Map is used as a tool for analyzing financial statements, with the focus on bankruptcy prediction. The phenomenon of going bankrupt is analyzed qualitatively, and companies are also classified into healthy and bankrupt-prone ones. In the qualitative analysis, the Self-Organizing Map is used in a supervised manner: both input and output vectors are represented in the weight vector of each unit, and during training, the whole weight vector is updated, but the best-matching unit search is based on the input vector part only. In the quantitative analysis, three classifiers that utilize the Self-Organizing Map are compared to Linear Discriminant Analysis and Learning Vector Quantization. A modification of the Learning Vector Quantization algorithm to accommodate the Neyman-Pearson classification criterion is also presented.