Forecasting classification of operating performance of enterprises by probabilistic neural network

Abstract

Classification of operating performance of the enterprises is not only a hot issue emphasized by the management, but it is even the important reference by investors in their decision-making. In general, the analysis of its performance is usually undertaken by models of financial prediction or credit rating. This paper address a lot of models to analyze it through the financial ratio from 287 private enterprises of traditional industry public listed in Taiwan's stock market and OTC as sample data. A hybrid methodology that combines both data mining and artificial intelligence is proposed to take advantage of the unique strength of single one model. First, we use the data mining technique, such as traditional principal components analysis, to select network input variables. Second, the various different models, including the Probabilistic Neural Network are also considered. Third, this paper shows that the classification ability of the Probabilistic Neural Network model, after the parameter adjusted by genetic algorithm, does significantly outperform other simple methods-back-propagation network, decision tree, and logistic regression model. In conclusion, experimental results with real data sets indicate that combined model can be an effective way to improve forecasting classification accuracy achieved by either of the one single models.