Use Probabilistic Neural Network to construct early warning model for business financial distress

Because artificial neural network possesses powerful capabilities in filtering and learning adaptability and functions of multi-I/O, thus, this method is expected to be more efficient than those of classical statistics modeling constructs. In the domain of artificial neural network, probabilistic neural network demonstrates broader and much more generalized capabilities, as of now, the latter has been successfully applied to different fields. In light of this, this thesis adopts probabilistic neural network to proceed constructs for early warning model of business financial distress. And this thesis started by collecting relevant information from selected 50 financial crisis prone companies and those respective pairing companies in Taiwan, then followed with principal component analysis in order to reduce model input variables. Finally, through constructing respective forecasting models with probabilistic neural network, back-propagation networks and logistic regression, researcher can compare the three models as intended. Results showed that, model forecasting on early warning model of business financial distress by probabilistic neural network with its parameters fine-tuned, exhibited excellent performance in this regard.