Comparison of Wavelet Network and Logistic Regression in Predicting Enterprise Financial Distress

Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks in non-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem. ARMA model need estimate the value of all parameters in the model, it has a large amount of computation. Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. It discussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rate and error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity exist a proposed of business failure prediction model (wavelet network model and logistic regression model). The empirical research which is comparison of Wavelet Network and Logistic Regression on training and forecasting sample, the result shows that this wavelet network model is high accurate and the overall prediction accuracy, Type I error and Type II error, wavelet networks model is better than logistic regression model.