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In this paper, we first construct a three-layer (one hidden layer) multilayer back propagation neural network (BPNN) model to forecast daily closing prices of stocks, but there are considerable errors between the actual values and predicted values. Then, to get better prediction results with higher accuracy, we fit the tendency of the errors by modeling a generalized autoregressive conditional heteroscedasticity (GARCH) model. Since it can better deal with the non-linearity and other characteristics of financial data, so the predictive effect of our method is better than that of the hybrid approach of BPNN and autoregressive integrated moving average (ARIMA) model. Finally, we verify this assertion through experimental results.

A Hybrid Approach of Combining BP Neural Network and GARCH Model for Forecasting Stock Price