Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis

Abstract

Put forward a novel combination forecasting method (M-ARIMA-BP) that could make a more accurate and concise prediction of stock market based on wavelet multiresolution analysis. This innovative method operated by parsing of the low-frequency trend series and the high-frequency volatility series of stock market and gives an insight into the price series. Using the daily closing price data of SSE (Shanghai Stock Exchange) Composite Index and Shenzhen Component Index as samples, compared with conventional wavelet prediction model, ARIMA model, and BP neural network model, the empirical results show that the new algorithm M-ARIMA-BP can improve the accuracy of volatility forecasting and perform better in predicting prices rising and falling.