Stock Forecast Method Based on Wavelet Modulus Maxima and Kalman Filter

Stock market has gradually become an absolutely necessary part of financial market in China. The trend analysis and forecasting of stock prices become key topics in investment and security, which have great theoretical significance and application value. In this paper, the wavelet modulus maxima method is proposed for the abnormal detection of the stock market. The abnormal points detected by wavelet modulus maxima are replaced by the new interpolation points which will be used as an important index of Kalman algorithm to predict stock. The experimental results show that the proposed method can predict the stock data with higher credibility than Kalman algorithm. Therefore, the proposed method can reduce the investment risk and plays an important role in the economic development and financial building.