A hybrid method for forecasting stock market trend using soft-thresholding de-noise model and SVM

Abstract

Stock market time series are inherently noisy. Although support vector machine has the noise-tolerant property, the noised data still affect the accuracy of classification. Compared with other studies only classify the movements of stock market into up-trend and down-trend which does not concern the noised data, this study uses wavelet soft-threshold de-noising model to classify the noised data into stochastic trend. In the experiment, we remove the stochastic trend data from the SSE Composite Index and get de-noised training data for SVM. Then we use the de-noised data to train SVM and to forecast the testing data. The hit ratio is 60.12%. Comparing with 54.25% hit ratio that is forecasted by noisy training data SVM, we enhance the forecasting performance. © Springer-Verlag Berlin Heidelberg 2007.