Forecasting Stock Returns Based on Spline Wavelet Support Vector
Lingbing Tang, Huanye Sheng · 2009
Stock returns forecast is vital important in finance to reduce risk and take better decisions. This paper propose a spline wavelet kernel for support vector machine (SVM), called spline wavelet support vector machine (SWSVM), to model nonstationary financial time series. The SWSVM is obtained by incorporating the spline wavelet theory into SVM. Because spline wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities, the SWSVM can forecast stock returns accurately. The applicability and validity of spline wavelet support vector machine (SWSVM) for stock returns forecast were analyzed through experiments on real-world stock data. It appears that the spline wavelet kernel perform better than the Gaussian kernel.