A New Kernel of Support Vector Regression for Forecasting High-Frequency Stock Returns

This paper investigates the value of designing a new kernel of support vector regression for the application of forecasting high-frequency stock returns. Under the assumption that each return is an event that triggers momentum and reversal periodically, we decompose each future return into a collection of decaying cosine waves that are functions of past returns. Under realistic assumptions, we reach an analytical expression of the nonlinear relationship between past and future returns and introduce a new kernel for forecasting future returns accordingly. Using high-frequency prices of Chinese CSI 300 index from January 4, 2010, to March 3, 2014, as empirical data, we have the following observations: (1) the new kernel significantly beats the radial basis function kernel and the sigmoid function kernel out-of-sample in both the prediction mean square error and the directional forecast accuracy rate. (2) Besides, the capital gain of a simple trading strategy based on the out-of-sample predictions with the new kernel is also significantly higher. Therefore, we conclude that it is statistically and economically valuable to design a new kernel of support vector regression for forecasting high-frequency stock returns.