Rejection based support vector machines for financial time series forecasting

Abstract

Much research has been conducted in recent years applying support vector machines (SVMs) for financial forecasting. Financial time series have been shown to be very noisy and difficult to generalize: directional accuracies are often close to the class distribution. In order to improve the accuracy of our predictions, we look at applying rejection to the decision outputs of the SVM model. We study whether accuracies can be improved by rejecting based on a) the distance from the separating hyperplane, b) the probabilistic SVM output, and c) the magnitude of the support vector regression output. We test on the gold future financial contract with 6125 out of sample points covering 2010. The results show small but insignificant accuracy gains but substantial economic improvements when applying the non-probabilistic reject methods. Further, for the margin based rejection method, we observe a strong relationship between the rejection rate and the classification accuracy; This helps with the heuristic that distance-from-the-margin can be associated with confidence.