The weighted Support Vector Machines for the stock turning point prediction

Abstract

This research treats the stock turning point prediction as the imbalanced data classification problems and proposes the evolving weighted support vector machines (EW-SVM) system that leads to superior predictions upon the direction-of-change of the market. However, many parameters of the w-SVM model have to be decided by the user beforehand. Therefore, the EW-SVM system combining both w-SVM with GA is applied to forecast stock turning points. In the experimental results, the EW-SVM system is used to predict stock turning points and is compared to other prediction models including the SVM, DT, NB and k-NN models. These experimental results show that our EW-SVM system has the better performance among all the different approaches.