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.