A hybrid two-stage financial stock forecasting algorithm based on clustering and ensemble learning

Ying Xu, Cuijuan Yang, Shaoliang Peng, Yusuke Nojima

Abstract: This paper investigates the problem of the stock closing price forecasting for the stock market. Based on existing two-stage fusion models in the literature, two new prediction models based on clustering have been proposed, where k-means clustering method is adopted to cluster several common technical indicators. In addition, ensemble learning has also been applied to improve the prediction accuracy. Finally, a hybrid prediction model, which combines both the k-means clustering and ensemble learning, has been proposed. The experimental results on a number of Chinese stocks demonstrate that the hybrid prediction model obtains the best predicting accuracy of the stock price. The k-means clustering on the stock technical indicators can further enhance the prediction accuracy of the ensemble learning.