The Enhanced Classification for the Stock Index Prediction
Hyeuk Kim, Sang Tae Han · Procedia Computer Science · 2016
It is one of the hardest challenges to predict the movement of the stock price. We propose the modified bootstrap method in random forests to predict the direction of movement of the stock index price. The training set generated by the modified bootstrapping considers the impact of response variable simultaneously and is applied in random forests. The real KOSPI data are used for the experiments and the result shows that the proposed method performs better than the original method in various situations.