Abstract

The financial market is a highly complex and dynamic system that has great commercial value; thus, many financial elite are drawn to research on the subject. Recent studies show that machine learning methods perform better than traditional statistical ones. In our study, based on the characteristics of financial sequence data, we propose a wrapped ensemble approach using a supervised learning algorithm to predict stock price volatility of China’s stock markets. To check our new approach, we developed an intelligent financial forecast system and used the Hushen 300 index data to test our model; it proves that our model performs better than a single algorithm. We also compared our model with the famous ensemble approach of bagging, and the result shows that our model is better.