Stock Return Prediction Based on Bagging-Decision Tree

摘要：

There is a vast amount of financial information on companies’ financial performance. This information is of great interest for different stakeholders, i.e., stockholders, creditors, auditors, financial analysts, and managers. For stakeholders it is important to extract performance information concerning the companies they are interested in. As a common method for classification and prediction, decision tree has a few merits, such as intelligible, rapid, and simple. In this paper, we perform a financial statement analysis with the method of decision tree. Fifty financial ratios are selected to predict the direction of one-year-ahead earnings changes. A Bagging technique is introduced to improve the classification accuracy of decision tree. Other methods are also examined in order to make comparison. The results show that, compared with the standard-decision tree model and Boosting-decision tree model, the Bagging-decision tree model works well in stock return prediction.

作者: Huacheng Wang Yanxia Jiang Hui Wang

作者单位: School of business, Renmin University of China,Beijing 100872, China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Grey System and Intelligent Services(2009 IEEE灰色系统与服务科学国际会议)

会议地点: 南京

会议语种:英文

页码: 1575-1580

在线出版日期: 2009-10-20（万方平台首次上网日期，不代表论文的发表时间）