Stock return prediction based on Bagging-decision tree

Abstract

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 relevant performance information of the companies they are interested in. As a common method for classification and prediction, decision tree has merits, such as intelligible, rapid, and simple. In this paper, we design a financial statement analysis using 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 better in stock return prediction.