Study of Financial Warning Ensemble Model for Listed Companies Based on Unbalanced Classification Perspective

Abstract

Using the ensemble learning method to mine valuable information from a sea of financial data accumulated on the market of financial securities is very important for studying data processing. On the basis of financial data from A-share companies listed on Shanghai Stock Market, this article takes the perspective of unbalanced classification of ST stocks to carry out a study of the construction of a financial warning model for the listed companies. In our experiment, HDRF (HDRandom Forest, Hellinger Distance based Random Forest), ensemble classification models of Bagging, AdaBoost, and Rotation Forest, which take Hellinger distance decision tree (HDDT) as the base classifier, and the ensemble classification model which takes the C4.5 decision tree as the base classifier, are compared in respect of both the area under the ROC curve and the F-measure. As shown in the experimental results, the HDRF and the HDDT based classifier, as an ensemble method, are effective for financial data of listed companies.