Financial distress prediction based on Adaboost algorithm combined with DEGWO-SVM

Abstract

Abstract: A new Adaboost-DEGWO-SVM combination model is proposed in this paper to solve the problems of difficult parameter selection and low classification accuracy of support vector machine (SVM) in financial distress prediction. Firstly, the financial data of all A-share listed companies were preprocessed in 2017, and 1∶1 modeling dataset of distressed companies (ST) and normal companies was extracted. Then, the difference evolutionary algorithm (DE) was used to improve the global search capability of GWO, and to solve the problem that the gray wolf algorithm is prone to fall into the local optimal, so as to realize the optimization of the parameters of SVM. Finally, Adaboost algorithm is used to improve the classification ability of DEGWO-SVM model. Experimental results show that the proposed Adaboost-DEGWO-SVM combined forecasting model has obvious advantage in dilemmaprediction, compared with DEGWO-SVM, the classification accuracy rate isincreased 4.34%, Ⅰ errors and Ⅱ errors aredecreased by 0.043 5, and compared with the single SVM, theclassification accuracy rate is increased 13.04%, Ⅰ errors and Ⅱ errors decreased 0.130 4 and 0.130 5, it is a kind of potential corporate financial distress prediction model.

Key words: distress prediction, support vector machine, improved grey wolf optimization algorithm, Adaboost algorithm

CLC Number:

TP391

ZHU Chang-sheng, TIAN Hui-xing, FENG Wen-fang. Financial distress prediction based on Adaboost algorithm combined with DEGWO-SVM[J]. Journal of Lanzhou University of Technology, 2021, 47(6): 100-107.

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