The extraction of early warning features for predicting financial distress based on XGBoost model and shap framework

He Yang (), Emma Li (), Yi Fang Cai (), Jiapei Li and George X. Yuan
Additional contact information
He Yang: School of Math. & Stats., Zhengzhou University, Zhengzhou 450001, P. R. China
Emma Li: #x2020;Henan Experimental High School, Zhengzhou 450001, P. R. China
Yi Fang Cai: School of Math. & Stats., Zhengzhou University, Zhengzhou 450001, P. R. China
Jiapei Li: #x2021;Henan Key Lab of Financial Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
George X. Yuan: #xA7;Business School, Guangxi University, Nanning 530004, P. R. China¶Business School, Sun Yat-Sen University, Guangzhou 510275, P. R. China∥Business School, Chengdu University, Chengdu 610106, P. R. China**BBD Technology Co., Ltd., No. 966-#9 Building, Tianfu Avenue, Chengdu 610093, P. R. China

International Journal of Financial Engineering (IJFE), 2021, vol. 08, issue 03, 1-24

Abstract: The purpose of this paper is to establish a framework for the extraction of early warning risk features for the predicting financial distress based on XGBoost model and SHAP. It is well known that the way to construct early warning risk features to predict financial distress of companies is very important, and by comparing with the traditional statistical methods, though the data-driven machine learning for the financial early warning, modelling has a better performance in terms of prediction accuracy, but it also brings the difficulty such as the one the corresponding model may be not explained well. Recently, eXtreme Gradient Boosting (XGBoost), an ensemble learning algorithm based on extreme gradient boosting, has become a hot topic in the area of machine learning research field due to its strong nonlinear information recognition ability and high prediction accuracy in the practice. In this study, the XGBoost algorithm is used to extract early warning features for the predicting financial distress for listed companies, with 76 financial risk features from seven categories of aspects, and 14 non-financial risk features from four categories of aspects, which are collected to establish an early warning system for the predication of financial distress. With applications, we conduct the empirical testing respect to AUC, KS and Kappa, the numerical results show that by comparing with the Logistic model, our method based on XGBoost model established in this paper has much better ability to predict the financial distress risk of listed companies. Moreover, under the framework of SHAP (SHAPley Additive exPlanations), we are able to give a reasonable explanation for important risk features and influencing ways affecting the financial distress visibly. The results given by this paper show that the XGBoost approach to model early warning features for financial distress does not only preform a better prediction accuracy, but also is explainable, which is significant for the identification of early warning to the financial distress risk for listed companies in the practice.

Keywords: Financial distress; early-warning feature; XGBoost; SHAP framework; AUC and KS testing; machine learning (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S2424786321410048

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