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Risk Management

, Volume 21, Issue 4, pp 215–242 | Cite as

Dynamic prediction of relative financial distress based on imbalanced data stream: from the view of one industry

  • Jie Sun
  • Mengjie Zhou
  • Wenguo Ai
  • Hui LiEmail author
Original Article

Abstract

Early studies on financial distress prediction (FDP) seldom consider the problem of industry’s relative financial distress concept drift and neglects how to dynamically predict industry’s relative financial distress. This paper proposes a novel model for dynamic prediction of relative financial distress based on imbalanced data stream of certain industry, and the whole model is divided into the three submodules: the financial feature selection module based on plus-L-minus-R approach, the financial condition evaluation module based on principal component analysis, and the FDP modeling module based on SMOTEBoost-SVM/DT/KNN/Logistic. After feature selection, the results of industry financial condition evaluation are used as class labels for industry’s relative FDP modeling, and the model keeps updating with time window sliding on. The empirical experiment is carried out based on the financial ratio data of Chinese iron and steel companies listed in Shanghai and Shenzhen Stock Exchange, and the results indicate the effectiveness of the dynamic model for industry’s relative FDP.

Keywords

Dynamic financial distress prediction Industry’s relative financial distress Concept drift Principal component analysis SMOTE–AdaBoost Chinese iron and steel industry 

Notes

Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant Numbers 71771162, 71571167 and 71371171). The authors gratefully thank reviewers for their useful comments and editors for their work.

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Copyright information

© Springer Nature Limited 2018

Authors and Affiliations

  1. 1.School of AccountancyTianjin University of Finance and EconomicsTianjinPeople’s Republic of China
  2. 2.School of Economics and ManagementZhejiang Normal UniversityJinhuaPeople’s Republic of China
  3. 3.Management SchoolHarbin Institute of TechnologyHarbinPeople’s Republic of China
  4. 4.College of Tourism and Service ManagementNankai UniversityTianjinPeople’s Republic of China

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