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

Jie Sun, Mengjie Zhou, Wenguo Ai and Hui Li ()
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Jie Sun: Tianjin University of Finance and Economics
Mengjie Zhou: Zhejiang Normal University
Wenguo Ai: Harbin Institute of Technology
Hui Li: Nankai University

Risk Management, 2019, vol. 21, issue 4, 215-242

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
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