Dynamic financial distress prediction based on class-imbalanced data batches

Jie Sun, Xin Liu, Wenguo Ai and Qianyuan Tian
Additional contact information
Jie Sun: Business School, Tianjin University of Finance and Economics, Tianjin 300222, P. R. China
Xin Liu: #x2020;School of Economics and Management, Zhejiang Normal University, Jinhua Zhejiang Province 321004, P. R. China
Wenguo Ai: #x2021;School of Management, Harbin Institute of Technology, Harbin, Heilongjiang Province 150001, P. R. China
Qianyuan Tian: #xA7;Finance Office, Institute of Exploration Techniques, China Geological Survey, Tianjin 300300, P. R. China

International Journal of Financial Engineering (IJFE), 2021, vol. 08, issue 03, 1-35

Abstract: This study proposes two approaches for dynamic financial distress prediction (FDP) based on class-imbalanced data batches by considering both concept drift and class imbalance. One is based on sliding time window and synthetic minority over-sampling technique (SMOTE) and the other is based on sliding time window and majority class partition. Support vector machine, multiple discriminant analysis (MDA) and logistic regression are used as base classifiers in the experiments on a real-world dataset. The results indicate that the two approaches perform better than the pure dynamic FDP (DFDP) models without class imbalance processing and the static FDP models either with or without class imbalance processing.

Keywords: Dynamic financial distress prediction; concept drift; class imbalance; synthetic minority over-sampling technique; majority class partition (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S2424786321500262
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijfexx:v:08:y:2021:i:03:n:s2424786321500262

Ordering information: This journal article can be ordered from

DOI: 10.1142/S2424786321500262

Access Statistics for this article

International Journal of Financial Engineering (IJFE) is currently edited by George Yuan

More articles in International Journal of Financial Engineering (IJFE) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().