A Comparison of Static, Dynamic and Machine Learning Models in Predicting the Financial Distress of Chinese Firms

Umair Bin Yousaf, Khalil Jebran () and Man Wang
Additional contact information
Umair Bin Yousaf: Doctoral Student at School of Accounting, Dongbei University of Finance and Economics, China Internal Control Research Center, Dalian, China
Khalil Jebran: School of Business Administration, Dongbei University of Finance and Economics, Dalian, China. Corresponding author.
Man Wang: Dongbei University of Finance and Economics, China Internal Control Research Center, Dalian, China

Journal for Economic Forecasting, 2022, issue 1, 122-138

Abstract: As recent studies have begun to pay increasing attention to financial distress prediction (FDP), this study compares the performance of static, dynamic and machine learning (ML) models in predicting the financial distress of firms. Balanced and imbalanced datasets of Chinese listed firms that span the years 1992 to 2018 (27 years) and contain 29,000 firm-year observations are used for the comparison. Results show an incremental improvement in the FDP accuracy of these models, with the random forest (RF) ML model demonstrating the best performance, followed by the Dynamic Hazard model and the static model. Including the growth variable improved the predictive ability of these models. Whilst all these models demonstrated a decreasing prediction accuracy in the imbalanced dataset, a weaker trend was observed for ML models. These results support the debate regarding the FDP superiority of computational methods. The findings provide new evidence that ML models can predict financial distress more accurately as compared to conventional models in the Chinese context.

Keywords: financial distress prediction; static; dynamic; machine learning; growth; China (search for similar items in EconPapers)
JEL-codes: C52 C63 G17 G33 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.ipe.ro/rjef/rjef1_2022/rjef1_2022p122-138.pdf

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:rjr:romjef:v::y:2022:i:1:p:122-138

Access Statistics for this article

Journal for Economic Forecasting is currently edited by Lucian Liviu Albu and Corina Saman

More articles in Journal for Economic Forecasting from Institute for Economic Forecasting Contact information at EDIRC.
Bibliographic data for series maintained by Corina Saman ( this e-mail address is bad, please contact ).