Long-horizon predictions of credit default with inconsistent customers

Guotai Chi, Bingjie Dong, Ying Zhou and Peng Jin

Technological Forecasting and Social Change, 2024, vol. 198, issue C

Abstract: We developed a decision support framework for default predictions that addresses two common issues: inconsistent customers and predictions of future defaults. We developed a T−m default prediction model using multivariate adaptive regression splines to address the methodological challenges. We confirm that this model outperforms typical approaches in terms of default prediction accuracy. Furthermore, an empirical application of our new framework involving unique data on defaults among Chinese-listed companies yields several substantive insights. Owing to the high interpretability of our predictions, we identify certain industry sectors that should receive high (and low) credit risk assessments. In addition, our research has important implications for the investment decisions of financial institutions and investors and government regulations.

Keywords: Chinese credit market; Credit characteristics; Default prediction; Inconsistent customers; Machine learning; Time window (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162523006935
Full text for ScienceDirect subscribers only

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:eee:tefoso:v:198:y:2024:i:c:s0040162523006935

DOI: 10.1016/j.techfore.2023.123008

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().