Dynamic Early-Warning of Enterprise Financial Distress Based on Gradient Boosting Algorithm

Ying Peng, Ziyi Chen, Jingyi Wang

2022

Abstract

One of the biggest problems of users of financial statements is whether the enterprise will face financial distress. In this study, an early-warning system model based on gradient boosting algorithm for enterprise dynamic early-warning is presented. Sometimes special treatment (ST) is the warning of abnormal financial or occurring other conditions in China stock exchange. We construct enterprise dynamic early-warning model based on gradient boosting algorithm using the data of ST companies and their matching companies before special treatment 3 years. Our model calculates the relative variable importance (RVI) of each financial distress indicators, and get the average results of models. Through comparing with logit model, the results show that model based on gradient boosting algorithm can get better warning results. Our paper provides a more accurate method for enterprise dynamic early-warning, which can provide reference for users of financial statements improve financial situation, change investment strategy and so on.

Paper Citation

in Harvard Style

Peng Y., Chen Z. and Wang J. (2022). Dynamic Early-Warning of Enterprise Financial Distress Based on Gradient Boosting Algorithm. In Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME; ISBN 978-989-758-636-1, SciTePress, pages 17-23. DOI: 10.5220/0012022800003620

in Bibtex Style

@conference{icemme22,
author={Ying Peng and Ziyi Chen and Jingyi Wang},
title={Dynamic Early-Warning of Enterprise Financial Distress Based on Gradient Boosting Algorithm},
booktitle={Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME},
year={2022},
pages={17-23},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012022800003620},
isbn={978-989-758-636-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Economic Management and Model Engineering - Volume 1: ICEMME
TI - Dynamic Early-Warning of Enterprise Financial Distress Based on Gradient Boosting Algorithm
SN - 978-989-758-636-1
AU - Peng Y.
AU - Chen Z.
AU - Wang J.
PY - 2022
SP - 17
EP - 23
DO - 10.5220/0012022800003620
PB - SciTePress