Abstract
The New Third Board (NTB) market is a non-publicly traded stock exchange in the Chinese securities market and is an essential component of the Chinese capital market. The distinctive features of the NTB market are its low entry barriers, high flexibility, and relatively minimal information disclosure requirements, which, in turn, introduce higher levels of risk. In order to effectively predict the financial risks of NTB-listed companies, a predictive model based on data mining and machine learning technologies needs to be developed. The purpose of this research is to construct a financial risk prediction model for NTB-listed companies, based on integrated feature engineering and learning models, to enhance risk warning capabilities and accuracy. In this study, 15 predictive indicators were formed based on collected financial data of listed companies, and the F-score was used to calculate risk prediction ground truth. Subsequently, through supervised learning, an ensemble learning model, Catboost, was trained for risk assessment and prediction in different time periods. The results of the study indicate that this framework aligns with professional scoring trends, and the mean squared error (MSE) and mean absolute error (MAE) metrics outperform traditional machine learning methods significantly. Notably, the MAE metric is as low as 0.124, suggesting a high level of precision in intelligent risk prediction, offering new perspectives for financial risk assessment of NTB-listed companies in the future.
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Data Availability
The dataset can be accessed upon request.
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Acknowledgements
The authors would like to thank the Key Research and Practice Project of Higher Education Teaching Reform in Henan Province in 2021 and Research and Practice of Higher Education Teaching Reform in Henan Province in 2021 General Project, for their strong support to this article.
Funding
This study was funded by the Key Research and Practice Project of Higher Education Teaching Reform in Henan Province in 2021: Research and practice of Marketing Skills Master Studio to improve the professional ability of higher vocational teachers, project number: 2021SJGLX693, and the Research and Practice of Higher Education Teaching Reform in Henan Province in 2021 General Project: Research and Practice of integration of accounting competition and teaching under the background of Vocational College Skills Competition, project number: 2021SJGLX826.
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Conceptualization and research methods: Haitao Lu; data collection and analysis: Xiaofeng Hu; investigation: Haitao Lu, Xiaofeng Hu; writing: Haitao Lu, Xiaofeng Hu.
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Lu, H., Hu, X. Enhancing Financial Risk Prediction for Listed Companies: A Catboost-Based Ensemble Learning Approach. J Knowl Econ (2023). https://doi.org/10.1007/s13132-023-01601-5
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DOI: https://doi.org/10.1007/s13132-023-01601-5