2025/02/08 by Lin Zhu, Zhihua Zhang, M. James C. Crabbe · 1 voice · 3 citations
Business, Management and Accounting · Computer Science · Economics, Econometrics and Finance · Engineering · #Business #Computer science #Coupling (piping) #Econometrics #Economics #Engineering #Finance #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Insurance and Financial Risk Management #Physics #Scale (ratio)
paper · pdf · doi:10.1186/s40854-024-00748-7
published in Financial Innovation 11(1) (Springer Nature)
openalex publication_date 2025/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
Abstract The financial health of leading enterprises has a significant impact on the sustainable development of the global economy. Most data-driven financial health forecasts are based on the direct use of small-scale machine learning. In this study, we proposed the idea of optimization coupling learning to improve these machine learning models in financial health forecasting. It not only revealed lagging, immediate, continuous impacts of various indicators in different fiscal year, but also had the same low computational cost and complexity as known small-scale machine learning models. We used our optimization coupling learning to investigate 3424 leading enterprises in China and revealed inner triggering mechanisms and differences of enterprises' financial health status from individual behavior to macro level.