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Exploring small-scale optimization coupling learning approaches for enterprises’ financial health forecasts

Author

Listed:
  • Lin Zhu

    (Shandong University)

  • Zhihua Zhang

    (Shandong University)

  • M. James C. Crabbe

    (University of Oxford)

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.

Suggested Citation

  • Lin Zhu & Zhihua Zhang & M. James C. Crabbe, 2025. "Exploring small-scale optimization coupling learning approaches for enterprises’ financial health forecasts," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-18, December.
    • Handle: RePEc:spr:fininn:v:11:y:2025:i:1:d:10.1186_s40854-024-00748-7
    DOI: 10.1186/s40854-024-00748-7
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    References listed on IDEAS

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    More about this item

    Keywords

    Financial health forecasts; Optimization coupling learning; Triggering mechanisms; Small-scale models;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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