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Determinants for predicting zero-leverage decisions: A machine learning approach

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  • Dong, Shengke
  • Jiang, Yuexiang

Abstract

The zero-leverage (ZL) phenomenon is widespread and receives much attention; however, its determinants remain unknown. Using random forest and LASSO regression methods, this study investigates the factors contributing to the ZL phenomenon. We are the first to show that the determinants of overall leverage cannot be directly applied to ZL companies. Findings reveal that cash holdings, tangible assets, industry leverage-level, and firm size are key determinants of ZL. Notably, compared with related studies, ZL shares only some of the determinants of overall leverage, despite being its component. Cash holdings are a determinant unique to ZL companies and the most important among all variables. Using machine learning methods, we identified determinants that are important and reliable, filling a critical gap in relevant research. Moreover, we demonstrate how sample imbalance affects the model’s ability to correctly identify ZL companies and propose a solution to this problem.

Suggested Citation

  • Dong, Shengke & Jiang, Yuexiang, 2025. "Determinants for predicting zero-leverage decisions: A machine learning approach," Finance Research Letters, Elsevier, vol. 71(C).
    • Handle: RePEc:eee:finlet:v:71:y:2025:i:c:s154461232401345x
    DOI: 10.1016/j.frl.2024.106316
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    More about this item

    Keywords

    Capital structure; Machine learning; Random forest; Zero leverage; Sample imbalance; LASSO;
    All these keywords.

    JEL classification:

    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • 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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