 Determinants for predicting zero-leverage decisions: A machine learning approachShengke Dong and Yuexiang Jiang Finance Research Letters, 2025, vol. 71, issue C 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. Keywords: Capital structure; Machine learning; Random forest; Zero leverage; Sample imbalance; LASSO (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:71:y:2025:i:c:s154461232401345x DOI: 10.1016/j.frl.2024.106316 Access Statistics for this article Finance Research Letters is currently edited by R. Gençay More articles in Finance Research Letters from Elsevier |
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