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.

中文翻译：

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预测零杠杆决策的决定因素：机器学习方法


零杠杆 （ZL） 现象很普遍，受到广泛关注;然而，其决定因素仍然未知。本研究使用随机森林和 LASSO 回归方法，调查了导致 ZL 现象的因素。我们是第一个证明整体杠杆的决定因素不能直接应用于 ZL 公司的人。研究结果表明，现金持有量、有形资产、行业杠杆水平和公司规模是 ZL 的关键决定因素。值得注意的是，与相关研究相比，ZL 尽管是其组成部分，但仅分享了整体杠杆的部分决定因素。现金持有量是 ZL 公司独有的决定因素，也是所有变量中最重要的。使用机器学习方法，我们确定了重要且可靠的决定因素，填补了相关研究中的关键空白。此外，我们演示了样本不平衡如何影响模型正确识别 ZL 公司的能力，并提出了这个问题的解决方案。