Can machines learn capital structure dynamics?

Shahram Amini, Ryan Elmore, Ozde Oztekin () and Jack Strauss

Journal of Corporate Finance, 2021, vol. 70, issue C

Abstract: Yes, they can! Machine learning models predict leverage better than linear models and identify a broader set of leverage determinants. They boost the out-of-sample R2 from 36% to 56% over OLS and LASSO. The best performing model (random forests) selects market-to-book, industry median leverage, cash and equivalents, Z-Score, profitability, stock returns, and firm size as reliable predictors of market leverage. More precise target estimation yields a 10%–33% faster speed of adjustment and improves prediction of financing actions relative to linear models. Machine learning identifies uncertainty, cash flow, and macroeconomic considerations among primary drivers of leverage adjustments.

Keywords: Machine learning; Target leverage; Speed of leverage adjustment; Financing actions (search for similar items in EconPapers)
JEL-codes: C10 C50 G17 G30 G32 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:corfin:v:70:y:2021:i:c:s0929119921001954

DOI: 10.1016/j.jcorpfin.2021.102073

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