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Can machines learn capital structure dynamics?

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  • Amini, Shahram
  • Elmore, Ryan
  • Öztekin, Özde
  • Strauss, Jack

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.

Suggested Citation

  • Amini, Shahram & Elmore, Ryan & Öztekin, Özde & Strauss, Jack, 2021. "Can machines learn capital structure dynamics?," Journal of Corporate Finance, Elsevier, vol. 70(C).
  • Handle: RePEc:eee:corfin:v:70:y:2021:i:c:s0929119921001954
    DOI: 10.1016/j.jcorpfin.2021.102073
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    1. Smith, Geoffrey Peter, 2022. "Predicting the debt-equity decision," Finance Research Letters, Elsevier, vol. 48(C).

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    More about this item

    Keywords

    Machine learning; Target leverage; Speed of leverage adjustment; Financing actions;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • 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
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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