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Predicting the debt-equity decision

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  • Smith, Geoffrey Peter

Abstract

I train the k-nearest neighbors (KNN) and random forests (RF) machine learning models to predict if a firm will issue debt, or equity, in the upcoming quarter. KNN predicts 94% of debt and 80% of equity issues correctly. RF predicts 95% of debt and 86% of equity issues correctly. KNN is 92% correct when predicting debt and 84% correct when predicting equity. RF is 94% correct when predicting debt and 88% correct when predicting equity. The overall prediction accuracy is 90% for KNN and 92% for RF. I conclude that machine learning models can “learn” to predict the debt-equity decision.

Suggested Citation

  • Smith, Geoffrey Peter, 2022. "Predicting the debt-equity decision," Finance Research Letters, Elsevier, vol. 48(C).
    • Handle: RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322001489
    DOI: 10.1016/j.frl.2022.102859
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    References listed on IDEAS

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    Cited by:

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    More about this item

    Keywords

    Debt-equity decision; Machine learning for classification; Capital structure;
    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
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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