Predicting the debt-equity decision

Geoffrey Peter Smith

Finance Research Letters, 2022, vol. 48, issue C

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.

Keywords: Debt-equity decision; Machine learning for classification; Capital structure (search for similar items in EconPapers)
JEL-codes: C14 G30 G32 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322001489

DOI: 10.1016/j.frl.2022.102859

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