Debt structure instability using machine learning

Qianru Qi and Jing Wang

Journal of Financial Stability, 2021, vol. 57, issue C

Abstract: Applying a machine-learning algorithm to a large sample of U.S. public firms, we document that more than 30% of the firms substantially alter debt structures in a year, even when leverage ratio is stable, when short-term debt is trivial, and when little cash outlay is required for operations. The instability of debt structure reveals new costs of financial constraints: compared to high-credit-quality firms, low-credit-quality firms have to change debt structure more frequently to accommodate their financing needs, even with increased borrowing costs; low-credit-quality firms lack the opportunity available to high-credit-quality firms to reduce borrowing costs through switching debt instruments.

Keywords: Debt structure; Financial constraints; Credit ratings; Borrowing costs; Machine learning (search for similar items in EconPapers)
JEL-codes: G32 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finsta:v:57:y:2021:i:c:s1572308921001078

DOI: 10.1016/j.jfs.2021.100948

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