Litigation risk and the cost of debt financing in M&As

Qing Liang and Zhaohua Li

International Review of Financial Analysis, 2024, vol. 96, issue PA

Abstract: In this paper, we use a semi-supervised machine learning technique, the word2vec word embedding model, to measure litigation risk for fixed-income issuers that use bond financing in primary market for mergers and acquisitions (M&As) in 28 countries. We investigate the relationship between the litigation risk and the offering yield of these securities, demonstrating that increased litigation risk increases financing costs. We analyze several ways to mitigate adverse effects, including the employment of more M&A advisors and assessing the legal environment in the issuing country. Our results are robust to an instrumental variable approach and alternative measures.

Keywords: Corporate governance; Word embedding model; Litigation risk; Yield (search for similar items in EconPapers)
JEL-codes: C45 G12 G23 K40 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:96:y:2024:i:pa:s1057521924005180

DOI: 10.1016/j.irfa.2024.103586

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