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

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  • Liang, Qing
  • Li, Zhaohua

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.

Suggested Citation

  • Liang, Qing & Li, Zhaohua, 2024. "Litigation risk and the cost of debt financing in M&As," International Review of Financial Analysis, Elsevier, vol. 96(PA).
  • Handle: RePEc:eee:finana:v:96:y:2024:i:pa:s1057521924005180
    DOI: 10.1016/j.irfa.2024.103586
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    More about this item

    Keywords

    Corporate governance; Word embedding model; Litigation risk; Yield;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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