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

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.