Work

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

Year: 2024

Type: article

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.

Source: International Review of Financial Analysis

DOI: 10.1016/j.irfa.2024.103586

Authors: Qing Liang, Zhaohua Li

Institutions: Jiaxing University, Lincoln University

Language: English

Topic: Corporate Finance and Governance

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,490

Funders: National Natural Science Foundation of China, National Social Science Fund of China