JOURNAL ARTICLE
Dark Knights: The Rise in Firm Intervention by Credit Default Swap Investors.
Published In: Management Science (INFORMS), 2024, v. 70, n. 2. P. 952 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Danis, András; Gamba, Andrea 3 of 3
Abstract
The article focuses on the theoretical analysis of credit default swap (CDS) investor intervention in the restructuring of financially distressed firms and its impact on firm value. It develops a model distinguishing two types of CDS intervention: (1) intervention by CDS buyers (typically lenders hedged with CDS protection) who push for tougher renegotiation terms, potentially increasing liquidation risk, and (2) intervention by CDS sellers who can inject capital to avoid liquidation. The key finding is that two-sided intervention—by both CDS buyers and sellers—decouples the commitment problem between equity and debt holders from the liquidation problem, thereby increasing firm value and potentially achieving first-best investment levels with zero probability of liquidation. The model’s predictions include that a concentrated CDS seller market can enhance firm value by reducing costly liquidations, while regulatory or market changes that limit protection seller intervention may increase bankruptcy risk and borrowing costs. The analysis is grounded in assumptions such as symmetric information and focuses on two specific types of CDS intervention, offering policy implications that challenge the view that CDS activism necessarily harms firms.
Additional Information
- Source:Management Science (INFORMS). 2024/02, Vol. 70, Issue 2, p952
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.4717
- Accession Number:175542978
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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