JOURNAL ARTICLE

Sponsor Reputation and Capital Structure Dynamics in Leveraged Buyouts.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 7. P. 5849 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Shive, Sophie; Forster, Margaret 3 of 3

Abstract

This article investigates how the reputations of leveraged buyout (LBO) sponsors as borrowers influence the refinancing terms of their portfolio companies. Analyzing 510 U.S. LBOs from 1988 to 2021, the study finds that 67% of refinancing events occur on time—one quarter before existing debt maturity—and generally improve borrowing terms, while early refinancing events are associated with higher leverage, more dividends, and increased borrowing costs. The key measure, SponsorFailure, defined as the proportion of a sponsor's exited deals resulting in bankruptcy or distress within the past two years, is linked to less favorable refinancing terms, including higher loan spreads and reduced likelihood of dividend issuance, particularly during periods of loosening credit conditions. The study shows that lenders adjust loan terms in response to changes in sponsor reputation, accurately pricing the increased risk of default at refinancing, and that these effects are not observed prior to the LBO or among matched non-private equity firms. The findings highlight the importance of sponsor reputation in intermediate financing decisions and suggest implications for LBO capital structure models and portfolio risk management.

Additional Information

  • Source:Management Science (INFORMS). 2025/07, Vol. 71, Issue 7, p5849
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2023.00971
  • Accession Number:187524668
  • 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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