JOURNAL ARTICLE
Offsetable Derivatives and Investor Risk Assessment.
Published In: Management Science (INFORMS), 2024, v. 70, n. 5. P. 2779 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Neilson, Jed J.; Wang, K. Philip; Williams, Christopher D.; Xie, Biqin 3 of 3
Abstract
This article examines the accounting treatment and risk implications of offsetable derivatives—derivative assets and liabilities that banks can legally offset against the same counterparty—under U.S. generally accepted accounting principles (GAAP) and international financial reporting standards (IFRS). U.S. GAAP permits offsetting and reporting only the net amount on the balance sheet, whereas IFRS applies more restrictive criteria, requiring recognition of gross amounts, leading to significant differences in banks' balance sheet presentations. Using a global sample of large banks, the study finds that offsetable derivatives are positively associated with multiple measures of bank risk, contradicting claims by some U.S. banks that these derivatives do not increase risk. Furthermore, the research shows that equity investors, particularly less sophisticated ones, assess bank risk differently depending on whether offsetable derivatives are recognized on the balance sheet or merely disclosed in footnotes, while more sophisticated credit default swap (CDS) investors do not exhibit this difference. These findings suggest that disclosure alone may not substitute for recognition in conveying risk information to all investors, with implications for accounting standards and financial stability.
Additional Information
- Source:Management Science (INFORMS). 2024/05, Vol. 70, Issue 5, p2779
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.4831
- Accession Number:177188247
- 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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