JOURNAL ARTICLE
Informativeness of Flexibility versus Uniformity in Cash Flow Classification Standards.
Published In: Journal of International Accounting Research, 2025, v. 24, n. 3. P. 83 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Liang, Mayer Chunzi 3 of 3
Abstract
This study explores how flexibility in classification standards affects the production of comparable cash flow information. In the context of cash flow reporting, comparability means users can infer similarities (differences) in transactions from similar (different) classification of items across firms. Current cash flow classification guidance is a collection of uniform and flexible standards with standard setters sometimes taking different approaches for the same items. I exploit variation in flexibility permitted across U.S. GAAP and IFRS in classifying cash interest paid, cash interest received and dividends received, and find that flexibility produces more comparable cash flows when firms have a more heterogeneous interest and dividend generating process than peers. Additionally, opportunistic classification incentives undermine cash flow comparability more under flexible standards. This is the first study to examine comparability implications of classification flexibility, thereby providing timely evidence for IASB's effort to improve cash flow statement comparability via changes in classification guidance. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G18; M41. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of International Accounting Research. 2025/10, Vol. 24, Issue 3, p83
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:1542-6297
- DOI:10.2308/JIAR-2023-039
- Accession Number:189037517
- Copyright Statement:Copyright of Journal of International Accounting Research is the property of American Accounting Association 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.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.