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Domestic Accounting Standards, International Accounting Standards, and the Predictability of Earnings

Ashbaugh, Hollis, Pincus, Morton·2001·Journal of Accounting Research

Abstract

We investigate (1) whether the variation in accounting standards across national boundaries relative to International Accounting Standards (IAS) has an impact on the ability of financial analysts to forecast non‐U.S. firms’ earnings accurately, and (2) whether analyst forecast accuracy changes after firms adopt IAS. IAS are a set of financial reporting policies that typically require increased disclosure and restrict management’s choices of measurement methods relative to the accounting standards of our sample firms’ countries of domicile. We develop indexes of differences in countries’ accounting disclosure and measurement policies relative to IAS, and document that greater differences in accounting standards relative to IAS are significantly and positively associated with the absolute value of analyst earnings forecast errors. Further, we show that analyst forecast accuracy improves after firms adopt IAS. More specifically, after controlling for changes in the market value of equity, changes in analyst following, and changes in the number of news reports, we find that the convergence in firms’ accounting policies brought about by adopting IAS is positively associated with the reduction in analyst forecast errors.

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Metadata

Authors
Ashbaugh, H., & Pincus, M.
Venue
Journal of Accounting Research
Publisher
Wiley
Published
2001-12
Volume
39
Issue
3
Pages
417-434
ISSN
  • 0021-8456 (ISSN-L)
  • 1475-679X
OpenAlex
W3121940960
Cited by
704
References
0
FWCI
21.46
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Domestic Accounting Standards, International Accounting Standards, and the Predictability of Earnings. https://app.cochat.ai/papers/10.1111/1475-679x.00020
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Ashbaugh, H., & Pincus, M. (2001). Domestic Accounting Standards, International Accounting Standards, and the Predictability of Earnings. Journal of Accounting Research. https://doi.org/10.1111/1475-679x.00020

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.