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Cited by 765

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Inefficiency in Analysts' Earnings Forecasts: Systematic Misreaction or Systematic Optimism?

Open access
Easterwood, John C., Nutt, Stacey R.·1999·The Journal of Finance
DOI 10.1111/0022-1082.00166
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Abstract

A rational analysis of analyst behavior predicts that analysts immediately and without bias incorporate information into their forecasts. Several studies document analysts' tendency to systematically underreact to information. Underreaction is inconsistent with rationality. Other studies indicate that analysts systematically overreact to new information or that they are systematically optimistic. This study discriminates between these three hypotheses by examining the interaction between the nature of information and the type of reaction by analysts. The evidence indicates that analysts underreact to negative information, but overreact to positive information. These results are consistent with systematic optimism in response to information.

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Metadata

Authors
Easterwood, J. C., & Nutt, S. R.
Venue
The Journal of Finance
Publisher
Wiley
Published
1999-10
Volume
54
Issue
5
Pages
1777-1797
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
DOI
10.1111/0022-1082.00166
OpenAlex
W1967330734
Cited by
765
References
21
FWCI
79.85
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Inefficiency in Analysts' Earnings Forecasts: Systematic Misreaction or Systematic Optimism? https://app.cochat.ai/papers/10.1111/0022-1082.00166
The paper
Easterwood, J. C., & Nutt, S. R. (1999). Inefficiency in Analysts' Earnings Forecasts: Systematic Misreaction or Systematic Optimism? The Journal of Finance. https://doi.org/10.1111/0022-1082.00166

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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