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Cited by 229

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

A Simultaneous Equations Analysis of Forecast Accuracy, Analyst Following, and Trading Volume

Open access
Alford, Andrew W., Berger, Philip G.·1999·Journal of Accounting, Auditing & Finance

Abstract

We use a simultaneous equations model to study forecast accuracy, analyst following, and trading volume. Forecast accuracy and analyst following are determined simultaneously, with greater accuracy associated with higher following. This result supports the idea that an analyst's private information complements, rather than substitutes for, factors that increase certainty about the firm's prospects. Stocks generating more trading volume (and thus greater brokerage commissions) have higher analyst following. Given the simultaneity we document between accuracy and following, stocks that generate greater brokerage commissions not only directly induce higher analyst following but also indirectly enhance the accuracy of earnings forecasts about their firms. Finally, special items and a broad accounting-based signal of change impair analysts' ability to predict future earnings. The negative effect of special items on accuracy is consistent with concerns raised by standard setters that unusual events impair investors' ability to interpret future earnings.

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Crossref · 10.1177/0148558x9901400303

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Metadata

Authors
Alford, A. W., & Berger, P. G.
Venue
Journal of Accounting, Auditing & Finance
Published
1999-07
Volume
14
Issue
3
Pages
219-240
ISSN
  • 0148-558X (ISSN-L)
  • 2160-4061
OpenAlex
W1671678821
Cited by
229
References
2
FWCI
28.88
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: A Simultaneous Equations Analysis of Forecast Accuracy, Analyst Following, and Trading Volume. https://app.cochat.ai/papers/10.1177/0148558x9901400303
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Alford, A. W., & Berger, P. G. (1999). A Simultaneous Equations Analysis of Forecast Accuracy, Analyst Following, and Trading Volume. Journal of Accounting, Auditing & Finance. https://doi.org/10.1177/0148558x9901400303

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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.