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The Use of Earnings Forecasts in Stock Recommendations: Are Accurate Analysts More Consistent?†

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The use of earnings forecasts in stock recommendations: Are accurate analysts more consistent?†

Andreas Simon Orfalea College of Business California Polytechnic State University San Luis Obispo, CA (email: ansimon@calpoly.edu)

Asher Curtis David Eccles School of Business The University of Utah Salt Lake City, UT (email: asher.curtis@business.utah.edu)

This draft: September, 2010. Forthcoming, Journal of Business Finance and Accounting. ABSTRACT: We examine how analysts’ conflicting incentives to be either accurate or optimistic affect their choice to generate stock recommendations with rigorous valuation models or growth-based heuristics. Consistent with prior research the average analyst recommendation is negatively associated with rigorous valuation models and positively associated with growth-based heuristics, we document that these associations are weakest for the most accurate analysts and strongest for the least accurate analysts. We also find evidence consistent with consistency between recommendations and valuation models underlying the positive future returns from trading on the most accurate analysts’ recommendations. Our results are consistent with reputation incentives to be accurate mitigating the use of optimistic growthbased models in generating stock recommendations. Keywords: Forecast Accuracy; Fundamental Valuation; Stock Recommendations; Analyst Reputation.



Andreas Simon and Asher Curtis are, respectively, from the Orfalea College of Business California Polytechnic State University and David Eccles School of Business, The University of Utah. This paper is related to work done by Andreas Simon in his Ph.D. dissertation at the University of Queensland. We gratefully acknowledge the helpful comments from an anonymous reviewer, Brian Cadman, Peter Clarkson, Russ Lundholm, Sarah McVay, Brian Rountree (AAA Annual Meeting Discussant),

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