Analyst Forecasts and Stock Returns

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Abstract
This study seeks to determine the relation between stock returns and analyst forecast properties, specifically, the dispersion and error of annual earnings forecasts. The results of portfolio sorts, Fama-MacBeth cross-sectional regression models, and Fama and French (1993) factor models indicate firms with low dispersion or error outperform firms with high dispersion or error. Robustness tests show the results are not explained by liquidity, momentum, industry, post-earnings announcement drift, or traditional risk measures. An investment strategy based on forecast properties is shown to produce zero-cost returns of 13% per year, yielding positive returns in all 19 years using an error measure. The results are not attributable to several potential theories. Risk-related theories are eliminated as firms with low dispersion or error ("transparent") outperform firms with high dispersion or error ("opaque"). This remains true even after controlling for volatility measures. Behavioral theories based on optimism are also eliminated as optimistic forecasts only explain a small part of the results. Finally, the results are not related to contrarian-value strategies as the transparent firms outperform in both up and down markets.