The Information Content of Analyst Stock Recommendations

Parker Center for Investment Research Working Papers

40 Pages Posted: 18 Feb 2001

See all articles by Susan D. Krische

Susan D. Krische

American University - Kogod School of Business

Charles M.C. Lee

Stanford University - Graduate School of Business

Date Written: December 18, 2000

Abstract

We investigate the relation between analyst stock recommendations and eight concurrently available variables that have predictive power for stock returns. We find that analysts generally pay little attention to the large sample predictive attributes of these variables. In seven out of eight cases, analysts' stock recommendations are directionally opposite to the variable's normative usage in returns prediction. In general, analysts exhibit a strong bias in favor of glamour stocks with growth characteristics.

Despite this general bias, analyst recommendations have incremental predictive power for future returns. In fact, after controlling for the other predictive variables, the predictive power of the level of the analyst recommendation increases. These findings suggest that analyst stock recommendations contain information that is largely orthogonal to the information in the other predictive variables. We discuss the implications of these results for analysts, and for investors who rely on their recommendations.

Keywords: Analyst, recommendations, market efficiency, investment, trading rules, quantitative analysis, fundamental analysis, stock screens

JEL Classification: G12, G14, G21, G24, G29, M41

Suggested Citation

Krische, Susan D. and Lee, Charles M.C., The Information Content of Analyst Stock Recommendations (December 18, 2000). Parker Center for Investment Research Working Papers. Available at SSRN: https://ssrn.com/abstract=254547 or http://dx.doi.org/10.2139/ssrn.254547

Susan D. Krische

American University - Kogod School of Business ( email )

4400 Massachusetts Ave NW
Washington, DC 20016
United States
202-885-2082 (Phone)
202-885-1992 (Fax)

Charles M.C. Lee (Contact Author)

Stanford University - Graduate School of Business ( email )

Stanford Graduate School of Business
655 Knight Way
Stanford, CA 94305-5015
United States
650-721-1295 (Phone)

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