In this study, we explore a view of expertise in which specific experiences and training create knowledge, and knowledge is combined with innate ability to perform specific audit tasks. Specifically, we test the extent to which we can explain cross-sectional variation in auditors' performance in several audit tasks using various types of knowledge and ability measures that have been identified in the psychology literature as important determinants of auditor expertise. We compare these results to the explanatory power of a simple measure of general audit experience. Our results indicate that, although more experienced auditors outperform less experienced auditors on average (and given our performance criteria), knowledge and innate ability provide a better explanation of variation in performance. Part of the motivation for this paper is to distinguish between general and expertise in the performance of information-processing tasks. Early studies of human information processing in accounting examined the effect of on performance in audit tasks (see, for example, Ashton and Brown [1980], Hamilton and Wright [1982], and Messier [1983]). Implicit in this research is the notion that . . a primary determinant of improved expertise ... is experience (Hamilton and Wright [1982, p. 757]). The reasoning behind this notion is that knowledge can be gained through and many audit tasks are knowl-

*University of Southern California; tUniversity of Colorado at Boulder. We would like to thank Gilbert Bloom of KPMG Peat Marwick, Bob Rosen of Ernst & Young, Wayne Gazur, Robert Jamison, Sally Jones, Stewart Karlinsky, and David Mason for their assistance in validating the instruments; Eugene Willis and the AICPA for allowing us to collect data at the National Tax Education Program; Stephen Conrad of Arthur Andersen, John Lanning of KPMG Peat Marwick, Jerry Marrs of Ernst & Young, and Randy Stein of Coopers & Lybrand for allowing us to collect data at their respective firms; Minou Bohlin, Linda Levy, David Mason, and Paul Walker for their research assistance; and Vairum Arunachalam for his assistance in collecting data. The authors also gratefully acknowledge the helpful comments of three anonymous referees, Alison Ashton, Robert Ashton, C. Brian Cloyd, David Frederick, Joan Luft, Robert Libby, Laureen Maines, Mark Nelson, Michael Roberts, Frank Selto, D. Shores, Ira Solomon, Rick Tubbs, S. Mark Young, and workshop participants at Arizona State University, Cornell University, Duke University, Indiana University, the University of Illinois Tax Symposium, the Journal of Accounting Research Conference, University of Texas at Arlington, University of Utah, and University of Wisconsin. Finally, the financial support of the KPMG Peat Marwick Foundation and the University of Colorado is gratefully acknowledged. 1 We infer expertise in this study from the level of performance in a specific task, here issue identification in tax planning. This inference is consistent with much of the literature on expertise in accounting and other disciplines (e.g., Bonner and Lewis [1990],

We examine the effects of analysts' celebrity on investor reaction to earnings forecast revisions. We measure celebrity as the quantity of media coverage analysts receive in sources included in the Dow Jones Interactive database, and find that media coverage is positively related to investor reaction to forecast revisions. The effect of celebrity on the reaction to forecast revisions remains significant after controlling for forecast performance variables examined in prior studies (ex post forecast accuracy, ex ante accuracy, award status, and other variables shown to be related to forecast accuracy). While these results are consistent with the familiarity of the analyst's name affecting the market reaction, we cannot rule out that our measure of celebrity is correlated with error in the performance measures we examine and/or correlated with other unexamined dimensions of forecast performance. A content analysis of a random subsample of the media coverage of our sample analysts suggests that our findings likely are not due to the increased availability of forecast revisions. Finally, an investigation of the excess returns around the quarterly earnings announcement date suggests that market participants react too strongly to forecast revisions issued by analysts with high levels of media coverage. Taken together, these findings suggest that an analyst's level of media coverage can affect the initial market reaction to his forecast revisions.