Meeting Individual Analyst Expectations

Abstract

The expectations management literature has so far focused on firms meeting the analyst consensus forecast—the expectations of analysts as a group—at earnings announcements. In this study we argue that investors may use individual analyst forecasts as additional benchmarks in evaluating reported earnings because the consensus forecast underutilizes private information contained in individual analyst forecasts. We predict that measures reflecting such private information have incremental explanatory power over the consensus forecast for the market's reaction to earnings news. We find results consistent with this prediction by examining two measures: (1) the percentage of individual forecasts met and (2) meeting the key analyst forecast. We extend the literature by documenting the role of individual analyst forecasts in investors' evaluations of reported earnings. JEL Classifications: G10; G11; G17; G14; G24. Data Availability: Data are publicly available from the sources identified in the paper.

Lured by the Consensus: The Implications of Treating All Analysts As Equal

“…The coefficient on HQ analysts' SUE is greater and significantly different from the coefficient on LQ analysts' SUE, which suggests that the market is partially aware of the accuracy differences among analysts. 10 This finding is in line with Kirk, Reppenhagen, and Tucker (2014), who show that the market reacts more strongly to the key analyst than to the least influential analyst following the firm. Importantly, the results indicate that the market does not sufficiently recognize analyst quality differences because its reaction to the consensus forecast is significantly stronger even in columns (4)-(6), where HQ analysts are, on average, more accurate than the consensus.…”

Internal Control Opinion Shopping and Audit Market Competition

“…Specifically, SHOP(normal) is negative and significant for clients of all incumbent Big 4 auditors across all three competition proxies when competition is high, and is not significant 13 We use this method because interpreting the results is more intuitive than interpreting an interaction term, and the estimation of separate models for two groups is a common method used in cross-sectional tests [e.g., Jayaraman and Milbourn (2015), Chen, Gul, Veeraghavan, and Zolotoy (2015), Kirk, Reppenhagen, and Tucker (2014), Beck and Mauldin (2014)]. when competition is low.…”

Analyst expertise and the value of cash holdings

“…Based on the implications of each analyst attribute suggested in prior studies, for our main test, we construct a composite measure, which is a function of analysts’ general forecasting experience, firm‐specific forecasting experience and All‐Star status, the size of the analyst's employer (i.e., brokerage firm), and the number of firms and industries covered by an analyst at the firm‐year level based on the various specific analyst expertise proxies used in the literature, consistent with prior studies (Kirk et al., ; Wu & Wilson, ). Kirk et al.…”

“…Specifically, we regress excess stock return on change in cash assets and its interaction with our measures of analyst expertise, while controlling for analyst coverage, analyst dispersion and a battery of other factors that could potentially confound our results. Consistent with prior studies (Kirk, Reppenhagen, & Tucker, ; Wu & Wilson, ), to identify expert analysts, we calculate a composite score measure based on certain characteristics that are found to affect analyst expertise. These characteristics include analysts’ general forecasting experience, firm‐specific forecasting experience, brokerage size, All‐Star status, and the number of firms and industries followed by an analyst (Clement, ; Kim, Lobo, & Song, ; Kirk et al., ; Stickel, ; Wu & Wilson, ).…”

“…Consistent with prior studies (Kirk, Reppenhagen, & Tucker, ; Wu & Wilson, ), to identify expert analysts, we calculate a composite score measure based on certain characteristics that are found to affect analyst expertise. These characteristics include analysts’ general forecasting experience, firm‐specific forecasting experience, brokerage size, All‐Star status, and the number of firms and industries followed by an analyst (Clement, ; Kim, Lobo, & Song, ; Kirk et al., ; Stickel, ; Wu & Wilson, ). The composite score aims to capture an analyst's overall expertise, which encompasses multiple aspects of analyst expertise.…”