By Tanja Klettke
Financial analysts offer info of their learn studies and thereby aid forming expectancies of a firm’s destiny company functionality. therefore, it truly is necessary to realize analysts who give you the so much designated forecasts and the accounting literature identifies features that support discovering the main actual analysts. Tanja Klettke detects new relationships and identifies new determinants of profits forecast accuracy. those new determinants are an analyst’s “general forecast attempt” and the “number of supplementary forecasts”. inside of accomplished empirical investigations she proves those measures’ energy to provide an explanation for accuracy alterations. Tanja Klettke’s learn is helping traders and researchers to spot extra actual profits forecasts.
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Additional info for New Determinants of Analysts’ Earnings Forecast Accuracy
Example text
Mikhail et al. (1999) find that analysts who issue less accurate forecasts are more likely to change to another brokerage house in the next period. However, they do not distinguish between analysts being fired and moving voluntarily to another brokerage house that might be of higher prestige. Similar to Hong and Kubik (2003) and Ertimur et al. (2011), I aim to differentiate between analysts’ movement to brokerage houses of higher or lower prestige. I argue that the forecast effort an analyst expends is recognized and rewarded by the brokerage houses, and therefore expect that analysts who expend a higher level of forecast effort have better career perspectives.
E. before moving to another brokerage house. 2. e. variables should be constant for analyst ݅ in year ݐand should not differ for each firm ݆. Thus, I include all firms when calculating the average forecast frequency: ܳܧܴܨܴܩ௧ ൌ ͳ ܴܳܧܴܨ௧ Ǥ ܬ (11) ୀଵ Forecast effort is now labeled ܳܧܴܨܴܩ௧ (instead of ܳܧܴܨܴܩ௧ ) and is constant for all firms analyst ݅ covers in year ݐ. Here, another advantage of a general effort measure becomes obvious as a firm-specific measure cannot be used for investigations on the analyst level.
G. O’Brien, 1988; Brown and Mohd, 2003). 26 For each of the ten AGE groups, I rerun the procedure described above, and partition each AGE subsample into terciles according to general forecast effort [firm-specific forecast effort respectively]. In Table 5, Panel A [Panel B], I report the mean accuracy values for the terciles according to the ܳܧܴܨܴܩdeviation [ܴ ܳܧܴܨdeviation]. 26 Due to the discrete distribution of ܴܧܩܣ, the decile sizes range between 11,032 and 30,492 observations. 7758 med.