How Does Prior Information Affect Analyst Forecast Revisions?

초록

This study investigates how observable prior information affects individual analysts' earnings forecast revisions. Following Stickel (1990), it first examines how new information observed by Korean analysts and uncertainty in the analysts' current forecast affect the analysts' forecast revision. Then, this study extends the prediction model by examining how characteristics of new information (positive vs. negative change in consensus forecast) and the characteristics of the analysts' current forecast (higher vs. lower than the mean forecast) affect their forecast revision. The empirical results show that the individual analyst revision, the change in consensus forecast, and the cumulative stock returns are positively related whereas the revision and the deviation of the analyst's current forecast from the consensus forecast are negatively related. Results also show more sensitive reactions to consensus change when analysts observe a negative consensus change. In addition, analysts' reaction to a negative consensus change is greater when their current forecast is greater than the prior mean forecast. These results suggest that analysts learn from market expectation changes and incorporate new information into their new forecasts.

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