Refining Financial Analysts’ Forecasts by Predicting Earnings Forecast Errors

Prior research on financial analyst' quarterly earnings forecasts has documented serial correlation in forecast errors. This paper examines the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm's industry and the analyst's experience and brokerage house affiliation. Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, I model consensus forecast errors as an autoregressive process. I demonstrate that the model of forecast errors that best fits the data is AR(1), and use the obtained autoregressive coefficients to predict consensus forecast errors. Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors, and proposes a series of refinements to the consensus. These refinements were not presented in prior literature, and can be useful to financial analysts and investors