An Information Interpretation of Financial Analyst Superiority in Forecasting Earnings

This paper develops and tests an information-based model for conditions under which analysts earnings forecasts are likely to be more accurate than forecasts of time-series models. Three information variables are considered, namely the dimensionality of the information set, the precision of the information items, and the correlation amongst the information items. The respective proxy variables for the information variables are firm size, extent of agreement amongst analysts, and the number of lines of business the firm operates in. Evidence is provided that analysts are likely to be more accurate than time series models for larger firms and for firms whereby analysts have more homogeneous earnings forecasts