Separating the Effects of Asymmetric Incentives and Inefficient Use of Information on Financial Analysts' Consensus Earnings Forecast Errors
Prior research on financial analysts' consensus earnings forecast errors has tended to explore either incentives-based or inefficient information use-based explanations for the properties of the analysts' forecast errors. This has limited our understanding of financial analysts' expectation formation process as incentives and cognitive biases are likely to simultaneously affect the properties of the analysts' consensus forecast errors. Our main contribution is in separating these two effects. In particular, using consensus quarterly earnings forecast data, we document that analysts have asymmetric loss function, and that they do not fully use past earnings and forecast errors information in minimizing their expected loss.
earnings forecasts, loss function, forecast optimality
SMU Cox: Accounting (Topic)