Beyond Consensus

We develop a sequential forecasting model for full-year earnings per share (EPS) that updates as quarterly earnings are reported. It combines an annual forecast, a forecast of the next unreported quarter, and realised earnings using only public information available at each forecast date. We call this sequential updating process a revision cascade. We evaluate the model against the I/B/E/S consensus without using analyst forecasts as model inputs. Across 66,914 firm-year-stage observations, the model’s mean absolute error is 0.723 times that of consensus, improving from 0.934 before any quarter is reported to 0.347 after three quarters. The difference between the model and consensus also predicts subsequent earnings surprises and supports a tradable long–short strategy. The results show that the model provides an independent benchmark for analyst consensus and that the disagreement between the two forecasts contains information about future earnings.