earnings-forecast

To forecast earnings and detect consensus deviations, you can use top-down and bottom-up forecasting chains to generate EPS estimates and compare them against analyst consensus to identify surprise opportunities.

Standardized Unexpected Earnings (SUE) is a scoring metric that measures the deviation between actual reported EPS and analyst consensus. SUE scoring helps identify earnings surprises and time trade entries based on post-earnings-announcement drift (PEAD) patterns.

You can use analyst revision momentum metrics like ERM and eps_change_pct to gauge market sentiment. Measuring dispersion and momentum in analyst estimates helps adjust positions by tracking shifts in consensus expectations before earnings announcements.

Yes, you need historical EPS data and analyst consensus forecasts as required inputs. These figures establish the baseline for top-down and bottom-up forecasting, SUE scoring, and momentum signal generation to detect potential earnings surprises.

Yes, you can configure risk controls including holding periods, rebalancing frequency, and stop-loss rules. This configurable framework adjusts entry timing based on SUE scores, PEAD patterns, and analyst revision momentum signals across quarterly cycles.

The best way to compare your EPS forecast with market consensus is providing a target ticker and forecast horizon. This generates an EPS projection measured against analyst estimates to produce actionable trading signals based on SUE and momentum metrics.