earnings-forecast

To calculate Standardized Unexpected Earnings (SUE), you compare actual corporate earnings against market consensus estimates. This skill applies SUE calculation to quantify expectation gaps and generate actionable trading signals based on earnings surprises. It requires integration with financial data providers for consensus EPS data.

Post-earnings announcement drift (PEAD) is the tendency of stock prices to continue moving in the direction of an earnings surprise. This skill implements PEAD strategy by tracking Analyst Expectation Revision Momentum (ERM) and historical deviation patterns to forecast upcoming drift opportunities.

You forecast earnings per share by applying top-down and bottom-up modeling frameworks within this skill. These models analyze corporate earnings forecasts and market consensus to identify mispriced assets driven by expectation gaps between actual performance and estimates.

Yes, analyzing earnings surprises requires integration with financial data providers to access consensus EPS and historical earnings data. The skill needs this external data to compute SUE, track analyst revisions, and execute PEAD strategy implementation accurately.

You generate buy/sell signals by calculating Analyst Expectation Revision Momentum (ERM) alongside SUE scores. This skill analyzes the gap between corporate earnings forecasts and market consensus, producing actionable trading signals when expectation deviations reach significant thresholds.

SUE and ERM trading signals depend heavily on the accuracy of consensus EPS data from integrated financial data providers. The approach assumes historical deviation patterns will persist, and may not account for sudden market regime changes or one-time corporate events that distort earnings predictability.