earnings-forecast

To calculate SUE and PEAD signals for A-share earnings surprises, you need historic analyst consensus EPS and SUE standard deviation history. The framework computes SUE, dispersion, and analyst expectation momentum to quantify surprise potential and output trade signals.

Building an EPS projection model that blends macro and bottom-up assumptions involves unifying macro GDP, industry revenue, and company-level revenue/expense data. This top-down and bottom-up forecasting framework generates EPS projections to compare against analyst consensus.

Setting holding periods and stop-loss rules for post-earnings announcement drift trading requires configuring PEAD holding thresholds. You align earnings calendar events with specific holding periods, stop-loss limits, and position limits to systematically capture post-announcement drift.

Yes, you can use this earnings forecasting framework for A-share consumer staples like Kweichow Moutai. It supports calibrating macro-to-bottom-up earnings stories across consumer, industrial, and growth sectors during earnings seasons.

Generating earnings-driven trading signals based on consensus deviation requires historic analyst consensus EPS and SUE standard deviation history. You also need configurable parameters for ERM, dispersion, and PEAD holding thresholds to surface surprise-driven signals.

Your earnings forecast model diverges from Wind consensus estimates due to expectation gaps between your macro-to-bottom-up projections and market consensus. Computing SUE, dispersion, and eps_change_pct metrics quantifies this surprise potential and builds trade conviction.