What problem does it solve?
It helps you turn earnings forecasts and analyst-consensus expectations into actionable signals by identifying where a company’s results meaningfully beat or miss what the market expects.
Core Features & Use Cases
- Forecasting frameworks: Build EPS forecasts using both top-down (macro → industry → company) and bottom-up (revenue/volume/price and business segment decomposition) approaches for earnings-quality research.
- Expectation-gap quantification: Compute standardized unexpected earnings (SUE) to classify surprise magnitude and direction, and assess analyst prevision behavior via expectation-revision momentum (e.g., ERM and consensus change).
- Post-announcement drift modeling (PEAD): Translate surprise direction into a practical holding-window plan to capture the tendency of prices to drift after earnings releases.
- Use Case: Evaluate an equity like a member of the CSI 300 by (1) forecasting EPS, (2) comparing against consensus EPS, and (3) generating a SUE/ERM-based signal for research or backtesting around earnings dates.
Quick Start
Analyze a ticker by computing your EPS estimate, comparing it to consensus EPS to derive SUE and ERM, and then producing a signal and PEAD-style action plan for the next earnings window.
