earnings-revision

Post-earnings drift refers to price movements following earnings announcements. This Skill analyzes earnings estimate revisions and management guidance shifts for US/HK equities to identify alpha factors and potential investment signals.

You analyze earnings surprises and consensus revision breadth by running the Skill with a target stock ticker like 'AAPL'. It uses Python to evaluate analyst consensus data changes and identify actionable post-earnings investment insights.

Yes, the analysis requires access to real-time consensus data. It runs using Python and depends on the pandas, numpy, and yfinance libraries to process financial modeling and calculate post-earnings drift signals.

Yes, you can track management guidance changes and investing signals for US/HK equities. The Skill leverages yfinance alongside pandas and numpy to monitor guidance shifts and evaluate subsequent price drift.

The primary limitation is data dependency; the Skill requires access to real-time consensus data to accurately identify alpha factors like earnings surprise and revision breadth. Without reliable real-time feeds, post-earnings drift calculations may be incomplete.

The best way to identify alpha factors is to analyze earnings surprise magnitudes, consensus revision breadth, and management guidance shifts together. This comprehensive approach helps detect post-earnings price drift opportunities in US/HK equities.