Building an Earnings-Surprise Strategy with Event and Factor Filters

Summary

This research note develops an A-share strategy around post-earnings-announcement drift. It groups surprise signals by whether they compare analyst forecasts, reported results, or revised company guidance, then studies events such as unanimous analyst upgrades and research headlines describing results as above expectations. The proposed event universe is rebalanced in selected reporting-season months and holds qualifying stocks for roughly 60 trading days. The report gives historical excess-return and portfolio statistics against the CSI 500, including results for a base event pool and for portfolios narrowed by fundamentals and technical signals.

For enhancement, it combines earnings surprise measures, year-over-year profitability improvement, analyst revision breadth, proximity to the 52-week high, announcement-related returns, size, and unusual turnover. It first selects 60 stocks using fundamental scores, then 30 using technical scores. The reported historical performance is descriptive and does not guarantee future results. The summary does not establish that the tests avoid look-ahead, selection, or implementation biases; results also depend on the data, benchmark, rebalancing rules, and stated transaction-cost assumptions.

Key ideas

• Post-announcement drift motivates buying stocks whose reported earnings or analyst revisions exceed prior expectations.
• The event pool focuses on unanimous analyst upgrades and research headlines signaling earnings surprises.
• Fundamental and announcement-related technical factors are used in two stages to narrow the pool.
• The report presents historical benchmark-relative results, which remain dependent on its data and backtest assumptions.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.