Post-Earnings Announcement Drift: Building Factor Models for Earnings Surprise Alpha

Earnings announcements create some of the market’s most persistent and tradable pricing dislocations, yet turning that phenomenon into a robust systematic strategy requires far more than a simple surprise screen. This book is written for quantitative equity traders, factor researchers, and systematic portfolio managers who want a disciplined framework for harvesting post-earnings announcement drift through investable long-short models rather than fragile backtests or anecdotal event studies.

Beginning with the statistical and market-structure foundations of event-driven equity research, the book leads readers through earnings data architecture, bias-resistant research design, and the empirical mechanics of PEAD itself. It then shows how to measure earnings surprise correctly, engineer stronger predictive signals, and transform those signals into neutralized cross-sectional factor portfolios. Readers will learn how to manage turnover, model transaction costs and shorting frictions, design realistic event-driven backtests, and evaluate performance using risk-adjusted and attribution-based diagnostics suitable for live deployment.

What distinguishes this treatment is its end-to-end, production-minded orientation. Rather than stopping at anomaly documentation, it follows the strategy all the way into validation, monitoring, governance, and research-to-production pipeline design. The result is a practical quantitative blueprint for building, testing, and maintaining earnings-surprise alpha in real institutional trading environments.