Independent Academic Research

Measuring portfolio gains from earnings announcement trading signals

Matthew Lyle, Kellogg School of Management, Northwestern University

Teri Lombardi Yohn, Goizueta Business School, Emory University


Wall Street Horizon Abstract

This study generates out-of-sample predictions from training data to construct investment portfolios that are mean-variance optimized and rebalanced daily to assess gains from incorporating signals based on post-earnings announcement drift (PEAD), the earnings announcement premium (EAP), and firms’ rescheduling of the earnings announcement (RES). The findings provide insight into the portfolio gains from implementable potential trading strategies that incorporate these fundamental earnings signals. In addition, the methodology used in this study can be used in future research to more accurately approximate gains from an implementable calendar-time trading strategy based on a proposed earnings signal, to add to the gains from implementing the strategy by identifying optimal signal lengths, and to estimate the gains from multiple signals.

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