Retail Investor Horizon and Earnings Announcements

TL;DR

This study distinguishes retail investor horizons to explain different earnings-related return patterns, revealing that long-term retail activity signals underreaction and drift, while short-term activity indicates sentiment-driven overreaction, with implications for trading strategies.

Contribution

It introduces a novel approach by classifying retail investor horizons using StockTwits data, uncovering their distinct impacts on price anomalies and developing a profitable trading strategy.

Findings

Long-horizon retail activity correlates with underreaction and post-earnings drift.

Short-horizon activity is linked to sentiment-driven overreaction and reversals.

A zero-cost long-short strategy based on horizons achieves 0.43% monthly alpha.

Abstract

This paper moves beyond aggregate measures of retail intensity to explore investment horizon as a distinguishing feature of earnings-related return patterns. Using self-reported holding periods from StockTwits (2010-2021), we observe that separating retail activity into "long-horizon" and "short-horizon" cohorts reveals divergent price anomalies. Long-horizon composition is associated with underreaction, characterized by larger initial reactions and pronounced Post-Earnings Announcement Drift (PEAD), suggesting a slow but persistent convergence toward fundamental value. In contrast, short-horizon activity parallels sentiment-driven overreaction, where elevated pre-event sentiment precedes weaker subsequent performance and price reversals. A zero-cost strategy exploiting this heterogeneity, going long on long-horizon stocks and short on short-horizon stocks, yields risk-adjusted alphas of 0.43% per month. These findings suggest that accounting for investment horizon helps disentangles the fundamental signal in retail flow from speculative noise.