Capitalizing on Earnings Surprises: Behavioral Finance and Momentum Strategies in Action

The stock market's reaction to earnings surprises has long defied the predictions of the efficient market hypothesis. According to a ScienceDirect review, historical data reveals a persistent phenomenon known as the Post-Earnings-Announcement Drift (PEAD), where stock prices continue to move in the direction of the earnings surprise for weeks after the announcement. This anomaly suggests that investors under-react or over-react to earnings news, creating opportunities for systematic strategies to exploit short-term price distortions.

Behavioral Biases and Momentum: A Symbiotic Relationship

Behavioral finance provides a framework to understand these market inefficiencies. Cognitive biases such as anchoring and overconfidence play a critical role in shaping investor reactions to earnings reports. For instance, overconfidence leads traders to overestimate their ability to interpret earnings data, often resulting in exaggerated price movements, as noted by Tactical Investor. During the dot-com bubble, investors' overconfidence in unproven tech firms fueled a speculative frenzy, only to face sharp corrections when fundamentals failed to justify valuations, a pattern discussed in the Tactical Investor piece. Similarly, anchoring-where investors fixate on past earnings or price levels-can delay market corrections, prolonging the drift observed in PEAD as the ScienceDirect review documents.

Momentum trading strategies capitalize on these behavioral tendencies. A 2025 Monty-Trader study highlights a hybrid approach combining PEAD with end-of-day momentum signals for S&P 500 stocks. This strategy targets firms with earnings surprises exceeding 15–20%, confirmed by technical indicators such as closing near the day's high or above key moving averages. By leveraging both fundamental and technical signals, the approach aims to capture sustained price trends driven by delayed investor reactions.

Systematic Strategies: Integrating Sentiment and Quantitative Metrics

The most effective strategies integrate behavioral finance metrics with quantitative analysis. A notable example is the EPS Surprise Enhanced Momentum Strategy, developed during a 2023 hackathon. This approach uses a Hidden Markov Model (HMM) to detect market regimes (bull or bear) and Random Forest algorithms to refine stock selection. By incorporating sentiment analysis from social media and earnings call transcripts, the strategy adapts to shifting investor psychology while maintaining risk controls such as tracking error limits and position sizing. Historical performance data underscores the potential of such strategies: backtesting from 2020 to 2023 shows the EPS Surprise Enhanced Momentum Strategy outperformed both a pure momentum approach and the S&P 500 index, achieving a Sharpe ratio of 1.8 versus 0.9 for the benchmark. However, challenges persist. In markets like China's A-shares, informed traders may manipulate technical indicators, reducing the effectiveness of traditional momentum signals, a finding highlighted in a ScienceDirect study. This highlights the need for adaptive models that account for regional market dynamics.

Risk Management: The Unsung Hero

While momentum and behavioral strategies offer compelling returns, they require disciplined risk management. Overconfidence bias, as documented by Tactical Investor, often leads to excessive trading and under-diversified portfolios. To mitigate this, successful strategies enforce strict rules: limiting drawdowns to 20% of equity, capping per-trade risk at 0.6% of capital, and avoiding averaging down on losing positions, practices also recommended in the Monty-Trader study. Additionally, stress-testing assumptions and adopting a probabilistic mindset-focusing on expected outcomes rather than certainties-can curb the emotional pitfalls of trading, as the Tactical Investor piece emphasizes.

Conclusion

The interplay between behavioral finance and momentum trading presents a robust framework for capitalizing on earnings surprises. By systematically addressing investor biases, integrating sentiment analysis, and refining entry/exit rules, traders can exploit the prolonged price drift observed in PEAD. However, success hinges on rigorous backtesting, adaptive modeling, and a commitment to disciplined risk management. As markets evolve, the fusion of behavioral insights with quantitative rigor will remain a cornerstone of profitable short-term trading.