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Seasonality in the Cross-Section of Expected Stock Returns

Last Updated on 10 February, 2024 by Rejaul Karim

The paper “Seasonality in the Cross-Section of Expected Stock Returns” by Steven L. Heston and Ronnie Sadka introduces an intriguing dimension of seasonality into the framework of expected stock returns.

Notably, the study challenges conventional assumptions by revealing substantial cross-sectional variation in expected stock returns when analyzed on a month-to-month basis, contrary to the absence of such variation when means are constrained to be constant throughout the year.

With a calculated annualized standard deviation of 13.8%, the research demonstrates distinct expected returns for stocks across each calendar month, notably witnessing particularly high variation during October, December, and January. Crucially, this observed seasonal pattern remains unaffected by industry, size, and earnings announcements, thereby advocating for the incorporation of seasonal structure into asset-pricing models.

This study’s findings mark a significant departure from traditional conceptions, offering thought-provoking insights into the impact of seasonality on expected stock returns.

Abstract Of Paper

This paper introduces seasonality into a model of expected stock returns. We confirm previous findings that there is no evidence for cross-sectional variation in expected stock returns when we restrict the means to be constant throughout the year. Yet, we show there is substantial variation when considering each month of the year separately. Applying a seasonal structure we estimate an annualized standard deviation of 13.8%. There is strong evidence stocks have distinct expected returns in January, February, … December. The estimated seasonal variation in expected returns is positive in every calendar month and especially high during October, December, and January. This structure is independent of industry, size, and earnings announcements. These results support the inclusion of seasonal structure into asset-pricing models.

Original paper – Download PDF

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Author

Steven L. Heston
University of Maryland – Department of Finance

Ronnie Sadka
Boston College – Carroll School of Management

Conclusion

In conclusion, “Seasonality in the Cross-Section of Expected Stock Returns” by Steven L. Heston and Ronnie Sadka significantly broadens our understanding of the dynamics of expected stock returns by incorporating a seasonality dimension into the model.

The research underscores the profound impact of month-to-month variations, dispelling the notion of uniformity in expected stock returns when means are held constant throughout the year. The estimated annualized standard deviation of 13.8% unveils compelling evidence for distinct expected returns in every calendar month, with particularly significant variations observed in October, December, and January.

Importantly, these findings persist independently of industry, size, and earnings announcements, advocating for the inclusion of seasonal structure in asset-pricing models. This study marks a pivotal advancement in the recognition and integration of seasonality as a crucial determinant of expected stock returns, fostering rich avenues for further exploration and refinement in financial research.

Related Reading:

Common Patterns of Predictability in the Cross-Section of International Stock Returns

Mood Beta and Seasonalities in Stock Returns

FAQ

What does the paper “Seasonality in the Cross-Section of Expected Stock Returns” contribute to the understanding of expected stock returns?

The paper introduces seasonality as a crucial factor in the model of expected stock returns. It challenges traditional assumptions by revealing significant cross-sectional variation in expected stock returns when analyzed on a month-to-month basis. This contrasts with the absence of such variation when means are constrained to be constant throughout the year.

What is the estimated annualized standard deviation of the seasonal variation in expected stock returns?

The research calculates an estimated annualized standard deviation of 13.8% for the seasonal variation in expected stock returns. This figure reflects the substantial variability in expected returns for stocks across each calendar month.

Which months exhibit particularly high variation in expected stock returns, according to the study?

The study identifies October, December, and January as months with particularly high variation in expected stock returns. These months stand out in terms of the observed seasonal pattern of distinct expected returns for stocks.

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