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The 52-Week High, Momentum, and Predicting Mutual Fund Returns

Last Updated on 10 February, 2024 by Rejaul Karim

In the dynamic realm of finance, Travis Sapp’s exploration in “The 52-Week High, Momentum, and Predicting Mutual Fund Returns” sheds light on innovative approaches to forecasting mutual fund performance. Published in the Review of Quantitative Finance and Accounting in 2011, the study delves into the predictive power of the 52-week high share price, a concept previously demonstrated in individual stock returns.

Sapp extends this insight to mutual funds, introducing measures like the 1-year high net asset value, prior extreme returns, and fund sensitivity to stock return momentum. The results unveil not only their independent predictive abilities for fund returns but also distinct patterns in momentum profits.

This comprehensive analysis offers valuable perspectives on mutual fund selection, adding layers to our understanding of return predictability and the intriguing dynamics of the financial landscape.

Abstract Of Paper

The 52-week high share price has been shown by George and Hwang (2004) to carry significant predictive ability for individual stock returns, dominating other common momentum-based trading strategies. This study examines the performance of trading strategies for mutual funds based on (1) an analogous 1-year high measure for the net asset value of fund shares, (2) prior extreme returns and (3) fund sensitivity to stock return momentum. All three measures have significant, independent, predictive ability for fund returns. Further, each produces a distinctive pattern in momentum profits, whether measured in raw or risk-adjusted returns, with profits from momentum loading being the least transitory. Nearness to the 1-year high and recent extreme returns are significant predictors of fund monthly cash flows, whereas fund momentum loading is not.

Original paper – Download PDF

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Author

Travis Sapp
Iowa State University – Department of Finance

Conclusion

In summary, this study delves into the predictive prowess of the 52-week high share price in the realm of mutual fund returns, extending the insights from George and Hwang (2004). The research introduces analogous measures for mutual funds, including the 1-year high net asset value, prior extreme returns, and fund sensitivity to stock return momentum.

Each measure emerges as a robust predictor of fund returns, showcasing unique patterns in momentum profits. Notably, the study unveils the resilience and least transitory nature of profits derived from momentum loading.

Furthermore, proximity to the 1-year high and recent extreme returns significantly influences fund monthly cash flows, underscoring the multifaceted role of these measures in shaping the dynamics of mutual fund performance and investor behavior.

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FAQ

1. What is the main focus of Travis Sapp’s study, “The 52-Week High, Momentum, and Predicting Mutual Fund Returns”?

The study explores the predictive power of the 52-week high share price, a concept previously demonstrated in individual stock returns, in the context of mutual fund performance. Travis Sapp investigates analogous measures for mutual funds, including the 1-year high net asset value, prior extreme returns, and fund sensitivity to stock return momentum.

2. How do the introduced measures for mutual funds perform in terms of predicting returns, and what distinctive patterns in momentum profits are identified?

The study finds that all three measures—1-year high net asset value, prior extreme returns, and fund sensitivity to stock return momentum—demonstrate significant and independent predictive ability for mutual fund returns. Each measure reveals distinctive patterns in momentum profits, with profits from momentum loading being identified as the least transitory.

3. What additional insights does the research provide regarding the influence of these measures on fund cash flows and their implications for mutual fund selection?

Proximity to the 1-year high and recent extreme returns are identified as significant predictors of mutual fund monthly cash flows. This highlights the multifaceted role of these measures in shaping investor behavior and the dynamics of mutual fund performance. The study contributes valuable perspectives for mutual fund selection, enriching our understanding of return predictability in the financial landscape.

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