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Momentum Turning Points

Last Updated on 10 February, 2024 by Rejaul Karim

The paper “Momentum Turning Points” by Christian L. Goulding, Campbell R. Harvey, and Michele G. Mazzoleni addresses the challenge of turning points in time-series momentum portfolios, where slow signals may not react promptly to changes in trend while fast signals often lead to false alarms.

By meticulously examining momentum portfolios with varying intermediate speeds, which are formed by blending slow and fast strategies, the authors shed light on how these portfolios tackle turning points. The research reveals that the intersection of slow and fast signal directions contains predictive information, including predictably negative returns when both signals are negative.

This study also introduces a novel decomposition of momentum strategy alpha, emphasizing the role of volatility timing, and proposes a mean-variance optimal dynamic speed-selection strategy that exhibits efficient out-of-sample performance across international equity markets. This insightful investigation of momentum turning points offers valuable contributions to the fields of asset pricing, market timing, and behavioral finance.

Abstract Of Paper

Turning points are the Achilles’ heel of time-series momentum portfolios. Slow signals fail to react quickly to changes in trend while fast signals are often false alarms. We analyze how momentum portfolios of various intermediate speeds, formed by blending slow and fast strategies, handle turning points. We find that the intersection of slow and fast signal directions possesses predictive information, including predictably negative returns when both signals are negative. We propose a novel decomposition of momentum strategy alpha, highlighting the role of volatility timing; and a mean-variance optimal dynamic speed-selection strategy with efficient out-of-sample performance across international equity markets.

Original paper – Download PDF

Here you can download the PDF and original paper of Momentum Turning Points.

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Author

Christian L. Goulding
Auburn University – Harbert College of Business

Campbell R. Harvey
Duke University – Fuqua School of Business; National Bureau of Economic Research (NBER)

Michele G. Mazzoleni
STRS Ohio

Conclusion

In conclusion, the paper provides a comprehensive examination of turning points in time-series momentum portfolios, addressing the limitations posed by slow and fast signals.

By exploring momentum portfolios of varying intermediate speeds, which are established through a blend of slow and fast strategies, the study uncovers notable insights about the handling of turning points. The intersection of slow and fast signal directions proves to possess predictive information and significant implications for returns.

Furthermore, the authors introduce a groundbreaking decomposition of momentum strategy alpha that emphasizes the role of volatility timing and propose a mean-variance optimal dynamic speed-selection strategy that demonstrates efficient performance across international equity markets.

This research makes an invaluable contribution to the understanding of time-series momentum, market timing, and asset pricing, offering potential benefits for investors and market participants seeking to navigate the complexities of momentum turning points.

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FAQ

Q1: What is the primary challenge addressed by the paper regarding time-series momentum portfolios, and how does it impact portfolio performance?

The primary challenge addressed is the issue of turning points in time-series momentum portfolios. Slow signals tend to react slowly to changes in trend, while fast signals often generate false alarms. This challenge can impact portfolio performance by leading to delayed reactions to changing market trends or, conversely, triggering premature responses based on false signals.

Q2: How does the study propose to address the challenge of turning points, and what insights are gained by examining momentum portfolios with varying intermediate speeds?

The study addresses the challenge of turning points by examining momentum portfolios with varying intermediate speeds, achieved through a blend of slow and fast strategies. The intersection of slow and fast signal directions is found to contain predictive information, especially indicating predictably negative returns when both signals are negative. This analysis provides insights into how portfolios with intermediate speeds handle turning points more effectively.

Q3: What novel contributions does the paper make in terms of decomposition of momentum strategy alpha and the proposed dynamic speed-selection strategy?

The paper introduces a novel decomposition of momentum strategy alpha, emphasizing the role of volatility timing. This decomposition provides a deeper understanding of the components contributing to the momentum strategy’s alpha. Additionally, the study proposes a mean-variance optimal dynamic speed-selection strategy, which exhibits efficient out-of-sample performance across international equity markets. These contributions offer new perspectives on enhancing momentum strategy performance and navigating market complexities.

You can find many more Research Papers here

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