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What Goes up Must Not Come Down – Time Series Momentum in Factor Risk Premiums

Last Updated on 10 February, 2024 by Rejaul Karim

Maximilian Renz from Frankfurt School of Finance & Management unravels the captivating narrative of time series momentum in factor risk premiums in his enlightening paper, “What Goes up Must Not Come Down – Time Series Momentum in Factor Risk Premiums.

With a keen focus on technical indicators designed to detect asset price trends, Renz unveils a dynamic world of time-varying risk premiums, where factors exhibit significant variation based on recent uptrends or downtrends. His trend-based dynamic factor strategy takes center stage, delivering annual utility gains of up to 500 basis points, doubling Sharpe ratios, and dramatically reducing maximum drawdowns.

Renz’s findings challenge traditional risk-based asset pricing theories, aligning more closely with sentiment theories, depicting investors’ initial underreaction and subsequent overreaction to new information. This exploration of factor risk premiums unfolds as a riveting journey through the dynamic landscape of financial markets.

Abstract Of Paper

I document significant time variation and predictability in a set of risk factors based on technical indicators. As these indicators are primarily designed to detect trends in asset prices, these findings imply substantial time series momentum in factor risk premiums. Specifically, risk premiums are significantly larger (lower) following recent uptrends (downtrends) in the underlying risk factor. A trend-based dynamic factor strategy, which uses the trend-based signals in order to lever up or down the risk factor, yields annual utility gains of up to 500 basis points, doubles the risk factors’ Sharpe ratio, and more than halves their maximum drawdown. In a conditional asset pricing context, employing technical indicators as conditioning information substantially improves a model’s explanatory power. Overall, my evidence poses several challenges to risk-based asset pricing theories and seems to be more in line with theories of sentiment and investors initial under- and subsequent overreaction to new information.

Original paper – Download PDF

Here you can download the PDF and original paper of What Goes up Must Not Come Down – Time Series Momentum in Factor Risk Premiums.

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Author

Maximilian Renz
Frankfurt School of Finance & Management

Conclusion

In summary, this study unravels compelling insights into time series momentum within factor risk premiums, shedding light on significant time variation and predictability influenced by technical indicators. The observed trend dynamics reveal that risk premiums experience noteworthy fluctuations following recent uptrends or downtrends in the underlying risk factor.

The application of a trend-based dynamic factor strategy, leveraging these signals, proves remarkably fruitful, offering annual utility gains of up to 500 basis points, doubling the risk factors’ Sharpe ratio, and significantly reducing their maximum drawdown. In a conditional asset pricing framework, incorporating technical indicators as conditioning information markedly enhances the model’s explanatory power.

These findings pose challenges to conventional risk-based asset pricing theories, aligning more closely with sentiment theories that highlight investors’ initial underreaction and subsequent overreaction to new information.

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FAQ

Q1: What is the primary focus of Maximilian Renz’s research, and what key insights does it offer regarding factor risk premiums?
A1: Renz’s research explores time series momentum in factor risk premiums, emphasizing significant time variation and predictability influenced by technical indicators designed to detect trends in asset prices. The findings suggest that risk premiums exhibit substantial fluctuations based on recent uptrends or downtrends in the underlying risk factor.

Q2: How does Renz’s trend-based dynamic factor strategy impact the performance of risk factors, and what are the key metrics indicating its effectiveness?
A2: The trend-based dynamic factor strategy, utilizing trend-based signals to adjust the risk factor, demonstrates remarkable effectiveness. It delivers annual utility gains of up to 500 basis points, doubles the Sharpe ratio of risk factors, and reduces their maximum drawdown by more than half.

Q3: What challenges do Renz’s findings pose to traditional risk-based asset pricing theories, and how do they align with theories of investor sentiment and reactions to new information?
A3: Renz’s evidence challenges conventional risk-based asset pricing theories, suggesting a closer alignment with sentiment theories. The observed time series momentum and fluctuations in risk premiums are more consistent with theories highlighting investors’ initial underreaction and subsequent overreaction to new information, adding a nuanced perspective to the understanding of factor risk premiums.

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