Last Updated on 10 February, 2024 by Rejaul Karim
In “Trend Momentum II: Driving Forces of Low Volatility and Momentum,” Wilhelm Berghorn, Markus Vogl, Martin T. Schulz, and Sascha Otto delve into the core of market anomalies, specifically exploring low volatility and momentum. Spanning 46 pages, their work seamlessly integrates statistical analyses and empirical findings, bridging the gap between violated assumptions in price series and observed market anomalies.
Employing an explicit trend model, the research unveils that both low volatility and momentum anomalies are intrinsically tied to trending behavior. The model not only highlights the log-normal trend characteristics but also reveals how low volatility employs implicitly asymmetric trends, while momentum directly exploits them.
By leveraging Mandelbrot’s fractional Brownian Motions model, the study establishes a connection between statistical analyses measuring the Hurst exponent and the empirically observed momentum factor, offering valuable insights into investment strategies.
Abstract Of Paper
In discussions and critiques on the validity of the Efficient Market Hypothesis, there are two important research focuses: statistical analyses showing that the basic assumption of statistical independence in price series is violated and empirical findings that show that significant market anomalies exist. In this work, we combine both viewpoints by analyzing two important mathematical factor anomalies: low volatility and momentum. By applying an explicit trend model, we show that both anomalies require trending. Additionally, we show that the trend model used exhibits log-normal trend characteristics. Furthermore, the model allows us to describe how low volatility uses implicitly asymmetric trend characteristics while momentum directly exploits trends. Using Mandelbrot’s model of fractional Brownian Motions, we can finally link statistical analyses (measuring the Hurst exponent and persistence in returns) to the empirically observed momentum factor. Experimentally, the Hurst exponent in itself allows for a momentum strategy, and it can also be utilized to significantly improve low volatility strategies. In contrast to Mandelbrot’s approach, we offer a non-stationary view that allows us to describe both investment strategies using the trend model.
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Mandelbrot Asset Management GmbH
Martin T. Schulz
University of Applied Sciences Aschaffenburg
Die Sparkasse Bremen AG
In conclusion, this study delves into the intricacies of two pivotal market anomalies—low volatility and momentum—bridging the gap between statistical analyses and empirical observations. Through the lens of an explicit trend model, the research unravels that both anomalies hinge on trending behavior.
The model, characterized by log-normal trend attributes, unveils the implicit asymmetry in low volatility and the direct exploitation of trends in momentum. Mandelbrot’s framework of fractional Brownian Motions serves as a crucial link, connecting statistical measures like the Hurst exponent and return persistence to the empirically observed momentum factor.
This novel approach, offering a non-stationary perspective, not only enhances our understanding of market anomalies but also provides valuable insights for refining investment strategies, accentuating the role of trending dynamics in shaping low volatility and momentum phenomena.
Q1: What is the main focus of the research paper?
A1: The research paper focuses on the driving forces behind two significant market anomalies: low volatility and momentum. It employs an explicit trend model to analyze how both anomalies are intrinsically tied to trending behavior in financial markets.
Q2: What does the explicit trend model reveal about low volatility and momentum?
A2: The model reveals that both low volatility and momentum anomalies require trending behavior. It further highlights that the trend model exhibits log-normal trend characteristics. Specifically, it shows how low volatility uses implicitly asymmetric trend characteristics, while momentum directly exploits trends.
Q3: How does the research connect statistical analyses to the empirically observed momentum factor?
A3: The study leverages Mandelbrot’s model of fractional Brownian Motions to establish a connection between statistical analyses (measuring the Hurst exponent and persistence in returns) and the empirically observed momentum factor. This connection offers valuable insights into understanding and potentially improving investment strategies related to momentum.