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Trend Momentum II: Driving Forces of Low Volatility and Momentum

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  • Wilhelm Berghorn
  • Martin T. Schulz
  • Markus Vogl
  • Sascha Otto

Abstract

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 paper, 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 exhibits lognormal 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 can 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.

Suggested Citation

  • Wilhelm Berghorn & Martin T. Schulz & Markus Vogl & Sascha Otto, 2021. "Trend Momentum II: Driving Forces of Low Volatility and Momentum," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 300-319, May.
  • Handle: RePEc:jfr:ijfr11:v:12:y:2021:i:3:p:300-319
    DOI: 10.5430/ijfr.v12n3p300
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    References listed on IDEAS

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    Cited by:

    1. Vogl, Markus, 2023. "Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framewo," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

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