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Fractal Optimization of Market Neutral Portfolio

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  • Sergey Kamenshchikov
  • Ilia Drozdov

Abstract

A fractal approach to the long-short portfolio optimization is proposed. The algorithmic system based on the composition of market-neutral spreads into a single entity was considered. The core of the optimization scheme is a fractal walk model of returns, optimizing a risk aversion according to the investment horizon. The covariance matrix of spread returns has been used for the optimization and modified according to the Hurst stability analysis. Out-of-sample performance data has been represented for the space of exchange traded funds in five period time period of observation. The considered portfolio system has turned out to be statistically more stable than a passive investment into benchmark with higher risk adjusted cumulative return over the observed period.

Suggested Citation

  • Sergey Kamenshchikov & Ilia Drozdov, 2016. "Fractal Optimization of Market Neutral Portfolio," Papers 1612.03698, arXiv.org, revised Dec 2016.
    • Handle: RePEc:arx:papers:1612.03698
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    References listed on IDEAS

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    1. Paolo Laureti & Matus Medo & Yi-Cheng Zhang, 2010. "Analysis of Kelly-optimal portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 689-697.
    2. Dubovikov, M.M & Starchenko, N.V & Dubovikov, M.S, 2004. "Dimension of the minimal cover and fractal analysis of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 339(3), pages 591-608.
    3. R. F. Engle & A. J. Patton, 2001. "What good is a volatility model?," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 237-245.
    4. Burton G. Malkiel, 2003. "Passive Investment Strategies and Efficient Markets," European Financial Management, European Financial Management Association, vol. 9(1), pages 1-10, March.
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