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Combining Independent Smart Beta Strategies for Portfolio Optimization

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  • Phil Maguire
  • Karl Moffett
  • Rebecca Maguire

Abstract

Smart beta, also known as strategic beta or factor investing, is the idea of selecting an investment portfolio in a simple rule-based manner that systematically captures market inefficiencies, thereby enhancing risk-adjusted returns above capitalization-weighted benchmarks. We explore the idea of applying a smart strategy in reverse, yielding a "bad beta" portfolio which can be shorted, thus allowing long and short positions on independent smart beta strategies to generate beta neutral returns. In this article we detail the construction of a monthly reweighted portfolio involving two independent smart beta strategies; the first component is a long-short beta-neutral strategy derived from running an adaptive boosting classifier on a suite of momentum indicators. The second component is a minimized volatility portfolio which exploits the observation that low-volatility stocks tend to yield higher risk-adjusted returns than high-volatility stocks. Working off a market benchmark Sharpe Ratio of 0.42, we find that the market neutral component achieves a ratio of 0.61, the low volatility approach achieves a ratio of 0.90, while the combined leveraged strategy achieves a ratio of 0.96. In six months of live trading, the combined strategy achieved a Sharpe Ratio of 1.35. These results reinforce the effectiveness of smart beta strategies, and demonstrate that combining multiple strategies simultaneously can yield better performance than that achieved by any single component in isolation.

Suggested Citation

  • Phil Maguire & Karl Moffett & Rebecca Maguire, 2018. "Combining Independent Smart Beta Strategies for Portfolio Optimization," Papers 1808.02505, arXiv.org, revised Aug 2018.
  • Handle: RePEc:arx:papers:1808.02505
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    References listed on IDEAS

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    1. Ang, Andrew & Hodrick, Robert J. & Xing, Yuhang & Zhang, Xiaoyan, 2009. "High idiosyncratic volatility and low returns: International and further U.S. evidence," Journal of Financial Economics, Elsevier, vol. 91(1), pages 1-23, January.
    2. Frazzini, Andrea & Pedersen, Lasse Heje, 2014. "Betting against beta," Journal of Financial Economics, Elsevier, vol. 111(1), pages 1-25.
    3. André F. Perold, 2004. "The Capital Asset Pricing Model," Journal of Economic Perspectives, American Economic Association, vol. 18(3), pages 3-24, Summer.
    4. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    5. Phil Maguire & Stephen Kelly & Robert Miller & Philippe Moser & Philip Hyland & Rebecca Maguire, 2017. "Further evidence in support of a low-volatility anomaly: Optimizing buy-and-hold portfolios by minimizing historical aggregate volatility," Journal of Asset Management, Palgrave Macmillan, vol. 18(4), pages 326-339, July.
    6. Yensen Ni & Yi-Ching Liao & Paoyu Huang, 2015. "Momentum in the Chinese Stock Market: Evidence from Stochastic Oscillator Indicators," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(S1), pages 99-110, January.
    7. Blitz, D.C. & van Vliet, P., 2007. "The Volatility Effect: Lower Risk without Lower Return," ERIM Report Series Research in Management ERS-2007-044-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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    Cited by:

    1. Kamil Korzeń & Robert Ślepaczuk, 2019. "Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach," Working Papers 2019-17, Faculty of Economic Sciences, University of Warsaw.
    2. Jordan Bowes & Marcel Ausloos, 2021. "Financial Risk and Better Returns through Smart Beta Exchange-Traded Funds?," JRFM, MDPI, vol. 14(7), pages 1-30, June.

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