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    Combining Independent Smart Beta Strategies for Portfolio Optimization

    Maguire, Phil and Moffett, Karl and Maguire, Rebecca (2018) Combining Independent Smart Beta Strategies for Portfolio Optimization. Working Paper. arXiv.

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    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.

    Item Type: Monograph (Working Paper)
    Additional Information: Cite as: arXiv:1808.02505 [q-fin.PM]
    Keywords: smart beta; factor investing; low-volatility anomaly; port- folio optimization; variance minimization; minimum volatility portfolio; long-short strategy, diversification;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Psychology
    Item ID: 10326
    Depositing User: Phil Maguire
    Date Deposited: 13 Dec 2018 16:47
    Publisher: arXiv
      Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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