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Surviving Black Swans III: Timing US Sector Funds

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  • Pankaj Topiwala

    (FastVDO LLC, 3097 Cortona Dr., Melbourne, FL 32940, USA)

Abstract

The typical small investor makes on average about 5% a year in investment gains, just half of what the market does. Moreover, most investment funds also underperform compared to the broader market. In two previous papers, we explored how a specific and simple approach to algorithmic trading can help both types of investors achieve strong results. For concreteness, we focused attention on investing in a single variable, in our case, a major US-based index such as SPX and IXIC, individually. For illustrative purposes, we also considered some highly traded tech stock examples. In this paper, we extend our work to study the US sector funds, and for the first time in our series, we also consider trading multiple variables at a time to see how that may differ from our single-variable investment strategy. To simplify matters, we consider an initial equal weighted portfolio of several sector funds, selected randomly without any analysis, and assume that each is traded independently. To simplify further, we do no rebalancing in our study, though that is an essential part of money management according to modern portfolio theory. We nevertheless obtain interesting and informative results. We can typically improve on the performance of most sector funds compared to buy-and-hold (hereafter referred to as BnH). Moreover, as an example of portfolio growth, a portfolio of five equal weighted sector funds in BnH achieves 6.5× growth over 20 years (ending in March 2023), whereas our approach achieves 12.4× growth—nearly 2× better, at roughly half the maximum drawdown. That is a strong win for both professional and home investors.

Suggested Citation

  • Pankaj Topiwala, 2023. "Surviving Black Swans III: Timing US Sector Funds," JRFM, MDPI, vol. 16(5), pages 1-18, May.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:5:p:275-:d:1149208
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    References listed on IDEAS

    as
    1. Pankaj Topiwala, 2023. "Surviving Black Swans II: Timing the 2020–2022 Roller Coaster," JRFM, MDPI, vol. 16(2), pages 1-26, February.
    2. Pankaj Topiwala & Wei Dai, 2022. "Surviving Black Swans: The Challenge of Market Timing Systems," JRFM, MDPI, vol. 15(7), pages 1-25, June.
    3. Chen, Yong & Liang, Bing, 2007. "Do Market Timing Hedge Funds Time the Market?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(4), pages 827-856, December.
    4. Merton, Robert C, 1981. "On Market Timing and Investment Performance. I. An Equilibrium Theory of Value for Market Forecasts," The Journal of Business, University of Chicago Press, vol. 54(3), pages 363-406, July.
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    Cited by:

    1. ChanKyu Paik & Jinhee Choi & Ivan Ureta Vaquero, 2024. "Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator, Williams%R, and Trading Volume for the S&P 500," JRFM, MDPI, vol. 17(11), pages 1-20, November.

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