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Making Leveraged Exchange-Traded Funds Work for your Portfolio

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  • Peter Forsyth
  • Pieter van Staden
  • Yuying Li

Abstract

We examine strategically incorporating broad stock market leveraged exchange-traded funds (LETFs) into investment portfolios. We demonstrate that easily understandable and implementable strategies can enhance the risk-return profile of a portfolio containing LETFs. Our analysis shows that seemingly reasonable investment strategies may result in undesirable Omega ratios, with these effects compounding across rebalancing periods. By contrast, relatively simple dynamic strategies that systematically de-risk the portfolio once gains are observed can exploit this compounding effect, taking advantage of favorable Omega ratio dynamics. Our findings suggest that LETFs represent a valuable tool for investors employing dynamic strategies, while confirming their well-documented unsuitability for passive or static approaches.

Suggested Citation

  • Peter Forsyth & Pieter van Staden & Yuying Li, 2025. "Making Leveraged Exchange-Traded Funds Work for your Portfolio," Papers 2506.19200, arXiv.org.
  • Handle: RePEc:arx:papers:2506.19200
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    References listed on IDEAS

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    1. S. G. Kou, 2002. "A Jump-Diffusion Model for Option Pricing," Management Science, INFORMS, vol. 48(8), pages 1086-1101, August.
    2. Bansal, Vipul K. & Marshall, John F., 2015. "A tracking error approach to leveraged ETFs: Are they really that bad?," Global Finance Journal, Elsevier, vol. 26(C), pages 47-63.
    3. Dang, D.M. & Forsyth, P.A., 2016. "Better than pre-commitment mean-variance portfolio allocation strategies: A semi-self-financing Hamilton–Jacobi–Bellman equation approach," European Journal of Operational Research, Elsevier, vol. 250(3), pages 827-841.
    4. Hubert Dichtl & Wolfgang Drobetz & Martin Wambach, 2016. "Testing rebalancing strategies for stock-bond portfolios across different asset allocations," Applied Economics, Taylor & Francis Journals, vol. 48(9), pages 772-788, February.
    5. Tim Leung & Ronnie Sircar, 2015. "Implied Volatility of Leveraged ETF Options," Applied Mathematical Finance, Taylor & Francis Journals, vol. 22(2), pages 162-188, April.
    6. Paolo Guasoni & Eberhard Mayerhofer, 2023. "Leveraged funds: robust replication and performance evaluation," Quantitative Finance, Taylor & Francis Journals, vol. 23(7-8), pages 1155-1176, August.
    7. Philippe Cogneau & Valeri Zakamouline, 2013. "Block bootstrap methods and the choice of stocks for the long run," Quantitative Finance, Taylor & Francis Journals, vol. 13(9), pages 1443-1457, September.
    8. Tim Leung & Matthew Lorig & Andrea Pascucci, 2014. "Leveraged {ETF} implied volatilities from {ETF} dynamics," Papers 1404.6792, arXiv.org, revised Apr 2015.
    9. Li, Yuying & Forsyth, Peter A., 2019. "A data-driven neural network approach to optimal asset allocation for target based defined contribution pension plans," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 189-204.
    10. Anarkulova, Aizhan & Cederburg, Scott & O’Doherty, Michael S., 2022. "Stocks for the long run? Evidence from a broad sample of developed markets," Journal of Financial Economics, Elsevier, vol. 143(1), pages 409-433.
    11. Pieter van Staden & Peter Forsyth & Yuying Li, 2024. "Smart leverage? Rethinking the role of Leveraged Exchange Traded Funds in constructing portfolios to beat a benchmark," Papers 2412.05431, arXiv.org, revised Mar 2025.
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