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Can NASDAQ-100 derivatives ETF portfolio beat QQQ?

Author

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  • Chi-Sheng Lo

    (Institute of Information Science, Academia Sinica)

Abstract

Portfolio optimization in derivative ETF markets presents complex challenges in balancing competing objectives across instruments with fundamentally different risk-return profiles. This paper constructs a portfolio strategy to optimize NASDAQ-100 derivative ETF allocations by balancing tracking error minimization relative to the Invesco QQQ Trust (QQQ) with excess return maximization. The approach dynamically allocates investments across three specialized ETFs: a short-position fund (YQQQ), an income-focused covered-call fund (QYLD), and a triple-leveraged fund (TQQQ). Using a deep reinforcement learning (DRL) framework, the strategy applies anomaly detection to optimize rebalancing timing, incorporating dividend payments to enhance returns. The approach achieves positive excess returns across all evaluation periods, though risk-adjusted performance progressively deteriorates from substantial outperformance during training to underperformance during testing. This progression reveals both the potential and limitations of reinforcement learning approaches for multi-objective portfolio optimization when encountering evolving market conditions.

Suggested Citation

  • Chi-Sheng Lo, 2025. "Can NASDAQ-100 derivatives ETF portfolio beat QQQ?," Economics Bulletin, AccessEcon, vol. 45(4), pages 1636-1648.
    • Handle: RePEc:ebl:ecbull:eb-25-00515
    as

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    References listed on IDEAS

    as
    1. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    2. Cui, Tianxiang & Du, Nanjiang & Yang, Xiaoying & Ding, Shusheng, 2024. "Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    3. Crama, Y. & Schyns, M., 2003. "Simulated annealing for complex portfolio selection problems," European Journal of Operational Research, Elsevier, vol. 150(3), pages 546-571, November.
    4. Guastaroba, G. & Speranza, M.G., 2012. "Kernel Search: An application to the index tracking problem," European Journal of Operational Research, Elsevier, vol. 217(1), pages 54-68.
    5. Ayman Chaouki & Stephen Hardiman & Christian Schmidt & Emmanuel S'eri'e & Joachim de Lataillade, 2020. "Deep Deterministic Portfolio Optimization," Papers 2003.06497, arXiv.org, revised Apr 2020.
    6. de Paulo, Wanderlei Lima & de Oliveira, Estela Mara & do Valle Costa, Oswaldo Luiz, 2016. "Enhanced index tracking optimal portfolio selection," Finance Research Letters, Elsevier, vol. 16(C), pages 93-102.
    7. Rudolf, Markus & Wolter, Hans-Jurgen & Zimmermann, Heinz, 1999. "A linear model for tracking error minimization," Journal of Banking & Finance, Elsevier, vol. 23(1), pages 85-103, January.
    8. Canakgoz, N.A. & Beasley, J.E., 2009. "Mixed-integer programming approaches for index tracking and enhanced indexation," European Journal of Operational Research, Elsevier, vol. 196(1), pages 384-399, July.
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    Keywords

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    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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