IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/125307.html
   My bibliography  Save this paper

Can NASDAQ-100 derivatives ETF portfolio beat QQQ?

Author

Listed:
  • Lo, Chi-Sheng

Abstract

This study explores whether a NASDAQ-100 derivatives ETF portfolio can outperform the Invesco QQQ Trust (QQQ) using a Deep Reinforcement Learning framework based on Proximal Policy Optimization (PPO). The portfolio dynamically allocates across three NASDAQ-100 derivative ETFs: YQQQ (short options income), QYLD (covered calls), and TQQQ (3x leveraged), employing Isolation Forest anomaly detection to optimize rebalancing timing. A train-validation-test framework (2010-2018 training, 2019-2023 validation, 2024-2025 testing) utilizes a multi-objective function to balance tracking error minimization and excess return maximization, integrating dividend payments and quarterly with event-driven rebalancing. The results show significant alpha generation over QQQ by leveraging YQQQ’s inverse exposure, QYLD’s income stability, and TQQQ’s leveraged growth. Though experiencing higher volatility and drawdowns, the PPO agent skillfully optimizes allocations, achieving positive excess returns in the testing phase, with performance varying by market condition, emphasizing the need for adaptive strategies in dynamic markets.

Suggested Citation

  • Lo, Chi-Sheng, 2025. "Can NASDAQ-100 derivatives ETF portfolio beat QQQ?," MPRA Paper 125307, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:125307
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/125307/1/MPRA_paper_125307.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Deep reinforcement learning; enhanced index tracking; isolation forest; QQQ; Nasdaq 100; exchange traded fund; options derivatives;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:125307. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.