IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2605.04004.html

Structural Limits of OHLCV-Based Intraday Momentum Signals in MNQ Futures: A Systematic Falsification Study

Author

Listed:
  • Mathias Mesfin

Abstract

This paper tests whether common intraday momentum signals built from OHLCV data generate a tradable edge in Micro E-Mini Nasdaq 100 (MNQ) futures after realistic execution costs. Fourteen signal families were evaluated on 947 trading days of five-minute data from 2021-2025 under expanding-window walk-forward validation. Each signal had to clear five criteria: T-statistic >= 2.0 on out-of-sample net returns, >= 30 trades per out-of-sample fold, positive net returns after instrument-appropriate friction, consistent direction across test years (2023, 2024, 2025), and permutation p

Suggested Citation

  • Mathias Mesfin, 2026. "Structural Limits of OHLCV-Based Intraday Momentum Signals in MNQ Futures: A Systematic Falsification Study," Papers 2605.04004, arXiv.org, revised Sep 2026.
    • Handle: RePEc:arx:papers:2605.04004
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2605.04004
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Steven L. Heston & Robert A. Korajczyk & Ronnie Sadka, 2010. "Intraday Patterns in the Cross‐section of Stock Returns," Journal of Finance, American Finance Association, vol. 65(4), pages 1369-1407, August.
    2. Gao, Lei & Han, Yufeng & Zhengzi Li, Sophia & Zhou, Guofu, 2018. "Market intraday momentum," Journal of Financial Economics, Elsevier, vol. 129(2), pages 394-414.
    3. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saketh Aleti & Tim Bollerslev & Mathias Siggaard, 2025. "Intraday Market Return Predictability Culled from the Factor Zoo," Management Science, INFORMS, vol. 71(9), pages 7731-7751, September.
    2. Yuan, Xianghui & Li, Xiang, 2022. "Delta-hedging demand and intraday momentum: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    3. Kang, Junqing & Lin, Shen & Xiong, Xiong, 2022. "What drives intraday reversal? illiquidity or liquidity oversupply?," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    4. Chan Kim & Peter Reinhard Hansen, 2026. "The Quarter-Hour Effect: Periodic Algorithmic Trading and Return Predictability in Cryptocurrency Futures," Papers 2607.09426, arXiv.org, revised Jul 2026.
    5. Wen, Zhuzhu & Gong, Xu & Ma, Diandian & Xu, Yahua, 2021. "Intraday momentum and return predictability: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 95(C), pages 374-384.
    6. Yan, Tingjin & Chiu, Mei Choi & Wong, Hoi Ying, 2023. "Portfolio liquidation with delayed information," Economic Modelling, Elsevier, vol. 126(C).
    7. Rui Fan & Oleksandr Talavera & Vu Tran, 2023. "Social media and price discovery: The case of cross‐listed firms," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 151-167, February.
    8. Dinesh Gajurel & Biplob Chowdhury, 2021. "Realized Volatility, Jump and Beta: evidence from Canadian Stock Market," Applied Economics, Taylor & Francis Journals, vol. 53(55), pages 6376-6397, November.
    9. Hollstein, Fabian & Wese Simen, Chardin, 2020. "Variance risk: A bird’s eye view," Journal of Econometrics, Elsevier, vol. 215(2), pages 517-535.
    10. Gao, Ya & Han, Xing & Li, Youwei & Xiong, Xiong, 2019. "Overnight momentum, informational shocks, and late informed trading in China," International Review of Financial Analysis, Elsevier, vol. 66(C).
    11. Li, Zeming & Sakkas, Athanasios & Urquhart, Andrew, 2022. "Intraday time series momentum: Global evidence and links to market characteristics," Journal of Financial Markets, Elsevier, vol. 57(C).
    12. Baltussen, Guido & Da, Zhi & Lammers, Sten & Martens, Martin, 2021. "Hedging demand and market intraday momentum," Journal of Financial Economics, Elsevier, vol. 142(1), pages 377-403.
    13. Mitja Steinbacher & Matej Steinbacher & Matjaz Steinbacher, 2025. "Using CNN to Model Stock Prices," Computational Economics, Springer;Society for Computational Economics, vol. 66(6), pages 5299-5340, December.
    14. Giovanni Cespa & Xavier Vives, 2016. "Market Transparency and Fragility," CESifo Working Paper Series 6279, CESifo.
    15. Cespa, Giovanni & Vives, Xavier, 2017. "High Frequency Trading and Fragility," IESE Research Papers D/1161, IESE Business School.
    16. Bogousslavsky, Vincent, 2021. "The cross-section of intraday and overnight returns," Journal of Financial Economics, Elsevier, vol. 141(1), pages 172-194.
    17. Kallinterakis, Vasileios & Karaa, Rabaa, 2023. "From dusk till dawn (and vice versa): Overnight-versus-daytime reversals and feedback trading," International Review of Financial Analysis, Elsevier, vol. 85(C).
    18. Chu, Xiaojun & Gu, Zherong & Zhou, Haigang, 2019. "Intraday momentum and reversal in Chinese stock market," Finance Research Letters, Elsevier, vol. 30(C), pages 83-88.
    19. Ikhlaas Gurrib & Firuz Kamalov & Elgilani E. Alshareif, 2022. "High Frequency Return and Risk Patterns in U.S. Sector ETFs during COVID-19," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 12(5), pages 441-456, September.
    20. Bernile, Gennaro & Hu, Jianfeng & Tang, Yuehua, 2016. "Can information be locked up? Informed trading ahead of macro-news announcements," Journal of Financial Economics, Elsevier, vol. 121(3), pages 496-520.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2605.04004. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    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.