IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v60y2024ics1544612323013089.html
   My bibliography  Save this article

Exploiting the potential of a directional changes-based trading algorithm in the stock market

Author

Listed:
  • Ao, Han
  • Li, Munan

Abstract

The performance of Directional Change based trading algorithms may be distorted due to omitting the difference between theoretical and actual Directional Change confirmations. The impact of the difference could be so vast so that trading algorithms are seemingly unusable. Applying theoretical confirmations instead of actual ones, we show the potential of these trading algorithms. Among 100 stocks, the number of overall-profit gains has improved from 61 to a theoretical 100, and a theoretical daily 0.1 % rate of return could be expected. The algorithm's overall return is related to the frequency of its trades. When designing a trading algorithm, the distortion should be considered. A threshold that minimizes the difference between actual and theoretical confirmations should be preferred so that the actual results could be closer to its theoretical values.

Suggested Citation

  • Ao, Han & Li, Munan, 2024. "Exploiting the potential of a directional changes-based trading algorithm in the stock market," Finance Research Letters, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:finlet:v:60:y:2024:i:c:s1544612323013089
    DOI: 10.1016/j.frl.2023.104936
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323013089
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.104936?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hatch, Brian C. & Johnson, Shane A. & Wang, Qin Emma & Zhang, Jun, 2021. "Algorithmic trading and firm value," Journal of Banking & Finance, Elsevier, vol. 125(C).
    2. Mestel, Roland & Murg, Michael & Theissen, Erik, 2018. "Algorithmic trading and liquidity: Long term evidence from Austria," Finance Research Letters, Elsevier, vol. 26(C), pages 198-203.
    3. Conlon, Thomas & Corbet, Shaen & McGee, Richard J., 2024. "The Bitcoin volume-volatility relationship: A high frequency analysis of futures and spot exchanges," International Review of Financial Analysis, Elsevier, vol. 91(C).
    4. Jarrow, Robert A., 2022. "High frequency trading and standard asset pricing models," Finance Research Letters, Elsevier, vol. 49(C).
    5. Snehal Banerjee & Ilan Kremer, 2010. "Disagreement and Learning: Dynamic Patterns of Trade," Journal of Finance, American Finance Association, vol. 65(4), pages 1269-1302, August.
    6. Markus Baldauf & Joshua Mollner, 2020. "High‐Frequency Trading and Market Performance," Journal of Finance, American Finance Association, vol. 75(3), pages 1495-1526, June.
    7. Ma, Junjun & Xiong, Xiong & He, Feng & Zhang, Wei, 2017. "Volatility measurement with directional change in Chinese stock market: Statistical property and investment strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 169-180.
    8. V. Petrov & A. Golub & R. Olsen, 2020. "Agent-based modelling in directional-change intrinsic time," Quantitative Finance, Taylor & Francis Journals, vol. 20(3), pages 463-482, March.
    9. repec:grz:wpsses:2018-03 is not listed on IDEAS
    10. Shengnan Li & Edward P. K. Tsang & John O'Hara, 2022. "Measuring relative volatility in high‐frequency data under the directional change approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(2), pages 86-102, April.
    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. Xu, Liao & Xu, Lu & Zhao, Jing & Zhao, Yang, 2020. "Information-based trading and information propagation: Evidence from the exchange traded fund market," International Review of Financial Analysis, Elsevier, vol. 70(C).
    2. Zhou, Deqing & Wang, Wenjie, 2020. "Insider, outsider and information heterogeneity," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    3. Tim Bollerslev & Jia Li & Yuan Xue, 2018. "Volume, Volatility, and Public News Announcements," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(4), pages 2005-2041.
    4. Sujoy Mukerji & Han N. Ozsoylev & Jean‐Marc Tallon, 2023. "Trading Ambiguity: A Tale Of Two Heterogeneities," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1127-1164, August.
    5. Khapko, Mariana & Zoican, Marius, 2021. "Do speed bumps curb low-latency investment? Evidence from a laboratory market," Journal of Financial Markets, Elsevier, vol. 55(C).
    6. Muñoz, Francisco, 2013. "Liquidity and firm investment: Evidence for Latin America," Journal of Empirical Finance, Elsevier, vol. 20(C), pages 18-29.
    7. Mark Wong & Adrian Wai Kong Cheung & Wei Hu, 2021. "When two anomalies meet: Volume and timing effects on earnings announcements," The Financial Review, Eastern Finance Association, vol. 56(2), pages 355-380, May.
    8. Peress, Joel & Schmidt, Daniel, 2021. "Noise traders incarnate: Describing a realistic noise trading process," Journal of Financial Markets, Elsevier, vol. 54(C).
    9. Erik Eyster & Matthew Rabin & Dimitri Vayanos, 2019. "Financial Markets Where Traders Neglect the Informational Content of Prices," Journal of Finance, American Finance Association, vol. 74(1), pages 371-399, February.
    10. Wayne B. Thomas & Yiding Wang & Ling Zhang, 2024. "Algorithmic Trading and Forward‐Looking MD&A Disclosures," Journal of Accounting Research, Wiley Blackwell, vol. 62(4), pages 1533-1569, September.
    11. Bergemann, Dirk & Ottaviani, Marco, 2021. "Information Markets and Nonmarkets," CEPR Discussion Papers 16459, C.E.P.R. Discussion Papers.
    12. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    13. Schmitt, Noemi & Westerhoff, Frank, 2014. "Speculative behavior and the dynamics of interacting stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 262-288.
    14. Sagade, Satchit & Scharnowski, Stefan & Westheide, Christian, 2022. "Broker colocation and the execution costs of customer and proprietary orders," SAFE Working Paper Series 366, Leibniz Institute for Financial Research SAFE.
    15. Kapetanios, George & Mitchell, James & Shin, Yongcheol, 2014. "A nonlinear panel data model of cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 179(2), pages 134-157.
    16. Hibbert, Ann Marie & Kang, Qiang & Kumar, Alok & Mishra, Suchi, 2020. "Heterogeneous beliefs and return volatility around seasoned equity offerings," Journal of Financial Economics, Elsevier, vol. 137(2), pages 571-589.
    17. ter Ellen, Saskia & Verschoor, Willem F.C. & Zwinkels, Remco C.J., 2019. "Agreeing on disagreement: Heterogeneity or uncertainty?," Journal of Financial Markets, Elsevier, vol. 44(C), pages 17-30.
    18. Zohar, Osnat, 2024. "Cyclicality of uncertainty and disagreement," Journal of Monetary Economics, Elsevier, vol. 143(C).
    19. Chen, Tao, 2020. "Does news affect disagreement in global markets?," Journal of Business Research, Elsevier, vol. 109(C), pages 174-183.
    20. Mark Marner-Hausen, 2022. "Developing a Framework for Real-Time Trading in a Laboratory Financial Market," ECONtribute Discussion Papers Series 172, University of Bonn and University of Cologne, Germany.

    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:eee:finlet:v:60:y:2024:i:c:s1544612323013089. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.