IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v50y2018i59p6397-6405.html
   My bibliography  Save this article

Trading by estimating the quantized forward distribution

Author

Listed:
  • Attila Ceffer
  • Norbert Fogarasi
  • Janos Levendovszky

Abstract

In this article, a novel algorithm is developed for electronic trading on financial time series. The new method uses quantization and volatility information together with feedforward neural networks for achieving high-frequency trading (HFT). The proposed procedures are based on estimating the Forward Conditional Probability Distribution (FCPD) of the quantized return values. From past samples, the conditional expected value can be learned, from which FCPD can be obtained by using a special encoding scheme. Based on this estimation, a trading signal is triggered if the probability of price change becomes significant as measured by a quadratic criterion. Due to the encoding scheme and quantization, the complexity of learning and estimation has been reduced for HFT. Extensive numerical analysis has been performed on financial time series and the new method has proven to be profitable on mid-prices. In order to beat the secondary effects, we focus on the most liquid assets, on which we managed to achieve positive profits.

Suggested Citation

  • Attila Ceffer & Norbert Fogarasi & Janos Levendovszky, 2018. "Trading by estimating the quantized forward distribution," Applied Economics, Taylor & Francis Journals, vol. 50(59), pages 6397-6405, December.
    • Handle: RePEc:taf:applec:v:50:y:2018:i:59:p:6397-6405
    DOI: 10.1080/00036846.2018.1486021
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2018.1486021
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2018.1486021?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arumugam, Devika & Prasanna, P. Krishna & Marathe, Rahul R., 2023. "Do algorithmic traders exploit volatility?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    2. Arumugam, Devika, 2023. "Algorithmic trading: Intraday profitability and trading behavior," Economic Modelling, Elsevier, vol. 128(C).

    More about this item

    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:taf:applec:v:50:y:2018:i:59:p:6397-6405. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    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.