IDEAS home Printed from https://ideas.repec.org/a/eee/jbfina/v103y2019icp113-129.html
   My bibliography  Save this article

Bulk volume classification and information detection

Author

Listed:
  • Panayides, Marios A.
  • Shohfi, Thomas D.
  • Smith, Jared D.

Abstract

Using European stock data from two different venues and time periods for which we can identify each trade's aggressor, we test the performance of the bulk volume classification (Easley et al. (2016); BVC) algorithm. BVC is data efficient, but may identify trade aggressors less accurately than “bulk” versions of traditional trade-level algorithms. BVC-estimated trade flow is the only algorithm related to proxies of informed trading, however. This is because traditional algorithms are designed to find individual trade aggressors, but we find that trade aggressor no longer captures information. Finally, we find that after calibrating BVC to trading characteristics in out-of-sample data, it is better able to detect information and to identify trade aggressors. In the new era of fast trading, sophisticated investors, and smart order execution, BVC appears to be the most versatile algorithm.

Suggested Citation

  • Panayides, Marios A. & Shohfi, Thomas D. & Smith, Jared D., 2019. "Bulk volume classification and information detection," Journal of Banking & Finance, Elsevier, vol. 103(C), pages 113-129.
    • Handle: RePEc:eee:jbfina:v:103:y:2019:i:c:p:113-129
    DOI: 10.1016/j.jbankfin.2019.04.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378426619300858
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbankfin.2019.04.001?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. Michael Frömmel & Eyup Kadioglu, 2023. "Impact of trading hours extensions on foreign exchange volatility: intraday evidence from the Moscow exchange," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    2. Frömmel, Michael & D'Hoore, Dick & Lampaert, Kevin, 2021. "The Accuracy of Trade Classification Systems on the Foreign Exchange Market: Evidence from the RUB/USD Market," Finance Research Letters, Elsevier, vol. 42(C).
    3. Mark Fedenia & Tavy Ronen & Seunghan Nam, 2024. "Machine learning and trade direction classification: insights from the corporate bond market," Review of Quantitative Finance and Accounting, Springer, vol. 63(1), pages 1-36, July.
    4. Jurkatis, Simon, 2022. "Inferring trade directions in fast markets," Journal of Financial Markets, Elsevier, vol. 58(C).
    5. Su, Fei, 2021. "Conditional volatility persistence and volatility spillovers in the foreign exchange market," Research in International Business and Finance, Elsevier, vol. 55(C).
    6. Allen Carrion & Madhuparna Kolay, 2020. "Trade signing in fast markets," The Financial Review, Eastern Finance Association, vol. 55(3), pages 385-404, August.

    More about this item

    Keywords

    Classification algorithms; Bulk volume; Informed trading strategies;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

    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:eee:jbfina:v:103:y:2019:i:c:p:113-129. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbf .

    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.