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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
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    Citations

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    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, 2020. "Inferring trade directions in fast markets," Bank of England working papers 896, Bank of England.
    5. Jurkatis, Simon, 2022. "Inferring trade directions in fast markets," Journal of Financial Markets, Elsevier, vol. 58(C).
    6. Su, Fei, 2021. "Conditional volatility persistence and volatility spillovers in the foreign exchange market," Research in International Business and Finance, Elsevier, vol. 55(C).
    7. 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

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