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Modeling Trade Direction

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  • Dale W. R. Rosenthal

Abstract

I propose a modeling approach to classifying trades as buys or sells. Modeled classifications consider information strengths, microstructure effects, and classification correlations. I also propose estimators for quotes prevailing at trade time. Comparisons using 2800 U.S. stocks show modeled classifications are 1%--2% more accurate than current methods across dates, sectors, and the spread. For Nasdaq and New York Stock Exchange stocks, 1% and 1.3% of improvement comes from using information strengths; 0.9% and 0.7% of improvement comes from estimating quotes. I find evidence past studies used unclean data and indications of short-term price predictability. The method may help detect destabilizing order flow. Copyright The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Suggested Citation

  • Dale W. R. Rosenthal, 2012. "Modeling Trade Direction," Journal of Financial Econometrics, Oxford University Press, vol. 10(2), pages 390-415, 2012 04.
    • Handle: RePEc:oup:jfinec:v:10:y:2012:i:2:p:390-415
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbr014
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    References listed on IDEAS

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    Cited by:

    1. Perlin, Marcelo & Brooks, Chris & Dufour, Alfonso, 2014. "On the performance of the tick test," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 42-50.
    2. Allen Carrion & Madhuparna Kolay, 2020. "Trade signing in fast markets," The Financial Review, Eastern Finance Association, vol. 55(3), pages 385-404, August.
    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. Aktas, Osman Ulas & Kryzanowski, Lawrence, 2014. "Trade classification accuracy for the BIST," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 259-282.

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    More about this item

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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