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Modeling Trade Direction

Author

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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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    Cited by:

    1. Jurkatis, Simon, 2022. "Inferring trade directions in fast markets," Journal of Financial Markets, Elsevier, vol. 58(C).
    2. 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.
    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. Allen Carrion & Madhuparna Kolay, 2020. "Trade signing in fast markets," The Financial Review, Eastern Finance Association, vol. 55(3), pages 385-404, August.
    5. 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.

    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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