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Modeling Trade Direction

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  • Rosenthal, Dale W.R.

Abstract

The problem of classifying trades as buys or sells is examined. I propose estimated quotes for midpoint and bid/ask tests and a modeling approach to classification. Prevailing quotes are estimated using flexible approximations to the distribution for delays of quotes relative to trade timestamps. Classification is done by a generalized linear model which includes improved versions of midpoint, tick, and bid/ask tests. The model also considers the relative strengths of these tests, can account for market microstructure peculiarities, and allows for autocorrelations and cross-correlations in trade direction. The correlation modeling corrects for pseudoreplication, yielding more accurate standard errors and fixed effect estimates. Further, the model estimates probabilities of correct classification. The model is compared to various trade classification methods using a sample of 2,836 domestic US stocks from an unexplored, recent, and readily-available dataset. Out of sample, modeled classifications are 1-2% more accurate overall than current methods; this improvement is consistent across dates, sectors, and locations relative to the inside quote. For Nasdaq and NYSE stocks, 1% and 1.3% of the improvement comes from using relative strengths of the various tests; 0.9% and 0.7% of the improvement, respectively, comes from using some form of estimated quotes. For AMEX stocks, a 0.4% improvement is attributed to using a lagged version of the bid/ask test. I also find indications of short- and ultra-short-term alpha.

Suggested Citation

  • Rosenthal, Dale W.R., 2008. "Modeling Trade Direction," MPRA Paper 10209, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:10209
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    References listed on IDEAS

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    2. Olivier Vergote, 2005. "How to Match Trades and Quotes for Nyse Stocks?," Working Papers of Department of Economics, Leuven ces0510, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
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    15. Steven Caudill & Beverly Marshall & Jacqueline Garner, 2004. "Improved trade classification rules: Estimates using a logit model based on misclassified data," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 32(3), pages 256-256, September.
    16. Peterson, Mark & Sirri, Erik, 2003. "Evaluation of the biases in execution cost estimation using trade and quote data," Journal of Financial Markets, Elsevier, vol. 6(3), pages 259-280, May.
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    Cited by:

    1. Jurkatis, Simon, 2020. "Inferring trade directions in fast markets," Bank of England working papers 896, Bank of England.
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    3. 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.
    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.

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    More about this item

    Keywords

    market microstructure; trade classification; generalized linear mixed model; ultra-high-frequency data analysis;
    All these keywords.

    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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