IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/10209.html
   My bibliography  Save this paper

Modeling Trade Direction

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/10209/1/MPRA_paper_10209.pdf
    File Function: original version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/36784/1/MPRA_paper_36784.pdf
    File Function: revised version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/40598/1/MPRA_paper_40598.pdf
    File Function: revised version
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Olivier Vergote, 2005. "How to Match Trades and Quotes for Nyse Stocks?," Working Papers Department of Economics ces0510, KU Leuven, Faculty of Economics and Business, Department of Economics.
    3. Kauermann G. & Carroll R.J., 2001. "A Note on the Efficiency of Sandwich Covariance Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1387-1396, December.
    4. Keim, Donald B & Madhaven, Ananth, 1996. "The Upstairs Market for Large-Block Transactions: Analysis and Measurement of Price Effects," Review of Financial Studies, Society for Financial Studies, vol. 9(1), pages 1-36.
    5. Hans R. Stoll, 2006. "Electronic Trading in Stock Markets," Journal of Economic Perspectives, American Economic Association, vol. 20(1), pages 153-174, Winter.
    6. Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    7. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    8. Henker, Thomas & Wang, Jian-Xin, 2006. "On the importance of timing specifications in market microstructure research," Journal of Financial Markets, Elsevier, vol. 9(2), pages 162-179, May.
    9. Ellis, Katrina & Michaely, Roni & O'Hara, Maureen, 2000. "The Accuracy of Trade Classification Rules: Evidence from Nasdaq," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(04), pages 529-551, December.
    10. 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.
    11. 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.
    12. Stoll, Hans R. & Schenzler, Christoph, 2006. "Trades outside the quotes: Reporting delay, trading option, or trade size?," Journal of Financial Economics, Elsevier, vol. 79(3), pages 615-653, March.
    13. George C. Chacko & Jakub W. Jurek & Erik Stafford, 2008. "The Price of Immediacy," Journal of Finance, American Finance Association, vol. 63(3), pages 1253-1290, June.
    14. Odders-White, Elizabeth R., 2000. "On the occurrence and consequences of inaccurate trade classification," Journal of Financial Markets, Elsevier, vol. 3(3), pages 259-286, August.
    15. Boehmer, Ekkehart, 2005. "Dimensions of execution quality: Recent evidence for US equity markets," Journal of Financial Economics, Elsevier, vol. 78(3), pages 553-582, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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

    Keywords

    market microstructure; trade classification; generalized linear mixed model; ultra-high-frequency data analysis;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:10209. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.