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Trade classification accuracy for the BIST

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  • Aktas, Osman Ulas
  • Kryzanowski, Lawrence

Abstract

The accuracy of five algorithms for classifying trades as buyer- or seller-initiated is assessed for BIST-30 index constituents over a period including the Lehman collapse. The highest classification accuracy rate (over 95%) is for the one-second lagged Lee & Ready (LR) algorithm. The LR's classification accuracy is highest (lowest) for trades representing mixed agency and principal (pure principal) relations between clients and executing brokers. Unlike for U.S. markets, almost all trades are classifiable with accuracy rates of 90-plus percent for both long and short trades. As for U.S. markets, higher misclassification rates occur for trades in the first versus last 30min of the trading day, as the time between consecutive trades decreases, and for decreasing trade sizes.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:intfin:v:33:y:2014:i:c:p:259-282
    DOI: 10.1016/j.intfin.2014.08.003
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    References listed on IDEAS

    as
    1. Glosten, Lawrence R. & Harris, Lawrence E., 1988. "Estimating the components of the bid/ask spread," Journal of Financial Economics, Elsevier, vol. 21(1), pages 123-142, May.
    2. Michael J. Barclay & Terrence Hendershott & D. Timothy McCormick, 2003. "Competition among Trading Venues: Information and Trading on Electronic Communications Networks," Journal of Finance, American Finance Association, vol. 58(6), pages 2637-2666, December.
    3. Marcel Blais & Philip Protter, 2012. "Signing trades and an evaluation of the Lee–Ready algorithm," Annals of Finance, Springer, vol. 8(1), pages 1-13, February.
    4. Boehmer, Ekkehart & Grammig, Joachim & Theissen, Erik, 2007. "Estimating the probability of informed trading--does trade misclassification matter?," Journal of Financial Markets, Elsevier, vol. 10(1), pages 26-47, February.
    5. Bessembinder, Hendrik, 2003. "Issues in assessing trade execution costs," Journal of Financial Markets, Elsevier, vol. 6(3), pages 233-257, May.
    6. Savickas, Robert & Wilson, Arthur J., 2003. "On Inferring the Direction of Option Trades," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 38(4), pages 881-902, December.
    7. Lesmond, David A. & Schill, Michael J. & Zhou, Chunsheng, 2004. "The illusory nature of momentum profits," Journal of Financial Economics, Elsevier, vol. 71(2), pages 349-380, February.
    8. Madhavan, Ananth & Richardson, Matthew & Roomans, Mark, 1997. "Why Do Security Prices Change? A Transaction-Level Analysis of NYSE Stocks," Review of Financial Studies, Society for Financial Studies, vol. 10(4), pages 1035-1064.
    9. Ahn, Hee-Joon & Cheung, Yan-Leung, 1999. "The intraday patterns of the spread and depth in a market without market makers: The Stock Exchange of Hong Kong," Pacific-Basin Finance Journal, Elsevier, vol. 7(5), pages 539-556, December.
    10. Asquith, Paul & Oman, Rebecca & Safaya, Christopher, 2010. "Short sales and trade classification algorithms," Journal of Financial Markets, Elsevier, vol. 13(1), pages 157-173, February.
    11. Theissen, Erik, 2001. "A test of the accuracy of the Lee/Ready trade classification algorithm," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 11(2), pages 147-165, June.
    12. Blume, Marshall E & MacKinley, A Craig & Terker, Bruce, 1989. " Order Imbalances and Stock Price Movements on October 19 and 20, 1987," Journal of Finance, American Finance Association, vol. 44(4), pages 827-848, September.
    13. Finucane, Thomas J., 2000. "A Direct Test of Methods for Inferring Trade Direction from Intra-Day Data," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(4), pages 553-576, December.
    14. Dale W. R. Rosenthal, 2012. "Modeling Trade Direction," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 10(2), pages 390-415, 2012 04.
    15. Lauren Cohen & Karl B. Diether & Christopher J. Malloy, 2007. "Supply and Demand Shifts in the Shorting Market," Journal of Finance, American Finance Association, vol. 62(5), pages 2061-2096, October.
    16. Paul Schultz, 2000. "Stock Splits, Tick Size, and Sponsorship," Journal of Finance, American Finance Association, vol. 55(1), pages 429-450, February.
    17. 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.
    18. Hasbrouck, Joel, 1988. "Trades, quotes, inventories, and information," Journal of Financial Economics, Elsevier, vol. 22(2), pages 229-252, December.
    19. Chakrabarty, Bidisha & Li, Bingguang & Nguyen, Vanthuan & Van Ness, Robert A., 2007. "Trade classification algorithms for electronic communications network trades," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3806-3821, December.
    20. Lawrence Kryzanowski & Hao Zhang, 1996. "Trading Patterns Of Small And Large Traders Around Stock Split Ex‐Dates," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 19(1), pages 75-90, March.
    21. Michael W. Brandt & Alon Brav & John R. Graham & Alok Kumar, 2010. "The Idiosyncratic Volatility Puzzle: Time Trend or Speculative Episodes?," Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 863-899, February.
    22. 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(4), pages 529-551, December.
    23. 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.
    24. Easley, David, et al, 1996. "Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-1436, September.
    25. Lee, Charles M. C. & Radhakrishna, Balkrishna, 2000. "Inferring investor behavior: Evidence from TORQ data," Journal of Financial Markets, Elsevier, vol. 3(2), pages 83-111, May.
    26. 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.
    27. Easley, David & O'Hara, Maureen, 1992. "Time and the Process of Security Price Adjustment," Journal of Finance, American Finance Association, vol. 47(2), pages 576-605, June.
    28. Aitken, Michael & Frino, Alex, 1996. "The accuracy of the tick test: Evidence from the Australian stock exchange," Journal of Banking & Finance, Elsevier, vol. 20(10), pages 1715-1729, December.
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    More about this item

    Keywords

    Trade classification algorithms; Market microstructure; Developing stock market; Short sales; Agency relations;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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