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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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    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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