Inferring trade direction from intraday data

Discussion in 'Automated Trading' started by stochastix, May 28, 2017.

  1. stargrazer likes this.
  2. Lol.
     
    lawrence-lugar likes this.
  3. what's so funny?
     
  4. Short Sales, Long Sales, and the Lee-Ready Trade Classification Algorithm Revisited

    Researchers are increasingly using data from the Nasdaq market to examine pricing behavior, market design, and other microstructure phenomena. The validity of any study that classifies trades as buys or sells depends on the accuracy of the classification method. Using a proprietary data set from Nasdaq that identifies trade direction, we examine the validity of several trade classification algorithms. We find that the quote rule, the tick rule and the Lee-Ready (1991) rule correctly classify 76.4%, 77.66% and 81.05% of the trades, respectively. However, all classification rules have only a very limited success in classifying trades executed inside the quotes, introducing a bias in the accuracy of classifying large trades, trades during high volume periods, and ECN trades. We also find that extant algorithms do a mediocre job when used for calculating effective spreads. We propose a new and simple classification algorithm for Nasdaq trades that improves over extant algorithms.
     
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