Discerning Information from Trade Data
Cornell University - Department of Economics
Marcos Lopez de Prado
Guggenheim Partners, LLC; Lawrence Berkeley National Laboratory; Harvard University - RCC
Cornell University - Samuel Curtis Johnson Graduate School of Management
Johnson School Research Paper Series No. 8-2012
How best to discern trading intentions from market data? We examine the accuracy of three methods for classifying trade data: bulk volume classification (BVC), Tick Rule and Aggregated Tick Rule. We develop a Bayesian model of inferring information from trade executions, and show the conditions in which tick rules or bulk volume classification will predominate.
Empirically, we find that Tick rule approaches and BVC are relatively good classifiers of the aggressor side of trading, but bulk volume classifications are better linked to proxies of information-based trading. Thus, BVC would appear to be a useful tool for discerning trading intentions from market data.
Number of Pages in PDF File: 56
Keywords: Trade Classification, Bulk Volume Classification, flow toxicity, volume imbalance, market microstructure
JEL Classification: C02, D52, D53, G14
Date posted: January 23, 2012 ; Last revised: March 19, 2015
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