Discerning information from trade data

• 成果类型: Article
• 署名作者: Easley, David; de Prado, Marcos Lopez; O'Hara, Maureen
• 署名单位: Cornell University; Guggenheim Partners, LLC; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Cornell University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2016.01.018
• 发表日期: 2016
• 页码: 269-285
• 关键词: Trade classification; Bulk volume classification; FLOW TOXICITY; Volume imbalance; Market microstructure

摘要

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 under which tick rules or bulk volume classification predominates. 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. (C) 2016 Elsevier B.V. All rights reserved.