Discerning information from trade data

Easley, D; de Prado, ML; O'Hara, M

Discerning information from trade data

Easley, D de Prado, ML O'Hara, M

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Publication Type:: Journal Article
Citation:: Journal of Financial Economics, 2016, 120 (2), pp. 269 - 285
Issue Date:: 2016-05-01

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Field	Value	Language
dc.contributor.author	Easley, D	en_US
dc.contributor.author	de Prado, ML	en_US
dc.contributor.author	O'Hara, M	en_US
dc.date.issued	2016-05-01	en_US
dc.identifier.citation	Journal of Financial Economics, 2016, 120 (2), pp. 269 - 285	en_US
dc.identifier.issn	0304-405X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121971
dc.description.abstract	© 2016.Elsevier B.V. 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.	en_US
dc.relation.ispartof	Journal of Financial Economics	en_US
dc.relation.isbasedon	10.1016/j.jfineco.2016.01.018	en_US
dc.subject.classification	Finance	en_US
dc.title	Discerning information from trade data	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	120	en_US
utslib.for	1502 Banking, Finance and Investment	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1606 Political Science	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	120	en_US

Abstract:

© 2016.Elsevier B.V. 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121971