IDEAS home Printed from https://ideas.repec.org/a/eee/jfinec/v120y2016i2p269-285.html
   My bibliography  Save this article

Discerning information from trade data

Author

Listed:
  • Easley, David
  • de Prado, Marcos Lopez
  • O'Hara, Maureen

Abstract

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.

Suggested Citation

  • Easley, David & de Prado, Marcos Lopez & O'Hara, Maureen, 2016. "Discerning information from trade data," Journal of Financial Economics, Elsevier, vol. 120(2), pages 269-285.
    • Handle: RePEc:eee:jfinec:v:120:y:2016:i:2:p:269-285
    DOI: 10.1016/j.jfineco.2016.01.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304405X16000246
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jfineco.2016.01.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hasbrouck, Joel & Saar, Gideon, 2009. "Technology and liquidity provision: The blurring of traditional definitions," Journal of Financial Markets, Elsevier, vol. 12(2), pages 143-172, May.
    2. Pierre Collin-Dufresne & Vyacheslav Fos, 2015. "Do Prices Reveal the Presence of Informed Trading?," Journal of Finance, American Finance Association, vol. 70(4), pages 1555-1582, August.
    3. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, vol. 18(3), pages 421-438.
    4. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    5. Shane A. Corwin & Paul Schultz, 2012. "A Simple Way to Estimate Bid‐Ask Spreads from Daily High and Low Prices," Journal of Finance, American Finance Association, vol. 67(2), pages 719-760, April.
    6. Torben G. Andersen & Oleg Bondarenko, 2015. "Assessing Measures of Order Flow Toxicity and Early Warning Signals for Market Turbulence," Review of Finance, European Finance Association, vol. 19(1), pages 1-54.
    7. Benoit Mandelbrot & Howard M. Taylor, 1967. "On the Distribution of Stock Price Differences," Operations Research, INFORMS, vol. 15(6), pages 1057-1062, December.
    8. Zoltán Eisler & Jean-Philippe Bouchaud & Julien Kockelkoren, 2012. "The price impact of order book events: market orders, limit orders and cancellations," Quantitative Finance, Taylor & Francis Journals, vol. 12(9), pages 1395-1419, September.
    9. Chakrabarty, Bidisha & Pascual, Roberto & Shkilko, Andriy, 2015. "Evaluating trade classification algorithms: Bulk volume classification versus the tick rule and the Lee-Ready algorithm," Journal of Financial Markets, Elsevier, vol. 25(C), pages 52-79.
    10. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    11. Bloomfield, Robert & O'Hara, Maureen & Saar, Gideon, 2005. "The "make or take" decision in an electronic market: Evidence on the evolution of liquidity," Journal of Financial Economics, Elsevier, vol. 75(1), pages 165-199, January.
    12. Craig W. Holden & Stacey Jacobsen, 2014. "Liquidity Measurement Problems in Fast, Competitive Markets: Expensive and Cheap Solutions," Journal of Finance, American Finance Association, vol. 69(4), pages 1747-1785, August.
    13. Ellis, Katrina & Michaely, Roni & O'Hara, Maureen, 2000. "The Accuracy of Trade Classification Rules: Evidence from Nasdaq," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(4), pages 529-551, December.
    14. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    15. Michael Goldstein & Shengwei Ding & John Hanna & Terrence Hendershott, 2014. "How Slow Is the NBBO? A Comparison with Direct Exchange Feeds," The Financial Review, Eastern Finance Association, vol. 49(2), pages 313-332, May.
    16. Lee, Charles M. C. & Radhakrishna, Balkrishna, 2000. "Inferring investor behavior: Evidence from TORQ data," Journal of Financial Markets, Elsevier, vol. 3(2), pages 83-111, May.
    17. Pierre Collin-Dufresne & Vyacheslav Fos, 2013. "Do Prices Reveal the Presence of Informed Trading?," Swiss Finance Institute Research Paper Series 13-69, Swiss Finance Institute, revised Sep 2015.
    18. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tarun Chordia & Jianfeng Hu & Avanidhar Subrahmanyam & Qing Tong, 2019. "Order Flow Volatility and Equity Costs of Capital," Management Science, INFORMS, vol. 65(4), pages 1520-1551, April.
    2. Acheson, Graeme G. & Coyle, Christopher & Turner, John D., 2018. "Prices and informed trading: Evidence from an early stock market," QUCEH Working Paper Series 2018-05, Queen's University Belfast, Queen's University Centre for Economic History.
    3. Chakrabarty, Bidisha & Pascual, Roberto & Shkilko, Andriy, 2015. "Evaluating trade classification algorithms: Bulk volume classification versus the tick rule and the Lee-Ready algorithm," Journal of Financial Markets, Elsevier, vol. 25(C), pages 52-79.
    4. Rzayev, Khaladdin & Ibikunle, Gbenga, 2019. "A state-space modeling of the information content of trading volume," Journal of Financial Markets, Elsevier, vol. 46(C).
    5. Jurkatis, Simon, 2022. "Inferring trade directions in fast markets," Journal of Financial Markets, Elsevier, vol. 58(C).
    6. Abad, David & Massot, Magdalena & Pascual, Roberto, 2018. "Evaluating VPIN as a trigger for single-stock circuit breakers," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 21-36.
    7. Allen Carrion & Madhuparna Kolay, 2020. "Trade signing in fast markets," The Financial Review, Eastern Finance Association, vol. 55(3), pages 385-404, August.
    8. Haghighi, Afshin & Zhang, Lei & Oliver, Barry & Faff, Robert, 2023. "Information acquisition and market liquidity: Evidence from EDGAR search activity," Global Finance Journal, Elsevier, vol. 57(C).
    9. Travis L. Johnson & Eric C. So, 2018. "A Simple Multimarket Measure of Information Asymmetry," Management Science, INFORMS, vol. 64(3), pages 1055-1080, March.
    10. Borochin, Paul & Rush, Stephen, 2022. "Information networks in the financial sector and systemic risk," Journal of Banking & Finance, Elsevier, vol. 134(C).
    11. Craig W. Holden & Stacey Jacobsen & Avanidhar Subrahmanyam, 2014. "The Empirical Analysis of Liquidity," Foundations and Trends(R) in Finance, now publishers, vol. 8(4), pages 263-365, December.
    12. Emiliano Pagnotta, 2016. "Chasing Private Information," 2016 Meeting Papers 1673, Society for Economic Dynamics.
    13. Joel Peress & Daniel Schmidt, 2020. "Glued to the TV: Distracted Noise Traders and Stock Market Liquidity," Journal of Finance, American Finance Association, vol. 75(2), pages 1083-1133, April.
    14. Lof, Matthijs & van Bommel, Jos, 2023. "Asymmetric information and the distribution of trading volume," Journal of Corporate Finance, Elsevier, vol. 82(C).
    15. Peress, Joel & Schmidt, Daniel, 2021. "Noise traders incarnate: Describing a realistic noise trading process," Journal of Financial Markets, Elsevier, vol. 54(C).
    16. Hagströmer, Björn, 2021. "Bias in the effective bid-ask spread," Journal of Financial Economics, Elsevier, vol. 142(1), pages 314-337.
    17. Frömmel, Michael & D'Hoore, Dick & Lampaert, Kevin, 2021. "The Accuracy of Trade Classification Systems on the Foreign Exchange Market: Evidence from the RUB/USD Market," Finance Research Letters, Elsevier, vol. 42(C).
    18. Zeynep Cobandag Guloglu & Cumhur Ekinci, 2022. "Liquidity measurement: A comparative review of the literature with a focus on high frequency," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 41-74, February.
    19. Blankespoor, Elizabeth & deHaan, Ed & Marinovic, Iván, 2020. "Disclosure processing costs, investors’ information choice, and equity market outcomes: A review," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    20. Marshall, Ben R. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2018. "Do liquidity proxies measure liquidity accurately in ETFs?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 94-111.

    More about this item

    Keywords

    Trade classification; Bulk volume classification; Flow toxicity; Volume imbalance; Market microstructure;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • D52 - Microeconomics - - General Equilibrium and Disequilibrium - - - Incomplete Markets
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jfinec:v:120:y:2016:i:2:p:269-285. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505576 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.