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Real-time market microstructure analysis: online transaction cost analysis

Author

Listed:
  • R. Azencott
  • A. Beri
  • Y. Gadhyan
  • N. Joseph
  • C.-A. Lehalle
  • M. Rowley

Abstract

Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of causes that lie behind poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. The common acronym for investigating the causes of bad and good performance of trading is transaction cost analysis Rosenthal [ Performance Metrics for Algorithmic Traders , 2009]). Automated algorithms take care of most of the traded flows on electronic markets (more than 70% in the US, 45% in Europe and 35% in Japan in 2012). Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance (like in Almgren and Chriss [ J. Risk , 2000, 3 (2), 5-39]), a stochastic control (e.g. Gu�ant et al. [ Math. Financ. Econ. , 2013, 7 (4), 477-507]), an impulse control (see Bouchard et al. [ SIAM J. Financ. Math. , 2011, 2 (1), 404-438]) or a statistical learning (as used in Laruelle et al . [ Math. Financ. Econ. , 2013, 7 (3), 359-403]) viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors , can be created for each market order (following for instance Cristianini and Shawe-Taylor [ An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000]). We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts (i.e. have influence , in the sense of predictive power or information defined in Basseville and Nikiforov [ Detection of Abrupt Changes: Theory and Application , 1993], Shannon [ Bell Syst. Tech. J. , 1948, 27 , 379-423] and Alkoot and Kittler [ Pattern Recogn. Lett. , 1999, 20 (11), 1361-1369]) which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis , we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method can be taken advantage of to automatically adjust their trading action in the post trade analysis of algorithms.

Suggested Citation

  • R. Azencott & A. Beri & Y. Gadhyan & N. Joseph & C.-A. Lehalle & M. Rowley, 2014. "Real-time market microstructure analysis: online transaction cost analysis," Quantitative Finance, Taylor & Francis Journals, vol. 14(7), pages 1167-1185, July.
  • Handle: RePEc:taf:quantf:v:14:y:2014:i:7:p:1167-1185
    DOI: 10.1080/14697688.2014.884283
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    References listed on IDEAS

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    1. Rosenthal, Dale W.R., 2009. "Performance metrics for algorithmic traders," MPRA Paper 36787, University Library of Munich, Germany, revised 04 Jan 2012.
    2. repec:hal:wpaper:hal-00422427 is not listed on IDEAS
    3. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    4. Olivier Gu'eant & Charles-Albert Lehalle & Joaquin Fernandez Tapia, 2011. "Dealing with the Inventory Risk. A solution to the market making problem," Papers 1105.3115, arXiv.org, revised Aug 2012.
    5. Charles-Albert Lehalle, 2013. "Market Microstructure Knowledge Needed for Controlling an Intra-Day Trading Process," Papers 1302.4592, arXiv.org.
    6. Esteban Moro & Javier Vicente & Luis G. Moyano & Austin Gerig & J. Doyne Farmer & Gabriella Vaglica & Fabrizio Lillo & Rosario N. Mantegna, 2009. "Market impact and trading profile of large trading orders in stock markets," Papers 0908.0202, arXiv.org.
    7. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
    8. Sitter, Randy R. & Wu, Changbao, 2001. "A note on Woodruff confidence intervals for quantiles," Statistics & Probability Letters, Elsevier, vol. 52(4), pages 353-358, May.
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    Cited by:

    1. Jackie Jianhong Shen, 2013. "A Pre-Trade Algorithmic Trading Model under Given Volume Measures and Generic Price Dynamics (GVM-GPD)," Papers 1309.5046, arXiv.org, revised Sep 2013.

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