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Realtime market microstructure analysis: online Transaction Cost Analysis

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  • Robert Azencott
  • Arjun Beri
  • Yutheeka Gadhyan
  • Nicolas Joseph
  • Charles-Albert Lehalle
  • Matthew 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 the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance, a stochastic control, an impulse control or a statistical learning 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. We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts 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 in the post trade analysis of algorithms can be taken advantage of to automatically adjust their trading action.

Suggested Citation

  • Robert Azencott & Arjun Beri & Yutheeka Gadhyan & Nicolas Joseph & Charles-Albert Lehalle & Matthew Rowley, 2013. "Realtime market microstructure analysis: online Transaction Cost Analysis," Papers 1302.6363, arXiv.org, revised Mar 2013.
  • Handle: RePEc:arx:papers:1302.6363
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    References listed on IDEAS

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    1. repec:hal:wpaper:hal-00422427 is not listed on IDEAS
    2. 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.
    3. Charles-Albert Lehalle, 2013. "Market Microstructure Knowledge Needed for Controlling an Intra-Day Trading Process," Papers 1302.4592, arXiv.org.
    4. 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.
    5. Rosenthal, Dale W.R., 2009. "Performance metrics for algorithmic traders," MPRA Paper 36787, University Library of Munich, Germany, revised 04 Jan 2012.
    6. 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.
    7. 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.
    8. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
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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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