IDEAS home Printed from https://ideas.repec.org/a/taf/eurjfi/v13y2007i8p717-739.html
   My bibliography  Save this article

Algorithmic Trading Patterns in Xetra Orders

Author

Listed:
  • Johannes Prix
  • Otto Loistl
  • Michael Huetl

Abstract

Computerized trading controlled by algorithms - “Algorithmic Trading” - has become a fashionable term in investment banking. We investigate a set of Xetra order data to find traces of algorithmic trading by studying the lifetimes of cancelled orders. Even though it is widely agreed that an algorithm must randomize its order activities to avoid exploitation by other traders, we still find systematic patterns in the submission and cancellation of certain Xetra orders, indicating the activity of algorithmic trading. The trading patterns observed might be interpreted as fishing for profitable roundtrips.

Suggested Citation

  • Johannes Prix & Otto Loistl & Michael Huetl, 2007. "Algorithmic Trading Patterns in Xetra Orders," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 717-739.
    • Handle: RePEc:taf:eurjfi:v:13:y:2007:i:8:p:717-739
    DOI: 10.1080/13518470701705538
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/13518470701705538
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13518470701705538?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, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Alfonso Dufour & Robert F. Engle, 2000. "Time and the Price Impact of a Trade," Journal of Finance, American Finance Association, vol. 55(6), pages 2467-2498, December.
    3. Anthony D. Hall & Nikolaus Hautsch, 2008. "Order aggressiveness and order book dynamics," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 133-165, Springer.
    4. Joachim Grammig & Kai-Oliver Maurer, 2000. "Non-monotonic hazard functions and the autoregressive conditional duration model," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 16-38.
    5. 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.
    6. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    7. Lo, Andrew W. & MacKinlay, A. Craig & Zhang, June, 2002. "Econometric models of limit-order executions," Journal of Financial Economics, Elsevier, vol. 65(1), pages 31-71, July.
    8. Robert F. Engle & Asger Lunde, 2003. "Trades and Quotes: A Bivariate Point Process," Journal of Financial Econometrics, Oxford University Press, vol. 1(2), pages 159-188.
    9. Handa, Puneet & Schwartz, Robert A, 1996. "Limit Order Trading," Journal of Finance, American Finance Association, vol. 51(5), pages 1835-1861, December.
    10. Ekkehart Boehmer & Gideon Saar & Lei Yu, 2005. "Lifting the Veil: An Analysis of Pre‐trade Transparency at the NYSE," Journal of Finance, American Finance Association, vol. 60(2), pages 783-815, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gomber, Peter & Gsell, Markus, 2009. "Algorithmic trading engines versus human traders: Do they behave different in securities markets?," CFS Working Paper Series 2009/10, Center for Financial Studies (CFS).
    2. Peter Gomber & Martin Haferkorn, 2013. "High-Frequency-Trading," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(2), pages 97-99, April.
    3. Dubey, Ritesh Kumar & Chauhan, Yogesh & Syamala, Sudhakara Reddy, 2017. "Evidence of algorithmic trading from Indian equity market: Interpreting the transaction velocity element of financialization," Research in International Business and Finance, Elsevier, vol. 42(C), pages 31-38.
    4. Le, Anh Tu & Le, Thai-Ha & Liu, Wai-Man & Fong, Kingsley Y., 2020. "Multiple duration analyses of dynamic limit order placement strategies and aggressiveness in a low-latency market environment," International Review of Financial Analysis, Elsevier, vol. 72(C).
    5. Boilard, J.-F. & Kanazawa, K. & Takayasu, H. & Takayasu, M., 2018. "Empirical scaling relations of market event rates in foreign currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1152-1161.
    6. Tim A. Herberger & Matthias Horn & Andreas Oehler, 2020. "Are intraday reversal and momentum trading strategies feasible? An analysis for German blue chip stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 179-197, June.
    7. Paweł Mielcarz & Dmytro Osiichuk & Jarosław Cymerski, 2020. "Algorithmic Sangfroid? The Decline of Sensitivity of Crude Oil Prices to News on Potentially Disruptive Terror Attacks and Political Unrest," Sustainability, MDPI, vol. 13(1), pages 1-24, December.
    8. Bruce Burton & Satish Kumar & Nitesh Pandey, 2020. "Twenty-five years of The European Journal of Finance (EJF): a retrospective analysis," The European Journal of Finance, Taylor & Francis Journals, vol. 26(18), pages 1817-1841, December.
    9. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    10. Gsell, Markus, 2008. "Assessing the impact of algorithmic trading on markets: A simulation approach," CFS Working Paper Series 2008/49, Center for Financial Studies (CFS).
    11. Martin Scholtus & Dick van Dijk, 2012. "High-Frequency Technical Trading: The Importance of Speed," Tinbergen Institute Discussion Papers 12-018/4, Tinbergen Institute.

    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. N. Taylor & Y. Xu, 2017. "The logarithmic vector multiplicative error model: an application to high frequency NYSE stock data," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 1021-1035, July.
    2. Spierdijk, Laura, 2004. "An empirical analysis of the role of the trading intensity in information dissemination on the NYSE," Journal of Empirical Finance, Elsevier, vol. 11(2), pages 163-184, March.
    3. Dionne, Georges & Zhou, Xiaozhou, 2016. "The Dynamics of Ex-ante High-Frequency Liquidity: An Empirical Analysis," Working Papers 15-5, HEC Montreal, Canada Research Chair in Risk Management.
    4. Collver, Charles, 2009. "Measuring the impact of option market activity on the stock market: Bivariate point process models of stock and option transactions," Journal of Financial Markets, Elsevier, vol. 12(1), pages 87-106, February.
    5. Clive Bowsher, 2002. "Modelling Security Market Events in Continuous Time: Intensity based, Multivariate Point Process Models," Economics Series Working Papers 2002-W22, University of Oxford, Department of Economics.
    6. Grammig, Joachim & Theissen, Erik & Wuensche, Oliver, 2007. "Time and price impact of a trade: A structural approach," CFR Working Papers 07-12, University of Cologne, Centre for Financial Research (CFR).
    7. Yang, Joey Wenling, 2011. "Transaction duration and asymmetric price impact of trades--Evidence from Australia," Journal of Empirical Finance, Elsevier, vol. 18(1), pages 91-102, January.
    8. repec:bla:jecsur:v:22:y:2008:i:4:p:711-751 is not listed on IDEAS
    9. Comerton-Forde, Carole & Tang, Kar Mei, 2009. "Anonymity, liquidity and fragmentation," Journal of Financial Markets, Elsevier, vol. 12(3), pages 337-367, August.
    10. Engle, Robert F. & Patton, Andrew J., 2004. "Impacts of trades in an error-correction model of quote prices," Journal of Financial Markets, Elsevier, vol. 7(1), pages 1-25, January.
    11. Spierdijk, L., 2002. "An Empirical Analysis of the Role of the Trading Intensity in Information Dissemination on the NYSE," Other publications TiSEM d495caf0-2f2a-425f-8e50-e, Tilburg University, School of Economics and Management.
    12. Rzayev, Khaladdin & Ibikunle, Gbenga, 2019. "A state-space modeling of the information content of trading volume," Journal of Financial Markets, Elsevier, vol. 46(C).
    13. Bowsher, Clive G., 2007. "Modelling security market events in continuous time: Intensity based, multivariate point process models," Journal of Econometrics, Elsevier, vol. 141(2), pages 876-912, December.
    14. Jondeau, Eric & Lahaye, Jérôme & Rockinger, Michael, 2015. "Estimating the price impact of trades in a high-frequency microstructure model with jumps," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 205-224.
    15. Manganelli, Simone, 2005. "Duration, volume and volatility impact of trades," Journal of Financial Markets, Elsevier, vol. 8(4), pages 377-399, November.
    16. Bhatti, Chad R., 2009. "Intraday trade and quote dynamics: A Cox regression analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(7), pages 2240-2249.
    17. Pascual, Roberto, 2000. "Adverse selection costs, trading activity and liquidity in the NYSE: an empirical analysis in a dynamic context," UC3M Working papers. Economics 7276, Universidad Carlos III de Madrid. Departamento de Economía.
    18. Ferriani, Fabrizio, 2010. "Informed and uninformed traders at work: evidence from the French market," MPRA Paper 24487, University Library of Munich, Germany.
    19. Stanislav Anatolyev & Dmitry Shakin, 2007. "Trade intensity in the Russian stock market: dynamics, distribution and determinants," Applied Financial Economics, Taylor & Francis Journals, vol. 17(2), pages 87-104.
    20. Zebedee, Allan A., 2001. "The impact of a trade on national best bid and offer quotes: a new approach to modeling irregularly spaced data," Journal of Multinational Financial Management, Elsevier, vol. 11(4-5), pages 363-383, December.
    21. GIOT, Pierre, 1999. "Time transformations, intraday data and volatility models," LIDAM Discussion Papers CORE 1999044, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    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:taf:eurjfi:v:13:y:2007:i:8:p:717-739. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/REJF20 .

    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.