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A Methodology to Assess the Benefits of Smart Order Routing

  • Bartholomäus Ende
  • Peter Gomber
  • Marco Lutat
  • Moritz C. Weber
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 341)

Abstract

Smart Order Routing technology promises to improve the efficiency of the securities trading value chain by selecting most favourable execution prices among fragmented markets. To measure the extent of sub-optimal order executions in Europe we develop a simulation framework which includes explicit costs associated with switching to a different market. By analysing historical order book data for EURO STOXX 50 securities across ten European lectronic markets we highlight an economically relevant potential of Smart Order Routing to improve the trading process on a gross basis. After the inclusion of switching costs (net basis), the realisability of this value potential depends on whether the user can directly access post-trading infrastructure of foreign markets or has to make use of intermediaries’ services.

Keywords

Electronic Markets Process Optimisation Smart Order Router 

References

  1. 1.
    Schwartz, R.A., Francioni, R.: Equity markets in action: The fundamentals of liquidity, market structure & trading. Wiley, Hoboken (2004)Google Scholar
  2. 2.
    Hitt, L.M., Brynjolfsson, E.: Productivity, Business Profitability, and Consumer Surplus: Three Different Measures of Information Technology Value. MIS Quarterly, 121–142 (June 1996)Google Scholar
  3. 3.
    Harris, L.: Trading and Exchanges – Market Microstructure for Practitioners. Oxford University Press, Oxford (2003)Google Scholar
  4. 4.
    CESAME Sub-Group on definitions, Commission Services Working Document on Definitions of Post-Trading Activities (October 2005)Google Scholar
  5. 5.
    Fidessa, Fidessa Fragmentation Index (2009), http://fragmentation.fidessa.com/ (last accessed 06/13/2009)
  6. 6.
    Giovannini Group, Cross-Border Clearing and Settlement Arrangements in the European Union (November 2001), http://ec.europa.eu/internal_market/financial-markets/docs/clearing/first_giovannini_report_en.pdf (last accessed: 08/11/2007)
  7. 7.
    Gomber, P., Pujol, G., Wranik, A.: The Implementation of European Best Execution Obligations – An Analysis for the German Market, FinanceCom, Paris, France (2008)Google Scholar
  8. 8.
    Kohli, R., Grover, V.: Business value of IT: An essay on expanding research directions to keep with the times. Journal of the Association for Information Systems 9(1), 23–39 (2008)Google Scholar
  9. 9.
    Chircu, A.M., Kauffman, R.J.: Limits to Value in Electronic Commerce-Related IT Investments. Journal of Management Information Systems 17(2), 59–80 (2000)Google Scholar
  10. 10.
    Davern, M.J., Kauffman, R.J.: Discovering potential and realizing value from information technology investments. J. Manage. Inf. Syst. 16(4), 121–143 (2000)Google Scholar
  11. 11.
    Mooney, J.G., Gurbaxani, V., Kraemer, K.L.: A process oriented framework for assessing the business value of information technology. SIGMIS Database 27(2), 68–81 (1996)CrossRefGoogle Scholar
  12. 12.
    Weyland, J.H., Engiles, M.: Towards simulation-based business process management: towards simulation-based business process management. In: Proc. of the 35th Conf. on Winter Simulation: Driving Innovation, pp. 225–227 (2003)Google Scholar
  13. 13.
    Yen, V.C.: An Integrated Model for Business Process Measurement. In: CONF-IRM 2008 Proceedings, Paper 9 (2008)Google Scholar
  14. 14.
    Qureshi, A., Weber, R., Balakrishnan, H., Guttag, J., Maggs, B.: Cutting the electric bill for internet-scale systems. In: Proc. of the ACM SIGCOMM 2009, pp. 123–134 (2009)Google Scholar
  15. 15.
    Foucault, T., Menkveld, A.J.: Competition for Order Flow and Smart Order Routing Systems. Journal of Finance (63), 119–158 (2008)CrossRefGoogle Scholar
  16. 16.
    Prix, J., Loistl, O., Huetl, M.: Algorithmic Trading Patterns in Xetra Orders. European Journal of Finance 13(8), 717–739 (2007)CrossRefGoogle Scholar
  17. 17.
    Gsell, M., Gomber, P.: Algorithmic trading engines versus human traders – Do they behave different in securities markets? In: Proc. of the 17th European Conference on Information Systems (2009)Google Scholar
  18. 18.
    Domowitz, I., Yegerman, H.: The Cost of Algorithmic Trading – A First Look at Comparative Performance. In: Bruce, B.R. (ed.) Algorithmic Trading: Precision, Control, Execution, pp. 30–40. Institutional Investor Inc. (2005)Google Scholar
  19. 19.
    Bakos, Y., Lucas, H.C., Wonseok, O., Viswanathan, S., Simon, G., Weber, B.: Electronic Commerce in the Retail Brokerage Industry: Trading Costs of Internet Versus Full Service Firms, Working Paper Series Stern#1S99-014 (1999)Google Scholar
  20. 20.
    Oxera. Methodology for monitoring prices, costs and volumes of trading and post-trading activities (July 2007), http://ec.europa.eu/internal_market/financialmarkets/docs/clearing/oxera_study_en.pdf (last accessed: 01/15/2008)

Copyright information

© IFIP 2010

Authors and Affiliations

  • Bartholomäus Ende
    • 1
  • Peter Gomber
    • 1
  • Marco Lutat
    • 1
  • Moritz C. Weber
    • 1
  1. 1.E-Finance LabGoethe-University FrankfurtFrankfurtGermany

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