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Intraday Anomalies and Market Efficiency: A Trading Robot Analysis


  • Guglielmo Maria Caporale
  • Luis Gil-Alana
  • Alex Plastun
  • Inna Makarenko


One of the leading criticisms of the Efficient Market Hypothesis (EMH) is the presence of so-called “anomalies”, i.e. empirical evidence of abnormal behaviour of asset prices which is inconsistent with market efficiency. However, most studies do not take into account transaction costs. Their existence implies that in fact traders might not be able to make abnormal profits. This paper examines whether or not anomalies such as intraday or time of the day effects give rise to exploitable profit opportunities by replicating the actions of traders. Specifically, the analysis is based on a trading robot which simulates their behaviour, and incorporates variable transaction costs (spreads). The results suggest that trading strategies aimed at exploiting daily patterns do not generate extra profits. Further, there are no significant differences between sub-periods (2005-2006 – “normal” , 2007- 2009 – “crisis” , 2010-2011 – “post-crisis).

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  • Guglielmo Maria Caporale & Luis Gil-Alana & Alex Plastun & Inna Makarenko, 2014. "Intraday Anomalies and Market Efficiency: A Trading Robot Analysis," Discussion Papers of DIW Berlin 1377, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1377

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    References listed on IDEAS

    1. A. Abhyankar & D. Ghosh & E. Levin & R.J. Limmack, 1997. "Bid-ask Spreads, Trading Volume and Volatility: Intra-day Evidence from the London Stock Exchange," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 24(3), pages 343-362.
    2. Wood, Robert A & McInish, Thomas H & Ord, J Keith, 1985. " An Investigation of Transactions Data for NYSE Stocks," Journal of Finance, American Finance Association, vol. 40(3), pages 723-739, July.
    3. Pawel STRAWINSKI & Robert SLEPACZUK, 2008. "Analysis Of High Frequency Data On The Warsaw Stock Exchange In The Context Of Efficient Market Hypothesis," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 3(3(5)_Fall), pages 306-319.
    4. Çankaya, Serkan & Eken, Hasan/M. & Ulusoy, Veysel, 2011. "The Impact of Short Selling on Intraday Volatility: Evidence from the Istanbul Stock Exchange," MPRA Paper 43658, University Library of Munich, Germany.
    5. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    6. Jensen, Michael C., 1978. "Some anomalous evidence regarding market efficiency," Journal of Financial Economics, Elsevier, vol. 6(2-3), pages 95-101.
    7. Anat R. Admati, Paul Pfleiderer, 1988. "A Theory of Intraday Patterns: Volume and Price Variability," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 3-40.
    8. CORONEO, Laura & VEREDAS, David, 2006. "Intradaily seasonality of returns distribution. A quantile regression approach and intradaily VaR estimation," CORE Discussion Papers 2006077, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Harris, Lawrence, 1989. "A Day-End Transaction Price Anomaly," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(01), pages 29-45, March.
    10. Thaler, Richard H, 1987. "Seasonal Movements in Security Prices II: Weekend, Holiday, Turn of the Month, and Intraday Effects," Journal of Economic Perspectives, American Economic Association, vol. 1(2), pages 169-177, Fall.
    11. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    12. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters,in: THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78 World Scientific Publishing Co. Pte. Ltd..
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    More about this item


    Efficient Market Hypothesis; intraday patterns; time of the day anomaly; trading strategy;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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