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Intraday Anomalies and Market Efficiency: A Trading Robot Analysis

Author

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  • Guglielmo Caporale

  • Luis Gil-Alana
  • Alex Plastun
  • Inna Makarenko

Abstract

One of the leading criticisms of the efficient market hypothesis 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). Copyright The Author(s) 2016

Suggested Citation

  • Guglielmo Caporale & Luis Gil-Alana & Alex Plastun & Inna Makarenko, 2016. "Intraday Anomalies and Market Efficiency: A Trading Robot Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 47(2), pages 275-295, February.
    • Handle: RePEc:kap:compec:v:47:y:2016:i:2:p:275-295
    DOI: 10.1007/s10614-015-9484-9
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    Cited by:

    1. Plastun, Alex & Sibande, Xolani & Gupta, Rangan & Wohar, Mark E., 2020. "Price gap anomaly in the US stock market: The whole story," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Umar, Zaghum & Adekoya, Oluwasegun Babatunde & Oliyide, Johnson Ayobami & Gubareva, Mariya, 2021. "Media sentiment and short stocks performance during a systemic crisis," International Review of Financial Analysis, Elsevier, vol. 78(C).
    3. V. Vismayaa & K. R. Pooja & A. Alekhya & C. N. Malavika & Binoy B. Nair & P. N. Kumar, 2020. "Classifier Based Stock Trading Recommender Systems for Indian stocks: An Empirical Evaluation," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 901-923, March.
    4. Su, Zhifang & Bao, Haohua & Li, Qifang & Xu, Boyu & Cui, Xin, 2022. "The prediction of price gap anomaly in Chinese stock market: Evidence from the dependent functional logit model," Finance Research Letters, Elsevier, vol. 47(PB).
    5. Alla A. Petukhina & Raphael C. G. Reule & Wolfgang Karl Härdle, 2021. "Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 8-30, January.
    6. I. Marta Miranda García & María‐Jesús Segovia‐Vargas & Usue Mori & José A. Lozano, 2023. "Early prediction of Ibex 35 movements," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1150-1166, August.
    7. Girardin, Eric & Salimi Namin, Fatemeh, 2019. "The January effect in the foreign exchange market: Evidence for seasonal equity carry trades," Economic Modelling, Elsevier, vol. 81(C), pages 422-439.
    8. Mohammad Arashi & Mohammad Mahdi Rounaghi, 2022. "Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model," Future Business Journal, Springer, vol. 8(1), pages 1-12, December.
    9. Maria Caporale, Guglielmo & Zakirova, Valentina, 2017. "Calendar anomalies in the Russian stock market," Russian Journal of Economics, Elsevier, vol. 3(1), pages 101-108.

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    Keywords

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    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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