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Does high frequency trading affect technical analysis and market efficiency? And if so, how?

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  • Manahov, Viktor
  • Hudson, Robert
  • Gebka, Bartosz

Abstract

In this paper we investigate how high frequency trading affects technical analysis and market efficiency in the foreign exchange (FX) market by using a special adaptive form of the Strongly Typed Genetic Programming (STGP)-based learning algorithm. We use this approach for real one-minute high frequency data of the most traded currency pairs worldwide: EUR/USD, USD/JPY, GBP/USD, AUD/USD, USD/CHF, and USD/CAD. The STGP performance is compared with that of parametric and non-parametric models and validated by two formal empirical tests. We perform in-sample and out-of-sample comparisons between all models on the basis of forecast performance and investment return. Furthermore, our paper shows the relative strength of these models with respect to the actual trading profit generated by their forecasts. Empirical experiments suggest that the STGP forecasting technique significantly outperforms the traditional econometric models. We find evidence that the excess returns are both statistically and economically significant, even when appropriate transaction costs are taken into account. We also find evidence that HFT has a beneficial role in the price discovery process.

Suggested Citation

  • Manahov, Viktor & Hudson, Robert & Gebka, Bartosz, 2014. "Does high frequency trading affect technical analysis and market efficiency? And if so, how?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 131-157.
  • Handle: RePEc:eee:intfin:v:28:y:2014:i:c:p:131-157
    DOI: 10.1016/j.intfin.2013.11.002
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    Cited by:

    1. repec:pdc:jrnbeh:v:13:y:2017:i:1:p:1-9 is not listed on IDEAS
    2. Manahov, Viktor, 2016. "A note on the relationship between high-frequency trading and latency arbitrage," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 281-296.
    3. Andrew Phiri, 2017. "Threshold convergence between the federal fund rate and South African equity returns around the colocation period," Business and Economic Horizons (BEH), Prague Development Center, vol. 13(1), pages 1-9, March.
    4. Gebka, Bartosz & Hudson, Robert S. & Atanasova, Christina V., 2015. "The benefits of combining seasonal anomalies and technical trading rules," Finance Research Letters, Elsevier, vol. 14(C), pages 36-44.
    5. Manahov, Viktor & Hudson, Robert & Hoque, Hafiz, 2015. "Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 85-98.
    6. repec:eee:intfin:v:52:y:2018:i:c:p:102-113 is not listed on IDEAS
    7. Narayan, Paresh Kumar & Mishra, Sagarika & Narayan, Seema & Thuraisamy, Kannan, 2015. "Is Exchange Rate Trading Profitable?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 217-229.
    8. Serbera, Jean-Philippe & Paumard, Pascal, 2016. "The fall of high-frequency trading: A survey of competition and profits," Research in International Business and Finance, Elsevier, vol. 36(C), pages 271-287.
    9. Simone Cirillo & Stefan Lloyd & Peter Nordin, 2014. "Evolving intraday foreign exchange trading strategies utilizing multiple instruments price series," Papers 1411.2153, arXiv.org.

    More about this item

    Keywords

    Technical trading rules; Genetic Programming; Exchange rate;

    JEL classification:

    • F31 - International Economics - - International Finance - - - Foreign Exchange
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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