Algorithmic Trading and Efficiency of the Stock Market in Poland

Rafał Jóźwicki; Paweł Trippner; Karolina Kłos

doi:10.18778/2391-6478.2.30.05

Authors

Rafał Jóźwicki University of Lodz, Institute of Finance https://orcid.org/0000-0002-4346-4046
Paweł Trippner University of Social Sciences, Institute of Finance https://orcid.org/0000-0003-1060-517X
Karolina Kłos Owner of Finance and Insurance Center

DOI:

https://doi.org/10.18778/2391-6478.2.30.05

Keywords:

algorithmic trading, market efficiency, trading system, investing, technical analysis

Abstract

The aim of the article is to investigate the impact of algorithmic trading on the returns obtained in the context of market efficiency theory. The research hypothesis is that algorithmic trading can contribute to a better rate of return than when using passive investment strategies. Technological progress can be observed in many different aspects of our lives, including investing in capital markets where we can see changes resulting from the spread of new technologies.

The methodology used in this paper consists in confronting a sample trading system based on classical technical analysis tools with a control strategy consisting in buying securities at the beginning of the test period and holding them until the end of this period.

The results obtained confirm the validity of the theory of information efficiency of the capital market, as the active investment strategy based on algorithmic trading did not yield better results than the control strategy.

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References

Alma, Y. Alanis, Arana-Daniel, N., Lopez-Franco, C., eds. (2019). Artificial Neural Networks for Engineering Applications, Elsevier.

Appel, G. (2005). Technical Analysis: Power Tools for Active Investors. New York: Pearson Education Inc.

Bilski, J., Kowalczyk, B., Marchlewska, A., Zurada, J.M. (2020). Local Levenberg-Marquardt Algorithm for Learning Feedforwad Neural Networks. Journal of Artificial Intelligence and Soft Computing Research, 10(4). DOI: https://doi.org/10.2478/jaiscr-2020-0020

Czekaj, J., Woś, M., Żarnowski, J. (2001). Efektywność giełdowego rynku akcji w Polsce. Z perspektywy dziesięciolecia. Warszawa: Wydawnictwo Naukowe PWN.

Dziwiński, P., Bartczuk, Ł., Paszkowski, J. (2020). A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm. Journal of Artificial Intelligence and Soft Computing Research, 10(2). DOI: https://doi.org/10.2478/jaiscr-2020-0007

Fama, E.F. (1970). Efficient Capital Markets: A review of Theory and Empirical Work. Journal of Finance, 2. DOI: https://doi.org/10.2307/2325486

Homenda, W., Jastrzębska, A., Pedrycz, W., Fusheng, Y. (2020). Combining Classifiers for Foreign Pattern Rejection, Journal of Artificial Intelligence and Soft Computing Research, 10(2). DOI: https://doi.org/10.2478/jaiscr-2020-0006

mql4.com, www.mql4.com [Accessed: 4.11.2020].

Murphy, J.J. (2019). Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications. New York: New York Institute of Finance.

Nowicki, R.K., Grzanek, K., Hayashi, Y. (2019). Rough Support Vector Machine for Classification with Interval and Incomplete Data. Journal of Artificial Intelligence and Soft Computing Research, 10(1). DOI: https://doi.org/10.2478/jaiscr-2020-0004

Starczewski, J.T., Goetzen, P., Napoli, Ch. (2020). Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems. Journal of Artificial Intelligence and Soft Computing Research, 10(4). DOI: https://doi.org/10.2478/jaiscr-2020-0018

Sysło, M.M. (2016). Algorytmy (Algorithms). Gliwice: Wydawnictwo HELION.

Szyszka, A. (2003). Efektywność giełdy papierów wartościowych w Warszawie na tle rynków dojrzałych (Efficiency of the Warsaw Stock Exchange in comparison with mature markets). Poznań: Wydawnictwo Akademii Ekonomicznej w Poznaniu.