Algorithmic Trading and Efficiency of the Stock Market in Poland

Autor

DOI:

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

Słowa kluczowe:

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

Abstrakt

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.

Pobrania

Brak dostępnych danych do wyświetlenia.

Bibliografia

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

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

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).
Google Scholar 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.
Google Scholar

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).
Google Scholar 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.
Google Scholar 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).
Google Scholar DOI: https://doi.org/10.2478/jaiscr-2020-0006

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

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.
Google Scholar

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).
Google Scholar 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).
Google Scholar DOI: https://doi.org/10.2478/jaiscr-2020-0018

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

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.
Google Scholar

Opublikowane

2021-06-30

Jak cytować

Jóźwicki, R., Trippner, P., & Kłos, K. (2021). Algorithmic Trading and Efficiency of the Stock Market in Poland. Finanse I Prawo Finansowe, 2(30), 75–85. https://doi.org/10.18778/2391-6478.2.30.05

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