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Candlestick—The Main Mistake of Economy Research in High Frequency Markets

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  • Michał Dominik Stasiak

    (Department of Investment and Real Estate, Poznan University of Economics and Business, al. Niepodleglosci 10, 61-875 Poznan, Poland)

Abstract

One of the key problems of researching the high-frequency financial markets is the proper data format. Application of the candlestick representation (or its derivatives such as daily prices, etc.), which is vastly used in economic research, can lead to faulty research results. Yet, this fact is consistently ignored in most economic studies. The following article gives examples of possible consequences of using candlestick representation in modelling and statistical analysis of the financial markets. Emphasis should be placed on the problem of research results being detached from the investing practice, which makes most of the results inapplicable from the investor’s point of view. The article also presents the concept of a binary-temporal representation, which is an alternative to the candlestick representation. Using binary-temporal representation allows for more precise and credible research and for the results to be applied in investment practice.

Suggested Citation

  • Michał Dominik Stasiak, 2020. "Candlestick—The Main Mistake of Economy Research in High Frequency Markets," IJFS, MDPI, vol. 8(4), pages 1-15, October.
    • Handle: RePEc:gam:jijfss:v:8:y:2020:i:4:p:59-:d:425855
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    References listed on IDEAS

    as
    1. Michał Dominik Stasiak, 2018. "Modelling of Currency Exchange Rates Using a Binary-Temporal Representation," Springer Proceedings in Business and Economics, in: Taufiq Choudhry & Jacek Mizerka (ed.), Contemporary Trends in Accounting, Finance and Financial Institutions, pages 97-110, Springer.
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    3. Michał Dominik Stasiak, 2018. "A study on the influence of the discretisation unit on the effectiveness of modelling currency exchange rates using the binary-temporal representation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(2), pages 57-70.
    4. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    5. Yun-Cheng Tsai & Chun-Chieh Wang, 2019. "Deep Reinforcement Learning for Foreign Exchange Trading," Papers 1908.08036, arXiv.org, revised Jun 2020.
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    7. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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    Cited by:

    1. Michał Dominik Stasiak, 2022. "Algoritmic Trading System Based on State Model for Moving Average in a Binary-Temporal Representation," Risks, MDPI, vol. 10(4), pages 1-15, March.

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