IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v8y2020i4p59-d425855.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/8/4/59/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7072/8/4/59/
    Download Restriction: no
    ---><---

    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.
    2. Dimitrios Vezeris & Themistoklis Kyrgos & Christos Schinas, 2018. "Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System," JRFM, MDPI, vol. 11(3), pages 1-23, September.
    3. Yun-Cheng Tsai & Chun-Chieh Wang, 2019. "Deep Reinforcement Learning for Foreign Exchange Trading," Papers 1908.08036, arXiv.org, revised Jun 2020.
    4. Philippe Jorion, 2000. "Risk management lessons from Long‐Term Capital Management," European Financial Management, European Financial Management Association, vol. 6(3), pages 277-300, September.
    5. 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.
    6. 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.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.
    2. Krzysztof Piasecki & Michał Dominik Stasiak, 2020. "Optimization Parameters of Trading System with Constant Modulus of Unit Return," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
    3. Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020. "Forex exchange rate forecasting using deep recurrent neural networks," Digital Finance, Springer, vol. 2(1), pages 69-96, September.
    4. Frédy Pokou & Jules Sadefo Kamdem & François Benhmad, 2024. "Hybridization of ARIMA with Learning Models for Forecasting of Stock Market Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1349-1399, April.
    5. Ignacio Escanuela Romana & Clara Escanuela Nieves, 2023. "A spectral approach to stock market performance," Papers 2305.05762, arXiv.org.
    6. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    7. Krzysztof Piasecki & Michał Dominik Stasiak, 2019. "The Forex Trading System for Speculation with Constant Magnitude of Unit Return," Mathematics, MDPI, vol. 7(7), pages 1-23, July.
    8. Chen, Rui & Ren, Jinjuan, 2022. "Do AI-powered mutual funds perform better?," Finance Research Letters, Elsevier, vol. 47(PA).
    9. 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.
    10. U, JuHyok & Lu, PengYu & Kim, ChungSong & Ryu, UnSok & Pak, KyongSok, 2020. "A new LSTM based reversal point prediction method using upward/downward reversal point feature sets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    11. Sang Il Lee, 2020. "Deeply Equal-Weighted Subset Portfolios," Papers 2006.14402, arXiv.org.
    12. Alireza Namdari & Tariq S. Durrani, 2021. "A Multilayer Feedforward Perceptron Model in Neural Networks for Predicting Stock Market Short-term Trends," SN Operations Research Forum, Springer, vol. 2(3), pages 1-30, September.
    13. Pastor, Lubos & Stambaugh, Robert F., 2003. "Liquidity Risk and Expected Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 111(3), pages 642-685, June.
    14. Wei Dai & Yuan An & Wen Long, 2021. "Price change prediction of ultra high frequency financial data based on temporal convolutional network," Papers 2107.00261, arXiv.org.
    15. Shao, Zhen & Zheng, Qingru & Yang, Shanlin & Gao, Fei & Cheng, Manli & Zhang, Qiang & Liu, Chen, 2020. "Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM," Energy Economics, Elsevier, vol. 86(C).
    16. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    17. Bell, Peter N, 2013. "New Testing Procedures to Assess Market Efficiency with Trading Rules," MPRA Paper 46701, University Library of Munich, Germany.
    18. Anderson, Robert M. & Bianchi, Stephen W. & Goldberg, Lisa R., 2013. "The Decision to Lever," Department of Economics, Working Paper Series qt8cg116sv, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    19. Michał Dominik Stasiak & Żaneta Staszak, 2024. "Modelling and Forecasting Crude Oil Prices Using Trend Analysis in a Binary-Temporal Representation," Energies, MDPI, vol. 17(14), pages 1-13, July.
    20. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijfss:v:8:y:2020:i:4:p:59-:d:425855. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.