IDEAS home Printed from https://ideas.repec.org/a/tec/journl/v8y2020i1p302-318.html
   My bibliography  Save this article

The Use of Technical Analysis in the US, European and Asian Stock Markets

Author

Listed:
  • Deimante Teresiene

    (Vilnius University, Lithuania)

  • Margarita Aleksynaite

    (Vilnius University, Lithuania)

Abstract

Technical analysis is a widely used tool in making investment decisions. Nowadays it becomes very popular in the context of big data analysis and artificial intelligence framework. Although the analysis of the results of indicators in certain markets often becomes the axis of technical analysis research, it is difficult to find articles aimed at applying and comparing this analysis in different markets. This paper attempts to answer the question of whether technical analysis indicators work in the same or different ways in the US, European, and Asian stock markets. For this purpose, 8 indicators are calculated, and their results are compared in three selected markets. The correlation between the indicators themselves in individual markets is also determined. It has been observed that the performance of technical analysis is similar in different markets so this type of analysis can be used in artificial intelligence framework.

Suggested Citation

  • Deimante Teresiene & Margarita Aleksynaite, 2020. "The Use of Technical Analysis in the US, European and Asian Stock Markets," Technium Social Sciences Journal, Technium Science, vol. 8(1), pages 302-318, June.
    • Handle: RePEc:tec:journl:v:8:y:2020:i:1:p:302-318
    as

    Download full text from publisher

    File URL: https://techniumscience.com/index.php/socialsciences/article/view/890/278
    Download Restriction: no
    File URL: https://techniumscience.com/index.php/socialsciences/article/view/890/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ivan Svetunkov & Fotios Petropoulos, 2018. "Old dog, new tricks: a modelling view of simple moving averages," International Journal of Production Research, Taylor & Francis Journals, vol. 56(18), pages 6034-6047, September.
    2. Ye, Cheng & Qiu, Yanjun & Lu, Guohao & Hou, Yawen, 2018. "Quantitative strategy for the Chinese commodity futures market based on a dynamic weighted money flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1009-1018.
    3. V Subramanian & K P Balakrishnan, 2014. "Efficacy of Refined Macd Indicators: Evidence from Indian Stock Markets," The IUP Journal of Applied Finance, IUP Publications, vol. 20(1), pages 76-91, January.
    4. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
    5. Aikman, David & Galesic, Mirta & Gigerenzer, Gerd & Kapadia, Sujit & Katsikopoulos, Konstantinos & Kothiyal, Amit & Murphy, Emma & Neumann, Tobias, 2014. "Financial Stability Paper No 28: Taking uncertainty seriously - simplicity versus complexity in financial regulation," Bank of England Financial Stability Papers 28, Bank of England.
    6. Gilat Levy & Ronny Razin, 2015. "Correlation Neglect, Voting Behavior, and Information Aggregation," American Economic Review, American Economic Association, vol. 105(4), pages 1634-1645, April.
    7. Alberto Abadie & Kasy, Maximilian, 2017. "The risk of machine learning," Working Paper 383316, Harvard University OpenScholar.
    8. Jying‐Nan Wang & Hung‐Chun Liu & Jiangze Du & Yuan‐Teng Hsu, 2019. "Economic benefits of technical analysis in portfolio management: Evidence from global stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 890-902, April.
    Full references (including those not matched with items on IDEAS)

    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. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    2. Popoyan, Lilit & Napoletano, Mauro & Roventini, Andrea, 2017. "Taming macroeconomic instability: Monetary and macro-prudential policy interactions in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 117-140.
    3. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    4. Makarewicz, Tomasz, 2021. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 626-673.
    5. repec:hal:spmain:info:hdl:2441/5bnglqth5987gaq6dhju3psjn3 is not listed on IDEAS
    6. Zhu, Manhong & Schmitz, Andrew & Schmitz, Troy G., "undated". "What are the Culprits Causing Obesity? A Machine Learning Approach in Variable Selection and Parameter Coefficient Inference," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 261220, Agricultural and Applied Economics Association.
    7. Steven Lehrer & Tian Xie & Tao Zeng, 2021. "Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures? [Regression Models with Mixed Sampling Frequencies]," Journal of Financial Econometrics, Oxford University Press, vol. 19(5), pages 910-933.
    8. Francesco Lamperti & Antoine Mandel & Mauro Napoletano & Alessandro Sapio & Andrea Roventini & Tomas Balint & Igor Khorenzhenko, 2017. "Taming macroeconomic instability," SciencePo Working papers Main hal-03399574, HAL.
    9. Haoge Chang & Yusuke Narita & Kota Saito, 2022. "Approximating Choice Data by Discrete Choice Models," Papers 2205.01882, arXiv.org, revised Dec 2023.
    10. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    11. Prato, Carlo & Wolton, Stephane, 2018. "Rational ignorance, populism, and reform," European Journal of Political Economy, Elsevier, vol. 55(C), pages 119-135.
    12. Nunnari, Salvatore & Zapal, Jan, 2017. "A Model of Focusing in Political Choice," CEPR Discussion Papers 12407, C.E.P.R. Discussion Papers.
    13. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
    14. William Forbes & Egor Kiselev & Len Skerratt, 2023. "The stability and downside risk to contrarian profits: Evidence from the S&P 500," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 733-750, January.
    15. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    16. Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
    17. Levy, Gilat & Razin, Ronny, 2018. "Information diffusion in networks with the Bayesian Peer Influence heuristic," Games and Economic Behavior, Elsevier, vol. 109(C), pages 262-270.
    18. Fiona Burlig & Christopher Knittel & David Rapson & Mar Reguant & Catherine Wolfram, 2020. "Machine Learning from Schools about Energy Efficiency," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(6), pages 1181-1217.
    19. Louis R. Nemzer & Florence Neymotin, 2020. "Concierge care and patient reviews," Health Economics, John Wiley & Sons, Ltd., vol. 29(8), pages 913-922, August.
    20. Razin, Ronny & Levy, Gilat, 2020. "Combining forecasts in the presence of ambiguity over correlation structures," LSE Research Online Documents on Economics 104641, London School of Economics and Political Science, LSE Library.
    21. Marco Migueis, 2017. "Forward-looking and Incentive-compatible Operational Risk Capital Framework," Finance and Economics Discussion Series 2017-087, Board of Governors of the Federal Reserve System (U.S.).

    More about this item

    Keywords

    Artificial intelligence; stock market; technical analysis;
    All these keywords.

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:tec:journl:v:8:y:2020:i:1:p:302-318. 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: Tasente Tanase (email available below). General contact details of provider: .

    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.