IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2212.08734.html
   My bibliography  Save this paper

Using Intermarket Data to Evaluate the Efficient Market Hypothesis with Machine Learning

Author

Listed:
  • N'yoma Diamond
  • Grant Perkins

Abstract

In its semi-strong form, the Efficient Market Hypothesis (EMH) implies that technical analysis will not reveal any hidden statistical trends via intermarket data analysis. If technical analysis on intermarket data reveals trends which can be leveraged to significantly outperform the stock market, then the semi-strong EMH does not hold. In this work, we utilize a variety of machine learning techniques to empirically evaluate the EMH using stock market, foreign currency (Forex), international government bond, index future, and commodities future assets. We train five machine learning models on each dataset and analyze the average performance of these models for predicting the direction of future S&P 500 movement as approximated by the SPDR S&P 500 Trust ETF (SPY). From our analysis, the datasets containing bonds, index futures, and/or commodities futures data notably outperform baselines by substantial margins. Further, we find that the usage of intermarket data induce statistically significant positive impacts on the accuracy, macro F1 score, weighted F1 score, and area under receiver operating characteristic curve for a variety of models at the 95% confidence level. This provides strong empirical evidence contradicting the semi-strong EMH.

Suggested Citation

  • N'yoma Diamond & Grant Perkins, 2022. "Using Intermarket Data to Evaluate the Efficient Market Hypothesis with Machine Learning," Papers 2212.08734, arXiv.org, revised Dec 2022.
  • Handle: RePEc:arx:papers:2212.08734
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2212.08734
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nikola Milosevic, 2016. "Equity forecast: Predicting long term stock price movement using machine learning," Papers 1603.00751, arXiv.org, revised Nov 2018.
    2. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda & Dirk Helbing, 2013. "Are Random Trading Strategies More Successful than Technical Ones?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-13, July.
    3. Gili Yen & Cheng-few Lee, 2008. "Efficient Market Hypothesis (EMH): Past, Present and Future," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 305-329.
    4. A. E. Biondo & A. Pluchino & A. Rapisarda & D. Helbing, 2013. "Are random trading strategies more successful than technical ones?," Papers 1303.4351, arXiv.org, revised Jul 2013.
    5. Ang, Andrew & Piazzesi, Monika & Wei, Min, 2006. "What does the yield curve tell us about GDP growth?," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 359-403.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    7. Marc Wildi & Branka Hadji Misheva, 2022. "A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection," Papers 2212.02906, arXiv.org.
    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. Tran Phuoc & Pham Thi Kim Anh & Phan Huy Tam & Chien V. Nguyen, 2024. "Applying machine learning algorithms to predict the stock price trend in the stock market – The case of Vietnam," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
    2. Yan Li & Bo Zheng & Ting-Ting Chen & Xiong-Fei Jiang, 2017. "Fluctuation-driven price dynamics and investment strategies," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-15, December.
    3. Piekunko-Mantiuk Iwona, 2019. "Parameterized Trade on the Futures Market on the WIG20," Folia Oeconomica Stetinensia, Sciendo, vol. 19(1), pages 114-125, June.
    4. Paulo Ferreira & Éder J.A.L. Pereira & Hernane B.B. Pereira, 2020. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena," JRFM, MDPI, vol. 13(7), pages 1-10, July.
    5. Alessandro Pluchino & Giulio Burgio & Andrea Rapisarda & Alessio Emanuele Biondo & Alfredo Pulvirenti & Alfredo Ferro & Toni Giorgino, 2019. "Exploring the role of interdisciplinarity in physics: Success, talent and luck," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-15, June.
    6. Yardley, Ben, 2020. "The Effects of Donald Trump’s Tweets on The Stock Exchange," MPRA Paper 102578, University Library of Munich, Germany.
    7. Alessandro Pluchino & Alessio Emanuele Biondo & Andrea Rapisarda, 2018. "Talent Versus Luck: The Role Of Randomness In Success And Failure," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 21(03n04), pages 1-31, May.
    8. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
    9. Huishu Zhang & Jianrong Wei & Jiping Huang, 2014. "Scaling and Predictability in Stock Markets: A Comparative Study," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-5, March.
    10. Stefan, F.M. & Atman, A.P.F., 2023. "Asymmetric rate of returns and wealth distribution influenced by the introduction of technical analysis into a behavioral agent-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    11. Stijn Claessens & M Ayhan Kose, 2017. "Asset prices and macroeconomic outcomes: a survey," BIS Working Papers 676, Bank for International Settlements.
    12. Mobarek, Asma & Fiorante, Angelo, 2014. "The prospects of BRIC countries: Testing weak-form market efficiency," Research in International Business and Finance, Elsevier, vol. 30(C), pages 217-232.
    13. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," JRFM, MDPI, vol. 12(2), pages 1-18, June.
    14. Mikio Ito & Akihiko Noda & Tatsuma Wada, 2016. "The evolution of stock market efficiency in the US: a non-Bayesian time-varying model approach," Applied Economics, Taylor & Francis Journals, vol. 48(7), pages 621-635, February.
    15. Charfeddine, Lanouar & Khediri, Karim Ben, 2016. "Time varying market efficiency of the GCC stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 487-504.
    16. F. M. Stefan & A. P. F. Atman, 2017. "Asymmetric return rates and wealth distribution influenced by the introduction of technical analysis into a behavioral agent based model," Papers 1711.08282, arXiv.org.
    17. Bianchi, Sergio & Pianese, Augusto, 2018. "Time-varying Hurst–Hölder exponents and the dynamics of (in)efficiency in stock markets," Chaos, Solitons & Fractals, Elsevier, vol. 109(C), pages 64-75.
    18. Hasan A?an Karaduman, 2016. "Stylized Facts And Weak-Form Efficiency In Turkish Stock Market," Proceedings of International Academic Conferences 4006651, International Institute of Social and Economic Sciences.
    19. Chengwei Liu, 2021. "In luck we trust: Capturing the diversity bonus through random selection," Journal of Organization Design, Springer;Organizational Design Community, vol. 10(2), pages 85-91, June.
    20. Caserta, Maurizio & Pluchino, Alessandro & Rapisarda, Andrea & Spagano, Salvatore, 2021. "Why lot? How sortition could help representative democracy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2212.08734. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.