IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2107.01031.html
   My bibliography  Save this paper

Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning

Author

Listed:
  • Sohrab Mokhtari
  • Kang K. Yen
  • Jin Liu

Abstract

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. In the technical analysis, the historical price data is exploited from Yahoo Finance, and in fundamental analysis, public tweets on Twitter associated with the stock market are investigated to assess the impact of sentiments on the stock market's forecast. The results show a median performance, implying that with the current technology of AI, it is too soon to claim AI can beat the stock markets.

Suggested Citation

  • Sohrab Mokhtari & Kang K. Yen & Jin Liu, 2021. "Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning," Papers 2107.01031, arXiv.org.
    • Handle: RePEc:arx:papers:2107.01031
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2107.01031
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, Wiley Blackwell, vol. 38, pages 1-41.
    2. Xiao Zhong & David Enke, 2019. "Predicting the daily return direction of the stock market using hybrid machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-20, December.
    3. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    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. Mohammad Javad Bazrkar & Soodeh Hosseini, 2023. "Predict Stock Prices Using Supervised Learning Algorithms and Particle Swarm Optimization Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 165-186, June.

    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. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    2. Bartram, Söhnke M. & Grinblatt, Mark, 2018. "Agnostic fundamental analysis works," Journal of Financial Economics, Elsevier, vol. 128(1), pages 125-147.
    3. Lu Zhang, 2017. "The Investment CAPM," European Financial Management, European Financial Management Association, vol. 23(4), pages 545-603, September.
    4. Lu Zhang, 2019. "Q-factors and Investment CAPM," NBER Working Papers 26538, National Bureau of Economic Research, Inc.
    5. Erica X. N. Li & Dmitry Livdan & Lu Zhang, 2009. "Anomalies," The Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4301-4334, November.
    6. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    7. Degenhardt, Thomas & Auer, Benjamin R., 2018. "The “Sell in May” effect: A review and new empirical evidence," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 169-205.
    8. Dewandaru, Ginanjar & Masih, Rumi & Bacha, Obiyathulla Ismath & Masih, A. Mansur. M., 2015. "Combining momentum, value, and quality for the Islamic equity portfolio: Multi-style rotation strategies using augmented Black Litterman factor model," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 205-232.
    9. Edward Lee & Konstantinos Stathopoulos & Mark Hon, 2006. "Investigating the return predictability of changes in corporate borrowing," Accounting and Business Research, Taylor & Francis Journals, vol. 36(2), pages 93-107.
    10. Agarwal, Vikas & Wang, Lingling, 2007. "Transaction costs and value premium," CFR Working Papers 07-06, University of Cologne, Centre for Financial Research (CFR).
    11. Fernando Rubio, 2005. "Eficiencia De Mercado, Administracion De Carteras De Fondos Y Behavioural Finance," Finance 0503028, University Library of Munich, Germany, revised 23 Jul 2005.
    12. Dionysia Dionysiou, 2015. "Choosing Among Alternative Long-Run Event-Study Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 158-198, February.
    13. Lee, Charles M. C., 2001. "Market efficiency and accounting research: a discussion of 'capital market research in accounting' by S.P. Kothari," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 233-253, September.
    14. Christiane Goodfellow & Dirk Schiereck & Steffen Wippler, 2013. "Are behavioural finance equity funds a superior investment? A note on fund performance and market efficiency," Journal of Asset Management, Palgrave Macmillan, vol. 14(2), pages 111-119, April.
    15. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    16. Dichev, Ilia D. & Qian, Jingyi, 2022. "The benefits of transaction-level data: The case of NielsenIQ scanner data," Journal of Accounting and Economics, Elsevier, vol. 74(1).
    17. Nam, Kiseok & Pyun, Chong Soo & Kim, Sei-Wan, 2003. "Is asymmetric mean-reverting pattern in stock returns systematic? Evidence from Pacific-basin markets in the short-horizon," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 13(5), pages 481-502, December.
    18. Ito, Akitoshi, 1999. "Profits on technical trading rules and time-varying expected returns: evidence from Pacific-Basin equity markets," Pacific-Basin Finance Journal, Elsevier, vol. 7(3-4), pages 283-330, August.
    19. Carlo Rosa & Giovanni Verga, 2006. "The Impact of Central Bank Announcements on Asset Prices in Real Time: Testing the Efficiency of the Euribor Futures Market," CEP Discussion Papers dp0764, Centre for Economic Performance, LSE.
    20. Xianfeng Jiang & Yongdong Shi, 2006. "The Impact of Insider Trading on the Secondary Market with Order-Driven System," Annals of Economics and Finance, Society for AEF, vol. 7(1), pages 129-143, May.

    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:2107.01031. 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.