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

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. John Phan & Hung-Fu Chang, 2024. "Leveraging Fundamental Analysis for Stock Trend Prediction for Profit," Papers 2410.03913, arXiv.org.
    2. Ullah, Rafid & Ismail, Hishamuddin Bin & Islam Khan, Mohammad Tariqul & Zeb, Ali, 2024. "Nexus between Chat GPT usage dimensions and investment decisions making in Pakistan: Moderating role of financial literacy," Technology in Society, Elsevier, vol. 76(C).
    3. 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.

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