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Artificial Intelligence in Trading the Financial Markets


  • Gil Cohen


Purpose: The purpose of this study is to review the methods that are used to construct Artificial Intelligence (AI) algorithmic trading systems. Design/Methodology/Approach: A review approach of the existing knowledge was used. Findings: We find that there are various methodologies that are used by researchers and practitioners when they contract algorithmic trading systems. Some of the systems combine data from the financial markets alone and some methods combine financial data with social media data. The ability of computerized algorithms to integrate a large set of data and react almost immediately according to it, does not come without risks of accelerating downtrends in times of panic in the financial market and therefore those systems must be institutionally monitored by the regulating authorities. Practice Implication: This study enables readers to understand the major methodologies that are used to predict trends of financial assets prices. The research identifies and explains the complexity of methods that helps traders to improve their trading results. Originality Value: No past study has summarized the major methodologies that are used to construct and optimize trading results.

Suggested Citation

  • Gil Cohen, 2022. "Artificial Intelligence in Trading the Financial Markets," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 101-110.
  • Handle: RePEc:ers:ijebaa:v:x:y:2022:i:1:p:101-110

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    References listed on IDEAS

    1. Ben Marshall & Martin Young & Rochester Cahan, 2008. "Are candlestick technical trading strategies profitable in the Japanese equity market?," Review of Quantitative Finance and Accounting, Springer, vol. 31(2), pages 191-207, August.
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    3. Marshall, Ben R. & Young, Martin R. & Rose, Lawrence C., 2006. "Candlestick technical trading strategies: Can they create value for investors?," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2303-2323, August.
    4. Yue Liu & Aijun Yang & Jijian Zhang & Jingjing Yao, 2020. "An Optimal Stopping Problem of Detecting Entry Points for Trading Modeled by Geometric Brownian Motion," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 827-843, March.
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    6. 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.
    7. Horton, Marshall J., 2009. "Stars, crows, and doji: The use of candlesticks in stock selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 283-294, May.
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    More about this item


    Algorithmic; trading; technical analysis; artificial intelligence.;
    All these keywords.

    JEL classification:

    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation


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