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Unveiling the Influence of Artificial Intelligence and Machine Learning on Financial Markets: A Comprehensive Analysis of AI Applications in Trading, Risk Management, and Financial Operations

Author

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  • Mohammad El Hajj

    (Business School, Holy Spirit University of Kaslik, Jounieh P.O. Box 446, Lebanon)

  • Jamil Hammoud

    (College of Business Administration, Rafik Hariri University, P.O. Box 10, Damour-Chouf 2010, Lebanon)

Abstract

This study explores the adoption and impact of artificial intelligence (AI) and machine learning (ML) in financial markets, utilizing a mixed-methods approach that includes a quantitative survey and a qualitative analysis of existing research papers, reports, and articles. The quantitative results demonstrate the growing adoption of AI and ML technologies in financial institutions and their most common applications, such as algorithmic trading, risk management, fraud detection, credit scoring, and customer service. Additionally, the qualitative analysis identifies key themes, including AI and ML adoption trends, challenges and barriers to adoption, the role of regulation, workforce transformation, and ethical and social considerations. The study highlights the need for financial professionals to adapt their skills and for organizations to address challenges, such as data privacy concerns, regulatory compliance, and ethical considerations. The research contributes to the knowledge on AI and ML in finance, helping policymakers, regulators, and professionals understand their benefits and challenges.

Suggested Citation

  • Mohammad El Hajj & Jamil Hammoud, 2023. "Unveiling the Influence of Artificial Intelligence and Machine Learning on Financial Markets: A Comprehensive Analysis of AI Applications in Trading, Risk Management, and Financial Operations," JRFM, MDPI, vol. 16(10), pages 1-16, October.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:10:p:434-:d:1253685
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    References listed on IDEAS

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    1. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    2. Jagtiani, Julapa & Lemieux, Catharine, 2018. "Do fintech lenders penetrate areas that are underserved by traditional banks?," Journal of Economics and Business, Elsevier, vol. 100(C), pages 43-54.
    3. Christopher Krauss & Anh Do & Nicolas Huck, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," Post-Print hal-01768895, HAL.
    4. Dixon, Matthew & Klabjan, Diego & Bang, Jin Hoon, 2017. "Classification-based financial markets prediction using deep neural networks," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 67-77.
    5. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    6. Jessica Morley & Josh Cowls & Mariarosaria Taddeo & Luciano Floridi, 2020. "Ethical guidelines for COVID-19 tracing apps," Nature, Nature, vol. 582(7810), pages 29-31, June.
    7. repec:imf:imfdps:2021/024 is not listed on IDEAS
    8. Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
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    Cited by:

    1. Babapour-Azar, Ali & Khanjani-Shiraz, Rashed, 2024. "A neural network framework for portfolio optimization under second-order stochastic dominance," Finance Research Letters, Elsevier, vol. 66(C).

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