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Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies

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  • Gil Cohen

    (Department of Management, Western Galilee Academic College, Acre 2412101, Israel)

Abstract

Artificial Intelligence (AI) has been recently recognized as an essential aid for human traders. The advantages of the AI systems over human traders are that they can analyze an extensive data set from different sources in a fraction of a second and perform actual high-frequency trading (HFT) that can take advantage of market anomalies and price differences. This paper reviews the most important papers published in recent years that use the most advanced techniques to forecast financial asset trends and answer the question of whether those techniques can be used to successfully trade the complex financial markets. All systems use deep learning (DL) and machine learning (ML) protocols to explore nonobvious correlations and phenomena that influence the probability of trading success. Their predictions are based on linear or nonlinear models often combined with social media investors’ sentiment derivations or pattern recognitions. Most of the reviewed papers have proven the successful ability of their developed system to trade the financial markets.

Suggested Citation

  • Gil Cohen, 2022. "Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies," Mathematics, MDPI, vol. 10(18), pages 1-13, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3302-:d:912640
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    References listed on IDEAS

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    Cited by:

    1. Alexander Brunhuemer & Lukas Larcher & Philipp Seidl & Sascha Desmettre & Johannes Kofler & Gerhard Larcher, 2022. "Supervised Machine Learning Classification for Short Straddles on the S&P500," Risks, MDPI, vol. 10(12), pages 1-25, December.
    2. Ardita TODRI & Petraq PAPAJORGJI, 2024. "Artificial Intelligence Waves In Financial Services Industry: An Evolution Factorial Analysis," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(2), pages 63-75, June.
    3. Muhammad Arslan & Ahmed Imran Hunjra & Wajid Shakeel Ahmed & Younes Ben Zaied, 2024. "Forecasting multi‐frequency intraday exchange rates using deep learning models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1338-1355, August.

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