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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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    1. Atsalakis, George S. & Atsalaki, Ioanna G. & Pasiouras, Fotios & Zopounidis, Constantin, 2019. "Bitcoin price forecasting with neuro-fuzzy techniques," European Journal of Operational Research, Elsevier, vol. 276(2), pages 770-780.
    2. Gil Cohen, 2022. "Algorithmic Strategies for Precious Metals Price Forecasting," Mathematics, MDPI, vol. 10(7), pages 1-12, April.
    3. Lahmiri, Salim & Bekiros, Stelios, 2020. "Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    4. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating cryptocurrency prices using machine learning," Papers 1805.08550, arXiv.org, revised Nov 2018.
    5. Ghosh, Pushpendu & Neufeld, Ariel & Sahoo, Jajati Keshari, 2022. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Finance Research Letters, Elsevier, vol. 46(PA).
    6. Gil Cohen, 2021. "Optimizing candlesticks patterns for Bitcoin's trading systems," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1155-1167, October.
    7. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating Cryptocurrency Prices Using Machine Learning," Complexity, Hindawi, vol. 2018, pages 1-16, November.
    8. Gil Cohen & Mahmoud Qadan, 2022. "The Complexity of Cryptocurrencies Algorithmic Trading," Mathematics, MDPI, vol. 10(12), pages 1-11, June.
    9. Young Bin Kim & Jurim Lee & Nuri Park & Jaegul Choo & Jong-Hyun Kim & Chang Hun Kim, 2017. "When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    10. Ni, Yensen & Day, Min-Yuh & Huang, Paoyu & Yu, Shang-Ru, 2020. "The profitability of Bollinger Bands: Evidence from the constituent stocks of Taiwan 50," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    11. Bizzi, Lorenzo & Labban, Alice, 2019. "The double-edged impact of social media on online trading: Opportunities, threats, and recommendations for organizations," Business Horizons, Elsevier, vol. 62(4), pages 509-519.
    12. 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.
    13. Suhwan Ji & Jongmin Kim & Hyeonseung Im, 2019. "A Comparative Study of Bitcoin Price Prediction Using Deep Learning," Mathematics, MDPI, vol. 7(10), pages 1-20, September.
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    Cited by:

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    2. 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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