IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2502.15853.html
   My bibliography  Save this paper

Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies

Author

Listed:
  • Daksh Dave
  • Gauransh Sawhney
  • Vikhyat Chauhan

Abstract

This paper presents a comprehensive study on stock price prediction, leveragingadvanced machine learning (ML) and deep learning (DL) techniques to improve financial forecasting accuracy. The research evaluates the performance of various recurrent neural network (RNN) architectures, including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and attention-based models. These models are assessed for their ability to capture complex temporal dependencies inherent in stock market data. Our findings show that attention-based models outperform other architectures, achieving the highest accuracy by capturing both short and long-term dependencies. This study contributes valuable insights into AI-driven financial forecasting, offering practical guidance for developing more accurate and efficient trading systems.

Suggested Citation

  • Daksh Dave & Gauransh Sawhney & Vikhyat Chauhan, 2025. "Multi-Agent Stock Prediction Systems: Machine Learning Models, Simulations, and Real-Time Trading Strategies," Papers 2502.15853, arXiv.org.
    • Handle: RePEc:arx:papers:2502.15853
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2502.15853
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dharmaraja Selvamuthu & Vineet Kumar & Abhishek Mishra, 2019. "Indian stock market prediction using artificial neural networks on tick data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-12, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shankhajyoti De & Arabin Kumar Dey & Deepak Kumar Gouda, 2022. "Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network," Annals of Data Science, Springer, vol. 9(2), pages 271-284, April.
    2. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
    3. Muhammad Ateeq ur REHMAN & Furman ALI & Shang XIE, 2022. "Impact of Foreign Investment News on the Return, Cost of Equity and Cash Flow Activities," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 112-127, December.
    4. Changshi Liu & Gang Kou & Yi Peng & Fawaz E. Alsaadi, 2019. "Location-Routing Problem for Relief Distribution in the Early Post-Earthquake Stage from the Perspective of Fairness," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    5. Goel Himanshu & Agarwal Monika & Chhabra Meghna & Som Bhupender Kumar, 2023. "The Predictive Power of Macroeconomic Variables on the Indian Stock Market Utilizing an Ann Model Approach: An Empirical Investigation Based on BSE Sensex," Folia Oeconomica Stetinensia, Sciendo, vol. 23(2), pages 116-131, December.
    6. Htet Htet Htun & Michael Biehl & Nicolai Petkov, 2023. "Survey of feature selection and extraction techniques for stock market prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-25, December.
    7. Fahad Mostafa & Pritam Saha & Mohammad Rafiqul Islam & Nguyet Nguyen, 2021. "GJR-GARCH Volatility Modeling under NIG and ANN for Predicting Top Cryptocurrencies," JRFM, MDPI, vol. 14(9), pages 1-22, September.
    8. Xiaojie Xu & Yun Zhang, 2022. "Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 169-181, July.
    9. Shankhyajyoti De & Arabin Kumar Dey & Deepak Gauda, 2020. "Construction of confidence interval for a univariate stock price signal predicted through Long Short Term Memory Network," Papers 2007.00254, arXiv.org.
    10. Dhruhi Sheth & Manan Shah, 2023. "Predicting stock market using machine learning: best and accurate way to know future stock prices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 1-18, February.
    11. Hussain, Syed Mujahid & Ahmad, Nisar & Ahmed, Sheraz, 2023. "Applications of high-frequency data in finance: A bibliometric literature review," International Review of Financial Analysis, Elsevier, vol. 89(C).
    12. Xie, Wen-Jie & Li, Mu-Yao & Zhou, Wei-Xing, 2021. "Learning representation of stock traders and immediate price impacts," Emerging Markets Review, Elsevier, vol. 48(C).
    13. Xiaojie Xu & Yun Zhang, 2022. "Forecasting the total market value of a shares traded in the Shenzhen stock exchange via the neural network," Economics Bulletin, AccessEcon, vol. 42(3), pages 1266-1279.
    14. Xiaojie Xu & Yun Zhang, 2023. "Neural network predictions of the high-frequency CSI300 first distant futures trading volume," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 191-207, June.
    15. Deniz Can Yıldırım & Ismail Hakkı Toroslu & Ugo Fiore, 2021. "Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-36, December.
    16. Tai-Liang Chen & Ching-Hsue Cheng & Jing-Wei Liu, 2019. "A Causal Time-Series Model Based on Multilayer Perceptron Regression for Forecasting Taiwan Stock Index," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(06), pages 1967-1987, November.
    17. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
    18. Suppawong Tuarob & Poom Wettayakorn & Ponpat Phetchai & Siripong Traivijitkhun & Sunghoon Lim & Thanapon Noraset & Tipajin Thaipisutikul, 2021. "DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-32, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2502.15853. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.