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Comprehensive Study on Stock Investment Behavior and Risk Based on Artificial Intelligence, Big Data and Multi-agent Simulation

In: Proceedings of the 2025 International Conference on Financial Innovation and Marketing Management (FIMM 2025)

Author

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  • Yixin Zhang

    (Western Michigan Institute, Guizhou University of Finance and Economics)

Abstract

In the context of the rapid development of science and technology in the 21st century, artificial intelligence has become a key factor in global competition and has promoted profound changes in the financial field. This article reviews the current status and trends of artificial intelligence applications in the financial field, especially in stock investment, and focuses on analyzing the impact of individual investors’ trading behavior on market fluctuations in the Chinese market. This paper aims to provide support for investment decision-making and risk management by exploring the use of big data, machine learning, and multi-agent simulation technology to collect and analyze multi-source information (including investor trading records, social media data, and market news), as well as multi-agent models to simulate investor behavioral interactions and evaluate the impact of financial technology on information dissemination. At the same time, this article deeply studies the practicality of artificial intelligence, big data, and multi-agent simulation, provides a new perspective for stock investment research, promotes the development of financial technology, and promotes market intelligence, efficiency and stability.

Suggested Citation

  • Yixin Zhang, 2025. "Comprehensive Study on Stock Investment Behavior and Risk Based on Artificial Intelligence, Big Data and Multi-agent Simulation," Advances in Economics, Business and Management Research, in: Maizaitulaidawati Md Husin (ed.), Proceedings of the 2025 International Conference on Financial Innovation and Marketing Management (FIMM 2025), pages 205-212, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-874-5_26
    DOI: 10.2991/978-94-6463-874-5_26
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