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A Survey of Financial AI: Architectures, Advances and Open Challenges

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  • Junhua Liu

Abstract

Financial AI empowers sophisticated approaches to financial market forecasting, portfolio optimization, and automated trading. This survey provides a systematic analysis of these developments across three primary dimensions: predictive models that capture complex market dynamics, decision-making frameworks that optimize trading and investment strategies, and knowledge augmentation systems that leverage unstructured financial information. We examine significant innovations including foundation models for financial time series, graph-based architectures for market relationship modeling, and hierarchical frameworks for portfolio optimization. Analysis reveals crucial trade-offs between model sophistication and practical constraints, particularly in high-frequency trading applications. We identify critical gaps and open challenges between theoretical advances and industrial implementation, outlining open challenges and opportunities for improving both model performance and practical applicability.

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  • Junhua Liu, 2024. "A Survey of Financial AI: Architectures, Advances and Open Challenges," Papers 2411.12747, arXiv.org.
    • Handle: RePEc:arx:papers:2411.12747
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    References listed on IDEAS

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    1. Jean Lee & Hoyoul Luis Youn & Josiah Poon & Soyeon Caren Han, 2023. "StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series," Papers 2301.09279, arXiv.org, revised Feb 2023.
    2. Han Ding & Yinheng Li & Junhao Wang & Hang Chen, 2024. "Large Language Model Agent in Financial Trading: A Survey," Papers 2408.06361, arXiv.org.
    3. Dong, Mengming Michael & Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2024. "A scoping review of ChatGPT research in accounting and finance," International Journal of Accounting Information Systems, Elsevier, vol. 55(C).
    4. Yu Zhao & Huaming Du & Ying Liu & Shaopeng Wei & Xingyan Chen & Fuzhen Zhuang & Qing Li & Ji Liu & Gang Kou, 2022. "Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks," Papers 2201.04965, arXiv.org, revised Jan 2022.
    5. Qian Hui & Tiandong Wang, 2024. "Mitigating Extremal Risks: A Network-Based Portfolio Strategy," Papers 2409.12208, arXiv.org.
    6. Zhizhuo Kou & Holam Yu & Jingshu Peng & Lei Chen, 2024. "Automate Strategy Finding with LLM in Quant investment," Papers 2409.06289, arXiv.org.
    7. Jean Lee & Nicholas Stevens & Soyeon Caren Han & Minseok Song, 2024. "A Survey of Large Language Models in Finance (FinLLMs)," Papers 2402.02315, arXiv.org.
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