综述金融AI三大方向：预测、决策与信息增强，揭示理论与落地的差距。
A Survey of Financial AI: Architectures, Advances and Open Challenges
- 构建三维度框架：市场预测、交易决策、非结构化信息利用
- 提出基础模型、图架构、分层优化等关键技术进展
- 指出高频交易中模型复杂度与实际约束的矛盾
金融AI推动了金融市场预测、投资组合优化和自动化交易的高级方法发展。本综述系统分析了三个主要维度：捕捉复杂市场动态的预测模型、优化交易与投资策略的决策框架，以及利用非结构化金融信息的知识增强系统。我们考察了关键创新，包括金融时间序列的基础模型、用于市场关系建模的图架构，以及用于投资组合优化的分层框架。分析揭示了模型复杂性与实际约束之间的显著权衡，特别是在高频交易应用中。文中识别出理论进展与工业实现间的重大差距，指明了提升模型性能与实用性的开放挑战与机遇。
原文摘要 · Abstract (English)
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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