从直觉到AI代理，重构金融超额收益的演化路径
The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents
- 提出五阶段演进框架，涵盖人工策略到LLM驱动智能体
- 强调实时推理与多模态决策能力，推动系统从预测转向自主决策
- 聚焦可解释性与合规性，适合关注AI金融落地的研究者
追求超越市场基准的超额收益（alpha）正经历深刻变革，从依赖直觉的投资转向由人工智能驱动的自主系统。本文提出一个完整的五阶段分类体系，系统梳理了从人工策略、统计模型、经典机器学习、深度学习到大语言模型（LLM）赋能的智能体架构的演进历程。不同于以往局限于建模技术的综述，本研究采用系统视角，整合表示学习、多模态数据融合与工具增强的LLM代理进展。重点强调从静态预测器向具备上下文感知、实时推理、情景模拟和跨模态决策能力的金融智能体的战略转变。同时探讨了可解释性、数据脆弱性、治理及监管合规等生产部署中的关键挑战。所提出的分类框架为评估系统成熟度、对齐基础设施、指导下一代alpha系统的负责任发展提供了统一范式。
原文摘要 · Abstract (English)
The pursuit of alpha returns that exceed market benchmarks has undergone a profound transformation, evolving from intuition-driven investing to autonomous, AI powered systems. This paper introduces a comprehensive five stage taxonomy that traces this progression across manual strategies, statistical models, classical machine learning, deep learning, and agentic architectures powered by large language models (LLMs). Unlike prior surveys focused narrowly on modeling techniques, this review adopts a system level lens, integrating advances in representation learning, multimodal data fusion, and tool augmented LLM agents. The strategic shift from static predictors to contextaware financial agents capable of real time reasoning, scenario simulation, and cross modal decision making is emphasized. Key challenges in interpretability, data fragility, governance, and regulatory compliance areas critical to production deployment are examined. The proposed taxonomy offers a unified framework for evaluating maturity, aligning infrastructure, and guiding the responsible development of next generation alpha systems.
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