LLM如何提升金融投资决策能力
Integrating Large Language Models in Financial Investments and Market Analysis: A Survey
- 按四大框架梳理LLM在金融中的应用方法
- 覆盖选股、风险评估、情绪分析等关键场景
- 适合关注AI金融落地的研究者与从业者
大型语言模型（LLMs）被应用于金融决策，增强了投资策略的分析能力。传统投资策略多依赖量化模型、基本面分析和技术指标，而LLMs可处理大量结构化与非结构化数据，实时提取洞察并辅助决策。本综述系统梳理了近期金融领域中LLM的研究进展，将其贡献归纳为四类：基于LLM的框架与流程、混合集成方法、微调与适配技术、基于代理的架构。研究涵盖股票选择、风险评估、情感分析、交易与金融预测等应用场景。通过回顾现有文献，本文揭示了LLM在金融市场中的潜力、挑战与未来方向。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have been employed in financial decision making, enhancing analytical capabilities for investment strategies. Traditional investment strategies often utilize quantitative models, fundamental analysis, and technical indicators. However, LLMs have introduced new capabilities to process and analyze large volumes of structured and unstructured data, extract meaningful insights, and enhance decision-making in real-time. This survey provides a structured overview of recent research on LLMs within the financial domain, categorizing research contributions into four main frameworks: LLM-based Frameworks and Pipelines, Hybrid Integration Methods, Fine-Tuning and Adaptation Approaches, and Agent-Based Architectures. This study provides a structured review of recent LLMs research on applications in stock selection, risk assessment, sentiment analysis, trading, and financial forecasting. By reviewing the existing literature, this study highlights the capabilities, challenges, and potential directions of LLMs in financial markets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。