标题： 将大型语言模型应用于金融投资与市场分析：综述 显示英文标题

标题： Integrating Large Language Models in Financial Investments and Market Analysis: A Survey

摘要： 大型语言模型（LLMs）已被应用于金融决策，增强了投资策略的分析能力。 传统投资策略通常利用量化模型、基本面分析和技术指标。 然而，LLMs 引入了新的能力，可以处理和分析大量结构化和非结构化数据，提取有意义的见解，并在实时中增强决策能力。 本综述提供了对金融领域内 LLMs 最新研究的结构化概述，将研究贡献分为四个主要框架： 基于 LLM 的框架和流程，混合集成方法，微调和适应方法，以及基于代理的架构。 本研究对最近在股票选择、风险评估、情感分析、交易和金融预测中的 LLMs 应用进行了结构化综述。 通过回顾现有文献，本研究突出了 LLMs 在金融市场中的能力、挑战和潜在发展方向。 显示英文摘要

摘要： 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.