广发证券资深金工分析师 李豪 lhao@gf.com.cn 广发证券首席金工分析师 安宁宁 anningning@gf.com.cn 广发金工安宁宁陈原文团队 摘要 大语言模型在金融领域的应用： 近年来，人工智能技术的快速发展推动了大语言模型(LLMs)的革新。作为最前沿的技术之一，大语言 模型正在广泛应用于各行各业。金融行业作为一个高度依赖数据分析和信息处理的领域，对先进的人工 智能技术有着极大的需求。而LLMs凭借其强大的文本理解能力、信息提取能力以及推理和预测能力， 正在逐步改变传统的金融分析和决策方式，为投资管理、市场分析、风险控制等多个领域带来了新的机 遇。 DeepSeek定量解析基金季报行业观点及行业轮动策略构建： 本文中，我们尝试通过DeepSeekV3模型，对于基金季报观点文本中的行业观点进行定量解析，并以此 出发构建行业轮动策略。具体来看，首先我们筛选存续时间较长的主动型权益基金样本，并提取样本基 金不同季度报告期季报中的观点部分文本；而后我们将观点文本输入至DeepSeek模型，加入特定提示 词控制输出的格式，并基于输出结果构建基金季报行业观点指标；最后我们基于基金季报行业观点指标 及观 ...

Group 1 - The core viewpoint of the article emphasizes the transformative potential of Large Language Models (LLMs) in the financial sector, particularly in investment management, market analysis, and risk control [1][7][8]. - LLMs can process vast amounts of unstructured data, such as news articles, social media, and financial reports, enabling faster access to critical information for investors [7][8]. - The DeepSeek model, a representative of advanced LLMs, showcases strong reasoning capabilities and cost-effectiveness, making high-performance AI technology more accessible [13][19]. Group 2 - The article discusses the quantitative analysis of fund quarterly reports using the DeepSeek V3 model to extract industry viewpoints and construct industry rotation strategies [2][22]. - Approximately 18,000 quarterly report texts were analyzed, focusing on active equity funds with a significant equity position over the past five years [26][31]. - The analysis revealed that the proportion of bullish and bearish viewpoints on various industries varies significantly, with certain sectors like electronics and pharmaceuticals receiving more attention [41][42]. Group 3 - The construction of industry viewpoint indicators is based on the quantitative analysis results, leading to the development of 14 indicators to capture the sentiment towards different industries [56][60]. - The article outlines various strategies for industry rotation based on the constructed indicators, highlighting the performance of different combinations during market conditions [62][66]. - The findings suggest that industries with high attention and bullish sentiment tend to perform better, while those with low attention and bearish sentiment may underperform [75][76].