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【广发金工】DeepSeek定量解析基金季报行业观点及行业轮动策略构建
广发金融工程研究·2025-04-08 03:35

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].