Core Insights - The report presents a systematic framework for automated factor research based on Large Language Models (LLM) and Prompt Engineering, aiming to explore the potential applications of AI in the entire quantitative investment chain [1] - The framework was first applied to low-frequency price-volume factors, optimizing the classic Alpha158 factor library and transitioning from an "optimization" paradigm to a "generation" paradigm [1] - AI demonstrated strong factor discovery capabilities in both fundamental and high-frequency data domains, successfully generating new factors and enhancing traditional factor libraries [1] - The report also explores AI's application in unstructured text analysis, utilizing the Gemini model to interpret sentiment from extensive research memos, creating unique sentiment indicators that effectively integrate into stock selection strategies [1] Group 1: Low-Frequency Price-Volume Factor Optimization - The framework was initially applied to the optimization of low-frequency price-volume factors, using the Alpha158 factor library as a foundation for optimization experiments [1] - AI identified logical flaws in original factors and proposed effective improvements, with optimization effects being consistent across multiple time windows from 5 to 60 days [1] - New factors generated by AI, with low correlation to sample factors, showed robust out-of-sample performance, with some factors achieving an Information Coefficient Information Ratio (ICIR) above 1.0 [1] Group 2: Fundamental and High-Frequency Factor Discovery - In the fundamental dimension, AI not only generated enhanced versions of classic factors but also innovatively expanded value, quality, and growth factors from novel perspectives [1] - In the high-frequency dimension, AI was empowered to directly generate Python code, uncovering a set of novel and high-performing high-frequency factors, with some strong signal factors achieving annualized returns exceeding 60% [1] - Integrating the AI-generated high-frequency factor library into the AGRU neural network model significantly improved annualized excess returns from 18.24% to 25.28% [1] Group 3: Alternative Data Processing and Sentiment Analysis - The report investigates AI's potential in processing alternative data, analyzing nearly one million words of research memos using the Gemini 2.5 Pro model [1] - A weekly sentiment factor was constructed, revealing unique asymmetric predictive capabilities, where negative sentiment strongly predicted future price declines, achieving annualized excess returns of 8.26% [1] - This sentiment factor exhibited low correlation with traditional price-volume and fundamental factors, serving as an independent and effective supplementary information source [1] Group 4: Comprehensive Strategy Development - A multi-dimensional information fusion strategy was developed, integrating AI-discovered high-frequency factors with low-frequency market data into the AGRU neural network to form a core Alpha [1] - The final strategy, enhanced by AI sentiment factors for risk adjustment, improved annualized excess returns from 11.15% to 11.81% while maintaining turnover rates [1] - The strategy demonstrated a significant increase in the information ratio from 2.18 to 2.31, validating AI's potential to empower quantitative research across multiple stages and achieve a "1+1>2" effect [1]

金融工程丨深度报告 [Table_Title] 多因子选股（二十一）：日历效应下的因子投资 %% %% %% %% research.95579.com 1 丨证券研究报告丨 报告要点 [Table_Summary] 受市场交易影响，不同类别的因子在年内表现有所不同，寻找因子的日历效应规律，可以对因 子选股的收益进行增强，并降低尾部风险。 分析师及联系人 [Table_Author] 郑起 覃川桃 SAC：S0490520060001 SAC：S0490513030001 SFC：BUT353 请阅读最后评级说明和重要声明 2 / 29 %% %% %% %% 2 [Table_Title2] 多因子选股（二十一）：日历效应下的因子投资 [Table_Summary2] 因子的日历效应 本文对 12 个大类因子，共统计了季度、年末年初、春节、两会、国庆五种日历效应： 日历效应指数增强 根据因子的日历效应，在大类因子合成权重上进行调整，构建月度调仓频率的增强策略： 短期展望 进入十二月后，年末年初效应规律为以低波、拥挤度、质量、价值为代表的低风险偏好因子更 为有效，考虑到当前高 Beta 的成长风格仍有一定概率 ...