Group 1 - The core concept of industry rotation strategy is to profit from structural market trends by switching between different industry sectors to maximize investment returns or mitigate systemic risks [1] - Industry rotation strategies are particularly effective in market environments where there are significant differences in returns among various industries during the same time period [1] Group 2 - Utilizing ETFs for industry rotation offers several advantages, including simplicity of operation, as investors can easily gain exposure to a basket of stocks representing an industry without the need to buy multiple individual stocks [2] - ETFs provide transparency in holdings, with daily disclosures of their portfolios, allowing investors to clearly understand their investments, unlike actively managed equity funds which may have delayed reporting [2] - The cost-effectiveness of ETFs is highlighted, as they can be traded at any time during the trading day, and generally have lower fees compared to traditional actively managed funds, enhancing investor returns [2] Group 3 - The method for constructing an industry rotation strategy using ETFs involves scoring ETFs based on a series of industry selection indicators, selecting those with higher scores for allocation, and making regular adjustments [3] - The scoring process includes evaluating the performance of the underlying stocks in terms of fundamentals, technicals, and capital flows, leading to an overall score for the ETF [3]

当ETF赛道深陷费率战与规模焦虑时，中证A500指数却以另类姿态撕开市场——这只诞生即被贴上"新锐"标 签的宽基指数，凭借对科创属性与中小市值的倾斜性覆盖，成为近两年机构博弈"贝塔收益"的主战场。 除了密集成立的指数基金以外，截至今年4月，全市场已有26只指数增强产品参与竞逐，不同产品之间分化 剧烈：两只成立时间间隔不到一个月的A500指数增强基金，目前的超额收益差值已经接近10%。 归根结底，A500指数"市值+行业双轮筛选"的编制原则，使得成份股市值和流动性分层显著，为量化模型留 足了"翻石头"的空间。因此，在选择A500指数增强基金时，基金经理的投资能力与增强策略变得至关重 要。 华安基金量化投资部助理总监、基金经理张序的突围密码，藏在八年磨一剑的"行业轮动+多因子"双擎模型 里。通过对行业轮动的深度理解和持续迭代，其管理的华安事件驱动量化基金自2020年执掌以来，连续五 年跑赢偏股混基指数，年化超额收益达9.3%，无论在公募量化还是主动股基均排名前1%。 而当市场还在争论主动量化与被动投资的边界时，华安基金已悄然完成中证A500产品线的战术合围。继 2024年精准卡位A500ETF之后，再次推出了由张 ...

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].

Group 1: Industry Insights - The drug regulatory authority has introduced a new data protection policy for drug trial data, granting 6 years of protection for innovative drugs and 3 years for improved and first-generic drugs [3] - The policy aims to accelerate the development of innovative drugs through faster approval processes and supportive pricing and reimbursement policies, particularly for differentiated innovative drugs [3] - The performance of major drug varieties is expected to accelerate, with significant growth anticipated for PD-1/IL-2 drugs by 2025 [3] Group 2: Automotive and Chip Industry - Local governments are promoting the implementation of L3 autonomous driving, particularly in cities like Beijing and Wuhan, supported by legal frameworks [4] - The cost of intelligent driving systems is significantly decreasing, with expectations that models priced above 150,000 yuan will standardize high-level intelligent driving systems [4] - Horizon Robotics, a leading domestic intelligent driving chip manufacturer, is expected to ship over 10 million units of its chips by 2025 [4] Group 3: Nuclear Fusion Industry - The nuclear fusion sector is attracting significant investment and participation from various companies, with strong government support for its development [5] - The U.S. is constructing the world's first commercial nuclear fusion power plant, with notable advancements in plasma confinement and fusion power output from international facilities [5] - The advantages of nuclear fusion power include stability and minimal greenhouse gas emissions, which are driving interest in the sector [5] Group 4: Chemical Industry - The demand structure for chromium salts is changing, with significant growth expected in the aerospace sector due to the material's properties [6] - Global gas turbine orders are projected to increase from 40 GW/year in 2023 to 80 GW/year by 2026, driving demand for chromium salts [6] - The chemical sector is anticipated to enter a restocking cycle as inventory levels are low and demand is expected to rebound [6]