投资本质是"科学"与"艺术"的结合。量化负责 "科学" 市场有很多客观规律,比如买高股息、低 估值,这些规律长期有效,而主动负责"艺术",处 理无法量化的内容 查晓磊 | 华泰证券资管 基 金 经 理 英 雄 榜 第 5 7 8 期 以下，我们先分享一些来自查晓磊的投资"金句"： 1、投资的本源是每笔投资都想盈利，而非为排名。落实到动作上，就是对每个标的算账，通过研究提高盈利概率，控制回撤下限，追求向上收 益，无关排名和基准 从博时基金到浙商基金，再到如今执掌华泰证券资管权益投研，十余年的从业经历里，查晓磊见证了公募行业对"相对收益"的追逐，也亲历了市场 波动下持有人的无奈：当产品短期内回撤超30%，再亮眼的长期业绩也难掩持有体验的糟糕。正是这份共情与反思，让他在加入华泰证券资管后， 完成了一次关键的认知跃迁：投资的本源从不是超越基准，而是力争让每一笔交易都挣到钱。 可A股高波动的底色，向来是绝对收益的"拦路虎"。如何在控制回撤的同时留住收益弹性？如何让"买得便宜、卖得明白"不再是口号？查晓磊给出的 答案，是一套看似简单却极具穿透力的"三价打分体系"：买入价、极限底价、卖出价，这三个价格如同投资的"锚"，解决 ...

研究报告合集下载链接（下载密码欢迎联系团队成员或对口销售）： https://pan.baidu.com/s/1d2oPPwOo4jMsF-kYQ5XpMg AI量化系列专题报告 | 《系列一：深度学习之股指期货日内交易策略》 | | --- | | 《系列二：深度学习算法掘金 Alpha 因子》 | | 《系列三：深度学习新进展，Alpha 因子的再挖掘》 | | 《系列四：趋势策略的深度学习增强》 | | 《系列五：风险中性的深度学习选股策略》 | | 《系列六：深度学习在指数增强策略上的应用》 | | 《系列七：深度学习框架下的高频数据因子挖掘》 | | 《系列八：基本面因子模型的深度学习增强》 | | 《系列九：基于条件随机场的周频择时策略》 | | 《系列十：机器学习多因子动态调仓策略》 | | 《系列十一：人工智能在资产管理行业的应用和展望》 | | 《系列十二：基于涨跌模式识别的指数和行业择时策略》 | | 《系列十三：再探西蒙斯投资之道：基于隐马尔科夫模型的选股策略研究》 | | 《系列十四：机器学习模型在因子选股上的比较分析》 | | 《系列十五：多周期机器学习选股模型》 | | 《系列十六 ...

Group 1: Fixed Income Strategies - The strategy for credit bonds and sci-tech bonds ETFs focuses on four main considerations: cash retention versus bond allocation, seeking flexibility versus static returns, duration versus credit risk for yield, and the duration structure of holdings being either barbell or bullet [4] - Historical review indicates that cash retention is typically a short-term phenomenon during periods of weak market conditions, and the likelihood of holding cash is low [4] - In the current low interest rate and low spread environment, actively seeking static returns through credit bond ETFs is not cost-effective, and these ETFs tend to extend duration to seek flexibility when interest rates stabilize or decline [4][5] Group 2: Credit Bond ETF Preferences - Given the current market environment, the preference for sci-tech bond ETFs may align with that of credit bond ETFs during correction periods, focusing on high flexibility and high ratings while favoring a barbell strategy with increased allocation to long-duration bonds [5] - The credit dimension shows that during volatile periods, credit bond ETFs have increased their allocation to high-rated bonds, and this trend is expected to continue for sci-tech bond ETFs, maintaining a dominant position in AAA-rated and above securities [5] Group 3: Selection Strategies for Sci-Tech Bonds - The selection strategy for sci-tech bonds during expansion expectations is based on the excess spread between component bonds and non-component bonds, with a narrowing spread observed as of August 29 [6] - There is an anticipated increase in demand for perpetual (non-subordinated) sci-tech bonds due to expansion expectations, with three of the first ten sci-tech bond ETFs including such bonds [6] - The issuance space for new sci-tech bonds has increased, with an average weekly issuance of 427 billion since July, indicating a growing opportunity for new issuances [6] Group 4: Market Trends in Consumer Goods - The food and beverage sector is expected to show performance advantages in growth, with a stable revenue scale and a deceleration in profit growth, particularly in the beverage and snack segments [15] - The overall performance of the food and beverage sector in Q2 2025 showed a slight increase in revenue and a decrease in net profit, with specific segments like soft drinks and snacks experiencing significant growth [16][17] - The high-end and sub-high-end liquor segments are facing pressure on demand, leading to a notable divergence in performance among brands, with top brands maintaining stability while others struggle [16]

Summary of the Conference Call on Pharmaceutical Investment Strategies Industry Overview - The pharmaceutical industry is categorized into three main segments: medical manufacturing, medical consumption, and medical technology [1][5][3]. Key Points and Arguments - **Quantitative Classification**: The classification combines subjective research and quantitative indicators (assets, expenses, personnel structure) to ensure accuracy and adaptability [1][5]. - **Selection Strategy**: Different stock selection models are constructed for each segment: - **Consumption**: Focuses on product, brand, and channel performance [1][11]. - **Manufacturing**: Emphasizes competitiveness, innovation capability, and international expansion [1][12]. - **Technology**: Concentrates on innovation output and efficiency [1][13]. - **Risk Control**: High volatility stocks are excluded to reduce the risk of sharp declines, with a focus on long-term volatility for technology stocks [1][14]. - **Margin of Safety Assessment**: Utilizes PB-ROE models for consumption and manufacturing, and PEG models for technology to eliminate overvalued stocks [1][15]. Performance Insights - **Strategy Effectiveness**: The comprehensive strategy has outperformed indices in most years, particularly in unfavorable market conditions [4][16]. - **Institutional Interest**: Stocks with lower institutional attention but solid fundamentals tend to show more stable returns and higher win rates [4][19]. - **Stock Pool Construction**: A refined stock pool of approximately 50-60 stocks is maintained, with adjustments made quarterly based on earnings reports [17][21]. Additional Considerations - **Dynamic Classification**: The classification system allows for dynamic adjustments based on changes in company attributes or business models [7]. - **Comparison with Thematic Funds**: The strategy has generally performed well against pharmaceutical thematic funds, especially in low-beta environments [18]. - **Elastic Market Strategies**: A reverse pool is created to capture high-elasticity stocks, which may not necessarily have strong fundamentals [20]. Conclusion - The pharmaceutical sector presents a robust investment opportunity through a structured quantitative approach, focusing on risk management and dynamic stock selection strategies. The emphasis on institutional interest and performance metrics provides a comprehensive framework for identifying potential investments.

Core Insights - Xu Zhongxiang is a key figure in the international quantitative investment circle, known for his contributions to the RAFI fundamental quantitative strategy and Smart Beta strategies, demonstrating a strong strategic confidence in the Chinese market [1] - The investment philosophy emphasizes a "slow money" approach, advocating for sustainable profit rather than chasing quick returns, which aligns with the long-term growth of companies [2][4] Group 1: Investment Philosophy - Xu advocates for a "slow money" investment philosophy, arguing that true growth often comes from companies that do not experience explosive growth in the short term [4] - The belief is that the pursuit of quick profits is unsustainable, and the focus should be on continuous profitability [4][5] - The investment approach is based on the understanding that high returns without risk are a myth, and investors must choose between high-risk, high-reward or low-risk, low-reward options [5] Group 2: Market Analysis - The analysis of various market data indicates that no investment strategy can consistently outperform the market, and all popular investment methods carry inherent risks [5] - Xu emphasizes the importance of understanding market dynamics and the need for a diversified investment strategy to mitigate risks [7] - The Chinese market is viewed as being in a critical development phase, with regulatory improvements and a shift towards institutionalization and professionalism [10][12] Group 3: Quantitative Investment - The core of quantitative research lies in validating patterns through vast amounts of data, which is more reliable than anecdotal success stories [8][9] - Xu's firm focuses on fundamental quantitative and low-frequency quantitative strategies, which involve long holding periods and deep engagement with companies' growth cycles [9] - The firm leverages extensive data from both domestic and international markets to adapt investment strategies to local conditions [9][11] Group 4: Regulatory Environment - The regulatory environment in China is seen as increasingly sophisticated, with a focus on protecting investors and ensuring market stability [10] - Xu argues that the perception of "weak regulation" in overseas markets is misleading, as it is built on a foundation of market maturity that China is still developing [10][12] - The evolution of the Chinese market is expected to follow a natural progression towards maturity, similar to that of established overseas markets [12]

Core Viewpoint - The new quantitative regulations have led to a significant decrease in trading volume, with a total turnover of 1.21 trillion yuan, down over 200 billion yuan, indicating a serious contraction in market activity [1] Group 1: Market Reactions - The initial impact of the new quantitative regulations has caused a short-term pain in the market, but it is expected to benefit the healthy development of the market in the long run [3] - Leading institutions are shifting towards fundamental quantitative strategies and AI stock selection models, which will favor long-term investors in the future [4] Group 2: Power Sector Insights - The power sector is experiencing a resurgence, with multiple stocks hitting the daily limit up due to high temperatures and increased electricity demand during the summer peak [5][6] - National statistics show that on July 4, the maximum national power load reached 1.465 billion kilowatts, an increase of approximately 200 million kilowatts from the end of June and nearly 150 million kilowatts year-on-year, marking a historical high [7] - Analysts suggest focusing on the power sector due to the rising electricity load and the positive performance of thermal power companies, which have seen nearly 70% of listed companies report year-on-year profit growth in Q1, largely due to falling coal prices [9] Group 3: Real Estate Sector Developments - The real estate sector has become active following a series of favorable policies released since June by the central bank and other regulatory bodies, leading to a warming market atmosphere [11] - Analysts recommend focusing on high-quality residential properties, particularly in core cities with strong land acquisition capabilities and product strength, as they are likely to benefit from the current policy environment [11]

Group 1 - The core viewpoint of the articles highlights the increasing popularity of dividend-themed funds as a key investment tool for investors amid a global preference for safe-haven assets and recent interest rate cuts by the central bank [1][2] - The central bank's recent adjustment of the Loan Prime Rate (LPR) and significant reductions in deposit rates have led to a decrease in household savings, prompting a renewed interest in dividend assets and related funds [1] - The Guangfa Stable Strategy fund, managed by Yang Dong, has achieved a return of 11.16% over the past six months, significantly outperforming the benchmark index, which only rose by 2.19% during the same period [1] Group 2 - Yang Dong is recognized for pioneering fundamental quantitative strategies in fund management, combining active stock selection with quantitative models to create a stable, outperforming equity fund [2] - The "active + quantitative" strategy involves subjective analysis for identifying trends and deep dives into individual stock fundamentals, while quantitative strategies utilize style factors to uncover patterns and enhance stock selection [2] - The team led by Yang Dong includes researchers with quantitative backgrounds, contributing to the development of specific style sub-strategies that provide flexibility in the fund's portfolio [2] Group 3 - The Guangfa Stable Strategy fund's holdings reflect a distinctive "active concentration + quantitative dispersion" approach, with a focus on a few concentrated top holdings while maintaining a diversified portfolio [3] - The fund has significantly increased its exposure to Hong Kong stocks, with a notable presence of H-shares in its top holdings, which tend to offer higher dividend yields compared to A-shares [3] - In the first quarter of 2025, the fund underwent a rebalancing, introducing six new stocks across various sectors, demonstrating its broad industry coverage and flexible adjustment capabilities [4]

Summary of Key Points from the Conference Call Industry or Company Involved - The conference call focuses on the **AI investment strategies** and **ETF market** in the context of the **capital market** as discussed by **Huatai Securities** during their **2025 Spring Strategy Meeting**. Core Points and Arguments - **AI Strategies in Investment**: AI strategies significantly enhance traditional multi-factor models by processing vast amounts of data and complex factors, particularly in volume and price data analysis, optimizing investment decisions [1][4][9]. - **Acceptance of AI in Asset Management**: The asset management industry is increasingly accepting AI strategies, particularly those based on statistical models, due to their strong performance. However, the ability of reasoning-based large language models to reach expert-level performance remains to be validated [1][13][14]. - **ETF Market Growth**: The ETF market has surpassed **3.8 trillion yuan**, with a focus on smart beta strategies to achieve stable returns through industry rotation and asset allocation models [1][22]. - **Investment Strategy Focus**: Huatai Securities emphasizes a robust return strategy, primarily focusing on bond investments, and utilizes global asset allocation models and qualitative analysis for market judgment [1][27]. - **Industry Rotation Strategy**: The industry rotation strategy combines macro, meso, and micro factors with AI identification and qualitative analysis, favoring technology, consumer, and pharmaceutical sectors while adjusting investment targets based on significant events like the Two Sessions [3][31]. - **AI's Role in Financial Engineering**: AI enhances traditional multi-factor frameworks by integrating diverse data types, leading to more precise and efficient data analysis, thus optimizing portfolio design and improving returns while reducing risks [7][18]. - **Performance of AI in Quantitative Investment**: AI strategies outperform traditional multi-factor methods by effectively aggregating information and conducting global analyses, leading to superior excess returns [9][12]. - **Future of Large Models in Finance**: Large models like DeepSeek and ChatGPT show potential in subjective analysis, suggesting a new paradigm of combining subjective and quantitative investment approaches, although their expert-level capabilities need further validation [11][15]. - **ETF Product Development**: Huatai Securities is committed to providing ETF products and solutions, focusing on smart beta strategies and offering professional services, including market reports and strategy analyses [1][23]. Other Important but Possibly Overlooked Content - **Historical Context of AI in Quantitative Investment**: The application of AI in quantitative investment began around 2003, evolving through various phases, with significant adoption starting in 2017, leading to substantial investment returns [2][13]. - **Impact of Two Sessions on Market**: The analysis of the Two Sessions' impact on the market involves reviewing historical key topics and market performance, indicating that different time periods around the event affect market dynamics [32]. - **Investment Heat and Valuation Levels**: The current investment heat in AI-related sectors is at historical highs, with significant trading activity and valuation levels, necessitating cautious investment strategies [62][64]. - **Differentiation of Index Products**: Index products vary significantly in valuation levels and stock resonance, suggesting that investors should choose based on their risk appetite and investment strategy [68][70]. - **Performance of Active Equity Fund Managers**: Different fund managers exhibit varying performance in the AI sector, categorized into stable allocation, focused sector, and flexible adjustment types, highlighting the importance of selecting managers based on their stability and risk-return profile [73][74]. This summary encapsulates the essential insights from the conference call, providing a comprehensive overview of the discussions surrounding AI investment strategies and the ETF market.