Group 1: Small Cap and Value Style Rotation Strategy - The latest quantitative model signals indicate a shift towards small-cap style for May, with historical data suggesting small-cap style is likely to outperform in this month [1] - The current market capitalization factor valuation spread is at 1.05, which is relatively low compared to historical highs of 1.7 to 2.6, indicating potential for small-cap outperformance [1] - Year-to-date, the small-cap style rotation strategy has achieved an excess return of 2.94% relative to an equal-weighted benchmark (CSI 300 and CSI 2000) [1] Group 2: Value and Growth Style Rotation Strategy - The latest quantitative model signals continue to favor value style for May, with expectations for value style to maintain its advantage [2] - Year-to-date, the value-growth style rotation strategy has generated an excess return of 4.6% compared to an equal-weighted benchmark (National Growth and National Value) [2] Group 3: Factor Performance Tracking - Among eight major factors, momentum and growth factors showed high positive returns in April, while liquidity and volatility factors exhibited high negative returns [2] - Year-to-date, momentum, analyst sentiment, and earnings volatility factors have shown positive returns, whereas industry momentum, liquidity, and short-term reversal factors have shown negative returns [2]

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更多国泰海通研究和服务 亦可联系对口销售获取 本订阅号所载内容仅面向国泰海通证券研究服务签约客户。因本资料暂时无法设置访问限制，根据《证 券期货投资者适当性管理办法》的要求，若您并非国泰海通证券研究服务签约客户，为保证服务质量、 控制投资风险，还请取消关注，请勿订阅、接收或使用本订阅号中的任何信息。我们对由此给您造成的 不便表示诚挚歉意，非常感谢您的理解与配合！如有任何疑问，敬请按照文末联系方式与我们联系。 法律声明 重要提醒 ...

Group 1 - The article emphasizes the importance of adhering to the guidelines set forth by the Securities and Futures Investor Suitability Management Measures, particularly for clients of Guotai Junan Securities [2] - It highlights that the content is exclusively for signed clients of Guotai Junan Securities research services, ensuring quality and risk control [2] - The article expresses gratitude for the understanding and cooperation of readers who may not be signed clients, indicating a focus on maintaining service quality [2] Group 2 - The document includes a legal disclaimer, reinforcing the need for compliance with regulations and the limitations of the information provided [3]

报告导读： 美国具有充足的安全垫机制，短期违约风险不大。美国债务风险被市场高估， 经济衰退仍是主要逻辑，当下的环境更像 2011 年，中长期来看美债配置价值优于欧日。 美国主权债务危机复盘：历史上未发生过主权债务违约，且当前有比较充足的风险垫机制，美债短期违约 风险不大。 回顾历史，美国国债未因债务上限发生过正式的债务违约，但曾有被视为技术性违约或接近违 约的案例。早在 18 至 20 世纪初期，美国曾多次因战争大量举债而最终无力偿还接近违约的案例，如 1779 年的独立战争时期和 1862 年的南北战争时期等。 20 世纪末至今则偶有几次发生技术性违约的情 况。总的来看，美国国债历史上并没有发生过真正意义上的违约（即未能按时支付本金或利息），过去几 轮技术性违约事件通常是由债务上限问题引发的，背后则是民主党和共和党对于财政扩张或财政保守之间 的政治博弈。解决方式主要是通过国会提高债务上限或采取非常规措施来维持支付能力。美联储、 TGA 账户资金和超常规措施提供安全垫，美债短期违约风险不大。 美债危机期间的资产变动：与全球经济、货币政策等因素紧密相关，衰退期间美债表现占优。 1 ） 1979 年美国经济衰退， ...

Group 1 - The core viewpoint of the article is the successful construction of a sentiment timing model that predicts market trends based on the number of stocks hitting the daily limit up or down, reflecting market sentiment and capital activity [1][2]. - The sentiment timing model integrates five key factors: board strategy return factor, next-day return factor for limit down stocks, proportion of limit up stocks, proportion of limit down stocks, and net limit up proportion factor, achieving an annualized return of 6.65% and a maximum drawdown of 29.39% [1][2]. - The model's performance is superior to the Wind All A Index and any single factor combination, demonstrating its effectiveness in predicting short-term market sentiment [1][2]. Group 2 - Improvement 1 involves incorporating overall market trend analysis, which enhances the model's ability to identify macroeconomic changes, resulting in an annualized return of 9.41% and a maximum drawdown of 46.35% [2]. - Improvement 2 includes weighting factor signals, leading to an annualized return of 9.68% and a maximum drawdown of 24.47%, significantly outperforming the Wind All A Index [2]. Group 3 - The underlying logic of the factors indicates that changes in the number of limit up and down stocks can directly reflect market sentiment, while the effectiveness of chasing and low-buy strategies reveals investor behavior and emotional tendencies [3]. - The limit up proportion factor and limit down proportion factor show strong performance in predicting short-term market sentiment, while the next-day return factor for limit up stocks is less reliable as a leading indicator [3].

核心观点 ①越南在"革新开放"后，经济迅速腾飞。提振经济的主要动力来自于外资和外贸。②在"革新开放"后，越 南产业结构得到不断优化。尤其是制造业逐步成为外贸和外贸的重点对象。同时，越南制造业在全球的地 位也得到不断提升。③中美贸易摩擦下，全球经贸秩序面临重置，越南在全球产业链中具有重要的战略地 位。中国与越南在未来的经贸上将有更加紧密的联系。 越南：新兴市场佼佼者 "革新开放"后，越南经济迅速腾飞，在全球经贸的地位不断提升。 越南经济腾飞的动能之一就是外资，在早期经济发展中发挥重要作用。 越南经济腾飞的另一动能就是外贸，也为越南经济的发展提供较大贡献。 越南：制造业逐步崛起 "革新开放"后，越南产业结构更加优化，二、三产业地位抬升。 文章来源 本文摘自：2025年5月14日发布的 越南制造业的崛起与前景 李 俊 ，资格证书编号： S0880525040063 汪立亭 ，资格证书编号： S0880525040004 无论是投资领域还是贸易领域，核心对象均在制造业领域。 越南产业逐步升级，资本密集型产品份额抬升，制造业地位不断提升。 越南：国际新秩序机遇 越南在劳动力领域具有较大的优势；同时，在地理位置以及政策领 ...

Core Viewpoint - The article discusses the integration of return factors into an orthogonal layer within deep learning models to enhance stock selection effectiveness while maintaining low correlation with existing return factors [1][2]. Group 1: Deep Learning Model Enhancements - By incorporating return factors into the orthogonal layer, deep learning factors can maintain good stock selection performance while ensuring low correlation with these return factors [1]. - The deep learning model's black-box nature makes it challenging to manually adjust factor weights during significant market style shifts; thus, the orthogonal layer allows for easier manual adjustments without compromising stock selection effectiveness [1]. Group 2: Performance Metrics - After adding return factors to the orthogonal layer, deep learning factors still exhibit strong stock selection capabilities, achieving an Information Coefficient (IC) above 0.02 and an IC Information Ratio (IR) exceeding 6 [2]. - The combination of deep learning factors with manually constructed return factors leads to significant improvements in overall market long positions compared to using deep learning factors alone, although the enhancement varies across different index-enhanced portfolios [2]. Group 3: Correlation and Performance - The correlation between deep learning factors and multi-granularity factors remains low after integrating return factors into the orthogonal layer, with high-frequency data inputs showing a correlation of no more than 0.01 [2]. - Utilizing deep learning factors alongside multi-granularity factors can significantly enhance the performance of overall market long positions, although the deep learning factors show limited predictive capability for mid to large-cap stock returns, resulting in less noticeable improvements for index-enhanced portfolios [2].

Core Viewpoint - The financial data indicates that the policy side continues to exert efforts to stabilize growth, including accelerated issuance and utilization of government bonds, while also highlighting that the recovery speed of domestic demand, particularly in the household sector, still needs to be boosted [1][4][17]. Group 1: Social Financing and Credit - In April, new social financing amounted to 1.2 trillion yuan, a year-on-year increase of 1.2 trillion yuan, raising the social financing stock growth rate to 8.7%, the highest since March 2024 [1][4]. - The increase in social financing was significantly influenced by a low base from the previous year, where new social financing in April 2022 was -65.8 billion yuan [4]. - New credit in April was 280 billion yuan, a decrease of 450 billion yuan year-on-year, with corporate bill financing being the main support for credit in April, amounting to 834.1 billion yuan [8][10]. Group 2: Government Bonds and Fiscal Policy - From January to April, net financing of government bonds reached 4.85 trillion yuan, with April's net financing at 976.2 billion yuan, an increase of over 1 trillion yuan year-on-year [4]. - The Ministry of Finance initiated the issuance of special government bonds on April 24, with the issuance pace advanced by about one month compared to 2024, indicating ongoing support for stabilizing growth and domestic demand [4][17]. Group 3: Household Sector and Demand Recovery - In April, household loans decreased by 521.6 billion yuan, indicating a need for improvement in the willingness of households to leverage [14]. - The transaction area of commercial housing in 30 major cities saw a year-on-year growth rate drop to -12%, reflecting a cooling in market activity and the need for recovery in household balance sheets [14][17]. Group 4: Monetary Supply - M2 growth rebounded to 8.0%, up 1 percentage point from March, primarily due to a low base effect from the previous year [17]. - The decline in M1 year-on-year was slight at 1.5%, indicating a mixed trend in monetary supply [17].

Macro - The rebound in social financing growth is primarily driven by government bonds, with April's new social financing reaching 1.2 trillion yuan, a year-on-year increase of 1.2 trillion yuan, raising the stock growth rate to 8.7%, the highest since March 2024 [1] - New loans in April amounted to 280 billion yuan, a decrease of 450 billion yuan year-on-year, with corporate note financing being the main support for credit in April, contributing 834.1 billion yuan [1] - The financial data for April reflects continued policy efforts to stabilize growth, including accelerated issuance and utilization of government bonds, while internal demand, particularly from households, still requires further support [1][2] Retail - Substantial progress in US-China trade negotiations has led to a significant reduction in bilateral tariff levels, with the US committing to cancel 91% of tariffs imposed on Chinese goods and China reciprocating similarly [4] - The negotiations took place on May 10-11, with a joint statement released on May 12, indicating a temporary suspension of 24% of tariffs for 90 days while retaining 10% [4] Machinery - The Chinese gas turbine industry is expected to benefit from two main developments: improvements in domestic manufacturers' gas turbine technology and increased demand from data center construction, leading to a positive outlook for the global gas turbine market [7] - The domestic gas turbine market is seeing breakthroughs in self-developed technology, which is expected to enhance the localization rate of both complete machines and core components [7] - The global demand for gas turbines is anticipated to rise significantly due to new requirements from data centers, providing opportunities for domestic manufacturers with core component technology [7]