Quantitative Models and Construction Methods 1. Model Name: Brinson Attribution Model - **Model Construction Idea**: The model is used to decompose the excess returns of active equity funds into stock selection and sector allocation contributions, providing insights into the sources of fund performance [16][19][22] - **Model Construction Process**: The Brinson model calculates excess returns as follows: $ R_{excess} = \sum_{i=1}^{n} (W_{i,f} - W_{i,b}) \cdot R_{i,b} + \sum_{i=1}^{n} W_{i,f} \cdot (R_{i,f} - R_{i,b}) $ - $ W_{i,f} $: Fund weight in sector $ i $ - $ W_{i,b} $: Benchmark weight in sector $ i $ - $ R_{i,f} $: Fund return in sector $ i $ - $ R_{i,b} $: Benchmark return in sector $ i $ The first term represents the allocation effect, and the second term represents the selection effect [16][19] - **Model Evaluation**: The model highlights that stock selection contributes more significantly to excess returns than sector allocation, with stock selection accounting for 83.17% of the total contribution on average [16][22] --- Model Backtesting Results 1. Brinson Attribution Model - Average stock selection contribution: 5.38% per half-year [22] - Probability of positive stock selection returns: 69.12% [23] - Probability of positive sector allocation returns: 53.66% [23] --- Quantitative Factors and Construction Methods 1. Factor Name: Fund Stability Factor - **Factor Construction Idea**: This factor measures the stability of a fund's sector allocation and its impact on outperforming benchmarks [10][12] - **Factor Construction Process**: Funds are categorized into 16 groups based on static and dynamic sector allocation characteristics: - Static categories: Highly diversified, diversified, concentrated, highly concentrated - Dynamic categories: Highly stable, stable, rotational, highly rotational The average probability of outperforming benchmarks is calculated for each group [10][12] - **Factor Evaluation**: Funds with highly stable and diversified sector allocations have the highest probability of outperforming benchmarks, exceeding 73% on average [12][14] 2. Factor Name: Style Consistency Factor - **Factor Construction Idea**: This factor evaluates the consistency of a fund's style (e.g., large-cap value) and its correlation with performance [27][30] - **Factor Construction Process**: Funds are classified based on their style consistency over time: - Long-term stable allocation - Majority-time allocation - Partial-time allocation - Rare-time allocation The probability of outperforming benchmarks is calculated for each group [27][28] - **Factor Evaluation**: Funds with long-term stable large-cap value styles have the highest probability of outperforming benchmarks, reaching 79.77% [28][30] --- Factor Backtesting Results 1. Fund Stability Factor - Highly diversified-highly stable funds: - Probability of outperforming benchmark: 73.12% - Probability of outperforming benchmark +10%: 57.29% [12] 2. Style Consistency Factor - Long-term stable large-cap value funds: - Probability of outperforming benchmark: 79.77% - Probability of outperforming benchmark +10%: 69.05% [28]

金融工程 | 金工定期报告 金融工程 证券研究报告 量化择时周报：步入震荡上沿，维持中性仓位 步入震荡上沿，维持中性仓位 上上周周报(20250525)认为：短期市场宏观不确定性增加和成交萎缩的压 制下，风险偏好较难快速提升，预计成交或将继续下行，等待地量信号， 预计成交缩量至 9000 亿附近有望迎来反弹。最终 wind 全 A 上周表现强势， 全周大涨 1.61%。市值维度上，上周代表小市值股票的中证 2000 上涨 2.29%， 中盘股中证 500 上涨 1.6%，沪深 300 上涨 0.88%，上证 50 上涨 0.38%;上周 中信一级行业中，表现较强行业包括通信、有色金属，通信上涨 5.06%，家 电、食品饮料表现较弱，家电下跌 1.75%。上周成交活跃度上，非银金融资 金流入明显。 从择时体系来看，我们定义的用来区别市场整体环境的 wind 全 A 长期均 线（120 日）和短期均线（20 日）的距离开始扩大，最新数据显示 20 日 线收于 5115，120 日线收于 5081 点，短期均线位于长线均线之上，两线 差值由上周的 0.29%变化至 0.68%，但距离绝对值继续小于 3%，市场继续处 ...

金融工程 | 金工定期报告 2025 年 05 月 25 日 作者 吴先兴 分析师 SAC 执业证书编号：S1110516120001 wuxianxing@tfzq.com 相关报告 1 《金融工程：金融工程-因子跟踪周 报：换手率、季度 sp 分位数因子表现较 好-20250524》 2025-05-24 2 《金融工程：金融工程-大模型总结 和解读行业研报（ 2025W20 ）》 2025-05-19 3 《金融工程：金融工程-量化择时周 报：等待缩量》 2025-05-18 金融工程 证券研究报告 量化择时周报：继续等待缩量 继续等待缩量 上周周报(20250518)认为：短期市场在利好兑现和成交萎缩的压制下，风 险偏好较难快速提升，预计成交或将继续下行，等待地量信号，预计成交 缩量至 9000 亿附近有望迎来有力反弹。最终 wind 全 A 全周下跌 0.63%。 市值维度上，上周代表小市值股票的中证 2000 下跌 1.52%，中盘股中证 500 下跌 1.1%，沪深 300 微跌 0.18%，上证 50 微跌 0.18%;上周中信一级行业中， 表现较强行业包括医药、汽车，医药上涨 1.92%，综 ...

- The report defines a market timing system using the distance between the long-term moving average (120 days) and the short-term moving average (20 days) of the Wind All A Index to distinguish the overall market environment[2][8][13] - The distance between the 20-day moving average and the 120-day moving average has narrowed from -2.80% to -1.33%, indicating the market is in a volatile state[2][8][13] - The industry allocation model recommends sectors such as Hang Seng Medical, Hong Kong automotive, and new consumption industries from a mid-term perspective[2][3][9] - The TWO BETA model continues to recommend the technology sector, focusing on information innovation and communication[2][3][9] - The Wind All A Index's overall PE is around the 60th percentile, indicating a medium level, while the PB is around the 10th percentile, indicating a relatively low level[3][9] - The position management model suggests an absolute return product with Wind All A as the main stock allocation should have a 50% position[3][9] - The market is expected to continue to decline in trading volume, with a potential rebound when the volume shrinks to around 900 billion[2][3][9] Model Backtest Results - The distance between the 20-day and 120-day moving averages is -1.33%[2][8][13] - The Wind All A Index's PE is at the 60th percentile[3][9] - The Wind All A Index's PB is at the 10th percentile[3][9] - The recommended position for absolute return products is 50%[3][9]

Quantitative Models and Construction Methods - **Model Name**: Industry Allocation Model **Model Construction Idea**: This model aims to recommend industry sectors based on medium-term perspectives, focusing on sectors with potential for recovery or growth trends[2][3][10] **Model Construction Process**: The model identifies sectors with recovery potential ("困境反转型板块") and growth opportunities. It recommends sectors such as healthcare (恒生医疗), export-related consumer sectors (e.g., light industry and home appliances), and technology sectors (信创, communication, solid-state batteries). Additionally, it highlights sectors with ongoing upward trends, such as banking and gold[2][3][10] **Model Evaluation**: The model provides actionable insights for medium-term industry allocation, emphasizing sectors with recovery potential and growth trends[2][3][10] - **Model Name**: TWO BETA Model **Model Construction Idea**: This model focuses on identifying technology-related sectors with growth potential[2][3][10] **Model Construction Process**: The TWO BETA model recommends technology sectors, including 信创, communication, and solid-state batteries, based on their growth potential and market trends[2][3][10] **Model Evaluation**: The model effectively identifies technology sectors with strong growth potential, aligning with market trends[2][3][10] - **Model Name**: Timing System Model **Model Construction Idea**: This model evaluates market conditions by analyzing the distance between short-term and long-term moving averages to determine market trends[2][9][14] **Model Construction Process**: 1. Define the short-term moving average (20-day) and long-term moving average (120-day) for the Wind All A Index 2. Calculate the difference between the two moving averages: $ \text{Difference} = \text{20-day MA} - \text{120-day MA} $ - Latest values: 20-day MA = 4946, 120-day MA = 5088 - Difference = -2.80% (previous week: -3.63%) 3. Monitor the absolute value of the difference; when it falls below 3%, the market is considered to be in a consolidation phase[2][9][14] **Model Evaluation**: The model provides a clear signal for market consolidation, aiding in timing decisions[2][9][14] - **Model Name**: Position Management Model **Model Construction Idea**: This model determines the recommended equity allocation based on valuation levels and short-term market trends[3][10] **Model Construction Process**: 1. Assess valuation levels of the Wind All A Index: - PE ratio: 50th percentile (medium level) - PB ratio: 10th percentile (low level) 2. Combine valuation levels with short-term market trends to recommend a 60% equity allocation for absolute return products[3][10] **Model Evaluation**: The model provides a systematic approach to position management, balancing valuation and market trends[3][10] Backtesting Results of Models - **Industry Allocation Model**: No specific numerical backtesting results provided[2][3][10] - **TWO BETA Model**: No specific numerical backtesting results provided[2][3][10] - **Timing System Model**: - Latest moving average difference: -2.80% - Previous week difference: -3.63% - Absolute difference < 3%, indicating a consolidation phase[2][9][14] - **Position Management Model**: - Recommended equity allocation: 60%[3][10]

Core Viewpoint - The article discusses the impact of the recent "reciprocal tariffs" announced by Trump on the global market, particularly focusing on the Chinese market and its recovery trends following the initial shock [1][3]. Market Performance Summary - Following the announcement of tariffs on April 2, the Hong Kong stock market experienced significant volatility, with a notable drop on April 7 that erased all gains for the year. However, by May 2, the Hang Seng Tech Index rebounded by 19.1%, while MSCI China, Hang Seng Index, and Hang Seng China Enterprises Index saw rebounds of 13.6%, 13.5%, and 13.3% respectively. The Shanghai Composite Index and CSI 300 had smaller rebounds of 5.9% and 5.0% [1]. - Sector performance from April 8 to May 2 showed that Information Technology (+29.0%), Healthcare (+19.2%), and Consumer Discretionary (+14.3%) led the gains, while sectors like Banking (+4.9%), Utilities (+5.6%), and Energy (+5.9%) lagged behind [1]. Industry Analysis Framework - The article proposes an industry analysis framework based on demand sources, categorizing industries into three main types: 1. Industries primarily dependent on the U.S. market, which face significant challenges in finding alternative demand. 2. Industries with demand from markets outside the U.S., which are less directly affected by U.S. tariffs. 3. Industries with domestic demand, which are influenced by domestic policy support [4][6]. Impact of Tariffs on Different Industries - Industries with primary demand from the U.S. are categorized based on their ability to find alternative markets and their bargaining power. Sectors like Media, Software Services, and Textiles have shown resilience due to higher profit margins and U.S. import dependency, while smaller firms in shipping and medical supplies face greater challenges [6][10]. - Industries with demand from other markets, particularly those with established market shares and competitive advantages, are expected to perform better. Sectors such as Technology Hardware and Home Appliances have shown potential for growth in non-U.S. markets [11][14]. - Domestic demand-driven industries, particularly in consumption and infrastructure, are closely tied to government policy support. The article highlights the importance of fiscal measures to mitigate external shocks [18][20]. Historical Context and Future Outlook - The article draws parallels with the 2018-2019 trade tensions, noting that the current market dynamics reflect similar patterns of initial decline followed by recovery phases. The sectors that are less dependent on U.S. demand have shown more resilience, while those heavily reliant on U.S. markets have faced significant declines [21][25]. - The potential impact of tariffs on GDP and corporate profits is discussed, with estimates suggesting that a significant drop in exports to the U.S. could lead to a decline in GDP growth and a downward adjustment in profit forecasts for Hong Kong stocks [34][35]. - The article concludes with a projection of market indices under different scenarios, emphasizing the need for policy support to counterbalance the negative effects of tariffs and the importance of sector-specific strategies for investors [37].

发布时间：2025-04-30 研究所 分析师:肖承志 SAC 登记编号:S1340524090001 Email:xiaochengzhi@cnpsec.com 近期研究报告 《年报效应边际递减，右侧买入信号 触发——微盘股指数周报 20250427》 - 2025.04.27 《动量波动分化，低波高涨占优—— 中邮因子周报 20250427》 - 2025.04.27 《OpenAI 发布 GPT-4.1，智谱发布 证券研究报告：金融工程报告 GLM-4-32B-0414 系列——AI 动态汇总 20250421》 - 2025.04.23 《国家队交易特征显著，短期指数仍 交易补缺预期，TMT 类题材仍需等待— —行业轮动周报 20250420》 - 2025.04.21 《小市值强势，动量风格占优——中 邮因子周报 20250420》 - 2025.04.21 《基本面与量价共振，如遇回调即是 买点——微盘股指数周报 20250420》 - 2025.04.21 《Meta LIama 4 开源，OpenAI 启动先 锋计划——AI 动态汇总 20250414》 - 2025.04.15 《融资盘被动 ...

- The report mentions the "TWO BETA" model, which continues to recommend the technology sector, focusing on communication equipment and military industry[3][4][9] - The industry allocation model suggests a mid-term focus on sectors experiencing a turnaround, recommending industries such as new energy[3][4][9] - The timing system signal shows that the distance between the 20-day and 120-day moving averages of the Wind All A Index has narrowed to 3.28%, indicating a market in a volatile state[2][4][11] - The report suggests that if the trading volume falls below 1.1 trillion yuan, the market is expected to rebound[2][4][11] - The current PE ratio of the Wind All A Index is around the 60th percentile, indicating a medium level, while the PB ratio is around the 20th percentile, indicating a relatively low level[3][12] - The position management model recommends maintaining a 50% position for absolute return products based on the Wind All A Index[3][12] Model and Factor Construction - **TWO BETA Model**: This model recommends the technology sector, focusing on communication equipment and military industry. The model's construction details are not provided in the report[3][4][9] - **Industry Allocation Model**: This model suggests a mid-term focus on sectors experiencing a turnaround, recommending industries such as new energy. The model's construction details are not provided in the report[3][4][9] - **Timing System**: The timing system uses the distance between the 20-day and 120-day moving averages of the Wind All A Index to determine market trends. The latest data shows the 20-day moving average at 5253 points and the 120-day moving average at 5086 points, with a distance of 3.28%[2][4][11] Model and Factor Evaluation - **TWO BETA Model**: Continues to recommend the technology sector, focusing on communication equipment and military industry[3][4][9] - **Industry Allocation Model**: Recommends a mid-term focus on sectors experiencing a turnaround, such as new energy[3][4][9] - **Timing System**: Indicates a market in a volatile state, with the distance between the 20-day and 120-day moving averages narrowing to 3.28%[2][4][11] Backtest Results - **Timing System**: The distance between the 20-day and 120-day moving averages of the Wind All A Index is 3.28%, indicating a market in a volatile state[2][4][11] - **Wind All A Index**: The current PE ratio is around the 60th percentile, and the PB ratio is around the 20th percentile[3][12] - **Position Management Model**: Recommends maintaining a 50% position for absolute return products based on the Wind All A Index[3][12]