证券研究报告 金工 6 月指数定期调样的影响估算 华泰研究 2025 年 6 月 02 日│中国内地 专题研究 0% 10% 20% 30% 40% ≤-3 (-3, -2] (-2, -1] (-1, -0.5] (-0.5, 0] (0, 0.5] (0.5, 1] (1, 2] (2, 3] >3 个股冲击系数分布 资料来源：Wind，中证指数有限公司，深圳证券信息有 限公司，华泰研究 被动市场大幅扩容，指数定期调整或带来短期影响 A 股被动市场持续扩容，近年来增长尤为迅速，截至 2025 年 Q1 已经达到 约 3.26 万亿元。对于部分跟踪产品规模较大的指数而言，产品调整会对被 调动成分带来一定的短期影响；而随着被动市场的扩容，该影响可能变得愈 发明显。从测算结果来看，资金净流入的高冲击系数股票中，有 10 只股票 冲击系数超过 2 倍，或在短期内对价格提供支撑；而呈现净流出的股票中， 有 26 只股票系数绝对值在 2 倍以上，可能面临一定的潜在压力。 近年来被动市场呈现高速扩张趋势，总规模突破 3 万亿元 作为资本市场的核心投资渠道之一，被动基金近年来在国内呈现较为强劲的 发展趋势。一方面，被动产 ...

Quantitative Models and Construction Methods 1. Model Name: Commodity Term Structure Simulation Portfolio - **Model Construction Idea**: This is a long-short strategy that dynamically holds long positions in commodities with high roll yields and short positions in commodities with low roll yields. The strategy aims to capture the term structure premium in commodity markets while reducing dependency on single market trends[33][35][34]. - **Model Construction Process**: 1. **Roll Yield Factor**: The roll yield is calculated to measure the contango or backwardation state of a commodity. 2. **Dynamic Positioning**: Commodities with high roll yields are dynamically allocated long positions, while those with low roll yields are allocated short positions. 3. **Portfolio Balancing**: The portfolio is rebalanced periodically to maintain the desired exposure to the roll yield factor[35][38]. - **Model Evaluation**: The strategy demonstrates flexibility in adapting to market risks and provides stable returns even in weak market trends[34]. 2. Model Name: Commodity Time-Series Momentum Simulation Portfolio - **Model Construction Idea**: This strategy captures medium- to long-term trends in commodity prices using multiple technical indicators. It dynamically allocates long positions to upward-trending assets and short positions to downward-trending assets[33][35]. - **Model Construction Process**: 1. **Trend Indicators**: Technical indicators such as moving averages and momentum are used to identify price trends. 2. **Dynamic Positioning**: Commodities with upward trends are allocated long positions, while those with downward trends are allocated short positions. 3. **Portfolio Rebalancing**: Positions are adjusted periodically based on updated trend signals[35][45]. - **Model Evaluation**: The strategy effectively tracks price trends but may underperform in volatile or trendless markets[45]. 3. Model Name: Commodity Cross-Sectional Inventory Simulation Portfolio - **Model Construction Idea**: This strategy uses inventory data to capture fundamental changes in commodity markets. Commodities with declining inventories are allocated long positions, while those with increasing inventories are allocated short positions[33][35]. - **Model Construction Process**: 1. **Inventory Factor**: Changes in inventory levels are calculated to assess supply-demand dynamics. 2. **Dynamic Positioning**: Commodities with declining inventories are dynamically allocated long positions, while those with increasing inventories are allocated short positions. 3. **Portfolio Rebalancing**: Positions are adjusted periodically based on updated inventory data[35][49]. - **Model Evaluation**: The strategy is effective in capturing fundamental supply-demand imbalances but may be sensitive to data accuracy and reporting delays[49]. --- Model Backtesting Results 1. Commodity Term Structure Simulation Portfolio - **Annualized Return**: 3.03% (YTD 2025)[33][38] - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned 2. Commodity Time-Series Momentum Simulation Portfolio - **Annualized Return**: -1.33% (YTD 2025)[45] - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned 3. Commodity Cross-Sectional Inventory Simulation Portfolio - **Annualized Return**: 2.88% (YTD 2025)[49] - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned --- Quantitative Factors and Construction Methods 1. Factor Name: Roll Yield Factor - **Factor Construction Idea**: Measures the contango or backwardation state of a commodity to capture the term structure premium[35]. - **Factor Construction Process**: 1. Calculate the roll yield as the difference between the spot price and the futures price. 2. Normalize the roll yield across commodities to ensure comparability. 3. Rank commodities based on their roll yields and allocate positions accordingly[35]. 2. Factor Name: Trend Factor - **Factor Construction Idea**: Captures medium- to long-term price trends using technical indicators[35]. - **Factor Construction Process**: 1. Use moving averages, momentum, and other technical indicators to identify trends. 2. Normalize trend signals across commodities to ensure comparability. 3. Rank commodities based on their trend strength and allocate positions accordingly[35]. 3. Factor Name: Inventory Factor - **Factor Construction Idea**: Measures changes in inventory levels to capture supply-demand imbalances[35]. - **Factor Construction Process**: 1. Calculate the percentage change in inventory levels over a specified period. 2. Normalize inventory changes across commodities to ensure comparability. 3. Rank commodities based on their inventory changes and allocate positions accordingly[35]. --- Factor Backtesting Results 1. Roll Yield Factor - **Annualized Return**: Not explicitly mentioned - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned 2. Trend Factor - **Annualized Return**: Not explicitly mentioned - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned 3. Inventory Factor - **Annualized Return**: Not explicitly mentioned - **Annualized Volatility**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Sharpe Ratio**: Not explicitly mentioned - **Calmar Ratio**: Not explicitly mentioned

证券研究报告 电力设备与新能源 周观点：蔚来充换电产业布局动作频 频，关注相关产业链 华泰研究 2025 年 6 月 01 日│中国内地 行业周报（第二十二周） 周观点：蔚来充换电产业布局动作频频，关注相关产业链 充电方面，5 月 29 日，极氪汽车与蔚来能源宣布双方达成充电网络双向互 联互通合作，双方通过充电平台间动态数据交互共享，实现全国范围充电基 础设施的双向互联互通，将进一步扩大新能源汽车的充电网络覆盖范围，提 升用户体验。目前蔚来充电桩数量已超过 26 万根。换电方面，5 月 28 日蔚 来能源实现天津换电县县通。截至 5 月 28 日，蔚来能源在全国已建成换电 站 3337 座。看好充换电模式逐步扩大覆盖范围，关注产业链相关环节增量。 子行业观点 1）新能源车：充换电板块进展积极；2）工控：关注 AIDC 板块机会；3） 储能：中国企业出海热潮持续，看好出海带来业绩增量；4）光伏：绿电直 连政策发布，或释放绿电需求；5）风电：国内海风招标中标稳步推进，装 机高增可期。 重点公司及动态 1）宁德时代：电池龙头地位稳固，持续扩大业务版图；2）富临精工：高 压密铁锂龙头，扩大机器人业务布局。 风险提示： ...

Investment Rating - The report maintains a "Buy" rating for Qiaoyin Co., Ltd. with a target price of 14.36 CNY [8][31]. Core Insights - The sanitation industry is transitioning towards digitalization and automation, with significant growth potential driven by the adoption of sanitation robots and supportive government policies [1][4][9]. - The total market size for sanitation operations in China is expected to reach 458.5 billion CNY by 2025, reflecting a year-on-year growth of 7% [2][10]. - The aging workforce and rising labor costs in the sanitation sector are accelerating the demand for robotic solutions, with projections indicating substantial profit increases for companies adopting these technologies [3][12][22]. Summary by Sections Market Size and Growth - The sanitation operation market in China is projected to exceed 458.5 billion CNY by 2025, with major cities like Beijing, Guangzhou, and Shenzhen implementing policies to promote the development of unmanned sanitation vehicles [2][9]. - The annualized amount of smart sanitation orders is expected to reach 600 million CNY in 2024, with a year-on-year increase of 53% [2][11]. Workforce and Cost Dynamics - As of 2023, over 8 million people are employed in the sanitation sector in China, with more than 60% of workers aged over 60, highlighting the urgency for robotic replacements [3][12]. - The report estimates that replacing 50% of the workforce with sanitation robots could lead to profit increases of 62% for Yuhua Tian and 94% for Qiaoyin Co., Ltd. in 2024 [22][23]. Technological Advancements - Companies like Yuhua Tian and Qiaoyin Co., Ltd. are actively investing in robotic technology, with Yuhua Tian acquiring a significant stake in Candela Intelligent and establishing a new subsidiary focused on smart sanitation solutions [4][27]. - Qiaoyin Co., Ltd. is collaborating with the National Local Joint Innovation Center to develop humanoid robots for urban service applications, aiming to enhance operational efficiency [24][32]. Policy Support - Various local governments are introducing policies to support the integration of unmanned sanitation vehicles, with Guangzhou planning to deploy 1,000 autonomous sanitation vehicles by 2026 [2][11]. - The report emphasizes the importance of government support in driving the adoption of sanitation robots and the overall digital transformation of the sanitation industry [9][11].

证券研究报告 能源/工业 绿电直连落地，新能源转向以荷定源 华泰研究 2025 年 5 月 30 日│中国内地 动态点评 近日，国家发改委、国家能源局印发《关于有序推动绿电直连发展有关事项 的通知》，明确绿电直连机制，即风电、太阳能发电、生物质发电等新能源 不直接接入公共电网，通过直连线路向单一电力用户供给绿电；明晰安全优 先、权责对等、源荷匹配的原则，以期实现满足企业绿色用能需求、提升新 能源就近就地消纳水平的目标。我们拆分顶层机制如下： 我们认为在绿电直连机制落地、"以荷定源"和责任划分要求明确下，新能 源加速向用电侧价值转移，对负荷控制、分布式电源管理和电网互动提出新 的应用场景和要求，相关设备构成投资窗口，推荐电力自动化龙头国电南瑞， 相关公司包括四方股份、东方电子等。 拓宽电源投资主体，鼓励社会资本多元参与，为未来发展打开空间 通知引入项目投资模式创新、扩宽投资主体范围，包括民营企业在内的各类 经营主体(不含电网企业)可投资绿电直连项目，鼓励民资参与。不同于此前 电源侧单一投资主体的规定，绿电直连项目电源可由负荷投资，也可由发电 企业或双方成立的合资公司投资，直连专线原则上应由负荷、电源主体投资； ...

Investment Rating - The report maintains an "Overweight" rating for the automotive industry [7] Core Insights - Retail sales in May are expected to reach 1.85 million units, supported by increased promotional discounts and new vehicle launches [1][39] - The penetration rate of new energy vehicles (NEVs) reached 51.6% in April, with a significant year-on-year increase [2][15] - The cumulative number of vehicle trade-in applications as of May 11, 2025, reached 3.225 million, indicating strong market support from the trade-in subsidy policy [3][39] Summary by Sections Sales Performance - In April, retail and wholesale sales of passenger vehicles were 1.781 million and 2.223 million units, respectively, with year-on-year increases of 14.8% and 11.0% [2][13] - The expected retail sales for May are projected to increase by 5.4% month-on-month [1] Discounts and Promotions - Discounts for both fuel and new energy vehicles increased in May, with fuel vehicles averaging a discount rate of 20.65% and NEVs at 5.56% [4] - Many automakers have introduced zero-interest and fixed-price schemes to lower purchase costs, with BYD offering a minimum price of 55,800 yuan for its models [5] Market Trends - The market for NEVs continues to grow, with April sales reaching 1.146 million units, a year-on-year increase of 42.2% [2][15] - The market share of domestic brands reached 65.8% in April, reflecting a strong competitive position [20] Inventory and Pricing - The overall inventory coefficient for automotive dealers decreased to 1.41 in April, indicating a healthy inventory level [29] - The average price of passenger vehicles remained stable at 170,000 yuan in April, with slight variations between fuel and NEV prices [28] Trade-in Policy Impact - The trade-in subsidy policy is expected to significantly boost vehicle sales, with an estimated increase of 960,000 to 1.93 million vehicles due to expanded eligibility [47]

Economic Growth - May tariffs reduction boosted growth momentum, but tariff impacts may still become evident[1] - US May Markit manufacturing and services PMI exceeded expectations, pushing composite PMI up to 52.5[1] - Eurozone May composite PMI fell to 49.5, dragged down by services PMI, while manufacturing PMI rose to 49.4[1] Inflation Trends - US April core PCE inflation remained flat at 0.1% month-on-month, with a year-on-year rate of 2.5%[1] - Japan's April core CPI rose 0.3 percentage points to 3.5%, exceeding expectations of 3.4%[1] Market Performance - As of May 30, US stock indices rose significantly: S&P 500 up 6.2%, Nasdaq up 9.6%, and Dow Jones up 3.9%[2] - US 2-year and 10-year Treasury yields increased by 29 basis points and 24 basis points to 3.89% and 4.41%, respectively[2] Policy Developments - US-China tariff reduction announced on May 12, with tariffs on both sides reduced from 125% to 34%[3] - The US House passed the "Beautiful Bill," which includes tax cuts and an increase in the debt ceiling, expected to raise the US fiscal deficit by $3.1 trillion over ten years[3] Risks - Uncertainty remains regarding Trump's tariff policies and potential geopolitical volatility[4]

Economic Growth - The easing of tariffs has boosted some U.S. survey indicators in May, with the composite PMI rising by 1.7 to 52.5, driven by improvements in manufacturing and services PMI, both exceeding expectations at 52.3[3] - The first quarter GDP growth rate was revised up by 0.1 percentage points to -0.2%, with inventory and investment contributions adjusted upward, while consumption and net exports were revised downward[3] - Retail sales showed a slight decline in April, with the Redbook retail index indicating a further drop in May's year-on-year retail growth[3] Financial Conditions - Goldman Sachs' Financial Conditions Index (FCI) relaxed by 26 basis points from May 1 to May 30, with the S&P 500 rising by 6.2% during the same period[4] - Investment-grade corporate spreads narrowed by 23 basis points to 1.14%, while the 2-year and 10-year U.S. Treasury yields increased by 29 basis points and 24 basis points, respectively, to 3.89% and 4.41%[4] Inflation - April's PCE inflation remained moderate, with the core PCE unchanged at 0.1% month-on-month and a year-on-year decline of 0.2 percentage points to 2.5%[5] - The CPI core goods related to China showed a significant rebound in growth rates from March to April, indicating the impact of tariffs on prices[5] Labor Market - In April, non-farm payrolls increased by 177,000, surpassing expectations of 138,000, while the unemployment rate remained stable at 4.2%[6] - The labor force participation rate rose to 62.6%, but the job vacancy rate has shown signs of decline, indicating potential future hiring slowdowns[6][6] Risks - There is an increasing uncertainty regarding Trump's policies, which may lead to a continued slowdown in U.S. economic growth[7]

Quantitative Models and Construction Methods AI Industry Rotation Model - **Model Name**: AI Industry Rotation Model - **Model Construction Idea**: The model uses a full-spectrum volume-price fusion factor to score 32 primary industries and constructs a weekly rebalancing strategy, selecting the top 5 industries for equal-weight allocation[1][16][23] - **Model Construction Process**: 1. **Industry Pool**: Includes 32 primary industries, with some industries split into subcategories (e.g., food and beverage split into food, beverage, and liquor)[23] 2. **Factor**: Full-spectrum volume-price fusion factor, derived from deep learning models extracting information from multi-frequency volume-price data[16][23] 3. **Strategy Rules**: - Select the top 5 industries with the highest scores on the last trading day of each week - Equal-weight allocation - Buy at the next week's first trading day's closing price - Weekly rebalancing, no transaction costs considered[23] - **Model Evaluation**: The model leverages AI's feature extraction capabilities to identify patterns in multi-frequency volume-price data, complementing top-down strategies[16] AI Theme Index Rotation Model - **Model Name**: AI Theme Index Rotation Model - **Model Construction Idea**: The model uses a full-spectrum volume-price fusion factor to score 133 thematic indices and constructs a weekly rebalancing strategy, selecting the top 10 indices for equal-weight allocation[2][6][9] - **Model Construction Process**: 1. **Index Pool**: Includes 133 thematic indices tracked by thematic ETFs, based on Wind's ETF classification[9] 2. **Factor**: Full-spectrum volume-price fusion factor, scoring each thematic index based on its constituent stocks[9] 3. **Strategy Rules**: - Select the top 10 indices with the highest scores on the last trading day of each week - Equal-weight allocation - Buy at the next week's first trading day's opening price - Weekly rebalancing, transaction costs set at 0.04% for both sides[9] - **Model Evaluation**: The model effectively identifies high-performing thematic indices using AI-driven factor scoring[6] AI Concept Index Rotation Model - **Model Name**: AI Concept Index Rotation Model - **Model Construction Idea**: The model uses a full-spectrum volume-price fusion factor to score 72 concept indices and constructs a weekly rebalancing strategy, selecting the top 10 indices for equal-weight allocation[11][15] - **Model Construction Process**: 1. **Index Pool**: Includes 72 concept indices based on Wind's popular concept indices[15] 2. **Factor**: Full-spectrum volume-price fusion factor, scoring each concept index based on its constituent stocks[15] 3. **Strategy Rules**: - Select the top 10 indices with the highest scores on the last trading day of each week - Equal-weight allocation - Buy at the next week's first trading day's opening price - Weekly rebalancing, transaction costs set at 0.04% for both sides[15] - **Model Evaluation**: The model efficiently captures trends in concept indices using AI-based factor scoring[11] AI CSI 1000 Enhanced Portfolio - **Model Name**: AI CSI 1000 Enhanced Portfolio - **Model Construction Idea**: The portfolio is constructed using the full-spectrum volume-price fusion factor to enhance the CSI 1000 index[3][26][29] - **Model Construction Process**: 1. **Factor**: Full-spectrum volume-price fusion factor, combining high-frequency deep learning factors and low-frequency multi-task learning factors[26] 2. **Portfolio Construction Rules**: - Constituent stock weight ≥ 80% - Individual stock weight deviation limit: 0.8% - Barra exposure < 0.3 - Weekly rebalancing, turnover rate controlled at 30% - Transaction costs set at 0.4% for both sides[29] - **Model Evaluation**: The portfolio demonstrates strong enhancement capabilities relative to the CSI 1000 index, with high IR and controlled tracking error[28] Text-based FADT_BERT Stock Selection Portfolio - **Model Name**: Text-based FADT_BERT Portfolio - **Model Construction Idea**: The portfolio is based on the forecast_adjust_txt_bert factor, which upgrades text factors in earnings forecast adjustment scenarios[32] - **Model Construction Process**: 1. **Factor**: Forecast_adjust_txt_bert factor, derived from text analysis of earnings forecast adjustments[32] 2. **Portfolio Construction Rules**: - Top 25 stocks from the long side of the factor's base stock pool - Active quantitative stock selection strategy[32] - **Model Evaluation**: The portfolio effectively integrates text-based factors into stock selection, achieving high long-term returns[32] --- Model Backtesting Results AI Industry Rotation Model - Annualized return: 24.95% - Annualized excess return: 20.80% - Maximum drawdown of excess return: 12.43% - Excess Sharpe ratio: 2.00 - YTD return: 4.88% - YTD excess return: 1.11%[1][22][25] AI Theme Index Rotation Model - Annualized return: 16.03% - Annualized excess return: 13.10% - Maximum drawdown of excess return: 16.55% - Excess Sharpe ratio: 1.02 - YTD return: 9.86% - YTD excess return: 10.43%[2][8][9] AI Concept Index Rotation Model - Annualized return: 22.42% - Annualized excess return: 12.68% - Maximum drawdown of excess return: 17.96% - Excess Sharpe ratio: 1.07 - YTD return: 11.48% - YTD excess return: 7.61%[11][13][15] AI CSI 1000 Enhanced Portfolio - Annualized return: 17.31% - Annualized excess return: 22.17% - Annualized tracking error: 6.07% - Maximum drawdown of excess return: 7.55% - IR: 3.65 - Calmar ratio: 2.93[3][28][30] Text-based FADT_BERT Stock Selection Portfolio - Annualized return since inception: 39.29% - Annualized excess return since inception: 31.74% - Maximum drawdown: 48.69% - Sharpe ratio: 1.36 - Calmar ratio: 0.81[32][36][38]

Economic Overview - Export demand index (HDET) recorded approximately 0% year-on-year growth from May 1-30, indicating a recovery in export sentiment post-tariff reduction[2] - From January to May, net issuance of national and local bonds increased by CNY 3.66 trillion year-on-year, supporting domestic demand[2] PMI Analysis - Manufacturing PMI rose from 49% in April to 49.5% in May, aligning with Bloomberg consensus expectations[4] - The production index within the manufacturing PMI increased by 0.9 percentage points to 50.7%, while new orders and new export orders rose to 49.8% and 47.5%, respectively[4] - Employment index in manufacturing improved marginally to 48.1%, suggesting a slight recovery in labor demand[4] Sector Performance - High-tech industries maintained expansion with a PMI of 50.9%, while high-energy industries saw a decline in PMI to 47.0%[7] - Non-manufacturing business activity index slightly decreased to 50.3%, with new orders index rising to 46.1%[7] Price Trends - Raw material purchase and factory price indices fell by 0.1 percentage points to 46.9% and 44.7%, respectively, indicating pressure on corporate profits[8] - Prices for coal, rebar, and Brent crude oil decreased by 6.4%, 0.2%, and 3.7% month-on-month, while domestic copper and aluminum prices increased by 2.1% and 0.9%[8] Risks - Potential risks include unexpected escalation in US-China trade tensions and weaker-than-expected domestic demand[9]