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： ...

证券研究报告 公用环保 智慧环卫应用加速，环卫机器人打开 成长空间 华泰研究 2025 年 6 月 01 日│中国内地 动态点评 现阶段环卫仍属于人力密集型行业，2024 年玉禾田员工人数超 9 万，侨银 股份、福龙马员工人数均超 5 万，玉禾田、侨银股份、福龙马、劲旅环境等 主要公司人工成本占总成本比例均超 60%。近年来行业主要公司持续布局 环卫机器人领域，推动城市服务数字化转型，同时多地政府出台无人环卫政 策规划。智慧环卫模式本质在于场景协同和人机一体化，玉禾田、侨银股份 等公司城市服务应用场景丰富。我们认为环卫机器人应用和迭代进程有望加 速，看好环卫机器人应用带来的城市服务数字化转型机遇和业绩增长空间， 推荐侨银股份。 环卫市场规模 2025 年或超 4500 亿，广州规划 2026 年无人装备达 1000 台 环卫运营具备城市服务刚需属性，我们预计 2025 年我国环卫运营市场规模 合计达 4585 亿元（yoy+7%）。北京、广州、深圳相继出台政策规划，鼓励 无人环卫发展，广州规划 2026 年自动驾驶环卫车达到 1000 台。2024 年全 国智慧环卫订单年化金额 6 亿元（yoy+53%），政 ...

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； ...

5 月折扣促销力度有所提升，零售销量预计达 185 万辆 在五一黄金周、上海车展以及政策补贴支持下，5 月 1-18 日零售/批发分别 为 93.2/85.8 万辆，同比+12%/+18%，较上月同期+18%/-2%。乘联会预计 5 月零售销量环比+5.4%至 185.0 万辆。展望后续，供给侧：车企或冲刺半 年度销量；需求侧：5 月市场折扣促销力度有所提升，以旧换新政策持续发 力，叠加新车密集上市，5 月-6 月终端需求预计回暖。我们推荐弹性标的吉 利汽车和小鹏汽车，稳健配置比亚迪、长城汽车和理想汽车。 证券研究报告 汽车 折扣有所提升，5 月零售预计 185 万 ——月度销量和折扣追踪系列 7 华泰研究 2025 年 5 月 30 日│中国内地 行业月报 4 月零售/批发同比增速均超 10%，新能源渗透率/自主品牌市占率维持高位 4 月乘用车零售/批发分别为 178.1/222.3 万辆，同比+14.8%/+11.0%，环比 -10.3%/-10.0%，其中新能源乘用车销量达 114.6 万辆，同环比分别 +42.2%/-1.0%，渗透率达 51.6%，环比提升 0.7pct。4 月出口 43.1 万辆， ...

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]

Economic Growth - In May, the easing of tariffs boosted growth momentum, but the impact of tariffs may still need to be fully realized[1] - The US Markit Manufacturing and Services PMI both exceeded expectations, pushing the composite PMI up to 52.5[1] - The Eurozone's composite PMI fell to 49.5, dragged down by the services sector, while manufacturing PMI rose to 49.4[1] Inflation Trends - The US April core PCE inflation remained flat at 0.1% month-on-month and 2.5% year-on-year, meeting expectations[1] - Japan's April core CPI rose by 0.3 percentage points to 3.5%, exceeding the expected 3.4%[1] Market Performance - As of May 30, US stock indices rose significantly, with the S&P 500, Nasdaq, and Dow Jones increasing by 6.2%, 9.6%, and 3.9% respectively[2] - The 2-year and 10-year US Treasury yields rose by 29 basis points and 24 basis points to 3.89% and 4.41% respectively[2] Policy Developments - On May 12, a joint statement announced a reduction of tariffs from 125% to 34%, with 24% of tariffs deferred for 90 days[3] - The US House passed the "Beautiful Bill," which includes tax cuts and an increase in the debt ceiling, expected to raise the fiscal deficit by $3.1 trillion over ten years[3] Risks - There is uncertainty surrounding the reversal of Trump's tariff policies and increased geopolitical volatility[4]

Investment Rating - The report does not explicitly state an investment rating for the industry or the specific technology discussed. Core Insights - The research explores the application of Multimodal Chain of Thought (MCoT) in investment research, particularly in technical analysis using K-line charts, leading to the development of an automated platform called GPT-Kline [1][4][13]. - MCoT enhances the reasoning capabilities of large models by combining multimodal understanding with logical reasoning, allowing for more sophisticated analysis of complex tasks [2][21]. - The O3 model, launched by OpenAI, demonstrates impressive image reasoning capabilities, marking a significant step towards achieving general artificial intelligence (AGI) [2][37]. Summary by Sections Multimodal Reasoning - Multimodal collaboration is essential for large models to progress towards AGI, requiring them to be proficient in various modalities beyond just language [17]. - MCoT represents a significant advancement, enabling models to think based on images rather than merely perceiving them [21][31]. Application in Investment Research - The report highlights the potential of MCoT in technical analysis, particularly with K-line charts, which encapsulate vital trading information and patterns suitable for analysis [3][42]. - The O3 model's application in technical analysis shows its ability to process K-line images, perform necessary pre-processing, and generate analytical reports [3][43]. Development of GPT-Kline - GPT-Kline integrates MCoT with the capabilities of large models to create a specialized tool for K-line technical analysis, automating the entire analysis process from drawing to reporting [4][65]. - The platform features a user-friendly web interface designed for intuitive interaction, allowing users to engage with the analysis process effectively [4][83]. Model Comparison and Performance - The report compares various large models, including OpenAI's GPT-4o and Gemini-2.5 series, assessing their capabilities in K-line analysis and identifying Gemini-2.5 Flash as a strong performer [66][96]. - The analysis results indicate that while OpenAI's models tend to be conservative in their outputs, the Gemini models provide more comprehensive and accurate annotations [95][96].