Investment Rating - The report maintains an "Outperform" rating for the electric equipment and new energy industry [1]. Core Insights - The photovoltaic supply-side reform is ongoing, with a focus on improving product quality and eliminating low-price competition [1][2]. - In June, the wholesale sales of new energy passenger vehicles in China reached 1.26 million units, a year-on-year increase of 29% [1][2]. - The demand for batteries and materials is expected to grow as new models of electric vehicles are launched in the second half of the year, with projections indicating high growth in domestic sales of new energy vehicles by 2025 [1]. - The solid-state battery industrialization trend is clear, with attention on related materials and equipment companies [1]. - In May, China's photovoltaic installed capacity reached 92.92 GW, a year-on-year increase of 388%, which may suppress demand for photovoltaic installations in the near term [1]. - The hydrogen energy sector is being driven by policies promoting industrialization, with a focus on companies with cost and technological advantages in electrolyzer production and hydrogen infrastructure [1]. Summary by Sections Industry Performance - The electric equipment and new energy sector rose by 1.99% this week, outperforming the Shanghai Composite Index, which increased by 1.4% [10]. - The photovoltaic sector saw a significant increase of 6.80%, while the lithium battery index rose by 3.84% [2][13]. New Energy Vehicles - Major players in the new energy vehicle market reported varying delivery figures for June, with BYD delivering 383,600 units (up 11.98% year-on-year) and Li Auto experiencing a decline of 24% [2][27]. - The report highlights the expected growth in new energy vehicle sales, driven by the introduction of new models [1][2]. Photovoltaic Sector - The central economic work conference emphasized the need to regulate low-price competition and improve product quality in the photovoltaic industry [1][2]. - The report notes the significant increase in installed capacity and the potential impact on future demand [1]. Hydrogen Energy - The approval of a large-scale green hydrogen pipeline project indicates ongoing support for the hydrogen energy sector [1][27]. Company Developments - Companies like EVE Energy and Xinwanda are planning to issue H-shares for overseas listings, indicating growth strategies in the electric equipment sector [2][28].

Quantitative Models and Construction Methods Model 1: High Prosperity Industry Rotation Strategy (S1) - **Model Construction Idea**: The model aims to select industries with upward profit expectations by tracking industry profitability using a multi-factor model based on analysts' consensus expectations[16] - **Model Construction Process**: - Construct three major types of factors based on the original value, slope, and curvature of profit expectations - Screen candidate factors with annualized excess return >3% - Use hierarchical clustering to classify candidate factors into 8 categories and select the highest excess return factor from each category for rank equal-weighted composite - Exclude overvalued industries and select the top 3 industries with the highest factor values weekly[16] - **Model Evaluation**: The model effectively captures industries with high profitability expectations[16] Model 2: Implicit Sentiment Momentum Tracking Strategy (S2) - **Model Construction Idea**: The strategy constructs a sentiment momentum model that runs ahead of earnings expectation data by capturing "unproven sentiment" in the market[19] - **Model Construction Process**: - Perform cross-sectional regression of industry daily returns on daily turnover rate changes to strip out "expected sentiment" - Calculate the residual as "unproven sentiment" - Construct half-month and 12-month momentum factors based on cumulative unproven sentiment factor net value - Rank and equal-weight composite the two momentum factors - Exclude overvalued industries and select the top 3 industries with the highest factor values weekly[20] - **Model Evaluation**: The model captures market sentiment ahead of earnings expectation data[19] Model 3: Macro Style Rotation Strategy (S3) - **Model Construction Idea**: The strategy predicts the long-short situation of four industry styles (high beta, high valuation, 12-month momentum, high volatility) based on current macro indicators and their correlation with the returns of these styles[22] - **Model Construction Process**: - Construct a fundamental indicator system from "economic growth," "inflation," "currency," "credit," and "market sentiment" - Calculate the exposure of each industry to the four styles and estimate the expected long-short returns of the style factors - Use a weak voting classifier to predict the long-short of the styles - Map the style predictions to industries and select the top 6 industries with the highest total scores monthly[23] - **Model Evaluation**: The model effectively integrates macro indicators with industry style predictions[22] Model 4: Long-term Reversal Strategy (S4) - **Model Construction Idea**: The strategy leverages the momentum effect within 2 years and the reversal effect beyond 3 years in industries[27] - **Model Construction Process**: - Construct a "1-year momentum" factor excluding the most recent month's returns - Construct a "3-year reversal" factor using the period from 3 years ago to 2 years ago - Construct a turnover factor using the turnover rate of free float market value - Rank and equal-weight composite the three factors - Select the top 5 industries with the highest factor values monthly[27] - **Model Evaluation**: The model captures long-term reversal and medium-term momentum effects in industries[27] Model 5: Fund Flow Industry Rotation Strategy (S5) - **Model Construction Idea**: The strategy constructs an industry rotation model based on "market main fund flow and strength" and "late trading fund flow and strength"[29] - **Model Construction Process**: - Construct an "institutional order trend strength factor" using the net buy amount of institutional orders - Construct a "late trading fund flow and strength factor" using the average daily inflow of late trading funds - Rank and equal-weight composite the two factors - Select the top 5 industries with the highest fund inflow strength monthly[30] - **Model Evaluation**: The model effectively captures the flow and strength of market funds[29] Model 6: Financial Report Factor Failure Reversal Strategy (S6) - **Model Construction Idea**: The strategy leverages the phenomenon of financial report factors performing poorly in recent years to construct an industry rotation model based on the mean reversion theory of factor effectiveness[34] - **Model Construction Process**: - Classify financial report factors into categories and screen for "long-term effective factors" with annualized excess return >5.5% - Identify "short-term failure factors" that underperform the industry equal-weight benchmark for 4 consecutive months - Composite the highest annualized excess return factors from each category - Select the top 5 industries with the highest factor values monthly[35] - **Model Evaluation**: The model captures the mean reversion of financial report factors[34] Model 7: Multi-factor Scoring Composite Strategy (S7) - **Model Construction Idea**: The strategy is a quarterly rebalancing strategy that composites factors from "momentum," "liquidity," "valuation," and "quality" dimensions[39] - **Model Construction Process**: - Exclude industries with a weight below 2% in the CSI 800 - Select 2 factors from each dimension and rank equal-weight composite - Select the top 5 industries with the highest factor values quarterly[40] - **Model Evaluation**: The model effectively integrates multiple factor dimensions[39] Model Backtest Results - **S1**: Annualized excess return -1.8% YTD[66] - **S2**: Annualized excess return 5.6% YTD[66] - **S3**: Annualized excess return 2.7% YTD[66] - **S4**: Annualized excess return 4.8% YTD[66] - **S5**: Annualized excess return -0.2% YTD[66] - **S6**: Annualized excess return 0.6% YTD[66] - **S7**: Annualized excess return 3.9% YTD[66] - **Composite Strategy**: Annualized excess return 2.0% YTD[66]

Group 1: Policy Signals and Market Reactions - The recent signals regarding the orderly exit of backward production capacity emerged before the July 1 meeting of the Central Financial Committee, with some product prices stabilizing in June[3] - The Central Financial Committee emphasized the need to promote the orderly exit of backward production capacity, marking a shift from industry self-discipline to top-level policy[3] - Despite the policy signals, related industry stock performances remained subdued until the July 1 meeting, indicating a delayed market reaction[21] Group 2: Market Characteristics and Trends - The current market is expected to exhibit "pulse-like" trends due to unclear demand-side signals, contrasting with the 2016 supply-side reform that saw simultaneous demand boosts[22] - The segmentation of industries will likely show significant differentiation between "old industries" (e.g., steel, coal, cement) and "new industries" (e.g., new energy vehicles, lithium batteries)[22] - Focus should be on new industries with external demand, which may offer higher profit elasticity under similar supply-side adjustments[22] Group 3: Economic Implications and Risks - The stabilization of related industries will significantly aid macroeconomic structural adjustments and improve price factors, contributing to the overall elevation of the A-share market[23] - Risks include the potential underperformance of the orderly exit of backward production capacity, unexpected macroeconomic fluctuations, and unforeseen tariff disputes[28]

Market Update - The production and demand expectations for June show a marginal recovery, indicating a potential repair in profit factors [1][2] - In May, industrial enterprise profits weakened significantly due to a decline in both volume and price, leading to a faster decline in revenue growth [2] - The forward-looking indicator, June PMI, has slightly rebounded, suggesting a continuation of a strong production pattern [2] Inventory and Price Trends - In May, finished product inventory showed a marginal decline, primarily due to weak prices affecting nominal inventory [2] - The June PMI inventory sub-indices have shown varying degrees of recovery, aligning with the economic indicators that suggest a weak May but a recovery in June [2] - Short-term price pressures may have peaked, and the negative impact of base effects is expected to diminish in the second half of the year [2] Profitability Outlook - Entering July, profitability contributions are expected to improve, with the A-share market showing a rebound supported by positive valuation contributions [2] - The market currently undervalues profit factors, and the mid-year performance window in July-August may lead to a phase of recovery for profitability factors [2] - The market is anticipated to exhibit an upward oscillation trend in the second half of the year, supported by a favorable liquidity environment and improved macroeconomic expectations [2]

Group 1 - The core view of the report indicates that risk assets are expected to maintain relative advantages, with the profitability factor likely to recover [2][4][10] - The asset allocation model is an improved version of the Black-Litterman (BL) model, which combines market consensus with active views to optimize asset allocation and enhance the Sharpe ratio [3][5] - The model predicts that in the third quarter of 2025, the allocation ratio for domestic stocks will continue to increase while the bond allocation ratio will remain relatively high [10][11] Group 2 - In the A-share market, the profitability factor is expected to recover, and the advantage of small-cap stocks is likely to continue [2][17] - As of June 30, 2025, the market style performance for the second quarter showed strong results for small-cap and low-valuation factors, with weak profitability and weak reversal [13][16] - The report recommends focusing on indices such as the ChiNext Index, CSI A500, and CSI 2000, which exhibit high profitability and small-cap attributes [20][21]

Core Insights - The report highlights the sustained high demand for domestic computing power driven by ongoing U.S. restrictions on advanced chip imports, accelerating the domestic substitution process [3][7] - Domestic cloud service providers are increasing capital expenditures, gradually releasing industrial demand, while the iteration of domestic AI large models and applications is further boosting computing power needs [3][7] Industry Performance - The report provides a snapshot of market indices, with the Shanghai Composite Index closing at 3454.79, down 0.09%, and the Shenzhen Component Index at 10412.63, down 0.61% [4] - The performance of various sectors is noted, with steel up 3.37% and electronics down 2.01% [5] Key Focus Areas - The domestic computing power market is experiencing a boom, with Huawei's Ascend 910C servers being deployed in significant quantities, indicating a new phase in domestic computing commercialization [7] - The Ascend 910C chip boasts a single-chip computing power of 320 TFLOPS (FP16), designed for efficiency and low power consumption, suitable for AI tasks [7] - Major domestic internet companies are ramping up investments in AI infrastructure, with Alibaba planning to invest 380 billion RMB over three years, and Tencent's capital expenditure reaching 275 billion RMB in Q1 2025, up 91% year-on-year [8] Demand Drivers - The report notes that application-side inference is expected to drive demand growth, with significant increases in token usage reported by major companies like Alphabet and ByteDance [9] - The domestic supply side, including chips and supernode deployments, has achieved technological breakthroughs, which will lead to increased demand for computing power as industry applications evolve [9]

Investment Rating - The industry investment rating is "Outperform the Market," indicating that the industry index is expected to perform better than the benchmark index over the next 6-12 months [11]. Core Insights - The report highlights the sustained high demand for domestic computing power driven by the ongoing restrictions on advanced chip imports from the US, which accelerates the domestic computing power substitution process. Domestic cloud vendors are increasing capital expenditures and gradually releasing industrial demand, while the iteration of domestic AI models and applications is boosting computing power demand [1][3]. Summary by Sections Investment Recommendations - It is recommended to prioritize attention on the construction and application of domestic AI computing power network infrastructure, including operators such as China Mobile, China Telecom, and China Unicom, as well as server and switch equipment manufacturers like ZTE, Unisoc, Inspur, Ruijie Networks, and Shengke Communication. Additionally, focus on optical modules and optical devices from companies like NewEase, Zhongji Xuchuang, Yuanjie Technology, Huagong Technology, Guangxun Technology, Shijia Photonics, and Huafeng Technology [3]. Industry Trends - The report notes that Huawei's Ascend 910C has begun mass shipments, marking a new phase in the commercialization of domestic computing power. A recent tender announcement indicated that a smart computing center project plans to use 4,500 Ascend 910C-2 servers, with an expected capacity of 20,000 P computing power. The Ascend 910C features a single-chip computing power of 320 TFLOPS (FP16), making it suitable for AI tasks such as natural language processing and computer vision [1][3]. - Domestic cloud vendors and operators are increasing capital expenditures on computing power, with Alibaba planning to invest 380 billion RMB in cloud construction and AI hardware infrastructure over the next three years, averaging over 120 billion RMB annually. Tencent's capital expenditure in Q1 2025 reached 27.5 billion RMB, a year-on-year increase of 91%, with a focus on resources for large model training and inference [1][3]. - The demand for computing power is expected to grow due to breakthroughs in application-side inference technology, which significantly lowers barriers to entry. The report cites Alphabet's inference volume reaching approximately 634 trillion tokens in Q1 2025, a 50-fold increase from the previous year [1][3].

证券研究报告——晨会聚焦 2025 年 7 月 2 日 中银晨会聚焦-20250702 资料来源：万得，中银证券 中银国际证券股份有限公司 具备证券投资咨询业务资格 产品组 证券分析师：王军 ■重点关注 【策略研究】2025 年中期策略报告*王君 徐沛东 郭晓希 徐亚 高天然。在科 技重估、关税冲击、政策储备等多维度下，怎么配 A 股才能成功突围？ 【宏观经济】一季度对外经济部门体检报告*管涛 刘立品 。2025 年一季度， 我国国际收支延续经常项目顺差、资本项目逆差的自主平衡格局，交易引起 的外汇储备资产减少但外汇储备余额增加，民间部门首次转为对外净资产。 | 市场指数 | | | | --- | --- | --- | | 指数名称 | 收盘价 | 涨跌% | | 上证综指 | 3457.75 | 0.39 | | 深证成指 | 10476.29 | 0.11 | | 沪深 300 | 3942.76 | 0.17 | 行业表现（申万一级） | 指数名称 | 涨跌% | 指数名称 | 涨跌% | | --- | --- | --- | --- | | 综合 | 2.60 | 计算机 | (1.18) | ...

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Investment Rating - The report rates the real estate industry as "Outperform" [1] Core Insights - New home transaction area increased on a month-on-month basis but decreased year-on-year, with a significant drop in the year-on-year rate of decline [1] - The inventory of new homes and the de-stocking cycle both decreased on a month-on-month and year-on-year basis [1] - The land market saw both volume and price increases, with a notable rise in the premium rate [1] - Domestic bond issuance by real estate companies decreased significantly, indicating tighter financing conditions [1] - The absolute return of the real estate sector increased, while the relative return compared to the CSI 300 also improved [1] Summary by Sections 1. Key City New Home Market, Second-hand Home Market, and Inventory Tracking - New home transaction area for 40 cities was 3.366 million square meters, up 37.0% month-on-month but down 25.7% year-on-year [1][18] - Second-hand home transaction area decreased by 2.7% month-on-month but saw a smaller year-on-year decline of 0.9% [1][18] - New home inventory area for 12 cities was 87.42 million square meters, down 0.3% month-on-month and down 16.3% year-on-year [1][45] 2. Land Market Tracking - Total land transaction area for 100 cities was 15.761 million square meters, up 47.9% month-on-month and up 25.6% year-on-year [1][14] - Total land transaction price reached 57.35 billion yuan, up 186.7% month-on-month and up 155.3% year-on-year [1][14] - The average floor price of land was 3,639 yuan per square meter, up 93.9% month-on-month and up 103.2% year-on-year [1][14] 3. Industry Policy Review - The report highlights ongoing government efforts to stabilize the real estate market through various supportive measures [1][6] 4. Sector Performance Review - The absolute return of the real estate sector was 3.1%, an increase of 4.8 percentage points from the previous week [1][15] - The sector's price-to-earnings ratio (PE) was 23.85X, up 0.68X from the previous week [1][15] 5. Company Announcements - The report includes a summary of key company announcements within the real estate sector for the week [1][15] 6. Bond Issuance Situation - The total bond issuance in the real estate sector was 4.79 billion yuan, down 43.0% month-on-month and down 37.1% year-on-year [1][15]