投资评级：看好（维持） 行业走势图 数据来源：聚源 -24% -12% 0% 12% 24% 36% 48% 2024-07 2024-11 2025-03 房地产 沪深300 相关研究报告 房地产 房地产 2025 年 07 月 01 日 《新房成交面积环比增长，多地公积 金政策优化—行业周报》-2025.6.29 《新房二手房成交面积环比增长，西 安公积金直付新房首付款—行业周 报》-2025.6.22 《新房杭州环比领涨，二手房价同比 降幅缩小—行业点评报告》-2025.6.16 2025 年 1-6 月强销售金额点评：1-6 月百强销售同比 收缩，建发金茂单月销售表现靓眼 ——行业点评报告 克而瑞、亿翰智库等第三方机构发布 2025 年 6 月百强销售榜单。根据榜单测算， TOP100 房企 1-6 月全口径累计销售金额 17820.0 亿元，同比下降 11.4%，累计权 益销售金额 113013.6 万平方米，同比下降 11.5%。累计销售均价 20727.2 元/平方 米。从数据端来看，1-6 月整体销售规模同比有所降温，百强房企不同梯队间销 售情况分化程度低，6 月单月销售方面，建发房产、中国金 ...

2025 年 07 月 01 日 金融工程研究团队 魏建榕（首席分析师） 证书编号：S0790519120001 张 翔（分析师） 证书编号：S0790520110001 傅开波（分析师） 证书编号：S0790520090003 高 鹏（分析师） 证书编号：S0790520090002 苏俊豪（分析师） 证书编号：S0790522020001 胡亮勇（分析师） 证书编号：S0790522030001 王志豪（分析师） 证书编号：S0790522070003 券商金股解析月报（2025 年 7 月） 魏建榕（分析师） 高鹏（分析师） weijianrong@kysec.cn 证书编号：S0790519120001 盛少成（分析师） 证书编号：S0790523060003 苏 良（分析师） 证书编号：S0790523060004 何申昊（分析师） 证书编号：S0790524070009 蒋 韬（研究员） 证书编号：S0790123070037 相关研究报告 ——金融工程定期 《开源量化评论（23）-"金股+"组 合的量化方案》-2021.4.26 《开源量化评论（32）-券商金股的内 部收益结构》-2021.8. ...

Manufacturing Sector - June official manufacturing PMI is 49.7%, slightly improved from 49.5% in May, indicating a marginal recovery but still below the expansion threshold[3] - New orders, new export orders, and imports PMI increased by 0.4, 0.2, and 0.7 percentage points respectively, suggesting stronger domestic demand compared to external demand[4] - The BCI index for private enterprises fell by 1.0 percentage points to 49.3%, indicating weakened sentiment among smaller firms[4] Construction Sector - June construction PMI rose by 1.8 percentage points to 52.8%, with infrastructure activity remaining robust despite a decline in the construction sub-index[5] - As of June 30, the issuance progress of special bonds reached approximately 49.1%, significantly faster than the same period in 2024[5] - The government has arranged 500 billion yuan for projects in 2025, aiming to expedite the completion of the 800 billion yuan "dual-heavy" construction project list[5] Service Sector - June service PMI stands at 50.1%, a slight decrease of 0.1 percentage points from the previous month, indicating relative stability in the sector[6] - New orders in the service sector improved by 0.3 percentage points to 46.9%, although consumer spending has weakened due to the fading holiday effect[6] Economic Outlook - Q2 GDP is projected to grow by approximately 5.2%, supported by the "dual-heavy" initiatives and resilient exports[7] - The growth forecast includes contributions from the primary, secondary, and tertiary sectors at approximately 3.5%, 5.2%, and 5.4% respectively[7] - Economic resilience is attributed to three factors: boosting exports, production, and consumer subsidies, although future export uncertainties remain[7] Risk Factors - Potential risks include unexpected policy changes and a possible recession in the U.S. economy[8]

Group 1 - The report highlights the recent performance of the A-share market, with the Shanghai Composite Index closing at 3424.23 points, up 1.91% during the week of June 23 to June 27, 2025 [16] - The report identifies the top-performing sectors, including defense and military, media, telecommunications, electronics, and textiles, with respective gains of 4.35%, 2.82%, 1.90%, 1.44%, and 1.41% [16] - Conversely, the report notes the underperforming sectors, such as non-bank financials, banks, and transportation, with declines of -0.77%, -0.34%, and -0.09% respectively [16] Group 2 - The semiconductor third-party laboratory testing market is projected to reach a market size of 180-200 billion yuan by 2027, driven by the increasing demand for testing and analysis due to technological advancements and domestic production trends [19] - The report emphasizes the growing trend of specialization in the semiconductor industry, with third-party testing services becoming essential for companies to reduce costs and improve efficiency [18] - Key recommended stocks in the semiconductor sector include Jiangbolong, Demingli, Baiwei Storage, Shannon Chip, and Langke Technology [34] Group 3 - The report discusses the stablecoin market, which is becoming a bridge between traditional finance and the crypto world, with a total market capitalization exceeding 250 billion USD as of June 23, 2025 [21] - It highlights the regulatory developments in the stablecoin sector, including the EU's MiCA regulation and similar frameworks in the US and Hong Kong, which are expected to support market growth [22] - Investment opportunities in the stablecoin and RWA markets are recommended, with specific stocks such as Longxin Group, Boyan Technology, and Zhuoyi Information identified as beneficiaries [25] Group 4 - The storage price cycle is showing signs of recovery, with NAND Flash prices expected to stabilize by Q2 2025 due to production cuts by major manufacturers [29] - The report notes that the demand for storage modules is expected to increase significantly due to the AI industry's growth, with domestic cloud service providers ramping up capital expenditures [30] - Recommended stocks in the storage sector include Jiangbolong, Demingli, Baiwei Storage, Shannon Chip, and Langke Technology [34] Group 5 - Century Internet has raised its performance guidance for FY 2025, with expected net revenue adjusted from 9.1-9.3 billion yuan to 9.15-9.35 billion yuan, reflecting a growth of 11-13% year-on-year [36] - The report indicates that the AI-driven applications are expected to boost demand for AIDC resources, particularly in urban areas [37] - The report recommends stocks related to AIDC infrastructure, including Yingweike, Xinyi Network Group, and Baoxin Software [38]

Investment Rating - The industry investment rating is "Positive" (maintained) [1] Core Insights - The report emphasizes that concerns regarding AI capital expenditure are diminishing, with sovereign AI opening up long-term demand potential. Major AI firms are expected to benefit from sustained token consumption, indicating a robust demand for computing power [4][5] - The report highlights significant growth in token consumption by leading internet applications, with a notable increase in daily token calls for major models, suggesting a strong underlying demand for AI capabilities [6] - Traditional AI sectors are expected to complement generative AI, particularly in scenarios requiring high accuracy and repeatability, indicating a favorable industry outlook [7] Summary by Sections Industry Overview - The report discusses the ongoing transformation in the AI sector, with a focus on sovereign AI and its implications for computing power demand [4][5] Market Trends - The report notes that as of June 11, 2025, Nvidia announced plans to build 20 AI factories in Europe, indicating a strong commitment to expanding AI infrastructure [5] - Token consumption data shows that major models like Doubao and Google have experienced exponential growth, with Doubao's daily token calls reaching 16.4 trillion, a 137-fold increase year-on-year [6] Investment Opportunities - The report identifies specific companies that are likely to benefit from the trends in AI, including Fourth Paradigm, which focuses on decision-making AI, and BaiRong Cloud-W, which targets the financial sector [4][7]

Quantitative Models and Construction Methods 1. Model Name: High-Frequency Macroeconomic Factors - **Construction Idea**: Utilize asset portfolio simulations to construct a high-frequency macroeconomic factor system, capturing market expectations of macroeconomic changes[11] - **Construction Process**: 1. Combine real macroeconomic indicators to synthesize low-frequency macroeconomic factors 2. Select assets leading low-frequency macroeconomic factors 3. Use rolling multivariate regression with asset returns as independent variables and low-frequency macroeconomic factors as dependent variables to determine asset weights and simulate macroeconomic factor trends - Example: - High-frequency economic growth: Rolling regression fitting with Hang Seng Index and CRB Metal Spot Index[12] - High-frequency consumer inflation: Rolling regression fitting with Food Price Index and Pork Price Index[12] - High-frequency production inflation: Rolling regression fitting with Production Material Price Index, CRB Industrial Spot Index, and CRB Composite Spot Index[12] - **Evaluation**: High-frequency indicators show a leading effect compared to low-frequency macroeconomic factors[12][16] 2. Model Name: Duration Timing Model - **Construction Idea**: Predict yield curves using an improved Diebold2006 model and map expected returns for bonds of different durations[18] - **Construction Process**: 1. Predict level, slope, and curvature factors 2. Level factor prediction: Based on macroeconomic variables and policy rate tracking 3. Slope and curvature factors: Predicted using AR(1) model[18] - **Evaluation**: The model provides actionable insights for short-duration bond holdings, though recent performance shows slight underperformance against benchmarks[19] 3. Model Name: Convertible Bond Allocation Model - **Construction Idea**: Compare convertible bonds with equities and credit bonds, and implement style rotation within convertible bonds[23] - **Construction Process**: 1. Relative valuation with equities: Construct "100-yuan conversion premium rate" and calculate rolling historical percentiles[23] 2. Relative valuation with credit bonds: Use "Adjusted YTM - Credit Bond YTM" to measure relative value[23] 3. Style rotation: Construct factors like conversion premium deviation and theoretical value deviation to exclude overvalued bonds; use 20-day momentum and volatility deviation to capture market sentiment[25] - **Evaluation**: The model effectively captures style rotation opportunities, achieving strong annualized returns and high IR[27] 4. Model Name: Gold Expected Return Model - **Construction Idea**: Link gold's forward real return to U.S. TIPS (Treasury Inflation-Protected Securities) real return[30] - **Construction Process**: - Formula: $ E[Real\_Return^{gold}] = k \times E[Real\_Return^{Tips}] $ $ E[R^{gold}] = \pi^{e} + k \times E[Real\_Return^{Tips}] $ - Parameters: - $ k $ estimated via extended window OLS - $ \pi^{e} $ approximated using the Federal Reserve's long-term inflation target of 2%[30] - **Evaluation**: The model has consistently issued bullish signals for gold, with strong historical returns[32] 5. Model Name: Active Risk Budget Model - **Construction Idea**: Combine risk parity with active signals to dynamically adjust equity and bond weights based on three dimensions: cross-asset valuation, equity valuation, and market liquidity[35][36] - **Construction Process**: 1. Cross-asset valuation: Use the Fed model to calculate equity risk premium (ERP) - Formula: $ ERP = \frac{1}{PE_{ttm}} - YTM_{TB}^{10Y} $[37] 2. Equity valuation: Calculate rolling historical percentiles of equity valuations over the past five years[40] 3. Market liquidity: Use the M2-M1 scissors difference to measure marginal changes in liquid funds[41] 4. Aggregate signals and convert them into risk budget weights using the softmax function: $ softmax(x) = \frac{\exp(\lambda x)}{\exp(\lambda x) + \exp(-\lambda x)} $[45] - **Evaluation**: The model provides stable returns with low drawdowns, outperforming risk parity and equal-weighted strategies over the long term[49] 6. Model Name: Industry Rotation Model 3.0 - **Construction Idea**: Construct sub-models across six dimensions (trading behavior, prosperity, capital flow, chip structure, macro drivers, technical analysis) and dynamically synthesize signals for bi-weekly industry selection[50] - **Construction Process**: 1. Trading behavior: Capture intraday momentum and overnight reversal effects 2. Prosperity: Capture earnings momentum effects 3. Capital flow: Capture active buying and passive selling behaviors 4. Chip structure: Capture holding returns and resistance-support effects 5. Macro drivers: Map high-frequency macro expectations to industries 6. Technical analysis: Capture trading signals from trends, oscillations, and volume indicators of industry constituents[50] - **Evaluation**: The model effectively identifies industry rotation opportunities, though recent performance shows challenges in achieving excess returns[56][59] --- Model Backtest Results 1. High-Frequency Macroeconomic Factors - High-frequency economic growth: Leading low-frequency indicators as of June 27, 2025[12][16] - High-frequency consumer inflation: Leading low-frequency indicators as of June 27, 2025[16] - High-frequency production inflation: Leading low-frequency indicators as of June 27, 2025[16] 2. Duration Timing Model - June 2025 return: 31.6bp (benchmark: 33.7bp, excess: -2.1bp)[19] - 1-year return: 1.78% (benchmark: 4.74%, excess: -2.96%)[19] 3. Convertible Bond Allocation Model - Annualized return (2018-2025): 23.87% - Maximum drawdown: 16.67% - IR: 1.43 - Monthly win rate: 64.77% - 2025 YTD return: 25.21%[27] 4. Gold Expected Return Model - 1-year expected return: 23.0% (as of June 30, 2025)[30] - 1-year absolute return: 40.72% (based on TIPS timing model)[32] 5. Active Risk Budget Model - June 2025 portfolio return: 0.92% - Annualized return (full sample): 6.51% - Maximum drawdown: 4.89% - Return-to-volatility ratio: 1.64 - Return-to-drawdown ratio: 1.33[49] 6. Industry Rotation Model 3.0 - June 2025 long portfolio return: 1.05% - Short portfolio return: 2.50% - Equal-weight benchmark return: 2.03% - Long excess return: -0.98% - Short excess return: -0.47% - Long-short return: -1.45%[56]

Investment Rating - The investment rating for the company is "Buy" (maintained) [4][16]. Core Views - The company is positioned as a listed platform for China Electronics Technology Group Corporation, expected to benefit from the satellite internet and low-altitude economy [4]. - The company has maintained its profit forecast, projecting net profits of 106 million, 142 million, and 207 million yuan for 2025, 2026, and 2027 respectively, with corresponding EPS of 0.16, 0.21, and 0.30 yuan per share [4]. - The current price-to-earnings (P/E) ratios are projected to be 150.0, 112.4, and 77.0 for the years 2025, 2026, and 2027 respectively [4]. Financial Summary - The company's revenue for 2023 was 5,463 million yuan, with a projected decrease to 4,973 million yuan in 2024, followed by an increase to 5,808 million yuan in 2025 [8][11]. - The net profit for 2023 was 36 million yuan, with a significant projected increase to 106 million yuan in 2025, reflecting a year-on-year growth of 850.8% [8][11]. - The gross margin is expected to improve from 17.3% in 2023 to 18.3% in 2025, while the net margin is projected to rise from 0.7% to 1.8% in the same period [12]. Strategic Developments - The company has signed a strategic cooperation agreement with Helios Starlink and Yixin Technology to establish a joint innovation center, aimed at supporting the construction of the "Trinity Computing Constellation" [5][6]. - The collaboration focuses on building an integrated space-ground computing network, enhancing data transmission technologies, and exploring commercial applications [6]. - The successful launch of the "Trinity Computing Constellation" marks a significant milestone, with the potential to process data in orbit and improve the efficiency of satellite data handling [7].

Investment Rating - The investment rating for the industry is "Positive" (First time) [1] Core Insights - Stablecoins are becoming a bridge between traditional finance and the crypto world, with applications expanding from cryptocurrency trading to broader payment fields, including cross-border trade payments and retail payment innovations [3][13] - The stablecoin market is rapidly growing, with a total market capitalization exceeding $250 billion as of June 26, 2025, and projections indicating it could reach $2 trillion within three years [4][24] - Regulatory frameworks in regions like the EU, the US, and Hong Kong are being established to support the healthy development of the stablecoin market [4][34] Summary by Sections 1. Stablecoins as a Bridge - Stablecoins are designed to address the volatility of cryptocurrencies, typically pegged to fiat currencies or commodities [13] - They can be categorized into fiat-collateralized, crypto-collateralized, and algorithmic stablecoins, with fiat-collateralized stablecoins like USDT and USDC dominating the market [14] 1.1 Expansion of Use Cases - The application of stablecoins is broadening from cryptocurrency trading to include cross-border payments, retail innovations, and inclusive finance [15] - In cross-border trade, stablecoins significantly reduce transaction times and costs compared to traditional banking methods [19][21] 1.2 Market Growth - As of June 26, 2025, there are 267 types of stablecoins with a total market cap exceeding $250 billion, and the trading volume in the past 12 months reached $33 trillion [24][28] - USDT and USDC are the leading stablecoins, holding 62.47% and 24.31% of the market share, respectively [26] 2. Regulatory Developments - The EU's MiCA regulation and the US's GENIUS Act are significant steps towards establishing a clear regulatory framework for stablecoins [34][38] - Hong Kong's stablecoin regulation aims to balance financial innovation with risk management, requiring licenses for stablecoin issuers [35] 3. Development of Major Stablecoins - USDT, the largest stablecoin, is pegged 1:1 to the US dollar and has become a fundamental tool in cryptocurrency trading and DeFi [41] - USDC, the second-largest stablecoin, has a business model centered around treasury yield from US government bonds, with a total revenue of $1.676 billion in 2024 [49][51] 4. Investment Recommendations - The report recommends investing in companies benefiting from the stablecoin and RWA market opportunities, including Longxin Group, Boyan Technology, and Zhuoyi Information, among others [6]

Quantitative Models and Construction Methods Barra Style Factors - **Model Name**: Barra Style Factors - **Construction Idea**: The model tracks the performance of common style factors such as size, value, growth, and profitability in the market - **Construction Process**: The factors are calculated based on specific financial metrics. For example: - Size factor: Based on market capitalization - Value factor: Measured by book-to-market ratio - Growth factor: Derived from growth-related metrics - Profitability factor: Based on earnings expectations - **Evaluation**: The model provides insights into the performance of different market styles, helping investors understand factor dynamics[4][14] --- Ideal Reversal Factor - **Factor Name**: Ideal Reversal Factor - **Construction Idea**: Captures the micro-level reversal force in the A-share market, primarily driven by large transaction volumes - **Construction Process**: 1. Retrieve the past 20 trading days' data for selected stocks 2. Calculate the average transaction amount per trade (transaction amount/number of trades) for each day 3. Identify the 10 days with the highest average transaction amounts and sum their returns (denoted as M_high) 4. Identify the 10 days with the lowest average transaction amounts and sum their returns (denoted as M_low) 5. Compute the factor value as M = M_high - M_low 6. Repeat the above steps for all stocks to calculate their respective factor values[43] - **Evaluation**: The factor effectively identifies trading days with strong reversal attributes, providing a robust alpha source[15] --- Smart Money Factor - **Factor Name**: Smart Money Factor - **Construction Idea**: Identifies institutional trading activity by analyzing price-volume information from intraday data - **Construction Process**: 1. Retrieve the past 10 days' minute-level data for selected stocks 2. Construct the indicator $S_t = \frac{|R_t|}{V_t^{0.25}}$, where $R_t$ is the return at minute $t$, and $V_t$ is the trading volume at minute $t$ 3. Sort the minute-level data by $S_t$ in descending order and select the top 20% of minutes by cumulative trading volume as "smart money" trades 4. Calculate the volume-weighted average price (VWAP) for smart money trades ($VWAP_{smart}$) and for all trades ($VWAP_{all}$) 5. Compute the factor value as $Q = \frac{VWAP_{smart}}{VWAP_{all}}$[42][44] - **Evaluation**: The factor successfully tracks institutional trading patterns, offering a unique perspective on market behavior[15] --- APM Factor - **Factor Name**: APM Factor - **Construction Idea**: Measures the difference in stock price behavior between morning (or overnight) and afternoon trading sessions - **Construction Process**: 1. Retrieve the past 20 days' data for selected stocks 2. Calculate daily overnight returns and afternoon returns for both stocks and indices 3. Perform a regression of stock returns on index returns for both periods to obtain residuals 4. Compute the difference between overnight and afternoon residuals for each day 5. Calculate the statistic $stat = \frac{\mu(\delta_t)}{\sigma(\delta_t)/\sqrt{N}}$, where $\mu$ is the mean, $\sigma$ is the standard deviation, and $N$ is the number of observations[45] 6. Perform a cross-sectional regression of $stat$ on momentum factors to remove their influence, and use the residuals as the APM factor[46] - **Evaluation**: The factor captures structural differences in trading behavior across time periods, providing valuable insights into intraday dynamics[15] --- Ideal Amplitude Factor - **Factor Name**: Ideal Amplitude Factor - **Construction Idea**: Measures the structural differences in amplitude information between high and low price states - **Construction Process**: 1. Retrieve the past 20 trading days' data for selected stocks 2. Calculate the daily amplitude as $(\text{High Price}/\text{Low Price}) - 1$ 3. Select the top 25% of trading days by closing price and compute the average amplitude (denoted as $V_{high}$) 4. Select the bottom 25% of trading days by closing price and compute the average amplitude (denoted as $V_{low}$) 5. Compute the factor value as $V = V_{high} - V_{low}$[48] - **Evaluation**: The factor effectively captures amplitude differences across price states, revealing hidden structural information[15] --- Composite Trading Behavior Factor - **Factor Name**: Composite Trading Behavior Factor - **Construction Idea**: Combines multiple trading behavior factors using ICIR-based weighting to enhance overall performance - **Construction Process**: 1. Perform outlier removal and standardization for individual factors within industries 2. Use the past 12 periods' ICIR values as weights to combine the factors 3. Compute the composite factor value as a weighted sum of the individual factors[32] - **Evaluation**: The composite factor demonstrates superior performance, particularly in small-cap stock pools, and provides a comprehensive view of trading behavior[32] --- Backtesting Results of Models and Factors Barra Style Factors - Size factor: Return of -0.42% - Book-to-market ratio factor: Return of 0.09% - Growth factor: Return of -0.05% - Profitability factor: Return of -0.11%[4][14] --- Ideal Reversal Factor - IC: -0.050 - RankIC: -0.061 - IR: 2.53 - Long-short monthly win rate: 78.1% - June 2025 long-short return: 1.09% - 12-month long-short monthly win rate: 66.7%[6][16] --- Smart Money Factor - IC: -0.037 - RankIC: -0.061 - IR: 2.74 - Long-short monthly win rate: 82.1% - June 2025 long-short return: 0.91% - 12-month long-short monthly win rate: 91.7%[6][19] --- APM Factor - IC: 0.029 - RankIC: 0.034 - IR: 2.27 - Long-short monthly win rate: 76.6% - June 2025 long-short return: -0.11% - 12-month long-short monthly win rate: 58.3%[6][23] --- Ideal Amplitude Factor - IC: -0.054 - RankIC: -0.073 - IR: 3.01 - Long-short monthly win rate: 83.5% - June 2025 long-short return: 2.43% - 12-month long-short monthly win rate: 75.0%[6][27] --- Composite Trading Behavior Factor - IC: 0.067 - RankIC: 0.092 - IR: 3.30 - Long-short monthly win rate: 82.4% - June 2025 long-short return: 1.12% - 12-month long-short monthly win rate: 83.3% - Outperformance in small-cap pools: IR of 2.93 in the CSI 2000, 2.85 in the CSI 1000, and 1.26 in the CSI 800[6][32]

Investment Rating - The industry investment rating is "Overweight" [1][10] Core Viewpoints - The report maintains a positive outlook on the domestic AIDC (Artificial Intelligence Data Center) industry chain, driven by the acceleration of AI applications in various sectors such as education, gaming, and video conferencing, which is expected to increase demand for AI computing power [5][6] - Century Internet has raised its revenue and adjusted EBITDA guidance for the fiscal year 2025, indicating a stronger than expected performance due to faster-than-anticipated migration of wholesale IDC customers and ongoing operational efficiency improvements [4] Summary by Sections AIDC Industry Development - The report highlights the potential for accelerated development in the domestic AIDC industry chain, with specific recommendations for investment in AIDC facilities, computing power leasing, and IT infrastructure [6] - Recommended stocks include Yingweike, New Idea Network Group, and Baoxin Software for AIDC facility construction, while companies like Ziguang Co. and ZTE are recommended for IT side investments [6] AI Computing Power Demand - The report emphasizes that the demand for AI computing power will continue to grow, particularly in low-latency applications such as online gaming and video calls, which are expected to drive resource demand in major cities [5][6] Performance Forecasts - Century Internet's revised guidance indicates a projected revenue increase from 9.1-9.3 billion to 9.15-9.35 billion, reflecting a year-on-year growth of 11-13% [4] - Adjusted EBITDA is also raised from 2.7-2.76 billion to 2.76-2.82 billion, with a year-on-year growth forecast of 14-16% [4]