炒股就看金麒麟分析师研报，权威，专业，及时，全面，助您挖掘潜力主题机会！ 证券时报记者根据Wind数据统计，今年以来，已有300家上市公司公布了评级机构对公司的评级报告， 289家公司的主体信用评级保持不变，但同时也有12家公司的主体信用评级被调整。 上市公司主体信用评级被调低，一般缘于公司基本面存在瑕疵，包括业绩亏损、债务压力攀升、征信状 况恶化、股票退市等情形。比如，山石网科发布公告称，信用评级机构联合资信在对公司经营状况及相 关行业进行综合分析与评估的基础上，于今年5月28日出具了《山石网科通信技术股份有限公司向不特 定对象发行可转换公司债券2025年跟踪评级报告》，下调公司主体信用等级为A，山石转债信用等级为 A，评级展望为"负面"。 联合资信认为，截至2025年3月末，公司发行的山石转债余额为2.67亿元。2024年，公司经营活动现金 流入量对山石转债的保障程度较强，但公司EBITDA和经营活动现金流量净额均为负，对山石转债均无 覆盖能力，整体看，公司长期偿债能力指标表现弱，且整体看公司对山石转债的保障能力有所下降。 联合资信进一步指出，未来，随着公司持续进行组织架构调整和优化，ASIC安全专用芯片 ...

- The report introduces the **Industry Basic Tracking Model**, which monitors industry fundamentals and identifies the top-performing industries based on their sentiment and activity levels. The model highlights transportation, food & beverage, and coal as the industries with the highest sentiment, while media, communication, and banking show lower sentiment levels[3][8][9] - The **Crowding Factor** is introduced to measure the disparity between leading and lagging stocks within an industry across three dimensions: volatility, liquidity, and systemic risk. Higher crowding factors indicate elevated risks such as high volatility, active trading turnover, or increased beta exposure. For example, the food & beverage industry shows historically high crowding factors, while industries like agriculture, pharmaceuticals, machinery, consumer services, and coal exhibit historically low crowding factors[3][17][18] - The **Crowding Factor** is calculated using metrics such as stock volatility, liquidity, and beta exposure. It reflects the degree of market concentration and trading activity within an industry. Higher values suggest speculative trading and heightened systemic risk, while lower values indicate reduced market activity and risk exposure[17][18][28] - The pharmaceutical industry demonstrates a divergence between sentiment and crowding factors, with sentiment decreasing by 0.06 and crowding factors increasing by 0.28. This is attributed to short-term policy benefits, event-driven catalysts, and market sentiment, despite the lack of comprehensive recovery in industry fundamentals[12][15][17] - The report emphasizes that industries with high crowding factors, such as food & beverage, may face risks of speculative trading and systemic volatility. Conversely, industries with low crowding factors, such as agriculture, pharmaceuticals, machinery, consumer services, and coal, may present opportunities for stable investment due to reduced speculative activity[17][18][28]

5月1日至31日，长洲船闸单月过货量突破2444万吨，同比增长9.24%，刷新历史纪录。 长洲船闸刷新单月过货量纪录 ...

6月4日，A股市场整体上涨。截至收盘，上证指数收报3376.2点，上涨0.42%；深证成指收报10144.58点，上涨 0.87%；创业板指收报2024.93点，上涨1.11%。两市合计成交11530.47亿元，较上一交易日增加116.38亿元。 1. 两市全天主力资金净流出近3亿元 | | | 沪深两市最近五个交易日主力资金流向情况(亿元) | | | | --- | --- | --- | --- | --- | | 日期 | | 净流入金额 开盘净流入 | 人得发起 | 超大单净买入 | | 2025-6-4 | -2. 61 | -33.75 | -0. 29 | 23. 41 | | 2025-6-3 | -91. 89 | -39. 38 | -8. 11 | -25. 46 | | 2025-5-30 | -352. 62 | -158. 05 | -37.83 | -193. 19 | | 2025-5-29 | 74. 33 | -19.58 | 23.89 | 121. 48 | | 2025-5-28 | -185. 39 | -73. 21 | -22. 45 | -57. 58 | ...

交通运输行业今日下跌0.58%，全天主力资金净流出5.85亿元，该行业所属的个股共125只，今日上涨的 有56只，涨停的有1只；下跌的有56只。以资金流向数据进行统计，该行业资金净流入的个股有52只， 其中，净流入资金超千万元的有18只，净流入资金居首的是嘉诚国际，今日净流入资金7248.74万元， 紧随其后的是重庆港、中国国航，净流入资金分别为5221.74万元、2995.26万元。交通运输行业资金净 流出个股中，资金净流出超3000万元的有7只，净流出资金居前的有德邦股份、保税科技、龙洲股份， 净流出资金分别为2.68亿元、1.07亿元、7382.63万元。（数据宝） 交通运输行业资金流入榜 沪指6月4日上涨0.42%，申万所属行业中，今日上涨的有28个，涨幅居前的行业为美容护理、综合，涨 幅分别为2.63%、2.53%。跌幅居前的行业为交通运输、国防军工、公用事业，跌幅分别为0.58%、 0.24%、0.12%。交通运输行业位居今日跌幅榜首位。 资金面上看，两市主力资金全天净流入10.74亿元，今日有17个行业主力资金净流入，电子行业主力资 金净流入规模居首，该行业今日上涨1.17%，全天净流入资金30 ...

主力资金净流出的行业有14个，汽车行业主力资金净流出规模居首，全天净流出资金17.87亿元，其次 是国防军工行业，净流出资金为17.40亿元，净流出资金较多的还有医药生物、机械设备、银行等行 业。 沪指6月4日上涨0.42%，申万所属行业中，今日上涨的有28个，涨幅居前的行业为美容护理、综合，涨 幅分别为2.63%、2.53%。跌幅居前的行业为交通运输、国防军工、公用事业，跌幅分别为0.58%、 0.24%、0.12%。公用事业行业位居今日跌幅榜第三。 资金面上看，两市主力资金全天净流入10.74亿元，今日有17个行业主力资金净流入，电子行业主力资 金净流入规模居首，该行业今日上涨1.17%，全天净流入资金30.70亿元，其次是非银金融行业，日涨幅 为0.99%，净流入资金为15.88亿元。 公用事业行业资金流出榜 | 代码 | 简称 | 今日涨跌幅（%） | 今日换手率（%） | 主力资金流量（万元） | | --- | --- | --- | --- | --- | | 600025 | 华能水电 | -1.51 | 0.20 | -5128.49 | | 600886 | 国投电力 | -1.56 | ...

Market Overview - The A-share market saw all major indices rise today, with the Shanghai Composite Index up by 0.42% closing at 3376.20 points, the Shenzhen Component Index up by 0.87% at 10144.58 points, and the ChiNext Index up by 1.11% at 2024.93 points [1] - The total trading volume in the Shanghai and Shenzhen markets reached 1153 billion yuan, an increase of 11.6 billion yuan compared to the previous trading day [1] Industry Performance - Among the Shenwan first-level industries, the leading sectors included Beauty Care, Comprehensive, Textile and Apparel, Communication, and Light Industry Manufacturing, with increases of 2.63%, 2.53%, 2.41%, 1.79%, and 1.61% respectively [1] - The only sectors that experienced declines were Transportation, National Defense and Military Industry, and Public Utilities, with decreases of 0.58%, 0.24%, and 0.12% respectively [1] Concept Sector Performance - The top-performing concept sectors today included Rare Earth, Rare Earth Permanent Magnet, Gold and Jewelry, Dairy Industry, and Lithium Battery, among others [2] - Conversely, sectors such as Air Transportation, Aircraft Carriers, and Intelligent Logistics experienced pullbacks [11]

沪深两市全天成交额1.15万亿元。个股涨多跌少，全市场超3900只个股上涨。 美容护理、啤酒、服装家纺、能源金属板块涨幅居前，机场航运、物流板块走低。 6月4日，截至收盘，沪指涨0.42%，深成指涨0.87%，创业板指涨1.11%。 | 代码 | 名称 | 现价 | 涨跌 | 涨跌幅 | | --- | --- | --- | --- | --- | | 000001 | 上证指数 | 3376.20 c | 14.23 | 0.42% | | 399001 | 深证成指 | 10144.58 c | 87.41 | 0.87% | | 899050 | 非证50 | 1438.73 c | 15.59 | 1.10% | | 881001 | 万得全A | 5136.95 c | 36.37 | 0.71% | | 000688 | 科创20 | 986.11 c | 4.40 | 0.45% | | 399006 | 创业板指 | 2024.93 c | 22.23 | 1.11% | 【资金流向】 主力资金净流入电子、有色金属、通信等板块，净流出银行、交通运输、汽车等板块。 具体到个股来看，沪电股份、光 ...

证券时报·数据宝统计，截至下午13:59，今日沪指涨0.39%，A股成交量777.48亿股，成交金额9310.49亿 元，比上一个交易日减少0.50%。个股方面，3748只个股上涨，其中涨停73只，1456只个股下跌，其中 跌停3只。从申万行业来看，美容护理、综合、纺织服饰等涨幅最大，涨幅分别为2.55%、2.36%、 1.75%；交通运输、煤炭、公用事业等跌幅最大，跌幅分别为0.62%、0.25%、0.24%。（数据宝） | 国防军工 | | | | 纵横股份 | | | --- | --- | --- | --- | --- | --- | | 公用事业 | -0.24 | 156.43 | -19.60 | 南网能源 | -5.30 | | 煤炭 | -0.25 | 40.02 | -23.25 | 陕西煤业 | -2.00 | | 交通运输 | -0.62 | 206.30 | -9.10 | 江西长运 | -5.99 | 注：本文系新闻报道，不构成投资建议，股市有风险，投资需谨慎。 今日各行业表现（截至下午13:59） | 申万行业 | 行业涨跌（%） | 成交额（亿元） | 比上日（%） | 领涨 ...

Quantitative Models and Construction Methods 1. Model Name: Hong Kong Stock Multi-Factor Model - **Model Construction Idea**: The model integrates four categories of factors: technical, capital flow, fundamental, and analyst expectations, to evaluate Hong Kong Stock Connect constituent stocks[38][39] - **Model Construction Process**: 1. Select Hong Kong Stock Connect constituent stocks as the sample universe 2. Construct four categories of factors: - **Technical factors**: Indicators derived from price and volume data - **Capital flow factors**: Metrics based on fund flow data, such as net inflow - **Fundamental factors**: Metrics like valuation ratios (e.g., PE, PB) and profitability indicators (e.g., ROE) - **Analyst expectation factors**: Metrics based on analyst ratings and earnings forecasts 3. Combine these factors into a composite score for each stock 4. Rank stocks based on their composite scores and select the top 20 stocks to form the portfolio[38][39][40] - **Model Evaluation**: The model demonstrates strong performance in historical backtesting, with significant excess returns over the benchmark[38][40] 2. Model Name: Hong Kong Stock Preferred 20 Portfolio - **Model Construction Idea**: This portfolio is constructed monthly by selecting the top 20 stocks with the highest composite scores from the multi-factor model, using equal weighting[40] - **Model Construction Process**: 1. At the end of each month, rank stocks based on their composite scores from the multi-factor model 2. Select the top 20 stocks 3. Allocate equal weights to each stock in the portfolio 4. Use the Hong Kong Composite Index (HKD, 930930.CSI) as the benchmark for performance comparison[40] - **Model Evaluation**: The portfolio has shown robust performance over the long term, with a high excess annualized return and a stable risk-return profile[40][44] --- Model Backtesting Results 1. Hong Kong Stock Multi-Factor Model - **Excess Annualized Return**: 13.3% (2015.1–2025.5)[40][44] - **Excess Annualized Volatility**: 13.4%[44] - **Excess Sharpe Ratio**: 1.0[44] - **Maximum Drawdown**: 18.2%[44] 2. Hong Kong Stock Preferred 20 Portfolio - **May 2025 Monthly Return**: 2.44%[40] - **May 2025 Excess Return**: -2.36% (Benchmark return: 4.80%)[40] - **Excess Annualized Return**: 13.3% (2015.1–2025.5)[40][44] - **Excess Annualized Volatility**: 13.4%[44] - **Excess Sharpe Ratio**: 1.0[44] - **Maximum Drawdown**: 18.2%[44] --- Quantitative Factors and Construction Methods 1. Factor Name: Technical Factors - **Factor Construction Idea**: Derived from price and volume data to capture market trends and momentum[38][39] - **Factor Construction Process**: 1. Calculate indicators such as moving averages, RSI, and MACD 2. Normalize and rank the indicators across the stock universe 3. Combine the normalized scores into a composite technical factor score[38][39] 2. Factor Name: Capital Flow Factors - **Factor Construction Idea**: Based on fund flow data to identify stocks with strong capital inflows[38][39] - **Factor Construction Process**: 1. Measure net fund inflows for each stock 2. Normalize and rank the net inflow data 3. Combine the normalized scores into a composite capital flow factor score[38][39] 3. Factor Name: Fundamental Factors - **Factor Construction Idea**: Focused on valuation and profitability metrics to identify undervalued stocks with strong fundamentals[38][39] - **Factor Construction Process**: 1. Calculate valuation ratios (e.g., PE, PB) and profitability indicators (e.g., ROE) 2. Normalize and rank these metrics across the stock universe 3. Combine the normalized scores into a composite fundamental factor score[38][39] 4. Factor Name: Analyst Expectation Factors - **Factor Construction Idea**: Based on analyst ratings and earnings forecasts to capture market sentiment and expectations[38][39] - **Factor Construction Process**: 1. Collect analyst ratings and earnings forecast data 2. Normalize and rank the data 3. Combine the normalized scores into a composite analyst expectation factor score[38][39] --- Factor Backtesting Results 1. Technical Factors - **Performance**: Demonstrated strong predictive power in identifying stocks with upward momentum[38][39] 2. Capital Flow Factors - **Performance**: Effective in capturing stocks with significant fund inflows, indicating strong market interest[38][39] 3. Fundamental Factors - **Performance**: Successfully identified undervalued stocks with robust financial performance[38][39] 4. Analyst Expectation Factors - **Performance**: Provided valuable insights into market sentiment and future earnings potential[38][39]