- The report does not contain any quantitative models or factors related to construction, testing, or evaluation

Market Liquidity Overview - R007 increased from 1.5811% to 1.591%, a rise of 0.99 basis points, while DR007 decreased from 1.502% to 1.4941%, a drop of 0.79 basis points. The spread between R007 and DR007 widened by 1.78 basis points [9][11] - The net outflow of funds this week was 320.904 billion, an increase of 391.818 billion from the previous week. Fund supply was 144.097 billion, and fund demand was 465.001 billion [11][12] Industry Sector Liquidity Tracking - Most sectors in the CITIC first-level industry recorded declines, with the banking sector showing the strongest performance, up 3.13%. The pharmaceutical and textile sectors led the declines, falling 4.16% and 4.10% respectively [16][17] - The pharmaceutical industry had the highest overall congestion score, indicating significant market activity and potential risk [27][28] Style Sector Liquidity Tracking - The consumer style index experienced the largest decline of 3.90%, while the financial style saw a slight increase of 0.05%, making it the only sector with positive returns [32][33] - The growth style maintained the highest daily trading volume share at 52.43%, significantly ahead of other styles, and also had the highest turnover rate at 2.33% [30][32]

[Table_Title] 每日复盘 证券研究报告 2025 年 06 月 23 日 2025 年 6 月 23 日市场全天低开高走，稳定币概念股再度 活跃 报告要点： 2025 年 6 月 23 日市场全天低开高走，稳定币概念股再度活跃。上证指 数上涨 0.65%，深证成指上涨 0.43%，创业板指上涨 0.39%。市场成交额 11224.57 亿元，较上一交易日增加 548.70 亿元。全市场 4501 只个股上涨， 862 只个股下跌。 风格上看，各指数涨跌幅排序为：成长>金融>周期>稳定>消费>0；小盘 成长>大盘价值>小盘价值>中盘价值>中盘成长>大盘成长；中证全指表现优 于基金重仓。 分行业看，30 个中信一级行业普遍上涨；表现相对靠前的是：综合金融 （5.03%），综合（3.22%），计算机（2.21%）；表现相对靠后的是：食品饮料 （-0.78%），家电（-0.43%），钢铁（0.02%）。概念板块方面，多数概念板块 上涨，昨日连板、昨日连板_含一字、数字货币等大幅上涨；低碳冶金、白 酒、啤酒等板块走低。 资金筹码方面，主力资金 6 月 23 日净流入 86.96 亿元。其中超大单净流 入 9 ...

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industry performance[27][28] - **Model Construction Process**: The diffusion index is calculated for each industry, ranking them based on their momentum. Industries with higher diffusion index values are considered to have stronger upward trends. The model selects industries with the highest diffusion index values for allocation. - Formula: Not explicitly provided in the report - **Model Evaluation**: The model has shown mixed performance over the years. It performed well in 2021 and 2022 but faced significant drawdowns in 2023 and 2024 due to market reversals and failure to adjust to cyclical changes[27] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency price and volume data, aiming to identify industry trends and generate excess returns[34][39] - **Model Construction Process**: The GRU network is trained on historical minute-level price and volume data to predict industry rankings. The model then allocates to industries with the highest GRU factor scores. - Formula: Not explicitly provided in the report - **Model Evaluation**: The model has shown strong adaptability in short-term cycles but struggles in long-term trends and extreme market conditions. It has faced challenges in capturing excess returns in 2025 due to concentrated market themes[34][39] --- Model Backtesting Results 1. Diffusion Index Model - **2025 YTD Excess Return**: 0.37%[26][31] - **June 2025 Excess Return**: 1.99%[31] - **Weekly Average Return (June 2025)**: -0.65%[31] - **Weekly Excess Return (June 2025)**: 0.79%[31] 2. GRU Factor Model - **2025 YTD Excess Return**: -3.83%[34][37] - **June 2025 Excess Return**: 0.25%[37] - **Weekly Average Return (June 2025)**: -1.18%[37] - **Weekly Excess Return (June 2025)**: 0.25%[37] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the momentum of industries by ranking them based on their upward trends[28] - **Factor Construction Process**: The diffusion index is calculated for each industry weekly. Industries are ranked based on their index values, with higher values indicating stronger momentum. - Example Values (as of June 20, 2025): - Top Industries: Comprehensive Finance (1.0), Non-Bank Finance (0.973), Banking (0.97)[28] - Bottom Industries: Coal (0.174), Food & Beverage (0.313), Oil & Gas (0.387)[28] - **Factor Evaluation**: The factor effectively captures upward trends but may underperform during market reversals[27][28] 2. Factor Name: GRU Factor - **Factor Construction Idea**: Utilizes GRU deep learning to analyze high-frequency trading data and rank industries based on predicted performance[34][39] - **Factor Construction Process**: The GRU network processes minute-level price and volume data to generate factor scores for each industry. Industries are ranked based on these scores. - Example Values (as of June 20, 2025): - Top Industries: Coal (3.48), Non-Bank Finance (3.15), Utilities (2.65)[35] - Bottom Industries: Communication (-17.95), Media (-15.45), Defense (-11.87)[35] - **Factor Evaluation**: The factor is effective in short-term trend identification but struggles with long-term stability and extreme market conditions[34][39] --- Factor Backtesting Results 1. Diffusion Index Factor - **Top Weekly Changes (June 20, 2025)**: - Oil & Gas: +0.09 - Textiles: +0.044 - Metals: +0.036[30] - **Bottom Weekly Changes (June 20, 2025)**: - Agriculture: -0.229 - Defense: -0.086 - Building Materials: -0.078[30] 2. GRU Factor - **Top Weekly Changes (June 20, 2025)**: - Non-Bank Finance: Significant increase - Consumer Services: Significant increase - Comprehensive: Significant increase[35] - **Bottom Weekly Changes (June 20, 2025)**: - Communication: Significant decrease - Electronics: Significant decrease - New Energy Equipment: Significant decrease[35]

Group 1 - The core viewpoint indicates that the non-ferrous materials sector has underperformed compared to the broader market, with a notable decline in various sub-sectors such as rare earths and gold [2][4] - COMEX gold futures closed at $3363.20 per ounce, down $69.4 per ounce, a decrease of 2%, while COMEX silver futures fell to $35.78 per ounce, down $0.5 per ounce, a decline of 1.38% [3][4] - Industrial metals showed slight price increases, with LME copper settling at $9945 per ton, reflecting a weekly increase of 3% [3][4] Group 2 - The non-ferrous materials sector saw a decline of 3.3%, with rare earths down 6.02% and gold down 5.34%, while aluminum remained stable [2][4] - Prices for various industrial metals, including LME aluminum and zinc, experienced weekly increases of 1.8% and 2%, respectively, while LME nickel saw a decrease of 1.34% [3][4] - The price of lithium carbonate and lithium hydroxide continued to decline, with lithium hydroxide prices dropping below 60,000 per ton, indicating potential losses for companies without cost advantages [3][4]

Quantitative Models and Construction Methods - **Model Name**: TWO BETA Model **Model Construction Idea**: This model is designed to identify and recommend sectors or themes with strong momentum, focusing on technology-related sectors and specific themes like military and Hong Kong automotive industries[2][3][10]. **Model Construction Process**: The report does not provide detailed steps or formulas for the construction of the TWO BETA model. However, it is used to track and recommend sectors based on their relative performance and momentum trends[2][3][10]. **Model Evaluation**: The model continues to recommend technology sectors, military themes, and Hong Kong automotive themes, indicating its focus on identifying upward trends in these areas[2][3][10]. - **Model Name**: Industry Allocation Model **Model Construction Idea**: This model aims to recommend sectors based on medium-term perspectives, focusing on sectors undergoing a turnaround or showing resilience in current market conditions[2][3][10]. **Model Construction Process**: The report does not provide detailed steps or formulas for the construction of the industry allocation model. It is used to identify sectors like innovative drugs in Hong Kong, new consumption themes, and financial sectors in Hong Kong[2][3][10]. **Model Evaluation**: The model highlights sectors with potential for recovery or sustained growth, such as Hong Kong innovative drugs, new consumption, and financial sectors, which are deemed to have intact trends[2][3][10]. - **Model Name**: Timing System **Model Construction Idea**: This model uses the distance between short-term and long-term moving averages to determine the market's overall environment and timing signals[1][9][13]. **Model Construction Process**: 1. Define the short-term moving average (20-day) and long-term moving average (120-day) for the Wind All A Index. 2. Calculate the distance between the two moving averages: $ \text{Distance} = \frac{\text{Short-term MA} - \text{Long-term MA}}{\text{Long-term MA}} $ - Short-term MA (20-day): 5130 - Long-term MA (120-day): 5075 - Distance: 1.09% 3. Interpret the signal: If the absolute value of the distance is less than 3%, the market is considered to be in a consolidation phase[1][9][13]. **Model Evaluation**: The model indicates that the market remains in a consolidation phase, with the short-term moving average above the long-term moving average, suggesting a lack of strong directional trends[1][9][13]. Backtesting Results of Models - **TWO BETA Model**: No specific backtesting results or quantitative metrics are provided in the report[2][3][10]. - **Industry Allocation Model**: No specific backtesting results or quantitative metrics are provided in the report[2][3][10]. - **Timing System**: - Short-term MA: 5130 - Long-term MA: 5075 - Distance: 1.09% - Absolute distance remains below 3%, confirming the market's consolidation phase[1][9][13]. Quantitative Factors and Construction Methods - **Factor Name**: None explicitly mentioned in the report. Backtesting Results of Factors - **Factors**: No specific factors or their backtesting results are provided in the report.

周三（6 月 18 日）大盘探底回升，小幅收涨。截至收 盘，上证综指收于 3388.81 点，上涨 0.04%；深成指 收于 10175.59 点，上涨 0.24%；科创 50 上涨 0.53%； 创业板指上涨 0.23%，万得全 A 成交额共 12218 亿元， 较前一日略有下降。 行业方面，30 个中信一级行业有 9 个行业收涨，其中 电子、通信及国防军工涨幅居前，而综合金融、房地 产及轻工制造则跌幅较大。概念方面，电路板、光模 块 CPO 及军工信息化等指数表现活跃。 海外市场综述 美国三大股指收盘涨跌不一，VISA 跌近 5% 周三（6 月 18 日），美国三大股指收盘涨跌不一，道 指跌 0.1%，标普 500 指数跌 0.03%，纳指涨 0.13%。万 得美国科技七巨头指数涨 0.04%，特斯拉涨超 1%，英伟 达涨近 1%。中概股多数下跌，比特小鹿跌逾 7%。 新闻精要 1. 2025 陆家嘴论坛召开 国家金融管理部门主要领导 重磅发声 2. 中央金融委员会印发《关于支持加快建设上海国际 金融中心的意见》 3. 国家有关部门：剩余支持资金将有序下达 国内市场综述 探底回升 小幅收涨 6. 全球重要 ...

Core Insights - Ping An Good Doctor has rebranded and launched its annual health service card, introducing a comprehensive "7+N+1" AI medical product matrix [1][4] Group 1: Brand and Service Development - Since its establishment in 2014, Ping An Good Doctor has become a leading provider of healthcare and elderly care management services in China, leveraging policy opportunities and user needs [2] - The company has built a service network covering 29 departments with approximately 50,000 internal and external doctors, linking 105,000 health service providers, 235,000 pharmacies, and 4,000 hospitals [2] - The platform has registered users reaching 400 million, with 20 million family doctor members and partnerships with 2,100 companies covering over 5 million employees [2] Group 2: New Service Offerings - The annual health service card emphasizes three key service areas: "Proactive Family Doctor Care," "Zero Distance to Experts," and "Comprehensive Medical Management" [3] - "Proactive Family Doctor Care" features a team of certified family doctors providing 24/7 case analysis and proactive services [3] - "Zero Distance to Experts" allows users to connect with over 3,000 experts within three hours, enhancing the service for critical patients [3] - "Comprehensive Medical Management" ensures full support for patients from registration to recovery, including surgical assistance and post-operative guidance [3] Group 3: AI Product Matrix - The "7+N+1" AI medical product matrix includes seven original AI medical products, such as "Ping An AI Doctor" and "AI Health Manager," aimed at enhancing service capabilities [4] - The "N" represents upgrades to existing AI Agent products, while "1" refers to the "Youyi" platform that extends AI capabilities to the broader society [4] - Collaboration with oncology experts aims to establish MDT diagnostic standards, focusing on the application of AI in clinical settings [4] Group 4: Strategic Direction - Ping An Group is committed to advancing its "comprehensive finance + healthcare and elderly care" strategy, focusing on customer-centric services [5] - The company aims to provide cost-effective healthcare and elderly care services, ensuring customers save time, money, and effort [5]

Domestic Market Overview - The domestic market experienced narrow consolidation with slight declines, with the Shanghai Composite Index closing at 3387.4 points, down 0.04%, and the Shenzhen Component Index at 10151.43 points, down 0.12% [1][4][9] - Among the 30 CITIC first-level industries, 16 sectors saw gains, with comprehensive finance and coal showing the largest increases, while pharmaceuticals, media, and light manufacturing faced significant declines [1][4][9] - The total trading volume for the A-share market was approximately 123.71 billion yuan, a slight decrease from the previous day [1][4][9] Overseas Market Overview - The three major U.S. stock indices all closed lower, with the Dow Jones down 0.7%, the S&P 500 down 0.84%, and the Nasdaq down 0.91% [2][4] - Notable declines included Merck and Nike, both dropping over 3%, leading the Dow's decline, while Tesla fell nearly 4% and Apple dropped over 1% [2][4] - Chinese concept stocks also saw widespread declines, with Zai Lab down over 8% and Canadian Solar down more than 6% [2][4] Key News Highlights - China's investment in Central Asian countries has exceeded 30 billion USD, reflecting a sustained increase in investment in green development and digital economy sectors [3][19] - The second China-Central Asia Summit was attended by President Xi Jinping, where he emphasized cooperation and signed multiple cooperation agreements [3][11][18] - The Beijing Municipal Bureau of Commerce announced a subsidy program for smart home products, aiming to boost consumer spending [3][20]

The provided content does not contain any quantitative models or factors, nor does it include their construction, evaluation, or backtesting results. The documents primarily focus on market performance, sector analysis, market sentiment, fund flows, ETF premiums/discounts, block trading, and institutional activity. These topics are descriptive and lack the quantitative modeling or factor analysis required for the task.