Quantitative Factors and Models Summary Quantitative Factors and Their Construction - **Factor Name**: Standardized Unexpected Revenue (SUR) - **Construction Idea**: Measures the deviation of actual revenue from analysts' expectations, standardized by the standard deviation of expected revenue[20][27][31] - **Construction Process**: $ SUR = \frac{Actual\ Revenue - Expected\ Revenue}{Standard\ Deviation\ of\ Expected\ Revenue} $ - The numerator represents the difference between actual and expected revenue - The denominator is the standard deviation of expected revenue, ensuring comparability across stocks[20][27][31] - **Evaluation**: Demonstrated strong performance across multiple indices, indicating its effectiveness in capturing unexpected revenue trends[8][27][31] - **Factor Name**: Delta ROA - **Construction Idea**: Tracks the year-over-year change in Return on Assets (ROA) to capture profitability trends[20][31][39] - **Construction Process**: $ \Delta ROA = ROA_{Current\ Quarter} - ROA_{Same\ Quarter\ Last\ Year} $ - ROA is calculated as $ \frac{Net\ Income}{Total\ Assets} $ - The factor highlights improvements or deteriorations in asset efficiency[20][31][39] - **Evaluation**: Consistently strong performance, particularly in small-cap indices like the CSI 1000 and CSI 2000, suggesting its relevance in growth-oriented stocks[8][39][43] - **Factor Name**: Standardized Unexpected Earnings (SUE) - **Construction Idea**: Similar to SUR, measures the deviation of actual earnings from analysts' expectations, standardized by the standard deviation of expected earnings[20][31][39] - **Construction Process**: $ SUE = \frac{Actual\ Earnings - Expected\ Earnings}{Standard\ Deviation\ of\ Expected\ Earnings} $ - The numerator captures the earnings surprise - The denominator ensures standardization for comparability[20][31][39] - **Evaluation**: Strong performance in indices like CSI 500 and CSI 800, indicating its ability to capture earnings surprises effectively[8][27][31] - **Factor Name**: Delta ROE - **Construction Idea**: Measures the year-over-year change in Return on Equity (ROE) to identify shifts in shareholder profitability[20][31][39] - **Construction Process**: $ \Delta ROE = ROE_{Current\ Quarter} - ROE_{Same\ Quarter\ Last\ Year} $ - ROE is calculated as $ \frac{Net\ Income}{Shareholders'\ Equity} $ - Highlights changes in equity efficiency over time[20][31][39] - **Evaluation**: Demonstrated strong performance in growth-oriented indices, particularly the CSI 1000 and Growth Enterprise Market (GEM) indices[8][39][43] Factor Backtesting Results - **Standardized Unexpected Revenue (SUR)** - CSI 500: Weekly return 1.43%, monthly return 1.66%, annualized return 12.83%[27] - CSI 800: Weekly return 1.36%, monthly return 2.61%, annualized return 4.26%[31] - CSI All Share: Weekly return 1.37%, monthly return 1.95%, annualized return 6.91%[47] - **Delta ROA** - CSI 1000: Weekly return 0.56%, monthly return 1.67%, annualized return 11.51%[35] - CSI 2000: Weekly return 1.90%, monthly return 1.90%, annualized return 27.67%[39] - CSI All Share: Weekly return 1.10%, monthly return 2.33%, annualized return 7.78%[47] - **Standardized Unexpected Earnings (SUE)** - CSI 500: Weekly return 1.39%, monthly return 2.75%, annualized return 7.19%[27] - CSI 800: Weekly return 0.52%, monthly return 1.33%, annualized return 3.04%[31] - CSI All Share: Weekly return 1.09%, monthly return 2.46%, annualized return 0.72%[47] - **Delta ROE** - CSI 1000: Weekly return 0.30%, monthly return 1.59%, annualized return 8.89%[35] - CSI 2000: Weekly return 1.24%, monthly return 1.21%, annualized return 90.84%[39] - GEM: Weekly return 1.03%, monthly return 2.76%, annualized return 21.85%[43] Quantitative Model Construction - **Model Name**: Maximized Factor Exposure (MFE) Portfolio - **Construction Idea**: Constructs portfolios that maximize exposure to a single factor while controlling for industry, style, and stock-specific constraints[62][63][66] - **Construction Process**: $ \begin{array}{ll} max & f^{T}w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & 0 \leq w \leq l \\ & 1^{T}w = 1 \\ & \Sigma|w-w_{0}| \leq to_{h} \end{array} $ - Maximizes factor exposure $ f^{T}w $ - Constraints include style, industry, stock-specific deviations, and turnover limits[62][63][66] - **Evaluation**: Effective in isolating factor performance under realistic portfolio constraints, widely used in index enhancement strategies[62][63][66] Model Backtesting Results - **MFE Portfolio** - CSI 300: Weekly excess return max 1.67%, min -0.65%, median 0.21%[54] - CSI 500: Weekly excess return max 1.13%, min -0.76%, median 0.24%[57] - CSI 1000: Weekly excess return max 1.11%, min -0.52%, median 0.24%[61]

- The report discusses the dividend prediction model for the August contracts of the SSE 50, CSI 300, CSI 500, and CSI 1000 indices, with respective dividend points of 1.42, 2.59, 4.93, and 3.19[5][9] - The annualized hedging costs for the August contracts, excluding dividends and calculated on a 365-day basis, are -1.22% for SSE 50, 6.13% for CSI 300, 17.91% for CSI 500, and 19.73% for CSI 1000[5][9] - The report suggests that investors should pay attention to the short-term positive arbitrage opportunities in the SSE 50 index futures, given its current slight premium state and relatively low hedging cost[6][9] - For the CSI 300 index futures, the report advises investors to closely monitor the potential for discount recovery and the changes in hedging costs due to increased volatility, as it has shifted from a neutral to a moderate discount state[6][9] - The CSI 500 and CSI 1000 index futures are currently in a deep discount state, with significantly expanded discount margins compared to the previous period. The report recommends that investors with arbitrage execution capabilities and risk management experience consider participating in the phase discount arbitrage opportunities for these small and mid-cap index futures[6][9] - The dividend prediction process involves estimating the net profit of constituent stocks, calculating the pre-tax total dividend for each stock, determining the impact of dividends on the index, and predicting the impact of dividends on each contract[7][19][22] - The formula used to estimate the weight of each stock in the index is: $$ \mathrm{w_{it}={\frac{w_{i0}\times\mathrm{\scriptsize{\boldmath~(~1+R~)}~}}{\sum_{1}^{n}w_{i0}\times\mathrm{\scriptsize{\boldmath~(~1+R~)}~}}}} $$ where \( w_{i0} \) is the accurate weight of stock \( i \) on day \( t0 \), and \( R \) is the rate of change in the stock price from \( t0 \) to \( t \)[22] - The theoretical pricing model for stock index futures under discrete dividend distribution is: $$ F (S D)(1 r) t t = − + $$ where \( F_t \) is the futures price at time \( t \), \( S_t \) is the spot price, \( D \) is the present value of the dividend stream during the period \( T-t \), and \( r \) is the risk-free rate during the period \( T-t \)[28] - The theoretical pricing model for stock index futures under continuous dividend distribution is: $$ (r d)(T-t) t t F S e − = $$ where \( F_t \) is the futures price at time \( t \), \( S_t \) is the spot price, \( d \) is the annualized dividend rate, and \( r \) is the annualized risk-free rate during the period \( T-t \)[29] Model Backtest Results - SSE 50 index futures (IH) August contract: actual spread -0.13, dividend-inclusive spread 1.29, annualized hedging cost -1.22%[1][10] - CSI 300 index futures (IF) August contract: actual spread -12.13, dividend-inclusive spread -9.53, annualized hedging cost 6.13%[1][11] - CSI 500 index futures (IC) August contract: actual spread -47.60, dividend-inclusive spread -42.67, annualized hedging cost 17.91%[1][12] - CSI 1000 index futures (IM) August contract: actual spread -53.67, dividend-inclusive spread -50.48, annualized hedging cost 19.73%[1][13]

Group 1 - The company, Ningbo Jintian Copper Industry (Group) Co., Ltd., issued 14.5 million convertible bonds with a total amount of 1.45 billion yuan, which will be traded on the Shanghai Stock Exchange starting from August 28, 2023 [1][2] - The initial conversion price of the bonds was set at 6.75 yuan per share, which was later adjusted to 6.63 yuan per share due to the company's annual equity distribution [2][3] - The conversion price was further adjusted to 5.90 yuan per share following a board resolution and will remain at this level until the next adjustment [3][4] Group 2 - The company has a conditional redemption clause for the convertible bonds, which allows redemption if the stock price exceeds 130% of the conversion price for a specified period [5] - The redemption condition was met as the stock price was above 7.53 yuan per share for fifteen trading days [5][6] - The company decided to exercise its right to redeem the convertible bonds early, with the redemption price being the face value plus accrued interest [6][7] Group 3 - The company’s major shareholders and executives did not trade the convertible bonds within six months prior to the redemption condition being met [6] - The sponsor institution, Dongfang Securities, confirmed that the redemption process complied with relevant regulations and internal procedures [7]

Group 1 - The core viewpoint of the article highlights the ongoing restructuring of brokerage branch layouts as firms aim to reduce costs and enhance efficiency, with over 20 brokerages having announced the closure of more than 60 branches by July 30 this year [2][3] - In July alone, three brokerages, including Founder Securities, announced the closure of their branches, with Founder Securities closing four branches, including its Henan branch [2][3] - The closures are primarily driven by high operational costs associated with physical branches, as well as a significant shift towards online trading platforms, which have become the mainstream mode of operation [3][5] Group 2 - Some brokerages are also actively expanding by opening new branches in regions with high business potential, such as the recent openings by Cheng Tong Securities and Wanlian Securities [5] - The restructuring of branch layouts is closely linked to the transformation towards wealth management, which is becoming increasingly important in the brokerage business model, contributing over 30% to brokerage income by 2024 [5][6] - Future adjustments in branch layouts are expected to focus on efficiency rather than scale, with a shift towards high-value, integrated service models, leveraging financial technology to enhance operations [6]

格隆汇8月1日｜东方证券研报指出，洽洽食品预计上半年归母净利润为8,000–9,750万元，同比下降约 71–76%，主因是原料采购价格大幅上涨带动毛利率下行。公司25Q1收入同比下降13.7%，反映出主力 品类动销放缓、渠道去库存延续。该行判断，公司当前主动压缩利润释放节奏，在保证终端投放与品牌 维护的前提下进行结构优化，为后续增长修复创造空间。考虑到原料端成本上行叠加主力品类出货节奏 偏慢，公司上半年利润显著下滑，我们修正了瓜子与坚果等核心品类的销售增速、新品导入的放量节奏 及毛利率改善幅度等关键假设。据此调整公司盈利预测，预计公司2025–2027年EPS分别为 1.25/1.42/1.67元（前次预测2025/2026年EPS为2.42/2.75元）。考虑公司在渠道端持续推进精耕、海外市 场具备中长期潜力，参考当前可比公司估值水平，给予公司2025年21倍PE，对应目标价26.25元，维 持"买入"评级。 ...

Core Viewpoint - The report from Dongfang Securities indicates a sustained high growth in overseas data center-related CAPEX since the first half of 2024, with upward revisions to future expectations driven by AI demand [1] Group 1: CAPEX Trends - Overseas CSPs have revised their CAPEX expectations upward, indicating an improving investment climate for data centers [1] - Microsoft, Google, and Meta have all increased their CAPEX forecasts for Q2 2025, with Meta raising its full-year CAPEX expectation to $66-72 billion (previously $64-72 billion) and Google adjusting its FY2025 CAPEX to $85 billion (previously $75 billion) [1] - Microsoft’s Q2 2025 CAPEX is projected at $24.2 billion, with expectations to exceed $30 billion in Q3 2025, reflecting a year-on-year growth of over 50% [1] Group 2: Importance of Power Supply - The transition from traditional computing to intelligent computing has led to significant increases in power consumption, necessitating advancements in AIDC power supply technology [2] - The importance of high efficiency, high density, and high stability power supplies is expected to continue rising as AI demand evolves [2] - The entire power supply chain, from grid power (10kV+) to final chip (<1V), is undergoing technological upgrades, enhancing the value of power systems in AIDC [2] Group 3: Export Opportunities - The overseas AIDC CSP cycle is leading, with AI power supply exports likely to be the first beneficiaries of the AI technology infrastructure wave [3] - High barriers to entry in the overseas AIDC power market favor companies like Delta and Vertiv, which possess strong technical and ecological advantages [3] - As domestic Chinese companies continue to close the technology gap with Taiwanese and foreign firms, they are expected to benefit from the overseas AICAPEX boom [3] Group 4: Investment Recommendations - Companies in the AIDC power sector that are likely to benefit from the improving overseas investment climate include Magpower (002851.SZ), Hewei Electric (603063.SH), Xinle Energy (300593.SZ), Tonghe Technology (300491.SZ), Oulu Tong (300870.SZ), and Zhongheng Electric (002364.SZ) [4] - In the AIDC power supply segment, key companies to watch include Jinpan Technology (688676.SH), Mingyang Electric (301291.SZ), Weiteng Electric (688226.SH), and Liangxin Co., Ltd. (002706.SZ) [4]

Group 1 - The core viewpoint of the article highlights that Aide Biology's semi-annual report shows strong performance, demonstrating growth resilience despite challenges in the IVD industry [1] - The IVD industry is currently facing pressure on growth due to medical insurance cost control and extended hospital payment cycles [1] - Starting from 2025, the value-added tax rate on the company's reagent sales will increase from 3% to 13% [1] Group 2 - Aide Biology is a leader in tumor drug companion diagnostics, with a comprehensive range of products and multiple exclusive approvals, contributing to stable revenue performance [1] - The main revenue source is testing reagents, which generated revenue of 480 million yuan, a year-on-year increase of 7.9%, accounting for 83.4% of total revenue, with a gross margin of 90.7% [1] - Technical services and testing services generated revenues of 60 million yuan (down 5.0% year-on-year) and 30 million yuan (up 0.9% year-on-year), respectively [1] Group 3 - The company's overseas business showed stable performance, achieving revenue of 110 million yuan in the first half of 2025, a year-on-year increase of 3.7%, accounting for 18.3% of total revenue [1] - Based on the average valuation of comparable companies for 2025, the company is given a target price of 26.56 yuan, corresponding to a price-to-earnings ratio of 32 times [1] - The rating for the company is maintained at "Buy" [1]

Core Viewpoint - The demand for localized AI deployment is rising, and the company is expected to benefit significantly from this trend [1] Group 1: AI Solutions and Performance - The company has upgraded its "Yiduo Cloud" solution to create an AI platform that better matches large model scenarios, focusing on intelligent computing and application development [1] - The AICP high-performance computing platform shows a performance improvement of 5-10 times compared to traditional Ollama architecture in multi-instance and high-concurrency scenarios, making it compatible with the latest large models, including DeepSeek [1] - The company's AI application innovation platform enables users to quickly build large model applications, simplifying the development and operation of AI applications [1] Group 2: Market Position and Valuation - The company's full-stack capabilities provide a "high-performance, high-security, high-value" one-stop solution for localized large model deployment, positioning it well to benefit from ongoing domestic substitution and growing AI demand [1] - Based on the comparable companies' 25-year PS level, the company is assigned a 25-year PS of 6.10 times, corresponding to a target price of 119.00 yuan, maintaining a "buy" rating [1]

东方证券研报指出，近期，朗新集团正式向全球首发自主研发的"朗新九功AI能源大模型"（简称"朗新 九功"），该模型数据智能体在BIRD-Bench国际评测基准中斩获双榜冠军。朗新九功模型深度融合 了"时序预测"与"AI智能体"双引擎技术，基于对中国能源市场全场景的数据沉淀与实践验证，打造出能 够解决能源行业复杂核心问题的垂直大模型。在全球权威的自然语言转SQL评测基准BIRD-Bench中， 九功模型凭借意图理解、推理分析与SQL生成能力获得了执行准确率（EX）与查询效率奖励（R- VES）双料冠军。认为九功模型能够斩获双榜冠军，彰显了朗新集团较强的AI技术实力，也为相关模型 在能源行业的落地打下良好基础。九功模型功能强大，赋能公司电力现货交易等新兴业务。随着香 港"稳定币+RWA"生态的发展，朗新有望凭借其较好的业务场景和先发优势，在绿色能源+区块链领域 成为重要的参与者和生态建设方，从而为自身业务发展和商业模式扩展提供助力。参照可比公司，认为 公司合理估值水平为2025年41倍市盈率，对应目标价为22.96元，维持"买入"评级。 ...

Core Viewpoint - Dongfang Securities report highlights that Ganli Pharmaceutical's innovative drug data is impressive, particularly the glucose-lowering effects of GZR18, indicating strong future growth potential for the company [1] Group 1: Drug Development and Clinical Data - Ganli Pharmaceutical has leading R&D expenditures in the industry, with multiple key products gradually disclosing Phase II clinical data since 2025 [1] - GZR18 (Bofang Glutide) shows superior glucose-lowering effects in Type 2 diabetes patients, with HbA1c and weight reductions exceeding those of the Semaglutide group after 24 weeks of biweekly injections [1] - After 23 weeks of treatment, GZR18 subjects experienced an average weight loss of 6.92 kg, representing a 9.3% reduction from baseline [1] - The Phase II clinical data for GZR4, an insulin weekly formulation, indicates good safety and a more significant reduction in HbA1c compared to daily insulin degludec (Novo Nordisk) in patients with poor baseline insulin control after 16 weeks of treatment [1] Group 2: Market Outlook and Valuation - The company anticipates a substantial 32.6% increase in the volume of its third-generation insulin agreements in the upcoming centralized procurement in 2024, alongside a rebound in product prices [1] - Leveraging the market coverage advantages gained from centralized procurement, the company's annual performance expectations are further solidified [1] - The company is assigned a 37x PE valuation for 2025, corresponding to a target price of 70.67 yuan, maintaining a "buy" rating [1]