Group 1 - The report highlights the deep analysis of Luzhou Laojiao (000568.SZ), emphasizing a consumer-centric approach and digital empowerment of the supply chain to enhance competitive barriers [9][10] - The company is expected to face a 21% underperformance compared to the liquor sector from H2 2023 to H1 2025, with a projected PE decline of 45% due to internal and external cyclical pressures [9][11] - Luzhou Laojiao is actively reducing channel burdens and advancing digital transformation, which is seen as a strategic advantage in a weak industry environment [9][10] Group 2 - The semiconductor industry is experiencing a significant demand increase driven by AI applications, with TI expanding production to meet future needs and storage prices continuing to rise [19][21] - The global semiconductor sales in May 2025 reached $58.98 billion, marking a 19.8% year-on-year growth, with China's semiconductor sales at $17.08 billion, up 13% [21][22] - The report recommends focusing on semiconductor manufacturing companies and AI-related enterprises, highlighting the importance of domestic AI development in the semiconductor supply chain [21][27] Group 3 - The medical and biological sector is witnessing a strong performance, with a focus on innovative drug development, particularly in radioactive ligand therapy, which is expected to grow rapidly [29][30] - The report identifies XTR005 and XTR008 as key products in the pipeline, with XTR005 being the first PET diagnostic radioactive ligand approved in China [30][31] - The overall medical sector is projected to continue its upward trend, with a notable increase in clinical data competitiveness for domestic innovative drugs [31]

Quantitative Models and Construction Methods - **Model Name**: Index Dividend Points Estimation Model **Model Construction Idea**: This model estimates the dividend points of index constituents to account for the natural drop in index levels caused by dividend ex-dates, which is critical for accurately calculating the basis and premium/discount levels of stock index futures[12][38][44] **Model Construction Process**: 1. Identify the index constituents and their weights. If daily weights are unavailable, adjust monthly weights using the formula: $$ W_{n,t} = \frac{w_{n0} \times (1 + r_{n})}{\sum_{i=1}^{N} w_{i0} \times (1 + r_{i})} $$ where \( w_{n0} \) is the weight of stock \( n \) on the last disclosed date, and \( r_{n} \) is the non-adjusted return of stock \( n \) from the last disclosed date to the current date[45][46] 2. Estimate the dividend amount for each constituent: - If disclosed, use the reported dividend amount - If not disclosed, estimate using: $$ \text{Dividend Amount} = \text{Net Profit} \times \text{Dividend Payout Ratio} $$ - Net profit is predicted using historical profit distribution patterns, distinguishing between stable and unstable profit distributions[47][50] - Dividend payout ratio is estimated using historical averages or prior-year values, with adjustments for outliers[51][53] 3. Predict the ex-dividend date using historical intervals and linear extrapolation, or default to specific dates if historical data is unavailable[55][56] 4. Calculate the dividend points for the index: $$ \text{Dividend Points} = \sum_{n=1}^{N} \left( \frac{\text{Dividend Amount}_n}{\text{Market Cap}_n} \times \text{Weight}_n \times \text{Index Closing Price} \right) $$ where \( n \) represents each constituent, and only constituents with ex-dividend dates between the current date and the futures contract expiration date are included[38][44] **Model Evaluation**: The model demonstrates high accuracy for indices like the SSE 50 and CSI 300, with prediction errors generally within 5 points. However, the error margin for the CSI 500 index is slightly larger, around 10 points[57][61] Model Backtesting Results - **Index Dividend Points Estimation Model**: - SSE 50 Index: Prediction error ~5 points[61] - CSI 300 Index: Prediction error ~5 points[61] - CSI 500 Index: Prediction error ~10 points[61] Quantitative Factors and Construction Methods - **Factor Name**: Historical Profit Distribution Factor **Factor Construction Idea**: This factor predicts net profit by analyzing historical profit distribution patterns, distinguishing between stable and unstable distributions[50] **Factor Construction Process**: 1. Classify companies into stable or unstable profit distribution categories based on historical quarterly profit data 2. For stable distributions, use historical patterns to predict future profits 3. For unstable distributions, use the previous year's profit as the prediction[50] **Factor Evaluation**: Effective for companies with consistent profit patterns but less reliable for those with volatile earnings[50] - **Factor Name**: Historical Dividend Payout Ratio Factor **Factor Construction Idea**: This factor estimates the dividend payout ratio using historical averages or prior-year values, with adjustments for extreme values[51] **Factor Construction Process**: 1. Use the prior year's payout ratio if the company paid dividends last year 2. Use the average payout ratio of the last three years if no dividends were paid last year 3. Assume no dividends if the company has never paid dividends 4. Apply truncation if the estimated payout ratio exceeds 100%[53] **Factor Evaluation**: Reliable for companies with stable dividend policies but may overestimate for companies with irregular payouts[51][53] - **Factor Name**: Ex-Dividend Date Prediction Factor **Factor Construction Idea**: This factor predicts ex-dividend dates using historical intervals and linear extrapolation[55] **Factor Construction Process**: 1. Use the disclosed ex-dividend date if available 2. If unavailable, estimate based on historical intervals between announcement and ex-dividend dates 3. Default to specific dates (e.g., July 31, August 31, or September 30) if historical data is insufficient[56] **Factor Evaluation**: Accurate for most companies, with 90% of predictions falling within expected timeframes[56] Factor Backtesting Results - **Historical Profit Distribution Factor**: Effective for stable profit companies, less so for volatile ones[50] - **Historical Dividend Payout Ratio Factor**: Reliable for stable dividend policies, prone to overestimation for irregular payouts[51][53] - **Ex-Dividend Date Prediction Factor**: 90% accuracy for companies with historical data, with most predictions aligning with expected timelines[56]

The provided content does not contain any specific quantitative models or factors, nor does it include their construction processes, formulas, evaluations, or backtesting results. The documents primarily focus on market performance, sector analysis, market sentiment, capital flows, ETF premiums/discounts, block trading, and institutional activities. These are descriptive analyses and statistics rather than detailed quantitative models or factor-based methodologies.

证券研究报告 | 2025年07月09日 通胀数据快评 CPI 同比转正 经济研究·宏观快评 | 证券分析师： | 邵兴宇 | 010-88005483 | shaoxingyu@guosen.com.cn | 执证编码：S0980523070001 | | --- | --- | --- | --- | --- | | 证券分析师： | 董德志 | 021-60933158 | dongdz@guosen.com.cn | 执证编码：S0980513100001 | 事项： 7 月 9 日，国家统计局公布数据显示，中国 6 月 PPI 同比-3.6%，预期-3.2%，前值-3.3%。中国 6 月 CPI 同 比 0.1%，预期 0%，前值-0.1%，环比-0.1%，预期 0%，前值-0.2%。 评论： CPI 同比转正 6 月 CPI 当月同比+0.1%，较上月-0.1%的水平有所回升，这也是 CPI 自 2025 年 1 月以来首次转正。CPI 环 比-0.1%，较上月-0.2%降幅有所收窄。从季节性看，6 月 CPI 环比-0.1%，略好于过去 5 年季节性表现（过 去 5 年环比平均为-0.2%）但仍 ...

政府债净融资第 27 周（6/30-7/6）2216 亿，第 28 周（7/7-7/13）3032 亿。截至第 27 周（6/30-7/6）累计 7.9 万亿，超出去年同期 4.4 万亿， 主要是置换隐债专项债错位和国债发行较快。 国债净融资+新增地方债发行第 27 周（6/30-7/6）2574 亿，第 28 周 （7/7-7/13）2714 亿。截至第 27 周（6/30-7/6）广义赤字累计 6.2 万 亿，进度 52.2%。 国债第 27 周（6/30-7/6）净融资 1999 亿，第 28 周（7/7-7/13）1929 亿。截至第 27 周（6/30-7/6）累计 3.6 万亿，进度 53.8%。 地方债净融资第 27 周（6/30-7/6）216 亿，第 28 周（7/7-7/13）1102 亿。截至第 27 周（6/30-7/6）累计 4.4 万亿，超出去年同期 2.6 万亿。 证券研究报告 | 2025年07月09日 政府债务周度观察 特殊新增专项债发行加速 新增专项债第 27 周（6/30-7/6）508 亿，第 28 周（7/7-7/13）640 亿。 截至第 27 周（6/30-7/6 ...

证券研究报告 | 2025年07月09日 进入 7 月份，针对信用债等传统"根据地"，理财持续买入，中短端收益向 新低试探，但理财对缺乏流动性的长久期资产仍偏谨慎。因为 7 月以来包括 银行理财等各类负债端增长较快，只要收益率出现调整，前期止盈资金更倾 向于快速回补仓位，导致收益率下行过程中阶段性反弹高度越来越低；尤其 "630"整改完成后，部分理财子出现减持长期限银行二永债与中低评级信 用债，转而加仓短期高评级债券的情况，通过"卖长买短"来压降理财产品 净值波动。 随着负债端规模增长，利差被增量资金买平，新一轮银行理财产品结构调整 迫切。后续理财一方面推进低波动稳健产品与多元主题产品并行；另一方面， 适当通过增加权益类资产、衍生品配置等方式提升收益，理财整改为权益市 场带来增量资金，我们判断年度量级在 800-1200 亿元左右，整体匹配中性 收益策略，如北交所打新、定增+股指对冲、红利等股票多头等。 风险提示：宏观经济、货币政策超预期变化、政策监管风险。 银行理财 2025 年 7 月月报 优于大市 理财整改为信用指数和权益市场带来增量资金 上半年银行理财规模同比保持稳定增长，结构上仍以固收类产品为主。根 ...

证券研究报告 | 2025年07月09日 泸州老窖（000568.SZ） 优于大市 以消费者为中心，数字化赋能供应链，提升竞争壁垒 市场表现：增速预期下修，估值出现折价。2023H2-2025H1 公司股价跑输白 酒板块 21%，预测 PE 下跌 45%，主因内外部周期叠加，公司收入及业绩增速 边际放缓：行业需求承压影响大单品国窖的量价表现，同时公司自身发展进 入阶段性调整。我们认为市场基于短期增速的定价有失偏颇，当前行业弱景 气度背景下公司积极去化渠道包袱，以消费者为中心推进数字化转型，进行 前瞻性产品布局，管理层面的相对优势仍是共识；当前 PE 接近 2013 年初水 平，于经营、于投资层面均有布局机会。 竞争壁垒：管理禀赋为核，全价位产品布局。公司在行业中为管理驱动类， 组织、渠道为核心优势，历史两轮调整通过深度改革实现破局。公司组织机 制市场化、激励充足，庞大且专业化的销售队伍开拓市场；组织力支撑精细 化的渠道管理模式得以落地跑通，因地制宜、强化管控。产品层面"浓香鼻 祖"名酒基因根植，双品牌多品系布局，全价位带均有可贡献增长的大单品。 数字化思辩：科技增密供应链，以消费者为中心提升竞争壁垒。近两年白 ...

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证券研究报告 | 2025年07月09日 非车险"报行合一"点评 重塑非车险生态，利好承保利润提升 |  行业研究·行业快评 | | |  | 非银金融·保险Ⅱ |  投资评级：优于大市（维持） | | --- | --- | --- | --- | --- | --- | | 证券分析师： | 孔祥 | 021-60375452 | | kongxiang@guosen.com.cn | 执证编码：S0980523060004 | | 联系人： | 王京灵 | 0755-22941150 | | wangjingling@guosen.com.cn | | 事项： 近日，国家金融监督管理总局下发《关于加强非车险监管有关事项的通知（征求意见稿）》（以下简称"《通 知》"），首次明确"非车险"定义，同时标志着非车险"报行合一"或将正式实施。 国信非银观点：近年来，非车险业务持续扩容，在财产险总保费中的占比已从 2019 年的 37.1%攀升至 2024 年的 47.4%，贡献接近半数财险保费规模。然而高速发展伴随恶性竞争加剧，部分机构为抢占份额，通过 拆分保额、更改标的使用性质变相降费，或虚列"会议费"" ...