Quantitative Models and Construction Methods - **Model Name**: Index Dividend Points Estimation Model **Model Construction Idea**: This model aims to estimate the dividend points of index futures by considering the dividend impact of constituent stocks, which is critical for accurately calculating the basis and premium/discount levels of index futures contracts[12][37] **Model Construction Process**: 1. **Dividend Points Formula**: Dividend points are calculated as: $ \text{Dividend Points} = \sum_{n=1}^{N} \frac{\text{Dividend Amount of Stock}_n}{\text{Market Cap of Stock}_n} \times \text{Weight of Stock}_n \times \text{Index Closing Price} $ Here, $N$ represents the number of constituent stocks, and only stocks with ex-dividend dates between the current date ($t$) and the contract expiration date ($T$) are included[37] 2. **Key Steps**: - Identify whether the company has disclosed dividend amounts and ex-dividend dates. If disclosed, use the provided data; otherwise, estimate these values[41] - For undisclosed dividend amounts, estimate based on the product of net profit and dividend payout ratio. Net profit is predicted using historical profit distribution patterns, while the payout ratio is estimated using historical averages[46][50] - For undisclosed ex-dividend dates, predict using historical intervals between announcement and ex-dividend dates, applying linear extrapolation if stable patterns exist[50][55] **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 prediction error for the CSI 500 index is slightly larger, around 10 points[60] Quantitative Factors and Construction Methods - **Factor Name**: Constituent Stock Weight Adjustment Factor **Factor Construction Idea**: Adjust the weights of index constituent stocks to reflect daily changes in stock prices and corporate actions, ensuring more precise dividend impact estimation[44] **Factor Construction Process**: 1. **Weight Adjustment Formula**: $ W_{n,t} = \frac{w_{n,0} \times (1 + r_{n})}{\sum_{i=1}^{N} w_{i,0} \times (1 + r_{i})} $ Here, $w_{n,0}$ is the weight of stock $n$ at the last disclosed date, and $r_{n}$ is the non-adjusted return of stock $n$ from the last disclosed date to the current date[44] 2. **Data Source**: Use daily closing weights disclosed by the China Securities Index Company to ensure accuracy and avoid biases from corporate actions like stock splits or rights issues[45] - **Factor Name**: Net Profit Prediction Factor **Factor Construction Idea**: Predict annual net profit for constituent stocks based on historical profit distribution patterns, distinguishing between stable and unstable profit distributions[46] **Factor Construction Process**: 1. Classify companies into two categories: stable and unstable profit distributions 2. For stable companies, predict profits using historical distribution patterns. For unstable companies, use the previous year's profit as the prediction[49] - **Factor Name**: Dividend Payout Ratio Prediction Factor **Factor Construction Idea**: Estimate the dividend payout ratio using historical averages, considering the stability of corporate dividend policies[50] **Factor Construction Process**: 1. If the company paid dividends last year, use the previous year's payout ratio as the estimate 2. If no dividends were paid last year, use the average payout ratio of the past three years 3. If the company has never paid dividends, assume no dividends for the current year[52] - **Factor Name**: Ex-Dividend Date Prediction Factor **Factor Construction Idea**: Predict ex-dividend dates using historical intervals between announcement and ex-dividend dates, applying default dates if historical patterns are unavailable[50] **Factor Construction Process**: 1. If the company has disclosed the ex-dividend date, use the disclosed value 2. If not disclosed, estimate based on historical intervals, applying linear extrapolation for stable patterns 3. If historical data is insufficient, use default dates based on typical dividend schedules[55] Model Backtesting Results - **Index Dividend Points Estimation Model**: - SSE 50 Index: Prediction error within 5 points[60] - CSI 300 Index: Prediction error within 5 points[60] - CSI 500 Index: Prediction error within 10 points[60] Factor Backtesting Results - **Constituent Stock Weight Adjustment Factor**: Improved accuracy in daily weight adjustments, ensuring precise dividend impact estimation[45] - **Net Profit Prediction Factor**: Effective for distinguishing between stable and unstable profit distributions, enhancing dividend amount predictions[49] - **Dividend Payout Ratio Prediction Factor**: Reliable for companies with stable dividend policies, though less effective for companies with irregular payouts[52] - **Ex-Dividend Date Prediction Factor**: High accuracy for companies with stable historical patterns, with default dates providing reasonable estimates for others[55]

Core Viewpoint - Guosen Securities has given a "better than market" rating to Adebiotech (300685.SZ, latest price: 23.95 yuan) based on its strong performance in testing reagents and stable financial metrics [2] Group 1: Company Performance - The testing reagent business is experiencing steady growth, with both domestic and overseas operations developing simultaneously [2] - Gross profit margin remains stable, while various expense ratios have decreased [2] - The operating cash flow is of high quality and remains robust [2]

发行人将按上述发行价格于 2025 年 8 月 13 日及 2025 年 8 月 14 日面向专业 机构投资者网下发行。具体认购方法请参考 2025 年 8 月 11 日刊登在深圳证券交 易所网站（http://www.szse.cn）及巨潮资讯网（http://www.cninfo.com.cn）上的《国 信证券股份有限公司 2025 年面向专业投资者公开发行公司债券（第二期）（续发 行）发行公告》。 国信证券股份有限公司 2025 年面向专业投资者公开发行 公司债券（第二期）（续发行）发行价格公告 发行人及全体董事、监事、高级管理人员保证本公告内容真实、准确和完 整，并对公告中的虚假记载、误导性陈述或者重大遗漏承担责任。 特此公告。 证券代码：524180 证券简称：25 国证 03 国信证券股份有限公司（以下简称"公司"或"发行人"）向专业投资者公开 发行面值总额不超过 200 亿元公司债券已获得中国证券监督管理委员会证监许 可[2024]1722 号文注册同意。 国信证券股份有限公司 2025 年面向专业投资者公开发行公司债券（第二期） （续发行）（以下简称"本期续发行债券"）发行面值总额不超过 30 ...

关于延长国信证券股份有限公司 2025 年面向专业投资者 公开发行公司债券（第二期）（续发行）簿记建档时间的公告 国信证券股份有限公司（以下简称"公司"或"发行人"）向专业投资者公开 发行面值总额不超过 200 亿元公司债券已获得中国证券监督管理委员会证监许 可[2024]1722 号文注册同意。 （以下无正文） 根据《国信证券股份有限公司 2025 年面向专业投资者公开发行公司债券（第 （续发行）发行公告》，发行人及簿记管理人原定于 2025 年 8 月 12 日（T-1 二期） 日）15:00 到 18:00 以簿记建档的方式向网下投资者进行发行价格询价，并根据 簿记建档结果确定本期续发行债券的最终发行价格。 因簿记建档当日市场变化较为剧烈，经全体簿记参与人协商一致，现将本期 续发行债券簿记建档结束时间由 2025 年 8 月 12 日 18:00 延长至 2025 年 8 月 12 日 19:00。 特此公告。 ...

公司债券（第五期）在深圳证券交易所上市的公告 根据深圳证券交易所债券上市的有关规定，国信证券股份有限公司 2025 年 面向专业投资者公开发行公司债券（第五期）符合深圳证券交易所债券上市条件， 将于 2025 年 8 月 13 日起在深圳证券交易所上市，并面向专业投资者中的机构投 资者交易，交易方式包括匹配成交、点击成交、询价成交、竞买成交和协商成交。 债券相关要素如下： 国信证券股份有限公司 2025 年面向专业投资者公开发行公司债券 债券名称 国信证券股份有限公司 2025 年面向专业投资者公开发行 | （第五期） | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 债券简称 | 25 | 国证 | | 07 | | | | | | | | | 债券代码 | 524386 | | | | | | | | | | | | 信用评级 | AAA/AAA | | | | | | | | | | | | 评级机构 | | | | | | 中诚信国际信用评级有限责任公司 ...

中国上市公司协会近期发布了2025年上市公司现金分红榜单（100家公司），共有3家券商登榜。中信证 券、华泰证券和国信证券分别排名第36位、67位和94位。数据显示，2024年三家券商全年拟派发现金分 红分别达77.07亿元、52.37亿元、33.64亿元。其中，中信证券自2003年A股上市以来，已连续22年实施 现金分红，累计分红总额超845亿元。中证协称，2024年度上市公司分红意识不断增强，用"真金白 银"积极回报投资者，最新出炉的现金分红榜单注重分红连续性、稳定性，对分红数据进行三到五年的 长周期评价，并增加多年连续分红、分红连续增长等前置条件。 ...

因簿记建档当日市场变化较为剧烈，经全体簿记参与人协商一致，现将本期 续发行债券簿记建档结束时间由 2025 年 8 月 12 日 18:00 延长至 2025 年 8 月 12 日 19:00。 特此公告。 （以下无正文） 1 关于延长国信证券股份有限公司 2025 年面向专业投资者 公开发行公司债券（第二期）（续发行）簿记建档时间的公告 国信证券股份有限公司（以下简称"公司"或"发行人"）向专业投资者公开 发行面值总额不超过 200 亿元公司债券已获得中国证券监督管理委员会证监许 可[2024]1722 号文注册同意。 根据《国信证券股份有限公司 2025 年面向专业投资者公开发行公司债券（第 二期）（续发行）发行公告》，发行人及簿记管理人原定于 2025 年 8 月 12 日（T-1 日）15:00 到 18:00 以簿记建档的方式向网下投资者进行发行价格询价，并根据 簿记建档结果确定本期续发行债券的最终发行价格。 国信证券股 24 2 3/7 （本页无正文，为《关于延长国信证券股份有限公司 2025 年面向专业投资者公 开发行公司债券（第二期）(续发行）簿记建档时间的公告》之盖章页 ） 司 12 日 2 ...

国信证券股份有限公司 2025 年面向专业投资者公开发行 公司债券（第五期）在深圳证券交易所上市的公告 | 债券名称 | 国信证券股份有限公司 | | | | 2025 年面向专业投资者公开发行公司债券 | | --- | --- | --- | --- | --- | --- | | | （第五期） | | | | | | 债券简称 | 国证 25 07 | | | | | | 债券代码 | 524386 | | | | | | 信用评级 | AAA/AAA | | | | | | 评级机构 | | | 中诚信国际信用评级有限责任公司 | | | | 发行总额（亿元） | 24.00 | | | | | | 债券期限 | 年 10 | | | | | | 票面年利率（％） | 2.15 | | | | | | 利率形式 | 固定利率 | | | | | | 付息频率 | | 每年付息一次，到期一次还本 | | | | | 发行日 | 2025 年 8 月 5 | | 日-2025 | 年 | 8 月 6 日 | | 起息日 | 年 月 2025 8 6 | 日 | | | | | 上市日 | 年 月 202 ...

Core Viewpoint - The launch of "Guoxin Stock Market Assistant" by Guoxin Securities in collaboration with Volcano Engine aims to enhance investors' ability to navigate the stock market through intelligent analysis and quick access to information [1][3]. Group 1: Product Features - "Guoxin Stock Market Assistant" is designed as a "thinking investment assistant" that utilizes the capabilities of Volcano Engine's Doubao model and intelligent platform [3]. - The assistant incorporates insights from the top 10% of investment advisors among over 3,000, creating a professional research and investment thinking chain [3]. - It features a dual-mode of "quick response" and "deep thinking," allowing it to provide fast, accurate answers for common inquiries and in-depth analysis for complex issues [3]. Group 2: Market Impact - The assistant helps investors avoid the overwhelming task of filtering through vast amounts of information, enhancing their ability to seize market opportunities [3]. - It processes real-time market data, captures authoritative financial news, and identifies market trends and sentiment, thereby improving decision-making for investors [3]. Group 3: Security Measures - The financial services sector places a high emphasis on content security, and the assistant is equipped with a security firewall from Volcano Engine to ensure reliable content [4].

Group 1 - The core viewpoint emphasizes the long-term opportunities in the smart automotive industry, focusing on the rise of domestic brands and the trend towards electric intelligence [1] - The global status of domestic automotive brands is improving, with three main types of companies involved: traditional automakers actively transforming, new force brands, and technology companies transitioning into automotive [1] - By 2025, it is expected that more vehicles will be equipped with lidar, domain controllers, and L2+ level models will enter mass production, marking the beginning of L3 high-level intelligent driving [1] Group 2 - The incremental components in the industry are developing along two main lines: data flow (sensors, domain controllers, etc.) and energy flow (power batteries, electric drive systems, etc.) [1] - The industry is transitioning from a growth phase to a maturity phase, with an expected annual compound growth rate of 2% over the next 20 years, and sales of new energy vehicles are projected to exceed 15.5 million units in 2025, representing a year-on-year growth of over 25% [1] - The Smart Automotive ETF (159889) tracks the CS Smart Automotive Index (930721), which selects listed companies involved in smart driving and vehicle networking technologies to reflect the overall performance of quality enterprises in the smart automotive sector [1]