证券之星消息，根据市场公开信息及10月22日披露的机构调研信息，国泰基金近期对3家上市公司进行了调研，相关名单如下： 1）科大讯飞 （国泰基金参与公司业绩说明会&价值在线会议） 个股亮点：公司在不同行业多年的规模化应用积累了超过50TB的行业语料和每天超10亿人次用户交互数据，通过挖掘、收集及清洗高质量、多领 域、多行业及多样性的海量丰富数据；公司与阿里一直有良好的交流合作；"讯飞星火大模型"升级，V4.0在底座能力上对标GPT-4 Turbo，多模态 能力、智能体能力显著提升。 2）百亚股份 （国泰基金管理有限公司参与公司投资者电话会议） 调研纪要：抖音仍是品牌曝光及拉新引流的重要阵地，公司保持投入力度，同时加大在小红书的资源投入，反馈趋势良好。即时零售增速较快， 占比逐季上升，被视为重要新兴渠道，具备成为行业新红利的潜力。线上平台推广效率高，传统电商平台盈利性优于新兴平台，公司坚持推新 品、拉新客，兼顾盈利与增长。外围市场扩张快于计划，前三季度营收增速超100%，渠道成本摊薄，净利率有望上升。大健康系列产品收入占比 超50%，有机纯棉和益生菌系列增长显著，未来将持续扩大规模并升级产品。新品测试进度符合预期 ...

一、证券市场回顾 南财金融终端数据显示，昨日(10月21日，下同)上证综指日内上涨1.36%，收于3916.33点，最高3919.32 点；深证成指日内上涨2.06%，收于13077.32点，最高13100.08点；创业板指日内上涨3.02%，收于 3083.72点，最高3101.93点。 二、ETF市场表现 1、股票型ETF整体市场表现 昨日股票型ETF收益率中位数为1.52%。其中按照不同分类，规模指数ETF中博时中证科创创业50ETF收 益率最高，为4.86%；行业指数ETF中嘉实国证通信ETF收益率最高，为4.71%；策略指数ETF中中金 MSCI中国A股国际质量ETF收益率最高，为2.4%；风格指数ETF中华夏创业板动量成长ETF收益率最 高，为4.6%；主题指数ETF中国泰中证全指通信设备ETF收益率最高，为6.76%。 2、股票型ETF涨跌幅排行 昨日股票型ETF资金流入最多的3只ETF及其流入金额分别为：国泰中证煤炭ETF（流入5.33亿元）、易 方达上证科创板50成份ETF（流入5.02亿元）、华夏中证A500ETF（流入3.74亿元）。资金流入前10详 情见下表： | 类别 | 代码 | ...

2025年10月21日，永鼎股份(600105.SH)发布2025年第三季报。截至2025年10月20日，共有8个机构投资 者披露持有永鼎股份A股股份，合计持股量达4.77亿股，占永鼎股份总股本的32.60%。其中，机构投资 者包括永鼎集团有限公司、上海东昌企业集团有限公司、香港中央结算有限公司、UBS AG、中国工商 银行股份有限公司-国泰中证全指通信设备交易型开放式指数证券投资基金、中国工商银行股份有限公 司-长安成长优选混合型证券投资基金、全国社保基金五零三组合、国联安基金-中国太平洋人寿保险股 份有限公司-分红险-国联安基金中国太平洋人寿股票相对收益型(个分红)单一资产管理计划，机构投资 者合计持股比例达32.60%。相较于上一季度，机构持股比例合计下跌了0.70个百分点。 以上内容与数据，与有连云立场无关，不构成投资建议。据此操作，风险自担。 公募基金方面，本期较上一期持股增加的公募基金共计1个，即国泰中证全指通信设备ETF，持股增加 占比达0.27%。本期较上一季度持股减少的公募基金共计1个，即长安成长优选混合A，持股减少占比小 幅下跌。本期较上一季未再披露的公募基金共计130个，主要包括兴全趋势 ...

Group 1: Report Information - Report Date: October 10, 2025 [1] - Report Title: Shenwan Hongyuan Gold ETF Portfolio 202510 [1] - Analysts: Shen Siyi, Deng Hu [3] - Research Support: Bai Haotian [3] - Contact: Shen Enyi [3] Group 2: Investment Ratings - No industry investment ratings are provided in the report. Group 3: Core Views - The report constructs four ETF portfolios, including the macro industry portfolio, macro + momentum industry portfolio, core - satellite portfolio, and trinity style rotation ETF portfolio, based on macro - sensitivity and momentum analysis, aiming to capture investment opportunities in different market environments [5][8]. - The current economic leading indicators are rising, liquidity indicators are slightly tight, and credit indicators remain positive. The portfolios are shifting towards a more balanced allocation, with an increased proportion of consumer sectors [5]. - The trinity style rotation model combines macro - liquidity, fundamental, and market sentiment factors to construct a medium - to long - term style rotation model, providing insights into market style preferences [5][9]. Group 4: ETF Portfolio Construction Methods 4.1 Based on Macro - Method - Calculate macro - sensitivity for broad - based, industry - theme, and Smart Beta ETFs based on economic, liquidity, and credit variables. Traditional cyclical industries are sensitive to the economy, TMT to liquidity, and consumption to credit [8]. - Construct three ETF portfolios (macro industry, macro + momentum industry, and core - satellite) using macro - sensitivity and momentum, and rebalance monthly [8]. 4.2 Trinity Style Rotation ETF Portfolio - Build a medium - to long - term style rotation model centered on macro - liquidity, comparing with the CSI 300 index. Screen macro, fundamental, and market sentiment factors to construct three types of models (growth/value, market - cap, and quality) [9]. Group 5: Portfolio Details 5.1 Macro Industry Portfolio - Select the top 6 industry - theme indices based on macro - sensitivity scores, and equally weight the largest - scale corresponding ETFs. Currently, the portfolio is more balanced with an increased consumer proportion [5][10]. - October 2025 holdings include ETFs related to tourism, home appliances, chemicals, etc. [14]. - In 2025, the portfolio had varying monthly excess returns, with positive excess returns in September [15]. 5.2 Macro + Momentum Industry Portfolio - Combine macro and momentum methods. The pharmaceutical sector's weight is further reduced, and rare earth and battery sectors are selected on the momentum side [5][16]. - October 2025 holdings include multiple industry - themed ETFs [18]. - The portfolio performed well in 2025, with positive excess returns in September after a drawdown in August [19]. 5.3 Core - Satellite Portfolio - Use the CSI 300 as the core and combine broad - based, industry, and Smart Beta portfolios. Weight them at 50%, 30%, and 20% respectively [20][21]. - October 2025 holdings include a mix of broad - based and industry - themed ETFs [24][25]. - The portfolio performed steadily in 2025, outperforming the index almost every month [25]. 5.4 Trinity Style Rotation ETF Portfolio - The model currently favors small - cap growth and high - quality styles. The portfolio's factor exposure and historical performance are presented [26][27]. - October 2025 holdings include ETFs related to small - cap indices and high - growth sectors [31]. - The portfolio has shown certain performance since 2021, with positive excess returns in September 2025 [30].

Core Viewpoint - The ETF market has experienced significant growth in 2023, with industry and thematic ETFs seeing their combined scale increase from less than 600 billion to over 1 trillion yuan, marking a growth of over 77% [1][2]. ETF Market Overview - As of September 30, there are 483 thematic ETFs and 84 industry ETFs, with total scales of 774.79 billion yuan and 287.63 billion yuan respectively, surpassing 1 trillion yuan in total scale [2]. - The combined scale of these ETFs has increased by 462.77 billion yuan this year, compared to a much smaller increase of 330 billion yuan for broad-based ETFs [2]. Fund Flow Dynamics - The shift in investor behavior is evident, with some investors buying more of underperforming ETFs while others take profits from those that have performed well [1][4]. - Notably, the coal ETF, despite a decline of 5.65%, saw its scale grow from 2.8 billion yuan to 11.4 billion yuan, a nearly 300% increase [1][7]. Performance of ETFs - 16 industry and thematic ETFs have seen gains exceeding 80% this year, with 150 products yielding over 50% returns [4]. - The top-performing ETF, the Guotai Chuangye Board AI ETF, has surged by 121.53%, while other notable performers include the Guotai Zhongzheng All Index Communication Equipment ETF and the Yongying Zhongzheng Hong Kong Gold Industry ETF, with increases of 96.19% and 87.3% respectively [4][5]. Sector Analysis - The top-performing ETFs are primarily from two sectors: technology, represented by AI, communication, and chips, and gold stocks [5]. - Conversely, the worst performers include energy and coal ETFs, with declines exceeding 6% [6]. Fund Size and Performance Correlation - Among ETFs with scales exceeding 10 billion yuan, the top three in terms of scale are the Guotai Fund's Securities ETF, the Jiashi Fund's Sci-Tech Chip ETF, and the Huabao Fund's Broker ETF [7]. - The Jiashi Fund's Sci-Tech Chip ETF leads in annual returns at 75.1%, while the only declining product among the large-scale ETFs is the Penghua Fund's Wine ETF, which has dropped over 4% [7][8]. Investment Strategies - The trend of "buying the dip" is evident, with significant inflows into ETFs that have underperformed, while some investors are also taking profits from high-performing ETFs [8]. - The top three ETFs in terms of scale increase this year are the Guotai Fund's Securities ETF, the Huaxia Fund's Robotics ETF, and the Jiashi Fund's Sci-Tech Chip ETF, indicating a mix of strategies among investors [8].

Core Viewpoint - The A-share and Hong Kong stock markets have shown a continuous upward trend since mid-April, achieving new highs in the third quarter, with equity funds yielding significant returns [1] Group 1: Active Equity Funds - A total of 37 funds have doubled their returns this year as of September 26, with 31 active equity funds in A-shares achieving over 100% returns [2][4] - The average return for active equity funds is 30.32%, with over 98% of these funds reporting positive returns [4] - The top-performing fund, Yongying Technology Smart Selection A, has a return rate of 189.58%, significantly boosted by its focus on AI concept stocks [4][6] Group 2: Passive Index Funds - Nearly 98% of index funds have achieved positive returns, with an average return of 27.53% [7] - Funds tracking innovative drugs, communications, and artificial intelligence have outperformed, with the top two funds yielding returns of 103.96% and 100.59% [7] - Underperforming index funds are primarily those tracking energy, food and beverage, and coal sectors, with losses exceeding 5% [7] Group 3: QDII Funds - QDII funds focused on the Hong Kong market, particularly in innovative drug assets, have performed well, with four funds exceeding 100% returns [3][8] - The top-performing QDII fund, Huatai Bairui Hang Seng Innovation Drug ETF, has a return of 152.25% [8] - Other notable funds in this category have also shown strong performance, with several exceeding 90% returns [8]

Quantitative Models and Construction Methods 1. Model Name: Counter-Cyclical Allocation Model - **Model Construction Idea**: Predict the macroeconomic environment using proxy variables and allocate assets that perform best under the predicted environment[26][31] - **Model Construction Process**: - Use proxy variables to forecast the macroeconomic environment (e.g., Inflation, Growth, etc.) - Allocate assets based on historical performance under the predicted environment - For Q3 2025, the model predicted an "Inflation" environment, leading to allocations in CSI 300, CSI 2000, Nanhua Commodity Index, and ChinaBond Total Wealth Index[26] - **Model Evaluation**: Provides a systematic approach to asset allocation based on macroeconomic conditions[26] 2. Model Name: Macro Momentum Model - **Model Construction Idea**: Constructed using multiple dimensions such as economic growth, inflation, interest rates, exchange rates, and risk sentiment to time asset classes like stocks and bonds[26] - **Model Construction Process**: - Incorporate macroeconomic indicators, positioning data, volume-price factors, and sentiment factors - Apply the model to time assets such as CSI 300, ChinaBond Total Wealth Index, and gold contracts (AU9999)[26] - **Model Evaluation**: Offers a multi-dimensional perspective for timing asset allocation[26] 3. Model Name: Multi-Factor Industry Rotation Model - **Model Construction Idea**: Combines historical fundamentals, expected fundamentals, sentiment, volume-price technicals, and macroeconomic factors to rotate among industries[27] - **Model Construction Process**: - Match ETFs with their corresponding CSI Level-1 industries - Use a pool of 23 industries to construct the benchmark - Allocate weights to ETFs based on the model's output[27][29] - **Model Evaluation**: Provides a structured approach to industry rotation, leveraging multiple factor dimensions[27] 4. Model Name: Absolute Return Strategies (Blended Models) - **Model Construction Idea**: Combine macro timing and industry rotation strategies with asset rebalancing to achieve absolute returns[31][37] - **Model Construction Process**: - Implement 20/80 stock-bond rebalancing and risk parity strategies - Enhance these strategies with macro timing and industry ETF rotation[31][37] - **Model Evaluation**: Enhances traditional rebalancing strategies with timing and rotation components for better returns[31][37] --- Model Backtesting Results 1. Counter-Cyclical Allocation Model - CSI 300 Q3 2025 Return: 14.38%[26] - CSI 2000 Q3 2025 Return: 16.58%[26] - Nanhua Commodity Index Q3 2025 Return: 4.17%[26] - ChinaBond Total Wealth Index Q3 2025 Return: -1.08%[26] 2. Macro Momentum Model - CSI 300 September 2025 Return: 0.11%[26] - ChinaBond Total Wealth Index September 2025 Return: -0.31%[26] - AU9999 Gold Contract September 2025 Return: 5.72%[26] 3. Multi-Factor Industry Rotation Model - Weekly Return: 0.61% (Excess Return: 0.79% over Wind All A Index)[27][28] - Monthly Return (September 2025): 0.82% (Excess Return: 0.28% over Wind All A Index)[27][28] 4. Absolute Return Strategies (Blended Models) - **Macro Timing + 20/80 Rebalancing**: - Weekly Return: -0.10% - Monthly Return: -0.09% - YTD Return: 3.85% - Annualized Volatility: 3.38% - Max Drawdown: 1.78% - Sharpe Ratio: 1.61[32] - **Macro Timing + Risk Parity**: - Weekly Return: -0.01% - Monthly Return: -0.15% - YTD Return: 1.58% - Annualized Volatility: 1.75% - Max Drawdown: 1.50% - Sharpe Ratio: 1.27[32] - **Macro Timing + Industry ETF Rotation + 20/80 Rebalancing**: - Weekly Return: 0.22% - Monthly Return: 0.21% - YTD Return: 7.83% - Annualized Volatility: 5.28% - Max Drawdown: 2.54% - Sharpe Ratio: 2.12[32] - **Macro Timing + Industry ETF Rotation + Risk Parity**: - Weekly Return: 0.11% - Monthly Return: -0.03% - YTD Return: 2.94% - Annualized Volatility: 2.18% - Max Drawdown: 1.45% - Sharpe Ratio: 1.90[32] --- Quantitative Factors and Construction Methods 1. Factor Name: PB Earnings - **Factor Construction Idea**: Focuses on price-to-book ratios and earnings growth to identify undervalued stocks with growth potential[39][41] - **Factor Construction Process**: - Calculate PB ratios for stocks - Combine with earnings growth metrics to rank stocks[39][41] - **Factor Evaluation**: Targets value-oriented opportunities with growth potential[39][41] 2. Factor Name: High Dividend Yield - **Factor Construction Idea**: Selects stocks with high dividend yields for stable income generation[39][41] - **Factor Construction Process**: - Rank stocks based on dividend yield - Adjust for payout sustainability metrics[39][41] - **Factor Evaluation**: Suitable for income-focused strategies[39][41] 3. Factor Name: Small-Cap Value - **Factor Construction Idea**: Targets small-cap stocks with low valuations for higher growth potential[39][41] - **Factor Construction Process**: - Identify small-cap stocks - Rank based on valuation metrics like P/E and P/B ratios[39][41] - **Factor Evaluation**: Captures the small-cap premium with a value tilt[39][41] 4. Factor Name: Small-Cap Growth - **Factor Construction Idea**: Focuses on small-cap stocks with high growth potential[39][41] - **Factor Construction Process**: - Identify small-cap stocks - Rank based on growth metrics like revenue and earnings growth rates[39][41] - **Factor Evaluation**: Targets high-growth opportunities in the small-cap space[39][41] --- Factor Backtesting Results 1. PB Earnings - **10/90 Rebalancing**: - Weekly Return: -0.18% - Monthly Return: -0.04% - YTD Return: 2.49% - Annualized Volatility: 2.34% - Max Drawdown: 1.82% - Sharpe Ratio: -0.01[41] - **20/80 Rebalancing**: - Weekly Return: -0.39% - Monthly Return: -0.11% - YTD Return: 4.06% - Annualized Volatility: 4.71% - Max Drawdown: 3.79% - Sharpe Ratio: 0.19[41] 2. High Dividend Yield - **10/90 Rebalancing**: - Weekly Return: -0.12% - Monthly Return: -0.09% - YTD Return: 1.91% - Annualized Volatility: 2.09% - Max Drawdown: 1.39% - Sharpe Ratio: -0.18[41] - **20/80 Rebalancing**: - Weekly Return: -0.28% - Monthly Return: -0.22% - YTD Return: 2.88% - Annualized Volatility: 4.19% - Max Drawdown: 3.47% - Sharpe Ratio: 0.05[41] 3. Small-Cap Value - **10/90 Rebalancing**: - Weekly Return: -0.27% - Monthly Return: -0.07% - YTD Return: 5.35% - Annualized Volatility: 3.55% - Max Drawdown: 3.69% - Sharpe Ratio: 0.47[41] - **20/80 Rebalancing**: - Weekly Return: -0.57% - Monthly Return: -0.16% - YTD Return: 9.91% - Annualized Volatility: 7.14% - Max Drawdown: 7.74% - Sharpe Ratio: 0.60[41]

Summary of Key Points Core Viewpoint - On September 17, a total of 39.78 billion yuan net inflow was recorded for industry-themed ETF funds, with a cumulative net inflow of 124.46 billion yuan over the past five trading days, indicating strong investor interest in these funds [1]. Fund Inflows - A total of 123 industry-themed ETF funds experienced net inflows on September 17, with the leading fund being the Guotai CSI All-Share Securities Company ETF (512880), which saw an increase of 9.36 million shares and a net inflow of 11.88 billion yuan [1][3]. - Other notable funds with significant net inflows include: - Huabao CSI Financial Technology Theme ETF (159851) with a net inflow of 5.25 billion yuan [3]. - Huaxia CSI Robotics ETF (562500) with a net inflow of 5.01 billion yuan [3]. - Guotai Securities ETF (512000) with a net inflow of 3.59 billion yuan [3]. Fund Outflows - On the same day, 164 industry-themed ETF funds experienced net outflows, with the Guotai CSI Coal ETF (515220) leading the outflows, which saw a reduction of 4.21 million shares and a net outflow of 4.6 billion yuan [4][5]. - Other funds with significant net outflows include: - Huabao CSI Medical ETF (512170) with a net outflow of 1.50 billion yuan [5]. - Penghua CSI Sub-Segment Chemical Industry ETF (159870) with a net outflow of 1.49 billion yuan [5]. - Huatai-PineBridge CSI Rare Earth Industry ETF (516780) with a net outflow of 1.36 billion yuan [5].

昨日股票型ETF涨幅最高的3只ETF及其收益率分别为：银华中证5G通信主题ETF（10.03%）、国泰中 证全指通信设备ETF（9.99%）、华夏中证5G通信主题ETF（9.98%）。涨幅前10详情见下表： | 类别 | 代码 | 基金名称 | 涨跌幅(%) | | --- | --- | --- | --- | | 股票型 | 159994.SZ | 银华中证5G通信主题ETF | 10.03% | | 股票型 | 515880.SH | 国泰中证全指通信设备ETF | 9.99% | | 股票型 | 515050.SH | 华夏中证5G通信主题ETF | 9.98% | | 股票型 | 159811.SZ | 博时中证5G产业50ETF | 9.18% | | 股票型 | 159246.SZ | 富国创业板人工智能ETF | 9.17% | | 股票型 | 159363.SZ | 华宝创业板人工智能ETF | 8.91% | | 股票型 | 560660.SII | 新华中证云计算50ETF | 8.9% | | 股票型 | 159381.SZ | 华夏创业板人工智能ETF | 8.74% | | 股票型 ...

Core Viewpoint - As of September 11, the total margin balance for ETFs in the Shanghai and Shenzhen markets is 112.707 billion yuan, showing a decrease of 1.386 billion yuan from the previous trading day [1] Group 1: ETF Financing and Margin Balance - The ETF financing balance stands at 104.962 billion yuan, down by 1.537 billion yuan from the previous trading day [1] - The ETF margin short balance is 7.745 billion yuan, which has increased by 0.151 billion yuan compared to the previous trading day [1] Group 2: Net Buy Insights - On September 11, 228 ETFs experienced net financing purchases, with the E Fund ChiNext ETF leading with a net purchase amount of 309 million yuan [1] - Other ETFs with significant net financing purchases include GF CSI Hong Kong Innovative Drug ETF, HuaBao ChiNext Artificial Intelligence ETF, Guotai CSI All-Share Communication Equipment ETF, Southern CSI 500 ETF, Bosera CSI Convertible Bonds and Exchangeable Bonds ETF, and E Fund CSI 300 Medical ETF [1]