| 证券代码 证券简称 | 涨跌幅 | 基金份额变化 净流入额 基金 | | | | --- | --- | --- | --- | --- | | | (%) | (亿份) | (亿元) | (亿; | | 512880.SH 国泰中证全指证券公司ETF | 0.32 | 13.41 | 16.91 | 57 | | 159755.SZ 广发国证新能源车电池ETF | 2.16 | 11.58 | 13.12 | 16 | | 510300.SH 华泰柏瑞沪深300ETF | 1.58 | 2.45 | 11.76 | 433 | | 159915.SZ 易方达创业板ETF | 0.56 | 3.17 | 10.30 | 112 | | 512400.SH 南方中证申万有色金属ETF | 8.71 | 5.49 | 9.64 | 16 | | 512000.SH 华宝中证全指证券公司ETF | 0.33 | 13.58 | 8.18 | 36 | | 159796.SZ 汇添富中证电池主题ETF | 0.39 | 7.36 | 7.55 | 9 | | 159869.SZ 华夏中证动漫游戏ETF | -2. ...

市场概况 | 代码 | 基金名称 | 融资买入额 | | --- | --- | --- | | 513090.SH | 易方达中证香港证券投资主题ETF | 24.96亿元 | | 511360.SH | 海富通中证短融ETF | 13.46亿元 | | 513120.SH | 广发中证香港创新药(QDII-ETF) | 12.97亿元 | | 513130.SH | 华泰柏瑞南方东英恒生科技指数(QDII-ETF) | 11.63亿元 | | 511380.SH | 博时中证可转债及可交换债券ETF | 9.8亿元 | | 588000.SH | 华夏上证科创板50成份ETF | 7.43亿元 | | 518880.SH | 华安易富黄金ETF | 6.89亿元 | | 512880.SH | 国泰中证全指证券公司ETF | 6.36亿元 | | 513330.SH | 华夏恒生互联网科技业(QDII-ETF) | 5.96亿元 | | 513180.SH | 华夏恒生科技(QDII-ETF) | 5.89亿元 | ETF融资净买入额 9月30日ETF融资净买入额前三位分别为：广发中证香港创新药(QDII ...

（原标题：23只ETF融资余额上月增逾亿元） 最新两市ETF两融余额为1110.65亿元，较8月末环比增加23.65亿元，其中，ETF融资余额环比增加24.04亿元，融券余额环比减少3890.68万元。 证券时报•数据宝统计显示，截至9月30日，两市ETF两融余额为1110.65亿元，较8月末增加23.65亿元，增幅为2.18%，其中，ETF融资余额为 1036.51亿元，较8月末增加24.04亿元，增长2.37%，ETF融券余额为74.14亿元，较8月末减少3890.68万元，下降0.52%。 具体来看，深市ETF最新两融余额346.12亿元，较8月末增加4.69亿元，增幅1.37%，其中，ETF融资余额337.02亿元，较8月末增加4.94亿元，增幅 1.49%，融券余量4.46亿份，较8月末减少4428.27万份，降幅9.04%，ETF最新融券余额9.10亿元，较8月末减少2486.80万元。 沪市ETF最新两融余额764.53亿元，较8月末增加18.96亿元，增幅2.54%，其中，ETF融资余额699.49亿元，较8月末增加19.10亿元，融券余量19.51 亿份，较8月末减少406.12万份，降幅0 ...

Group 1 - On September 29, the company reported the launch of the CCB Schroder CSI Selected Hong Kong and Shanghai Technology 50 ETF, marking a significant return to ETF offerings after 14 years since the establishment of the CCB ShenZhen 300 Value ETF in September 2011 [1][2][4] - The ETF aims to reflect the performance of 50 technology companies with strong research capabilities and good fundamentals selected from the mainland and Hong Kong markets, with the top five sectors being electronics, machinery, communications, power equipment, and computers [4] - The company currently has only two existing ETF products, with the latest sizes being 227 million and 63 million respectively for the CCB 180 Governance ETF and the CCB ShenZhen 300 Value ETF [4] Group 2 - On September 25, another active equity firm, Xingzheng Global Fund, submitted its first ETF product application, the Xingzheng Global CSI 300 Quality ETF, indicating a trend among active managers to expand into the ETF market [5] - The ETF market in China has seen significant growth, with total ETF assets increasing from 373 billion at the beginning of the year to 547 billion by September 26, representing a growth rate of over 46% [6] - Major fund companies like Huaxia Fund and E Fund have ETF management scales exceeding 800 billion, while others like Huatai-PB Fund and Southern Fund have also seen substantial growth in their ETF management scales [6][7]

Group 1 - On September 29, the company reported the launch of the CCB Schroder CSI Selected Hong Kong and Shanghai Technology 50 ETF, marking a significant return to the ETF market after 14 years since the establishment of the CCB Schroder Shenzhen 300 Value ETF in September 2011 [1][2][4] - The ETF aims to reflect the performance of 50 technology companies with strong research capabilities and good fundamentals selected from the mainland and Hong Kong markets, with the top five industries by weight being electronics, machinery, communications, power equipment, and computers [4] - The ETF market in China has seen significant expansion, with total ETF assets increasing from 373 billion to 547 billion this year, representing a growth of over 46% [6] Group 2 - On September 25, another active equity firm, Xingzheng Global Fund, submitted its first ETF product, the Xingzheng Global CSI 300 Quality ETF, indicating a trend among active managers to diversify into ETF offerings [5] - Major fund companies such as Huaxia Fund and E Fund have seen their ETF management scales exceed 800 billion, while other firms like Huatai-PB and Southern Fund have also reported substantial growth in their ETF assets [6][7] - Several "super products" in the ETF market have attracted significant capital this year, with the Fortune CSI Hong Kong Internet ETF leading with an increase of over 70 billion, followed by other major ETFs with substantial growth figures [7]

国泰基金基金经理艾小军公开表示，国内经济将继续稳步恢复，政策层面的支持和市场信心的逐步修 复，将为A股市场提供支撑。另外，证券公司并购重组等事件，也将给行业带来持续催化。 华西证券发布研报称，2025年第三季度证券市场各项指标逐渐清晰。预计45家上市券商在2025第三季度 单季度实现调整后营收1581亿元，同比增加50%，环比增加21%。2025年前三季度，预计45家上市券商 实现调整后营业收入3987亿元，同比增加44%。 9月29日，上证指数在券商等大金融板块的带领下，盘中一度逼近3900点，广发证券和华泰证券收盘涨 停。据Wind数据统计，9月1日至9月26日，合计超270亿元资金涌入券商相关ETF，仅国泰中证全指证券 公司ETF一只就获得超100亿元资金买入。 券商板块作为"牛市旗手"，大部分券商ETF规模呈现出快速增长，部分券商ETF规模持续创新高。截至9 月26日，华宝中证全指证券公司ETF规模达到607亿份，较8月底增长超两成；资产规模达到353.34亿 元，较8月底增长超一成。按照9月成交均值计算，9月以来接近70亿元资金在不断增持该ETF。另外，9 月至今，天弘中证全指证券公司ETF、银华中 ...

Group 1 - The A-share market has experienced high-level fluctuations since September, with significant capital inflow into brokerage-related ETFs, totaling over 27 billion yuan [1][2] - The Guotai CSI All-Share Securities Company ETF alone received over 10 billion yuan in capital inflow [1][2] - As of September 26, 16 brokerage-related ETFs collectively attracted approximately 27.41 billion yuan in capital [2] Group 2 - The Guotai CSI All-Share Securities Company ETF's fund size reached 44.48 billion units by September 26, marking a 28.96% increase since the end of August, with total assets growing by 18.4% to 54.18 billion yuan [5] - Other brokerage ETFs also showed rapid growth, with the Huabao CSI All-Share Securities Company ETF's size increasing by over 20% and total assets rising by over 10% [5] - The Tianhong, Yinhua, and Nanfang CSI All-Share Securities Company ETFs each received over 1 billion yuan in capital inflow since September [5] Group 3 - The technology sector continues to attract significant capital, with the E Fund National Robot Industry ETF receiving over 6 billion yuan in inflow, doubling its size to 7.74 billion units [7] - The Guangfa National New Energy Vehicle Battery ETF and the Huitianfu CSI Battery Theme ETF each gained over 4 billion yuan in capital inflow, with their sizes reaching 12.55 billion units and 7.21 billion units, respectively [7] - The technology growth sector has shown strong performance in the first three quarters, with expectations for continued positive trends in the fourth quarter [7] Group 4 - The new energy vehicle sector is expected to receive ongoing policy support, leading to rapid growth in battery demand, particularly for solid-state batteries [8] - The battery sector is anticipated to maintain a high level of prosperity, with expectations for a favorable market in the fourth quarter [8] Group 5 - The domestic economy is expected to continue its steady recovery, supported by policy measures and improved market confidence, which will benefit the securities sector [6]

国泰中证全指证券公司ETF（512880）成立日期2016年7月26日，最新规模297.95亿。今年以来收益 4.16%，同类排名3826/4221；近一年收益30.63%，同类排名2474/3836；成立以来收益21.8%。 国泰中证全指证券公司ETF（512880）基金经理为艾小军。 截至发稿，艾小军累计任职时间11年263天，现任基金资产总规模1418.59亿元，任职期间最佳基金回报 239.96%， 任职期间最差基金回报-46.54%。 风险提示：市场有风险，投资需谨慎。本文为AI大模型自动发布，任何在本文出现的信息（包括但不 限于个股、评论、预测、图表、指标、理论、任何形式的表述等）均只作为参考，不构成个人投资建 议。 9月29日，中国银河涨5.06%，截至发稿，报17.85元/股，成交12.88亿元，换手率1.02%，总市值1951.79 亿元。 资料显示，中国银河证券股份有限公司位于北京市丰台区西营街8号院1号楼青海金融大厦，成立日期 2007年1月26日，上市日期2017年1月23日，公司主营业务涉及提供经纪、销售和交易、投资银行和投资 管理等综合性证券服务。主营业务收入构成为：其他母子公司一 ...

市场概况 ETF融资买入额 9月25日两市ETF两融余额为1166.63亿元，较前一交易日增加12.37亿元。其中融资余额为1086.18亿元， 较前一交易日增加12.22亿元；融券余额为80.45亿元，较前一交易日增加1466.84万元。 分市场来看，沪市ETF两融余额为816.33亿元，较前一交易日增加8.78亿元。其中融资余额为745.28亿 元，较前一交易日增加8.72亿元；融券余额为71.04亿元，较前一交易日增加593.4万元。深市ETF两融 余额为350.3亿元，较前一交易日增加3.59亿元。其中融资余额为340.9亿元，较前一交易日增加3.5亿 元；融券余额为9.4亿元，较前一交易日增加873.44万元。 ETF融资融券余额 | 代码 | 基金名称 | 融资融券余额 | | --- | --- | --- | | 518880.SH | 华安易富黄金ETF | 73.66亿元 | | 159934.SZ | 易方达黄金ETF | 58.68亿元 | | 511520.SH | 富国中债7-10年政策性金融债ETF | 42.75亿元 | | 159920.SZ | 华夏恒生(QDII-ETF) ...

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]