具体来看，11月21日有279只ETF获融资净买入，其中，易方达创业板ETF获融资净买入额居首，净买 入3.29亿元；融资净买入金额居前的还有华泰柏瑞沪深300ETF、国泰中证全指证券公司ETF、南方中证 500ETF、华夏上证科创板50ETF、富国上证综指ETF、华夏中证1000ETF等。 数据显示，截至11月21日，沪深两市ETF两融余额为1212.89亿元，较上一交易日增加3.7亿元。其中， ETF融资余额为1142.66亿元，较上一交易日增加9.86亿元；ETF融券余额为70.23亿元，较上一交易日减 少6.16亿元。 ...

具体来看，11月21日有279只ETF获融资净买入，其中，易方达创业板ETF获融资净买入额居首，净买 入3.29亿元；融资净买入金额居前的还有华泰柏瑞沪深300ETF、国泰中证全指证券公司ETF、南方中证 500ETF、华夏上证科创板50ETF、富国上证综指ETF、华夏中证1000ETF等。 （文章来源：证券时报网） Wind统计显示，截至11月21日，沪深两市ETF两融余额为1212.89亿元，较上一交易日增加3.7亿元。其 中，ETF融资余额为1142.66亿元，较上一交易日增加9.86亿元；ETF融券余额为70.23亿元，较上一交易 日减少6.16亿元。 ...

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 recent appointment of Miao Fu as a fund manager for two quantitative stock selection funds by the company reflects an attempt to revitalize fund performance amid declining assets under management and investor redemptions [1][4]. Fund Manager Overview - Wang Baohe, a seasoned fund manager with extensive experience in quantitative investment, has been managing multiple funds since 2006, including both passive index and active management products [1][3]. - His management of the "Fuguo CSI 300 ETF" and "Fuguo CSI 300 ETF Connect" has yielded returns of 89.42% and 77.17% respectively since March 2011, showcasing his capability in managing passive index funds [3]. Fund Performance and Asset Management - The two newly appointed funds, "Fuguo Zhi Hong Quantitative Stock Selection" and "Fuguo Zhi Hang Quantitative Stock Selection," have recorded cumulative returns of 9.79% and 13.12% since their inception in 2023, ranking them in the middle tier among similar products [4]. - However, these funds have seen significant reductions in their asset sizes, with "Fuguo Zhi Hong" and "Fuguo Zhi Hang" shrinking by 87.47% and 87.78% respectively from their initial sizes [4]. Challenges in Active Management - Wang Baohe's transition to active management has not met expectations, as evidenced by continuous outflows from his actively managed products, leading to a decline in total assets under management from 194.94 billion to 107.58 billion, a drop of 44.84% [5][4]. - The only mixed equity fund managed by Wang has also experienced a drastic reduction in size, down 96.51% since its inception, with a modest cumulative return of only 0.37% [4]. Fee Structure and Market Competitiveness - The management fee rates for Wang's funds are relatively high, with many equity funds charging 1.2%, which may deter investors in a market where lower fees are becoming the norm [10][12]. - The "Fuguo CSI 300 ETF," despite its strong performance, has seen significant outflows attributed to its higher management fee of 0.5%, compared to the market average [10][12]. Market Trends and Recommendations - The recent trend in the market has seen many fund companies reducing management fees, which has impacted the competitiveness of Wang's funds that have not adjusted their fee structures [12][13]. - The company needs to reassess its fee strategy to retain investor interest and compete effectively in the current market landscape [13].

Quantitative Models and Construction Methods Jump Imbalance Indicator - **Model Name**: Jump Imbalance Indicator - **Model Construction Idea**: Measures the difference in the strength of upward and downward jumps in stock prices[2] - **Model Construction Process**: - Formula: $$D_{i,t}^{N J}=\frac{\mathrm{No.of~Pjumps}_{i}^{d}\mathrm{\-~No.of~Njumps}_{i}^{d}}{\mathrm{No.of~Tjumps}_{i}^{d}}$$[14] - Parameters: - No.of Pjumps: Number of days with positive jumps in the past 20 trading days - No.of Njumps: Number of days with negative jumps in the past 20 trading days - No.of Tjumps: Number of days with jumps in the past 20 trading days[15] - **Model Evaluation**: Effective for timing the market but not outstanding[20] - **Model Testing Results**: - Annualized return: 6.23% - Sharpe ratio: 0.57 - Profit-loss ratio: 1.46 - Annualized excess return: 4.48% - Sharpe ratio (excess): 0.34[22] Implied Jump Imbalance Indicator - **Model Name**: Implied Jump Imbalance Indicator - **Model Construction Idea**: Reflects the jump information of stocks not affected by market jumps, potentially containing expectations of future performance or insider trading probability[23] - **Model Construction Process**: - Formula: $$D_{i,t}^{IJ}=\frac{\text{No.of Pumps}_{i}|\text{No Market Jump-No.of Numps}_{i}|\text{No Market Jump}}{\text{No.of Tumps}_{i}|\text{No Market Jump}}$$[23] - Parameters: - No.of Pjumps | No Market Jump: Number of days with positive jumps when the market index did not jump - No.of Njumps | No Market Jump: Number of days with negative jumps when the market index did not jump - No.of Tjumps | No Market Jump: Number of days with jumps when the market index did not jump[23] - **Model Evaluation**: Shows better performance compared to the Jump Imbalance Indicator[31] - **Model Testing Results**: - Annualized return: 9.93% - Sharpe ratio: 0.82 - Calmar ratio: 0.75 - Profit-loss ratio: 2.05 - Annualized excess return: 8.46% - Sharpe ratio (excess): 0.77 - Calmar ratio (excess): 1.12[34] Jump Imbalance Dispersion Indicator - **Model Name**: Jump Imbalance Dispersion Indicator - **Model Construction Idea**: Represents the dispersion of jump imbalance among stocks, indicating market sentiment divergence[39] - **Model Construction Process**: - Formula: $$\Delta J_{R_{Std}}$$[39] - Parameters: - Standard deviation of implied jump imbalance indicator among stocks[39] - **Model Evaluation**: Effective for timing the market[39] - **Model Testing Results**: - Annualized return: 9.41% - Sharpe ratio: 0.74 - Calmar ratio: 0.70 - Profit-loss ratio: 1.50 - Annualized excess return: 7.91% - Sharpe ratio (excess): 0.69 - Calmar ratio (excess): 0.72[42] Composite Indicator - **Model Name**: Composite Indicator - **Model Construction Idea**: Combines implied jump imbalance indicator and jump imbalance dispersion indicator for better market timing[40] - **Model Construction Process**: - Formula: $$\Delta J_{R} > 0 \text{ and } \Delta J_{R_{Std}} < 0$$[40] - Parameters: - Implied jump imbalance indicator - Jump imbalance dispersion indicator[40] - **Model Evaluation**: Shows significant improvement in market timing effectiveness[40] - **Model Testing Results**: - Annualized return: 16.5% - Sharpe ratio: 1.28 - Calmar ratio: 2.41 - Annualized excess return: 15.49% - Sharpe ratio (excess): 0.82 - Calmar ratio (excess): 0.88[45] Quantitative Factors and Construction Methods Jump Imbalance Factor - **Factor Name**: Jump Imbalance Factor - **Factor Construction Idea**: Measures the difference in the strength of upward and downward jumps in stock prices[2] - **Factor Construction Process**: - Formula: $$D_{i,t}^{N J}=\frac{\mathrm{No.of~Pjumps}_{i}^{d}\mathrm{\-~No.of~Njumps}_{i}^{d}}{\mathrm{No.of~Tjumps}_{i}^{d}}$$[14] - Parameters: - No.of Pjumps: Number of days with positive jumps in the past 20 trading days - No.of Njumps: Number of days with negative jumps in the past 20 trading days - No.of Tjumps: Number of days with jumps in the past 20 trading days[15] - **Factor Evaluation**: Effective for timing the market but not outstanding[20] - **Factor Testing Results**: - Annualized return: 6.23% - Sharpe ratio: 0.57 - Profit-loss ratio: 1.46 - Annualized excess return: 4.48% - Sharpe ratio (excess): 0.34[22] Implied Jump Imbalance Factor - **Factor Name**: Implied Jump Imbalance Factor - **Factor Construction Idea**: Reflects the jump information of stocks not affected by market jumps, potentially containing expectations of future performance or insider trading probability[23] - **Factor Construction Process**: - Formula: $$D_{i,t}^{IJ}=\frac{\text{No.of Pumps}_{i}|\text{No Market Jump-No.of Numps}_{i}|\text{No Market Jump}}{\text{No.of Tumps}_{i}|\text{No Market Jump}}$$[23] - Parameters: - No.of Pjumps | No Market Jump: Number of days with positive jumps when the market index did not jump - No.of Njumps | No Market Jump: Number of days with negative jumps when the market index did not jump - No.of Tjumps | No Market Jump: Number of days with jumps when the market index did not jump[23] - **Factor Evaluation**: Shows better performance compared to the Jump Imbalance Factor[31] - **Factor Testing Results**: - Annualized return: 9.93% - Sharpe ratio: 0.82 - Calmar ratio: 0.75 - Profit-loss ratio: 2.05 - Annualized excess return: 8.46% - Sharpe ratio (excess): 0.77 - Calmar ratio (excess): 1.12[34] Jump Imbalance Dispersion Factor - **Factor Name**: Jump Imbalance Dispersion Factor - **Factor Construction Idea**: Represents the dispersion of jump imbalance among stocks, indicating market sentiment divergence[39] - **Factor Construction Process**: - Formula: $$\Delta J_{R_{Std}}$$[39] - Parameters: - Standard deviation of implied jump imbalance indicator among stocks[39] - **Factor Evaluation**: Effective for timing the market[39] - **Factor Testing Results**: - Annualized return: 9.41% - Sharpe ratio: 0.74 - Calmar ratio: 0.70 - Profit-loss ratio: 1.50 - Annualized excess return: 7.91% - Sharpe ratio (excess): 0.69 - Calmar ratio (excess): 0.72[42] Composite Factor - **Factor Name**: Composite Factor - **Factor Construction Idea**: Combines implied jump imbalance factor and jump imbalance dispersion factor for better market timing[40] - **Factor Construction Process**: - Formula: $$\Delta J_{R} > 0 \text{ and } \Delta J_{R_{Std}} < 0$$[40] - Parameters: - Implied jump imbalance factor - Jump imbalance dispersion factor[40] - **Factor Evaluation**: Shows significant improvement in market timing effectiveness[40] - **Factor Testing Results**: - Annualized return: 16.5% - Sharpe ratio: 1.28 - Calmar ratio: 2.41 - Annualized excess return: 15.49% - Sharpe ratio (excess): 0.82 - Calmar ratio (excess): 0.88[45] Factor Backtesting Results Jump Imbalance Factor - **Annualized return**: 6.23% - **Sharpe ratio**: 0.57 - **Profit-loss ratio**: