Core Insights - As of November 21, the total margin balance for ETFs in the Shanghai and Shenzhen markets reached 121.29 billion yuan, an increase of 370 million yuan from the previous trading day [2] - The financing balance for ETFs was 114.27 billion yuan, up by 986 million yuan, while the margin short balance decreased to 7.02 billion yuan, down by 616 million yuan [2] ETF Financing Activity - On November 21, 279 ETFs experienced net financing inflows, with the E Fund ChiNext ETF leading the way with a net inflow of 329 million yuan [2] - Other ETFs with significant net financing inflows included the Huatai-PB CSI 300 ETF, the Guotai CSI All Share Securities Company ETF, the Southern CSI 500 ETF, the Huaxia SSE Sci-Tech Innovation Board 50 ETF, the Fortune SSE Composite Index ETF, and the Huaxia CSI 1000 ETF [2]

Core Viewpoint - The total margin balance of ETFs in the Shanghai and Shenzhen markets reached 121.289 billion yuan as of November 21, showing an increase of 370 million yuan from the previous trading day [1] Group 1: ETF Financing and Margin Balance - The financing balance of ETFs was 114.266 billion yuan, increasing by 986 million yuan compared to the previous trading day [1] - The margin balance for ETF short selling was 7.023 billion yuan, decreasing by 616 million yuan from the previous trading day [1] Group 2: Net Inflows into ETFs - On November 21, 279 ETFs experienced net inflows, with the E Fund ChiNext ETF leading with a net inflow of 329 million yuan [1] - Other ETFs with significant net inflows included Huatai-PB CSI 300 ETF, Guotai Junan CSI All Share Securities Companies ETF, Southern CSI 500 ETF, and others [1]

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**: