Core Viewpoint - The A-share market has experienced a significant rebound since the "924" rally began in 2024, driven by economic recovery, supportive policies, and increased capital inflows, with notable performance from various fund products, particularly those managed by Guotou Ruijin Fund [2][3] Group 1: Active Equity Funds - Active equity funds are the core area for measuring the research and investment capabilities of fund companies, with Guotou Ruijin Fund's products showing over 30% returns in the past year, primarily driven by active equity funds [3][6] - Industry-themed funds have outperformed significantly, with Guotou Ruijin Ruiyi Reform A achieving a return of 72.58%, surpassing its benchmark by 62.81% [3][5] - Other notable performers include Guotou Ruijin Industrial Upgrade Two-Year Holding A with a return of 69.17% and Guotou Ruijin Advanced Manufacturing with a return of 50.64%, both also outperforming their benchmarks [3][5] Group 2: Quantitative Products - Active quantitative products aim for sustained excess returns through systematic investment, with Guotou Ruijin Specialized and New Quantitative Stock Selection A achieving a return of 69.46%, outperforming its benchmark by 41.31% [4][6] Group 3: Index Products - Guotou Ruijin Fund has developed a robust index product system to capture beta returns efficiently, with Guotou Ruijin CSI 500 Quantitative Enhancement A returning 29.62% over the past year, outperforming its benchmark [7][9] - The Guotou Ruijin CSI Upstream A also performed well, achieving a return of 25.90% [7][9] Group 4: Diversified Asset Allocation - The company has made significant strides in QDII, commodities, and FOF sectors, with Guotou Ruijin China Value Discovery QDII returning 22.34% and Guotou Ruijin Silver Futures A returning 33.60% [10][11] - Guotou Ruijin Balanced Pension Target Three-Year Holding A achieved a return of 20.98%, contributing to long-term pension investments [11][12] Group 5: Research and Team Development - Guotou Ruijin Fund emphasizes building a professional value creation capability, enhancing its research and investment integration, and fostering a diverse research team through mentorship [13] - The company aims to balance active and passive strategies, as well as domestic and cross-border investments, to effectively respond to investor demands [13]

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure (MFE) Portfolio **Model Construction Idea**: The MFE portfolio aims to maximize the exposure of a single factor while controlling for constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach evaluates the effectiveness of factors under realistic constraints in enhanced index portfolios [56][57][59] **Model Construction Process**: The optimization model is formulated as follows: $ \begin{array}{ll} max & f^{T}w \\ s.t. & s_{l}\leq X(w-w_{b})\leq s_{h} \\ & h_{l}\leq H(w-w_{b})\leq h_{h} \\ & w_{l}\leq w-w_{b}\leq w_{h} \\ & b_{l}\leq B_{b}w\leq b_{h} \\ & 0\leq w\leq l \\ & 1^{T}w=1 \\ & \Sigma|w-w_{0}|\leq to_{h} \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector - **Constraints**: 1. Style exposure deviation (\( X \)): \( s_{l} \) and \( s_{h} \) are the lower and upper bounds for style factor deviation 2. Industry exposure deviation (\( H \)): \( h_{l} \) and \( h_{h} \) are the lower and upper bounds for industry deviation 3. Stock weight deviation (\( w_{l} \) and \( w_{h} \)): Limits on individual stock weight deviation relative to the benchmark 4. Component weight limits (\( b_{l} \) and \( b_{h} \)): Constraints on the weight of benchmark components 5. No short selling and upper limits on stock weights 6. Full investment constraint: \( 1^{T}w=1 \) 7. Turnover constraint: \( \Sigma|w-w_{0}|\leq to_{h} \), where \( w_{0} \) is the previous period's weight [56][57][59] **Model Evaluation**: The model effectively balances factor exposure and practical constraints, ensuring stable returns and avoiding excessive concentration in specific stocks [60] --- Quantitative Factors and Construction Methods - **Factor Name**: Six-Month UMR **Factor Construction Idea**: The six-month UMR factor measures risk-adjusted momentum over a six-month window, capturing medium-term momentum trends [19][8][44] **Factor Construction Process**: - The UMR (Up-Minus-Down Ratio) is calculated as the ratio of upward movements to downward movements in stock prices over a specified period - The six-month UMR specifically uses a six-month window to compute this ratio, adjusted for risk [19][8][44] **Factor Evaluation**: This factor demonstrates strong performance in various index spaces, particularly in the CSI 500 and CSI All Share indices, indicating its effectiveness in capturing medium-term momentum [8][44] - **Factor Name**: Three-Month UMR **Factor Construction Idea**: Similar to the six-month UMR, this factor focuses on shorter-term momentum trends over a three-month window [19][8][44] **Factor Construction Process**: - The three-month UMR is calculated using the same methodology as the six-month UMR but with a three-month window for data aggregation [19][8][44] **Factor Evaluation**: This factor shows consistent performance across multiple indices, including the CSI 500 and CSI All Share indices, making it a reliable short-term momentum indicator [8][44] - **Factor Name**: Pre-Tax Earnings to Total Market Value (EPTTM) **Factor Construction Idea**: This valuation factor evaluates the earnings yield of a stock, providing insights into its relative valuation [19][8][44] **Factor Construction Process**: - EPTTM is calculated as the ratio of pre-tax earnings to the total market value of a stock, with adjustments for rolling time windows (e.g., one year) [19][8][44] **Factor Evaluation**: EPTTM consistently ranks among the top-performing valuation factors, particularly in the CSI 300 and CSI 800 indices, reflecting its robustness in identifying undervalued stocks [8][44] --- Backtesting Results of Models - **MFE Portfolio**: - The MFE portfolio demonstrates strong performance under various constraints, with backtesting results showing significant alpha generation relative to benchmarks like CSI 300, CSI 500, and CSI 1000 [60][61] --- Backtesting Results of Factors - **Six-Month UMR**: - CSI 500: Weekly return of 0.99%, monthly return of 1.65%, annualized return of -4.07% [26] - CSI All Share: Weekly return of 1.23%, monthly return of 1.59%, annualized return of 7.43% [44] - **Three-Month UMR**: - CSI 500: Weekly return of 0.94%, monthly return of 1.31%, annualized return of 0.68% [26] - CSI All Share: Weekly return of 1.02%, monthly return of 1.63%, annualized return of 5.64% [44] - **EPTTM**: - CSI 300: Weekly return of 0.74%, monthly return of 1.42%, annualized return of 3.89% [22] - CSI 800: Weekly return of 1.00%, monthly return of 1.91%, annualized return of 2.87% [30]