Quantitative Models and Construction Methods 1. Model Name: Optimization Replication - **Model Construction Idea**: Simplify the replication of index returns into an optimization model that minimizes tracking error[31][32] - **Model Construction Process**: 1. Define the return sequence of the portfolio as: $ \tilde{R}_{t} = \Sigma_{i=1}^{M} \widetilde{W}_{i,t} \cdot Y_{i,t} = Y_{t} \cdot \overline{W}_{t} $ where $ \widetilde{W}_{i,t} $ represents the weight of asset $i$ at time $t$, and $Y_{i,t}$ is the return of asset $i$ at time $t$[31] 2. Minimize the tracking error (TE): $ w = arg\,min\;TE $ $ TE = \frac{1}{T} \Sigma_{t=1}^{T} (\tilde{R}_{t} - R_{t})^2 $ where $R_{t}$ is the benchmark return at time $t$[32] 3. Add constraints: - Full investment: $ \Sigma_{i=1}^{N} w_{i} = 1 $ - Non-negativity: $ 0 \leq w_{i} \leq 1 $[33][35] 4. Incorporate style and industry neutrality constraints to reduce overfitting: $ z_{low} \leq \frac{X_{s}^{T}w - X_{s}^{T}\tilde{w}}{s_{b}} \leq z_{up} $ $ w_{low}^{I} \leq X_{I}^{T}w - X_{I}^{T}\bar{w} \leq w_{up}^{I} $[36] 5. Solve the optimization problem to determine the weights of individual stocks[37] 2. Model Name: Pair Trading - **Model Construction Idea**: Identify pairs of stocks or sectors with similar trends and exploit mean-reversion characteristics to generate excess returns[57] - **Model Construction Process**: 1. Identify pairs of stocks or sectors with stable relationships based on quantitative or fundamental analysis 2. Overweight weaker-performing assets and underweight stronger-performing ones 3. Capture excess returns when the price spread reverts to its mean, particularly during events like macroeconomic data releases or seasonal effects[57] --- Model Backtesting Results 1. Optimization Replication - **Annualized Return**: 5.76% - **Excess Return**: 3.74% - **Sharpe Ratio**: 0.41 - **Excess Drawdown**: 3.86% - **Excess Win Rate**: 72% - **Information Ratio (IR)**: 1.51 - **Tracking Error**: 2.22%[23][27] --- Quantitative Factors and Construction Methods 1. Factor Name: Quantitative Multi-Factor - **Factor Construction Idea**: Select long-term effective alpha factors and construct a multi-factor portfolio to achieve stable excess returns[46] - **Factor Construction Process**: 1. Define the multi-factor model: $ \begin{bmatrix} r_{1} \\ r_{2} \\ \vdots \\ r_{n} \end{bmatrix} = \begin{bmatrix} x_{11} \\ x_{21} \\ \vdots \\ x_{n1} \end{bmatrix} f_{1} + \begin{bmatrix} x_{12} \\ x_{22} \\ \vdots \\ x_{n2} \end{bmatrix} f_{2} + \cdots + \begin{bmatrix} x_{1m} \\ x_{2m} \\ \vdots \\ x_{nm} \end{bmatrix} f_{m} + \begin{bmatrix} u_{1} \\ u_{2} \\ \vdots \\ u_{n} \end{bmatrix} $ where $r_{i}$ is the excess return of stock $i$, $x_{ij}$ is the exposure of stock $i$ to factor $j$, and $f_{j}$ is the return of factor $j$[46] 2. Select effective single factors, such as volatility, short-selling intention, and liquidity, based on theoretical direction and empirical validation[48] 3. Construct a multi-factor portfolio by combining these factors and optimizing weights[47] 2. Factor Name: Negative Enhancement - **Factor Construction Idea**: Underweight stocks expected to underperform or incur losses, leveraging negative factors or events to generate stable excess returns[56] - **Factor Construction Process**: 1. Identify stocks with negative attributes, such as analyst downgrades, equity pledges, or poor earnings reports 2. Underweight these stocks in the portfolio to reduce potential losses and achieve alpha[56] 3. Factor Name: Machine Learning-Based Alpha - **Factor Construction Idea**: Use deep learning models like TCN (Temporal Convolutional Networks) to extract complex and effective alpha factors from price and volume data[52] - **Factor Construction Process**: 1. Train neural networks on historical price and volume data to identify patterns and relationships 2. Generate alpha factors that outperform traditional genetic programming methods in terms of depth and complexity[51][52] --- Factor Backtesting Results 1. Quantitative Multi-Factor - **Excess Return**: Stable across long-term horizons - **Key Factors**: Volatility, short-selling intention, liquidity, and local pricing[48] 2. Negative Enhancement - **Excess Return**: Achieved through underweighting stocks with negative attributes or events[56] 3. Machine Learning-Based Alpha - **Performance**: Demonstrated superior results compared to traditional factor generation methods[52] --- Index Enhancement Methods 1. IPO Enhancement - **Annualized Return**: 2.13% in 2025 - **Segment Performance**: Sci-Tech Innovation Board (4.34%), ChiNext (2.52%)[67][68] 2. Stock Index Futures - **Annualized Basis Spread**: - CSI 300: -6.75% - SSE 50: -2.48% - CSI 500: -13.60% - CSI 1000: -18.09%[72][73] 3. Block Trades - **Median Discount Rate**: 5.38% (2017-2025), 8.23% in 2025[74][75] 4. Private Placements - **Median Discount Rate**: 14.55% (2017-2025), 11.87% in 2025[77]

Core Viewpoint - The recent self-purchase actions by public fund companies, such as 富国基金 and 摩根基金, reflect confidence in their investment management capabilities and serve as a positive signal to investors [2][9]. Group 1: 富国基金's Self-Purchase - On May 6, 富国基金 announced a self-purchase of at least 25 million yuan for its 富国均衡投资混合型证券投资基金, with the company and senior management contributing at least 20 million yuan and the proposed fund manager contributing at least 5 million yuan [3][4]. - The proposed fund manager, 范妍, has a strong background in investment analysis and has achieved a return of 1.40% since October 2022, with a net value increase of 2.71% this year [4]. - The fund's scale increased from 520 million yuan at the end of Q3 2022 to 7.461 billion yuan at the end of Q1 2023, representing a growth of over 13 times [4]. Group 2: 摩根基金's Self-Purchase - On April 30, 摩根基金 announced a self-purchase of at least 54 million yuan for its new equity public fund, with 30 million yuan allocated to the 摩根中证A500增强策略ETF [5]. - The 摩根中证A500增强策略ETF is designed to enhance returns through a quantitative multi-factor model and aims to meet the dual needs of flexible trading and return enhancement for investors [5]. - The total scale of passive index and enhanced index funds linked to the 中证A500 index reached 256.3 billion yuan by March 31, 2025, indicating its growing significance in the A-share market [6]. Group 3: Overall Market Self-Purchase Trends - As of May 6, 114 fund companies have collectively self-purchased over 108 billion yuan in their funds this year, with money market funds accounting for the majority at approximately 103.79 billion yuan [8]. - In April alone, several fund companies, including 安信 and 博时, engaged in self-purchases totaling 4.94 billion yuan, demonstrating a widespread trend among fund managers to invest in their own products [8]. - The self-purchase behavior of public funds not only stabilizes market confidence but also conveys a message of shared risk and reward between fund managers and investors [9].