Core Viewpoint - The article emphasizes the potential of Index Enhanced ETFs, which combine passive tracking with active management strategies to seek excess returns beyond traditional broad-based ETFs [2][5]. Summary by Sections What is Index Enhanced ETF? - Index Enhanced ETFs are designed to closely track broad market indices like the CSI 300 while employing quantitative or stock selection strategies to achieve excess returns. They offer a dual benefit of following the index and aiming for additional profits [5]. How to Find Index Enhanced ETFs? - Investors can easily locate Index Enhanced ETFs using the Go-Goal App or the ETF search mini-program. By selecting the "Index Enhanced" option in the ETF screening feature, users can access a list of 51 Index Enhanced ETFs available in the market, with 50 already listed [6][9]. ETF Screening Features - The Go-Goal platform provides a comprehensive ETF screening tool with eight categories of features, allowing investors to filter based on their specific needs: 1. Asset Class: Domestic stocks, foreign stocks, domestic bonds, commodities, etc. 2. Major Sectors: Public utilities, upstream resources, midstream materials, etc. 3. Style Characteristics: High dividend, growth, value, low valuation, quality, etc. 4. Trading Tags: T+0, Stock Connect, margin trading, etc. 5. Management Method: Full replication, index enhancement. 6. Investment Regions: China, Hong Kong, USA, Japan, Germany, France, etc. 7. Index Type: Core index, secondary core index, other indices. 8. Business Nature: State-owned enterprises, private enterprises [9].

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio recorded an excess return of -0.17% for the week and 16.49% year-to-date [1][6] - The CSI 500 index enhanced portfolio achieved an excess return of 0.26% for the week and 8.94% year-to-date [1][6] - The CSI 1000 index enhanced portfolio had an excess return of 0.91% for the week and 17.72% year-to-date [1][6] - The CSI A500 index enhanced portfolio reported an excess return of -0.21% for the week and 9.06% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as quarterly earnings surprises, year-on-year revenue growth, and quarterly ROE performed well [1][9] - In the CSI 500 component stocks, factors like three-month turnover, quarterly revenue year-on-year growth, and EPTTM one-year percentile showed strong performance [1][9] - For the CSI 1000 component stocks, factors including three-month institutional coverage, quarterly ROE, and executive compensation performed well [1][9] - In the CSI A500 index component stocks, factors such as quarterly revenue year-on-year growth, EPTTM one-year percentile, and quarterly ROE were notable [1][9] - Among publicly offered fund heavy stocks, factors like executive compensation, quarterly ROE, and three-month institutional coverage performed well [1][9] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 0.91%, a minimum of -1.54%, and a median of -0.17% for the week [1][22] - The CSI 500 index enhanced products recorded a maximum excess return of 1.63%, a minimum of -1.35%, and a median of -0.01% for the week [1][23] - The CSI 1000 index enhanced products achieved a maximum excess return of 1.66%, a minimum of -0.37%, and a median of 0.44% for the week [1][24] - The CSI A500 index enhanced products had a maximum excess return of 0.53%, a minimum of -0.76%, and a median of -0.11% for the week [1][26]

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices. The construction process includes three main components: return prediction, risk control, and portfolio optimization[12][14][42] - The MFE (Maximized Factor Exposure) portfolio is used to test the effectiveness of single factors under real-world constraints. The optimization model maximizes single-factor exposure while controlling for industry exposure, style exposure, stock weight deviation, and turnover rate. The objective function is defined as: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ Here, `f` represents factor values, `w` is the stock weight vector, and constraints include style factor exposure (`X`), industry exposure (`H`), stock weight deviation (`w`), and component stock weight control (`B_b`). The weights are normalized to sum to 1[42][43][44] - The MFE portfolio construction process involves setting constraints, optimizing the portfolio at the end of each month, and calculating historical returns during the backtesting period. Transaction costs of 0.3% are deducted on both sides to compute risk-return statistics relative to the benchmark[46] - The report monitors the performance of 30+ factors across different sample spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy-holding indices. Factors are categorized into valuation, reversal, growth, profitability, liquidity, governance, and analyst-related dimensions. Examples include BP (Book-to-Price), ROE (Return on Equity), and momentum factors[15][16][17] - Factor performance varies across sample spaces. For example, in the CSI 300 space, factors such as single-quarter ROE, single-quarter revenue growth, and single-quarter surprise magnitude performed well recently, while factors like BP and expected net profit growth performed poorly[18][19] - In the CSI 500 space, factors such as three-month turnover, single-quarter revenue growth, and EPTTM percentile performed well recently, while factors like one-year momentum and standardized unexpected income performed poorly[20][21] - In the CSI 1000 space, factors such as three-month institutional coverage, single-quarter ROE, and executive compensation performed well recently, while factors like one-year momentum and DELTAROA performed poorly[22][23] - In the CSI A500 space, factors such as single-quarter revenue growth, EPTTM percentile, and single-quarter ROE performed well recently, while factors like one-year momentum and DELTAROE performed poorly[24][25] - In the public fund heavy-holding index space, factors such as executive compensation, single-quarter ROE, and three-month institutional coverage performed well recently, while factors like one-year momentum and expected EPTTM performed poorly[26][27] - The report tracks the performance of public fund index enhancement products for CSI 300, CSI 500, CSI 1000, and CSI A500 indices. For example, CSI 300 index enhancement products had a maximum excess return of 0.91% and a minimum of -1.54% in the past week, with a median of -0.17%[28][32] - CSI 500 index enhancement products had a maximum excess return of 1.63% and a minimum of -1.35% in the past week, with a median of -0.01%[35] - CSI 1000 index enhancement products had a maximum excess return of 1.66% and a minimum of -0.37% in the past week, with a median of 0.44%[38] - CSI A500 index enhancement products had a maximum excess return of 0.53% and a minimum of -0.76% in the past week, with a median of -0.11%[41]

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to maximize the exposure of a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures that the factor's predictive power is tested under realistic constraints, making it more applicable in actual portfolio construction [42][43][44] **Model Construction Process**: The MFE portfolio is constructed using the following optimization model: $ \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} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, \( f^{T}w \) is the weighted exposure of the portfolio to the factor, and \( w \) is the stock weight vector - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style factor exposure 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation 4. **Constituent Stock Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark constituent, and \( b_l, b_h \) are the lower and upper bounds for constituent stock weights 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \) 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T}w = 1 \) - **Implementation**: 1. Set constraints for style, industry, and stock weights 2. Construct MFE portfolios for each factor at the end of each month 3. Backtest the MFE portfolios, calculate historical returns, and adjust for transaction costs (0.3% on both sides) [42][43][46] **Model Evaluation**: The MFE portfolio approach is effective in testing factor validity under realistic constraints, ensuring that factors deemed "effective" can contribute to actual portfolio performance [42][43] Quantitative Factors and Construction Methods - **Factor Name**: Single-Quarter ROE **Factor Construction Idea**: Measures the return on equity for a single quarter to capture profitability trends [17] **Factor Construction Process**: $ \text{Single-Quarter ROE} = \frac{\text{Net Income (Quarterly)} \times 2}{\text{Average Shareholders' Equity}} $ - **Net Income (Quarterly)**: Quarterly net income attributable to shareholders - **Average Shareholders' Equity**: Average of beginning and ending equity for the quarter [17] - **Factor Name**: Single-Quarter Revenue Growth (YoY) **Factor Construction Idea**: Tracks the year-over-year growth in quarterly revenue to identify growth trends [17] **Factor Construction Process**: $ \text{Single-Quarter Revenue Growth (YoY)} = \frac{\text{Revenue (Current Quarter)} - \text{Revenue (Same Quarter Last Year)}}{\text{Revenue (Same Quarter Last Year)}} $ [17] - **Factor Name**: Analyst Coverage (3-Month) **Factor Construction Idea**: Measures the number of analysts covering a stock over the past three months to gauge market attention [17] **Factor Construction Process**: $ \text{3-Month Analyst Coverage} = \text{Number of Analysts Covering the Stock in the Last 3 Months} $ [17] Factor Backtesting Results - **Single-Quarter ROE**: - **CSI 300**: Weekly excess return: 0.42%, monthly: 2.94%, YTD: 15.41%, historical annualized: 4.92% [19] - **CSI 500**: Weekly excess return: 0.47%, monthly: 0.89%, YTD: 4.43%, historical annualized: 5.85% [21] - **CSI 1000**: Weekly excess return: 1.20%, monthly: 1.70%, YTD: -0.61%, historical annualized: 7.62% [23] - **CSI A500**: Weekly excess return: 0.30%, monthly: 1.68%, YTD: 13.78%, historical annualized: 3.35% [25] - **Single-Quarter Revenue Growth (YoY)**: - **CSI 300**: Weekly excess return: 0.48%, monthly: 2.34%, YTD: 17.35%, historical annualized: 4.94% [19] - **CSI 500**: Weekly excess return: 1.28%, monthly: 2.58%, YTD: 15.18%, historical annualized: 3.81% [21] - **CSI 1000**: Weekly excess return: 0.69%, monthly: 2.73%, YTD: 15.73%, historical annualized: 5.17% [23] - **CSI A500**: Weekly excess return: 0.47%, monthly: 1.15%, YTD: 15.65%, historical annualized: 2.96% [25] - **3-Month Analyst Coverage**: - **CSI 300**: Weekly excess return: 0.17%, monthly: 0.90%, YTD: 10.33%, historical annualized: 3.07% [19] - **CSI 500**: Weekly excess return: 0.29%, monthly: 0.07%, YTD: 4.10%, historical annualized: 5.56% [21] - **CSI 1000**: Weekly excess return: 1.30%, monthly: 0.52%, YTD: 5.98%, historical annualized: 7.22% [23] - **CSI A500**: Weekly excess return: -0.21%, monthly: 0.97%, YTD: 8.12%, historical annualized: 3.93% [25]

Summary of Conference Call Records Industry or Company Involved - The discussion primarily revolves around the **Chinese active investment fund industry** and its comparison with the **U.S. active investment fund industry**. Core Points and Arguments 1. **Alpha Generation in China**: Chinese active fund managers demonstrate stronger alpha generation capabilities over the long term, especially in volatile market conditions, achieving significant excess returns. This year, the median return of many public sector active funds exceeded 30 percentage points [1][5][11]. 2. **Market Opportunities**: The Chinese market offers more opportunities for excess returns compared to the U.S. market, attributed to differences in index composition and the emergence of new industries such as robotics, innovative pharmaceuticals, new energy, and AI during China's economic transition [1][4][9]. 3. **Benchmark Selection**: Under the new regulatory framework, it is essential to choose a representative broad-based index that aligns with the investment style, and to regularly compare performance against this benchmark to ensure transparency and accuracy [1][6][18]. 4. **Performance of Chinese Active Funds**: Chinese active public funds have performed exceptionally well this year, with stock-type public funds rising over 20% since the peak on October 8 of the previous year. The proportion of equity public funds outperforming the CSI 300 index reached 70%, a historical high [1][13][14]. 5. **Comparison with U.S. Active Funds**: U.S. active funds are increasingly moving towards passive strategies due to the difficulty of beating indices, with only 27% of active funds outperforming the S&P 500. In contrast, over 90% of Chinese products have historically outperformed their passive counterparts [2][4][18]. 6. **Investment Environment**: Active investment thrives in volatile market environments, where selective stock picking and industry allocation can yield significant excess returns. The outlook for Chinese active investment remains positive as skilled fund managers are expected to continue outperforming market benchmarks [5][17]. 7. **Sector Performance**: Key sectors that have shown strong performance this year include electronics, new energy, communications, and pharmaceuticals, indicating a recovery in the active investment landscape [15][14]. 8. **Investment Strategy Recommendations**: Different investment styles should adopt specific strategies: - **Balanced**: Prefer broad-based indices like CSI 300 or A500. - **Growth**: Opt for growth-oriented indices such as CSI 300 Growth. - **Value and Dividend**: Choose broad-based indices rather than specialized value indices. - **Industry-Specific**: Match benchmarks to specific sectors of interest [29]. Other Important but Possibly Overlooked Content 1. **Impact of Economic Cycles**: The past few years saw a "barbell" investment strategy due to macroeconomic downturns, but the current environment is different, with many industries entering a harvest phase, leading to clearer investment signals [16]. 2. **Benchmark Performance**: The performance of benchmarks like the CSI 300 has been relatively weak compared to the S&P 500, but Chinese fund managers have shown a greater ability to generate alpha over the long term [8][20]. 3. **Investor Behavior**: The shift towards passive investment in the U.S. is influenced by historical financial crises that made investors wary of high volatility risks, leading to a preference for more stable investment strategies [2][10].

Group 1 - The core viewpoint of the article is to track and analyze the performance of various index enhancement portfolios and the factors influencing stock selection across different indices [1][2][3][17]. Group 2 - The performance of the CSI 300 index enhancement portfolio showed an excess return of -0.65% for the week and 16.53% year-to-date [5][21]. - The CSI 500 index enhancement portfolio had an excess return of -0.37% for the week and 8.50% year-to-date [5][23]. - The CSI 1000 index enhancement portfolio recorded an excess return of -0.53% for the week and 16.52% year-to-date [5][26]. - The CSI A500 index enhancement portfolio achieved an excess return of 0.02% for the week and 9.22% year-to-date [5][27]. Group 3 - In the CSI 300 component stocks, factors such as one-year momentum, quarterly revenue growth year-on-year, and three-month institutional coverage performed well [6][8]. - In the CSI 500 component stocks, factors like executive compensation, standardized expected non-operating income, and quarterly revenue growth year-on-year showed strong performance [6][10]. - For the CSI 1000 component stocks, factors such as expected PEG, standardized expected non-operating income, and three-month institutional coverage performed well [6][12]. - In the CSI A500 index component stocks, factors like executive compensation, three-month institutional coverage, and quarterly revenue growth year-on-year were notable [6][14]. Group 4 - The public fund index enhancement products for the CSI 300 had a maximum excess return of 1.16% and a minimum of -1.26% for the week, with a median of -0.17% [21][19]. - The CSI 500 public fund index enhancement products had a maximum excess return of 1.09% and a minimum of -1.70% for the week, with a median of -0.25% [23][20]. - The CSI 1000 public fund index enhancement products recorded a maximum excess return of 0.96% and a minimum of -1.05% for the week, with a median of -0.08% [26][24]. - The CSI A500 public fund index enhancement products achieved a maximum excess return of 0.77% and a minimum of -0.96% for the week, with a median of -0.07% [27][25].

Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of individual factors under realistic constraints, such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures that the factors deemed "effective" can genuinely contribute to the portfolio's predictive power in real-world scenarios [39][40]. - **Model Construction Process**: - The optimization model maximizes single-factor exposure while adhering to constraints such as style and industry neutrality, stock weight limits, and turnover control. - The objective function is expressed as: $ \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} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Explanation**: - \( f \): Factor values - \( w \): Stock weight vector - \( X \): Style factor exposure matrix - \( H \): Industry exposure matrix - \( w_b \): Benchmark stock weights - \( s_l, s_h \): Lower and upper bounds for style exposure - \( h_l, h_h \): Lower and upper bounds for industry exposure - \( w_l, w_h \): Lower and upper bounds for stock weight deviation - \( b_l, b_h \): Lower and upper bounds for benchmark stock weight proportions [39][40] - The process involves: 1. Setting constraints for style, industry, and stock weight deviations 2. Constructing the MFE portfolio at the end of each month 3. Backtesting the portfolio with historical data, accounting for transaction costs [41][43] - **Model Evaluation**: The MFE model is effective in testing factor performance under realistic constraints, ensuring that selected factors contribute to portfolio returns in practical scenarios [39][40] --- Factor Construction and Methods 1. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: SUE measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings. It captures the market's reaction to earnings surprises [17]. - **Factor Construction Process**: - Formula: $ SUE = \frac{(Actual\ Net\ Profit - Expected\ Net\ Profit)}{Standard\ Deviation\ of\ Expected\ Net\ Profit} $ - Parameters: - Actual Net Profit: Reported earnings for the quarter - Expected Net Profit: Consensus analyst estimates for the quarter - Standard Deviation of Expected Net Profit: Variability in analyst estimates [17] 2. Factor Name: Momentum (1-Year Momentum) - **Factor Construction Idea**: Momentum captures the tendency of stocks with strong past performance to continue outperforming in the near term [17]. - **Factor Construction Process**: - Formula: $ Momentum = \text{Cumulative Return over the Past Year (Excluding the Most Recent Month)} $ - Parameters: - Cumulative Return: Total return over the specified period, excluding the most recent month to avoid short-term reversal effects [17] 3. Factor Name: Single-Quarter Revenue Growth (YoY) - **Factor Construction Idea**: This factor measures the year-over-year growth in quarterly revenue, reflecting a company's growth potential [17]. - **Factor Construction Process**: - Formula: $ Revenue\ Growth = \frac{(Current\ Quarter\ Revenue - Revenue\ from\ Same\ Quarter\ Last\ Year)}{Revenue\ from\ Same\ Quarter\ Last\ Year} $ - Parameters: - Current Quarter Revenue: Revenue reported for the current quarter - Revenue from Same Quarter Last Year: Revenue reported for the same quarter in the previous year [17] --- Factor Backtesting Results 1. Factor: 1-Year Momentum - **Performance**: - **CSI 300 Universe**: Weekly excess return of 0.67%, monthly excess return of 3.06%, annualized historical return of 2.70% [19] - **CSI 500 Universe**: Weekly excess return of 0.92%, monthly excess return of 0.21%, annualized historical return of 3.07% [21] - **CSI 1000 Universe**: Weekly excess return of -0.27%, monthly excess return of -2.23%, annualized historical return of -0.46% [23] 2. Factor: Single-Quarter Revenue Growth (YoY) - **Performance**: - **CSI 300 Universe**: Weekly excess return of 0.66%, monthly excess return of 4.36%, annualized historical return of 4.93% [19] - **CSI 500 Universe**: Weekly excess return of 1.05%, monthly excess return of 2.95%, annualized historical return of 3.70% [21] - **CSI 1000 Universe**: Weekly excess return of -0.16%, monthly excess return of 4.94%, annualized historical return of 5.11% [23] 3. Factor: Standardized Unexpected Earnings (SUE) - **Performance**: - **CSI 300 Universe**: Weekly excess return of 0.02%, monthly excess return of 1.49%, annualized historical return of 3.98% [19] - **CSI 500 Universe**: Weekly excess return of 0.35%, monthly excess return of 0.22%, annualized historical return of 9.14% [21] - **CSI 1000 Universe**: Weekly excess return of -1.37%, monthly excess return of 0.77%, annualized historical return of 10.44% [23] --- Model Backtesting Results 1. CSI 300 Enhanced Portfolio - Weekly excess return: -0.65% - Year-to-date excess return: 16.53% [5][14] 2. CSI 500 Enhanced Portfolio - Weekly excess return: -0.37% - Year-to-date excess return: 8.50% [5][14] 3. CSI 1000 Enhanced Portfolio - Weekly excess return: -0.53% - Year-to-date excess return: 16.52% [5][14] 4. CSI A500 Enhanced Portfolio - Weekly excess return: 0.02% - Year-to-date excess return: 9.22% [5][14]

Group 1 - The A-share market has been recovering this year, leading investors to focus on index products with clear strategies and stable styles, particularly enhanced index funds [1] - The CSI A500 Index is gaining attention as a benchmark for enhanced strategy products due to its scientific compilation, industry balance, and historically low valuation [1][3] - The CSI A500 Index is considered a "future-oriented" benchmark, covering many emerging industries and growth-oriented companies, showing strong long-term return potential [1][3] Group 2 - The enhanced index products aim to achieve excess returns while controlling tracking errors, utilizing quantitative models and industry rotation [2] - As of September 17, the National Gold CSI A500 Enhanced A fund achieved a year-to-date return of 27.50%, with an excess return of 7.74%, ranking second among 57 similar products [2] - National Gold Fund has been conducting quantitative live investment since 2016 and entered the public enhanced index product field in November 2022, focusing on machine learning and multi-dimensional information mining [2] Group 3 - The CSI A500 Index is deemed suitable for quantitative enhanced products due to its industry diversification and strong representation of constituent stocks [3] - In uncertain market conditions, broad-based index funds are highlighted for their risk diversification and opportunity capture, with the CSI A500 Index showing long-term allocation value [3]

Core Viewpoint - Index-enhanced funds are designed to provide a balance between the ease of index investing and the potential for higher returns than the market average [1] Group 1: Mechanism of Index-Enhanced Funds - Index-enhanced funds aim to achieve returns that exceed a specific index through a combination of active management and quantitative strategies, seeking to outperform the index by losing less in downturns and gaining more in upturns [2] - The returns of index-enhanced funds can be divided into two components: β returns (market returns) and α returns (excess returns) [3] Group 2: Performance Data - Historical data indicates that index-enhanced products have consistently outperformed the index, achieving significant excess returns over time [4] - From 2010 to 2024, the average return of the CSI 300 enhanced funds has consistently surpassed that of the CSI 300 index, with a cumulative excess return exceeding 67% [5][6] Group 3: Market Context - The potential for obtaining excess returns in the A-share market is greater compared to overseas markets, primarily due to a higher proportion of individual investors in China, leading to more significant valuation fluctuations [7] - Given the current market conditions, index-enhanced products may be more suitable for ordinary investors seeking excess returns compared to passive index products [7]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.61% this week and 17.47% year-to-date [1][6] - The CSI 500 index enhanced portfolio recorded an excess return of -0.85% this week and 8.97% year-to-date [1][6] - The CSI 1000 index enhanced portfolio had an excess return of -0.05% this week and 17.24% year-to-date [1][6] - The CSI A500 index enhanced portfolio reported an excess return of -0.55% this week and 9.19% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month institutional coverage, one-month volatility, and one-year momentum performed well [1][7] - In the CSI 500 component stocks, one-year momentum, expected net profit month-on-month, and single-quarter revenue year-on-year growth showed strong performance [1][7] - In the CSI 1000 component stocks, single-quarter EP, three-month earnings adjustments, and single-quarter ROE were notable factors [1][7] - In the CSI A500 index component stocks, one-year momentum, standardized expected external income, and single-quarter revenue year-on-year growth performed well [1][7] - Among public fund heavy stocks, one-year momentum, single-quarter revenue year-on-year growth, and expected net profit month-on-month were strong factors [1][7] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 2.13%, a minimum of -1.11%, and a median of 0.05% this week [1][20] - The CSI 500 index enhanced products had a maximum excess return of 0.44%, a minimum of -1.86%, and a median of -0.42% this week [1][22] - The CSI 1000 index enhanced products had a maximum excess return of 0.63%, a minimum of -1.37%, and a median of 0.03% this week [1][26] - The CSI A500 index enhanced products had a maximum excess return of 0.84%, a minimum of -0.79%, and a median of -0.02% this week [1][27]