Group 1 - The core viewpoint of the article highlights the rise of enhanced index funds as a new growth driver in the asset management market, driven by the dual trends of passive investment and refined active management [1] - Enhanced index funds combine the low cost and transparency of index funds with the stock selection capabilities of quantitative models, aiming for excess returns while controlling tracking errors [1][2] - As of the end of Q2 this year, 83 new enhanced index funds have been established, setting a historical record, with many products achieving positive excess returns compared to benchmarks [1][2] Group 2 - The total scale of passive index funds in China reached 3.55 trillion yuan, growing by 1.42 trillion yuan in just one year, reflecting strong market demand for index tools [2] - Enhanced index funds are positioned as a bridge between passive allocation and active returns, with annualized excess returns for mainstream products typically ranging from 3% to 8% [2][3] - Huashang Fund has developed a comprehensive product matrix covering both broad-based and technology-focused enhanced index funds to meet diverse investor needs [2][3] Group 3 - Huashang Fund's enhanced index family includes products that cover various market segments, such as the Huashang CSI A500 Enhanced Index Fund, which focuses on mid-cap growth [3] - The newly launched Huashang CSI 300 Enhanced Index Fund aims to complete the product layout, catering to both growth-oriented and conservative investors [3] Group 4 - The core competitiveness of enhanced index funds lies in the quantitative strategies employed, with Huashang Fund's quantitative investment team utilizing over 300 factors for stock selection and risk control [4][5] - The team integrates AI technology to enhance data mining and optimize multiple models, showcasing a robust quantitative framework [4] Group 5 - Huashang Fund employs industry rotation strategies based on valuation, market conditions, and trading factors, alongside style rotation strategies to balance growth and value investments [5] - The team is led by experienced fund managers who combine quantitative insights with active management, ensuring a comprehensive investment approach [5][6] Group 6 - Huashang Fund has consistently ranked among the top in the industry for its active management capabilities, with a 147.27% return for its active equity products over the past seven years, placing it third out of 115 [7] - The fund's proactive approach in both equity and fixed income sectors has earned it high ratings from authoritative institutions, including a 5A rating for its comprehensive management [7] Group 7 - The enhanced index family from Huashang Fund represents an innovative practice within the passive investment trend, extending active management capabilities into the quantitative domain [8] - The fund's products are designed to provide investors with tools that balance risk and return, embodying a philosophy of "quantitative wisdom + active management" [8]

Core Viewpoint - The market is currently favoring small-cap stocks, particularly the CSI 2000 Enhanced ETF (159552), which has seen a significant increase in performance and popularity compared to larger indices like the CSI 300 [1][5]. Group 1: Performance of CSI 2000 Enhanced ETF - The CSI 2000 Enhanced ETF (159552) has achieved a year-to-date return of over 43%, with its scale increasing by 31 times, indicating strong investor demand [1][6]. - The fund's performance is significantly higher than its peers, making it the top performer in its category [6]. - The fund's trading volume has reached 15 million shares, reflecting a robust market interest [2]. Group 2: Market Dynamics and Investor Behavior - The margin trading balance is approaching 2 trillion, indicating a surge in leveraged funds targeting small-cap stocks [3]. - Investors are shifting from large-cap blue chips to small-cap stocks due to lower resistance and easier price movements in the latter [3]. - The CSI 2000 index includes many "small giant" companies in trending sectors such as robotics, artificial intelligence, and innovative pharmaceuticals, contributing to its outperformance [5]. Group 3: Investment Strategy and Risks - The use of quantitative models in index enhancement can provide a safety net for the high volatility associated with small-cap stocks [5][6]. - While the CSI 2000 Enhanced ETF has shown impressive returns, the current valuation is at historical highs, suggesting caution for new investors [6].

Core Viewpoint - The investment value of China's core assets is increasingly prominent amid the ongoing high-quality economic development and accelerated industrial transformation in China [1] Group 1: Fund Launch and Management - Huashang Fund launched the Huashang CSI 300 Index Enhanced Fund on August 4, aiming to leverage active management advantages to create excess returns through a quantitative stock selection model [1] - The fund will be managed by Dr. Deng Mo and Dr. Ai Dingfei, both of whom are recognized experts in quantitative investment [4][5] Group 2: Index Characteristics - The CSI 300 Index is a representative core broad-based index of the A-share market, comprising 300 leading companies with high market capitalization and liquidity, reflecting the economic trends in China [1] - As of the second quarter of 2025, the top three sectors in the CSI 300 Index—finance, industry, and information technology—account for 57.6%, indicating a balanced mix of cyclical, defensive, and growth sectors [1] Group 3: Valuation Metrics - The CSI 300 Index's price-to-earnings ratio has decreased from approximately 17.4 times in February 2021 to 13.19 times, placing it at the 70th percentile over the past decade, with a dividend yield close to 2.8% [3] Group 4: Investment Strategy - The fund aims to achieve "index β + quantitative α" dual return potential by combining big data analysis with quantitative models for stock selection [4] - The investment strategy will focus on diversified allocation within a small deviation from the benchmark index to capture excess returns [6] Group 5: Performance and Outlook - Huashang Fund's active equity funds ranked 3rd and 8th in absolute returns over the past 7 and 5 years, respectively, showcasing strong active management capabilities [6] - The current market valuation is considered reasonable, and liquidity remains ample, which is favorable for the performance of multi-factor quantitative models [6]

Quantitative Models and Construction Methods Style Factors 1. **Factor Name**: Beta Factor - **Construction Idea**: Captures the market sensitivity of stocks - **Construction Process**: - Calculate the daily returns of individual stocks and the market index (CSI All Share Index) over the past 252 trading days - Perform an exponentially weighted regression with a half-life of 63 trading days - The regression coefficient is taken as the Beta factor - **Evaluation**: High Beta stocks outperformed low Beta stocks in the recent week, indicating a preference for market-sensitive stocks[15][16] 2. **Factor Name**: Leverage Factor - **Construction Idea**: Measures the financial leverage of companies - **Construction Process**: - Calculate three sub-factors: Market Leverage (MLEV), Debt to Assets (DTOA), and Book Leverage (BLEV) - MLEV = Non-current liabilities / Total market value - DTOA = Total liabilities / Total assets - BLEV = Non-current liabilities / Shareholders' equity - Combine the three sub-factors equally to form the Leverage factor - **Evaluation**: Low leverage companies outperformed high leverage companies, indicating a market preference for financially stable companies[15][16] 3. **Factor Name**: Growth Factor - **Construction Idea**: Measures the growth potential of companies - **Construction Process**: - Calculate two sub-factors: Sales Growth (SGRO) and Earnings Growth (EGRO) - SGRO = Regression slope of past five years' annual sales per share divided by the average sales per share - EGRO = Regression slope of past five years' annual earnings per share divided by the average earnings per share - Combine the two sub-factors equally to form the Growth factor - **Evaluation**: The Growth factor showed a negative return, indicating a decline in market preference for high-growth stocks[15][16] Stock Selection Factors 1. **Factor Name**: Single Quarter ROA - **Construction Idea**: Measures the return on assets for a single quarter - **Construction Process**: - Single Quarter ROA = Net income attributable to parent company / Total assets - **Evaluation**: Performed well in the CSI 300 stock pool over the past week[21][24] 2. **Factor Name**: 240-Day Skewness - **Construction Idea**: Measures the skewness of daily returns over the past 240 trading days - **Construction Process**: - Calculate the skewness of daily returns over the past 240 trading days - **Evaluation**: Performed well in the CSI 300 stock pool over the past week[21][24] 3. **Factor Name**: Single Quarter ROE - **Construction Idea**: Measures the return on equity for a single quarter - **Construction Process**: - Single Quarter ROE = Net income attributable to parent company / Shareholders' equity - **Evaluation**: Performed well in the CSI 300 stock pool over the past week[21][24] Factor Backtesting Results 1. **Beta Factor**: Weekly long-short return: 1.86%, Monthly long-short return: 1.64%[17] 2. **Leverage Factor**: Weekly long-short return: -3.07%, Monthly long-short return: -1.58%[17] 3. **Growth Factor**: Weekly long-short return: -1.73%, Monthly long-short return: -5.13%[17] Stock Selection Factor Backtesting Results 1. **Single Quarter ROA**: Weekly excess return: 0.98%, Monthly excess return: 2.61%, Annual excess return: 9.49%, Ten-year annualized return: 3.69%[22] 2. **240-Day Skewness**: Weekly excess return: 0.75%, Monthly excess return: 2.48%, Annual excess return: 6.40%, Ten-year annualized return: 2.85%[22] 3. **Single Quarter ROE**: Weekly excess return: 0.74%, Monthly excess return: 1.55%, Annual excess return: 8.96%, Ten-year annualized return: 3.46%[22]

Group 1 - The core viewpoint of the article is to track and analyze the performance of various index enhancement portfolios and stock selection factors across different indices, highlighting their excess returns and factor performance [2][3][20]. Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of 0.47% for the week and 9.69% year-to-date [8][24]. - The performance of the Zhongzheng 500 index enhancement portfolio showed an excess return of 0.92% for the week and 10.86% year-to-date [8][26]. - The Zhongzheng 1000 index enhancement portfolio had an excess return of 0.08% for the week and 15.70% year-to-date [8][30]. - The Zhongzheng A500 index enhancement portfolio reported an excess return of 1.00% for the week and 10.95% year-to-date [8][31]. Group 3 - In the HuShen 300 component stocks, factors such as single-season ROA, standardized expected external income, and single-season revenue year-on-year growth performed well [9][11]. - For Zhongzheng 500 component stocks, factors like standardized expected external income, single-season net profit year-on-year growth, and standardized expected external profit showed strong performance [11][12]. - In the Zhongzheng 1000 component stocks, standardized expected external income, standardized expected external profit, and single-season revenue year-on-year growth were notable [11][14]. - The Zhongzheng A500 index component stocks had strong performances in single-season ROA, DELTAROA, and DELTAROE [11][17]. Group 4 - The public fund index enhancement products for HuShen 300 showed a maximum excess return of 1.58% and a minimum of -0.61% for the week, with a median of 0.13% [24]. - The Zhongzheng 500 index enhancement products had a maximum excess return of 1.06% and a minimum of -0.83% for the week, with a median of 0.16% [26]. - The Zhongzheng 1000 index enhancement products reported a maximum excess return of 1.08% and a minimum of -0.54% for the week, with a median of 0.21% [30]. - The Zhongzheng A500 index enhancement products had a maximum excess return of 0.86% and a minimum of -0.58% for the week, with a median of 0.09% [31].

Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure (MFE) Portfolio - **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of single 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 return prediction in the final portfolio[38][39]. - **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, 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 exposure[39]. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation[39]. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation[39]. 4. **Constituent Weight Control**: \( B_b \) is a 0-1 vector indicating benchmark constituents, and \( b_l, b_h \) are the lower and upper bounds for constituent weights[39]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights[39]. 6. **Full Investment**: Ensures the portfolio is fully invested (\( \mathbf{1}^{T}w = 1 \))[40]. - **Implementation**: - Constraints are set monthly, and the MFE portfolio is rebalanced accordingly. - Historical returns are calculated, and transaction costs of 0.3% (double-sided) are deducted to evaluate the portfolio's performance relative to the benchmark[42]. - **Model Evaluation**: The MFE portfolio effectively identifies factors that can predict returns under realistic constraints, making it a robust tool for factor validation[38][39]. --- Quantitative Factors and Construction Methods 1. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings, to capture earnings surprises[15]. - **Factor Construction Process**: $ SUE = \frac{\text{Actual Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}} $ - **Parameters**: - Actual Net Profit: Reported quarterly net profit. - Expected Net Profit: Consensus analyst forecast for the quarter. - Standard Deviation: Variability in analyst forecasts[15]. 2. Factor Name: Delta ROA (DELTAROA) - **Factor Construction Idea**: Tracks the change in return on assets (ROA) compared to the same quarter in the previous year to capture profitability trends[15]. - **Factor Construction Process**: $ \Delta ROA = \text{ROA}_{\text{current quarter}} - \text{ROA}_{\text{same quarter last year}} $ - **Parameters**: - ROA: \( \frac{\text{Net Income} \times 2}{\text{Average Total Assets}} \)[15]. 3. Factor Name: Standardized Unexpected Revenue (SUR) - **Factor Construction Idea**: Measures the deviation of actual revenue from expected revenue, standardized by the standard deviation of expected revenue, to capture revenue surprises[15]. - **Factor Construction Process**: $ SUR = \frac{\text{Actual Revenue} - \text{Expected Revenue}}{\text{Standard Deviation of Expected Revenue}} $ - **Parameters**: - Actual Revenue: Reported quarterly revenue. - Expected Revenue: Consensus analyst forecast for the quarter. - Standard Deviation: Variability in analyst forecasts[15]. --- Factor Backtesting Results 1. **Performance in CSI 300 Universe** - **Top-Performing Factors (1 Week)**: Single-quarter ROA (1.09%), Standardized Unexpected Revenue (0.73%), Single-quarter Revenue Growth (0.71%)[17]. - **Underperforming Factors (1 Week)**: Specificity (-0.93%), 3-Month Reversal (-0.53%), 1-Month Volatility (-0.46%)[17]. 2. **Performance in CSI 500 Universe** - **Top-Performing Factors (1 Week)**: Standardized Unexpected Revenue (1.07%), Single-quarter Net Profit Growth (1.00%), Standardized Unexpected Earnings (0.99%)[19]. - **Underperforming Factors (1 Week)**: 3-Month Volatility (-1.08%), BP (-0.28%), 1-Month Volatility (-1.14%)[19]. 3. **Performance in CSI 1000 Universe** - **Top-Performing Factors (1 Week)**: Standardized Unexpected Revenue (1.07%), Standardized Unexpected Earnings (1.00%), Single-quarter Revenue Growth (0.90%)[21]. - **Underperforming Factors (1 Week)**: 1-Month Volatility (-1.14%), 3-Month Volatility (-1.08%), 3-Month Reversal (-1.02%)[21]. 4. **Performance in CSI A500 Universe** - **Top-Performing Factors (1 Week)**: Single-quarter ROA (1.14%), Delta ROA (1.12%), Delta ROE (1.02%)[23]. - **Underperforming Factors (1 Week)**: Specificity (-0.65%), Non-Liquidity Shock (-0.64%), 1-Month Volatility (-0.62%)[23]. 5. **Performance in Public Fund Heavyweight Index** - **Top-Performing Factors (1 Week)**: Delta ROA (1.12%), Expected PEG (0.94%), Standardized Unexpected Earnings (0.99%)[25]. - **Underperforming Factors (1 Week)**: 3-Month Volatility (-0.60%), 1-Month Volatility (-0.62%), 1-Month Reversal (-0.37%)[25].

Core Viewpoint - The article emphasizes the importance of gathering and summarizing valuable investment knowledge and data-driven insights for better learning and decision-making in investment strategies [1][2]. Group 1: Investment Strategies - The article discusses a promotional event for the "Ding Series Investment Advisory Combination," offering a 50% discount on advisory fees from July 1, 2025, to December 31, 2025, with a cap of 180 yuan per year for larger investments [5]. - It highlights the principle of value investing, referencing Warren Buffett's approach, which focuses on buying companies with strong earnings growth, as a foundation for long-term investment success [7]. - The article outlines six enhancement methods for index investment, including fundamental enhancement and quantitative enhancement, which can increase returns beyond the index's inherent growth [9]. Group 2: Market Analysis - The article presents insights on the current market valuation, indicating that the market remains relatively undervalued, suggesting continued investment in active selection and index enhancement strategies [12]. - It discusses the relationship between index valuation and company earnings growth, noting that recent favorable policies are expected to positively impact earnings growth, leading to a dual boost in valuation and earnings [11]. - The article provides an overview of the Hong Kong technology index, noting its higher long-term returns compared to broader indices, while also highlighting the volatility associated with sector-specific investments [18]. Group 3: Financial Products and Tools - The article introduces a new "Golden Star Rating" and "Bull-Bear Signal Board" for gold assets, providing insights into gold pricing, historical ratings, and its relationship with real interest rates [6]. - It discusses the recent trend of lowering the investment threshold for trusts to 300,000 yuan, making them more accessible for wealth management among ordinary investors [17]. - The article emphasizes the importance of global investment through index funds, suggesting that they provide a diversified approach to capturing opportunities across various markets [14].

Group 1 - The article highlights the increasing frequency of "black swan" events in the A-share market, leading to significant drawdowns in excess returns for public index-enhanced products in 2024, marking the largest historical drawdown [1][4][6] - The "black swan index" has shown a higher proportion of extreme events occurring in 2024 compared to previous years, indicating a substantial increase in the probability of extreme tail risks [1][10][14] Group 2 - The evolution of risk models has transitioned from single-factor to multi-factor approaches, and from explicit to implicit risks, reflecting a deeper understanding of market risks [18][19][21] - Implicit risks are defined as those that change with market conditions and are not fully captured by traditional explicit risk models, making them crucial for comprehensive risk management [46][52] Group 3 - A comprehensive risk control process is proposed, consisting of three stages: preemptive measures, in-process control, and post-event handling, aimed at effectively managing both explicit and implicit risks [60][63] - The introduction of a full-process risk control model has shown to significantly reduce drawdowns and volatility without adversely affecting long-term returns [3][61] Group 4 - The traditional multi-factor index-enhanced model has demonstrated an annualized excess return of 18.77% with a maximum drawdown of 9.68%, while the model incorporating full-process risk control has achieved an annualized excess return of 16.51% with a maximum drawdown of only 4.90% [3][5] - The performance metrics indicate that the full-process risk control model enhances the stability of excess returns while minimizing drawdowns and volatility [3][5][61]

Core Viewpoint - The personal pension fund market is experiencing significant expansion with the introduction of new Y-class fund shares specifically for individual pension investments, increasing the number of index-enhanced funds from 19 to 23, covering various benchmark indices [1][3]. Group 1: Fund Expansion - The new Y-class fund shares for personal pensions were announced by major asset management firms including Guotai Asset Management, Tianhong Fund, and others, enhancing their offerings in index-enhanced funds [2]. - The index-enhanced funds introduced are considered "star" quantitative products managed by experienced teams, indicating a focus on quality and performance [2][4]. Group 2: Performance Metrics - As of the second quarter of this year, several index-enhanced funds have shown significant asset sizes, with Tianhong's fund exceeding 3 billion yuan and Guotai's fund surpassing 2 billion yuan, indicating strong market interest [3]. - Notably, the performance of these funds has been impressive, with some achieving over 10 percentage points of excess returns compared to their benchmark indices over the past year [3]. Group 3: Industry Context - The expansion of personal pension products aligns with the broader implementation of the personal pension system set to roll out nationwide by December 2024, which will include a total of 85 index funds, 19 of which are index-enhanced [3][4]. - The regulatory framework allows for ongoing additions to the list of index funds eligible for personal pensions, ensuring a dynamic and responsive market [4]. Group 4: Investment Strategy - The characteristics of index funds, such as clear benchmark tracking and stable investment styles, make them suitable for long-term pension asset allocation, particularly for individual investors [5][6]. - The focus on balancing tracking error and excess returns is crucial for fund managers, as they aim to provide sustainable long-term returns while adhering to benchmark indices [6].

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Construction Idea**: The MFE portfolio is designed to maximize single-factor exposure while controlling for various real-world constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures the factor's effectiveness under practical constraints [39][40][41] **Construction Process**: The optimization model is formulated as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ - **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 to be solved [39][40] - **Constraints**: - **Style Exposure**: $X$ is the matrix of stock exposures to style factors, $w_b$ is the benchmark weight vector, and $s_l$, $s_h$ are the lower and upper bounds for style factor exposure [40] - **Industry Exposure**: $H$ is the matrix of stock exposures to industries, $h_l$, $h_h$ are the lower and upper bounds for industry exposure [40] - **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for stock weight deviation relative to the benchmark [40] - **Component Weight Control**: $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark, $b_l$, $b_h$ are the lower and upper bounds for component weight control [40] - **No Short Selling**: Ensures non-negative weights and limits individual stock weights [40] - **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [41] **Evaluation**: This model effectively tests factor validity under real-world constraints, ensuring the factor's predictive power in practical portfolio construction [39][40][41] Quantitative Factors and Construction Methods - **Factor Name**: Specificity **Construction Idea**: Measures the uniqueness of stock returns by evaluating the residuals from a Fama-French three-factor regression [16][19][23] **Construction Process**: - Formula: $1 - R^2$ from the Fama-French three-factor regression, where $R^2$ represents the goodness-of-fit of the regression model [16] **Evaluation**: Demonstrates strong performance in multiple sample spaces, indicating its effectiveness in capturing unique stock characteristics [19][23][25] - **Factor Name**: EPTTM Year Percentile **Construction Idea**: Represents the percentile rank of trailing twelve-month earnings-to-price ratio (EPTTM) over the past year [16][19][23] **Construction Process**: - Formula: Percentile rank of $EPTTM = \frac{\text{Net Income (TTM)}}{\text{Market Cap}}$ over the past year [16] **Evaluation**: Performs well in various sample spaces, particularly in growth-oriented indices [19][23][25] - **Factor Name**: Three-Month Reversal **Construction Idea**: Captures short-term price reversal by measuring the return over the past 60 trading days [16][19][23] **Construction Process**: - Formula: $\text{Return}_{60\text{days}} = \frac{\text{Price}_{t} - \text{Price}_{t-60}}{\text{Price}_{t-60}}$ [16] **Evaluation**: Effective in identifying short-term reversal opportunities, especially in volatile indices [19][23][25] Factor Backtesting Results - **Specificity Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 1.18% - Monthly Excess Return: 2.02% - Year-to-Date Excess Return: 4.23% - Historical Annualized Return: 0.51% [19] - **Sample Space**: CSI A500 - Weekly Excess Return: 1.43% - Monthly Excess Return: 2.14% - Year-to-Date Excess Return: 2.71% - Historical Annualized Return: 1.72% [25] - **EPTTM Year Percentile Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 0.54% - Monthly Excess Return: 2.01% - Year-to-Date Excess Return: 6.74% - Historical Annualized Return: 3.26% [19] - **Sample Space**: CSI 500 - Weekly Excess Return: 1.01% - Monthly Excess Return: 1.54% - Year-to-Date Excess Return: 1.90% - Historical Annualized Return: 5.24% [21] - **Three-Month Reversal Factor**: - **Sample Space**: CSI 300 - Weekly Excess Return: 0.49% - Monthly Excess Return: 1.35% - Year-to-Date Excess Return: 4.31% - Historical Annualized Return: 1.13% [19] - **Sample Space**: CSI 1000 - Weekly Excess Return: 1.10% - Monthly Excess Return: 2.15% - Year-to-Date Excess Return: 2.59% - Historical Annualized Return: -0.67% [23] Index Enhancement Portfolio Backtesting Results - **CSI 300 Enhanced Portfolio**: - Weekly Excess Return: 0.78% - Year-to-Date Excess Return: 9.31% [5][14] - **CSI 500 Enhanced Portfolio**: - Weekly Excess Return: -0.52% - Year-to-Date Excess Return: 9.90% [5][14] - **CSI 1000 Enhanced Portfolio**: - Weekly Excess Return: 0.07% - Year-to-Date Excess Return: 15.69% [5][14] - **CSI A500 Enhanced Portfolio**: - Weekly Excess Return: 0.26% - Year-to-Date Excess Return: 9.96% [5][14] Public Fund Index Enhancement Product Performance - **CSI 300 Public Fund Products**: - Weekly Excess Return: Max 1.28%, Min -0.98%, Median 0.12% - Monthly Excess Return: Max 4.10%, Min -0.99%, Median 0.61% - Quarterly Excess Return: Max 5.71%, Min -0.90%, Median 1.52% - Year-to-Date Excess Return: Max 9.84%, Min -0.77%, Median 2.87% [31] - **CSI 500 Public Fund Products**: - Weekly Excess Return: Max 1.41%, Min -1.31%, Median 0.04% - Monthly Excess Return: Max 2.56%, Min -0.60%, Median 0.60% - Quarterly Excess Return: Max 5.51%, Min -0.10%, Median 2.60% - Year-to-Date Excess Return: Max 9.88%, Min -0.77%, Median 4.19% [34] - **CSI 1000 Public Fund Products**: - Weekly Excess Return: Max 0.82%, Min -0.47%, Median 0.15% - Monthly Excess Return: Max 3.55%, Min -0.67%, Median 1.07% - Quarterly Excess Return: Max 7.14%, Min -0.58%, Median 3.21% - Year-to-Date Excess Return: Max 15.34%, Min 0.49%, Median 6.75% [36] - **CSI A500 Public Fund Products**: - Weekly Excess Return: Max 1.16%, Min -0.57%, Median -0.04% - Monthly Excess Return: Max 1.89%, Min -1.55%, Median 0.68% - Quarterly Excess Return: Max 3.76%, Min -1.67%, Median 2.20% [38]