Quantitative Models and Construction Methods 1. Model Name: Guosen Quantitative Index Enhanced Portfolio - **Model Construction Idea**: The model aims to construct enhanced portfolios benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500, with the goal of consistently outperforming their respective benchmarks [10][11]. - **Model Construction Process**: 1. **Revenue Prediction**: Predict stock returns using multiple factors. 2. **Risk Control**: Apply constraints on industry exposure, style exposure, stock weight deviation, and turnover rate. 3. **Portfolio Optimization**: Optimize the portfolio to maximize single-factor exposure while adhering to constraints. The optimization model is 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, \( w \) is the stock weight vector, and \( f^{T}w \) is the weighted exposure to the factor. - **Constraints**: - **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. - **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. - **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. - **Component Stock Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark component, and \( b_l, b_h \) are the lower and upper bounds for component stock weight. - **No Short Selling**: Ensure non-negative weights and limit individual stock weights. - **Full Investment**: Ensure the portfolio is fully invested with weights summing to 1 [40][41][42]. 4. **Backtesting**: Rebalance the portfolio monthly, calculate historical returns, and evaluate performance metrics such as excess returns and risk statistics [44]. 2. Model Name: Public Fund Heavyweight Index - **Model Construction Idea**: Construct an index based on the holdings of public funds to evaluate factor performance under "institutional style" [42][43]. - **Model Construction Process**: 1. **Sample Selection**: Include ordinary equity funds and partial equity hybrid funds with a minimum size of 50 million RMB and at least six months of listing history. Exclude recently transformed funds or those with insufficient data. 2. **Data Collection**: Use fund periodic reports (annual, semi-annual, or quarterly) to gather holding information. 3. **Weight Calculation**: Average the stock weights across eligible funds. 4. **Index Construction**: Sort stocks by weight in descending order and select those accounting for 90% of cumulative weight to form the index [43]. --- Model Backtesting Results 1. Guosen Quantitative Index Enhanced Portfolio - **CSI 300 Enhanced Portfolio**: - Weekly excess return: -0.71% - Year-to-date excess return: 16.74% [13] - **CSI 500 Enhanced Portfolio**: - Weekly excess return: 0.12% - Year-to-date excess return: 6.85% [13] - **CSI 1000 Enhanced Portfolio**: - Weekly excess return: -0.94% - Year-to-date excess return: 14.08% [13] - **CSI A500 Enhanced Portfolio**: - Weekly excess return: -1.37% - Year-to-date excess return: 7.55% [13] 2. Public Fund Heavyweight Index - **CSI 300 Index Enhanced Products**: - Weekly excess return: Max 0.70%, Min -1.26%, Median 0.09% - Year-to-date excess return: Max 9.92%, Min -4.53%, Median 2.58% [31] - **CSI 500 Index Enhanced Products**: - Weekly excess return: Max 1.17%, Min -1.13%, Median 0.11% - Year-to-date excess return: Max 13.14%, Min -9.17%, Median 3.94% [33] - **CSI 1000 Index Enhanced Products**: - Weekly excess return: Max 0.89%, Min -1.38%, Median -0.05% - Year-to-date excess return: Max 19.12%, Min -1.84%, Median 8.24% [36] - **CSI A500 Index Enhanced Products**: - Weekly excess return: Max 0.71%, Min -0.86%, Median -0.04% - Year-to-date excess return: Max 2.67%, Min -4.14%, Median -0.76% [39] --- Quantitative Factors and Construction Methods 1. Factor Name: Maximized Factor Exposure (MFE) - **Factor Construction Idea**: Evaluate factor effectiveness under real-world constraints by maximizing single-factor exposure in a portfolio [40][41]. - **Factor Construction Process**: 1. Define constraints for style exposure, industry exposure, stock weight deviation, and component stock weight. 2. Optimize the portfolio to maximize single-factor exposure while adhering to constraints. 3. Rebalance monthly and calculate historical returns [40][41][44]. 2. Factor Name: Public Fund Heavyweight Factors - **Factor Construction Idea**: Test factor performance in the public fund heavyweight index to reflect institutional preferences [42][43]. - **Factor Construction Process**: 1. Use public fund holdings to construct the index. 2. Evaluate factor performance within this index using metrics such as excess returns and risk-adjusted returns [42][43]. --- Factor Backtesting Results 1. Maximized Factor Exposure (MFE) - **CSI 300 Sample Space**: - Best-performing factors (weekly): One-month volatility (0.83%), one-month turnover (0.68%), three-month volatility (0.65%) - Worst-performing factors (weekly): Single-quarter profit growth (-0.26%), three-month institutional coverage (-0.24%), one-year momentum (-0.24%) [18] - **CSI 500 Sample Space**: - Best-performing factors (weekly): Three-month institutional coverage (1.09%), one-month reversal (1.01%), three-month reversal (0.99%) - Worst-performing factors (weekly): Standardized unexpected earnings (-1.00%), DELTAROA (-0.81%), DELTAROE (-0.81%) [20] - **CSI 1000 Sample Space**: - Best-performing factors (weekly): One-month turnover (1.08%), three-month institutional coverage (1.06%), single-quarter ROA (1.04%) - Worst-performing factors (weekly): Single-quarter SP (-1.29%), expected PEG (-1.25%), SPTTM (-1.22%) [22] - **CSI A500 Sample Space**: - Best-performing factors (weekly): One-month turnover (0.82%), three-month turnover (0.75%), one-month volatility (0.74%) - Worst-performing factors (weekly): Expected net profit QoQ (-0.91%), single-quarter net profit growth (-0.61%), expected PEG (-0.41%) [24] - **Public Fund Heavyweight Index**: - Best-performing factors (weekly): One-month volatility (1.32%), one-month turnover (1.23%), three-month turnover (0.89%) - Worst-performing factors (weekly): Single-quarter revenue growth (-0.89%), single-quarter profit growth (-0.88%), single-quarter ROE (-0.81%) [26]

Core Viewpoint - The article discusses several noteworthy fund managers and their performance, highlighting their unique investment styles and the potential for future tracking by investors [4]. Group 1: Yang Shijin - Xingquan Multi-Dimensional Value - Yang Shijin has been managing Xingquan Multi-Dimensional Value since July 16, 2021, demonstrating strong investment capabilities with an 18.02% increase in 2021 despite market downturns [5][6]. - The fund has shown resilience during bear markets in 2022 and 2023, maintaining a single-year decline of around 10% [6]. - Yang's investment strategy includes a concentrated position in the electronics sector, with long-term holdings in stocks like Haiguang Information and Tencent Holdings [10][11]. Group 2: Wu Yuanyi - GF Growth Navigator - Wu Yuanyi is recognized for his balanced industry allocation and impressive performance, with the GF Growth Navigator fund achieving a 143.14% increase year-to-date as of November 17 [12][14]. - The fund maintains a maximum industry allocation of 20%, showcasing a diversified approach that has led to strong returns without heavy reliance on specific sectors [14]. - Wu's ability to rotate stocks effectively has contributed to the fund's success, even amidst a challenging market environment [15]. Group 3: Shen Cheng - Huafu New Energy - Shen Cheng has managed Huafu New Energy since December 29, 2021, achieving consistent excess returns relative to its benchmark despite the sector's overall struggles [18][20]. - The fund's annual returns from 2022 to 2025 have outperformed its benchmark, with a notable 76.76% increase in the latest year [20]. - Shen's investment strategy includes holding industry leaders like Ningde Times while also actively trading to capitalize on short-term opportunities [21][22].

Performance of Index Enhancement Portfolios - The CSI 300 index enhancement portfolio recorded an excess return of -0.22% for the week and 18.41% year-to-date [1][6] - The CSI 500 index enhancement portfolio had an excess return of -0.52% for the week and 7.09% year-to-date [1][6] - The CSI 1000 index enhancement portfolio showed an excess return of -0.12% for the week and 16.38% year-to-date [1][6] - The CSI A500 index enhancement portfolio achieved an excess return of 0.01% for the week and 9.75% year-to-date [1][6] Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month volatility, one-month volatility, and three-month reversal performed well [1][9] - In the CSI 500 component stocks, factors like one-month turnover, BP, and illiquidity shock showed strong performance [1][9] - For the CSI 1000 component stocks, factors such as illiquidity shock, expected net profit month-on-month, and EPTTM one-year percentile performed well [1][9] - In the CSI A500 index component stocks, factors like three-month volatility, one-month volatility, and one-month turnover performed well [1][9] Public Fund Index Enhancement Products Performance Tracking - The CSI 300 index enhancement products had a maximum excess return of 1.15%, a minimum of -2.04%, and a median of 0.19% for the week [1][20] - The CSI 500 index enhancement products recorded a maximum excess return of 2.03%, a minimum of -0.65%, and a median of 0.27% for the week [1][21] - The CSI 1000 index enhancement products had a maximum excess return of 1.84%, a minimum of -0.95%, and a median of 0.00% for the week [1][23] - The CSI A500 index enhancement products achieved a maximum excess return of 0.94%, a minimum of -0.47%, and a median of 0.16% for the week [1][25] Public Fund Index Enhancement Product Quantity and Scale - There are currently 76 CSI 300 index enhancement products with a total scale of 77.9 billion [1][19] - There are 74 CSI 500 index enhancement products with a total scale of 50.5 billion [1][19] - There are 46 CSI 1000 index enhancement products with a total scale of 21.4 billion [1][19] - There are 68 CSI A500 index enhancement products with a total scale of 25.3 billion [1][19]

- The report tracks the performance of Guosen Financial Engineering's index enhancement portfolios, which are constructed based on multi-factor stock selection models targeting benchmarks such as CSI 300, CSI 500, CSI 1000, and CSI A500 indices[10][11][13] - The construction process of the index enhancement portfolios includes three main components: return prediction, risk control, and portfolio optimization[11] - The report monitors the performance of single-factor Maximized Factor Exposure (MFE) portfolios across different stock selection spaces, including CSI 300, CSI 500, CSI 1000, CSI A500 indices, and public fund heavy positions index[10][14][39] - The MFE portfolio construction process involves optimizing the portfolio to maximize single-factor exposure while controlling for constraints such as style exposure, industry exposure, individual stock weight deviation, and turnover rate[39][40][41] - The optimization model for MFE portfolios is defined as follows: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style factor deviation, industry deviation, individual stock weight deviation, and component stock weight limits[39][40] - The report highlights the weekly, monthly, and yearly performance of various factors in different stock selection spaces, such as CSI 300, CSI 500, CSI 1000, CSI A500 indices, and public fund heavy positions index[17][19][21][23][25] - Factors such as three-month volatility, one-month volatility, and three-month turnover performed well in the CSI 300 space recently, while factors like one-year momentum and single-quarter profit growth rate performed poorly[17][18] - In the CSI 500 space, factors like one-month turnover and BP showed strong performance recently, while one-year momentum and standardized unexpected earnings performed poorly[19][20] - In the CSI 1000 space, factors such as illiquidity shock and expected net profit growth performed well recently, while standardized unexpected revenue and one-year momentum showed weak performance[21][22] - In the CSI A500 space, factors like three-month volatility and one-month turnover performed well recently, while one-year momentum and standardized unexpected earnings performed poorly[23][24] - In the public fund heavy positions index space, factors such as one-month volatility and three-month turnover performed well recently, while standardized unexpected revenue and one-year momentum showed weak performance[25][26] - The report tracks the performance of public fund index enhancement products, including CSI 300, CSI 500, CSI 1000, and CSI A500 index enhancement funds, with detailed statistics on excess returns across different time periods[27][28][31][33][35][38]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.01% this week and 18.92% year-to-date [6][18] - The CSI 500 index enhanced portfolio recorded an excess return of -0.26% this week and 7.89% year-to-date [6][18] - The CSI 1000 index enhanced portfolio had an excess return of -0.63% this week and 16.63% year-to-date [6][18] - The CSI A500 index enhanced portfolio posted an excess return of 0.20% this week and 9.84% year-to-date [6][18] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as EPTTM, expected BP, and BP performed well [9][10] - In the CSI 500 component stocks, three-month volatility, expected EPTTM, and expected BP showed strong performance [9][10] - For the CSI 1000 component stocks, EPTTM, three-month volatility, and expected EPTTM were the top-performing factors [9][10] - In the CSI A500 index component stocks, expected EPTTM, EPTTM, and BP were the best-performing factors [9][10] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 0.89%, a minimum of -1.44%, and a median of -0.18% this week [22][24] - The CSI 500 index enhanced products achieved a maximum excess return of 1.65%, a minimum of -1.05%, and a median of 0.05% this week [24][28] - The CSI 1000 index enhanced products recorded a maximum excess return of 0.94%, a minimum of -1.66%, and a median of -0.30% this week [24][28] - The CSI A500 index enhanced products had a maximum excess return of 0.59%, a minimum of -1.02%, and a median of -0.16% this week [24][29]

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 single factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, 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 objective function is to maximize single-factor exposure, represented as $f^{T}w$, where $f$ is the factor value, and $w$ is the stock weight vector. - The optimization model includes the following constraints: 1. **Style Exposure Constraint**: Limits the portfolio's deviation from the benchmark in terms of style factors. $X$ 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[39]. 2. **Industry Exposure Constraint**: Limits the portfolio's deviation from the benchmark in terms of industry exposure. $H$ is the industry exposure matrix, and $h_l, h_h$ are the lower and upper bounds for industry exposure[39]. 3. **Stock Weight Deviation Constraint**: Limits individual stock weight deviations from the benchmark. $w_l, w_h$ are the lower and upper bounds for stock weight deviations[39]. 4. **Constituent Stock Weight Constraint**: Limits the weight of constituent stocks within the portfolio. $B_b$ is a binary vector indicating whether a stock is a benchmark constituent, and $b_l, b_h$ are the lower and upper bounds for constituent stock weights[39]. 5. **No Short Selling Constraint**: Ensures no short positions and limits individual stock weights to a maximum value $l$[39]. 6. **Full Investment Constraint**: Ensures the portfolio is fully invested, with the sum of weights equal to 1[40]. - The optimization model is expressed as: $$ \begin{array}{ll} max & f^{T}w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array} $$ - The MFE portfolio is constructed monthly, and historical returns are backtested with a 0.3% transaction cost applied on both sides[42]. - **Model Evaluation**: The MFE portfolio effectively tests factor performance under realistic constraints, making it a robust tool for evaluating factor predictability in practical scenarios[38][39]. --- Factor Construction and Methods 1. Factor Name: EPTTM (Earnings to Price Trailing Twelve Months) - **Factor Construction Idea**: Measures the profitability of a company relative to its market value, using trailing twelve months' earnings[15]. - **Factor Construction Process**: - Formula: $EPTTM = \frac{\text{Net Income (TTM)}}{\text{Market Value}}$ - The numerator represents the trailing twelve months' net income, while the denominator is the company's total market value[15]. 2. Factor Name: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Evaluates the valuation of a company by comparing its book value to its market value[15]. - **Factor Construction Process**: - Formula: $BP = \frac{\text{Book Value}}{\text{Market Value}}$ - The numerator is the company's book value, and the denominator is its total market value[15]. 3. Factor Name: Three-Month Volatility - **Factor Construction Idea**: Captures the stock's price fluctuation over the past three months, reflecting its risk level[15]. - **Factor Construction Process**: - Formula: $Volatility = \text{Average True Range (ATR)}$ over the past 60 trading days. - The ATR is calculated as the average of the daily high-low range over the specified period[15]. 4. Factor Name: One-Month Reversal - **Factor Construction Idea**: Measures the short-term reversal effect by analyzing the stock's return over the past month[15]. - **Factor Construction Process**: - Formula: $Reversal = \text{Return over the past 20 trading days}$ - Positive values indicate a reversal effect, while negative values suggest momentum continuation[15]. --- Factor Backtesting Results 1. EPTTM - **HS300**: Weekly return 1.35%, monthly return 4.28%, YTD return 5.95%, historical annualized return 4.60%[18]. - **CSI500**: Weekly return 1.54%, monthly return 3.55%, YTD return -3.61%, historical annualized return 4.78%[20]. - **CSI1000**: Weekly return 1.44%, monthly return 2.78%, YTD return 0.15%, historical annualized return 6.84%[22]. - **CSIA500**: Weekly return 1.72%, monthly return 3.92%, YTD return 2.62%, historical annualized return 3.71%[24]. - **Public Fund Index**: Weekly return 1.82%, monthly return 5.32%, YTD return 4.75%, historical annualized return 1.42%[26]. 2. BP - **HS300**: Weekly return 1.25%, monthly return 2.83%, YTD return -1.86%, historical annualized return 2.72%[18]. - **CSI500**: Weekly return 1.36%, monthly return 2.23%, YTD return 3.09%, historical annualized return 3.47%[20]. - **CSI1000**: Weekly return 0.99%, monthly return 1.56%, YTD return -0.45%, historical annualized return 3.07%[22]. - **CSIA500**: Weekly return 1.50%, monthly return 3.44%, YTD return -4.52%, historical annualized return 2.89%[24]. - **Public Fund Index**: Weekly return 1.45%, monthly return 3.20%, YTD return -8.75%, historical annualized return 0.74%[26]. 3. Three-Month Volatility - **HS300**: Weekly return 0.52%, monthly return 1.75%, YTD return -3.56%, historical annualized return 1.84%[18]. - **CSI500**: Weekly return 1.76%, monthly return 3.07%, YTD return -7.17%, historical annualized return 3.50%[20]. - **CSI1000**: Weekly return 1.40%, monthly return 2.54%, YTD return -8.22%, historical annualized return 4.33%[22]. - **CSIA500**: Weekly return 0.79%, monthly return 2.15%, YTD return -9.34%, historical annualized return 2.77%[24]. - **Public Fund Index**: Weekly return 0.97%, monthly return 2.04%, YTD return -15.34%, historical annualized return 1.54%[26]. 4. One-Month Reversal - **HS300**: Weekly return -0.93%, monthly return 0.98%, YTD return -0.57%, historical annualized return -0.33%[18]. - **CSI500**: Weekly return -1.83%, monthly return -0.84%, YTD return 2.56%, historical annualized return -0.84%[20]. - **CSI1000**: Weekly return -1.49%, monthly return -0.55%, YTD return -4.63%, historical annualized return -3.84%[22]. - **CSIA500**: Weekly return -1.28%, monthly return 0.51%, YTD return -1.07%, historical annualized return -2.34%[24]. - **Public Fund Index**: Weekly return -1.11%, monthly return 0.95%, YTD return 4.67%, historical annualized return -1.80%[26].

Quantitative Models and Construction 国证 2000 Index Enhancement Strategy - **Model Name**: 国证 2000 Index Enhancement Strategy - **Model Construction Idea**: Focused on the small-cap stock rotation phenomenon in A-shares, aiming to select stocks effectively within 国证 2000 index components to enhance returns [11] - **Model Construction Process**: - Selected factors such as technical, reversal, and idiosyncratic volatility, which showed strong performance on 国证 2000 index components [12] - Addressed high correlation among factors by regressing volatility factors on technical and reversal factors to obtain residual volatility factors [12] - Combined all major factors equally and performed industry and market capitalization neutralization to construct the 国证 2000 enhancement factor [12] - Formula: Residual volatility factor = Volatility factor - Regression(Technical factor, Reversal factor) [12] - **Model Evaluation**: Demonstrated strong predictive performance with an IC mean of 12.63% and T-statistic of 12.70 [12] - **Strategy Construction**: - Monthly rebalancing at the end of each month, buying the top 10% ranked stocks based on factor values, constructing an equal-weighted long portfolio [15] - Backtesting period: April 2014 to present, benchmarked against 国证 2000 index, with a transaction fee rate of 0.2% per side [15] Machine Learning Index Enhancement Strategy - **Model Name**: TSGRU+LGBM Machine Learning Index Enhancement Strategy - **Model Construction Idea**: Improved machine learning stock selection model by integrating TimeMixer framework with GRU and LightGBM, leveraging multi-scale mixing and seasonal/trend decomposition mechanisms [21] - **Model Construction Process**: - Original strategy used GBDT and NN models trained on different feature datasets and prediction labels, but showed signs of failure due to market style adjustments [21] - Enhanced model incorporated TimeMixer framework into GRU, combined LightGBM with TSGRU latent vectors and traditional quantitative factors [21] - Optimized portfolio construction by controlling tracking error and individual stock weight deviation to maximize factor exposure [25] - **Model Evaluation**: Improved ability to capture recent market information, showing strong performance [21] Dividend Style Timing + Dividend Stock Selection Strategy - **Model Name**: Dividend Style Timing + Dividend Stock Selection Strategy - **Model Construction Idea**: Leveraged the long-term stability and high dividend characteristics of dividend stocks to reduce risk during weak market conditions [36] - **Model Construction Process**: - Used 10 indicators related to economic growth and monetary liquidity to construct a dynamic event factor system for dividend index timing [36] - Applied AI models to test stock selection within 中证红利 index components, achieving stable excess returns [36] - **Model Evaluation**: Demonstrated significant stability improvement compared to 中证红利 index total return [36] --- Model Backtesting Results 国证 2000 Index Enhancement Strategy - **IC Mean**: 12.63% [12] - **Latest Month IC**: 25.34% [12] - **Annualized Excess Return**: 13.30% [16] - **Information Ratio (IR)**: 1.73 [16] - **Tracking Error**: 7.68% [19] - **October Excess Return**: 2.92% [16] TSGRU+LGBM Machine Learning Index Enhancement Strategy - **沪深 300 Index**: - **Annualized Excess Return**: 6.96% [26] - **Information Ratio (IR)**: 1.40 [26] - **Tracking Error**: 4.97% [26] - **October Excess Return**: 2.25% [26] - **中证 500 Index**: - **Annualized Excess Return**: 10.11% [30] - **Information Ratio (IR)**: 1.96 [30] - **Tracking Error**: 5.16% [30] - **October Excess Return**: -0.59% [30] - **中证 1000 Index**: - **Annualized Excess Return**: 13.52% [35] - **Information Ratio (IR)**: 2.37 [35] - **Tracking Error**: 5.70% [35] - **October Excess Return**: 2.63% [35] Dividend Style Timing + Dividend Stock Selection Strategy - **Stock Selection Strategy**: - **Annualized Return**: 18.98% [38] - **Sharpe Ratio**: 0.90 [38] - **October Return**: 2.52% [38] - **Timing Strategy**: - **Annualized Return**: 13.83% [38] - **Sharpe Ratio**: 0.90 [38] - **October Return**: 3.28% [38] - **固收+ Strategy**: - **Annualized Return**: 7.39% [38] - **Sharpe Ratio**: 2.19 [38] - **October Return**: 0.92% [38]

Core Insights - The Shanghai Composite Index ETF (510760) has achieved significant excess returns, allowing investors to effectively "stand on 5100 points" ahead of the market, with a reported excess return of 30.05% since its launch [1][2]. Performance Summary - The ETF has outperformed the Shanghai Composite Index since its inception, with a secondary market return of 49.3% compared to the index's 19.25%, resulting in an excess return of 30.05% [2][3]. - Over the past year, the ETF's market return was 24.42%, while the Shanghai Composite Index returned 20.58%, yielding an excess return of 3.84% [3]. - In the past three years, the ETF achieved a return of 52.58% against the index's 36.68%, leading to an excess return of 15.90% [3]. Dividend Yield and Strategy - The ETF benefits from a dividend yield exceeding 2%, which enhances its return base. The index's total market capitalization weighting, particularly with "state-owned enterprises," contributes to this yield [4]. - The ETF's performance is bolstered by the inclusion of dividend income, as the fund's benchmark is based on the net price index, which does not account for dividends [4]. Market Outlook - The outlook for the A-share market remains positive, supported by ongoing growth policies, active market sentiment, and easing monetary policy. The ETF is seen as a key channel for investing in quality Chinese assets [5]. - The ETF has shown a notable excess return of 50.45% compared to the CSI 300 Index since its inception, further highlighting its strong performance [5][6]. Cost Efficiency - The ETF is positioned as a cost-effective investment tool, with a management fee of 0.15% per year and a custody fee of 0.05% per year, making it one of the lowest-cost options in the market [6].

Core Insights - The Shanghai Composite Index ETF (510760) has achieved significant excess returns, leading investors to a level above 5100 points, while the A-share market is around the 4000 points mark [1][5]. Performance Summary - The Shanghai Composite Index ETF has outperformed the Shanghai Composite Index by over 30% since its inception, with a total return of 49.30% compared to the index's 19.25% [2][3]. - The ETF's performance over various time frames shows consistent excess returns: 24.42% in the past year, 52.58% in the past three years, and 53.54% in the past five years, with excess returns of 3.84%, 15.90%, and 30.89% respectively [3]. Dividend Yield and Strategy - The ETF benefits from a dividend yield exceeding 2%, which enhances its return base. The index's total market capitalization weighting, particularly with a high allocation to state-owned enterprises, contributes to this yield [4]. - The ETF's performance is further supported by its strategy of tracking the index while controlling tracking error, allowing for enhanced returns through sampling replication [2][4]. Market Outlook - The outlook for the A-share market remains positive, with expectations of a slow bull market driven by ongoing growth policies, active market sentiment, and easing monetary policy [5]. - The Shanghai Composite Index ETF is positioned as a key channel for investing in quality Chinese assets, with notable excess returns compared to the CSI 300 Index, reaching 50.45% since inception [5][6]. Cost Efficiency - The ETF is noted for its low management fees of 0.15% per year and custody fees of 0.05% per year, making it an attractive investment vehicle for those looking to track the market [6].

Core Insights - The article provides a summary of key investment strategies and insights for October, focusing on various investment portfolios and market conditions [1][2]. Investment Strategies - The article introduces a free investment guide titled "Fund Investment Advisory Guide," which aims to help readers understand fund advisory and investment strategies [2]. - The "Screw Nail Gold Nail Treasure Index Enhanced Advisory Portfolio" has outperformed the CSI 800 Index by 5.49% as of August 2025, indicating its effectiveness in generating returns [4]. - The "Screw Nail Gold Nail Treasure Active Selection Advisory Portfolio" has outperformed the CSI 300 Index by 6.94% as of August 2025, showcasing the benefits of selecting high-quality fund managers [4]. - The "Screw Nail Silver Nail Treasure Monthly Salary Advisory Portfolio" employs a balanced stock-bond strategy, maintaining a 40:60 ratio, and has shown significant excess returns since its inception [5]. - The "Screw Nail Silver Nail Treasure 365-Day Advisory Portfolio" focuses on a conservative allocation of 15% stocks and 85% bonds, outperforming the secondary bond fund index by 3.01% as of August 2025 [6]. - The "Screw Nail Silver Nail Treasure 90-Day Advisory Portfolio" primarily invests in short-term bond funds, achieving returns that exceed the CSI Money Market Fund Index [7]. Market Insights - The article discusses the characteristics of bull markets in A-shares and Hong Kong stocks, emphasizing the importance of avoiding impulsive trading and focusing on long-term investments [15]. - It highlights the current low valuation of the consumer sector, which has been underperforming but is expected to recover as valuations have decreased significantly [16]. - The article explains the impact of high tariffs on investments, suggesting that while they may cause short-term volatility, they do not significantly affect long-term investment strategies [21]. Investment Principles - The article outlines the four principles of value investing, which include understanding that buying stocks means buying companies, maintaining a margin of safety, recognizing market fluctuations, and operating within one's circle of competence [22]. - It emphasizes the importance of asset allocation and rebalancing strategies to manage market volatility and enhance returns [22]. Additional Resources - The article mentions the creation of a "Screw Nail Index Map" to help investors quickly reference various indices, including their codes, selection rules, and industry distributions [13]. - It also introduces the "Screw Nail Gold Star Rating" and "Bull-Bear Signal Board" for assessing gold asset valuations and market conditions [12].