- The report highlights the launch of multiple active quantitative strategies since July 2023, including Dividend Selection Strategy and High Winning Rate Industry Strategy, aimed at tracking market trends and selecting industry-specific stocks [6][14][15] - Active quantitative strategies follow a top-down stock selection logic, leveraging industry and thematic insights to refine factor selection from a large fundamental factor pool, enabling precise identification of potential stocks within specific sectors [14] - The Dividend Series includes two products: "Central SOE High Dividend 30 Portfolio" and "Balanced Growth Dividend 50 Portfolio," focusing on stable and growth-oriented dividend strategies [15] - The Electronics Series includes two products: "Electronics Balanced Allocation Enhanced Portfolio" and "Electronics Sector Preferred Enhanced Portfolio," targeting mature sub-sector leaders within the electronics industry [15] - Weekly performance tracking shows that the Electronics Balanced Allocation Enhanced Portfolio and Electronics Sector Preferred Enhanced Portfolio achieved positive excess returns of approximately 1.86% and 1.62%, respectively, outperforming the benchmark [7][25][32] - The Dividend Series underperformed the benchmark this week, with the Central SOE High Dividend 30 Portfolio and Balanced Growth Dividend 50 Portfolio failing to exceed the returns of the CSI Dividend Total Return Index [7][16][22] - The CSI Dividend Index achieved a weekly return of 0.25%, while sub-indices like CSI Dividend Growth and CSI Dividend Low Volatility outperformed with average weekly returns of approximately 1.31% and 1.08%, respectively [7][16][19] - The Electronics Series portfolios ranked in the top 26%-28% among active technology-themed funds based on weekly returns [32] - The report emphasizes the differentiation of active quantitative strategies from traditional ones, highlighting their ability to integrate thematic and industry logic for enhanced stock selection and strategy validation [14]

- The report focuses on the performance of Smart Beta portfolios, including Value, Growth, and Small-cap styles, constructed based on high beta elasticity and long-term stable excess returns objectives[6][7][8] - Value Smart Beta portfolios include "Value 50 Portfolio" and "Value Balanced 50 Portfolio", with weekly returns of 2.58% and 2.40%, respectively, and annual returns of 19.22% and 26.57%[4][7][8] - Growth Smart Beta portfolios include "Growth 50 Portfolio" and "Growth Balanced 50 Portfolio", with weekly returns of -0.09% and -0.37%, respectively, and annual returns of 26.63% and 30.58%[4][7][17] - Small-cap Smart Beta portfolios include "Small-cap 50 Portfolio" and "Small-cap Balanced 50 Portfolio", with weekly returns of 2.55% and -0.17%, respectively, and annual returns of 48.97% and 41.26%[4][7][20] - The report highlights the excess returns of these portfolios relative to their benchmarks, such as "Value 50 Portfolio" outperforming the benchmark by 0.41% weekly and 8.89% annually[7][8][13] - Growth portfolios showed mixed results, with "Growth Balanced 50 Portfolio" achieving positive annual excess returns of 1.07%, while "Growth 50 Portfolio" underperformed by -2.88% annually[7][17][19] - Small-cap portfolios demonstrated strong performance, with "Small-cap 50 Portfolio" achieving weekly excess returns of 2.02% and annual excess returns of 19.09%[7][20][25] - The report provides detailed performance metrics, including absolute returns, excess returns, and maximum relative drawdowns for each portfolio[7][8][20]

Core Viewpoint - The report tracks the performance of a long-term stock selection strategy focusing on profitability and growth, which was initially published by the GF Financial Engineering team on August 26, 2020 [3][30]. Empirical Analysis - The backtesting period for the strategy spans from January 1, 2009, to October 31, 2025, with three rebalancing periods each year on April 30, August 31, and October 31 [5]. - The equal-weighted strategy achieved a cumulative return of 3458.94% and an annualized return of 23.55%, significantly outperforming the CSI 800 index, which had a cumulative return of 179.16% during the same period [6][31]. - The average number of stocks held in the portfolio was approximately 55, with an average market capitalization of around 14 billion [23][31]. - The strategy's annualized volatility relative to the CSI 800 index was 13.63%, with an information ratio of 1.19 [12][13]. Sector Distribution - The sectors with the highest frequency of stock selections included pharmaceuticals, chemicals, electronics, machinery, and food and beverages, while sectors like leisure services, construction, defense, steel, and non-bank financials were selected less frequently [26][31]. Market Capitalization Weighted Strategy - The market capitalization weighted strategy yielded a cumulative return of 2553.16% and an annualized return of 21.42%, with a relative annualized excess return of 13.88% compared to the CSI 800 index [14][21]. - The annualized volatility for the market capitalization weighted strategy was 14.17%, with an information ratio of 1.00 [21][22]. Summary - The report provides a comprehensive follow-up on the long-term stock selection strategy, emphasizing the importance of profitability and growth as key variables in stock selection, and highlights the strong performance of both equal-weighted and market capitalization weighted strategies [30][31].

Quantitative Models and Construction Methods - **Model Name**: Multi-cycle timing model for domestic interest rate price-volume trends **Model Construction Idea**: The model uses kernel regression algorithms to capture interest rate trend patterns, identifying support and resistance lines of interest rate data. It provides timing signals based on the shape of interest rate movements across different investment cycles [11][24][25] **Model Construction Process**: 1. **Data Input**: Utilize 5-year, 10-year, and 30-year government bond YTM data [11][24][25] 2. **Kernel Regression**: Apply kernel regression to identify support and resistance lines for interest rate trends [11][24][25] 3. **Cycle Analysis**: - Long cycle: Monthly frequency - Medium cycle: Bi-weekly frequency - Short cycle: Weekly frequency 4. **Signal Generation**: - If at least two cycles show downward breakthroughs of support lines and the trend is not upward, allocate fully to long-duration bonds - If at least two cycles show downward breakthroughs but the trend is upward, allocate 50% to medium-duration bonds and 50% to long-duration bonds - If at least two cycles show upward breakthroughs of resistance lines and the trend is not downward, allocate fully to short-duration bonds - If at least two cycles show upward breakthroughs but the trend is downward, allocate 50% to medium-duration bonds and 50% to short-duration bonds - Otherwise, allocate equally across short, medium, and long durations [24][25][29] **Model Evaluation**: The model demonstrates robust performance with high annualized returns and low drawdowns across different cycles [25][28][33] Model Backtesting Results - **5-Year YTM Model**: - Long-term annualized return: 5.5% - Maximum drawdown: 2.88% - Return-to-drawdown ratio: 1.91 - Short-term annualized return (since 2024): 2.21% - Maximum drawdown: 0.59% - Return-to-drawdown ratio: 3.74 - Long-term excess return: 1.07% - Short-term excess return: 0.87% - Historical win rate for annual absolute returns: 100% - Historical win rate for annual excess returns: 100% [25][37] - **10-Year YTM Model**: - Long-term annualized return: 6.09% - Maximum drawdown: 2.74% - Return-to-drawdown ratio: 2.22 - Short-term annualized return (since 2024): 2.64% - Maximum drawdown: 0.58% - Return-to-drawdown ratio: 4.57 - Long-term excess return: 1.65% - Short-term excess return: 1.43% - Historical win rate for annual absolute returns: 100% - Historical win rate for annual excess returns: 100% [28][37] - **30-Year YTM Model**: - Long-term annualized return: 7.37% - Maximum drawdown: 4.27% - Return-to-drawdown ratio: 1.73 - Short-term annualized return (since 2024): 3.28% - Maximum drawdown: 0.92% - Return-to-drawdown ratio: 3.59 - Long-term excess return: 2.41% - Short-term excess return: 2.68% - Historical win rate for annual absolute returns: 94.44% - Historical win rate for annual excess returns: 94.44% [33][37] Quantitative Factors and Construction Methods - **Factor Name**: Interest rate structure indicators (level, term, convexity) **Factor Construction Idea**: Transform YTM data into structural indicators to analyze the interest rate market from a mean-reversion perspective [8] **Factor Construction Process**: 1. **Level Structure**: - Formula: $ \text{Level} = \text{Average YTM across maturities} $ - Current reading: 1.61%, positioned at 21%, 12%, and 6% percentiles for 3, 5, and 10-year historical views, respectively [8] 2. **Term Structure**: - Formula: $ \text{Term} = \text{Difference between long and short maturity YTM} $ - Current reading: 0.41%, positioned at 27%, 17%, and 18% percentiles for 3, 5, and 10-year historical views, respectively [8] 3. **Convexity Structure**: - Formula: $ \text{Convexity} = \text{Second derivative of YTM curve} $ - Current reading: -0.04%, positioned at 10%, 6%, and 5% percentiles for 3, 5, and 10-year historical views, respectively [8] **Factor Evaluation**: These indicators provide a comprehensive view of the interest rate market's structural dynamics, aiding in timing and allocation decisions [8] Factor Backtesting Results - **Level Structure**: Current reading: 1.61% [8] - **Term Structure**: Current reading: 0.41% [8] - **Convexity Structure**: Current reading: -0.04% [8]

- Factor Name: EPD (Valuation Deviation Factor) - Construction Idea: Combining the Bollinger Bands mean reversion strategy commonly used in the CTA field with the logic of fundamental valuation repair, utilizing the mean reversion characteristic of the PE valuation indicator[7] - Construction Process: The EPD factor is constructed by using the mean reversion characteristic of the PE valuation indicator[7] - Evaluation: The EPD factor aims to capture valuation deviations and mean reversion in stock prices[7] - Factor Name: EPDS (Slow Deviation Factor) - Construction Idea: To eliminate the impact of changes in individual stock valuation logic, the EPD factor is used to remove the probability of individual stock valuation logic being altered (represented by the individual stock information ratio)[7] - Construction Process: The EPDS factor is constructed by using the EPD factor to remove the probability of individual stock valuation logic being altered[7] - Evaluation: The EPDS factor aims to provide a more stable measure of valuation deviations by accounting for changes in individual stock valuation logic[7] - Factor Name: EPA (Valuation Anomaly Factor) - Construction Idea: Removing the influence of Beta, growth, and value styles that affect the "valuation anomaly" logic[7] - Construction Process: The EPA factor is constructed by removing the influence of Beta, growth, and value styles from the EPD factor[7] - Evaluation: The EPA factor aims to capture valuation anomalies by eliminating the influence of common market factors[7] Factor Backtesting Results - EPD Factor - Annualized Return: 17.46%[2][8][12] - Annualized Volatility: 9.92%[2][8][12] - Information Ratio (IR): 1.76[2][8][12] - Monthly Win Rate: 70.37%[2][8][12] - Maximum Drawdown: 8.93%[2][8][12] - EPDS Factor - Annualized Return: 16.03%[2][8][12] - Annualized Volatility: 5.74%[2][8][12] - Information Ratio (IR): 2.79[2][8][12] - Monthly Win Rate: 78.31%[2][8][12] - Maximum Drawdown: 3.10%[2][8][12] - EPA Factor - Annualized Return: 17.15%[2][8][12] - Annualized Volatility: 5.16%[2][8][12] - Information Ratio (IR): 3.33[2][8][12] - Monthly Win Rate: 80.42%[2][8][12] - Maximum Drawdown: 3.12%[2][8][12]

Quantitative Models and Construction Methods - **Model Name**: "Day and Night Convergence" New Momentum Factor **Model Construction Idea**: The model is based on the price-volume relationship during intraday and overnight trading sessions. It improves traditional momentum factors by incorporating transaction volume information and separating the trading periods into day and night to explore their respective characteristics and logic[6][7] **Model Construction Process**: 1. The trading period is divided into intraday and overnight sessions 2. The price-volume relationship is analyzed separately for each session to identify distinct features 3. The improved intraday and overnight factors are synthesized into a new momentum factor 4. The factor is tested on the entire A-share market (excluding Beijing Stock Exchange stocks) from February 2014 to October 2025, using a 10-group long-short hedging strategy[7] **Model Evaluation**: The model demonstrates significant stock selection ability, outperforming traditional momentum factors in terms of stability and performance[6][7] Model Backtesting Results - **"Day and Night Convergence" New Momentum Factor**: - Annualized Return: 18.15% - Annualized Volatility: 8.68% - Information Ratio (IR): 2.09 - Monthly Win Rate: 78.01% - Maximum Drawdown: 9.07%[1][7][14] Quantitative Factors and Construction Methods - **Factor Name**: "Day and Night Convergence" New Momentum Factor **Factor Construction Idea**: The factor leverages the distinct characteristics of price-volume relationships during intraday and overnight trading sessions to enhance the signal strength of momentum factors[7] **Factor Construction Process**: 1. Separate the trading period into intraday and overnight sessions 2. Analyze the price-volume relationship for each session to identify unique features 3. Combine the improved intraday and overnight factors into a single momentum factor 4. Test the factor on the entire A-share market (excluding Beijing Stock Exchange stocks) from February 2014 to October 2025, using a 10-group long-short hedging strategy[7] **Factor Evaluation**: The factor significantly outperforms traditional momentum factors, with higher stability and better stock selection ability[6][7] Factor Backtesting Results - **"Day and Night Convergence" New Momentum Factor**: - Annualized Return: 18.15% - Annualized Volatility: 8.68% - Information Ratio (IR): 2.09 - Monthly Win Rate: 78.01% - Maximum Drawdown: 9.07%[1][7][14] - **Traditional Momentum Factor**: - Information Ratio (IR): 1.09 - Monthly Win Rate: 62.75% - Maximum Drawdown: 20.35%[6] October 2025 Performance - **"Day and Night Convergence" New Momentum Factor**: - Long Portfolio Return: 0.85% - Short Portfolio Return: -2.35% - Long-Short Hedging Return: 3.20%[1][10]

- The report focuses on the performance of various Smart beta portfolios, specifically the Value 50, Value Balanced 50, Growth 50, Growth Balanced 50, Small Cap 50, and Small Cap Balanced 50 portfolios[4][6] - The Value Balanced 50 portfolio outperformed last week with a weekly return of 2.28%, generating an excess return of 2.13% relative to the China Securities Value Index[1][4] - The Growth Balanced 50 portfolio achieved a weekly return of 1.52% last week, with an annual return of 31.07% year-to-date[4][6] - The Small Cap 50 portfolio had a negative weekly return of -0.21% last week, but it has an impressive annual return of 45.27% year-to-date[4][6] - The Small Cap Balanced 50 portfolio also had a negative weekly return of -0.50% last week, with an annual return of 41.50% year-to-date[4][6] - The report includes detailed performance metrics such as absolute returns, excess returns, and maximum relative drawdowns for each portfolio[7] - The Value 50 portfolio had a weekly return of 0.55% and an annual return of 16.22% year-to-date[4][7] - The Growth 50 portfolio had a weekly return of 0.08% and an annual return of 26.74% year-to-date[4][7] - The report provides visual representations of the weekly, monthly, and year-to-date performance of each portfolio[8][17][24]

- The "Dividend 50 Combination" strategy is designed to outperform the CSI Dividend Total Return Index by focusing on a balanced approach between growth and stability. The strategy includes stocks with high dividend yields and stable financial performance, aiming to capture excess returns in volatile markets[6][13][20] - The "Electronic Sector Preferred Enhanced Combination" strategy targets leading companies in mature sub-sectors within the electronic industry. It emphasizes stocks with strong fundamentals and growth potential, aiming to achieve positive excess returns relative to technology-themed funds[13][23][30] - The "Dividend 50 Combination" strategy achieved a weekly excess return of approximately 0.85% relative to the CSI Dividend Total Return Index, and a cumulative excess return of 7.35% since the beginning of 2025, placing it in the top 32% of all dividend-related fund products[20][22] - The "Electronic Sector Preferred Enhanced Combination" strategy delivered a weekly excess return of approximately 0.42%, outperforming the median return of active technology-themed funds during the same period[30][31]

Quantitative Models and Construction Quantitative Factors and Construction Process - **Factor Name**: Large Buy and Sell Factor **Construction Idea**: Reflects the market's active trading behavior and short-term price movement prediction[8][86][88] **Construction Process**: 1. Define "large orders" as transactions exceeding the rolling average by 1 standard deviation[8] 2. Calculate net buy amount as the difference between large buy and large sell orders[8] 3. Analyze the factor under three scenarios: full trading session, excluding the last 30 minutes, and only the first 30 minutes of trading[8] **Evaluation**: Strong short-term positive correlation with index returns due to momentum effects, but reverses over longer periods due to mean reversion[13][87][88] - **Factor Name**: Small Buy and Large Sell Factor **Construction Idea**: Captures the behavior of smaller investors and their impact on short-term market trends[8][86][88] **Construction Process**: 1. Define "large orders" as transactions exceeding the rolling average by 1 standard deviation[8] 2. Calculate the net buy amount for small buy and large sell orders[8] 3. Analyze the factor under three scenarios: full trading session, excluding the last 30 minutes, and only the first 30 minutes of trading[8] **Evaluation**: Strong short-term positive correlation with index returns due to momentum effects, but reverses over longer periods due to mean reversion[13][87][88] - **Factor Name**: Large Net Buy Factor **Construction Idea**: Represents the influence of large-scale net buying on market trends[8][86][88] **Construction Process**: 1. Define "large orders" as transactions exceeding the rolling average by 1 standard deviation[8] 2. Calculate net buy amount as the difference between large buy and large sell orders[8] 3. Analyze the factor under three scenarios: full trading session, excluding the last 30 minutes, and only the first 30 minutes of trading[8] **Evaluation**: Weak short-term negative correlation with index returns due to overbuying effects, but positive correlation over longer periods due to market support from large capital inflows[13][87][88] Optimal Parameters for Factors - **Large Buy and Sell Factor**: Optimal parameters are MA10-MA40 and MA10-MA60 for short-term and medium-term trends[9][32][88] - **Small Buy and Large Sell Factor**: Optimal parameters are MA5-MA20 and MA10-MA40 for short-term and medium-term trends[9][32][88] - **Large Net Buy Factor**: Optimal parameters are MA10-MA20 and MA10-MA40 for medium-term trends[9][32][88] --- Backtesting Results of Factors Single Factor Performance - **Large Buy and Sell Factor**: - **HS300**: Annualized return 12.2%-12.5%, Sharpe ratio 0.82-0.84, max drawdown -27.7%[36][38] - **CSI500**: Annualized return 10.6%, Sharpe ratio 0.60, max drawdown -32.0%[45] - **CSI1000**: Annualized return 11.4%, Sharpe ratio 0.64, max drawdown -45.0%[53] - **Small Buy and Large Sell Factor**: - **HS300**: Annualized return 12.5%, Sharpe ratio 0.84-0.85, max drawdown -24.4%[36][38] - **CSI500**: Annualized return 11.8%, Sharpe ratio 0.66, max drawdown -37.7%[45] - **CSI1000**: Annualized return 12.7%, Sharpe ratio 0.71, max drawdown -42.7%[53] - **Large Net Buy Factor**: - **HS300**: Annualized return 5.0%, Sharpe ratio 0.23, max drawdown -46.7%[36] - **CSI500**: Annualized return 6.8%, Sharpe ratio 0.27, max drawdown -65.2%[45] - **CSI1000**: Annualized return 5.1%, Sharpe ratio 0.22, max drawdown -57.9%[53] Composite Strategy Performance - **HS300**: - Aggressive long strategy: Annualized return 11.3%, Sharpe ratio 0.84, max drawdown -23.5%[64][66] - Conservative long strategy: Annualized return 10.1%, Sharpe ratio 0.85, max drawdown -29.9%[64][66] - Aggressive long-short strategy: Annualized return 17.2%, Sharpe ratio 0.84, max drawdown -32.1%[64][66] - Conservative long-short strategy: Annualized return 15.1%, Sharpe ratio 0.82, max drawdown -32.5%[64][66] - **CSI500**: - Aggressive long strategy: Annualized return 13.5%, Sharpe ratio 0.81, max drawdown -33.5%[69][72] - Conservative long strategy: Annualized return 16.1%, Sharpe ratio 1.21, max drawdown -15.0%[69][72] - Aggressive long-short strategy: Annualized return 16.1%, Sharpe ratio 0.69, max drawdown -53.3%[69][72] - Conservative long-short strategy: Annualized return 17.6%, Sharpe ratio 0.86, max drawdown -27.8%[69][72] - **CSI1000**: - Aggressive long strategy: Annualized return 12.1%, Sharpe ratio 0.65, max drawdown -50.3%[79][81] - Conservative long strategy: Annualized return 19.7%, Sharpe ratio 1.28, max drawdown -18.1%[79][81] - Aggressive long-short strategy: Annualized return 26.9%, Sharpe ratio 1.03, max drawdown -52.4%[79][81] - Conservative long-short strategy: Annualized return 28.5%, Sharpe ratio 1.25, max drawdown -38.8%[79][81] Annual Performance of Composite Strategies - **HS300**: Annual win rate exceeds 60%, with stable returns even during market downturns[66][67][70] - **CSI500**: Average annual win rate of 56%, higher elasticity compared to HS300, suitable for risk-tolerant strategies[75][76][77] - **CSI1000**: Annual win rate exceeds 70%, with the highest stability among all indices, especially for conservative strategies[82][83][84] --- Key Observations - Large Buy and Sell Factor and Small Buy and Large Sell Factor exhibit strong short-term positive correlation with index returns, while Large Net Buy Factor shows weak short-term negative correlation but positive long-term correlation[10][13][87] - Optimal parameters for high-frequency factors are concentrated in short-term (MA5, MA10) and medium-term (MA20, MA40) moving average distances[9][32][88] - Composite strategies outperform single-factor strategies in terms of stability and risk-adjusted returns, especially on indices with higher volatility like CSI500 and CSI1000[64][72][81] - Conservative strategies are more suitable for volatile indices, while aggressive strategies yield higher win rates on stable indices like HS300[85][89]

Group 1 - The report introduces a quantitative model solution for addressing the value-growth style switching issue, combining investment expectations based on odds and win rates [1][8] - The overall market growth style portfolio achieved a return of 4.58%, while the value style portfolio returned 2.24% in the last week [1][8] Group 2 - The estimated odds for the growth style is 1.08, while for the value style it is 1.12, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rate for the growth style is 63.24%, compared to 36.76% for the value style, based on seven win rate indicators [3][19] Group 3 - The latest investment expectation for the growth style is calculated to be 0.32, while the value style has an investment expectation of -0.22, leading to a recommendation for the growth style [4][21] - Since 2013, the annualized return of the style rotation model based on investment expectations is 27.99%, with a Sharpe ratio of 1.04 [4][22]