Core Viewpoint - The article highlights the growing significance of quantitative strategies in the investment landscape, particularly within private equity funds, showcasing their ability to generate excess returns compared to benchmark indices [2][3]. Group 1: Quantitative Strategies Overview - Quantitative strategies, especially quantitative long strategies, have become essential in the market, focusing on stock selection and optimization through models and algorithms to achieve excess returns [2]. - In May, 574 quantitative long products reported an average return of 3.77%, with an average excess return of 2.45%, indicating strong performance [2][3]. - The average excess returns for specific indices were as follows: CSI 300 at 0.97%, CSI 500 at 3.03%, and CSI 1000 at 2.84% [3]. Group 2: Performance of Specific Strategies - The top-performing products in the CSI 300 index over the past six months included those from Hainan Pengpai Private Equity and Ningbo Huansheng Quantitative, with excess returns of 6.81% and 5.67% respectively [4][5]. - For the CSI 500 index, the leading product was from Jintong Investment, achieving an excess return of 11.91% [8][10]. - In the CSI 1000 index, the top product was managed by Xiaoxiongmao Asset, with an excess return of 13.26% [10][12]. Group 3: Other Index Strategies - Other index products reported an average excess return of 14.41%, with the top performers coming from Liangchuang Investment and Longqi Technology [13][15]. - The strategy shift of certain products, such as the change from CSI 500 to other indices, has led to significant performance improvements [15]. Group 4: Quantitative Stock Selection - The average return for quantitative stock selection products was 9.83%, with an average excess return of 12.34% [17]. - The leading product in this category was managed by Zhuhai Zhengfeng Private Equity, achieving an excess return of ***% [19].

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: State-Owned Enterprise (SOE) Fundamental Factor Stock Selection Strategy **Model Construction Idea**: This model selects stocks from a pool of SOEs based on fundamental factors, emphasizing dividend characteristics and industry-specific metrics to evaluate profitability, operational efficiency, and solvency [3][5][6] **Model Construction Process**: 1. Define the SOE sample pool using the CSI SOE Index constituents and SOEs listed on the Beijing Stock Exchange for over six months [3] 2. Classify industries into two categories: dividend-oriented (e.g., resources, utilities, real estate) and growth-oriented (e.g., advanced manufacturing, software services) [3][4] 3. Select general factors such as ROE (TTM), operating cash ratio, asset-liability ratio, and dividend yield, alongside industry-specific factors like ROIC, inventory turnover, and R&D intensity [5][6][7] 4. Assign weights to factors, emphasizing dividend yield for all industries and adjusting weights for growth-oriented industries (e.g., lower weight for asset-liability ratio) [9] 5. Calculate scores for each stock based on weighted averages of general and industry-specific factors, normalize the scores, and rank stocks [10] 6. Allocate weights to the top 50 stocks using the formula: $$ w_{i} = \frac{score_{i}^3}{\sum_{i=1}^{N} score_{i}^3} $$ where \( score_{i} \) represents the normalized score of stock \( i \) [10] - **Model Evaluation**: The model effectively captures the dividend and growth characteristics of SOEs, providing a balanced approach to stock selection [3][5] - **Model Name**: Technology Theme Fundamental Factor Stock Selection Strategy **Model Construction Idea**: This model identifies technology stocks with high R&D intensity and strong fundamental performance, focusing on profitability, growth, and operational efficiency [17][18][21] **Model Construction Process**: 1. Define the technology stock pool based on industry classification (e.g., electronics, communication, computing) and R&D intensity (e.g., R&D expenses > 5% of revenue or R&D personnel > 10% of total employees) [17][18][19] 2. Exclude stocks in the "shakeout" and "decline" lifecycle stages, focusing on "introduction," "growth," and "maturity" stages [20][21] 3. Select fundamental factors, including general factors (e.g., gross margin growth, net profit growth) and unique factors (e.g., R&D expense ratio, PB-ROE) [22][23] 4. Calculate scores for each stock using the formula: $$ \hat{\mathbb{E}}_{i}^{s} = \frac{1}{5} Mean(S_{i}) + \frac{Mean(S_{i})}{Std(S_{i})} $$ where \( S_{i} \) represents the scores of eight factors for stock \( i \) [23][24] 5. Adjust scores using an R&D multiplier: $$ R&D \, Multiplier = 0.9 + 0.2 \times Normalization \left( \frac{Mean_{industry}(R&D/MarketCap)}{Mean_{A\_stock}(R&D/MarketCap)} \right) $$ Adjusted scores are then used to rank stocks [25][26] 6. Allocate weights to the top 50 stocks using the formula: $$ weight_{i} = \frac{score_{i}}{\sum_{i=1}^{50} score_{i}} $$ [27] - **Model Evaluation**: The model emphasizes R&D intensity and lifecycle stages, effectively identifying high-potential technology stocks [17][21] --- Model Backtest Results - **SOE Fundamental Factor Stock Selection Strategy**: - Annualized Return: 23.09% - Annualized Volatility: 21.77% - Sharpe Ratio: 1.0648 - Calmar Ratio: 0.9799 - Maximum Drawdown: -23.56% - Excess Return (vs. CSI SOE Index): 21.01% - Excess Sharpe Ratio: 1.7000 - Excess Calmar Ratio: 1.5867 - Excess Maximum Drawdown: -13.24% [11][12] - **Technology Theme Fundamental Factor Stock Selection Strategy**: - Annualized Return: 25.25% - Annualized Volatility: 28.22% - Sharpe Ratio: 0.9404 - Calmar Ratio: 0.7476 - Maximum Drawdown: -33.78% - Excess Return (vs. Technology Stock Pool): 10.62% - Excess Sharpe Ratio: 1.4755 - Excess Calmar Ratio: 1.2638 - Excess Maximum Drawdown: -8.40% [29][30] --- Quantitative Factors and Construction Methods - **Factor Name**: SOE General Factors **Factor Construction Idea**: Evaluate SOE performance using profitability, efficiency, and solvency metrics [5][6] **Factor Construction Process**: - Dividend Yield (TTM): Reflects SOE dividend stability - ROE (TTM): Measures profitability - Operating Cash Ratio: Indicates sales quality - Asset-Liability Ratio: Reflects financial stability - Labor Productivity: Measures operational efficiency [6] - **Factor Name**: Technology General and Unique Factors **Factor Construction Idea**: Assess technology stocks based on profitability, growth, R&D intensity, and supply chain metrics [22][23] **Factor Construction Process**: - Gross Margin Growth: Reflects profitability - Net Profit Growth: Indicates growth potential - R&D Expense Ratio: Measures R&D intensity - PB-ROE: Combines valuation and profitability - Supply Chain Metrics: Evaluate upstream and downstream risks [22][23] --- Factor Backtest Results - **SOE General Factors**: Incorporated into the SOE Fundamental Factor Stock Selection Strategy, contributing to its annualized return of 23.09% and Sharpe Ratio of 1.0648 [11][12] - **Technology General and Unique Factors**: Incorporated into the Technology Theme Fundamental Factor Stock Selection Strategy, contributing to its annualized return of 25.25% and Sharpe Ratio of 0.9404 [29][30]

- The short-term A-share models include the volume model (neutral), low volatility model (neutral), characteristic institutional model (neutral), characteristic volume model (bearish), intelligent CSI 300 model (bullish), and intelligent CSI 500 model (bearish) [1][10][68] - The mid-term A-share models include the limit-up and limit-down model (neutral) and the calendar effect model (neutral) [11][69] - The long-term A-share model is the long-term momentum model, which is neutral for all broad-based indices [12][70] - The comprehensive A-share models include the A-share comprehensive weapon V3 model (bearish) and the A-share comprehensive Guozheng 2000 model (bearish) [13][71] - The mid-term Hong Kong stock model is the turnover amplitude model, which is bullish [14][72] - The backtesting results for the models are as follows: - Volume model: neutral [1][10][68] - Low volatility model: neutral [1][10][68] - Characteristic institutional model: neutral [1][10][68] - Characteristic volume model: bearish [1][10][68] - Intelligent CSI 300 model: bullish [1][10][68] - Intelligent CSI 500 model: bearish [1][10][68] - Limit-up and limit-down model: neutral [11][69] - Calendar effect model: neutral [11][69] - Long-term momentum model: neutral [12][70] - A-share comprehensive weapon V3 model: bearish [13][71] - A-share comprehensive Guozheng 2000 model: bearish [13][71] - Turnover amplitude model: bullish [14][72]

Core Viewpoint - The article discusses the contrasting performance of micro-cap stocks and dividend stocks, highlighting a recent trend where both categories have shown strong performance simultaneously, breaking the previous inverse relationship between them [2][5]. Group 1: Market Performance - Micro-cap stocks experienced a significant drop of over 16% after a previous surge, while major banks reached historical highs, indicating a clear "see-saw" effect between these two stock categories [2]. - The recent week saw the micro-cap stock index and the bank index both reaching new highs, suggesting a potential shift in market dynamics [2]. Group 2: Investment Strategies - Dividend stocks are characterized by their large market capitalization, stable earnings, and high dividend yields, making them attractive for conservative investors [5]. - Recent monetary policies, including an 800 billion yuan liquidity support initiative, have bolstered the micro-cap growth market, enhancing its appeal to risk-tolerant investors [5][6]. - The article notes that high dividend strategies have gained traction, particularly as long-term interest rates decline, which positively impacts the pricing of dividend assets [5]. Group 3: Private Equity Products - There are 19 known micro-cap strategy products, all of which are quantitative, with six from billion-yuan private equity managers, all achieving positive returns [6][7]. - Notably, the "Zijie Growth Select No. 1" managed by Zijie Private Equity has reported a return of ***% over the past year, indicating strong performance in the micro-cap sector [8]. - The article highlights that the performance of high dividend and micro-cap strategies has become a primary focus for many investors, with various private equity products available for participation [5][10]. Group 4: Quantitative and Dividend Strategies - The article identifies 15 high dividend products, with a mix of quantitative and subjective strategies, showcasing the growing interest in dividend-focused investments [10][11]. - Noteworthy products include "Abama Four Seasons Dividend Quantitative Hedge" and "Century Frontier Dividend Preferred No. 1B," both of which have shown strong returns over the past year [11]. - The increasing recognition of the value of dividend strategies has led to greater volatility in related indices, presenting opportunities for skilled fund managers to achieve excess returns [12][13].

Quantitative Models and Construction 1. Model Name: Volume Model - **Construction Idea**: This model evaluates market trends based on trading volume dynamics to provide short-term signals [12][65] - **Construction Process**: The model analyzes trading volume data to determine whether the market is in a neutral, bullish, or bearish state. Specific formulas or parameters are not disclosed in the report [12][65] - **Evaluation**: The model currently provides a neutral signal for the short term, indicating no strong directional bias [12][65] 2. Model Name: Low Volatility Model - **Construction Idea**: This model assesses market conditions by analyzing the volatility of stock prices over a short-term horizon [12][65] - **Construction Process**: The model calculates the volatility of stock prices and categorizes the market state as neutral, bullish, or bearish. Detailed formulas are not provided [12][65] - **Evaluation**: The model is currently neutral, suggesting a lack of significant market movement [12][65] 3. Model Name: Institutional Feature Model (LHB) - **Construction Idea**: This model uses institutional trading data from the "Dragon and Tiger List" (龙虎榜) to predict short-term market trends [12][65] - **Construction Process**: The model aggregates institutional trading activity and evaluates its impact on market direction. Specific formulas are not disclosed [12][65] - **Evaluation**: The model is neutral, indicating no clear institutional bias in the market [12][65] 4. Model Name: Feature Volume Model - **Construction Idea**: This model combines trading volume features to assess short-term market trends [12][65] - **Construction Process**: The model analyzes specific volume-related features to determine market sentiment. Detailed formulas are not provided [12][65] - **Evaluation**: The model is bearish, suggesting a negative outlook for the short term [12][65] 5. Model Name: Smart HS300 Model - **Construction Idea**: This model uses intelligent algorithms to predict short-term trends for the CSI 300 Index [12][65] - **Construction Process**: The model applies machine learning or algorithmic techniques to analyze market data. Specific methodologies are not disclosed [12][65] - **Evaluation**: The model is bearish, indicating a negative outlook for the CSI 300 Index [12][65] 6. Model Name: Smart CSI500 Model - **Construction Idea**: This model uses intelligent algorithms to predict short-term trends for the CSI 500 Index [12][65] - **Construction Process**: Similar to the Smart HS300 Model, this model applies algorithmic techniques to analyze market data. Specific methodologies are not disclosed [12][65] - **Evaluation**: The model is bullish, indicating a positive outlook for the CSI 500 Index [12][65] 7. Model Name: Limit-Up/Down Model - **Construction Idea**: This model evaluates mid-term market trends based on the frequency of limit-up and limit-down events [13][66] - **Construction Process**: The model tracks the occurrence of daily limit-up and limit-down events to assess market sentiment. Specific formulas are not disclosed [13][66] - **Evaluation**: The model is neutral, indicating no strong mid-term market bias [13][66] 8. Model Name: Calendar Effect Model - **Construction Idea**: This model analyzes seasonal or calendar-based patterns to predict mid-term market trends [13][66] - **Construction Process**: The model evaluates historical market performance during specific calendar periods. Detailed methodologies are not provided [13][66] - **Evaluation**: The model is neutral, suggesting no significant calendar-based market trends [13][66] 9. Model Name: Long-Term Momentum Model - **Construction Idea**: This model assesses long-term market trends based on momentum indicators [14][67] - **Construction Process**: The model calculates momentum metrics for broad-based indices to determine long-term market direction. Specific formulas are not disclosed [14][67] - **Evaluation**: The model is neutral for all broad-based indices, indicating no strong long-term market trends [14][67] 10. Model Name: A-Share Comprehensive Weapon V3 Model - **Construction Idea**: This composite model integrates multiple short, mid, and long-term signals to provide an overall market outlook [15][68] - **Construction Process**: The model combines signals from various sub-models (e.g., volume, volatility, institutional activity) to generate a comprehensive market view. Specific integration methods are not disclosed [15][68] - **Evaluation**: The model is bearish, indicating an overall negative outlook for the A-share market [15][68] 11. Model Name: A-Share Comprehensive Guozheng 2000 Model - **Construction Idea**: This composite model focuses on the Guozheng 2000 Index, integrating multiple signals to provide an overall market outlook [15][68] - **Construction Process**: Similar to the V3 Model, this model aggregates signals from various sub-models. Specific methodologies are not disclosed [15][68] - **Evaluation**: The model is bearish, indicating a negative outlook for the Guozheng 2000 Index [15][68] 12. Model Name: HK Stock Turnover-to-Volatility Model - **Construction Idea**: This model evaluates mid-term trends in the Hong Kong market by analyzing the ratio of turnover to volatility [16][69] - **Construction Process**: The model calculates the turnover-to-volatility ratio to assess market sentiment. Specific formulas are not disclosed [16][69] - **Evaluation**: The model is bearish, suggesting a negative outlook for the Hong Kong market [16][69] --- Backtesting Results of Models 1. Volume Model - **Signal**: Neutral [12][65] 2. Low Volatility Model - **Signal**: Neutral [12][65] 3. Institutional Feature Model (LHB) - **Signal**: Neutral [12][65] 4. Feature Volume Model - **Signal**: Bearish [12][65] 5. Smart HS300 Model - **Signal**: Bearish [12][65] 6. Smart CSI500 Model - **Signal**: Bullish [12][65] 7. Limit-Up/Down Model - **Signal**: Neutral [13][66] 8. Calendar Effect Model - **Signal**: Neutral [13][66] 9. Long-Term Momentum Model - **Signal**: Neutral [14][67] 10. A-Share Comprehensive Weapon V3 Model - **Signal**: Bearish [15][68] 11. A-Share Comprehensive Guozheng 2000 Model - **Signal**: Bearish [15][68] 12. HK Stock Turnover-to-Volatility Model - **Signal**: Bearish [16][69]

Core Viewpoint - The article discusses the upcoming launch of the "招商红利量化选股混合" fund, which combines dividend investing with quantitative strategies, highlighting its potential advantages in the current market environment [2][17]. Group 1: Dividend Asset Value - Dividend assets continue to hold investment value, especially in a weak economic environment, characterized by market fluctuations and low interest rates [2][4]. - The current yield on China's 10-year government bonds is at 1.6279%, one of the lowest in over a decade, indicating a favorable environment for dividend assets [2][4]. Group 2: Advantages of Quantitative Strategies - The combination of dividend assets and quantitative stock selection offers several advantages over passive funds, including a broader selection of stocks beyond index limitations [5]. - In a weak recovery and low-interest-rate scenario, dividend assets, with a current dividend yield of 6.53%, provide stable cash flow, making them attractive to investors [6]. - Active management allows for more flexible rebalancing compared to passive funds, enabling the fund manager to respond to market changes and capture potential opportunities [7]. - The integration of quantitative strategies aims to enhance returns by pursuing both stable beta and potential alpha, increasing the overall performance of dividend assets [7]. Group 3: Fund Manager and Strategy - The fund "招商红利量化选股混合" is set to launch on May 14, with a focus on selecting stocks from a proprietary dividend-themed stock pool using a multi-factor quantitative investment model [8]. - The appointed fund manager, Cai Zhen, has nearly 11 years of investment research experience and a strong background in active quantitative investment [9]. - Cai Zhen's previous management of funds has yielded impressive results, with the "招商中证1000指增基金" achieving a return of 20.59% over the past year, outperforming its benchmark [10][11].