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].