Quantitative Models and Factor Construction Quantitative Models and Construction Methods 1. **Model Name**: Dynamic Factor Adjustment Model **Model Construction Idea**: Combines factor momentum and reversal characteristics to dynamically adjust factor selection based on historical performance and failure probabilities[4][80][82] **Model Construction Process**: - Evaluate factor momentum using the average RankIC over the past 6 months and the average RankICIR over the past 3-12 months[4][82] - Calculate conditional failure probabilities by rolling one year of historical data to assess the likelihood of a factor transitioning from effective to ineffective[74][87] - Exclude factors with high failure probabilities and assign scores based on momentum and failure probabilities. Select the top N factors with the highest scores for equal-weighted scoring in each period[82][87][88] **Model Evaluation**: The model effectively balances momentum and reversal characteristics, reducing the impact of unstable factors and improving robustness in factor selection[82][87] 2. **Model Name**: "2+3" Dynamic Factor Model for Small-Cap Stocks **Model Construction Idea**: Combines two fixed factors (valuation and volatility) with three dynamically selected high-momentum factors to construct a robust small-cap stock selection model[98][99] **Model Construction Process**: - Fixed factors: Valuation (BTOP) and volatility (VOLATILITY) are always included due to their stable and significant performance in small-cap pools[98][99] - Dynamic factors: Exclude factors with conditional failure probabilities above 80% and select the top 3 factors based on medium- and long-term momentum scores[98][99] - Construct a portfolio of 50 equally weighted stocks based on the selected factors[98][103] **Model Evaluation**: The model demonstrates strong performance in small-cap pools, with high momentum and low reversal failure probabilities, making it robust against overfitting[98][103] 3. **Model Name**: "Exclusion + Scoring" Model for Large-Cap Stocks **Model Construction Idea**: Focuses on stricter exclusion of high-failure-probability factors and integrates failure information into the scoring process for large-cap stock selection[109][110] **Model Construction Process**: - Exclude factors with conditional failure probabilities above 70%[109][110] - Combine failure indicators into the momentum scoring model, selecting the top 5 factors with the highest comprehensive scores[109][110] - Construct a portfolio of 50 equally weighted stocks based on the selected factors[109][113] **Model Evaluation**: The model effectively addresses the high sensitivity and extreme reversals in large-cap pools, improving stability and performance[109][113] Model Backtesting Results 1. **Dynamic Factor Adjustment Model**: - Annualized return: 8.83% - Sharpe ratio: 0.42 - Excess annualized return: 11.47% - Maximum drawdown: 38.67%[103] 2. **"2+3" Dynamic Factor Model for Small-Cap Stocks**: - Annualized return: 8.83% - Sharpe ratio: 0.42 - Excess annualized return: 11.47% - Maximum drawdown: 38.67%[103] 3. **"Exclusion + Scoring" Model for Large-Cap Stocks**: - Annualized return: 8.40% - Sharpe ratio: 0.40 - Excess annualized return: 8.32% - Maximum drawdown: 36.40%[113] Quantitative Factors and Construction Methods 1. **Factor Name**: Valuation (BTOP) **Factor Construction Idea**: Measures the book-to-price ratio to capture undervalued stocks[8][39] **Factor Construction Process**: Calculate the ratio of book value to current market value for each stock[8][39] **Factor Evaluation**: Demonstrates stable and significant performance in small-cap pools, with strong selection ability in various market conditions[39][98] 2. **Factor Name**: Volatility (VOLATILITY) **Factor Construction Idea**: Measures the residual volatility of stock returns to identify low-risk stocks[8][50] **Factor Construction Process**: Calculate the standard deviation of residuals from a time-series regression of stock returns[8][50] **Factor Evaluation**: Performs well in both small-cap and large-cap pools, with low failure probabilities and consistent selection ability[50][98] 3. **Factor Name**: Earnings (EARNING) **Factor Construction Idea**: Measures earnings yield to capture profitability[8][39] **Factor Construction Process**: Calculate the ratio of earnings to market value for each stock[8][39] **Factor Evaluation**: Strong selection ability in large-cap pools, with stable performance across different market conditions[39][113] Factor Backtesting Results 1. **Valuation (BTOP)**: - RankICIR: Consistently ranks in the top 2 across small-cap pools[39][98] 2. **Volatility (VOLATILITY)**: - RankICIR: Demonstrates stable negative expression across all pools, with low failure probabilities[50][98] 3. **Earnings (EARNING)**: - RankICIR: Strong performance in large-cap pools, with high selection ability and stable expression[39][113]

Core Viewpoint - The article discusses the challenges and performance of dividend-enhanced strategies compared to broader indices, highlighting that achieving excess returns in dividend indices is more difficult than in broader indices like the CSI 2000 [2][4]. Group 1: Dividend Indices Performance - The speaker, Deng Tong, notes that the excess return of the CSI 2000 index can exceed 30%, while the excess return for dividend indices is modest, often just a few percentage points [2][3]. - The article compares three main dividend indices: CSI Dividend, Low Volatility Dividend, and Hong Kong Dividend, revealing significant performance differences among them [9]. - The CSI Dividend index showed a 3-year return of 5.75%, while the Hong Kong Dividend index had a much stronger performance with a 3-year return of 16.97% [9]. Group 2: Active Management of Dividend Strategies - The analysis of several actively managed dividend strategy funds indicates that they do not consistently outperform the Low Volatility Dividend index, supporting Deng's assertion [13]. - The article highlights that if fund managers make poor stock selections, the funds may underperform, as seen with the "Huashang Dividend Preferred" fund [14]. - The "Guangfa Stable Strategy" fund has shown significant excess returns this year, attributed to the use of unique factors like "economic cycle factor" and "price-volume factor" [15]. Group 3: Investment Recommendations - For high Beta indices like the Low Volatility Dividend, it is suggested that investors may achieve better results by directly purchasing ordinary index funds or ETFs rather than relying on randomly selected active funds [15]. - The article emphasizes that the current trend in dividend index funds includes monthly distributions, contrasting with actively managed funds that typically do not distribute dividends [16]. - For lower Beta indices such as the CSI 2000 and CSI 300, it is recommended to prioritize index-enhanced funds to avoid stagnation in long-term performance [16].