Quantitative Models and Construction Methods Model: PB-ROE-50 Combination - **Construction Idea**: The PB-ROE-50 combination aims to capture excess returns by selecting stocks with favorable Price-to-Book (PB) and Return on Equity (ROE) metrics. - **Construction Process**: The combination is constructed by selecting the top 50 stocks based on their PB and ROE metrics from the entire market stock pool, the CSI 500 stock pool, and the CSI 800 stock pool. The selection is updated periodically to maintain the combination's effectiveness.[23][24] - **Evaluation**: The PB-ROE-50 combination has shown the ability to generate positive excess returns in the overall market stock pool, although it has underperformed in the CSI 500 and CSI 800 stock pools this week.[23][24] Model: Institutional Research Combination - **Construction Idea**: This model leverages the insights from public and private institutional research to select stocks that are expected to outperform. - **Construction Process**: The combination is constructed by tracking the stocks that have been researched by public and private institutions. The performance of these stocks is then compared to the CSI 800 index to measure excess returns.[25][26] - **Evaluation**: Both the public and private institutional research strategies have generated positive excess returns this week, indicating the effectiveness of institutional insights in stock selection.[25][26] Model: Block Trade Combination - **Construction Idea**: This model aims to capture the information embedded in block trades, which are large transactions that can indicate significant investor interest. - **Construction Process**: The combination is constructed by selecting stocks with high block trade transaction amounts and low 6-day transaction amount volatility. The combination is rebalanced monthly to maintain its effectiveness.[29][30] - **Evaluation**: The block trade combination has generated positive excess returns this week, suggesting that the "high transaction, low volatility" principle is effective in identifying outperforming stocks.[29][30] Model: Private Placement Combination - **Construction Idea**: This model focuses on the event-driven opportunities presented by private placements, which can indicate significant corporate actions and investor interest. - **Construction Process**: The combination is constructed by selecting stocks involved in private placements, considering factors such as market capitalization, rebalancing cycle, and position control. The combination is updated based on the announcement date of the shareholders' meeting.[35][36] - **Evaluation**: The private placement combination has underperformed this week, generating negative excess returns, which raises questions about the current effectiveness of private placement event-driven strategies.[35][36] Model Backtesting Results PB-ROE-50 Combination - **CSI 500**: Excess return this week: -0.57%, Year-to-date excess return: 2.97%, Absolute return this week: 2.69%, Year-to-date absolute return: 13.29%[24] - **CSI 800**: Excess return this week: -0.45%, Year-to-date excess return: 7.47%, Absolute return this week: 1.64%, Year-to-date absolute return: 14.12%[24] - **Overall Market**: Excess return this week: 0.06%, Year-to-date excess return: 9.34%, Absolute return this week: 2.22%, Year-to-date absolute return: 20.17%[24] Institutional Research Combination - **Public Research**: Excess return this week: 1.02%, Year-to-date excess return: 7.37%, Absolute return this week: 3.15%, Year-to-date absolute return: 14.02%[26] - **Private Research**: Excess return this week: 2.72%, Year-to-date excess return: 18.45%, Absolute return this week: 4.88%, Year-to-date absolute return: 25.78%[26] Block Trade Combination - **Excess return this week**: 0.83%, Year-to-date excess return: 27.95%, Absolute return this week: 3.01%, Year-to-date absolute return: 40.62%[30] Private Placement Combination - **Excess return this week**: -0.46%, Year-to-date excess return: 7.55%, Absolute return this week: 1.69%, Year-to-date absolute return: 18.19%[36] Quantitative Factors and Construction Methods Single Factors - **Top Performing Factors in CSI 300**: Single-quarter operating profit YoY growth rate (2.40%), Price-to-Book ratio (2.30%), Turnover rate relative volatility (2.19%)[12][13] - **Top Performing Factors in CSI 500**: Downside volatility proportion (3.85%), Intraday volatility and transaction amount correlation (3.44%), Price-to-Earnings TTM inverse (2.31%)[14][15] - **Top Performing Factors in Liquidity 1500**: Price-to-Book ratio (1.67%), Price-to-Earnings TTM inverse (1.20%), Price-to-Earnings ratio (0.97%)[16][17] Factor Backtesting Results CSI 300 - **Single-quarter operating profit YoY growth rate**: 2.40%[12][13] - **Price-to-Book ratio**: 2.30%[12][13] - **Turnover rate relative volatility**: 2.19%[12][13] CSI 500 - **Downside volatility proportion**: 3.85%[14][15] - **Intraday volatility and transaction amount correlation**: 3.44%[14][15] - **Price-to-Earnings TTM inverse**: 2.31%[14][15] Liquidity 1500 - **Price-to-Book ratio**: 1.67%[16][17] - **Price-to-Earnings TTM inverse**: 1.20%[16][17] - **Price-to-Earnings ratio**: 0.97%[16][17]

Quantitative Models and Construction Methods 1. Model Name: Volume Timing Signal - **Model Construction Idea**: The model uses volume-based signals to determine market timing, identifying bullish or cautious market views based on volume trends[23][24] - **Model Construction Process**: 1. Analyze the volume trends of major broad-based indices 2. Assign a "bullish" or "cautious" signal based on the volume dynamics 3. For example, as of April 11, 2025, the Beixin 50 index showed a "bullish" signal, while other indices like the Shanghai Composite and CSI 300 showed "cautious" signals[23][24] - **Model Evaluation**: The model provides a straightforward and intuitive approach to market timing but may lack robustness in highly volatile markets[23][24] 2. Model Name: Momentum Sentiment Indicator - **Model Construction Idea**: This model captures market sentiment by analyzing the proportion of stocks with positive returns in the CSI 300 index over a specific period[24][25] - **Model Construction Process**: 1. Calculate the proportion of CSI 300 constituent stocks with positive returns over the past N days $ \text{CSI 300 N-day Upward Proportion} = \frac{\text{Number of stocks with positive returns in N days}}{\text{Total number of stocks in CSI 300}} $ 2. Smooth the indicator using two moving averages with different windows (N1 and N2, where N1 > N2) 3. Generate signals: - If the short-term moving average (fast line) exceeds the long-term moving average (slow line), the market is considered bullish - If the fast line falls below the slow line, the market sentiment is turning cautious[27] - **Model Evaluation**: The indicator is effective in capturing upward opportunities but may fail to avoid risks in declining markets[25][27] 3. Model Name: Moving Average Sentiment Indicator - **Model Construction Idea**: This model uses an eight-moving-average system to assess the trend state of the CSI 300 index[32][33] - **Model Construction Process**: 1. Calculate the closing prices of the CSI 300 index for eight moving averages (parameters: 8, 13, 21, 34, 55, 89, 144, 233) 2. Assign values to the indicator based on the number of moving averages above or below the current price: - If the current price exceeds five or more moving averages, the market is considered bullish - Otherwise, the market is neutral or bearish[32][33] - **Model Evaluation**: The model provides a clear trend-following signal but may lag in rapidly changing markets[35] --- Model Backtesting Results 1. Volume Timing Signal - Beixin 50 Index: Bullish signal[23][24] - Other indices (e.g., Shanghai Composite, CSI 300): Cautious signal[23][24] 2. Momentum Sentiment Indicator - CSI 300 Upward Proportion: Approximately 53% in the most recent week[25] 3. Moving Average Sentiment Indicator - CSI 300 Index: Currently in a non-bullish sentiment zone[35] --- Quantitative Factors and Construction Methods 1. Factor Name: Cross-Sectional Volatility - **Factor Construction Idea**: Measures the dispersion of stock returns within an index to assess the alpha environment[37] - **Factor Construction Process**: 1. Calculate the cross-sectional volatility of constituent stocks in indices like CSI 300, CSI 500, and CSI 1000 2. Compare the recent quarter's average volatility with historical periods to determine the alpha environment[41] - **Factor Evaluation**: Higher cross-sectional volatility indicates a better alpha environment for stock selection[37][41] 2. Factor Name: Time-Series Volatility - **Factor Construction Idea**: Measures the volatility of index returns over time to assess the alpha environment[41][44] - **Factor Construction Process**: 1. Calculate the time-series volatility of indices like CSI 300, CSI 500, and CSI 1000 2. Compare the recent quarter's average volatility with historical periods to evaluate the alpha environment[44] - **Factor Evaluation**: Higher time-series volatility suggests a favorable alpha environment for active strategies[41][44] --- Factor Backtesting Results 1. Cross-Sectional Volatility - CSI 300: 1.90% (recent quarter), 77.80% of the past six months' percentile[41] - CSI 500: 2.16% (recent quarter), 42.06% of the past six months' percentile[41] - CSI 1000: 2.52% (recent quarter), 64.54% of the past six months' percentile[41] 2. Time-Series Volatility - CSI 300: 0.63% (recent quarter), 78.84% of the past six months' percentile[44] - CSI 500: 0.48% (recent quarter), 55.56% of the past six months' percentile[44] - CSI 1000: 0.29% (recent quarter), 70.12% of the past six months' percentile[44]