Quantitative Models and Construction Methods 1. Model Name: Volume Model - **Construction Idea**: The model uses trading volume data to predict market trends. - **Construction Process**: The model analyzes the trading volume of various broad-based indices to determine market sentiment. It categorizes the indices as neutral based on the volume data. - **Evaluation**: The model is considered neutral for all broad-based indices in the short term.[2][11] 2. Model Name: Low Volatility Model - **Construction Idea**: This model uses the volatility of stock prices to predict market trends. - **Construction Process**: The model evaluates the volatility of stock prices and categorizes the indices as neutral. - **Evaluation**: The model is considered neutral in the short term.[2][11] 3. Model Name: Institutional Feature Model - **Construction Idea**: This model uses institutional trading data from the "Dragon and Tiger List" to predict market trends. - **Construction Process**: The model analyzes the trading behavior of institutions listed on the "Dragon and Tiger List" and categorizes the indices as bullish. - **Evaluation**: The model is considered bullish in the short term.[2][11] 4. Model Name: Feature Volume Model - **Construction Idea**: This model uses specific volume features to predict market trends. - **Construction Process**: The model analyzes specific volume features and categorizes the indices as bearish. - **Evaluation**: The model is considered bearish in the short term.[2][11] 5. Model Name: Smart Algorithm Model (CSI 300) - **Construction Idea**: This model uses smart algorithms to predict market trends for the CSI 300 index. - **Construction Process**: The model applies smart algorithms to the CSI 300 index and categorizes it as neutral. - **Evaluation**: The model is considered neutral in the short term.[2][11] 6. Model Name: Smart Algorithm Model (CSI 500) - **Construction Idea**: This model uses smart algorithms to predict market trends for the CSI 500 index. - **Construction Process**: The model applies smart algorithms to the CSI 500 index and categorizes it as bearish. - **Evaluation**: The model is considered bearish in the short term.[2][11] 7. Model Name: Limit Up/Down Model - **Construction Idea**: This model uses the occurrence of limit up and limit down events to predict market trends. - **Construction Process**: The model analyzes the frequency of limit up and limit down events and categorizes the indices as neutral. - **Evaluation**: The model is considered neutral in the medium term.[2][12] 8. Model Name: Calendar Effect Model - **Construction Idea**: This model uses calendar effects to predict market trends. - **Construction Process**: The model analyzes historical calendar effects and categorizes the indices as neutral. - **Evaluation**: The model is considered neutral in the medium term.[2][12] 9. Model Name: Long-term Momentum Model - **Construction Idea**: This model uses long-term momentum to predict market trends. - **Construction Process**: The model analyzes long-term momentum indicators and categorizes the indices as bullish. - **Evaluation**: The model is considered bullish in the long term.[2][13] 10. Model Name: Comprehensive Weapon V3 Model - **Construction Idea**: This model combines multiple factors to predict market trends. - **Construction Process**: The model integrates various factors and categorizes the indices as bearish. - **Evaluation**: The model is considered bearish in the long term.[2][14] 11. Model Name: Comprehensive National Certificate 2000 Model - **Construction Idea**: This model combines multiple factors to predict market trends for the National Certificate 2000 index. - **Construction Process**: The model integrates various factors and categorizes the indices as bearish. - **Evaluation**: The model is considered bearish in the long term.[2][14] 12. Model Name: Turnover Inverse Amplitude Model - **Construction Idea**: This model uses the inverse amplitude of turnover to predict market trends. - **Construction Process**: The model analyzes the inverse amplitude of turnover and categorizes the indices as bullish. - **Evaluation**: The model is considered bullish in the medium term.[2][15] Model Backtest Results - **Volume Model**: Neutral for all broad-based indices in the short term.[2][11] - **Low Volatility Model**: Neutral in the short term.[2][11] - **Institutional Feature Model**: Bullish in the short term.[2][11] - **Feature Volume Model**: Bearish in the short term.[2][11] - **Smart Algorithm Model (CSI 300)**: Neutral in the short term.[2][11] - **Smart Algorithm Model (CSI 500)**: Bearish in the short term.[2][11] - **Limit Up/Down Model**: Neutral in the medium term.[2][12] - **Calendar Effect Model**: Neutral in the medium term.[2][12] - **Long-term Momentum Model**: Bullish in the long term.[2][13] - **Comprehensive Weapon V3 Model**: Bearish in the long term.[2][14] - **Comprehensive National Certificate 2000 Model**: Bearish in the long term.[2][14] - **Turnover Inverse Amplitude Model**: Bullish in the medium term.[2][15]

Quantitative Models and Construction Methods Jump Imbalance Indicator - **Model Name**: Jump Imbalance Indicator - **Model Construction Idea**: Measures the difference in the strength of upward and downward jumps in stock prices[2] - **Model Construction Process**: - Formula: $$D_{i,t}^{N J}=\frac{\mathrm{No.of~Pjumps}_{i}^{d}\mathrm{\-~No.of~Njumps}_{i}^{d}}{\mathrm{No.of~Tjumps}_{i}^{d}}$$[14] - Parameters: - No.of Pjumps: Number of days with positive jumps in the past 20 trading days - No.of Njumps: Number of days with negative jumps in the past 20 trading days - No.of Tjumps: Number of days with jumps in the past 20 trading days[15] - **Model Evaluation**: Effective for timing the market but not outstanding[20] - **Model Testing Results**: - Annualized return: 6.23% - Sharpe ratio: 0.57 - Profit-loss ratio: 1.46 - Annualized excess return: 4.48% - Sharpe ratio (excess): 0.34[22] Implied Jump Imbalance Indicator - **Model Name**: Implied Jump Imbalance Indicator - **Model Construction Idea**: Reflects the jump information of stocks not affected by market jumps, potentially containing expectations of future performance or insider trading probability[23] - **Model Construction Process**: - Formula: $$D_{i,t}^{IJ}=\frac{\text{No.of Pumps}_{i}|\text{No Market Jump-No.of Numps}_{i}|\text{No Market Jump}}{\text{No.of Tumps}_{i}|\text{No Market Jump}}$$[23] - Parameters: - No.of Pjumps | No Market Jump: Number of days with positive jumps when the market index did not jump - No.of Njumps | No Market Jump: Number of days with negative jumps when the market index did not jump - No.of Tjumps | No Market Jump: Number of days with jumps when the market index did not jump[23] - **Model Evaluation**: Shows better performance compared to the Jump Imbalance Indicator[31] - **Model Testing Results**: - Annualized return: 9.93% - Sharpe ratio: 0.82 - Calmar ratio: 0.75 - Profit-loss ratio: 2.05 - Annualized excess return: 8.46% - Sharpe ratio (excess): 0.77 - Calmar ratio (excess): 1.12[34] Jump Imbalance Dispersion Indicator - **Model Name**: Jump Imbalance Dispersion Indicator - **Model Construction Idea**: Represents the dispersion of jump imbalance among stocks, indicating market sentiment divergence[39] - **Model Construction Process**: - Formula: $$\Delta J_{R_{Std}}$$[39] - Parameters: - Standard deviation of implied jump imbalance indicator among stocks[39] - **Model Evaluation**: Effective for timing the market[39] - **Model Testing Results**: - Annualized return: 9.41% - Sharpe ratio: 0.74 - Calmar ratio: 0.70 - Profit-loss ratio: 1.50 - Annualized excess return: 7.91% - Sharpe ratio (excess): 0.69 - Calmar ratio (excess): 0.72[42] Composite Indicator - **Model Name**: Composite Indicator - **Model Construction Idea**: Combines implied jump imbalance indicator and jump imbalance dispersion indicator for better market timing[40] - **Model Construction Process**: - Formula: $$\Delta J_{R} > 0 \text{ and } \Delta J_{R_{Std}} < 0$$[40] - Parameters: - Implied jump imbalance indicator - Jump imbalance dispersion indicator[40] - **Model Evaluation**: Shows significant improvement in market timing effectiveness[40] - **Model Testing Results**: - Annualized return: 16.5% - Sharpe ratio: 1.28 - Calmar ratio: 2.41 - Annualized excess return: 15.49% - Sharpe ratio (excess): 0.82 - Calmar ratio (excess): 0.88[45] Quantitative Factors and Construction Methods Jump Imbalance Factor - **Factor Name**: Jump Imbalance Factor - **Factor Construction Idea**: Measures the difference in the strength of upward and downward jumps in stock prices[2] - **Factor Construction Process**: - Formula: $$D_{i,t}^{N J}=\frac{\mathrm{No.of~Pjumps}_{i}^{d}\mathrm{\-~No.of~Njumps}_{i}^{d}}{\mathrm{No.of~Tjumps}_{i}^{d}}$$[14] - Parameters: - No.of Pjumps: Number of days with positive jumps in the past 20 trading days - No.of Njumps: Number of days with negative jumps in the past 20 trading days - No.of Tjumps: Number of days with jumps in the past 20 trading days[15] - **Factor Evaluation**: Effective for timing the market but not outstanding[20] - **Factor Testing Results**: - Annualized return: 6.23% - Sharpe ratio: 0.57 - Profit-loss ratio: 1.46 - Annualized excess return: 4.48% - Sharpe ratio (excess): 0.34[22] Implied Jump Imbalance Factor - **Factor Name**: Implied Jump Imbalance Factor - **Factor Construction Idea**: Reflects the jump information of stocks not affected by market jumps, potentially containing expectations of future performance or insider trading probability[23] - **Factor Construction Process**: - Formula: $$D_{i,t}^{IJ}=\frac{\text{No.of Pumps}_{i}|\text{No Market Jump-No.of Numps}_{i}|\text{No Market Jump}}{\text{No.of Tumps}_{i}|\text{No Market Jump}}$$[23] - Parameters: - No.of Pjumps | No Market Jump: Number of days with positive jumps when the market index did not jump - No.of Njumps | No Market Jump: Number of days with negative jumps when the market index did not jump - No.of Tjumps | No Market Jump: Number of days with jumps when the market index did not jump[23] - **Factor Evaluation**: Shows better performance compared to the Jump Imbalance Factor[31] - **Factor Testing Results**: - Annualized return: 9.93% - Sharpe ratio: 0.82 - Calmar ratio: 0.75 - Profit-loss ratio: 2.05 - Annualized excess return: 8.46% - Sharpe ratio (excess): 0.77 - Calmar ratio (excess): 1.12[34] Jump Imbalance Dispersion Factor - **Factor Name**: Jump Imbalance Dispersion Factor - **Factor Construction Idea**: Represents the dispersion of jump imbalance among stocks, indicating market sentiment divergence[39] - **Factor Construction Process**: - Formula: $$\Delta J_{R_{Std}}$$[39] - Parameters: - Standard deviation of implied jump imbalance indicator among stocks[39] - **Factor Evaluation**: Effective for timing the market[39] - **Factor Testing Results**: - Annualized return: 9.41% - Sharpe ratio: 0.74 - Calmar ratio: 0.70 - Profit-loss ratio: 1.50 - Annualized excess return: 7.91% - Sharpe ratio (excess): 0.69 - Calmar ratio (excess): 0.72[42] Composite Factor - **Factor Name**: Composite Factor - **Factor Construction Idea**: Combines implied jump imbalance factor and jump imbalance dispersion factor for better market timing[40] - **Factor Construction Process**: - Formula: $$\Delta J_{R} > 0 \text{ and } \Delta J_{R_{Std}} < 0$$[40] - Parameters: - Implied jump imbalance factor - Jump imbalance dispersion factor[40] - **Factor Evaluation**: Shows significant improvement in market timing effectiveness[40] - **Factor Testing Results**: - Annualized return: 16.5% - Sharpe ratio: 1.28 - Calmar ratio: 2.41 - Annualized excess return: 15.49% - Sharpe ratio (excess): 0.82 - Calmar ratio (excess): 0.88[45] Factor Backtesting Results Jump Imbalance Factor - **Annualized return**: 6.23% - **Sharpe ratio**: 0.57 - **Profit-loss ratio**: