Quantitative Models and Construction A-Share Market Timing Model - **Model Name**: A-Share Multi-Dimensional Timing Model [10] - **Construction Idea**: The model integrates valuation, sentiment, capital, and technical dimensions to assess the directional outlook of the A-share market [10][12][16] - **Construction Process**: - Signals are generated daily for each dimension, with values of 0, ±1 representing neutral, bullish, and bearish views respectively [10] - **Valuation Dimension**: Uses equity risk premium (ERP) to capture mean-reversion characteristics [12][16] - **Sentiment Dimension**: Includes option put-call ratio, implied volatility, and futures member position ratio to reflect market sentiment [12][16] - **Capital Dimension**: Tracks financing purchase amounts to identify market trends [12][16] - **Technical Dimension**: Employs Bollinger Bands and individual stock turnover ratio differences to capture trend continuation [12][16] - The final market view is determined by the sum of scores across all dimensions [10] - **Evaluation**: The model effectively combines mean-reversion and trend-following strategies, balancing risk avoidance and opportunity capture [10] Style Timing Model - **Model Name**: Dividend Style Timing Model [18] - **Construction Idea**: Targets the relative performance of the CSI Dividend Index against the CSI All Index using trend-based indicators [18][22] - **Construction Process**: - Three indicators are used to generate daily signals (0, ±1 for neutral, bullish, bearish views) [18] - **Relative Momentum**: Positive indicator for dividend style [22] - **10Y-1Y Term Spread**: Negative indicator for dividend style, as wider spreads favor growth assets [22] - **Interbank Repo Volume**: Positive indicator for dividend style, reflecting asset scarcity [22] - Signals are aggregated to determine the overall view on dividend style [18] - **Evaluation**: The model captures dividend style trends effectively, leveraging macroeconomic and liquidity factors [18] - **Model Name**: Large-Cap vs Small-Cap Style Timing Model [23] - **Construction Idea**: Differentiates between macro-driven trends in low congestion and fund-driven reversals in high congestion [23][25] - **Construction Process**: - **Momentum Difference**: Calculates the difference in momentum between the Wind Micro-Cap Index and CSI 300 Index across multiple windows, averaging the top/bottom results for small/large-cap scores [27] - **Turnover Ratio**: Similar calculation for turnover ratio differences across windows, averaged for small/large-cap scores [27] - **Congestion Score**: Combines momentum and turnover scores to determine congestion levels (high congestion >90% for small-cap, <10% for large-cap) [27] - **Trend Model**: Uses small/large parameter double moving average models based on congestion levels [25] - **Evaluation**: The model adapts to market conditions, balancing long-term trends and short-term reversals [23][25] Sector Rotation Model - **Model Name**: Genetic Programming Sector Rotation Model [30] - **Construction Idea**: Directly mines factors from sector index data using genetic programming without relying on predefined scoring rules [30][33] - **Construction Process**: - **Factor Mining**: Utilizes NSGA-II algorithm to optimize for monotonicity and top-group performance simultaneously [33][34] - **Factor Combination**: Combines factors with weak collinearity using greedy strategy and variance inflation coefficient [34] - **Weekly Rebalancing**: Selects top five sectors based on multi-factor scores for equal-weight allocation [30] - **Example Factor**: Calculates covariance between standardized weekly low prices and monthly open prices over 25 days, adjusted by standardized weekly high prices over 15 days [38] - **Evaluation**: The model enhances factor diversity and reduces overfitting risks, achieving robust sector rotation performance [33][34] All-Weather Enhanced Portfolio - **Model Name**: China All-Weather Enhanced Portfolio [39] - **Construction Idea**: Implements macro factor risk parity to diversify risks across underlying macro drivers rather than assets [39][42] - **Construction Process**: - **Macro Quadrant Division**: Divides growth and inflation dimensions into four quadrants based on whether they exceed or fall short of expectations [42] - **Quadrant Portfolio Construction**: Constructs sub-portfolios within each quadrant, focusing on downside risk [42] - **Risk Budgeting**: Adjusts quadrant weights monthly based on macro momentum indicators combining buy-side and sell-side expectations [42] - **Evaluation**: The strategy demonstrates strong defensive attributes during market downturns while maintaining consistent returns [40][43] --- Backtesting Results A-Share Market Timing Model - **Annualized Return**: 24.94% [15] - **Maximum Drawdown**: -28.46% [15] - **Sharpe Ratio**: 1.16 [15] - **Calmar Ratio**: 0.88 [15] - **YTD Return**: 43.84% [15] - **Weekly Return**: 5.28% [15] Dividend Style Timing Model - **Annualized Return**: 15.67% [21] - **Maximum Drawdown**: -25.52% [21] - **Sharpe Ratio**: -0.26 [21] - **Calmar Ratio**: 0.85 [21] - **YTD Return**: 20.86% [21] - **Weekly Return**: -3.63% [21] Large-Cap vs Small-Cap Style Timing Model - **Annualized Return**: 27.04% [28] - **Maximum Drawdown**: -32.05% [28] - **Sharpe Ratio**: 1.13 [28] - **Calmar Ratio**: 0.84 [28] - **YTD Return**: 71.14% [28] - **Weekly Return**: -7.80% [28] Sector Rotation Model - **Annualized Return**: 30.83% [33] - **Annualized Volatility**: 17.74% [33] - **Sharpe Ratio**: 1.74 [33] - **Maximum Drawdown**: -19.63% [33] - **Calmar Ratio**: 1.57 [33] - **YTD Return**: 35.44% [33] - **Weekly Return**: -4.39% [33] All-Weather Enhanced Portfolio - **Annualized Return**: 11.51% [43] - **Annualized Volatility**: 6.18% [43] - **Sharpe Ratio**: 1.86 [43] - **Maximum Drawdown**: -6.30% [43] - **Calmar Ratio**: 1.83 [43] - **YTD Return**: 10.75% [43] - **Weekly Return**: -1.53% [43]

Quantitative Models and Construction Methods - **Model Name**: A-Share Multi-Dimensional Timing Model **Model Construction Idea**: The model evaluates the directional judgment of the A-share market using four dimensions: valuation, sentiment, capital, and technical indicators. Valuation and sentiment dimensions adopt a mean-reversion logic, while capital and technical dimensions use trend-following logic. The model combines these dimensions to provide a comprehensive view of market trends [2][9][15]. **Model Construction Process**: 1. The model uses the Wind All A Index as a proxy for the A-share market. 2. Each dimension generates daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. 3. Valuation indicators include equity risk premium (ERP). 4. Sentiment indicators include option put-call ratio, implied volatility, and futures member position ratio. 5. Capital indicators include financing purchase amount. 6. Technical indicators include Bollinger Bands and the difference in the proportion of individual stock trading volume [11][15]. 7. The final multi-dimensional score is calculated as the sum of the scores from the four dimensions, determining the overall market view [9][15]. **Model Evaluation**: The model effectively captures market trends and provides actionable insights for timing decisions [9]. - **Model Name**: Style Timing Model **Model Construction Idea**: The model evaluates timing for dividend and size styles using trend-based indicators and crowding metrics [3][17][22]. **Model Construction Process**: 1. **Dividend Style Timing**: - The model uses three indicators: relative momentum of the CSI Dividend Index vs. CSI All Index, 10Y-1Y term spread, and interbank pledged repo transaction volume. - Each indicator generates daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. - The final score is the sum of the three indicators, determining the overall view on dividend style [17][21]. 2. **Size Style Timing**: - The model uses the crowding degree of small-cap and large-cap styles, calculated based on momentum difference and trading volume ratio between the Wind Micro-Cap Index and CSI 300 Index. - Crowding degree is determined by averaging the top three results of six different window lengths for small-cap and large-cap styles. - High crowding is triggered when small-cap crowding exceeds 90% or large-cap crowding falls below 10%. - In high crowding zones, a small parameter double moving average model is used to capture short-term reversals. In low crowding zones, a large parameter double moving average model is used to follow medium- to long-term trends [22][24][26]. **Model Evaluation**: The model provides effective timing signals for style rotation, especially in different market conditions [22][24]. - **Model Name**: Industry Rotation Model **Model Construction Idea**: The model uses genetic programming to directly extract factors from industry index data, focusing on price-volume and valuation characteristics. It employs a dual-objective genetic programming approach to enhance factor diversity and reduce overfitting [4][29][32]. **Model Construction Process**: 1. The model uses 32 CITIC industry indices as underlying assets. 2. Factors are updated quarterly, and the model rebalances weekly. 3. The dual-objective genetic programming approach evaluates factors using |IC| and NDCG@5 metrics to assess monotonicity and performance of long positions. 4. Factors are combined using a greedy strategy and variance inflation factor to reduce collinearity. 5. The highest-weight factor is constructed as follows: - Perform cross-sectional regression of standardized monthly trading volume against the rolling 4-year percentile of price-to-book ratio (P/B). Take residuals as variable A. - Sum the smallest 9 values of variable A over the past 15 trading days to obtain variable B. - Standardize variable B using z-score, reverse values greater than 2.5, and sum the standardized values over the past 15 trading days [29][33][37]. **Model Evaluation**: The model effectively identifies industry rotation factors with strong monotonicity and performance, while reducing overfitting risks [29][33]. - **Model Name**: China Domestic All-Weather Enhanced Portfolio **Model Construction Idea**: The model adopts a macro factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively allocates based on macro expectation momentum [5][38][41]. **Model Construction Process**: 1. **Macro Quadrant Division and Asset Selection**: Divide growth and inflation dimensions into four quadrants based on whether they exceed or fall short of expectations. Determine suitable assets for each quadrant using quantitative and qualitative methods. 2. **Quadrant Portfolio Construction and Risk Measurement**: Construct sub-portfolios with equal weights for assets within each quadrant, focusing on downside risk. 3. **Risk Budgeting Model for Quadrant Weights**: Adjust quadrant risk budgets monthly based on "quadrant views" derived from macro expectation momentum indicators, which consider buy-side expectation momentum and sell-side expectation deviation momentum [38][41]. **Model Evaluation**: The model effectively balances macro risks and enhances portfolio performance through active allocation [38][41]. --- Model Backtesting Results - **A-Share Multi-Dimensional Timing Model**: - Annualized Return: 25.23% - Maximum Drawdown: -28.46% - Sharpe Ratio: 1.17 - Calmar Ratio: 0.89 - Year-to-Date (YTD): 40.98% - Last Week's Return: 0.15% [14] - **Style Timing Model**: - **Dividend Style Timing**: - Annualized Return: 16.04% - Maximum Drawdown: -25.52% - Sharpe Ratio: 0.87 - Calmar Ratio: 0.63 - YTD: 21.75% - Last Week's Return: 0.23% [20] - **Size Style Timing**: - Annualized Return: 26.25% - Maximum Drawdown: -30.86% - Sharpe Ratio: 1.09 - Calmar Ratio: 0.85 - YTD: 65.89% - Last Week's Return: 1.07% [27] - **Industry Rotation Model**: - Annualized Return: 32.60% - Annualized Volatility: 17.95% - Sharpe Ratio: 1.82 - Maximum Drawdown: -19.63% - Calmar Ratio: 1.66 - Last Week's Return: 0.27% - YTD: 36.44% [32] - **China Domestic All-Weather Enhanced Portfolio**: - Annualized Return: 11.53% - Annualized Volatility: 6.16% - Sharpe Ratio: 1.87 - Maximum Drawdown: -6.30% - Calmar Ratio: 1.83 - Last Week's Return: 0.66% - YTD: 9.02% [42]