五月配置建议：主权CDS下行预示AH股机会

Quantitative Models and Construction 1. Model Name: CDS Timing Strategy - Model Construction Idea: The model uses the 20-day difference signal of China's sovereign CDS as a timing indicator for Hong Kong stocks, leveraging the negative correlation between CDS and stock performance[12][13] - Model Construction Process: 1. Calculate the 20-day difference of China's sovereign CDS 2. Use the signal to time Hong Kong stock investments 3. Evaluate the annualized excess return relative to the benchmark - Model Evaluation: The model demonstrates a strong fit with Hong Kong stock returns and provides a reliable timing signal[12][13] 2. Model Name: Duration Timing Strategy - Model Construction Idea: The model estimates the expected return of government bonds for any duration and holding period using a multi-step process[17] - Model Construction Process: 1. Decompose government bond yields 2. Predict interest rates using modeling techniques 3. Simulate scenarios via Monte Carlo methods 4. Calculate expected returns for different durations and holding periods - Model Evaluation: The strategy is effective for short-term bond timing and provides actionable insights for duration allocation[17][19] 3. Model Name: Equity Index Return Prediction Model - Model Construction Idea: Predict the future returns of broad-based indices using a combination of macroeconomic and valuation factors[22][27] - Model Construction Process: 1. Use macroeconomic indicators and valuation metrics 2. Apply the model to predict returns for indices like CSI 300, CSI 500, etc. 3. Compare predicted returns to historical benchmarks - Model Evaluation: The model shows strong predictive power for large-cap indices like CSI 300, while small-cap indices like CSI 500 exhibit lower reliability[22][27] 4. Model Name: Industry Rotation Strategy - Model Construction Idea: Evaluate industries based on momentum, turnover, volatility, and beta to identify rotation opportunities[60] - Model Construction Process: 1. Calculate 12-month information ratios for industry momentum 2. Assess turnover, volatility, and beta for crowding metrics 3. Combine these dimensions to rank industries - Model Evaluation: The strategy effectively identifies high-potential industries and provides actionable rotation insights[60][63] 5. Model Name: Odds + Win Rate Strategy - Model Construction Idea: Combine odds and win rate metrics to allocate assets dynamically[65][70] - Model Construction Process: 1. Construct odds and win rate indicators for each asset 2. Combine the two metrics into a unified score 3. Allocate assets based on the combined score - Model Evaluation: The strategy balances risk and return effectively, achieving stable performance over time[65][70] --- Model Backtest Results 1. CDS Timing Strategy - Annualized Return: 11.8% - Annualized Volatility: 13.9% - Maximum Drawdown: 19.1% - Sharpe Ratio: 0.851[15] 2. Duration Timing Strategy - Annualized Return: 6.8% - Annualized Volatility: 2.1% - Maximum Drawdown: 2.3% - Calmar Ratio: 2.94[19] 3. Equity Index Return Prediction Model - CSI 300: Predicted Return 19.7% - CSI 500: Predicted Return -27.8%[22][26] 4. Industry Rotation Strategy - Annualized Excess Return: 12.2% (since 2011) - Tracking Error: 10.9% - Maximum Drawdown: 25.4% - IR: 1.12[61] 5. Odds + Win Rate Strategy - Annualized Return: 6.9% (since 2011) - Annualized Volatility: 2.3% - Maximum Drawdown: 2.8% - Sharpe Ratio: 3.03[72] --- Quantitative Factors and Construction 1. Factor Name: Quality Factor - Factor Construction Idea: Combines odds, trend, and crowding metrics to evaluate quality stocks[46] - Factor Construction Process: 1. Calculate odds (valuation) at 1.3 standard deviations 2. Assess trend at -0.1 standard deviations 3. Measure crowding at -1.1 standard deviations 4. Combine metrics into a composite score - Factor Evaluation: High composite score indicates strong potential for long-term allocation[46] 2. Factor Name: Growth Factor - Factor Construction Idea: Evaluates growth stocks based on trend, odds, and crowding metrics[47] - Factor Construction Process: 1. Calculate trend at 0.5 standard deviations 2. Assess odds at -1.1 standard deviations 3. Measure crowding at 0.2 standard deviations 4. Combine metrics into a composite score - Factor Evaluation: Low composite score suggests limited allocation value[47] 3. Factor Name: Dividend Factor - Factor Construction Idea: Focuses on dividend-paying stocks with moderate odds and low crowding[50] - Factor Construction Process: 1. Calculate trend at -1.7 standard deviations 2. Assess odds at -0.2 standard deviations 3. Measure crowding at -1.6 standard deviations 4. Combine metrics into a composite score - Factor Evaluation: Low composite score indicates limited allocation potential[50] 4. Factor Name: Small-Cap Factor - Factor Construction Idea: Evaluates small-cap stocks based on trend, odds, and crowding metrics[53] - Factor Construction Process: 1. Calculate trend at -0.06 standard deviations 2. Assess odds at -0.05 standard deviations 3. Measure crowding at 0.3 standard deviations 4. Combine metrics into a composite score - Factor Evaluation: High uncertainty and low composite score suggest caution[53] --- Factor Backtest Results 1. Quality Factor - Odds: 1.3 SD - Trend: -0.1 SD - Crowding: -1.1 SD - Composite Score: 3[46] 2. Growth Factor - Odds: -1.1 SD - Trend: 0.5 SD - Crowding: 0.2 SD - Composite Score: 0[47] 3. Dividend Factor - Odds: -0.2 SD - Trend: -1.7 SD - Crowding: -1.6 SD - Composite Score: 0[50] 4. Small-Cap Factor - Odds: -0.05 SD - Trend: -0.06 SD - Crowding: 0.3 SD - Composite Score: 0[53]