七月配置建议：不轻易低配A股

Quantitative Models and Construction 1. Model Name: Odds Ratio + Win Rate Strategy - Model Construction Idea: This strategy combines the odds ratio and win rate metrics to allocate risk budgets across assets, aiming to optimize returns under historical data constraints [3][46] - Model Construction Process: - The odds ratio and win rate metrics are calculated for each asset based on historical data - The risk budgets derived from these two metrics are summed to form a composite score - Asset allocation is determined by the composite score, with higher scores receiving higher allocations - Current allocation recommendation: 11.5% equities, 2.2% gold, 86.3% bonds [3][46] - Model Evaluation: The model demonstrates stable performance with low drawdowns, making it suitable for risk-averse investors [3][46] 2. Model Name: Odds Ratio Enhanced Strategy - Model Construction Idea: Focuses on maximizing returns by overweighting high-odds assets and underweighting low-odds assets under a volatility constraint [40][41] - Model Construction Process: - Odds ratios are calculated for each asset - A fixed volatility constraint is applied to ensure risk control - Asset allocation is adjusted dynamically based on odds ratios - Current allocation recommendation: 15.6% equities, 2.9% gold, 81.5% bonds [40][41] - Model Evaluation: The strategy effectively balances risk and return, achieving consistent performance over time [40][41] 3. Model Name: Win Rate Enhanced Strategy - Model Construction Idea: Utilizes macroeconomic factors (e.g., monetary policy, credit, growth, inflation, and overseas conditions) to derive win rate scores for asset allocation [43][44] - Model Construction Process: - Win rate scores are calculated based on macroeconomic indicators - Asset allocation is determined by the win rate scores, favoring assets with higher scores - Current allocation recommendation: 6.6% equities, 1.7% gold, 91.7% bonds [43][44] - Model Evaluation: The strategy is robust in capturing macroeconomic trends, providing a defensive allocation approach [43][44] --- Model Backtesting Results 1. Odds Ratio + Win Rate Strategy - Annualized Return: 7.0% (2011–2025), 7.6% (2014–2025), 7.2% (2019–2025) - Maximum Drawdown: 2.8% (2011–2025), 2.7% (2014–2025), 2.8% (2019–2025) - Sharpe Ratio: 2.86 (2011–2025), 3.26 (2014–2025), 2.85 (2019–2025) [3][46][47] 2. Odds Ratio Enhanced Strategy - Annualized Return: 6.6% (2011–2025), 7.5% (2014–2025), 7.0% (2019–2025) - Maximum Drawdown: 3.0% (2011–2025), 2.4% (2014–2025), 2.4% (2019–2025) - Sharpe Ratio: 2.72 (2011–2025), 3.19 (2014–2025), 3.02 (2019–2025) [40][41][42] 3. Win Rate Enhanced Strategy - Annualized Return: 7.0% (2011–2025), 7.7% (2014–2025), 6.3% (2019–2025) - Maximum Drawdown: 2.8% (2011–2025), 2.3% (2014–2025), 2.3% (2019–2025) - Sharpe Ratio: 2.96 (2011–2025), 3.36 (2014–2025), 2.87 (2019–2025) [43][44][45] --- Quantitative Factors and Construction 1. Factor Name: Value Factor - Factor Construction Idea: Measures the relative attractiveness of value stocks based on odds, trends, and crowding metrics [18][20] - Factor Construction Process: - Odds: 0.2 standard deviations (higher indicates cheaper valuation) - Trend: -0.1 standard deviations (moderate level) - Crowding: -1.0 standard deviations (low crowding) - Composite Score: 1.0 (highest among all factors) [18][20] - Factor Evaluation: Strong trend and low crowding make it a top-performing factor [18][20] 2. Factor Name: Quality Factor - Factor Construction Idea: Focuses on high-quality stocks with favorable odds and low crowding, awaiting trend confirmation [20][21] - Factor Construction Process: - Odds: 1.4 standard deviations (high level) - Trend: -0.3 standard deviations (weak level) - Crowding: -0.8 standard deviations (low level) - Composite Score: 0.6 [20][21] - Factor Evaluation: Promising long-term potential but requires trend confirmation for stronger performance [20][21] 3. Factor Name: Growth Factor - Factor Construction Idea: Targets growth stocks with improving odds and moderate crowding [23][25] - Factor Construction Process: - Odds: 0.6 standard deviations (moderate level) - Trend: 0.02 standard deviations (neutral level) - Crowding: -0.1 standard deviations (moderate level) - Composite Score: 0.4 [23][25] - Factor Evaluation: Suitable for neutral allocation due to balanced metrics [23][25] 4. Factor Name: Small-Cap Factor - Factor Construction Idea: Captures small-cap stocks with strong trends but high crowding and low odds [26][28] - Factor Construction Process: - Odds: -0.5 standard deviations (low level) - Trend: 0.9 standard deviations (high level) - Crowding: 0.6 standard deviations (high level) - Composite Score: 0.0 [26][28] - Factor Evaluation: High uncertainty due to low odds and high crowding, requiring cautious approach [26][28] --- Factor Backtesting Results 1. Value Factor - Odds: 0.2 standard deviations - Trend: -0.1 standard deviations - Crowding: -1.0 standard deviations - Composite Score: 1.0 [18][20] 2. Quality Factor - Odds: 1.4 standard deviations - Trend: -0.3 standard deviations - Crowding: -0.8 standard deviations - Composite Score: 0.6 [20][21] 3. Growth Factor - Odds: 0.6 standard deviations - Trend: 0.02 standard deviations - Crowding: -0.1 standard deviations - Composite Score: 0.4 [23][25] 4. Small-Cap Factor - Odds: -0.5 standard deviations - Trend: 0.9 standard deviations - Crowding: 0.6 standard deviations - Composite Score: 0.0 [26][28]