大盘或进入高波动状态

Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - Model Construction Idea: The model aims to fully explore technical information to depict market conditions, breaking down the vague concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of timing signals from 10 selected indicators[9][14][15] - Model Construction Process: 1. Select 10 effective market observation indicators across the five dimensions (e.g., 20-day Bollinger Bands, 20-day price deviation rate, 60-day turnover rate volatility, etc.)[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score[9][14] - Model Evaluation: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Dividend Style Timing Model - Model Construction Idea: The model times the dividend style by analyzing the relative performance of the CSI Dividend Index against the CSI All Share Index, using three indicators: relative momentum, 10Y-1Y term spread, and interbank pledged repo trading volume[16][19] - Model Construction Process: 1. Generate daily signals (0, +1, -1) for each indicator, representing neutral, bullish, and bearish views, respectively 2. Aggregate the scores to determine the overall long/short view on the dividend style 3. When bullish, fully allocate to the CSI Dividend Index; when bearish, fully allocate to the CSI All Share Index[16][19] - Model Evaluation: The model has consistently maintained a bearish view on the dividend style this year, favoring growth style instead[16] 3. Model Name: Large-Cap vs. Small-Cap Style Timing Model - Model Construction Idea: The model evaluates the crowding level of large-cap and small-cap styles based on momentum and trading volume differences, adjusting the strategy based on whether the market is in a high or low crowding state[20][22][24] - Model Construction Process: 1. Calculate momentum differences and trading volume ratios between the Wind Micro-Cap Index and the CSI 300 Index over multiple time windows 2. Derive crowding scores for both large-cap and small-cap styles based on percentile rankings of the calculated metrics 3. Use a dual moving average model with smaller parameters in high crowding states and larger parameters in low crowding states to determine trends[20][22][24] - Model Evaluation: The model effectively captures the medium- to long-term trends in low crowding states and reacts to potential reversals in high crowding states[22] 4. Model Name: Industry Rotation Model (Genetic Programming) - Model Construction Idea: The model employs genetic programming to directly extract factors from industry index data (e.g., price, volume, valuation) without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[27][30][31] - Model Construction Process: 1. Use NSGA-II algorithm to optimize two objectives: |IC| and NDCG@5 2. Combine multiple factors with weak collinearity into industry scores using greedy strategies and variance inflation factors 3. Select the top five industries with the highest composite scores for equal-weighted allocation[30][33][37] - Model Evaluation: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks[30][33] 5. Model Name: China Domestic All-Weather Enhanced Portfolio - Model Construction Idea: The model adopts a macro factor risk parity framework, emphasizing diversification across underlying macro risk sources (growth and inflation surprises) rather than asset classes[38][41] - Model Construction Process: 1. Divide macroeconomic scenarios into four quadrants based on growth and inflation surprises 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, actively overweighting favorable quadrants[41][42] - Model Evaluation: The strategy achieves enhanced performance by actively allocating based on macroeconomic expectations[38][41] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.67% - Annualized Volatility: 17.33% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.87[15] 2. Dividend Style Timing Model - Annualized Return: 16.65% - Maximum Drawdown: -25.52% - Sharpe Ratio: 0.91 - Calmar Ratio: 0.65 - YTD Return: 5.78%[17] 3. Large-Cap vs. Small-Cap Style Timing Model - Annualized Return: 27.79% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.16 - Calmar Ratio: 0.87 - YTD Return: 6.27%[25] 4. Industry Rotation Model (Genetic Programming) - Annualized Return: 31.95% - Annualized Volatility: 17.44% - Maximum Drawdown: -19.62% - Sharpe Ratio: 1.83 - Calmar Ratio: 1.63 - YTD Return: 3.31%[30] 5. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.82% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.91 - Calmar Ratio: 1.88 - YTD Return: 2.02%[42]