行业轮动周报：连板高度打开情绪持续发酵，GRU行业轮动调入房地产-20251118

Quantitative Models and Construction Methods - Model Name: Diffusion Index Model Model Construction Idea: Based on price momentum principles, the model identifies upward trends in industries to optimize allocation decisions[23][24][27] Model Construction Process: 1. Calculate the diffusion index for each industry based on price momentum 2. Rank industries by their diffusion index values 3. Allocate to industries with the highest diffusion index values Evaluation: The model performs well in capturing upward trends but struggles during market reversals or when trends shift to oversold rebounds[23][27] - Model Name: GRU Factor Model Model Construction Idea: Utilizes GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level volume and price data for industry rotation[31][32][36] Model Construction Process: 1. Input minute-level volume and price data into the GRU network 2. Train the model on historical data to identify industry rotation signals 3. Rank industries based on GRU factor scores and allocate accordingly Evaluation: The model adapts well to short-term market dynamics but faces challenges in long-term performance and extreme market conditions[31][38] Model Backtesting Results - Diffusion Index Model: - Weekly average return: -1.26% - Excess return over equal-weighted industry index: -1.99% - November excess return: -0.74% - Year-to-date excess return: 1.84%[22][27] - GRU Factor Model: - Weekly average return: 1.72% - Excess return over equal-weighted industry index: 1.00% - November excess return: 2.69% - Year-to-date excess return: -3.34%[31][36] Quantitative Factors and Construction Methods - Factor Name: Diffusion Index Factor Construction Idea: Measures industry momentum by tracking price trends and ranking industries accordingly[24][25][26] Factor Construction Process: 1. Calculate the diffusion index for each industry using price trend data 2. Rank industries based on diffusion index values 3. Identify industries with the highest and lowest diffusion index values for allocation decisions Evaluation: Effective in identifying upward trends but sensitive to market reversals[23][24] - Factor Name: GRU Factor Factor Construction Idea: Derived from GRU deep learning networks, the factor captures industry rotation signals based on volume and price dynamics[31][32][36] Factor Construction Process: 1. Train GRU networks on historical minute-level data 2. Generate GRU factor scores for industries 3. Rank industries by GRU factor scores for allocation decisions Evaluation: Strong adaptability to short-term market changes but limited robustness in long-term scenarios[31][38] Factor Backtesting Results - Diffusion Index Factor: - Top industries by diffusion index: Nonferrous metals (0.991), Banking (0.968), Steel (0.949), Communication (0.918), Electric equipment & new energy (0.914), Comprehensive (0.885)[24][25][26] - Weekly average return: -1.26% - Excess return over equal-weighted industry index: -1.99% - November excess return: -0.74% - Year-to-date excess return: 1.84%[22][27] - GRU Factor: - Top industries by GRU factor: Comprehensive (3.41), Real estate (2.63), Petroleum & petrochemical (2.13), Light manufacturing (1.67), Steel (0.53), Comprehensive finance (0.52)[32][35][36] - Weekly average return: 1.72% - Excess return over equal-weighted industry index: 1.00% - November excess return: 2.69% - Year-to-date excess return: -3.34%[31][36]