- Model Name: Diffusion Index Model; Model Construction Idea: The model is based on the observation of industry diffusion indices; Detailed Construction Process: The model tracks the weekly changes in diffusion indices for various industries, ranking them based on their performance. The formula used is $ \text{Diffusion Index} = \frac{\text{Number of Advancing Stocks}}{\text{Total Number of Stocks}} $; Model Evaluation: The model has shown varying performance over the years, with significant returns in some periods and notable drawdowns in others[6][14][27] - Model Name: GRU Factor Model; Model Construction Idea: The model utilizes GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level price and volume data; Detailed Construction Process: The model ranks industries based on GRU factor scores, which are derived from the GRU network's analysis of trading data. The formula used is $ \text{GRU Factor Score} = \text{GRU Network Output} $; Model Evaluation: The model has achieved substantial excess returns by capturing trading information, though it has faced challenges in certain market conditions[7][14][34] Model Backtest Results - Diffusion Index Model, Average Weekly Return: 0.72%, Excess Return: 0.11%, Year-to-Date Excess Return: -2.26%[32] - GRU Factor Model, Average Weekly Return: 1.07%, Excess Return: 0.44%, Year-to-Date Excess Return: -3.71%[37] Factor Construction and Evaluation - Factor Name: GRU Industry Factor; Factor Construction Idea: The factor is constructed using GRU deep learning networks to analyze minute-level trading data; Detailed Construction Process: The factor scores are calculated based on the GRU network's output, which evaluates the trading data to rank industries. The formula used is $ \text{GRU Factor Score} = \text{GRU Network Output} $; Factor Evaluation: The factor has shown significant improvements in certain industries, indicating its effectiveness in capturing trading information[7][14][35] Factor Backtest Results - GRU Industry Factor, Top Industries: Automotive (2.84), Steel (1.85), Media (1.48), Power Equipment and New Energy (1.35), Communication (0.88), Coal (0.66)[7][14][35]

Quantitative Models and Construction 1. Model Name: Diffusion Index Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industry performance[28][38] - **Model Construction Process**: The model calculates the diffusion index for each industry, ranking them based on their relative performance. Industries with higher diffusion indices are recommended for allocation. The model tracks weekly and monthly changes in the diffusion index to adjust allocations dynamically[5][14][29] - **Model Evaluation**: The model has shown strong performance in capturing momentum trends during upward markets but may underperform during market reversals[28][38] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency volume and price data, aiming to identify industry rotation opportunities[39] - **Model Construction Process**: The GRU network is trained on historical minute-level data to predict industry factor rankings. The model dynamically adjusts allocations based on the predicted rankings, focusing on industries with higher GRU factor scores[6][34][39] - **Model Evaluation**: The model performs well in short-term scenarios due to its adaptability but may face challenges in long-term or extreme market conditions[39] --- Backtesting Results of Models 1. Diffusion Index Model - **2025 YTD Excess Return**: -2.75%[27][32] - **April 2025 Excess Return**: -0.68%[32] - **Weekly Portfolio Return**: -0.18%[32] 2. GRU Factor Model - **2025 YTD Excess Return**: -3.54%[34][37] - **April 2025 Excess Return**: 0.68%[37] - **Weekly Portfolio Return**: -0.78%[37] --- Quantitative Factors and Construction 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the breadth of industry performance to identify upward trends[5][14] - **Factor Construction Process**: The diffusion index is calculated as the proportion of stocks in an industry with positive momentum. Weekly and monthly changes in the index are tracked to adjust rankings dynamically[5][14][29] - **Factor Evaluation**: Effective in capturing momentum trends but sensitive to market reversals[28][38] 2. Factor Name: GRU Industry Factor - **Factor Construction Idea**: Utilizes GRU deep learning to analyze high-frequency trading data and predict industry rankings[39] - **Factor Construction Process**: The GRU network processes minute-level volume and price data to generate factor scores for industries. Industries with higher scores are prioritized for allocation[6][34][39] - **Factor Evaluation**: Strong adaptability in short-term scenarios but limited in long-term or extreme market conditions[39] --- Backtesting Results of Factors 1. Diffusion Index Factor - **Top 6 Industries (as of April 30, 2025)**: Banking (0.988), Non-Banking Financials (0.94), Comprehensive Financials (0.928), Computers (0.884), Retail (0.88), Automobiles (0.872)[5][14][29] - **Weekly Change Leaders**: Steel (0.17), Comprehensive (0.095), Automobiles (0.065)[5][31] 2. GRU Industry Factor - **Top 6 Industries (as of April 30, 2025)**: Real Estate (4.62), Textiles & Apparel (4.14), Comprehensive Financials (2.89), Transportation (1.71), Light Manufacturing (1.7), Construction (1.41)[6][35] - **Weekly Change Leaders**: Pharmaceuticals, Real Estate, Comprehensive Financials[6][35]

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