Quantitative Models and Construction Methods 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[27][28] - **Model Construction Process**: The diffusion index is calculated for each industry, ranking them based on their momentum. Industries with higher diffusion index values are considered to have stronger upward trends. The model selects industries with the highest diffusion index values for allocation. - Formula: Not explicitly provided in the report - **Model Evaluation**: The model has shown mixed performance over the years. It performed well in 2021 and 2022 but faced significant drawdowns in 2023 and 2024 due to market reversals and failure to adjust to cyclical changes[27] 2. Model Name: GRU Factor Model - **Model Construction Idea**: This model leverages GRU (Gated Recurrent Unit) deep learning networks to process high-frequency price and volume data, aiming to identify industry trends and generate excess returns[34][39] - **Model Construction Process**: The GRU network is trained on historical minute-level price and volume data to predict industry rankings. The model then allocates to industries with the highest GRU factor scores. - Formula: Not explicitly provided in the report - **Model Evaluation**: The model has shown strong adaptability in short-term cycles but struggles in long-term trends and extreme market conditions. It has faced challenges in capturing excess returns in 2025 due to concentrated market themes[34][39] --- Model Backtesting Results 1. Diffusion Index Model - **2025 YTD Excess Return**: 0.37%[26][31] - **June 2025 Excess Return**: 1.99%[31] - **Weekly Average Return (June 2025)**: -0.65%[31] - **Weekly Excess Return (June 2025)**: 0.79%[31] 2. GRU Factor Model - **2025 YTD Excess Return**: -3.83%[34][37] - **June 2025 Excess Return**: 0.25%[37] - **Weekly Average Return (June 2025)**: -1.18%[37] - **Weekly Excess Return (June 2025)**: 0.25%[37] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the momentum of industries by ranking them based on their upward trends[28] - **Factor Construction Process**: The diffusion index is calculated for each industry weekly. Industries are ranked based on their index values, with higher values indicating stronger momentum. - Example Values (as of June 20, 2025): - Top Industries: Comprehensive Finance (1.0), Non-Bank Finance (0.973), Banking (0.97)[28] - Bottom Industries: Coal (0.174), Food & Beverage (0.313), Oil & Gas (0.387)[28] - **Factor Evaluation**: The factor effectively captures upward trends but may underperform during market reversals[27][28] 2. Factor Name: GRU Factor - **Factor Construction Idea**: Utilizes GRU deep learning to analyze high-frequency trading data and rank industries based on predicted performance[34][39] - **Factor Construction Process**: The GRU network processes minute-level price and volume data to generate factor scores for each industry. Industries are ranked based on these scores. - Example Values (as of June 20, 2025): - Top Industries: Coal (3.48), Non-Bank Finance (3.15), Utilities (2.65)[35] - Bottom Industries: Communication (-17.95), Media (-15.45), Defense (-11.87)[35] - **Factor Evaluation**: The factor is effective in short-term trend identification but struggles with long-term stability and extreme market conditions[34][39] --- Factor Backtesting Results 1. Diffusion Index Factor - **Top Weekly Changes (June 20, 2025)**: - Oil & Gas: +0.09 - Textiles: +0.044 - Metals: +0.036[30] - **Bottom Weekly Changes (June 20, 2025)**: - Agriculture: -0.229 - Defense: -0.086 - Building Materials: -0.078[30] 2. GRU Factor - **Top Weekly Changes (June 20, 2025)**: - Non-Bank Finance: Significant increase - Consumer Services: Significant increase - Comprehensive: Significant increase[35] - **Bottom Weekly Changes (June 20, 2025)**: - Communication: Significant decrease - Electronics: Significant decrease - New Energy Equipment: Significant decrease[35]

- 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]

- The report discusses two main quantitative models: the Diffusion Index Model and the GRU Factor Model[6][7][14][33] Diffusion Index Model 1. **Model Name**: Diffusion Index Model 2. **Model Construction Idea**: The model is based on the principle of price momentum, capturing industry trends by observing the diffusion index of various sectors[6][27] 3. **Model Construction Process**: - Calculate the diffusion index for each industry - Rank industries based on their diffusion index values - Select top industries for investment based on their diffusion index rankings - Formula: $ \text{Diffusion Index} = \frac{\text{Number of advancing stocks}}{\text{Total number of stocks}} $ 4. **Model Evaluation**: The model has shown varying performance over the years, with significant returns in some periods and notable drawdowns in others[26][30] 5. **Model Test Results**: - 2025 YTD excess return: -3.16%[25] - April 2025 excess return: -1.08%[30] - Weekly excess return: 0.43%[30] GRU Factor Model 1. **Model Name**: GRU Factor Model 2. **Model Construction Idea**: The model leverages GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level price and volume data, aiming to capture trading information and trends[7][33] 3. **Model Construction Process**: - Collect minute-level price and volume data - Train a GRU network on historical data to identify patterns - Rank industries based on GRU factor scores - Select top industries for investment based on their GRU factor rankings - Formula: $ \text{GRU Factor} = \text{GRU Network Output} $ 4. **Model Evaluation**: The model has shown strong performance in short cycles but may struggle in long cycles or extreme market conditions[33][36] 5. **Model Test Results**: - 2025 YTD excess return: -3.33%[33] - April 2025 excess return: 0.92%[36] - Weekly excess return: -0.31%[36] Factor Rankings and Performance 1. **Diffusion Index Rankings (as of April 25, 2025)**: - Top industries: Banking (0.986), Non-Banking Financials (0.948), Comprehensive Financials (0.926), Computers (0.873), Retail (0.847), Communication (0.841)[14][27] - Bottom industries: Coal (0.105), Oil & Petrochemicals (0.175), Food & Beverage (0.257), Agriculture (0.396), Steel (0.423), Utilities (0.491)[27][28] 2. **GRU Factor Rankings (as of April 25, 2025)**: - Top industries: Banking (3.81), Transportation (2.77), Non-Banking Financials (2.37), Textiles & Apparel (2.34), Media (1.98), Light Manufacturing (1.81)[7][34] - Bottom industries: Automobiles (-5.31), Agriculture (-4.05), Pharmaceuticals (-4.03), Home Appliances (-3), Coal (-2.67), Defense (-2.64)[34] Weekly and Monthly Performance 1. **Diffusion Index Weekly Performance**: - Top weekly gainers: Construction (0.189), Real Estate (0.187), Building Materials (0.136), Light Manufacturing (0.089), Textiles & Apparel (0.081), Communication (0.069)[29] - Top weekly losers: Steel (-0.111), Utilities (-0.038), Non-Ferrous Metals (-0.018), Coal (0.003), Transportation (0.007), Computers (0.009)[29] 2. **GRU Factor Weekly Performance**: - Top weekly gainers: Banking, Textiles & Apparel, Consumer Services[34] - Top weekly losers: Coal, Automobiles, Construction[34]