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]

- The diffusion index model tracks industry rotation based on momentum principles, focusing on upward trends in industry performance. It has been monitored for four years, with notable performance in 2021 achieving excess returns of over 25% before a significant drawdown in September due to cyclical stock adjustments. In 2025, the model suggests allocating to industries such as non-ferrous metals, banking, communication, steel, electronics, and power equipment & new energy[22][23][26] - The GRU factor model utilizes minute-level volume and price data processed through GRU deep learning networks. It has shown strong adaptability in short cycles but performs less effectively in long cycles. In 2025, the model's industry rotation includes sectors like agriculture, power & utilities, basic chemicals, transportation, steel, and petrochemicals. Weekly average returns were 2.56%, with excess returns of 1.65% against equal-weighted industry benchmarks. Year-to-date excess returns stand at -4.49%[29][30][32] - Diffusion index weekly tracking shows top-ranked industries as non-ferrous metals (0.991), banking (0.931), power equipment & new energy (0.925), communication (0.92), steel (0.871), and electronics (0.864). Industries with the most significant weekly changes include power equipment & new energy (+0.083), petrochemicals (+0.082), and light manufacturing (+0.078)[23][24][25] - GRU factor weekly tracking ranks industries such as comprehensive (7.22), basic chemicals (3.37), building materials (2.7), transportation (2.36), power & utilities (1.96), and food & beverages (1.94) as top performers. Industries with notable weekly increases include power & utilities, non-bank finance, and basic chemicals[30][33][37]

- The diffusion index model has been tracking out-of-sample performance for four years, with notable results in 2021 when momentum strategies captured industry trends, achieving excess returns of over 25% before a significant drawdown in September due to cyclical stock adjustments. In 2022, the strategy maintained stable returns with an annual excess return of 6.12%. However, in 2023, excess returns declined to -4.58%, and in 2024, a major drawdown occurred after September due to the model's focus on upward trends, missing rebound industries, resulting in an annual excess return of -5.82%[24][28] - The diffusion index model suggests allocating to industries such as non-bank finance, construction, and defense military, which showed significant week-on-week improvement in rankings. The top six industries based on diffusion index rankings as of October 24, 2025, are non-bank finance (0.988), banking (0.967), steel (0.952), communication (0.946), comprehensive (0.913), and non-bank finance (0.9)[25][26][27] - The GRU factor model, based on minute-level volume and price data processed through GRU deep learning networks, has shown strong performance in short cycles but weaker performance in long cycles. The model has been effective in capturing trading information since 2021, achieving significant excess returns. However, since February 2025, the model has faced challenges in generating excess returns due to market focus on thematic trading[31][37] - The GRU factor model ranks industries based on their GRU factor scores. As of October 24, 2025, the top six industries are non-bank finance (1.13), banking (1), electric power and utilities (0.54), textile and apparel (0.03), automotive (-0.58), and machinery (-0.73). Industries with the lowest GRU factor scores include food and beverage (-17.79), non-ferrous metals (-10.81), basic chemicals (-8.82), agriculture (-8.76), coal (-6.57), and building materials (-6.48)[6][13][32] - The GRU factor model's weekly industry rotation suggests allocating to non-bank finance, electric power and utilities, textile and apparel, transportation, steel, and petrochemicals. For the week ending October 24, 2025, the model achieved an average return of 1.89%, underperforming the equal-weighted return of the CSI first-tier industries by -0.77%. For October, the model's excess return is 1.80%, while the year-to-date excess return stands at -6.41%[6][34][39]

Quantitative Models and Construction Methods 1. Model Name: Diffusion Index Industry Rotation Model - **Model Construction Idea**: The model is based on the principle of price momentum, aiming to capture upward trends in industries through a diffusion index[26][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. Select top industries for allocation based on their rankings 4. Adjust the portfolio monthly or weekly based on updated diffusion index rankings[26][27] - **Model Evaluation**: The model has shown stable performance in certain years (e.g., 2022 with an annual excess return of 6.12%) but struggled during market reversals or concentrated market themes, such as in 2024 and 2025[26][33] 2. Model Name: GRU Factor Industry Rotation 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[38] - **Model Construction Process**: 1. Input high-frequency volume and price data into the GRU network 2. Train the GRU model on historical data to identify patterns in industry rotation 3. Generate factor scores for industries based on the GRU model's output 4. Rank industries by their GRU factor scores and allocate to top-ranked industries[38][34] - **Model Evaluation**: The model performs well in short cycles but struggles in long cycles or extreme market conditions. It has shown difficulty in capturing excess returns in concentrated market themes during 2025[33][38] --- Model Backtesting Results 1. Diffusion Index Industry Rotation Model - **Weekly Average Return**: -1.74%[30] - **Excess Return (Weekly)**: -1.41%[30] - **Excess Return (September 2025)**: -1.88%[30] - **Excess Return (2025 YTD)**: 2.76%[25][30] 2. GRU Factor Industry Rotation Model - **Weekly Average Return**: -0.72%[36] - **Excess Return (Weekly)**: -0.38%[36] - **Excess Return (September 2025)**: -0.10%[36] - **Excess Return (2025 YTD)**: -7.78%[33][36] --- Quantitative Factors and Construction Methods 1. Factor Name: Diffusion Index - **Factor Construction Idea**: Measures the breadth of price momentum across industries to identify upward trends[26][27] - **Factor Construction Process**: 1. Calculate the proportion of stocks in an industry with positive price momentum 2. Aggregate these proportions to derive the diffusion index for the industry 3. Rank industries based on their diffusion index values[27][28] - **Factor Evaluation**: Effective in capturing upward trends but vulnerable to reversals and underperformance in counter-trend markets[26][33] 2. Factor Name: GRU Factor - **Factor Construction Idea**: Utilizes GRU deep learning to analyze high-frequency trading data and generate predictive scores for industry rotation[38] - **Factor Construction Process**: 1. Input high-frequency trading data into the GRU network 2. Train the model to recognize patterns in industry rotation 3. Output factor scores for industries based on the model's predictions[38][34] - **Factor Evaluation**: Strong in short-term predictions but less effective in long-term or extreme market conditions[33][38] --- Factor Backtesting Results 1. Diffusion Index - **Top Industries (Weekly)**: Non-ferrous Metals (0.978), Banking (0.968), Communication (0.946), Electronics (0.877), Automotive (0.874), Retail (0.873)[27] - **Bottom Industries (Weekly)**: Food & Beverage (0.354), Real Estate (0.46), Coal (0.487), Transportation (0.543), Construction (0.574), Building Materials (0.618)[27] 2. GRU Factor - **Top Industries (Weekly)**: Non-ferrous Metals (7.4), Petrochemicals (5.38), Coal (4.17), Steel (4.15), Building Materials (3.46), Non-banking Financials (3.08)[34] - **Bottom Industries (Weekly)**: Comprehensive Finance (-19.42), Utilities (-13.41), Electronics (-13.18), Pharmaceuticals (-11.14), Automotive (-10.07), Consumer Services (-10.04)[34]

- Model Name: Diffusion Index Model; Construction Idea: The model is based on the observation of industry diffusion indices to capture industry trends; Construction Process: The model tracks the weekly and monthly changes in diffusion indices for various industries, ranking them based on their diffusion index values. The formula used is not explicitly mentioned, but the ranking is based on the diffusion index values observed; Evaluation: The model has shown varying performance over the years, with notable returns in some years and significant drawdowns in others[4][24][25] - Model Name: GRU Factor Model; Construction Idea: The model utilizes GRU (Gated Recurrent Unit) neural networks to process minute-level volume and price data to generate industry factors; Construction Process: The model ranks industries based on GRU-generated factors, which are derived from deep learning on historical volume and price data. The specific formula is not provided, but the ranking is based on the GRU factor values; Evaluation: The model has shown strong performance in short cycles but struggles in longer cycles and extreme market conditions[5][30][31] Model Backtest Results - Diffusion Index Model, Average Weekly Return: 3.95%, Excess Return over Equal-weighted Index: 1.94%, August Excess Return: 1.51%, Year-to-date Excess Return: 1.75%[28] - GRU Factor Model, Average Weekly Return: -0.06%, Excess Return over Equal-weighted Index: -2.07%, August Excess Return: -1.78%, Year-to-date Excess Return: -6.66%[33] Factor Construction and Evaluation - Factor Name: Diffusion Index; Construction Idea: The factor is constructed by observing the weekly and monthly changes in industry diffusion indices; Construction Process: The factor ranks industries based on their diffusion index values, with higher values indicating stronger trends. The specific formula is not provided, but the ranking is based on the observed diffusion index values; Evaluation: The factor has shown varying performance, capturing industry trends effectively in some periods while underperforming in others[4][24][25] - Factor Name: GRU Industry Factor; Construction Idea: The factor is generated using GRU neural networks to process minute-level volume and price data; Construction Process: The factor ranks industries based on GRU-generated values, which are derived from deep learning on historical data. The specific formula is not provided, but the ranking is based on the GRU factor values; Evaluation: The factor performs well in short cycles but faces challenges in longer cycles and extreme market conditions[5][30][31] Factor Backtest Results - Diffusion Index Factor, Top Industries: Comprehensive Finance (1.0), Steel (1.0), Non-bank Finance (0.999), Comprehensive (0.998), Non-ferrous Metals (0.997), Communication (0.997)[25] - GRU Industry Factor, Top Industries: Non-ferrous Metals (5.67), Non-bank Finance (4.65), Building Materials (4.14), Real Estate (4.08), Steel (3.64), Basic Chemicals (2.71)[31][13]

- Model Name: Diffusion Index Model; Model Construction Idea: The model is based on the principle of price momentum; Model Construction Process: The model tracks the weekly and monthly changes in the diffusion index of various industries, ranking them accordingly. The formula used is $ \text{Diffusion Index} = \frac{\text{Number of Upward Trends}}{\text{Total Number of Trends}} $; Model Evaluation: The model has shown varying performance over the years, with significant returns in some periods and notable drawdowns in others[27][28][31] - Model Name: GRU Factor Model; Model Construction Idea: The model utilizes GRU deep learning networks to analyze minute-level volume and price data; Model Construction Process: The model ranks industries based on GRU factors, which are derived from deep learning algorithms processing historical trading data. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Model Evaluation: The model performs well in short cycles but has mixed results in longer cycles[33][34][36] - Diffusion Index Model, Average Weekly Return: 2.06%, Excess Return: -0.00%, August Excess Return: -0.45%, Year-to-Date Excess Return: -0.41%[31] - GRU Factor Model, Average Weekly Return: 2.71%, Excess Return: 0.65%, August Excess Return: 0.32%, Year-to-Date Excess Return: -4.35%[36] - Factor Name: GRU Industry Factor; Factor Construction Idea: The factor is derived from GRU deep learning networks analyzing minute-level trading data; Factor Construction Process: The factor ranks industries based on GRU network outputs, which are calculated from historical volume and price data. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Factor Evaluation: The factor has shown significant changes in rankings, indicating its sensitivity to market conditions[6][14][34] - GRU Industry Factor, Steel: 2.82, Building Materials: 1.72, Transportation: 1.3, Oil & Petrochemicals: 0.27, Construction: -0.46, Comprehensive: -1.87[6][14][34]

- Model Name: Diffusion Index Model; Construction Idea: The model is based on the principle of price momentum, capturing industry trends through diffusion indices; Construction Process: The model tracks the weekly and monthly changes in the diffusion indices of various industries, ranking them accordingly. The formula for the diffusion index is not explicitly provided; Evaluation: The model has shown varying performance over the years, with significant drawdowns during market reversals[24][25][28] - Model Name: GRU Factor Model; Construction Idea: The model utilizes GRU (Gated Recurrent Unit) deep learning networks to process minute-level volume and price data, aiming to capture trading information; Construction Process: The model ranks industries based on GRU factors, which are derived from the deep learning network's analysis of trading data. The specific formula for GRU factors is not provided; Evaluation: The model has performed well in short cycles but has shown general performance in longer cycles[31][32][35] - Diffusion Index Model, Average Weekly Return: 0.89%, Excess Return Since July: -3.47%, Excess Return YTD: -0.45%[28] - GRU Factor Model, Average Weekly Return: 4.27%, Excess Return Since July: 1.34%, Excess Return YTD: -4.25%[35] - Factor Name: Diffusion Index; Construction Idea: The factor is based on the momentum of industry prices, capturing upward trends; Construction Process: The factor is calculated by observing the weekly and monthly changes in the diffusion indices of various industries. The specific formula is not provided; Evaluation: The factor has shown varying performance, with significant drawdowns during market reversals[24][25][28] - Factor Name: GRU Factor; Construction Idea: The factor is derived from GRU deep learning networks, capturing trading information from minute-level volume and price data; Construction Process: The factor is calculated by ranking industries based on the GRU network's analysis of trading data. The specific formula is not provided; Evaluation: The factor has performed well in short cycles but has shown general performance in longer cycles[31][32][35] - Diffusion Index Factor, Top Industries: Comprehensive Finance (1.0), Steel (1.0), Non-Bank Finance (0.999), Comprehensive (0.998), Non-Ferrous Metals (0.997), Home Appliances (0.995)[25] - GRU Factor, Top Industries: Banking (3.3), Real Estate (0.58), Oil & Petrochemicals (-1.26), Textile & Apparel (-1.73), Light Manufacturing (-2.49), Electric Power & Utilities (-2.83)[32]

- Model Name: Diffusion Index Model; Model Construction Idea: The model is based on the principle of price momentum; Model Construction Process: The model tracks the weekly changes in the diffusion index of various industries, ranking them based on their diffusion index values. The formula used is $ \text{Diffusion Index} = \frac{\text{Number of Stocks with Positive Momentum}}{\text{Total Number of Stocks}} $; Model Evaluation: The model captures industry trends effectively but may face challenges during market reversals[5][27][28] - 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; Model Construction Process: The model ranks industries based on their GRU factor values, which are derived from the GRU network's analysis of trading information. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Model Evaluation: The model performs well in short cycles but may struggle in long cycles or extreme market conditions[6][13][33] - Diffusion Index Model, IR value 2.05%, weekly average return 0.24%, monthly excess return -1.00%, annual excess return 2.05%[25][30] - GRU Factor Model, IR value -4.52%, weekly average return 1.32%, monthly excess return 0.77%, annual excess return -4.52%[32][37] - Factor Name: GRU Industry Factor; Factor Construction Idea: The factor is derived from GRU deep learning networks analyzing minute-level trading data; Factor Construction Process: The factor values are calculated based on the GRU network's output, ranking industries accordingly. The formula used is $ \text{GRU Factor} = \text{GRU Network Output} $; Factor Evaluation: The factor captures short-term trading information effectively but may face challenges in long-term or extreme market conditions[6][13][33] - GRU Industry Factor, IR value -4.52%, weekly average return 1.32%, monthly excess return 0.77%, annual excess return -4.52%[32][37]

- The diffusion index model tracks industry rotation and has achieved an excess return of 0.37% since 2025[26][27][31] - The diffusion index ranks industries weekly based on momentum, with top industries including non-bank finance (1.0), comprehensive finance (1.0), and media (0.976)[4][28][30] - The diffusion index suggests monthly industry allocation, recommending sectors such as non-bank finance, banking, and media for June 2025[27][31] - GRU factor model focuses on industry rotation based on transaction data, achieving an excess return of -4.76% in 2025[33][36][34] - GRU factor ranks industries weekly, with top industries including textile & apparel (3.7), construction (3.34), and real estate (3.28)[5][13][34] - GRU factor suggests weekly industry allocation, recommending sectors such as real estate, transportation, and coal for the current week[36][34][33]

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]