证券研究报告：金融工程报告 发布时间：2025-09-28 研究所 分析师:黄子崟 SAC 登记编号:S1340523090002 Email:huangziyin@cnpsec.com 研究助理:李子凯 SAC 登记编号:S1340124100014 Email:lizikai@cnpsec.com 近期研究报告 《指数震荡反内卷方向领涨，ETF 持续 净流入金融地产——行业轮动周报 20250921》 - 2025.09.22 《成长风格占优，小盘股活跃——中邮 因子周报 20250914》 - 2025.09.15 《金融地产获 ETF 持续净流入，连板情 绪偏修复等待合力方向——行业轮动 周报 20250914》 - 2025.09.15 《融资资金净流入电力设备及新能源， 光通信概念趋势未破——行业轮动周 报 20250907》 - 2025.09.08 《双创涨速明显提升，ETF 资金配置思 路偏补涨——行业轮动周报 20250831》 - 2025.09.01 《指数上行重返十年高位，涨幅超 10% 芯片相关 ETF 净流出较多——行业轮动 周报 20250824》 – 2025.08.25 《 ...

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]

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Construction Idea**: The model is based on price momentum principles, aiming to capture upward trends in industry performance[25][37] **Construction Process**: 1. Calculate the diffusion index for each industry based on price momentum 2. Rank industries by their diffusion index values 3. Select industries with the highest diffusion index values for portfolio allocation **Formula**: Not explicitly provided in the report **Evaluation**: The model performs well during upward trends but struggles during reversals, as seen in historical performance[25][37] - **Model Name**: GRU Factor Model **Construction Idea**: The model leverages GRU (Gated Recurrent Unit) deep learning networks to analyze minute-level volume and price data for industry rotation[38][33] **Construction Process**: 1. Input minute-level volume and price data into the GRU network 2. Train the model using historical data to identify industry rotation signals 3. Generate GRU factor scores for each industry and rank them 4. Allocate portfolio weights based on GRU factor rankings **Formula**: Not explicitly provided in the report **Evaluation**: The model performs well in short cycles but faces challenges in long cycles and extreme market conditions[38][33] Model Backtesting Results - **Diffusion Index Model**: - Monthly average return: -0.81% - Excess return over equal-weighted industry benchmark: -1.61% (July 2025)[29] - Year-to-date excess return: 1.48%[24][29] - **GRU Factor Model**: - Weekly average return: -0.46% - Excess return over equal-weighted industry benchmark: -1.27% (July 2025)[36] - Year-to-date excess return: -5.75%[33][36] Quantitative Factors and Construction Methods - **Factor Name**: Diffusion Index **Construction Idea**: Measures industry momentum based on price trends[25][26] **Construction Process**: 1. Calculate the diffusion index for each industry using price data 2. Rank industries by diffusion index values 3. Select industries with the highest diffusion index values for portfolio allocation **Formula**: Not explicitly provided in the report **Evaluation**: Effective in capturing upward trends but vulnerable to reversals[25][26] - **Factor Name**: GRU Factor **Construction Idea**: Utilizes GRU deep learning networks to analyze minute-level volume and price data for industry rotation[38][33] **Construction Process**: 1. Input minute-level volume and price data into the GRU network 2. Train the model using historical data to identify industry rotation signals 3. Generate GRU factor scores for each industry and rank them 4. Allocate portfolio weights based on GRU factor rankings **Formula**: Not explicitly provided in the report **Evaluation**: Performs well in short cycles but struggles in long cycles and extreme market conditions[38][33] Factor Backtesting Results - **Diffusion Index Factor**: - Top-ranked industries (July 18, 2025): Comprehensive Finance (1.0), Comprehensive (0.998), Non-Banking Finance (0.996), Steel (0.995), Nonferrous Metals (0.994), Communication (0.993)[26][27] - Weekly changes in rankings: Consumer Services (+0.224), Food & Beverage (+0.208), National Defense (+0.091)[28] - **GRU Factor**: - Top-ranked industries (July 18, 2025): Banking (2.68), Transportation (2.42), Nonferrous Metals (-0.87), Steel (-1.92), Construction (-2.19), Coal (-2.36)[34] - Weekly changes in rankings: Building Materials (+), Banking (+), Comprehensive Finance (+)[34]

- 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]

Quantitative Models and Construction Methods Diffusion Index Model - Model Name: Diffusion Index Model - Model Construction Idea: The model monitors the critical point of future diffusion index changes to predict market trends. - Model Construction Process: - The horizontal axis represents the relative price change of stocks in the future, ranging from 1.1 to 0.9, indicating a 10% rise to a 10% fall. - The vertical axis represents the length of the review period or future days, ranging from 20 to 10 days. - Example: A value of 0.07 at the horizontal axis 0.95 and vertical axis 15 days indicates that if all stocks in the micro-cap index fall by 5% after 5 days, the diffusion index value is 0.07. - Formula: $ \text{Diffusion Index} = \frac{\text{Number of stocks rising}}{\text{Total number of stocks}} $ - Model Evaluation: The model is useful for monitoring the critical point of future diffusion index changes and predicting market trends.[6][17][40] First Threshold Method (Left-side Trading) - Model Name: First Threshold Method - Model Construction Idea: The model triggers a signal based on the first threshold value to indicate trading actions. - Model Construction Process: - The model triggered a no-position signal at the closing value of 0.9850 on May 8, 2025. - Formula: $ \text{Threshold Value} = \text{Current Index Value} $ - Model Evaluation: The model provides early signals for trading actions based on threshold values.[6][43][44] Delayed Threshold Method (Right-side Trading) - Model Name: Delayed Threshold Method - Model Construction Idea: The model triggers a signal based on the delayed threshold value to indicate trading actions. - Model Construction Process: - The model triggered a no-position signal at the closing value of 0.8975 on May 15, 2025. - Formula: $ \text{Delayed Threshold Value} = \text{Current Index Value} $ - Model Evaluation: The model provides delayed signals for trading actions based on threshold values.[6][45][47] Dual Moving Average Method (Adaptive Trading) - Model Name: Dual Moving Average Method - Model Construction Idea: The model uses dual moving averages to trigger trading signals. - Model Construction Process: - The model triggered a no-position signal at the closing value on June 11, 2025. - Formula: $ \text{Signal} = \text{Short-term Moving Average} - \text{Long-term Moving Average} $ - Model Evaluation: The model adapts to market changes using dual moving averages to provide trading signals.[6][48][49] Model Backtesting Results Diffusion Index Model - Diffusion Index Model, Current Value: 0.34[40] First Threshold Method (Left-side Trading) - First Threshold Method, Closing Value: 0.9850[43] Delayed Threshold Method (Right-side Trading) - Delayed Threshold Method, Closing Value: 0.8975[47] Dual Moving Average Method (Adaptive Trading) - Dual Moving Average Method, Closing Value: Not specified[48] Quantitative Factors and Construction Methods Past Year Volatility Factor - Factor Name: Past Year Volatility Factor - Factor Construction Idea: The factor measures the volatility of stocks over the past year. - Factor Construction Process: - Formula: $ \text{Volatility} = \sqrt{\frac{\sum (R_i - \bar{R})^2}{N}} $ - This week's rank IC: 0.171, Historical average: -0.033 - Factor Evaluation: The factor is effective in capturing the volatility of stocks over the past year.[5][16][33] Beta Factor - Factor Name: Beta Factor - Factor Construction Idea: The factor measures the sensitivity of stocks to market movements. - Factor Construction Process: - Formula: $ \beta = \frac{\text{Cov}(R_i, R_m)}{\text{Var}(R_m)} $ - This week's rank IC: 0.145, Historical average: 0.004 - Factor Evaluation: The factor is effective in capturing the sensitivity of stocks to market movements.[5][16][33] Logarithmic Market Value Factor - Factor Name: Logarithmic Market Value Factor - Factor Construction Idea: The factor measures the logarithmic market value of stocks. - Factor Construction Process: - Formula: $ \text{Log Market Value} = \log(\text{Market Value}) $ - This week's rank IC: 0.138, Historical average: -0.033 - Factor Evaluation: The factor is effective in capturing the logarithmic market value of stocks.[5][16][33] Nonlinear Market Value Factor - Factor Name: Nonlinear Market Value Factor - Factor Construction Idea: The factor measures the nonlinear market value of stocks. - Factor Construction Process: - Formula: $ \text{Nonlinear Market Value} = (\text{Market Value})^2 $ - This week's rank IC: 0.138, Historical average: -0.033 - Factor Evaluation: The factor is effective in capturing the nonlinear market value of stocks.[5][16][33] Non-liquidity Factor - Factor Name: Non-liquidity Factor - Factor Construction Idea: The factor measures the non-liquidity of stocks. - Factor Construction Process: - Formula: $ \text{Non-liquidity} = \frac{\text{Number of non-trading days}}{\text{Total number of days}} $ - This week's rank IC: 0.125, Historical average: 0.038 - Factor Evaluation: The factor is effective in capturing the non-liquidity of stocks.[5][16][33] Factor Backtesting Results Past Year Volatility Factor - Past Year Volatility Factor, This week's rank IC: 0.171, Historical average: -0.033[5][16][33] Beta Factor - Beta Factor, This week's rank IC: 0.145, Historical average: 0.004[5][16][33] Logarithmic Market Value Factor - Logarithmic Market Value Factor, This week's rank IC: 0.138, Historical average: -0.033[5][16][33] Nonlinear Market Value Factor - Nonlinear Market Value Factor, This week's rank IC: 0.138, Historical average: -0.033[5][16][33] Non-liquidity Factor - Non-liquidity Factor, This week's rank IC: 0.125, Historical average: 0.038[5][16][33]