Group 1: Overall Market Conditions - The proportion of short positions in commodities increased this week, mainly due to the decline in the factor strength of the energy and chemical sector. Currently, the relatively strong sectors in the cross - section are precious metals and agricultural products, while the relatively weak sector is the energy sector [3]. - In the precious metals sector, the sequential momentum of gold has marginally recovered, and the differentiation within the sector has narrowed. In the non - ferrous sector, the position factor has continued to decline slightly, and the cross - sectional differentiation has expanded, with copper and zinc being on the weaker end [3]. - In the black sector, the short - term momentum factor has marginally decreased, but the long - term factor has gradually stabilized, and the term structure differentiation has narrowed. In the energy and chemical sector, the cross - sectional momentum has declined overall, and PTA, soda ash, and glass are on the weaker end of the sector's cross - section [3]. - In the agricultural products sector, the positions of oilseeds and meals have both decreased slightly, and the differentiation has narrowed [3]. Group 2: Factor Returns - Last week's returns and monthly returns for different factors: supply factor had a last - week return of 1.64% and a monthly return of 0.00%; demand factor had a last - week return of 1.51% and a monthly return of 0.00%; inventory factor had a last - week return of 1.20% and a monthly return of - 2.28%; spread factor had a last - week return of 3.90% and a monthly return of 2.50%; profit factor had a last - week return of 0.00% and a monthly return of 0.00%; the cumulative return of major categories last week was 1.64% and this month was - 0.04% [7]. Group 3: Methanol Analysis - In terms of strategy net value, last week, the supply factor strengthened by 0.21%, the demand factor increased by 0.13%, the inventory factor weakened by 0.19%, the spread factor increased by 0.09%, and the composite factor increased by 0.16%. This week, the comprehensive signal for short positions continues. On the fundamental factor side, the capacity utilization rate of domestic methanol plants has increased, and the import arrival volume has increased slightly, indicating a short position on the supply side; the operating rates of formaldehyde and chloride plants have increased, but the capacity utilization rates of acetic acid and MTBE plants have decreased, making the demand side neutral; the inventory of domestic methanol manufacturers has decreased, indicating a long position on the inventory side; the closing price of the main methanol futures contract and the 9 - 1 spread on the futures market have both released short - position signals, and the spread side has turned to a short position [4]. Group 4: Glass Analysis - In terms of strategy net value, last week, the inventory factor decreased by 2.28%, the spread factor increased by 2.50%, and the composite factor weakened by 0.04%. This week, the comprehensive signal is a short position. On the fundamental factor side, the capacity utilization rate of float glass has remained flat month - on - month, keeping the supply side neutral; the number of commercial housing transactions in 30 large - and medium - sized Chinese cities has increased slightly, making the demand side neutral; float glass enterprises have continued to reduce inventory, indicating a long position on the inventory side; the spot prices of the float glass markets in Central China, North China, and South China have all released short - position signals, indicating a short position on the spread side; the pre - tax gross profit of float glass made from steam coal has declined, indicating a short position on the profit side [7]. Group 5: Iron Ore Analysis - In terms of strategy net value, last week, each factor remained unchanged, and this week, the comprehensive signal remains neutral. The arrival volume at Qingdao Port has increased significantly, and the shipping volumes of BHP and Rio Tinto have increased, turning the supply side into a short - position feedback, but the overall signal remains neutral. The consumption of iron ore powder for sintering in steel mills and the proportion of sintered ore in the furnace have decreased, and the strength of the long - position feedback on the demand side has decreased slightly, but the signal is still neutral. The iron ore concentrate at ports and the domestic sintering iron ore powder in steel mills have both reduced inventory slightly, weakening the short - position feedback on the inventory side. The price center of PB powder has shifted down, further weakening the strength of the long - position feedback on the spread side [7]. Group 6: Lead Analysis - In terms of strategy net value, last week, the supply factor strengthened by 0.52%, the demand factor decreased by 0.51%, the spread factor increased by 0.46%, and the composite factor strengthened by 0.15%. This week, the comprehensive signal has changed from a short position to neutral. The price of domestic lead concentrates from SMM has decreased, and the profit of tax - free recycled lead has decreased, turning the supply - side signal to neutral. The LME lead inventory and registered warrants have increased, turning the inventory side into a short - position feedback, but the overall signal remains neutral. The spread between the near and far months of LME lead has widened, strengthening the short - position feedback on the spread side [7].

Quantitative Models and Construction Methods - **Model Name**: Value Smart Beta Portfolio **Model Construction Idea**: The Value Smart Beta portfolios are constructed based on the goal of achieving high beta elasticity and long-term stable excess returns. The portfolios are designed to capture the value style with low historical correlation to other styles[7] **Model Construction Process**: Two portfolios are constructed under the value style: the "Value 50 Portfolio" and the "Value Balanced 50 Portfolio". These portfolios are selected and weighted to optimize for the value factor while maintaining diversification and minimizing correlation with other factors[7] **Model Evaluation**: The Value Smart Beta portfolios demonstrate a focus on capturing value-oriented excess returns, but their performance is sensitive to market conditions[7] - **Model Name**: Growth Smart Beta Portfolio **Model Construction Idea**: The Growth Smart Beta portfolios aim to capture the growth style with high beta elasticity and long-term stable excess returns. These portfolios are designed to have low historical correlation with other styles[7] **Model Construction Process**: Two portfolios are constructed under the growth style: the "Growth 50 Portfolio" and the "Growth Balanced 50 Portfolio". The portfolios are optimized to emphasize growth characteristics while maintaining diversification[7] **Model Evaluation**: The Growth Smart Beta portfolios are effective in capturing growth-oriented returns but may underperform in value-dominated market conditions[7] - **Model Name**: Small-Cap Smart Beta Portfolio **Model Construction Idea**: The Small-Cap Smart Beta portfolios are designed to capture the small-cap style with high beta elasticity and long-term stable excess returns. These portfolios are constructed to have low historical correlation with other styles[7] **Model Construction Process**: Two portfolios are constructed under the small-cap style: the "Small-Cap 50 Portfolio" and the "Small-Cap Balanced 50 Portfolio". The portfolios are optimized to emphasize small-cap characteristics while maintaining diversification[7] **Model Evaluation**: The Small-Cap Smart Beta portfolios show strong performance in capturing small-cap excess returns, particularly in favorable market environments[7] --- Model Backtesting Results - **Value 50 Portfolio**: - Weekly Absolute Return: -2.12% - Weekly Excess Return: -0.41% - Monthly Absolute Return: 0.20% - Monthly Excess Return: 0.33% - Year-to-Date Absolute Return: 12.44% - Year-to-Date Excess Return: 8.78% - Maximum Relative Drawdown: 2.35%[8] - **Value Balanced 50 Portfolio**: - Weekly Absolute Return: -0.46% - Weekly Excess Return: 1.26% - Monthly Absolute Return: 0.48% - Monthly Excess Return: 0.61% - Year-to-Date Absolute Return: 10.16% - Year-to-Date Excess Return: 6.50% - Maximum Relative Drawdown: 3.99%[8] - **Growth 50 Portfolio**: - Weekly Absolute Return: -1.48% - Weekly Excess Return: 0.68% - Monthly Absolute Return: -0.71% - Monthly Excess Return: -0.31% - Year-to-Date Absolute Return: 4.50% - Year-to-Date Excess Return: 2.38% - Maximum Relative Drawdown: 3.61%[8] - **Growth Balanced 50 Portfolio**: - Weekly Absolute Return: -1.64% - Weekly Excess Return: 0.53% - Monthly Absolute Return: 0.06% - Monthly Excess Return: 0.46% - Year-to-Date Absolute Return: 8.71% - Year-to-Date Excess Return: 6.59% - Maximum Relative Drawdown: 6.11%[8] - **Small-Cap 50 Portfolio**: - Weekly Absolute Return: 1.25% - Weekly Excess Return: 1.43% - Monthly Absolute Return: 1.07% - Monthly Excess Return: 0.85% - Year-to-Date Absolute Return: 36.52% - Year-to-Date Excess Return: 19.90% - Maximum Relative Drawdown: 6.23%[8] - **Small-Cap Balanced 50 Portfolio**: - Weekly Absolute Return: -1.09% - Weekly Excess Return: -0.90% - Monthly Absolute Return: 0.61% - Monthly Excess Return: 0.39% - Year-to-Date Absolute Return: 26.60% - Year-to-Date Excess Return: 9.98% - Maximum Relative Drawdown: 4.56%[8]

- The report tracks the performance of style factors, including Beta, liquidity, leverage, profitability, and market capitalization, with Beta and liquidity showing strong long positions, while leverage, profitability, and market capitalization exhibit strong short positions [2][16] - Style factors are constructed using various metrics, such as historical Beta, logarithm of total market capitalization, historical excess return averages for momentum, and a weighted combination of volatility measures for the volatility factor. For example, the volatility factor is calculated as $ 0.74 * historical excess return volatility + 0.16 * cumulative excess return deviation + 0.1 * historical residual return volatility $ [15] - Fundamental factors, including growth-related financial metrics and static financial metrics, are tested across different stock pools (e.g., CSI 300, CSI 500, CSI 1000). Growth-related financial factors generally show mixed or negative performance, while static financial factors exhibit varied results depending on the stock pool [3][4][5][6][18][20][23][25] - Technical factors, such as momentum and volatility, generally show positive performance across stock pools, with high-volatility and high-momentum stocks being dominant. For example, the 120-day momentum factor and 20-day volatility factor are highlighted for their significant contributions [3][4][5][6][18][20][23][26] - GRU factors are tested using different models (e.g., barra1d, barra5d, close1d), with performance varying across stock pools. For instance, barra1d shows strong positive performance in CSI 500 and CSI 1000 pools, while close1d experiences significant drawdowns in CSI 1000 [3][4][5][6][18][20][23][26] - Multi-factor strategies and GRU-based long portfolios are evaluated against the CSI 1000 index. GRU long portfolios show weak performance this week, with relative drawdowns of 0.11%-0.25%, while the barra5d model demonstrates strong year-to-date performance, achieving an excess return of 8.36% [7][30][31]

智通财经记者 | 龙力 量化私募开始"卷调研"了？ 私募排排网数据显示，截至7月24日，今年以来一共有137家量化私募累计调研达581次，涉及到29个申 万一级行业中的408只个股。其中，有15家量化私募今年以来的调研总次数不少于10次。 | 表 | | --- | 格：今年以来调研总次数不少于10次的量化私募名单（截至7月24日）数据来源：私募排排网、智通财 经整理 排排网集团旗下融智投资FOF基金经理李春瑜对智通财经表示，量化私募机构对上市公司的调研活动明 显升温，这一现象主要受两大因素推动：其一，部分头部量化机构正着手组建主观投研团队，以丰富其 多策略投资体系，增强收益的多元化；其二，在行业管理规模快速扩张的背景下，传统量价因子的超额 收益持续衰减，促使量化私募转向更深入的基本面研究，而上市公司调研正是获取高质量基本面数据的 关键环节。 "量化私募调研升温的核心动因是策略收益来源的多元化需求和市场环境与监管驱动的范式转型。" 尚 艺投资总经理王峥对智通财经表示。 王峥进一步对智通财经解释称，首先传统量价因子因策略同质化与市场有效性提升，超额收益持续衰 减，量化私募可以通过组建主观研究团队，将基本面分析融 ...

Quantitative Models and Construction Methods - **Model Name**: GRU **Model Construction Idea**: GRU is used for industry rotation and stock selection based on historical data and market trends[3][5][7] **Model Construction Process**: GRU utilizes gated recurrent units to process sequential data, capturing temporal dependencies in stock price movements and industry performance. It incorporates multiple factors such as momentum, volatility, and valuation metrics to predict future trends[3][5][7] **Model Evaluation**: GRU demonstrates strong performance in multi-factor combinations and industry rotation strategies, with notable differentiation across different stock pools[3][5][7] - **Model Name**: Barra **Model Construction Idea**: Barra focuses on style factors to explain stock returns and risks[14][15][16] **Model Construction Process**: Barra includes multiple style factors such as Beta, Size, Momentum, Volatility, Non-linear Size, Valuation, Liquidity, Profitability, Growth, and Leverage. Each factor is calculated using specific formulas: - **Beta**: Historical beta - **Size**: Natural logarithm of total market capitalization - **Momentum**: Mean of historical excess return series - **Volatility**: $0.74 \times \text{historical excess return volatility} + 0.16 \times \text{cumulative excess return deviation} + 0.1 \times \text{historical residual return volatility}$ - **Non-linear Size**: Cubic transformation of market capitalization - **Valuation**: Reciprocal of price-to-book ratio - **Liquidity**: $0.35 \times \text{monthly turnover rate} + 0.35 \times \text{quarterly turnover rate} + 0.3 \times \text{annual turnover rate}$ - **Profitability**: Weighted combination of analyst forecast earnings-price ratio, reciprocal of cash flow ratio, reciprocal of trailing twelve-month P/E ratio, and forecasted growth rates - **Growth**: Weighted combination of earnings growth rate and revenue growth rate - **Leverage**: Weighted combination of market leverage, book leverage, and debt-to-asset ratio[15] **Model Evaluation**: Barra style factors provide a comprehensive framework for analyzing stock returns, with strong differentiation in multi-factor strategies[14][15][16] Model Backtesting Results - **GRU Model**: - **open1d**: Weekly excess return 0.61%, monthly 1.56%, yearly 7.78% - **close1d**: Weekly excess return 0.02%, monthly 1.45%, yearly 7.28% - **barra1d**: Weekly excess return -0.24%, monthly -0.07%, yearly 3.61% - **barra5d**: Weekly excess return 0.06%, monthly 1.35%, yearly 8.63% - **Multi-factor combination**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] Quantitative Factors and Construction Methods - **Factor Name**: Beta **Factor Construction Idea**: Measures historical sensitivity to market movements[15] **Factor Construction Process**: Calculated as historical beta using regression analysis of stock returns against market returns[15] - **Factor Name**: Size **Factor Construction Idea**: Captures the impact of market capitalization on stock returns[15] **Factor Construction Process**: Natural logarithm of total market capitalization[15] - **Factor Name**: Momentum **Factor Construction Idea**: Reflects the persistence of stock price trends[15] **Factor Construction Process**: Mean of historical excess return series[15] - **Factor Name**: Volatility **Factor Construction Idea**: Measures risk associated with stock price fluctuations[15] **Factor Construction Process**: $0.74 \times \text{historical excess return volatility} + 0.16 \times \text{cumulative excess return deviation} + 0.1 \times \text{historical residual return volatility}$[15] - **Factor Name**: Non-linear Size **Factor Construction Idea**: Captures non-linear effects of market capitalization on returns[15] **Factor Construction Process**: Cubic transformation of market capitalization[15] - **Factor Name**: Valuation **Factor Construction Idea**: Reflects the relative attractiveness of stock prices[15] **Factor Construction Process**: Reciprocal of price-to-book ratio[15] - **Factor Name**: Liquidity **Factor Construction Idea**: Measures ease of trading stocks[15] **Factor Construction Process**: $0.35 \times \text{monthly turnover rate} + 0.35 \times \text{quarterly turnover rate} + 0.3 \times \text{annual turnover rate}$[15] - **Factor Name**: Profitability **Factor Construction Idea**: Captures earnings quality and growth potential[15] **Factor Construction Process**: Weighted combination of analyst forecast earnings-price ratio, reciprocal of cash flow ratio, reciprocal of trailing twelve-month P/E ratio, and forecasted growth rates[15] - **Factor Name**: Growth **Factor Construction Idea**: Reflects revenue and earnings growth trends[15] **Factor Construction Process**: Weighted combination of earnings growth rate and revenue growth rate[15] - **Factor Name**: Leverage **Factor Construction Idea**: Measures financial risk associated with debt levels[15] **Factor Construction Process**: Weighted combination of market leverage, book leverage, and debt-to-asset ratio[15] Factor Backtesting Results - **Beta**: Weekly excess return -0.24%, monthly -0.07%, yearly 3.61%[31][32][33] - **Size**: Weekly excess return 0.02%, monthly 1.45%, yearly 7.28%[31][32][33] - **Momentum**: Weekly excess return 0.61%, monthly 1.56%, yearly 7.78%[31][32][33] - **Volatility**: Weekly excess return 0.06%, monthly 1.35%, yearly 8.63%[31][32][33] - **Non-linear Size**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Valuation**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Liquidity**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Profitability**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Growth**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Leverage**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33]

Report Title - The report is titled "Commodity Quantitative CTA Weekly Tracking" by Guotou Futures [1] Core View - The proportion of long and short positions in commodities has changed little this week. The short positions in the non - ferrous sector have increased, and there are local long signals in the chemical and agricultural product sectors. The cross - sectionally strong sectors are agricultural products and chemicals, while the non - ferrous sector is weak. The overall signal for methanol is neutral, for float glass is long, and for lead is short [3][5][8][9] Industry Investment Rating - Not provided in the report Detailed Summaries Commodity Sector Analysis - **Non - ferrous Sector**: Momentum is marginally declining, short positions are increasing,持仓量 is decreasing, and cross - sectional differentiation is narrowing. Zinc and nickel are relatively weak. Gold's time - series momentum has stabilized, but the trading volume of Shanghai silver continues to rise, and intra - sector differentiation may widen [3] - **Black Sector**: The overall position factor is marginally declining, and the term structure differentiation is narrowing [3] - **Energy and Chemical Sector**: Cross - sectional momentum is differentiated. Ethylene glycol is cross - sectionally strong, while styrene is weak [3] - **Agricultural Product Sector**: The position of oils and fats has slightly declined [3] Factor Performance - **Last Week's and Current Month's Returns**: For the supply factor, last week's return was - 0.03% and the current month's return was - 0.09%; for the demand factor, last week was 0.00% and the current month was - 0.65%; for the inventory factor, last week was 0.02% and the current month was 1.00%; for the spread factor, last week was 0.00% and the current month was 0.35%; the cumulative return of major categories last week was - 0.03% and the current month was - 0.95% [4] Strategy Net Value and Fundamental Factors - **Methanol**: Last week, the supply factor weakened by 0.03%, the inventory factor increased by 0.02%, and the synthetic factor decreased by 0.03%. This week's comprehensive signal is neutral. On the fundamental side, the supply is bearish, demand is bearish, inventory is bullish but weakening, and the spread is bullish [5] - **Float Glass**: Last week, the supply factor strengthened by 1.47%, the demand factor increased by 1.58%, the inventory factor increased by 1.47%, the spread factor weakened by 0.04%, and the synthetic factor increased by 1.04%. This week's comprehensive signal is long. Supply is neutral, demand is neutral, inventory is bullish, and the spread is neutral - bearish [8] - **Lead**: Last week, the supply factor strengthened by 0.52%, the demand factor weakened by 0.40%, the inventory factor strengthened by 0.56%, the spread factor strengthened by 0.51%, and the synthetic factor strengthened by 0.32%. This week's comprehensive signal remains short. Supply is bearish, demand is bullish, inventory is bearish, and the spread is bearish [9] Factor Intensity and Momentum Indicators - **Factor Intensity**: For different factors such as supply, demand, inventory, and spread, their intensities vary in different commodities and time periods (last week and the current week) [4][8][9] - **Momentum Indicators**: Different sectors (black, non - ferrous, energy and chemical, agricultural products, equity index, and precious metals) have different values for momentum time - series, momentum cross - section, term structure, and position indicators [6]

Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model integrates fundamental and technical features to predict stock performance[3][19] - **Model Construction Process**: The GRU model is a recurrent neural network (RNN) variant designed to handle sequential data. It uses gating mechanisms to control the flow of information, allowing it to capture temporal dependencies in financial data. Specific details on the input features or training process are not provided in the report[3][19] - **Model Evaluation**: The GRU model shows mixed performance, with significant drawdowns in certain market segments[3][19] 2. Model Name: Barra1d - **Model Construction Idea**: A short-term factor model based on the Barra framework, focusing on daily data[3][19] - **Model Evaluation**: Barra1d exhibits significant drawdowns in multiple market segments, indicating weaker performance[3][19] 3. Model Name: Barra5d - **Model Construction Idea**: A medium-term factor model based on the Barra framework, focusing on 5-day data[3][19] - **Model Evaluation**: Barra5d demonstrates strong performance, achieving positive returns in various market segments[3][19] 4. Model Name: Close1d - **Model Construction Idea**: A short-term model focusing on daily closing prices[3][19] - **Model Evaluation**: Close1d performs well in certain market segments, achieving positive returns[3][19] 5. Model Name: Open1d - **Model Construction Idea**: A short-term model focusing on daily opening prices[3][19] - **Model Evaluation**: Open1d shows weaker performance, with significant drawdowns in certain market segments[3][19] --- Model Backtesting Results 1. GRU Model - **Weekly Excess Return**: -0.08% to -0.54% relative to the CSI 1000 Index[7][30] 2. Barra1d - **Weekly Excess Return**: -0.54%[31] - **Year-to-Date Excess Return**: 3.75%[31] 3. Barra5d - **Weekly Excess Return**: -0.31%[31] - **Year-to-Date Excess Return**: 7.42%[31] 4. Close1d - **Weekly Excess Return**: -0.40%[31] - **Year-to-Date Excess Return**: 5.73%[31] 5. Open1d - **Weekly Excess Return**: -0.08%[31] - **Year-to-Date Excess Return**: 6.68%[31] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures historical beta to capture market sensitivity[15] 2. Factor Name: Market Capitalization - **Factor Construction Idea**: Logarithm of total market capitalization[15] 3. Factor Name: Momentum - **Factor Construction Idea**: Average historical excess returns[15] 4. Factor Name: Volatility - **Factor Construction Process**: $ Volatility = 0.74 * \text{Historical Excess Return Volatility} + 0.16 * \text{Cumulative Excess Return Deviation} + 0.1 * \text{Residual Return Volatility} $ - **Parameters**: - Historical Excess Return Volatility: Measures the standard deviation of excess returns - Cumulative Excess Return Deviation: Captures deviations in cumulative returns - Residual Return Volatility: Measures the volatility of residual returns[15] 5. Factor Name: Nonlinear Market Capitalization - **Factor Construction Idea**: Cubic transformation of market capitalization[15] 6. Factor Name: Valuation - **Factor Construction Idea**: Inverse of price-to-book ratio[15] 7. Factor Name: Liquidity - **Factor Construction Process**: $ Liquidity = 0.35 * \text{Monthly Turnover} + 0.35 * \text{Quarterly Turnover} + 0.3 * \text{Annual Turnover} $ - **Parameters**: - Monthly Turnover: Measures trading activity over a month - Quarterly Turnover: Measures trading activity over a quarter - Annual Turnover: Measures trading activity over a year[15] 8. Factor Name: Profitability - **Factor Construction Process**: $ Profitability = 0.68 * \text{Analyst Forecast Earnings Yield} + 0.21 * \text{Inverse Price-to-Cash Flow} + 0.11 * \text{Inverse Price-to-Earnings (TTM)} $ $ + 0.18 * \text{Analyst Long-Term Growth Forecast} + 0.11 * \text{Analyst Short-Term Growth Forecast} $ - **Parameters**: - Analyst Forecast Earnings Yield: Measures expected earnings relative to price - Inverse Price-to-Cash Flow: Captures cash flow efficiency - Analyst Growth Forecasts: Reflects expected growth rates[15] 9. Factor Name: Growth - **Factor Construction Process**: $ Growth = 0.24 * \text{Earnings Growth Rate} + 0.47 * \text{Revenue Growth Rate} $ - **Parameters**: - Earnings Growth Rate: Measures growth in earnings - Revenue Growth Rate: Measures growth in revenue[15] 10. Factor Name: Leverage - **Factor Construction Process**: $ Leverage = 0.38 * \text{Market Leverage} + 0.35 * \text{Book Leverage} + 0.27 * \text{Debt-to-Asset Ratio} $ - **Parameters**: - Market Leverage: Measures leverage based on market value - Book Leverage: Measures leverage based on book value - Debt-to-Asset Ratio: Captures the proportion of debt in total assets[15] --- Factor Backtesting Results 1. Momentum Factors - **120-Day Momentum**: Weekly return -2.37%[28] - **60-Day Momentum**: Weekly return -2.17%[28] - **20-Day Momentum**: Weekly return -1.69%[28] 2. Volatility Factors - **60-Day Volatility**: Weekly return -1.53%[28] - **20-Day Volatility**: Weekly return -0.96%[28] - **120-Day Volatility**: Weekly return 0.78%[28] 3. Median Deviation - **Weekly Return**: -0.40%[28]

- The report tracks style factors including profitability, volatility, and momentum, which showed strong long positions, while nonlinear market capitalization, valuation, and leverage factors demonstrated strong short positions[3][16] - Barra style factors include Beta (historical beta), market capitalization (logarithm of total market capitalization), momentum (mean of historical excess return series), volatility (weighted combination of historical excess return volatility, cumulative excess return deviation, and residual return volatility), nonlinear market capitalization (third power of market capitalization style), valuation (inverse of price-to-book ratio), liquidity (weighted turnover rates across monthly, quarterly, and yearly periods), profitability (weighted combination of analyst forecast earnings-price ratio, inverse cash flow ratio, and inverse trailing twelve-month PE ratio), growth (weighted combination of earnings growth rate and revenue growth rate), and leverage (weighted combination of market leverage, book leverage, and debt-to-asset ratio)[15] - GRU factors demonstrated strong multi-directional performance across various stock pools, with models like barra5d showing particularly strong positive returns[4][5][7] - GRU long-only portfolio outperformed the CSI 1000 index with excess returns ranging from 0.06% to 0.95% this week, while the barra5d model achieved a year-to-date excess return of 7.75%[8][30][31]

Report Investment Rating - No information available Core Viewpoints - The proportion of short positions in commodities has slightly increased, with differentiation in the precious metals sector and a slight rebound in the agricultural products sector. Currently, the relatively strong sectors are agricultural products and precious metals, while the relatively weak one is energy and chemicals [2]. - In terms of strategy net worth, different factors showed varying trends last week, and the comprehensive signals for different commodities this week are either long, short, or neutral [4][7]. Summaries by Relevant Content Commodity Market Conditions - Precious metals: Gold's time - series momentum declined, while the marginal position of Shanghai silver increased, and short - cycle momentum significantly recovered [2]. - Non - ferrous metals: There were some differences in positions, and the cross - sectional differentiation narrowed, with copper remaining relatively strong [2]. - Black metals: The term structure differentiation narrowed, the position factors of iron ore and rebar increased, and short - cycle momentum factors rose [2]. - Energy and chemicals: The overall short - cycle momentum declined [2]. - Agricultural products: The positions of oilseeds and meals slightly increased, and palm oil remained relatively strong in the term structure [2]. Factor Returns | Factor | Last Week's Return (%) | Current Month's Return (%) | | --- | --- | --- | | Supply | 0.55 (Methanol), - 0.23 (Float glass), - 0.07 (Iron ore), - 0.07 (Aluminum) | 0.00 (Float glass), 0.42 (Iron ore), - 0.19 (Aluminum) | | Demand | 0.00 (Methanol), 0.00 (Float glass), 0.00 (Iron ore), 0.00 (Aluminum) | 0.54 (Methanol), 0.00 (Float glass), - 0.45 (Aluminum) | | Inventory | - 0.19 (Methanol), 0.82 (Float glass), 0.00 (Iron ore), 0.12 (Aluminum) | 0.99 (Methanol), 0.91 (Float glass), - 0.44 (Aluminum) | | Spread | 0.41 (Methanol), 1.11 (Float glass), 0.28 (Iron ore), - 0.28 (Aluminum) | 0.41 (Methanol), 0.28 (Iron ore), - 0.75 (Aluminum) | | Synthetic Factor | 0.43 (Methanol), 0.63 (Float glass), 0.09 (Iron ore), - 0.07 (Aluminum) | 0.27 (Methanol), 0.70 (Float glass), 0.09 (Iron ore), - 0.47 (Aluminum) | [3][4][7] Momentum and Structure Data of Different Sectors | Sector | Momentum Time - series | Momentum Cross - section | Term Structure | Position | | --- | --- | --- | --- | --- | | Black Metals | | 0.09 | 0 | - 0.08 | | Non - ferrous Metals | 0.05 | - 0.21 | 0.52 | 1.13 | | Energy and Chemicals | - 0.02 | 0.18 | 0.37 | 0.69 | | Agricultural Products | 0.13 | 0.35 | 0.41 | - 0.19 | | Stock Index | - 0.71 | 0.46 | - 0.63 | 1.06 | | Precious Metals | 0.12 | | | 0.88 | [5] Fundamental Factors of Different Commodities - **Methanol**: The domestic device capacity utilization rate increased, the supply - side long - position intensity weakened to neutral; traditional downstream manufacturers' raw material procurement decreased, the demand side was neutral to bearish; inland and port inventories continued to increase, the inventory side was bearish; the market price in East and South China coastal areas released a long - position signal, and the spread side was neutral to bullish [4]. - **Float glass**: The enterprise start - up load decreased slightly, the supply side remained neutral; second - tier city commercial housing transaction data released a long - position signal but with weakened intensity, the demand side was neutral to bullish; the inventory of Shanxi enterprises decreased, the inventory side was bullish; the Shenyang - Shahe regional spread factor released a long - position signal, and the spread side was neutral to bullish [7]. - **Iron ore**: The cumulative amount of raw ore continued to decline, the supply - side signal turned neutral; the monthly output of WSA blast furnace pig iron in China continued to decline, the demand - side signal turned neutral; the inventory of 45 ports of iron ore concentrate continued to decline, the inventory side remained neutral; the freight rate of Brazilian Tubarao to Qingdao continued to decline, the spread - side signal remained neutral [7]. - **Aluminum**: SMM domestic lead concentrate processing fees continued to decline, the supply - side signal remained bearish; China's lead alloy exports in May continued to decrease compared to April, the demand - side signal remained neutral; SMM aluminum concentrate monthly balance continued to decline, the inventory side turned neutral; the 0 - 1 spread declined, the spread - side signal remained bearish [7].

Report Summary 1. Report Industry Investment Rating No information about the industry investment rating is provided in the report. 2. Core View of the Report The report presents the market conditions of treasury bond futures on May 27, 2025, including price changes, trading volume, and related factors, and also mentions the situation of the equity market, money market, and macro - industry news, indicating that the expectations for treasury bond futures are limited and the market is in a state of waiting for a direction [1]. 3. Summary by Related Catalogs 3.1 Treasury Bond Futures Market Conditions - On May 27, treasury bond futures closed down across the board, with the 30 - year, 10 - year, 5 - year, and 2 - year main contracts down 0.26%, 0.11%, 0.03%, and 0.02% respectively [1]. - The treasury bond futures index was - 0.12. The volume - price factor was bullish, and the fundamental factor was bearish. Without leverage, the cumulative returns of the strategy in the past 20, 60, 120, and 240 days were 0.04%, - 0.53%, 0.14%, and 1.27% respectively [1]. - The trading volume of the 2 - year, 5 - year, 10 - year, and 30 - year main contracts was 32,028, 43,924, 58,575, and 62,401 respectively, and the open interest was 104,798, 128,934, 165,848, and 92,091 respectively [3]. - The IRR of the 2 - year, 5 - year, 10 - year, and 30 - year active CTD bonds was 1.95%, 2.07%, 1.88%, and 3.58% respectively, and the current R007 was about 1.6794% [3]. 3.2 Equity Market Conditions - On May 27, the equity market oscillated and adjusted throughout the day, with the ChiNext Index leading the decline. The Shanghai Composite Index fell 0.18%, the Shenzhen Component Index fell 0.61%, and the ChiNext Index fell 0.68%. The market hotspots were scattered, and the number of rising and falling stocks was basically the same [1]. 3.3 Money Market Conditions - On May 27, the overnight shibor was 1.4520%, down 5.4bp from the previous trading day; the 7 - day shibor was 1.5980%, up 1.9bp; the 14 - day shibor was 1.6670%, down 2.1bp; the 1 - month shibor was 1.6140%, up 0.2bp [2]. - The bank - to - bank pledged repurchase market traded 2.4 billion yuan, an increase of 1.62%. The overnight rate closed at 1.45%, up 1bp from the previous trading day; the 7 - day rate closed at 1.70%, up 19bp; the 14 - day rate closed at 1.65%, down 4bp; the 1 - month rate closed at 1.60%, down 6bp [4]. 3.4 Bond Yield Curve Conditions - The treasury bond yield curve rose by 0.29 - 1.10BP (the 2 - year yield rose 0.29BP to 1.47%; the 5 - year yield rose 0.78BP to 1.57%; the 10 - year yield rose 0.38BP to 1.72%; the 30 - year yield rose 1.10BP to 1.90%). The credit bond yield curve showed mixed changes [4]. 3.5 Net Long Position Changes by Institution Type - The daily net long position of private funds decreased by 3.27%, foreign capital decreased by 2.46%, and wealth management subsidiaries decreased by 2.4%. The weekly net long position of private funds decreased by 5.28%, foreign capital decreased by 4.11%, and wealth management subsidiaries decreased by 3.69% [6]. 3.6 Macro and Industry News - On May 27, the central bank conducted 448 billion yuan of 7 - day reverse repurchase operations at an operating rate of 1.40%, unchanged from before. There were 357 billion yuan of reverse repurchases due on the same day [8]. 3.7 Trend Intensity - The trend intensity of treasury bond futures was 0, indicating a neutral state [9].