Report Summary 1. Report Industry Investment Rating No relevant information provided. 2. Core Viewpoints - This week, the proportion of short positions in commodities has rebounded, mainly due to the decline in the factor strength of the black sector and the recovery in the agricultural products sector. Currently, the relatively strong sectors in the cross - section are non - ferrous metals and agricultural products, while the relatively weak ones are black metals and energy [3]. - The comprehensive signal for methanol this week is short, while for iron ore, it has turned to long, and for lead, it remains short, and for glass, it is long [4][13][15]. 3. Summary by Related Content Commodity Sector Analysis - **Black Sector**: The short - cycle momentum has declined. The positions of iron ore and rebar have decreased, indicating a cautious sentiment after the positive news is realized. Coking coal is relatively strong in the cross - section [3]. - **Non - ferrous Sector**: The position factor has marginally recovered, the long - cycle momentum continues to rise. Copper is relatively strong and alumina is relatively weak in the cross - section [3]. - **Energy and Chemical Sector**: The short - cycle momentum cross - section differentiation has expanded, and the chemical sector is at the short end of the cross - section [3]. - **Agricultural Products Sector**: There is a reversal in the cross - section. The short - cycle momentum of soybean oil has marginally decreased, while that of soybean meal has increased, and soybean meal is relatively strong in the short - term cross - section [3]. - **Precious Metals Sector**: The marginal time - series momentum of gold has recovered, the decline in the position of silver is small, and the differentiation at both ends of the cross - section has narrowed [3]. Strategy Net Value and Factor Analysis - **Methanol**: Last week, the supply factor increased by 0.98%, the demand factor decreased by 0.64%, the inventory factor decreased by 0.48%, and the synthetic factor weakened by 0.62%. The comprehensive signal this week is short. In terms of fundamental factors, the supply side is more bearish, the demand side is neutral to bearish, the inventory side is neutral, and the spread side is neutral [3][4]. - **Iron Ore**: Last week, the supply factor increased by 0.49%, the demand factor strengthened by 0.47%, the spread factor decreased by 0.09%, and the synthetic factor strengthened by 0.2%. The comprehensive signal this week is long. The supply side's bullish feedback has weakened, the demand side has turned to bullish feedback, the inventory side has turned to bullish feedback, and the spread side's bullish feedback has weakened [13]. - **Lead**: Last week, the supply factor increased by 0.49%, the demand factor strengthened by 0.47%, the spread factor decreased by 0.09%, and the synthetic factor strengthened by 0.2%. The comprehensive signal this week remains short. The supply side signal remains bearish, the inventory side signal remains neutral, and the spread side signal turns bearish [13]. - **Glass**: Last week, the inventory factor decreased by 0.05%, the spread factor weakened by 0.05%, and the synthetic factor decreased by 0.04%. The comprehensive signal this week is long. The supply side is neutral to bearish, the demand side is bullish, the inventory side remains bearish, and the spread side is bullish [15].

- The report tracks the performance of style factors, including liquidity, volatility, and nonlinear market capitalization, which showed strong long positions, while valuation, profitability, and leverage factors exhibited strong short positions [2][16] - Barra style factors are constructed using various financial and technical metrics, such as historical beta, logarithm of total market capitalization, historical excess return momentum, and volatility calculated as a weighted combination of historical excess return volatility, cumulative excess return deviation, and residual return volatility [14][15] - Liquidity factor is calculated as a weighted combination of monthly turnover rate (35%), quarterly turnover rate (35%), and annual turnover rate (30%) [15] - Profitability factor is constructed using a weighted combination of analyst forecast earnings-to-price ratio (68%), inverse cash flow ratio (21%), inverse PE ratio (11%), forecast long-term earnings growth rate (18%), and forecast short-term earnings growth rate (11%) [15] - Growth factor is calculated using a weighted combination of earnings growth rate (24%) and revenue growth rate (47%) [15] - Leverage factor is constructed using market leverage ratio (38%), book leverage (35%), and asset-liability ratio (27%) [15] - GRU models, including open1d, close1d, barra1d, and barra5d, are tracked for their multi-factor performance across different stock pools, showing varied results in terms of long-short returns [3][4][5][6] - GRU models demonstrated strong performance in certain configurations, such as close1d and barra5d, while open1d and barra1d showed weaker returns in specific periods [31][33] - Multi-factor portfolios underperformed this week, with relative excess returns against the CSI 1000 index showing a decline of 0.95% [33][34] - Barra5d model exhibited strong year-to-date performance, achieving an excess return of 5.81% against the CSI 1000 index [33][34] - Technical factors, including short-term and long-term momentum and volatility metrics, showed mixed results across different stock pools, with short-term metrics generally outperforming [19][21][24][26] - Basic financial factors, such as static financial metrics and growth-related metrics, generally showed negative long-short returns, with low-growth stocks outperforming [19][21][24][26] - GRU models' long-short returns varied across stock pools, with close1d and barra5d models showing strong positive returns, while open1d and barra1d models experienced slight pullbacks [31][33] - The liquidity factor achieved a weekly return of 1.39%, while the volatility factor returned 0.92% over the same period [17] - Profitability factor showed a weekly return of -1.31%, and valuation factor returned -1.53% [17] - Growth factor achieved a weekly return of 0.21%, while leverage factor returned -0.83% [17] - GRU models' weekly returns included -0.82% for open1d, 2.88% for close1d, -0.45% for barra1d, and 1.23% for barra5d [31] - Multi-factor portfolio weekly return was -0.95% relative to the CSI 1000 index [34]

- **Barra style factors**: The report tracks several style factors including Beta, Market Cap, Momentum, Volatility, Non-linear Market Cap, Valuation, Liquidity, Profitability, Growth, and Leverage. These factors are constructed using historical data and financial metrics such as turnover rates, earnings growth rates, and market leverage ratios. For example, the Beta factor represents historical beta, while the Valuation factor is calculated as the inverse of the price-to-book ratio. The formulas for constructing these factors include weighted combinations of metrics like turnover rates and earnings ratios [14][15][16] - **Factor performance tracking**: The report evaluates the recent performance of style factors across the market. Beta, Liquidity, and Momentum factors showed strong long positions, while Market Cap, Non-linear Market Cap, and Valuation factors performed better in short positions. The tracking methodology involves selecting stocks from the Wind All A pool, excluding ST stocks, suspended stocks, and newly listed stocks under 120 days. Long positions are taken in the top 10% of stocks with the highest factor values, and short positions in the bottom 10%, with equal weight allocation [16][19][20] - **Factor backtesting results**: The report provides detailed backtesting results for style factors. For example, Beta achieved a weekly return of 4.58%, while Market Cap showed a negative weekly return of -3.55%. Other factors like Momentum and Liquidity also demonstrated varied performance across different time horizons, such as one week, one month, and year-to-date. The report highlights the annualized returns for three-year and five-year periods for each factor [17][18][19] - **GRU factor performance**: GRU factors showed weaker performance overall, with only the barra1d model achieving positive returns. Other GRU models experienced drawdowns in their long-short portfolios. This indicates potential challenges in the effectiveness of GRU factors under current market conditions [20][25][29] - **Technical factors**: Technical factors such as 20-day Momentum, 60-day Momentum, and various volatility measures (e.g., 120-day Volatility) were tracked. These factors generally showed positive returns in long positions, particularly in high-volatility and high-momentum stocks. For example, 120-day Volatility achieved a weekly return of 5.92% in the CSI 300 stock pool [24][27][31] - **Fundamental factors**: Fundamental factors like ROA growth, ROC growth, and Net Profit growth were analyzed. In the CSI 300 stock pool, Net Profit growth achieved a weekly return of 2.51%, while ROA growth showed a return of 1.19%. These factors generally favored stocks with stable and strong growth metrics [23][25][30] - **Multi-factor portfolio performance**: The report evaluates the performance of multi-factor portfolios. The barra5d model outperformed the CSI 1000 index by 0.27% this week and achieved a year-to-date excess return of 5.91%. Other models showed mixed results, with some experiencing slight drawdowns. The multi-factor portfolio achieved a weekly excess return of 0.04% relative to the CSI 1000 index [8][33][34]

- The report tracks high-frequency stock selection factors, including price range factor, price-volume divergence factor, regret avoidance factor, and slope convexity factor, with their out-of-sample performance being generally strong[2][3][11] - **Price Range Factor**: Measures the activity of stock transactions within different intraday price ranges, reflecting investors' expectations of future stock trends. High price range transaction volume and transaction count factors are negatively correlated with future stock returns, while low price range average transaction volume factor is positively correlated with future stock returns. The factor is constructed by combining three sub-factors: high price 80% range transaction volume factor (VH80TAW), high price 80% range transaction count factor (MIH80TAW), and low price 10% range average transaction volume factor (VPML10TAW). These sub-factors are weighted at 25%, 25%, and 50%, respectively, and are industry market value neutralized[12][14][17] - **Price-Volume Divergence Factor**: Measures the correlation between stock price and trading volume. When price and volume diverge, the likelihood of future price increases is higher, while convergence indicates a higher likelihood of price decreases. The factor is constructed using high-frequency snapshot data to calculate the correlation between snapshot transaction price and snapshot trading volume, as well as snapshot transaction price and transaction count. Two sub-factors are used: price and transaction count correlation factor (CorrPM) and price and trading volume correlation factor (CorrPV). These sub-factors are equally weighted and industry market value neutralized[22][23][25] - **Regret Avoidance Factor**: Based on behavioral finance theory, this factor utilizes investors' regret avoidance emotions to construct effective stock selection factors. It examines the proportion and degree of stock price rebound after being sold by investors. The factor is constructed using transaction data to identify active buy/sell directions, with additional restrictions on small orders and closing trades to enhance performance. Two sub-factors are used: sell rebound proportion factor (LCVOLESW) and sell rebound deviation factor (LCPESW). These sub-factors are equally weighted and industry market value neutralized[26][32][35] - **Slope Convexity Factor**: Derived from the elasticity of supply and demand, this factor uses high-frequency snapshot data from limit order books to calculate the slope and convexity of buy and sell orders. The factor is constructed by aggregating order volume data by level and calculating the slope of buy and sell order books. Two sub-factors are used: low-level slope factor (Slope_abl) and high-level seller convexity factor (Slope_alh). These sub-factors are equally weighted and industry market value neutralized[36][41][43] - **High-frequency "Gold" Portfolio Strategy**: Combines the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with equal weights to construct an enhanced strategy for the CSI 1000 Index. The strategy includes mechanisms to reduce transaction costs, such as weekly rebalancing and turnover rate buffering. The strategy's annualized excess return is 10.20%, with an IR of 2.38 and maximum excess drawdown of 6.04%[44][46][47] - **High-frequency & Fundamental Resonance Portfolio Strategy**: Combines high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to construct an enhanced strategy for the CSI 1000 Index. The strategy's annualized excess return is 14.49%, with an IR of 3.46 and maximum excess drawdown of 4.52%[48][50][52]

Core Insights - The article provides a comprehensive analysis of market factors and their recent performance, highlighting the positive returns from liquidity and leverage factors, while noting negative returns from beta and growth factors [4][5]. Factor Performance - In the last two weeks, the liquidity factor and leverage factor yielded positive returns of 0.36% and 0.34% respectively, while the profitability factor achieved a positive return of 0.27%. Other factors like valuation and market capitalization also showed positive returns, albeit lower [4]. - For the CSI 300 stock pool, the best-performing factors included quarterly operating profit growth rate (2.54%) and quarterly net profit growth rate (2.36%), while total asset growth rate showed a negative return of -1.94% [5]. - In the CSI 500 stock pool, the top factors were the inverse of price-to-sales ratio (1.90%) and net profit gap (1.55%), with the worst performers being quarterly total asset gross margin (-2.12%) [5]. - The liquidity 1500 stock pool saw strong performance from the price-to-earnings ratio (2.19%) and inverse price-to-earnings ratio (2.09%), while total asset gross margin factors performed poorly [5]. Industry Factor Performance - Recent weeks showed a divergence in fundamental factors across industries, with net asset growth rate and net profit growth rate performing well in textiles, non-bank financials, and leisure services [6][7]. - Valuation factors, particularly the BP factor, achieved positive returns across multiple industries, while liquidity factors showed significant positive returns in the beauty and personal care sector [7]. Combination Tracking - The PB-ROE-50 combination achieved positive excess returns in the CSI 800 and overall market stock pools, with a notable excess return of 1.45% in the CSI 800 pool [8]. - Public and private fund research strategies yielded positive excess returns, with public research strategies outperforming the CSI 800 by 1.03% and private strategies by 1.89% [9]. Block Trade and Directed Issuance Tracking - The block trade combination underperformed relative to the CSI All Index, with an excess return of -0.57% [10]. - Similarly, the directed issuance combination also showed negative excess returns of -1.13% compared to the CSI All Index [11].

Report Summary 1. Report Industry Investment Rating - Not provided in the given content 2. Core Viewpoints - The proportion of short positions in commodities increased slightly this week, with the intensity of black and energy - chemical factors declining and the differentiation between non - ferrous and black sectors expanding. The cross - sectionally strong sectors are precious metals and non - ferrous metals, while the weak sectors are energy and black sectors [2]. - The comprehensive signals of strategies for methanol, float glass, iron ore, lead, and aluminum are neutral this week, except for iron ore which is bearish [3][6][9]. 3. Summary by Related Catalogs Commodity Market Overview - The proportion of short positions in commodities increased slightly this week, with the intensity of black and energy - chemical factors falling and the differentiation between non - ferrous and black sectors widening. Precious metals and non - ferrous metals are strong, while energy and black sectors are weak. Gold's time - series momentum rebounded significantly, but the internal difference between gold and silver continued to expand. The position factor of the non - ferrous sector increased marginally, with copper being strong. In the black sector, the momentum factor increased marginally, and iron ore was stronger than rebar in the term structure. In the energy - chemical sector, cross - sectional momentum was differentiated, with chemicals weaker than energy, and soda ash being weak. In the agricultural products sector, the positions of soybean oil and palm oil decreased, while that of soybean meal increased, and one can short the oil - meal ratio [2]. Methanol - Last week, the supply factor of the strategy net value weakened by 0.09%, the demand factor strengthened by 0.11%, the spread factor decreased by 0.09%, and the synthetic factor decreased by 0.07%. This week, the comprehensive signal is neutral. Fundamentally, the capacity utilization rate of domestic methanol decreased (bullish on the supply side); the average start - up of traditional downstream industries continued to decline, but the start - up of the olefin industry rebounded (neutral on the demand side); ports continued to accumulate inventory significantly (bearish on the inventory side); overseas methanol spot market prices and import profits released bearish signals, and the bullish strength of the spread side weakened and turned neutral [3]. Float Glass - Last week, the returns of major category factors were flat month - on - month, and this week, the comprehensive signal remains neutral. Fundamentally, the start - up load of float glass was flat compared with last week (neutral on the supply side); the transaction area of commercial housing in 30 large - and medium - sized Chinese cities decreased slightly (neutral on the demand side); the inventory of float glass enterprises decreased (slightly bullish on the inventory side); the profit of pipeline - gas - made float glass declined, and the bullish strength of the profit side weakened and remained neutral; the spread factor in the Shenyang - Shahe area released a bearish signal (slightly bearish on the spread side) [6]. Iron Ore - Last week, the supply factor of the strategy net value weakened by 0.21%, the spread factor decreased by 0.25%, and the synthetic factor decreased by 0.16%. This week, the comprehensive signal remains bearish. Fundamentally, the import volume in August increased, and the shipment volume from Brazil rose (bearish on the supply side); the consumption of sintering ore powder by steel mills increased, and the bullish feedback on the demand side strengthened, but the signal remained neutral; the inventory of major port iron ore continued to accumulate, and the bearish feedback on the inventory side strengthened, with the signal remaining neutral; the freight rate decreased, but the spot price increased, and the bearish feedback on the spread side weakened, with the signal remaining bearish [9]. Lead - Last week, the supply factor of the strategy net value weakened by 0.27%, the inventory factor increased by 0.04%, the spread factor decreased by 0.03%, and the synthetic factor decreased by 0.07%. This week, the comprehensive signal turned neutral. Fundamentally, the profit of SMM recycled lead was repaired, and the supply - side signal turned from bearish to neutral; LME lead registered warehouses and inventory continued to reduce, and the inventory - side signal remained neutral; the LME near - far - month spread widened, and the spread - side signal turned from neutral to bullish [9]. Aluminum - Last week, the supply factor of the strategy net value weakened, and the spread factor decreased by 0.03%, and the synthetic factor decreased by 0.07%. This week, the comprehensive signal is neutral. Fundamentally, the recovery speed of the supply side slowed down, and the supply - side signal turned from bearish to neutral [9].

Quantitative Models and Factor Analysis Quantitative Models and Construction - **Model Name**: GRU-based Models - **Construction Idea**: GRU (Gated Recurrent Unit) models are used to capture sequential patterns in financial data, aiming to predict stock movements based on historical trends and other input features [3][4][5] - **Construction Process**: GRU models are trained on historical data to optimize their predictive capabilities. Specific variations of GRU models include `barra1d`, `barra5d`, `open1d`, and `close1d`, which differ in their input features and time horizons [3][4][5] - **Evaluation**: GRU models show mixed performance, with `barra1d` consistently achieving positive returns, while other variations like `close1d` and `barra5d` experience significant drawdowns [3][4][5] Model Backtesting Results - **GRU Models**: - `barra1d`: Weekly excess return of 0.14%, monthly return of 1.20%, and YTD return of 4.77% [32][33] - `barra5d`: Weekly excess return of -0.59%, monthly return of -2.84%, and YTD return of 5.03% [32][33] - `open1d`: Weekly excess return of 0.22%, monthly return of -1.23%, and YTD return of 5.45% [32][33] - `close1d`: Weekly excess return of -0.20%, monthly return of -2.64%, and YTD return of 2.92% [32][33] --- Quantitative Factors and Construction - **Factor Name**: Style Factors (Barra) - **Construction Idea**: Style factors are designed to capture systematic risks and returns associated with specific stock characteristics, such as size, momentum, and valuation [14][15] - **Construction Process**: - **Beta**: Historical beta of the stock - **Size**: Natural logarithm of total market capitalization - **Momentum**: Mean of historical excess returns - **Volatility**: Weighted combination of historical excess return volatility, cumulative excess return deviation, and residual return volatility - **Valuation**: Inverse of price-to-book ratio - **Liquidity**: Weighted turnover rates over monthly, quarterly, and yearly periods - **Profitability**: Weighted combination of analyst-predicted earnings yield, cash flow yield, and other profitability metrics - **Growth**: Weighted combination of earnings and revenue growth rates - **Leverage**: Weighted combination of market leverage, book leverage, and debt-to-asset ratio [15] - **Evaluation**: Style factors exhibit varying performance, with size, non-linear size, and liquidity factors showing strong long positions, while valuation and growth factors perform better in short positions [16][17] - **Factor Name**: Fundamental Factors - **Construction Idea**: Fundamental factors are derived from financial statements and aim to capture the financial health and growth potential of companies [17][18][20] - **Construction Process**: - **ROA Growth**: Growth in return on assets - **ROC Growth**: Growth in return on capital - **Net Profit Growth**: Growth in net profit - **Sales-to-Price Ratio**: Inverse of price-to-sales ratio - **Operating Profit Growth**: Growth in operating profit [21][25][27] - **Evaluation**: Fundamental factors like ROA and ROC growth show positive returns, while static financial metrics like sales-to-price ratio exhibit mixed results [21][25][27] - **Factor Name**: Technical Factors - **Construction Idea**: Technical factors are based on price and volume data, aiming to capture momentum and volatility patterns [18][20][24] - **Construction Process**: - **Momentum**: Calculated over 20, 60, and 120-day periods - **Volatility**: Measured over similar time horizons - **Median Deviation**: Deviation of stock prices from the median [25][27][30] - **Evaluation**: High-momentum stocks generally outperform, while long-term volatility factors show weaker performance [25][27][30] --- Factor Backtesting Results - **Style Factors**: - Size: Weekly return of 0.22%, monthly return of 1.20%, and YTD return of 4.77% [16][17] - Valuation: Weekly return of -0.20%, monthly return of -2.64%, and YTD return of 2.92% [16][17] - **Fundamental Factors**: - ROA Growth: Weekly return of 1.31%, monthly return of 12.03%, and YTD return of 33.49% [21][25] - ROC Growth: Weekly return of 1.74%, monthly return of 4.75%, and YTD return of 10.89% [21][25] - **Technical Factors**: - 20-day Momentum: Weekly return of 3.25%, monthly return of 12.92%, and YTD return of 2.35% [25][27] - 60-day Volatility: Weekly return of 3.65%, monthly return of 16.15%, and YTD return of 28.43% [25][27]

Quantitative Models and Construction 1. Model Name: barra1d - **Model Construction Idea**: This model is part of the GRU factor family and is designed to capture short-term market dynamics through daily data inputs[4][6][8] - **Model Construction Process**: The barra1d model uses daily market data to calculate factor exposures and returns. It applies industry-neutralization and standardization processes to ensure comparability across stocks. The model is rebalanced monthly, selecting the top 10% of stocks with the highest factor scores for long positions and the bottom 10% for short positions, with equal weighting[17][28][29] - **Model Evaluation**: The barra1d model demonstrated strong performance in multiple stock pools, showing resilience in volatile market conditions[4][6][8] 2. Model Name: barra5d - **Model Construction Idea**: This model extends the barra1d framework to a five-day horizon, aiming to capture slightly longer-term market trends[4][6][8] - **Model Construction Process**: Similar to barra1d, the barra5d model uses five-day aggregated data for factor calculation. It follows the same industry-neutralization, standardization, and rebalancing processes as barra1d[17][28][29] - **Model Evaluation**: The barra5d model experienced significant drawdowns in recent periods, indicating sensitivity to market reversals[4][6][8] 3. Model Name: open1d - **Model Construction Idea**: This model focuses on open price data to identify short-term trading opportunities[4][6][8] - **Model Construction Process**: The open1d model calculates factor exposures based on daily opening prices. It applies the same industry-neutralization and rebalancing methodology as other GRU models[17][28][29] - **Model Evaluation**: The open1d model showed moderate performance, with some drawdowns in recent periods[4][6][8] 4. Model Name: close1d - **Model Construction Idea**: This model emphasizes closing price data to capture end-of-day market sentiment[4][6][8] - **Model Construction Process**: The close1d model uses daily closing prices for factor calculation. It follows the same construction and rebalancing methodology as other GRU models[17][28][29] - **Model Evaluation**: The close1d model demonstrated stable performance, with positive returns in certain stock pools[4][6][8] --- Model Backtesting Results 1. barra1d Model - Weekly Excess Return: +0.57%[29][30] - Monthly Excess Return: +0.75%[29][30] - Year-to-Date Excess Return: +4.38%[29][30] 2. barra5d Model - Weekly Excess Return: -2.17%[29][30] - Monthly Excess Return: -3.76%[29][30] - Year-to-Date Excess Return: +4.13%[29][30] 3. open1d Model - Weekly Excess Return: -0.97%[29][30] - Monthly Excess Return: -2.85%[29][30] - Year-to-Date Excess Return: +4.20%[29][30] 4. close1d Model - Weekly Excess Return: -1.68%[29][30] - Monthly Excess Return: -4.50%[29][30] - Year-to-Date Excess Return: +1.90%[29][30] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures historical market sensitivity of a stock[15] - **Factor Construction Process**: Calculated as the regression coefficient of a stock's returns against market returns over a specified period[15] 2. Factor Name: Size - **Factor Construction Idea**: Captures the size effect, where smaller firms tend to outperform larger ones[15] - **Factor Construction Process**: Defined as the natural logarithm of total market capitalization[15] 3. Factor Name: Momentum - **Factor Construction Idea**: Identifies stocks with strong recent performance[15] - **Factor Construction Process**: Combines historical excess return mean, volatility, and cumulative deviation into a weighted formula: $ Momentum = 0.74 * \text{Volatility} + 0.16 * \text{Cumulative Deviation} + 0.10 * \text{Residual Volatility} $[15] 4. Factor Name: Volatility - **Factor Construction Idea**: Measures the risk or variability in stock returns[15] - **Factor Construction Process**: Weighted combination of historical residual volatility and other measures[15] 5. Factor Name: Valuation - **Factor Construction Idea**: Captures the value effect, where undervalued stocks tend to outperform[15] - **Factor Construction Process**: Defined as the inverse of the price-to-book ratio[15] 6. Factor Name: Liquidity - **Factor Construction Idea**: Measures the ease of trading a stock[15] - **Factor Construction Process**: Weighted combination of turnover rates over monthly, quarterly, and yearly horizons: $ Liquidity = 0.35 * \text{Monthly Turnover} + 0.35 * \text{Quarterly Turnover} + 0.30 * \text{Yearly Turnover} $[15] 7. Factor Name: Profitability - **Factor Construction Idea**: Identifies stocks with strong earnings performance[15] - **Factor Construction Process**: Weighted combination of various profitability metrics, including analyst forecasts and financial ratios[15] 8. Factor Name: Growth - **Factor Construction Idea**: Captures the growth potential of a stock[15] - **Factor Construction Process**: Weighted combination of earnings and revenue growth rates[15] --- Factor Backtesting Results 1. Beta Factor - Weekly Return: +0.14%[21] - Monthly Return: +1.65%[21] - Year-to-Date Return: +5.29%[21] 2. Size Factor - Weekly Return: +0.36%[21] - Monthly Return: +1.00%[21] - Year-to-Date Return: +6.37%[21] 3. Momentum Factor - Weekly Return: +2.21%[24] - Monthly Return: +8.80%[24] - Year-to-Date Return: +23.30%[24] 4. Volatility Factor - Weekly Return: +2.82%[24] - Monthly Return: +12.29%[24] - Year-to-Date Return: +25.25%[24] 5. Valuation Factor - Weekly Return: +1.47%[21] - Monthly Return: +2.30%[21] - Year-to-Date Return: -2.26%[21] 6. Liquidity Factor - Weekly Return: +1.80%[21] - Monthly Return: +5.91%[21] - Year-to-Date Return: +19.70%[21] 7. Profitability Factor - Weekly Return: +4.57%[21] - Monthly Return: +7.53%[21] - Year-to-Date Return: +27.56%[21] 8. Growth Factor - Weekly Return: +2.76%[24] - Monthly Return: +6.51%[24] - Year-to-Date Return: +14.51%[24]

Core Viewpoint - The article aims to demystify the jargon associated with quantitative investing, making it more accessible to a broader audience [2]. Group 1: Key Concepts in Quantitative Investing - Beta represents the market's earnings, while Alpha refers to the excess returns earned beyond the market, also known as "excess returns" [5]. - Factors are elements that influence the price movements of a stock [9]. - Fundamental factors are a series of quantitative indicators based on a company's financial and operational data [13]. - Technical factors are quantitative indicators derived from market trading behavior data, such as historical prices, trading volumes, and positions [16]. - Alternative factors are constructed using non-traditional, non-financial alternative data [20]. - Industry deviation, also known as risk exposure, indicates the extent to which a product's industry allocation differs from its benchmark index [22]. - Style drift occurs when a quantitative product's holdings significantly deviate from the benchmark index, leading to a mismatch between actual investment style and declared investment strategy [27].

Report Overview - Report Title: Commodity Quantitative CTA Weekly Tracking [1] - Report Author: Research and Development Department of Guotou Futures, Financial Engineering Group [2] - Report Date: August 26, 2025 [2] Investment Rating - No investment rating information is provided in the report. Core Viewpoint - The proportion of long positions in commodities increased this week, with concentrated changes at both ends of the sectors. The factor intensity of the black sector significantly rebounded, and the internal differentiation of the agricultural and energy-chemical sectors continued to widen. Currently, the relatively strong sectors in cross-section are chemicals and black, while the relatively weak sector is energy. [3] Summary by Commodity Sector Overall Market Conditions - Gold's time-series momentum stabilized, but the internal differences in the precious metals sector continued to expand, with silver outperforming gold. - The position factor of the non-ferrous sector marginally rebounded, and the cross-sectional differentiation narrowed. - In the black sector, the momentum factor marginally rebounded, and iron ore was stronger than rebar in the term structure. - The cross-sectional momentum of the energy-chemical sector was differentiated, with chemicals at the stronger end and energy at the weaker end. - In the agricultural sector, the positions of oilseeds and meals both rebounded, and the short-term momentum of palm oil recovered. [3] Sector-specific Performance | Sector | Momentum Time-series | Momentum Cross-section | Term Structure | Position | | --- | --- | --- | --- | --- | | Black | 0.21 | -0.29 | 0.85 | 1.25 | | Non-ferrous | 0.06 | 0.93 | -2.2 | -0.64 | | Energy-chemical | -0.37 | 0.57 | 0.02 | 0.16 | | Agricultural | 0.75 | -0.67 | 0.93 | 1.37 | | Stock Index | 0.31 | -0.1 | -0.32 | 0.48 | | Precious Metals | 0 | - | - | -0.15 | [3] Summary by Strategy and Fundamental Factors Methanol - Strategy Net Value: Last week, the supply factor decreased by 0.22%, the inventory factor decreased by 0.18%, and the synthetic factor weakened by 0.11%. This week, the comprehensive signal is short. - Fundamental Factors: The arrival volume of imported methanol decreased month-on-month, weakening the short strength on the supply side and turning it neutral; the operating rates of traditional downstream formaldehyde and acetic acid plants both decreased, making the demand side neutral to bearish; port inventories continued to increase, and the inventory side remained bearish; the spot prices of methanol in Shanxi and southern Shandong released bullish signals, but the factor contribution was not high, and the spread side was neutral to bullish. [3] Glass - Strategy Net Value: Last week, the inventory factor increased by 0.55%, the spread factor weakened by 0.10%, the profit factor decreased by 0.11%, and the synthetic factor strengthened by 0.26%. This week, the comprehensive signal is long. - Fundamental Factors: The number of commercial housing transactions in third-tier cities released a bearish signal, but the factor intensity was not high, making the demand side neutral; the inventory of Chinese float glass enterprises slightly increased, making the inventory side neutral; the profit loss of pipeline gas-made float glass slightly narrowed, making the profit side neutral; the spot price of float glass in the Hubei market released a bullish signal, making the spread side bullish. [5] Iron Ore - Strategy Net Value: Last week, the supply factor weakened by 0.03%, the inventory factor increased by 0.22%, the spread factor decreased by 0.2%, and the synthetic factor weakened by 0.03%. This week, the comprehensive signal turned long. - Fundamental Factors: The arrival volume of iron ore at northern ports significantly decreased, turning the supply-side signal to bullish; the daily average port clearance volume decreased, and the consumption of imported sintering ore powder by steel mills slightly declined, turning the demand side to bearish feedback, but the signal remained neutral; the average available days of imported iron ore for steel mills decreased, and the inventory accumulation speed of major ports slowed down, weakening the bearish feedback on the inventory side and turning the signal to neutral; the freight rate from Tubarao, Brazil, to Qingdao decreased, and the spread-side signal remained bullish, but the intensity slightly weakened. [7] Lead - Strategy Net Value: Last week, the supply factor strengthened by 0.07%, the spread factor decreased by 0.06%, and the synthetic factor remained the same as last week. This week, the comprehensive signal turned long. - Fundamental Factors: The loss of SMM recycled lead widened, and the price of domestic lead concentrate declined, turning the supply-side signal to neutral; both LME lead inventory and SHFE warehouse receipts showed a de-stocking trend last week, turning the inventory-side signal to bullish; the average price of SMM lead ingots and the spot price of silver declined, weakening the bullish feedback on the spread side and turning the signal to neutral. [7]