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]

Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model is used to predict stock returns based on historical data and incorporates various factors to optimize portfolio performance [3][4][5] - **Model Construction Process**: - The GRU model is trained on historical data to capture temporal dependencies in stock returns - It uses multiple input features, including technical and fundamental factors, to predict future returns - The model is applied to different stock pools (e.g., CSI 300, CSI 500, CSI 1000) to evaluate its performance [5][6][7] - **Model Evaluation**: The GRU model demonstrates strong performance in most stock pools, with positive long-short returns across various factors. However, certain sub-models (e.g., `barra5d`) show occasional underperformance [5][6][7] 2. Model Name: Open1d and Close1d Models - **Model Construction Idea**: These models focus on short-term price movements and are designed to capture daily return patterns [8][31] - **Model Construction Process**: - Open1d and Close1d models are trained on daily open and close price data, respectively - They are evaluated based on their ability to generate excess returns relative to the CSI 1000 index [8][31] - **Model Evaluation**: These models show mixed performance, with occasional drawdowns relative to the benchmark index [8][31] 3. Model Name: Barra1d and Barra5d Models - **Model Construction Idea**: These models are based on the Barra factor framework and aim to capture short-term and medium-term return patterns [8][31] - **Model Construction Process**: - Barra1d focuses on daily factor returns, while Barra5d aggregates returns over a 5-day horizon - Both models are tested for their ability to generate excess returns relative to the CSI 1000 index [8][31] - **Model Evaluation**: Barra5d demonstrates strong year-to-date performance, significantly outperforming the benchmark, while Barra1d shows consistent but less pronounced gains [8][31] --- Model Backtest Results 1. GRU Model - **Excess Return**: Positive across most stock pools, with occasional underperformance in specific sub-models like `barra5d` [5][6][7] 2. Open1d Model - **Weekly Excess Return**: -0.01% - **Year-to-Date Excess Return**: 5.23% [32] 3. Close1d Model - **Weekly Excess Return**: -0.38% - **Year-to-Date Excess Return**: 3.64% [32] 4. Barra1d Model - **Weekly Excess Return**: 0.65% - **Year-to-Date Excess Return**: 3.80% [32] 5. Barra5d Model - **Weekly Excess Return**: 0.02% - **Year-to-Date Excess Return**: 6.44% [32] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures historical beta to capture market sensitivity [15] - **Factor Construction Process**: Historical beta is calculated based on the covariance of stock returns with market returns [15] 2. Factor Name: Momentum - **Factor Construction Idea**: Captures historical excess return trends [15] - **Factor Construction Process**: - Momentum = 0.74 * Historical Excess Return Volatility + 0.16 * Cumulative Excess Return Deviation + 0.1 * Historical Residual Return Volatility [15] 3. Factor Name: Volatility - **Factor Construction Idea**: Measures stock price fluctuations to identify high-volatility stocks [15] - **Factor Construction Process**: - Volatility = Weighted combination of historical residual return volatility and other metrics [15] 4. Factor Name: Growth - **Factor Construction Idea**: Focuses on earnings and revenue growth rates [15] - **Factor Construction Process**: - Growth = 0.24 * Earnings Growth Rate + 0.47 * Revenue Growth Rate [15] 5. Factor Name: Liquidity - **Factor Construction Idea**: Measures stock turnover to identify liquid stocks [15] - **Factor Construction Process**: - Liquidity = 0.35 * Monthly Turnover + 0.35 * Quarterly Turnover + 0.3 * Annual Turnover [15] --- Factor Backtest Results 1. Beta Factor - **Weekly Long-Short Return**: Positive [16][18] 2. Momentum Factor - **Weekly Long-Short Return**: Negative [16][18] 3. Volatility Factor - **Weekly Long-Short Return**: Positive [16][18] 4. Growth Factor - **Weekly Long-Short Return**: Positive [16][18] 5. Liquidity Factor - **Weekly Long-Short Return**: Positive [16][18]

Quantitative Models and Construction Methods 1. Model Name: Layered Progressive Stock Selection for Profitability Factor - **Model Construction Idea**: The model aims to enhance the profitability factor by progressively filtering stocks based on historical ROE and financial stability, ensuring higher future ROE probabilities [38][35][36] - **Model Construction Process**: - Step 1: Select the top 100 stocks based on historical ROE (ROE_ttm) [38] - Step 2: From the top 100, further filter the top 50 stocks with the highest financial stability scores, which include metrics like ROE stability, revenue growth stability, and leverage stability [27][38] - Step 3: Construct an equal-weighted portfolio with the final 50 stocks [38] - **Model Evaluation**: The layered approach effectively reduces the probability of ROE decline by one interval (5%) and increases the likelihood of maintaining high ROE levels in the future [38][36] 2. Model Name: Dividend Growth Factorization - **Model Construction Idea**: This model predicts future dividend growth by constructing a stock pool based on historical dividend stability and earnings growth expectations [49][51] - **Model Construction Process**: - Step 1: Select stocks with stable dividend payout ratios over the past three years and positive earnings growth expectations [49] - Step 2: Select stocks with dividend amounts growing over the past two years and positive earnings growth expectations [49] - Step 3: Combine the two pools to form a comprehensive stock pool [49] - Step 4: Construct sub-factors such as dividend payout deviation, sell-side forecast count, and recent financial report growth, standardize and sum them, and take the maximum value across perspectives [51] - **Model Evaluation**: The model improves the prediction accuracy of dividend growth, achieving over a 10% improvement in win rates for both the CSI 300 and CSI 500 indices [51][52] 3. Model Name: Growth Factor Improvement via Reverse Exclusion - **Model Construction Idea**: Instead of further refining high-growth stocks, this model excludes stocks unlikely to achieve future net profit growth, enhancing the growth factor's predictive power [70][69] - **Model Construction Process**: - Step 1: Start with 100 high-growth stocks based on historical growth factors [70] - Step 2: Exclude stocks meeting any of the following conditions: - FY1 consensus forecast ≤ 0 - FY1 consensus forecast is null - Consensus forecast downgraded in the past 4, 13, or 26 weeks [70] - Step 3: Construct a portfolio with the remaining stocks [70] - **Model Evaluation**: The exclusion method significantly improves the prediction rate of actual net profit growth and reduces the probability of selecting companies with declining net profits [70][69] 4. Model Name: Composite Three-Factor Portfolio - **Model Construction Idea**: This model integrates the improved profitability, dividend, and growth factors into a unified portfolio to enhance index performance [81][83] - **Model Construction Process**: - Step 1: Combine the stock pools from the three improved factors (profitability, dividend, growth) [81] - Step 2: Select approximately 120 stocks from the combined pool, ensuring industry neutrality and periodic rebalancing [83] - **Model Evaluation**: The composite portfolio demonstrates consistent performance improvement over the equal-weighted three-factor portfolio, with notable gains in the CSI 300 and CSI 500 indices [83][86] 5. Model Name: Three-Factor Portfolio + Volume-Price Factors - **Model Construction Idea**: This model incorporates volume-price factors (low volatility, low liquidity, momentum) into the three-factor portfolio to capture additional returns during strong volume-price factor periods [100][97] - **Model Construction Process**: - Step 1: Start with the three-factor composite portfolio [100] - Step 2: Select the top 75 stocks based on volume-price factor scores (low volatility, low liquidity, momentum) [100] - Step 3: Construct an equal-weighted portfolio with the selected stocks [100] - **Model Evaluation**: The addition of volume-price factors further enhances long-term returns and maintains stable excess returns compared to the equal-weighted six-factor portfolio [100][103] 6. Model Name: 75+25 Composite Portfolio - **Model Construction Idea**: This model combines the three-factor portfolio with a 25-stock pool selected based on volume-price factors across the entire market, aiming to maximize expected returns [109][112] - **Model Construction Process**: - Step 1: Select 75 stocks from the three-factor portfolio [109] - Step 2: Select 25 stocks from the entire market based on volume-price factors (growth, profitability, low volatility, small market cap) [109] - Step 3: Combine the two pools into a 100-stock portfolio [109] - **Model Evaluation**: The 75+25 portfolio achieves significant improvements in annualized returns and Sharpe ratios, benefiting from the strong performance of volume-price factors in recent years [112][125] --- Model Backtest Results 1. Layered Progressive Stock Selection for Profitability Factor - CSI 300: Win rate improved from 78.03% to 86.28% [36] - CSI 500: Win rate improved from 78.72% to 86.55% [36] 2. Dividend Growth Factorization - CSI 300: Win rate improved from 54.90% to 73.24% [51] - CSI 500: Win rate improved from 40.14% to 54.28% [51] 3. Growth Factor Improvement via Reverse Exclusion - CSI 300: Win rate improved from 83.38% to 92.88% [69] - CSI 500: Win rate improved from 80.21% to 90.13% [69] 4. Composite Three-Factor Portfolio - CSI 300: Annualized return improved from 6.36% to 9.34%, Sharpe ratio improved from 0.34 to 0.49 [86] - CSI 500: Annualized return improved from 5.46% to 7.36%, Sharpe ratio improved from 0.26 to 0.34 [86] 5. Three-Factor Portfolio + Volume-Price Factors - CSI 300: Annualized return improved from 7.81% to 11.55%, Sharpe ratio improved from 0.40 to 0.62 [103] - CSI 500: Annualized return improved from 6.75% to 9.15%, Sharpe ratio improved from 0.32 to 0.45 [103] 6. 75+25 Composite Portfolio - CSI 300: Annualized return improved from 7.84% to 14.56%, Sharpe ratio improved from 0.41 to 0.75 [112] - CSI 500: Annualized return improved from 7.35% to 13.18%, Sharpe ratio improved from 0.36 to 0.62 [112]

Group 1: Report Industry Investment Rating - No relevant content provided Group 2: Report's Core View - The commodity market shows different trends in various sectors. The bearish proportion has slightly increased this week, with significant changes in the black and agricultural sectors. The overall market signals are mainly bearish, with some exceptions like the iron ore market turning neutral [1]. Group 3: Summary by Related Content Commodity Market Sector Analysis - The agricultural sector's momentum is rising, while the energy sector is relatively weak. In the black sector, the momentum factor has decreased marginally, and the term - structure differentiation has narrowed. In the non - ferrous sector, the position - holding factor has decreased marginally, and the cross - sectional differentiation has widened. In the energy - chemical sector, there is cross - sectional momentum differentiation. In the agricultural sector, the position - holding of oilseeds and meals has increased, and the short - cycle momentum of palm oil has rebounded [1]. Performance of Different Commodities Methanol - Last week, the supply factor strengthened by 0.64%, the inventory factor declined by 0.66%, and the comprehensive signal this week is bearish. Fundamentally, the supply side remains bearish, the demand side is neutral to bearish, the inventory side turns bearish, and the spread side is neutral to bearish [1]. Float Glass - Last week, the inventory factor increased by 2.47%, the spread factor weakened by 0.17%, the profit factor decreased by 0.20%, and the synthetic factor declined by 0.13%, with a bearish comprehensive signal this week. Fundamentally, the supply side is neutral, the demand side is neutral to bearish, the inventory side is bearish, and the profit side is neutral to bullish [1]. Iron Ore - Last week, the supply factor strengthened by 0.38%, the synthetic factor increased by 0.08%, and the comprehensive signal this week turns neutral. Fundamentally, the supply side's bearish feedback weakens to neutral, the demand side turns to bullish feedback but remains neutral, the inventory side turns bearish, and the spread side turns bullish [3][4].

- 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 showing overall excellence[2][3][11] - Price Range Factor measures the activity level of stocks traded within different intraday price ranges, reflecting investors' expectations for future stock trends. It demonstrates strong predictive power and stable performance this year[3][11][17] - Price-Volume Divergence Factor evaluates the correlation between stock prices and trading volumes. Lower correlation typically indicates higher potential for future stock price increases. However, its performance has been unstable in recent years, with multi-long net value curves flattening[3][22][26] - Regret Avoidance Factor examines the proportion and degree of stock rebounds after being sold by investors, showcasing good predictive power. Its out-of-sample excess returns are stable, indicating that A-share investors' regret avoidance sentiment significantly impacts stock price expectations[3][27][36] - Slope Convexity Factor analyzes the slope and convexity of order books to assess the impact of investor patience and supply-demand elasticity on expected returns. It is constructed using high-frequency snapshot data from limit order books[3][37][42] - The report combines three high-frequency factors into an equal-weighted "Gold" portfolio for CSI 1000 Index enhancement strategy, achieving an annualized excess return rate of 10.51% and a maximum excess drawdown of 6.04%[3][44][45] - To further enhance strategy performance, the report integrates high-frequency factors with three effective fundamental factors (Consensus Expectations, Growth, and Technical Factors) to construct a high-frequency & fundamental resonance portfolio for CSI 1000 Index enhancement strategy. This strategy achieves an annualized excess return rate of 14.57% and a maximum excess drawdown of 4.52%[4][49][51] Factor Backtesting Results - Price Range Factor: Weekly excess return 0.40%, monthly excess return 0.51%, annual excess return 5.86%[2][13][17] - Price-Volume Divergence Factor: Weekly excess return -0.24%, monthly excess return 1.53%, annual excess return 9.00%[2][13][26] - Regret Avoidance Factor: Weekly excess return 0.27%, monthly excess return -0.49%, annual excess return 2.32%[2][13][36] - Slope Convexity Factor: Weekly excess return -1.74%, monthly excess return -2.46%, annual excess return -5.90%[2][13][42] Strategy Performance Metrics - "Gold" Portfolio: Annualized return 9.49%, annualized excess return 10.51%, Sharpe ratio 0.39, IR 2.47, maximum excess drawdown 6.04%[45][47][48] - High-frequency & Fundamental Resonance Portfolio: Annualized return 13.62%, annualized excess return 14.57%, Sharpe ratio 0.58, IR 3.50, maximum excess drawdown 4.52%[51][53][55]

Group 1 - The core viewpoint is that quantitative strategies in investment rely on diverse data sources, including traditional financial reports and non-traditional data such as social media sentiment and supply chain information, to generate forward-looking investment signals [1][2] - The performance of growth factors has been relatively strong in the current year, indicating a favorable market environment for growth-oriented investments [1] - A rich factor library is essential for diversifying sources of returns and enhancing cyclical resilience, necessitating regular updates to the factor pool to include both economically supported and algorithmically derived factors [1] Group 2 - Continuous model iteration and an open attitude towards new technologies, particularly AI, are crucial for improving the efficiency of factor development and constructing stronger predictive signals [2] - The application of AI in quantitative investment processes has become increasingly prevalent, including the use of large models for text data analysis and advanced models like transformers for end-to-end factor mining [2]