Quantitative Models and Construction Methods Model Name: DFQ-FactorGCL - **Model Construction Idea**: Based on hypergraph convolutional neural networks and temporal residual contrastive learning for stock return prediction[6] - **Model Construction Process**: The model uses hypergraph convolutional neural networks to capture complex relationships between stocks and temporal residual contrastive learning to enhance prediction accuracy[6] - **Model Evaluation**: The model is effective in capturing stock trends and improving prediction accuracy[6] Model Name: Neural ODE - **Model Construction Idea**: Reconstructing time series dynamic systems for deep learning factor mining[6] - **Model Construction Process**: The model uses ordinary differential equations to model the continuous dynamics of stock prices, allowing for more accurate factor extraction[6] - **Model Evaluation**: The model provides a novel approach to factor mining, improving the robustness and accuracy of predictions[6] Model Name: DFQ-FactorVAE-pro - **Model Construction Idea**: Incorporating feature selection and environmental variable modules into the FactorVAE model[6] - **Model Construction Process**: The model uses variational autoencoders with additional modules for feature selection and environmental variables to enhance stock selection[6] - **Model Evaluation**: The model improves stock selection by considering more comprehensive factors and environmental variables[6] Quantitative Factors and Construction Methods Factor Name: Beta - **Factor Construction Idea**: Bayesian compressed market Beta[16] - **Factor Construction Process**: The factor is constructed by compressing the market Beta using Bayesian methods to capture market sensitivity[16] - **Factor Evaluation**: The factor is effective in identifying stocks with high market sensitivity[12] Factor Name: Volatility - **Factor Construction Idea**: Average logarithmic turnover rate over the past 243 days[16] - **Factor Construction Process**: The factor is calculated using the average logarithmic turnover rate and its regression with the market turnover rate over the past 243 days[16] - **Factor Evaluation**: The factor captures the demand for high volatility assets[12] Factor Name: Liquidity - **Factor Construction Idea**: Average logarithmic turnover rate over the past 243 days[16] - **Factor Construction Process**: The factor is calculated using the average logarithmic turnover rate and its regression with the market turnover rate over the past 243 days[16] - **Factor Evaluation**: The factor indicates the demand for high liquidity assets[12] Factor Name: Value - **Factor Construction Idea**: Book-to-market ratio (BP) and earnings yield (EP)[16] - **Factor Construction Process**: The factor is calculated using the book-to-market ratio and earnings yield[16] - **Factor Evaluation**: The factor shows limited recognition of value investment strategies[12] Factor Name: Growth - **Factor Construction Idea**: State-owned enterprise stock proportion[16] - **Factor Construction Process**: The factor is calculated using the proportion of state-owned enterprise stocks[16] - **Factor Evaluation**: The factor indicates the market's attention to state-owned enterprise stocks[12] Factor Name: Cubic Size - **Factor Construction Idea**: Market capitalization power term[16] - **Factor Construction Process**: The factor is calculated using the market capitalization power term[16] - **Factor Evaluation**: The factor shows the market's reduced attention to micro-cap stocks[12] Factor Name: Trend - **Factor Construction Idea**: EWMA with different half-lives[18] - **Factor Construction Process**: The factor is calculated using EWMA with half-lives of 20, 120, and 240 days, standard volatility, FF3 specific volatility, range, and maximum and minimum returns over the past 243 days[18] - **Factor Evaluation**: The factor indicates the market's reduced preference for trend investment strategies[12] Factor Name: Certainty - **Factor Construction Idea**: Sales growth, institutional holding percentage, net asset growth, analyst coverage, and listing days[18] - **Factor Construction Process**: The factor is calculated using sales growth, institutional holding percentage, net asset growth, analyst coverage, and listing days[18] - **Factor Evaluation**: The factor shows the market's reduced confidence in certainty investment strategies[12] Factor Performance Monitoring Performance in Different Index Spaces - **CSI 300 Index**: Factors like expected PEG, DELTAROE, and single-quarter EP performed well, while three-month reversal and one-month volatility performed poorly[7][24][26] - **CSI 500 Index**: Factors like one-year momentum and expected ROE change performed well, while three-month reversal and three-month institutional coverage performed poorly[7][28][30] - **CSI 800 Index**: Factors like expected ROE change and DELTAROE performed well, while one-month volatility and three-month reversal performed poorly[7][32][34] - **CSI 1000 Index**: Factors like DELTAROA and single-quarter net profit growth performed well, while public holding market value and standardized unexpected revenue performed poorly[7][36][37] - **CNI 2000 Index**: Factors like non-liquidity impact and expected PEG performed well, while public holding market value and one-month volatility performed poorly[7][39][41] - **ChiNext Index**: Factors like three-month earnings adjustment and single-quarter EP performed well, while expected net profit change and expected ROE change performed poorly[7][43][45] - **CSI All Index**: Factors like one-month UMR and one-month reversal performed well, while one-month volatility and three-month volatility performed poorly[7][47][50] Factor Backtesting Results CSI 300 Index - **Expected PEG**: 0.75% (recent week), 2.07% (recent month), 7.23% (year-to-date), 5.96% (annualized)[24] - **DELTAROE**: 0.73% (recent week), 2.19% (recent month), 7.91% (year-to-date), 5.07% (annualized)[24] - **Single-quarter EP**: 0.71% (recent week), 0.96% (recent month), 5.93% (year-to-date), 7.58% (annualized)[24] CSI 500 Index - **One-year momentum**: 0.84% (recent week), 2.33% (recent month), 3.83% (year-to-date), 3.00% (annualized)[28] - **Expected ROE change**: 0.76% (recent week), 0.28% (recent month), 6.15% (year-to-date), 7.67% (annualized)[28] - **Three-month UMR**: 0.74% (recent week), -0.38% (recent month), 0.29% (year-to-date), -1.06% (annualized)[28] CSI 800 Index - **Expected ROE change**: 0.93% (recent week), 1.76% (recent month), 2.27% (year-to-date), -3.20% (annualized)[32] - **Expected PEG**: 0.83% (recent week), 2.60% (recent month), 10.99% (year-to-date), 10.96% (annualized)[32] - **DELTAROE**: 0.79% (recent week), 2.64% (recent month), 11.60% (year-to-date), 8.99% (annualized)[32] CSI 1000 Index - **DELTAROA**: 0.63% (recent week), 1.57% (recent month), 8.06% (year-to-date), 15.10% (annualized)[36] - **Single-quarter net profit growth**: 0.57% (recent week), 1.03% (recent month), 8.04% (year-to-date), 10.77% (annualized)[36] - **One-month UMR**: 0.47% (recent week), -0.92% (recent month), 1.13% (year-to-date), -3.13% (annualized)[36] CNI 2000 Index - **Non-liquidity impact**: 1.26% (recent week), 1.99% (recent month), 12.11% (year-to-date), 21.51% (annualized)[39] - **Expected PEG**: 0.54% (recent week), 0.32% (recent month), 10.32% (year-to-date), 36.23% (annualized)[39] - **Three-month institutional coverage**: 0.54% (recent week), 4.56% (recent month), 5.41% (year-to-date), -1.19% (annualized)[39] ChiNext Index - **Three-month earnings adjustment**: 0.66% (recent week), 0.53% (recent month), -12.72% (year-to-date), -28.10% (annualized)[43] - **Single-quarter EP**: 0.66% (recent week), 0.69% (recent month), 2.90% (year-to-date), 24.70% (annualized)[43] - **PB_ROE rank difference**: 0.61% (recent week), -0.26% (

Quantitative Factor and Model Analysis Quantitative Models and Construction 1. **Model Name**: GAN_GRU Model **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for generating realistic price-volume sequential features and Gated Recurrent Units (GRU) for encoding these sequential features into predictive signals for stock selection [2][9]. **Model Construction Process**: - **GRU Component**: - Input features include 18 price-volume features such as closing price, opening price, turnover, and turnover rate [10][13]. - Training data consists of the past 400 trading days' features, sampled every 5 trading days, forming a 40x18 feature matrix to predict the cumulative return over the next 20 trading days [14]. - Data preprocessing includes outlier removal and standardization at both time-series and cross-sectional levels [14]. - The GRU network consists of two layers (GRU(128, 128)) followed by an MLP (256, 64, 64), with the final output being the predicted return (pRet) [18]. - **GAN Component**: - The generator (G) uses an LSTM model to preserve the sequential nature of the input features, while the discriminator (D) employs a CNN to process the two-dimensional price-volume feature "images" [29][32]. - The generator's loss function is: $$ L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))] $$ where \( z \) represents random noise, \( G(z) \) is the generated data, and \( D(G(z)) \) is the discriminator's output probability [20][21]. - The discriminator's loss function is: $$ L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))] $$ where \( x \) is real data, \( D(x) \) is the discriminator's output for real data, and \( D(G(z)) \) is the output for generated data [23][25]. - Training alternates between updating the discriminator and generator parameters until convergence [26]. **Model Evaluation**: The GAN_GRU model effectively captures both sequential and cross-sectional price-volume features, leveraging the strengths of GANs and GRUs for stock selection [2][9][29]. --- Quantitative Factors and Construction 1. **Factor Name**: GAN_GRU Factor **Factor Construction Idea**: The GAN_GRU factor is derived from the GAN_GRU model's output, representing the encoded price-volume sequential features as a stock selection signal [2][9]. **Factor Construction Process**: - The factor is derived from the predicted return (pRet) output of the GAN_GRU model [18]. - The factor undergoes industry and market capitalization neutralization, followed by standardization [18]. **Factor Evaluation**: The GAN_GRU factor demonstrates strong predictive power across various industries, with consistent performance in both IC and excess returns [36][40]. --- Model Backtest Results 1. **GAN_GRU Model**: - **IC Mean**: 11.54% - **ICIR**: 0.89 - **Turnover Rate**: 0.83 - **Recent IC**: 8.34% - **1-Year IC Mean**: 11.09% - **Annualized Return**: 37.71% - **Annualized Volatility**: 24.95% - **IR**: 1.56 - **Max Drawdown**: 27.29% - **Annualized Excess Return**: 24.95% [36][37]. --- Factor Backtest Results 1. **GAN_GRU Factor**: - **IC Mean**: 11.54% - **ICIR**: 0.89 - **Turnover Rate**: 0.83 - **Recent IC**: 8.34% - **1-Year IC Mean**: 11.09% - **Annualized Return**: 37.71% - **Annualized Volatility**: 24.95% - **IR**: 1.56 - **Max Drawdown**: 27.29% - **Annualized Excess Return**: 24.95% [36][37].

Quantitative Factors and Construction Methods Factor Name: Liquidity - **Construction Idea**: Measures the market's preference for high-liquidity assets, reflecting the demand for stocks with higher turnover rates [9][14] - **Construction Process**: - **TO**: Average logarithmic turnover rate over the past 243 trading days - **Liquidity Beta**: Regression of individual stock turnover rates against market turnover rates over the past 243 trading days [14] - **Evaluation**: Demonstrated the highest positive return among style factors in the recent week, indicating a significant increase in demand for high-liquidity assets [9] - **Performance**: Weekly return of 5.44%, monthly return of 13.08%, and annualized return of 33.79% over the past year [11] Factor Name: Volatility - **Construction Idea**: Captures the market's preference for high-volatility stocks, reflecting risk appetite [9][14] - **Construction Process**: - **Stdvol**: Standard deviation of daily returns over the past 243 trading days - **Ivff**: Fama-French 3-factor idiosyncratic volatility over the past 243 trading days - **Range**: Difference between the highest and lowest prices over the past 243 trading days - **MaxRet_6**: Average return of the six highest daily returns over the past 243 trading days - **MinRet_6**: Average return of the six lowest daily returns over the past 243 trading days [14] - **Evaluation**: Showed a significant improvement in weekly performance, reflecting increased market risk appetite [9] - **Performance**: Weekly return of 5.03%, monthly return of 12.37%, and annualized return of 25.55% over the past year [11] Factor Name: Beta - **Construction Idea**: Represents the market's preference for high-beta stocks, indicating sensitivity to market movements [9][14] - **Construction Process**: Bayesian shrinkage of market beta [14] - **Evaluation**: Significant weekly performance improvement, indicating a strong preference for high-beta stocks [9] - **Performance**: Weekly return of 4.28%, monthly return of 12.51%, and annualized return of 33.02% over the past year [11] Factor Name: Growth - **Construction Idea**: Measures the market's preference for growth-oriented stocks, focusing on financial growth metrics [9][14] - **Construction Process**: - **Delta ROE**: Average change in ROE over the past three years - **Sales Growth**: 3-year compound growth rate of TTM sales revenue - **Na Growth**: 3-year compound growth rate of TTM net assets [14] - **Evaluation**: Improved weekly performance, reflecting increased market interest in growth stocks [9] - **Performance**: Weekly return of 1.65%, monthly return of 0.77%, and annualized return of 1.32% over the past year [11] Factor Name: Certainty - **Construction Idea**: Reflects the market's preference for stocks with higher predictability and stability [10][14] - **Construction Process**: - **Instholder Pct**: Proportion of holdings by mutual funds - **Cov**: Analyst coverage adjusted for market capitalization - **Listdays**: Number of days since the stock's listing [14] - **Evaluation**: Experienced a significant decline in weekly performance, indicating reduced confidence in certainty-based strategies [10] - **Performance**: Weekly return of -3.99%, monthly return of -9.10%, and annualized return of -17.07% over the past year [11] Factor Name: Value - **Construction Idea**: Measures the market's preference for undervalued stocks based on valuation metrics [10][14] - **Construction Process**: - **BP**: Book-to-price ratio - **EP**: Earnings yield [14] - **Evaluation**: Significant decline in weekly performance, reflecting reduced market interest in value-based strategies [10] - **Performance**: Weekly return of -4.75%, monthly return of -8.10%, and annualized return of -16.96% over the past year [11] Factor Name: Size - **Construction Idea**: Captures the market's preference for small-cap stocks [10][14] - **Construction Process**: Logarithm of total market capitalization [14] - **Evaluation**: Experienced the largest decline among style factors, indicating reduced market interest in small-cap stocks [10] - **Performance**: Weekly return of -5.96%, monthly return of -12.84%, and annualized return of -54.81% over the past year [11] --- Factor Backtesting Results Weekly Performance - **Liquidity**: 5.44% - **Volatility**: 5.03% - **Beta**: 4.28% - **Growth**: 1.65% - **Certainty**: -3.99% - **Value**: -4.75% - **Size**: -5.96% [11] Monthly Performance - **Liquidity**: 13.08% - **Volatility**: 12.37% - **Beta**: 12.51% - **Growth**: 0.77% - **Certainty**: -9.10% - **Value**: -8.10% - **Size**: -12.84% [11] Annualized Performance (Past Year) - **Liquidity**: 33.79% - **Volatility**: 25.55% - **Beta**: 33.02% - **Growth**: 1.32% - **Certainty**: -17.07% - **Value**: -16.96% - **Size**: -54.81% [11]

Quantitative Models and Factor Analysis Quantitative Factors and Construction Methods - **Factor Name**: Non-liquidity Shock **Construction Idea**: Measures the impact of illiquidity on stock returns **Construction Process**: Calculated as the average absolute daily return over the past 20 trading days divided by the corresponding daily trading volume[6][16][19] **Evaluation**: Demonstrated strong performance across multiple indices, indicating its effectiveness in capturing illiquidity effects[6][19][21] - **Factor Name**: Six-Month UMR **Construction Idea**: Captures momentum adjusted for risk over a six-month window **Construction Process**: Risk-adjusted momentum is calculated using a six-month rolling window, incorporating volatility adjustments[6][16][19] **Evaluation**: Consistently performed well in recent periods, showing robustness across different market conditions[6][19][21] - **Factor Name**: One-Year UMR **Construction Idea**: Similar to Six-Month UMR but uses a one-year window for risk-adjusted momentum **Construction Process**: Momentum is adjusted for risk using a one-year rolling window, factoring in volatility[6][16][19] **Evaluation**: Effective in capturing long-term momentum trends, though performance varies by index[6][19][21] - **Factor Name**: Three-Month Volatility **Construction Idea**: Measures short-term price fluctuations **Construction Process**: Calculated as the standard deviation of daily returns over the past 60 trading days[6][16][19] **Evaluation**: Demonstrated strong negative correlation with returns, indicating its utility in identifying high-risk assets[6][19][21] - **Factor Name**: One-Month Turnover **Construction Idea**: Reflects trading activity and liquidity over a short period **Construction Process**: Average daily turnover rate over the past 20 trading days[6][16][19] **Evaluation**: Effective in capturing liquidity dynamics, though performance varies across indices[6][19][21] Factor Backtesting Results - **Non-liquidity Shock**: - Recent Week: 0.58% (HS300), 0.91% (CSI500), 0.93% (CSI800), 0.87% (CSI1000), 1.14% (CSI All)[19][23][27][31][42] - Recent Month: 0.31% (HS300), 0.64% (CSI500), 0.77% (CSI800), 2.40% (CSI1000), 1.33% (CSI All)[19][23][27][31][42] - **Six-Month UMR**: - Recent Week: 0.54% (HS300), -0.09% (CSI500), 0.57% (CSI800), 0.73% (CSI1000), 0.73% (CSI All)[19][23][27][31][42] - Recent Month: 1.53% (HS300), 2.09% (CSI500), 2.35% (CSI800), 3.49% (CSI1000), 3.85% (CSI All)[19][23][27][31][42] - **One-Year UMR**: - Recent Week: 0.46% (HS300), 0.06% (CSI500), 0.88% (CSI800), 0.52% (CSI1000), 0.76% (CSI All)[19][23][27][31][42] - Recent Month: 1.15% (HS300), 2.19% (CSI500), 2.50% (CSI800), 2.85% (CSI1000), 3.74% (CSI All)[19][23][27][31][42] - **Three-Month Volatility**: - Recent Week: 0.24% (HS300), 0.78% (CSI500), 0.59% (CSI800), 0.65% (CSI1000), 0.86% (CSI All)[19][23][27][31][42] - Recent Month: 0.84% (HS300), 3.24% (CSI500), 2.17% (CSI800), 3.63% (CSI1000), 3.60% (CSI All)[19][23][27][31][42] - **One-Month Turnover**: - Recent Week: -0.05% (HS300), 0.48% (CSI500), 0.04% (CSI800), 0.57% (CSI1000), 0.50% (CSI All)[19][23][27][31][42] - Recent Month: 0.19% (HS300), 2.47% (CSI500), 0.19% (CSI800), 3.87% (CSI1000), 1.65% (CSI All)[19][23][27][31][42] Quantitative Model Construction - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Construction Idea**: Optimizes portfolio weights to maximize exposure to a single factor while controlling for constraints **Construction Process**: - Objective Function: Maximize $f^T w$, where $f$ is the factor value and $w$ is the weight vector - Constraints: Include style exposure, industry deviation, stock weight limits, turnover, and full investment constraints - Formula: $\begin{array}{ll}max&f^{T}w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &0\leq w\leq l\\ &1^{T}w=1\\ &\Sigma|w-w_{0}|\leq to_{h}\end{array}$[57][58][61] **Evaluation**: Provides a robust framework for testing factor effectiveness under realistic constraints[57][58][61] Model Backtesting Results - **MFE Portfolio**: - Demonstrated strong performance in capturing factor-specific returns while adhering to constraints such as turnover and industry exposure[57][58][61]

Quantitative Models and Construction Methods Factor: 3-Month Earnings Revision - **Construction Idea**: Measures the upward or downward revisions in earnings estimates over the past three months, reflecting changes in analysts' expectations[6][23][42] - **Construction Process**: Calculated as the difference between the number of upward and downward revisions in earnings estimates over the last three months, normalized by the total number of estimates[19][42] - **Evaluation**: Demonstrates strong performance in multiple index universes, indicating its effectiveness in capturing short-term earnings momentum[6][23][42] Factor: UMR (Up-Market Ratio) - **Construction Idea**: Captures momentum by analyzing risk-adjusted returns over different time windows (1 month, 3 months, 6 months, 1 year)[6][19][42] - **Construction Process**: - **1-Month UMR**: Risk-adjusted momentum over a 1-month window - **3-Month UMR**: Risk-adjusted momentum over a 3-month window - **6-Month UMR**: Risk-adjusted momentum over a 6-month window - **1-Year UMR**: Risk-adjusted momentum over a 12-month window[19][42] - **Evaluation**: Consistently performs well across multiple index universes, particularly in capturing medium-term momentum trends[6][23][42] Factor: EPTTM (Earnings-to-Price Trailing Twelve Months) - **Construction Idea**: A valuation factor that measures the earnings yield based on trailing twelve months' earnings[19][42] - **Construction Process**: Calculated as the ratio of trailing twelve months' earnings to the current market price[19][42] - **Evaluation**: Shows strong performance in certain index universes, particularly in value-oriented strategies[6][23][42] Factor: DeltaROE - **Construction Idea**: Measures the change in return on equity (ROE) over a specific period, reflecting improvements or deteriorations in profitability[19][42] - **Construction Process**: Calculated as the difference in ROE between the current period and the same period in the previous year[19][42] - **Evaluation**: Effective in identifying companies with improving profitability trends[6][23][42] Factor: Analyst Coverage (3-Month) - **Construction Idea**: Tracks the number of analysts covering a stock over the past three months, reflecting market attention and sentiment[19][42] - **Construction Process**: Count of unique analysts issuing reports on a stock in the last three months[19][42] - **Evaluation**: Performs well in identifying stocks with increasing market interest[6][23][42] --- Factor Backtesting Results 3-Month Earnings Revision - **Recent 1 Week**: 1.94% (China Securities All Index)[43] - **Recent 1 Month**: 0.82% (China Securities All Index)[43] - **Year-to-Date**: 2.50% (China Securities All Index)[43] UMR (Up-Market Ratio) - **1-Month UMR**: - **Recent 1 Week**: 1.30% (China Securities All Index)[43] - **Recent 1 Month**: 2.57% (China Securities All Index)[43] - **Year-to-Date**: 3.85% (China Securities All Index)[43] - **3-Month UMR**: - **Recent 1 Week**: 0.75% (China Securities All Index)[43] - **Recent 1 Month**: 2.14% (China Securities All Index)[43] - **Year-to-Date**: 2.48% (China Securities All Index)[43] - **6-Month UMR**: - **Recent 1 Week**: 0.72% (China Securities All Index)[43] - **Recent 1 Month**: 4.19% (China Securities All Index)[43] - **Year-to-Date**: 1.12% (China Securities All Index)[43] - **1-Year UMR**: - **Recent 1 Week**: 0.74% (China Securities All Index)[43] - **Recent 1 Month**: 3.92% (China Securities All Index)[43] - **Year-to-Date**: 0.80% (China Securities All Index)[43] EPTTM - **Recent 1 Week**: 0.83% (China Securities All Index)[43] - **Recent 1 Month**: 3.70% (China Securities All Index)[43] - **Year-to-Date**: -0.22% (China Securities All Index)[43] DeltaROE - **Recent 1 Week**: 0.19% (China Securities All Index)[43] - **Recent 1 Month**: -0.31% (China Securities All Index)[43] - **Year-to-Date**: 1.66% (China Securities All Index)[43] Analyst Coverage (3-Month) - **Recent 1 Week**: 1.86% (China Securities All Index)[43] - **Recent 1 Month**: 2.24% (China Securities All Index)[43] - **Year-to-Date**: 4.89% (China Securities All Index)[43] --- MFE Portfolio Construction - **Construction Method**: - Maximizes single-factor exposure while controlling for industry, style, and stock-specific deviations relative to the benchmark index[56][57][59] - Constraints include: - Style exposure limits - Industry exposure limits - Stock weight deviation limits - Turnover limits[56][57][59] - **Optimization Model**: $\begin{array}{ll}max&f^{T}w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &0\leq w\leq l\\ &1^{T}w=1\\ &\Sigma|w-w_{0}|\leq to_{h}\end{array}$[56][57] - **Evaluation**: Effective in isolating factor performance under realistic portfolio constraints[56][57][60]

Group 1 - The core viewpoint of the article highlights the performance of various market factors, indicating that the leverage factor achieved a positive return of 0.60%, while liquidity and market capitalization factors recorded negative returns of -0.77% and -0.52% respectively, suggesting a significant small-cap style in the market [2] Group 2 - In the CSI 300 stock pool, the best-performing factors included momentum-adjusted large orders (2.40%), net inflow of large orders (2.32%), and momentum-adjusted small orders (1.82%). Conversely, the worst-performing factors were gross profit margin on total assets for the quarter (-2.60%), total asset growth rate (-2.48%), and return on assets for the quarter (-2.14%) [3] - In the CSI 500 stock pool, the top-performing factors were momentum-adjusted small orders (2.99%), price-to-earnings ratio factor (2.89%), and price-to-book ratio factor (2.75%). The underperforming factors included the standard deviation of 5-day trading volume (-0.57%), TTM net profit margin (-0.48%), and TTM operating profit margin (-0.27%) [3] - In the liquidity 1500 stock pool, the leading factors were price-to-earnings ratio factor (2.79%), inverse of TTM price-to-earnings ratio (2.10%), and correlation between intraday volatility and trading volume (1.89%). The lagging factors were gross profit margin on total assets for the quarter (-2.31%), standardized unexpected income (-2.15%), and standardized unexpected profit (-2.06%) [3] Group 3 - The performance of fundamental factors varied across industries, with net asset growth rate, net asset per share, and TTM operating profit per share factors showing consistent performance in the environmental protection industry. Valuation factors such as BP and EP factors performed consistently in the home appliances, machinery, environmental protection, building materials, and chemical industries. Residual volatility and liquidity factors yielded consistent positive returns in the food and beverage, non-ferrous metals, oil and petrochemicals, real estate, and comprehensive industries. The small-cap style was notably significant across most industries this week [4] Group 4 - The PB-ROE-50 combination experienced a pullback in excess returns across various stock pools, with the CSI 500 pool recording an excess return of -1.20%, the CSI 800 pool at -2.24%, and the overall market stock pool at -2.22% [5] Group 5 - This week, the public fund research stock selection strategy and private fund research tracking strategy achieved positive excess returns, with the public fund strategy gaining 0.33% relative to the CSI 800 and the private fund strategy gaining 0.51% [8] Group 6 - The block trading combination achieved a positive excess return of 0.78% relative to the CSI All Share Index this week [9] Group 7 - The targeted issuance combination gained a significant excess return of 3.07% relative to the CSI All Share Index this week [10]