Core Insights - The report proposes a method to identify institutional trading behavior through the "order-cancellation time difference," emphasizing the importance of this approach in the context of algorithmic trading becoming the mainstream execution method for institutions [1][10] - The Buy Algorithmic Cancellation Ratio (BABR) factor demonstrates strong stock selection performance, with an annualized return of 27.8% for long-short portfolios [2][42] - The BABR factor aligns closely with the investment style of public funds, indicating its potential to track institutional behavior and holdings effectively [3][45] Algorithmic Trading Cancellation Identification - The report highlights that cancellation behavior is more revealing of algorithmic trading characteristics than the order itself, with significant pulse-like concentrations observed in cancellation statistics [1][17] - A method is developed to identify algorithmic trading cancellations based on specific time intervals, particularly focusing on cancellations occurring at discrete time points [22][24] Algorithmic Cancellation Ratio Factors - Two key factors are constructed: Algorithmic Cancellation Volume Ratio (ACVR) and Algorithmic Cancellation Counts Ratio (ACCR), both measuring the proportion of algorithmic cancellations relative to total cancellations [28][30] - The ACCR factor shows improved performance metrics, with an IC of 0.051 and an annualized return of 25.1% for long-short portfolios, indicating a strong correlation with institutional trading behavior [32][33] Buy Algorithmic Cancellation Ratio Factor - The BABR factor, which measures the ratio of buy algorithmic cancellations to total cancellations, shows superior stock selection performance compared to the original ACCR factor, with an IC of 0.058 [42][44] - The BABR factor's performance is consistent with the overall performance of public funds, suggesting its utility in capturing institutional trading dynamics [45][48] Factor Characteristics and Correlations - The BABR factor exhibits distinct style exposures, preferring high valuation, high elasticity, and low financial leverage stocks, with a correlation of 0.59 to the Wind Mixed Equity Fund Index [3][49] - The factor's performance is influenced by market capitalization, with a slight negative correlation to log market value, indicating a potential small-cap bias [49]

- The Beta factor achieved a positive return of 1.22% this week, while the size factor recorded a negative return of -0.79%, indicating a small-cap style in the market. Residual volatility and liquidity factors also showed negative returns of -0.77% and -0.56%, respectively[1][18][20] - In the CSI 300 stock pool, the top-performing factors this week were the 6-day moving average of transaction amounts (3.60%), 5-day average turnover rate (3.53%), and net profit gap (3.35%). The worst-performing factors were net inflow of large orders (-1.48%), the correlation between intraday volatility and transaction amounts (-1.30%), and the price-to-book ratio factor (-1.29%)[12][13] - In the CSI 500 stock pool, the best-performing factors this week were total asset growth rate (1.23%), post-morning return factor (1.12%), and single-quarter ROA YoY (1.02%). The worst-performing factors were the correlation between intraday volatility and transaction amounts (-2.89%), net inflow of large orders (-2.35%), and the price-to-book ratio factor (-2.30%)[14][15] - In the liquidity 1500 stock pool, the top-performing factors this week were single-quarter ROE (1.67%), total asset gross profit margin TTM (1.47%), and single-quarter ROA (1.33%). The worst-performing factors were the price-to-book ratio factor (-1.77%), the proportion of downside volatility (-1.39%), and the correlation between intraday volatility and transaction amounts (-1.19%)[16][17] - The PB-ROE-50 portfolio achieved positive excess returns this week, with -0.20% in the CSI 500 stock pool, 1.98% in the CSI 800 stock pool, and 2.85% in the overall market stock pool[23][24] - The institutional research portfolios also delivered positive excess returns this week. The public fund research stock selection strategy achieved an excess return of 3.24% relative to the CSI 800, while the private fund research tracking strategy achieved an excess return of 2.59% relative to the CSI 800[25][26] - The block trade portfolio, constructed based on the "high transaction, low volatility" principle, achieved an excess return of 3.94% relative to the CSI All Share Index this week[29][30] - The directed issuance portfolio, constructed around event-driven stock selection strategies, achieved an excess return of 1.16% relative to the CSI All Share Index this week[35][36]

Group 1: Construction of High-Growth Earnings Portfolio - The selection of high-growth earnings stocks is based on the top 80% of total market capitalization and average daily trading volume within the CSI All Share Index, excluding those with negative net profit from the previous year, and selecting the top 50% based on analysts' consensus earnings growth expectations [7][9][13]. - The average number of stocks in the high-growth earnings portfolio during the backtesting period from August 31, 2011, to October 31, 2025, is 571 [9]. - The portfolio is rebalanced at the end of April, August, and October, and stocks are selected based on the expected earnings growth factor [15][21]. Group 2: Achievement of High Earnings Growth - From April 27, 2012, to October 31, 2024, the median earnings growth rate of the high-growth earnings portfolio is 99%, significantly higher than the median earnings growth rate of 36.86% for the overall stock pool [24][28]. - The high-growth earnings portfolio consistently ranks in the top two deciles of the overall market earnings growth distribution during the backtesting period [28][30]. Group 3: Predictive Factors for Earnings Growth - The analysis identifies several factors that effectively predict annual earnings growth, including analyst ratings, growth, profitability, and valuation [35][37]. - The RankIC (Rank Information Coefficient) values indicate that analyst and growth factors have the most significant predictive power for identifying stocks with higher earnings growth [37]. Group 4: Differences Between Progressive and Parallel Stock Selection - The high-growth earnings portfolio employs a progressive stock selection method, first filtering stocks based on expected earnings growth and then refining the selection using changes in analyst earnings forecasts [43][46]. - In contrast, parallel stock selection methods, which use both factors simultaneously, have shown to be less effective in terms of annual returns over the backtesting period [47][49].

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Combination - **Model Construction Idea**: The PB-ROE-50 combination is designed to capture excess returns by selecting stocks with favorable Price-to-Book (PB) and Return on Equity (ROE) characteristics within specific stock pools[24] - **Model Construction Process**: The model selects stocks based on PB and ROE metrics, focusing on stocks with high ROE and low PB ratios. The combination is rebalanced periodically to maintain its focus on these metrics. Detailed construction methodology is referenced in earlier reports[24] - **Model Evaluation**: The model demonstrates significant excess returns in certain stock pools, indicating its effectiveness in capturing value and profitability factors[24] --- Model Backtesting Results 1. PB-ROE-50 Combination - **Excess Return (Weekly)**: - CSI 500: -0.62% - CSI 800: 1.31% - All Market: 1.36%[25] - **Excess Return (Year-to-Date)**: - CSI 500: 2.48% - CSI 800: 18.55% - All Market: 20.81%[25] - **Absolute Return (Weekly)**: - CSI 500: 3.39% - CSI 800: 3.85% - All Market: 4.18%[25] - **Absolute Return (Year-to-Date)**: - CSI 500: 33.50% - CSI 800: 43.89% - All Market: 51.01%[25] --- Quantitative Factors and Construction Methods 1. Factor Name: Beta Factor - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market returns, capturing systematic risk[20] - **Factor Construction Process**: Calculated as the covariance of a stock's returns with market returns divided by the variance of market returns $ \beta = \frac{\text{Cov}(R_i, R_m)}{\text{Var}(R_m)} $ where $R_i$ is the stock return and $R_m$ is the market return[20] - **Factor Evaluation**: Demonstrates positive returns in the current week, indicating its relevance in capturing market trends[20] 2. Factor Name: Scale Factor - **Factor Construction Idea**: Captures the size effect by focusing on the market capitalization of stocks[20] - **Factor Construction Process**: Stocks are ranked by market capitalization, and the factor is constructed by taking the difference in returns between small-cap and large-cap stocks[20] - **Factor Evaluation**: Positive returns this week suggest the dominance of large-cap stocks in the market[20] 3. Factor Name: Nonlinear Market Cap Factor - **Factor Construction Idea**: Captures nonlinear effects of market capitalization on stock returns[20] - **Factor Construction Process**: Incorporates higher-order terms of market capitalization in the regression model to account for nonlinear relationships[20] - **Factor Evaluation**: Positive returns this week highlight its effectiveness in capturing nonlinear size effects[20] 4. Factor Name: Leverage Factor - **Factor Construction Idea**: Measures the impact of financial leverage on stock returns[20] - **Factor Construction Process**: Calculated as the ratio of total debt to equity, adjusted for industry and market effects[20] - **Factor Evaluation**: Negative returns this week suggest that high-leverage stocks underperformed[20] 5. Factor Name: Early Morning Return Factor - **Factor Construction Idea**: Captures the return patterns of stocks during early trading hours[12] - **Factor Construction Process**: Calculated as the return of a stock during the first trading hour of the day, adjusted for market and industry effects[12] - **Factor Evaluation**: Strong positive performance this week indicates its ability to capture intraday momentum[12] 6. Factor Name: Single-Quarter Net Profit YoY Growth Rate - **Factor Construction Idea**: Measures the year-over-year growth in net profit for a single quarter, reflecting profitability trends[12][14][18] - **Factor Construction Process**: $ \text{Growth Rate} = \frac{\text{Net Profit}_{t} - \text{Net Profit}_{t-1}}{\text{Net Profit}_{t-1}} $ where $t$ is the current quarter and $t-1$ is the same quarter in the previous year[12][14][18] - **Factor Evaluation**: Consistently positive performance across multiple stock pools highlights its robustness in capturing profitability[12][14][18] 7. Factor Name: 5-Day Reversal Factor - **Factor Construction Idea**: Captures short-term mean-reversion effects in stock prices[18] - **Factor Construction Process**: Calculated as the negative return of a stock over the past 5 trading days, adjusted for market and industry effects[18] - **Factor Evaluation**: Strong positive performance in the liquidity 1500 stock pool indicates its effectiveness in identifying short-term reversals[18] --- Factor Backtesting Results 1. Beta Factor - Weekly Return: 1.31%[20] 2. Scale Factor - Weekly Return: 0.62%[20] 3. Nonlinear Market Cap Factor - Weekly Return: 0.58%[20] 4. Leverage Factor - Weekly Return: -0.13%[20] 5. Early Morning Return Factor - Weekly Return: - CSI 300: 2.16% - CSI 500: 0.25% - Liquidity 1500: 1.22%[12][14][18] 6. Single-Quarter Net Profit YoY Growth Rate - Weekly Return: - CSI 300: 1.75% - CSI 500: 1.11% - Liquidity 1500: 1.58%[12][14][18] 7. 5-Day Reversal Factor - Weekly Return: - CSI 300: 0.77% - CSI 500: 1.04% - Liquidity 1500: 3.33%[12][14][18]

Group 1: High Growth Portfolio Construction - The high growth profit stock pool is selected from the top 80% of stocks by market capitalization and average daily trading volume, excluding those with negative net profit from the previous year, and then selecting the top 50% based on analyst consensus profit growth expectations[6] - During the backtesting period from August 31, 2011, to October 31, 2025, the average number of stocks in the high growth profit stock pool was 571[11] - The high growth profit portfolio achieved an annualized return of 13.32% compared to the CSI 500 total return index's 7.31% during the same period, with a Sharpe ratio of 0.50[13] Group 2: Performance Attribution and Comparison - The median profit growth rate of the high growth profit portfolio was 99% during the period from April 27, 2012, to October 31, 2024, significantly higher than the median profit growth rate of 36.86% for the stock pool[29] - The high growth profit portfolio consistently ranked in the top two deciles of market profit growth during the same period, indicating strong performance relative to the market[32] - The portfolio's performance was primarily driven by growth and analyst factors, aligning with the portfolio construction logic[23] Group 3: Factor Analysis and Selection Methodology - The analysis identified that the analyst factor, growth factor, and profitability factor had significant predictive power for annual profit growth, with average RankIC values of 37.81%, 30.22%, and 33.46% respectively[41] - The sequential selection of factors in constructing the portfolio outperformed parallel selection methods, with the sequential method winning in 9 out of 14 years from 2012 to 2025[59] - The high growth profit portfolio's average market capitalization during the backtesting period was 15.8 billion yuan, indicating it is a mid-cap growth portfolio[20]

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]