Group 1 - The report aims to track large purchases and net active purchases through transaction detail data, building relevant indicators [1] - The top five industries for large purchases in the last five trading days are: Banking, Real Estate, Steel, Comprehensive, and Textile & Apparel [2] - The top five industries for net active purchases in the last five trading days are: Banking, Transportation, Pharmaceuticals, Real Estate, and Oil & Petrochemicals [2] Group 2 - The top five ETFs for large purchases in the last five trading days are: Guotai CSI A500 ETF, Guotai SSE 10-Year Treasury ETF, Harvest S&P Oil & Gas Exploration and Production Selected Industry ETF, Southern Growth Enterprise Board AI ETF, and Hai Futong SSE Urban Investment Bond ETF [2] - The top five ETFs for net active purchases in the last five trading days are: Guotai SSE 10-Year Treasury ETF, E Fund CSI 300 Non-Bank ETF, Yinhua SSE Sci-Tech Innovation Board 100 ETF, Huabao CSI Nonferrous Metals ETF, and Penghua CSI Liquor ETF [2]

Quantitative Factors and Construction Methods - **Factor Name**: UTR2.0 (Upgraded Turnover Rate 2.0) **Factor Construction Idea**: The UTR2.0 factor is an upgraded version of the original UTR factor. It combines the "volume stability factor" (STR) and the "small volume factor" (Turn20) using a new methodology. The key improvement involves transitioning from ordinal scale to ratio scale for factor values, which retains more information and adjusts the impact of the small volume factor based on the stability of the volume[6][7]. **Factor Construction Process**: 1. At the end of each month, calculate the small volume factor (Turn20) and the volume stability factor (STR) for all stocks[6]. 2. Sort all samples by STR in ascending order and assign scores (1, 2, ..., N), where N is the total number of samples. This is recorded as "Score 1"[6]. 3. For the top 50% of samples ranked by STR, sort them by Turn20 in descending order and assign scores (1, 2, ..., N/2). This is recorded as "Score 2". The final score for these stocks is "Score 1 + Score 2"[6]. 4. For the bottom 50% of samples ranked by STR, sort them by Turn20 in ascending order and assign scores (1, 2, ..., N/2). This is recorded as "Score 3". The final score for these stocks is "Score 1 + Score 3"[6]. 5. Transition from ordinal scale to ratio scale by introducing a coefficient for Turn20, which is a function of STR. The coefficient reflects the impact of Turn20 on returns: the more stable the volume, the stronger the positive impact; the less stable the volume, the stronger the negative impact. The formula for UTR2.0 is: $$ \mathrm{UTR2.0} = \mathrm{STR} + \text{softsign}(\mathrm{STR}) \cdot \mathrm{Turn20} $$ where $\text{softsign}(x) = \frac{x}{1 + |x|}$[7]. **Factor Evaluation**: The UTR2.0 factor improves upon the original UTR factor by achieving better performance in terms of volatility, information ratio (IR), and monthly win rate, although its returns are slightly lower[6][7]. --- Factor Backtesting Results - **UTR2.0 Factor**: - Annualized Return: 40.48% - Annualized Volatility: 14.98% - Information Ratio (IR): 2.70 - Monthly Win Rate: 75.53% - Maximum Drawdown: 11.03%[8][12] - **October 2025 Performance**: - Long Portfolio Return: 4.64% - Short Portfolio Return: -1.50% - Long-Short Portfolio Return: 6.14%[10]

Group 1 - The core viewpoint of the article emphasizes the performance of the "brokerage golden stocks" and their ability to track the performance of mixed equity funds, showcasing the analytical capabilities of brokerage firms [2][10][31] - In October 2025, the top-performing stocks in the brokerage golden stock pool included GuoDun Quantum, Rongxin Culture, and JiangBolong, with significant monthly increases [1][3][4] - The top three brokerages in terms of monthly returns were Western Securities, Great Wall Securities, and Guoyuan Securities, with returns of 5.84%, 5.43%, and 4.03% respectively, while the mixed equity fund index returned -2.14% [6][8] Group 2 - As of November 3, 2025, a total of 42 brokerages released their golden stocks for the month, resulting in 275 unique A-shares after deduplication [21][27] - The sectors with the highest allocation in the current golden stock pool were electronics (15.26%), non-ferrous metals (8.68%), and basic chemicals (6.84%) [27] - The brokerage golden stock performance enhancement portfolio had an absolute return of -0.77% for the month and a relative excess return of 1.37% compared to the mixed equity fund index [35] Group 3 - The article highlights the performance of various selection factors within the brokerage golden stock pool, noting that total market capitalization and quarterly revenue growth rates performed well recently [18][16] - The article also discusses the stocks that received multiple recommendations from analysts, indicating higher market attention, with stocks like Industrial Fulian and Kingsoft receiving recommendations from five or more analysts [22][23] - The brokerage golden stock index showed a year-to-date return of 28.59%, compared to the mixed equity fund index's return of 32.47% [14][35]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.53% this week and 18.86% year-to-date [1][7] - The CSI 500 index enhanced portfolio recorded an excess return of 0.45% this week and 9.03% year-to-date [1][7] - The CSI 1000 index enhanced portfolio had an excess return of 0.34% this week and 19.00% year-to-date [1][7] - The CSI A500 index enhanced portfolio experienced an excess return of -0.46% this week and 8.18% year-to-date [1][7] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as quarterly ROA, quarterly ROE, and one-year momentum performed well [1][10] - In the CSI 500 component stocks, factors like SPTTM, executive compensation, and three-month institutional coverage showed strong performance [1][10] - For the CSI 1000 component stocks, factors such as three-month earnings revisions, standardized unexpected revenue, and standardized unexpected earnings performed well [1][10] - In the CSI A500 index component stocks, factors like one-year momentum, quarterly revenue year-on-year growth, and DELTAROA showed good performance [1][10] - Among publicly offered fund heavy stocks, factors like one-year momentum, standardized unexpected revenue, and three-month earnings revisions performed well [1][10] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 2.02%, a minimum of -1.13%, and a median of 0.06% this week [1][23] - The CSI 500 index enhanced products recorded a maximum excess return of 1.24%, a minimum of -1.61%, and a median of 0.19% this week [1][25] - The CSI 1000 index enhanced products achieved a maximum excess return of 1.52%, a minimum of -1.23%, and a median of 0.45% this week [1][29] - The CSI A500 index enhanced products had a maximum excess return of 0.84%, a minimum of -0.53%, and a median of 0.03% this week [1][30]

Quantitative Factor and Construction Methodology - **Factor Name**: Turnover Distribution Uniformity (UTD) Factor [1][6][7] - **Factor Construction Idea**: The UTD factor is an improvement over the traditional turnover rate factor, leveraging minute-level transaction volume data to reduce misclassification of stock samples and enhance stock selection performance [1][6][7] - **Factor Construction Process**: 1. Collect minute-level transaction volume data for individual stocks [1][7] 2. Calculate the turnover rate distribution uniformity based on the dispersion of turnover rates across different time intervals [7] 3. Construct the UTD factor by quantifying the uniformity of turnover rate distribution [7] 4. Perform style, industry, and proprietary factor neutralization to ensure the purity of the UTD factor [1] - **Factor Evaluation**: The UTD factor significantly reduces the misclassification of stock samples and demonstrates superior stock selection performance compared to traditional turnover rate factors [1][6][7] --- Factor Backtesting Results - **Traditional Turnover Rate Factor (Turn20)**: - Monthly IC Mean: -0.072 [6] - Annualized ICIR: -2.10 [6] - Annualized Return: 33.41% [6] - IR: 1.90 [6] - Monthly Win Rate: 71.58% [6] - **UTD Factor (2014/01-2025/09)**: - Annualized Return: 19.82% [1][7][12] - Annualized Volatility: 7.39% [1][7][12] - IR: 2.68 [1][7][12] - Monthly Win Rate: 77.30% [1][7][12] - Maximum Drawdown: 5.51% [1][7][12] - **UTD Factor (September 2025)**: - 10-group long portfolio return: 0.91% [1][11] - 10-group short portfolio return: 0.52% [1][11] - 10-group long-short portfolio return: 0.39% [1][11]

- The report introduces the **RPV factor (Renewed Correlation of Price and Volume)**, which is constructed by combining intraday and overnight price-volume correlation information. The factor leverages the reversal effect of closing price sequences and the momentum effect of overnight returns, enhanced by turnover rate sequences. The construction process involves identifying the best representatives for intraday and overnight price-volume correlations (CCOIV and COV), and integrating their information into a unified factor. This factor is designed to capture both reversal and momentum effects effectively[6][7][10] - The report also introduces the **SRV factor (Smart Correlation of Price and Volume)**, which is a refined version of the RPV factor. The SRV factor splits intraday price movements into morning and afternoon sessions, calculates a "smart" indicator for the afternoon session, and identifies the 20% of time intervals with the highest informed trading activity. It then uses the turnover rate during these intervals to calculate the correlation with afternoon price movements. For overnight price-volume correlation, the turnover rate is replaced with the turnover rate of the last half-hour of the previous trading day, which is considered to have a higher proportion of informed trading. The SRV factor combines the improved intraday and overnight price-volume correlation factors into a single composite factor[6][10][11] - The **RPV factor** is evaluated as a novel and effective factor that incorporates both reversal and momentum effects, making it a robust tool for stock selection[6][7] - The **SRV factor** is evaluated as an improvement over the RPV factor, with better performance metrics, including higher annualized returns, information ratio, and lower maximum drawdown. It is considered a more effective factor for stock selection[6][10] - The **RPV factor** achieved an annualized return of 14.26%, annualized volatility of 7.70%, IR of 1.85, monthly win rate of 72.14%, and maximum drawdown of 10.63% during the backtesting period from January 2014 to September 2025[7][10] - The **SRV factor** achieved an annualized return of 17.07%, annualized volatility of 6.51%, IR of 2.62, monthly win rate of 74.29%, and maximum drawdown of 3.93% during the same backtesting period[7][10] - In September 2025, the **RPV factor** achieved a 10-group long portfolio return of 1.24%, short portfolio return of -0.89%, and long-short portfolio return of 2.12%[10] - In September 2025, the **SRV factor** achieved a 10-group long portfolio return of 1.70%, short portfolio return of -1.51%, and long-short portfolio return of 3.21%[10]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The MFE portfolio is designed to maximize the exposure of a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover limits. This approach ensures that the factor's predictive power is tested under realistic portfolio constraints, making it more applicable in practice [39][40]. **Model Construction Process**: The MFE portfolio is constructed using the following 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} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. 4. **Constituent Weight Control**: \( B_b \) is a binary vector indicating benchmark constituents, and \( b_l, b_h \) are the lower and upper bounds for constituent weights. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights. 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T} w = 1 \) [39][40][41]. **Model Evaluation**: The MFE portfolio is effective in testing factor performance under realistic constraints, making it a practical tool for portfolio construction [39][40]. Quantitative Factors and Construction Methods - **Factor Name**: DELTAROE **Factor Construction Idea**: Measures the change in return on equity (ROE) over a specific period to capture improvements in profitability [16]. **Factor Construction Process**: $ \text{DELTAROE} = \text{ROE}_{\text{current quarter}} - \text{ROE}_{\text{same quarter last year}} $ Where ROE is calculated as: $ \text{ROE} = \frac{\text{Net Income} \times 2}{\text{Beginning Equity} + \text{Ending Equity}} $ [16]. **Factor Evaluation**: DELTAROE is a profitability factor that has shown strong performance in multiple sample spaces, including CSI 300, CSI 500, and CSI A500 indices [17][19][24]. - **Factor Name**: Pre-expected PEG (Pre-expected Price-to-Earnings Growth) **Factor Construction Idea**: Incorporates analysts' earnings growth expectations to evaluate valuation relative to growth potential [16]. **Factor Construction Process**: $ \text{Pre-expected PEG} = \frac{\text{Forward P/E}}{\text{Expected Earnings Growth Rate}} $ Where forward P/E is based on analysts' consensus earnings estimates [16]. **Factor Evaluation**: This factor has demonstrated strong predictive power in growth-oriented sample spaces such as CSI 300 and CSI A500 indices [17][24]. - **Factor Name**: DELTAROA **Factor Construction Idea**: Measures the change in return on assets (ROA) over a specific period to capture improvements in asset efficiency [16]. **Factor Construction Process**: $ \text{DELTAROA} = \text{ROA}_{\text{current quarter}} - \text{ROA}_{\text{same quarter last year}} $ Where ROA is calculated as: $ \text{ROA} = \frac{\text{Net Income} \times 2}{\text{Beginning Total Assets} + \text{Ending Total Assets}} $ [16]. **Factor Evaluation**: DELTAROA has shown consistent performance across multiple indices, including CSI 1000 and public fund-heavy indices [22][26]. Factor Backtesting Results - **DELTAROE**: - CSI 300: Weekly excess return 0.75%, monthly 2.28%, YTD 8.04% [17]. - CSI 500: Weekly excess return 0.07%, monthly 0.59%, YTD 6.67% [19]. - CSI A500: Weekly excess return 0.68%, monthly 3.61%, YTD 9.20% [24]. - **Pre-expected PEG**: - CSI 300: Weekly excess return 0.72%, monthly 2.10%, YTD 7.22% [17]. - CSI 500: Weekly excess return 0.15%, monthly 1.34%, YTD 9.62% [19]. - CSI A500: Weekly excess return 0.85%, monthly 2.07%, YTD 10.35% [24]. - **DELTAROA**: - CSI 300: Weekly excess return 0.44%, monthly 2.27%, YTD 7.10% [17]. - CSI 1000: Weekly excess return 0.66%, monthly 1.57%, YTD 8.57% [22]. - Public Fund Index: Weekly excess return 0.66%, monthly 1.57%, YTD 8.57% [26].

Quantitative Factors and Construction Factor Name: Stability of Turnover Rate (STR) - **Factor Construction Idea**: The STR factor is designed to evaluate the stability of daily turnover rates. It aims to identify stocks with stable turnover rates, as opposed to focusing solely on low or high turnover rates. This approach addresses the limitations of traditional turnover rate factors, which may misjudge stocks with high turnover but significant future returns [1][8]. - **Factor Construction Process**: - The STR factor is constructed using daily turnover rate data. - The stability of turnover rates is calculated, inspired by the Uniformity of Turnover Rate Distribution (UTD) factor, which measures turnover rate volatility at the minute level. - The STR factor is then adjusted to remove the influence of common market styles and industry effects, ensuring a "pure" factor signal [8]. - **Factor Evaluation**: The STR factor demonstrates strong stock selection capabilities, even after controlling for market and industry influences. It is considered an effective and straightforward factor [6][8]. Traditional Turnover Rate Factor (Turn20) - **Factor Construction Idea**: The Turn20 factor calculates the average daily turnover rate over the past 20 trading days. It assumes that stocks with lower turnover rates are more likely to outperform in the future, while those with higher turnover rates are more likely to underperform [6][7]. - **Factor Construction Process**: - At the end of each month, the daily turnover rates of all stocks over the past 20 trading days are averaged. - The resulting values are neutralized for market capitalization to eliminate size effects [6]. - **Factor Evaluation**: While the Turn20 factor has historically performed well, its logic has limitations. Specifically, stocks with high turnover rates exhibit significant variability in future returns, leading to potential misjudgments of high-performing stocks within this group [7]. --- Backtesting Results of Factors STR Factor - **Annualized Return**: 40.75% [9][10] - **Annualized Volatility**: 14.44% [9][10] - **Information Ratio (IR)**: 2.82 [9][10] - **Monthly Win Rate**: 77.02% [9][10] - **Maximum Drawdown**: 9.96% [9][10] - **July 2025 Performance**: - Long Portfolio Return: 1.29% [10] - Short Portfolio Return: -0.02% [10] - Long-Short Portfolio Return: 1.32% [10] Turn20 Factor - **Monthly IC Mean**: -0.072 [6] - **Annualized ICIR**: -2.10 [6] - **Annualized Return**: 33.41% [6] - **Information Ratio (IR)**: 1.90 [6] - **Monthly Win Rate**: 71.58% [6]

Quantitative Models and Construction Methods Barra Style Factors - **Model Name**: Barra Style Factors - **Construction Idea**: The Barra style factors are designed to capture the performance of different market styles, such as size, value, growth, and profitability, through specific factor definitions[4][14] - **Construction Process**: - **Size Factor**: Measures the market capitalization of stocks - **Value Factor**: Captures the book-to-market ratio of stocks - **Growth Factor**: Reflects the growth potential of stocks - **Profitability Factor**: Based on earnings expectations[4][14] - **Evaluation**: These factors are widely used in the industry to analyze market trends and style rotations[4][14] --- Open-source Trading Behavior Factors - **Factor Name**: Ideal Reversal Factor - **Construction Idea**: Identifies the strongest reversal days by analyzing the average transaction size of large trades[5][15] - **Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the average transaction size per day (transaction amount/number of transactions) 3. Identify the 10 days with the highest transaction sizes and sum their returns (M_high) 4. Identify the 10 days with the lowest transaction sizes and sum their returns (M_low) 5. Compute the factor as $M = M_{high} - M_{low}$[43] - **Evaluation**: Captures the microstructure of reversal forces in the A-share market[5][15] - **Factor Name**: Smart Money Factor - **Construction Idea**: Tracks institutional trading activity by analyzing minute-level price and volume data[5][15] - **Construction Process**: 1. Retrieve the past 10 days' minute-level data for a stock 2. Construct the indicator $S_t = |R_t| / V_t^{0.25}$, where $R_t$ is the return at minute $t$, and $V_t$ is the trading volume at minute $t$ 3. Sort minute-level data by $S_t$ in descending order and select the top 20% of minutes by cumulative trading volume 4. Calculate the volume-weighted average price (VWAP) for smart money trades ($VWAP_{smart}$) and all trades ($VWAP_{all}$) 5. Compute the factor as $Q = VWAP_{smart} / VWAP_{all}$[42][44] - **Evaluation**: Effectively identifies institutional trading patterns[5][15] - **Factor Name**: APM Factor - **Construction Idea**: Measures the difference in trading behavior between morning (or overnight) and afternoon sessions[5][15] - **Construction Process**: 1. Retrieve the past 20 days' data for a stock 2. Calculate daily overnight and afternoon returns for both the stock and the index 3. Perform a regression of stock returns on index returns to obtain residuals 4. Compute the difference between overnight and afternoon residuals for each day 5. Calculate the statistic $\mathrm{stat} = \frac{\mu(\delta_t)}{\sigma(\delta_t) / \sqrt{N}}$, where $\mu$ is the mean, $\sigma$ is the standard deviation, and $N$ is the sample size 6. Regress the statistic on momentum factors and use the residual as the APM factor[43][45][46] - **Evaluation**: Captures intraday trading behavior differences[5][15] - **Factor Name**: Ideal Amplitude Factor - **Construction Idea**: Measures the structural differences in amplitude information between high and low price states[5][15] - **Construction Process**: 1. Retrieve the past 20 trading days' data for a stock 2. Calculate the daily amplitude as $(\text{High Price}/\text{Low Price}) - 1$ 3. Compute the average amplitude for the top 25% of days with the highest closing prices ($V_{high}$) 4. Compute the average amplitude for the bottom 25% of days with the lowest closing prices ($V_{low}$) 5. Compute the factor as $V = V_{high} - V_{low}$[48] - **Evaluation**: Highlights amplitude differences across price states[5][15] - **Factor Name**: Composite Trading Behavior Factor - **Construction Idea**: Combines the above trading behavior factors using ICIR-based weights to enhance predictive power[31] - **Construction Process**: 1. Standardize and winsorize the individual factors within industries 2. Use the past 12 periods' ICIR values as weights to compute the composite factor[31] - **Evaluation**: Demonstrates superior performance in small-cap stock pools[32] --- Backtesting Results of Models and Factors Barra Style Factors - **Size Factor**: Return of 0.64% in July 2025[4][14] - **Value Factor**: Return of 0.59% in July 2025[4][14] - **Growth Factor**: Return of 0.16% in July 2025[4][14] - **Profitability Factor**: Return of -0.32% in July 2025[4][14] Open-source Trading Behavior Factors - **Ideal Reversal Factor**: - IC: -0.050 - RankIC: -0.061 - IR: 2.52 - Long-short monthly win rate: 78.3% (historical), 66.7% (last 12 months) - July 2025 long-short return: 0.47%[6][16] - **Smart Money Factor**: - IC: -0.037 - RankIC: -0.061 - IR: 2.76 - Long-short monthly win rate: 82.2% (historical), 91.7% (last 12 months) - July 2025 long-short return: 1.78%[6][19] - **APM Factor**: - IC: 0.029 - RankIC: 0.034 - IR: 2.30 - Long-short monthly win rate: 77.4% (historical), 58.3% (last 12 months) - July 2025 long-short return: 1.42%[6][23] - **Ideal Amplitude Factor**: - IC: -0.054 - RankIC: -0.073 - IR: 3.03 - Long-short monthly win rate: 83.6% (historical), 75.0% (last 12 months) - July 2025 long-short return: 3.86%[6][28] - **Composite Trading Behavior Factor**: - IC: 0.067 - RankIC: 0.092 - IR: 3.30 - Long-short monthly win rate: 82.6% (historical), 83.3% (last 12 months) - July 2025 long-short return: 2.13%[6][31]

- The report aims to track large purchases and net active purchases using transaction detail data[1] - The indicators used are the proportion of large order transaction amounts and the proportion of net active purchase amounts[7] - The proportion of large order transaction amounts reflects the buying behavior of large funds[7] - The proportion of net active purchase amounts reflects the active buying behavior of investors[7] - The top 5 stocks with the highest average proportion of large order transaction amounts over the past 5 days are: Sobute, China Railway Industry, Tibet Tianlu, Poly United, and China Power Construction[4][9] - The top 5 stocks with the highest average proportion of net active purchase amounts over the past 5 days are: Weixing Co., HNA Holdings, Kaili Medical, Liaogang Co., and Hengyi Petrochemical[4][10] - The top 5 industries with the highest average proportion of large order transaction amounts over the past 5 days are: Banking, Real Estate, Petroleum and Petrochemical, Transportation, and Coal[4] - The top 5 industries with the highest average proportion of net active purchase amounts over the past 5 days are: Media, Textile and Apparel, Computers, Electronics, and Light Manufacturing[4] - The top 5 ETFs with the highest average proportion of large order transaction amounts over the past 5 days are: China Agricultural Theme ETF, E Fund CSI 300 Medical and Health ETF, Huabao CSI Medical ETF, Bosera SSE STAR 100 ETF, and Guotai CSI Livestock Breeding ETF[4][15] - The top 5 ETFs with the highest average proportion of net active purchase amounts over the past 5 days are: Penghua CSI Subdivision Chemical Industry Theme ETF, GF SSE STAR 50 ETF, Harvest CSI Rare Metals Theme ETF, E Fund Guozheng Robotics Industry ETF, and Harvest CSI Software Services ETF[4][16]