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]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure (MFE) Portfolio **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of individual factors under realistic constraints, such as industry exposure, style exposure, stock weight limits, and turnover constraints. This approach ensures that the factors deemed "effective" can genuinely contribute to return prediction in the final portfolio[41][42]. **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 \( f^{T} w \) represents the weighted exposure of the portfolio to the factor \( f \), and \( w \) is the stock weight vector. - **Constraints**: 1. **Style Exposure**: \( X \) represents the factor exposure matrix for stocks, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style factor exposure[42]. 2. **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviations[42]. 3. **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviations relative to the benchmark[42]. 4. **Constituent Weight**: \( B_b \) is a binary vector indicating whether a stock is part of the benchmark, and \( b_l, b_h \) are the lower and upper bounds for constituent weights[42]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \)[42]. 6. **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T} w = 1 \)[43]. - **Implementation**: 1. Define constraints for style, industry, and stock weights. For example, for CSI 500 and CSI 300 indices, industry exposure is neutralized, and stock weight deviations are capped at 1%[45]. 2. Construct the MFE portfolio at the end of each month based on the constraints[45]. 3. Backtest the portfolio, accounting for transaction costs (0.3% per side), and calculate performance metrics relative to the benchmark[45]. **Model Evaluation**: The MFE portfolio effectively tests factor performance under realistic constraints, making it a robust tool for evaluating factor predictability in practical scenarios[41][42]. Quantitative Factors and Construction Methods - **Factor Name**: DELTAROA **Factor Construction Idea**: Measures the change in return on assets (ROA) compared to the same quarter in the previous year, capturing improvements in asset utilization efficiency[16]. **Factor Construction Process**: $ DELTAROA = ROA_{current\ quarter} - ROA_{same\ quarter\ last\ year} $ Where \( ROA = \frac{Net\ Income}{Total\ Assets} \)[16]. **Factor Evaluation**: DELTAROA is a growth-oriented factor that has shown strong performance in multiple sample spaces, particularly in the CSI A500 index[19][25]. - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings, to capture earnings surprises[16]. **Factor Construction Process**: $ SUE = \frac{Actual\ Earnings - Expected\ Earnings}{Standard\ Deviation\ of\ Expected\ Earnings} $[16]. **Factor Evaluation**: SUE is a profitability factor that performs well in growth-oriented indices like CSI 1000 and CSI A500[19][23][25]. - **Factor Name**: One-Year Momentum **Factor Construction Idea**: Captures the trend-following behavior of stocks by measuring price momentum over the past year, excluding the most recent month[16]. **Factor Construction Process**: $ Momentum = \frac{Price_{t-12} - Price_{t-1}}{Price_{t-1}} $ Where \( t-12 \) and \( t-1 \) represent the stock price 12 months and 1 month ago, respectively[16]. **Factor Evaluation**: Momentum is a widely used factor that has shown consistent performance in large-cap indices like CSI 300 and CSI 500[19][21]. Factor Backtesting Results - **CSI 300 Sample Space**: - **Best-Performing Factors (1 Week)**: Single-quarter revenue growth, DELTAROA, single-quarter ROE[19]. - **Worst-Performing Factors (1 Week)**: Three-month volatility, one-month volatility, three-month turnover[19]. - **CSI 500 Sample Space**: - **Best-Performing Factors (1 Week)**: One-year momentum, standardized unexpected revenue, standardized unexpected earnings[21]. - **Worst-Performing Factors (1 Week)**: SPTTM, single-quarter SP, dividend yield[21]. - **CSI 1000 Sample Space**: - **Best-Performing Factors (1 Week)**: Three-month reversal, standardized unexpected revenue, single-quarter surprise magnitude[23]. - **Worst-Performing Factors (1 Week)**: Dividend yield, one-month volatility, BP[23]. - **CSI A500 Sample Space**: - **Best-Performing Factors (1 Week)**: DELTAROA, standardized unexpected earnings, single-quarter ROA[25]. - **Worst-Performing Factors (1 Week)**: Three-month volatility, one-month turnover, one-month volatility[25]. - **Public Fund Heavyweight Index Sample Space**: - **Best-Performing Factors (1 Week)**: One-year momentum, standardized unexpected revenue, expected net profit QoQ[27]. - **Worst-Performing Factors (1 Week)**: Dividend yield, one-month volatility, three-month volatility[27].

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 actual investment scenarios [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**: - **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. - **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. - **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. - **Component Weight Control**: \( B_b \) is a binary vector indicating benchmark components, and \( b_l, b_h \) are the lower and upper bounds for component weights. - **No Short Selling**: Ensures non-negative weights and limits individual stock weights. - **Full Investment**: Ensures the portfolio is fully invested (\( \mathbf{1}^{T}\ w=1 \)) [40][41]. **Model Evaluation**: The MFE portfolio effectively tests factor efficacy under realistic constraints, making it a robust tool for factor validation in enhanced index strategies [39][40]. --- Quantitative Factors and Construction Methods - **Factor Name**: Three-Month Reversal **Factor Construction Idea**: Measures the reversal effect by calculating the return over the past 60 trading days, assuming stocks with recent underperformance may outperform in the future [17]. **Factor Construction Process**: $ \text{Three-Month Reversal} = \text{Cumulative Return over the Past 60 Trading Days} $ **Factor Evaluation**: Demonstrates strong performance in certain index spaces, such as CSI 1000 and CSI A500, but underperforms in others like CSI 500 [17][22][25]. - **Factor Name**: One-Year Momentum **Factor Construction Idea**: Captures the momentum effect by excluding the most recent month and calculating the cumulative return over the prior 11 months [17]. **Factor Construction Process**: $ \text{One-Year Momentum} = \text{Cumulative Return over the Past 11 Months (Excluding the Most Recent Month)} $ **Factor Evaluation**: Performs well in CSI 500 and CSI 1000 spaces but shows mixed results in other index spaces [17][21][23]. - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of expected earnings [17]. **Factor Construction Process**: $ \text{SUE} = \frac{\text{Actual Earnings} - \text{Expected Earnings}}{\text{Standard Deviation of Expected Earnings}} $ **Factor Evaluation**: Consistently performs well across multiple index spaces, indicating its robustness as a predictive factor [17][22][25]. - **Factor Name**: Delta ROE (DELTAROE) **Factor Construction Idea**: Measures the change in return on equity (ROE) compared to the same quarter in the previous year [17]. **Factor Construction Process**: $ \text{DELTAROE} = \text{Current Quarter ROE} - \text{ROE from the Same Quarter Last Year} $ **Factor Evaluation**: Demonstrates strong predictive power in CSI 500 and CSI A500 spaces, with moderate performance in other index spaces [17][21][25]. --- Factor Backtesting Results - **Three-Month Reversal**: - CSI 300: Weekly excess return 0.66%, monthly excess return 0.65%, YTD excess return 3.01% [19]. - CSI 500: Weekly excess return 0.79%, monthly excess return 1.17%, YTD excess return 4.07% [21]. - CSI 1000: Weekly excess return 1.09%, monthly excess return 1.40%, YTD excess return 0.38% [23]. - CSI A500: Weekly excess return 1.08%, monthly excess return 0.36%, YTD excess return 3.64% [25]. - **One-Year Momentum**: - CSI 300: Weekly excess return 0.46%, monthly excess return 0.36%, YTD excess return -1.85% [19]. - CSI 500: Weekly excess return 1.26%, monthly excess return 1.18%, YTD excess return 2.77% [21]. - CSI 1000: Weekly excess return 1.45%, monthly excess return 1.73%, YTD excess return 0.26% [23]. - CSI A500: Weekly excess return 0.74%, monthly excess return 0.87%, YTD excess return -2.03% [25]. - **SUE**: - CSI 300: Weekly excess return 0.51%, monthly excess return 2.15%, YTD excess return 3.03% [19]. - CSI 500: Weekly excess return -0.41%, monthly excess return 0.13%, YTD excess return 2.86% [21]. - CSI 1000: Weekly excess return -0.08%, monthly excess return 2.77%, YTD excess return 4.41% [23]. - CSI A500: Weekly excess return 0.47%, monthly excess return 1.63%, YTD excess return 2.04% [25]. - **Delta ROE (DELTAROE)**: - CSI 300: Weekly excess return 0.26%, monthly excess return 2.27%, YTD excess return 5.32% [19]. - CSI 500: Weekly excess return 0.58%, monthly excess return 2.49%, YTD excess return 4.03% [21]. - CSI 1000: Weekly excess return -1.15%, monthly excess return 0.74%, YTD excess return 3.01% [23]. - CSI A500: Weekly excess return 0.52%, monthly excess return 2.82%, YTD excess return 5.13% [25].

Group 1: Equity Quantitative Analysis - The equity market risk factor returns have normalized in H1 2025, with small-cap and momentum factors performing prominently. The net profit of the entire A-share market has turned positive year-on-year for the first time since Q2 2023, indicating a significant recovery in the industrial sector. The TMT sector is expected to continue its growth in the second half of the year, with some cyclical industries likely to see a performance inflection point [3][14][23] - The technical analysis suggests that the broad market index may continue in a volatile pattern, with a focus on breakout directions. The overall ranking of indices is as follows: CSI 1000 > CSI 500 > CSI 300 > CSI 2000 [3][39][41] Group 2: Interest Rate Quantitative Analysis - The 10-year government bond yield has dropped below 2% and stabilized at a low level of 1.6%-1.7%. The recovery in economic activity and credit impulses has suppressed further declines in interest rates. The current willingness to hold inflation assets has weakened again, compounded by negative ROE in the real estate sector and low leverage in high ROE industries, leading to a lack of upward momentum in interest rates [3][49][63] - The future direction of interest rates will depend on the demand for funds from high ROE and high-leverage industries, which are currently lacking [3][70][75] Group 3: Gold Quantitative Analysis - Gold is viewed as a hedge against risk, with current fiscal factors dominating its price movements. The geopolitical risks and economic policy uncertainties globally are expected to support gold prices. The technical analysis indicates that gold has consolidated and accumulated support, with a target price set at $3,885 per ounce [3][81][87] Group 4: Industry Quantitative Analysis - The rotation speed among industries is expected to accelerate, with opportunities becoming more dispersed. Long-term investments in growth industries are anticipated to yield higher expected returns. The lifecycle model indicates that overall growth in primary industries is insufficient, with growth concentrated in tertiary industries, particularly in the basic chemical sector [4][14][23] - The TMT sector is projected to continue its growth trajectory, while industries such as basic chemicals and building materials are expected to stabilize and recover due to ongoing fiscal support for infrastructure projects [23][31][37]

Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE model is designed to test the effectiveness of individual factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover constraints. By maximizing single-factor exposure while adhering to these constraints, the model evaluates the predictive power of factors in a controlled environment [40][41]. - **Model Construction Process**: - The optimization model aims to maximize single-factor exposure: $ \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 $f^{T}w$, where $f$ represents factor values and $w$ represents stock weights [40][41]. - **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 [41]. 2. **Industry Exposure**: $H$ is the industry exposure matrix, and $h_l$, $h_h$ are the lower and upper bounds for industry deviation [41]. 3. **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for stock weight deviation [41]. 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 [41]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights [41]. 6. **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [42]. - **Implementation**: - At the end of each month, MFE portfolios are constructed for each factor under the specified constraints. - Historical returns are calculated for the MFE portfolios, adjusted for transaction costs (0.3% per side), and compared to the benchmark [44]. - **Model Evaluation**: The MFE model is effective in testing factor performance under realistic constraints, making it a practical tool for portfolio construction and factor validation [40][41]. --- Quantitative Factors and Construction Methods 1. Factor Name: Book-to-Price Ratio (BP) - **Factor Construction Idea**: Measures valuation by comparing book value to market capitalization [18]. - **Factor Construction Process**: - Formula: $ BP = \frac{\text{Net Assets}}{\text{Market Capitalization}} $ [18]. 2. Factor Name: Earnings-to-Price Ratio (EP) - **Factor Construction Idea**: Evaluates profitability relative to market capitalization [18]. - **Factor Construction Process**: - Formula: $ EP = \frac{\text{Net Income (Quarterly)}}{\text{Market Capitalization}} $ [18]. 3. Factor Name: Earnings-to-Price TTM (EPTTM) - **Factor Construction Idea**: Tracks trailing twelve-month earnings relative to market capitalization [18]. - **Factor Construction Process**: - Formula: $ EPTTM = \frac{\text{Net Income (TTM)}}{\text{Market Capitalization}} $ [18]. 4. Factor Name: Momentum (1-Year Momentum) - **Factor Construction Idea**: Captures price trends by measuring returns over the past year, excluding the most recent month [18]. - **Factor Construction Process**: - Formula: $ \text{1-Year Momentum} = \text{Cumulative Return (Year)} - \text{Return (Last Month)} $ [18]. 5. Factor Name: Analyst Coverage (3-Month Coverage) - **Factor Construction Idea**: Measures the number of analysts covering a stock over the past three months [18]. - **Factor Construction Process**: - Formula: $ \text{3-Month Coverage} = \text{Number of Analysts Covering Stock (Last 3 Months)} $ [18]. --- Factor Backtesting Results 1. Factor Performance in CSI 300 Universe - **Best-Performing Factors (Recent Week)**: EPTTM, Single-Quarter EP, EPTTM Percentile [20]. - **Worst-Performing Factors (Recent Week)**: 1-Year Momentum, Executive Compensation, Illiquidity Shock [20]. 2. Factor Performance in CSI 500 Universe - **Best-Performing Factors (Recent Week)**: BP, Expected BP, Expected EPTTM [22]. - **Worst-Performing Factors (Recent Week)**: 1-Year Momentum, 3-Month Coverage, Illiquidity Shock [22]. 3. Factor Performance in CSI 1000 Universe - **Best-Performing Factors (Recent Week)**: BP, 1-Month Turnover, 3-Month Volatility [24]. - **Worst-Performing Factors (Recent Week)**: 1-Year Momentum, 3-Month Coverage, Single-Quarter ROE [24]. 4. Factor Performance in CSI A500 Universe - **Best-Performing Factors (Recent Week)**: Single-Quarter EP, Expected EPTTM, Expected PEG [26]. - **Worst-Performing Factors (Recent Week)**: 3-Month Reversal, 1-Year Momentum, 1-Month Reversal [26]. 5. Factor Performance in Public Fund Heavyweight Index - **Best-Performing Factors (Recent Week)**: Expected EPTTM, Single-Quarter EP, Expected PEG [28]. - **Worst-Performing Factors (Recent Week)**: 1-Year Momentum, 3-Month Coverage, Expected Net Profit QoQ [28].

- Model Name: RPV (Renewed Correlation of Price and Volume); Model Construction Idea: The RPV factor integrates intraday and overnight information by dividing price and volume into four quadrants, effectively identifying the reversal and momentum effects of price-volume correlation factors through the monthly IC mean; Model Construction Process: The RPV factor is constructed by combining the best representatives of intraday and overnight price-volume correlations, incorporating "trading volume" information in the form of correlation, and completing information integration; Model Evaluation: The RPV factor is novel and effective[1][6][7] - Model Name: SRV (Smart Relative Volume); Model Construction Idea: The SRV factor splits intraday price changes into morning and afternoon changes, calculates the "smart" indicator by minute, and uses the correlation coefficient between the afternoon "smart" turnover rate and afternoon price changes; Model Construction Process: The SRV factor combines the more effective intraday price-volume correlation factor and the overnight price-volume correlation factor, where the turnover rate is replaced by the turnover rate of the last half-hour of the previous day, which has a higher proportion of informed trading; Model Evaluation: The SRV factor performs better than the RPV factor[1][6][7] Model Backtest Results - RPV Model, Annualized Return: 14.69%, Annualized Volatility: 7.75%, IR: 1.90, Monthly Win Rate: 72.79%, Maximum Drawdown: 10.63%[1][7][10] - SRV Model, Annualized Return: 17.48%, Annualized Volatility: 6.50%, IR: 2.69, Monthly Win Rate: 75.74%, Maximum Drawdown: 3.74%[1][7][10] Factor Construction and Evaluation - Factor Name: RPV; Factor Construction Idea: The RPV factor integrates intraday and overnight information by dividing price and volume into four quadrants, effectively identifying the reversal and momentum effects of price-volume correlation factors through the monthly IC mean; Factor Construction Process: The RPV factor is constructed by combining the best representatives of intraday and overnight price-volume correlations, incorporating "trading volume" information in the form of correlation, and completing information integration; Factor Evaluation: The RPV factor is novel and effective[1][6][7] - Factor Name: SRV; Factor Construction Idea: The SRV factor splits intraday price changes into morning and afternoon changes, calculates the "smart" indicator by minute, and uses the correlation coefficient between the afternoon "smart" turnover rate and afternoon price changes; Factor Construction Process: The SRV factor combines the more effective intraday price-volume correlation factor and the overnight price-volume correlation factor, where the turnover rate is replaced by the turnover rate of the last half-hour of the previous day, which has a higher proportion of informed trading; Factor Evaluation: The SRV factor performs better than the RPV factor[1][6][7] Factor Backtest Results - RPV Factor, Annualized Return: 14.69%, Annualized Volatility: 7.75%, IR: 1.90, Monthly Win Rate: 72.79%, Maximum Drawdown: 10.63%[1][7][10] - SRV Factor, Annualized Return: 17.48%, Annualized Volatility: 6.50%, IR: 2.69, Monthly Win Rate: 75.74%, Maximum Drawdown: 3.74%[1][7][10]

Group 1 - The core viewpoint of the article emphasizes the performance and characteristics of the "brokerage golden stocks" pool, highlighting its ability to track the performance of mixed equity funds and the strong selection capabilities of industry analysts [2][9][32] - In May 2025, the top-performing stocks in the brokerage golden stock pool included Mankalon, Chaohongji, and Kexing Pharmaceutical, with monthly returns of 10.78%, 7.30%, and 5.70% respectively [3][8] - Year-to-date, Northeast Securities, Huaxin Securities, and Dongxing Securities ranked highest in returns, achieving 45.28%, 40.52%, and 39.15% respectively, while the mixed equity fund index returned 3.37% and the CSI 300 index returned -2.41% [7][10] Group 2 - As of June 3, 2025, 43 brokerages published golden stocks, resulting in a total of 286 unique A-shares after deduplication [19] - The current allocation of golden stocks is highest in the machinery (9.56%), pharmaceuticals (9.04%), electronics (8.01%), basic chemicals (7.75%), and food and beverage (6.46%) sectors [23] - The brokerage golden stock pool has a higher exposure to small-cap stocks this month, indicating a shift in investment style [19][23] Group 3 - The performance of the brokerage golden stock performance enhancement portfolio showed an absolute return of 0.99% for the month and 4.97% year-to-date, outperforming the mixed equity fund index by 1.61% [28][29] - The article highlights the strong alpha generation capability of the brokerage golden stocks, which can provide significant investment opportunities [32][33] - The article also discusses the interaction between brokerage analysts and public fund managers, indicating that stocks recommended by multiple analysts tend to receive higher market attention [9][21]