Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The model aims to maximize single-factor exposure while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach tests the effectiveness of factors under real-world constraints, ensuring their predictive power in portfolio construction [39][40][41] **Model Construction Process**: The optimization model is formulated as follows: $ \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 $f^{T}w$ is the weighted exposure of the portfolio to the factor. $w$ is the stock weight vector to be optimized. - **Constraints**: - **Style Exposure**: $X$ is the matrix of stock exposures to style factors, $w_b$ is the benchmark weight vector, and $s_l$, $s_h$ are the lower and upper bounds for style factor exposure deviation. - **Industry Exposure**: $H$ is the industry exposure matrix, $h_l$, $h_h$ are the lower and upper bounds for industry deviation. - **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for individual stock weight deviation. - **Component Weight Control**: $B_b$ is a binary vector indicating whether a stock belongs to the benchmark index, $b_l$, $b_h$ are the lower and upper bounds for component stock weight. - **No Short Selling**: Ensures non-negative weights and limits individual stock weights. - **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [39][40][41] **Model Evaluation**: The model effectively tests factor validity under real-world constraints, ensuring factors contribute to portfolio returns in practical scenarios [39][40][41] Factor Construction and Methods - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual quarterly net profit from expected net profit, standardized by the standard deviation of expected net profit [17] **Factor Construction Process**: $ SUE = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}} $ **Factor Evaluation**: Useful for identifying stocks with earnings surprises, which may lead to price adjustments [17] - **Factor Name**: One-Month Reversal **Factor Construction Idea**: Captures short-term price reversal by measuring the return over the past 20 trading days [17] **Factor Construction Process**: $ \text{One-Month Reversal} = \text{Return over the past 20 trading days} $ **Factor Evaluation**: Effective in detecting short-term mean-reverting behavior in stock prices [17] - **Factor Name**: Delta ROA **Factor Construction Idea**: Measures the change in return on assets (ROA) compared to the same quarter of the previous year [17] **Factor Construction Process**: $ \Delta ROA = \text{Current Quarter ROA} - \text{ROA of the Same Quarter Last Year} $ **Factor Evaluation**: Indicates improvement or deterioration in asset efficiency, which can signal fundamental changes [17] Factor Backtesting Results **Performance in CSI 300 Sample Space** - **Standardized Unexpected Earnings**: Weekly excess return 1.35%, monthly excess return 3.78%, annual excess return 8.35% [19] - **One-Year Momentum**: Weekly excess return 1.27%, monthly excess return 1.98%, annual excess return -1.17% [19] - **Single-Quarter Revenue Growth**: Weekly excess return 1.08%, monthly excess return 3.86%, annual excess return 11.82% [19] **Performance in CSI 500 Sample Space** - **EPTTM Year Percentile**: Weekly excess return 1.69%, monthly excess return 1.74%, annual excess return 3.77% [21] - **Delta ROA**: Weekly excess return 1.00%, monthly excess return 2.43%, annual excess return 9.72% [21] - **Standardized Unexpected Earnings**: Weekly excess return 0.87%, monthly excess return 3.32%, annual excess return 7.87% [21] **Performance in CSI 1000 Sample Space** - **Standardized Unexpected Earnings**: Weekly excess return 0.75%, monthly excess return 3.69%, annual excess return 7.64% [23] - **Three-Month Reversal**: Weekly excess return 1.34%, monthly excess return 0.24%, annual excess return 5.36% [23] - **Single-Quarter Revenue Growth**: Weekly excess return 1.43%, monthly excess return 4.58%, annual excess return 11.12% [23] **Performance in CSI A500 Sample Space** - **Single-Quarter Revenue Growth**: Weekly excess return 1.43%, monthly excess return 4.58%, annual excess return 11.12% [25] - **Delta ROA**: Weekly excess return 0.63%, monthly excess return 4.33%, annual excess return 10.97% [25] - **Three-Month Reversal**: Weekly excess return 1.34%, monthly excess return 0.24%, annual excess return 5.36% [25] **Performance in Public Fund Heavy Index Sample Space** - **One-Year Momentum**: Weekly excess return 1.11%, monthly excess return 3.36%, annual excess return 1.15% [27] - **Delta ROA**: Weekly excess return 0.63%, monthly excess return 4.33%, annual excess return 10.97% [27] - **Standardized Unexpected Earnings**: Weekly excess return 0.75%, monthly excess return 3.69%, annual excess return 7.64% [27]

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].

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 1.06% this week and 4.21% year-to-date [1][5] - The CSI 500 index enhanced portfolio recorded an excess return of -0.05% this week and 6.45% year-to-date [1][5] - The CSI 1000 index enhanced portfolio had an excess return of 0.72% this week and 9.41% year-to-date [1][5] - The CSI A500 index enhanced portfolio reported an excess return of 0.36% this week and 6.44% year-to-date [1][5] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as three-month volatility, one-month volatility, and standardized unexpected earnings performed well [1][6] - In the CSI 500 component stocks, factors like quarterly revenue growth year-on-year, standardized unexpected revenue, and non-liquidity shocks showed strong performance [1][6] - For the CSI 1000 component stocks, factors such as EPTTM one-year percentile, SPTTM, and BP performed well [1][6] - In the CSI A500 index component stocks, factors like BP, quarterly EP, and three-month volatility showed good performance [1][6] - Among publicly offered fund heavy stocks, factors like quarterly unexpected magnitude, standardized unexpected earnings, and standardized unexpected revenue performed well [1][6] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.37%, a minimum of -0.21%, and a median of 0.32% this week [1][19] - The CSI 500 index enhanced products had a maximum excess return of 0.92%, a minimum of -0.09%, and a median of 0.35% this week [1][20] - The CSI 1000 index enhanced products had a maximum excess return of 0.98%, a minimum of -0.21%, and a median of 0.24% this week [1][22] - The CSI A500 index enhanced products had a maximum excess return of 0.70%, a minimum of -0.19%, and a median of 0.36% this week [1][24]

Group 1 - The core viewpoint of the article is to track the performance of index enhancement portfolios and the effectiveness of various stock selection factors across different indices [1][2][3] Group 2 - The performance of the HuShen 300 index enhancement portfolio showed an excess return of 0.37% for the week and 2.84% year-to-date [5] - The performance of the Zhongzheng 500 index enhancement portfolio showed an excess return of 1.06% for the week and 5.87% year-to-date [5] - The Zhongzheng 1000 index enhancement portfolio had an excess return of 1.73% for the week and 8.10% year-to-date [5] - The Zhongzheng A500 index enhancement portfolio reported an excess return of 0.53% for the week and 5.78% year-to-date [5] Group 3 - In the HuShen 300 component stocks, factors such as one-month reversal, expected PEG, and expected EPTTM performed well [6] - In the Zhongzheng 500 component stocks, one-month reversal, single-quarter SP, and SPTTM factors showed strong performance [6] - For Zhongzheng 1000 component stocks, factors like DELTAROA, executive compensation, and standardized expected external earnings performed well [6] - In the Zhongzheng A500 index component stocks, three-month reversal, single-quarter ROE, and one-month reversal factors were effective [6] - Among public fund heavy stocks, one-month reversal, three-month reversal, and single-quarter EP factors performed well [6] Group 4 - The HuShen 300 index enhancement products had a maximum excess return of 1.10%, a minimum of -0.76%, and a median of 0.06% for the week [19] - The Zhongzheng 500 index enhancement products had a maximum excess return of 0.99%, a minimum of -0.08%, and a median of 0.40% for the week [21] - The Zhongzheng 1000 index enhancement products had a maximum excess return of 0.81%, a minimum of -0.28%, and a median of 0.26% for the week [20] - The Zhongzheng A500 index enhancement products had a maximum excess return of 0.39%, a minimum of -0.52%, and a median of 0.23% for the week [22] Group 5 - The total number of public fund HuShen 300 index enhancement products is 67, with a total scale of 778 billion [16] - There are 70 Zhongzheng 500 index enhancement products with a total scale of 454 billion [16] - The Zhongzheng 1000 index enhancement products consist of 46 products with a total scale of 150 billion [16] - The Zhongzheng A500 index enhancement products have 35 products with a total scale of 223 billion [16]