Quantitative Models and Construction Methods 1. Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of individual factors under real-world constraints, such as controlling for industry exposure, style exposure, stock weight limits, and turnover rates. The goal is to maximize the exposure of a single factor while adhering to these constraints[39][40] - **Model Construction Process**: - The objective function is to maximize single-factor exposure, where $f$ represents the factor values, $f^T w$ is the weighted exposure of the portfolio to the single factor, and $w$ is the vector of stock weights - The optimization model is as follows: $$ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $$ - The first constraint limits the portfolio's style exposure relative to the benchmark index, where $X$ is the factor exposure matrix, $w_b$ is the weight vector of the benchmark index constituents, and $s_l$ and $s_h$ are the lower and upper bounds of style factor exposure, respectively - The second constraint limits the portfolio's industry deviation, where $H$ is the industry exposure matrix, and $h_l$ and $h_h$ are the lower and upper bounds of industry deviation, respectively - The third constraint limits individual stock deviations relative to the benchmark index constituents, where $w_l$ and $w_h$ are the lower and upper bounds of individual stock deviations - The fourth constraint limits the weight proportion of the portfolio within the benchmark index constituents, where $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark index, and $b_l$ and $b_h$ are the lower and upper bounds of the weight proportion - The fifth constraint prohibits short selling and limits the upper bound of individual stock weights - The sixth constraint ensures that the portfolio is fully invested, with the sum of weights equal to 1[39][40][41] - The MFE portfolio is constructed for a given benchmark index by applying the above optimization model. To avoid excessive concentration, the deviation of individual stock weights relative to the benchmark is typically set between 0.5% and 1%[41][43] - **Model Evaluation**: The MFE portfolio is used to evaluate the effectiveness of individual factors under realistic constraints, ensuring that the selected factors can contribute to the actual return prediction in the final portfolio[39][40] --- Model Backtesting Results 1. National Trust Quantitative Engineering Index Enhanced Portfolio - **CSI 300 Index Enhanced Portfolio**: - Weekly excess return: 0.63% - Year-to-date excess return: 17.65%[13] - **CSI 500 Index Enhanced Portfolio**: - Weekly excess return: 0.30% - Year-to-date excess return: 8.35%[13] - **CSI 1000 Index Enhanced Portfolio**: - Weekly excess return: 0.77% - Year-to-date excess return: 18.22%[13] - **CSI A500 Index Enhanced Portfolio**: - Weekly excess return: 1.57% - Year-to-date excess return: 11.17%[13] --- Quantitative Factors and Construction Methods 1. Factor Name: BP - **Factor Construction Idea**: Measures valuation by comparing book value to market value[16] - **Factor Construction Process**: - Formula: $BP = \frac{\text{Net Asset}}{\text{Total Market Value}}$[16] 2. Factor Name: Single Quarter EP - **Factor Construction Idea**: Measures profitability by comparing quarterly net profit to market value[16] - **Factor Construction Process**: - Formula: $Single\ Quarter\ EP = \frac{\text{Quarterly Net Profit}}{\text{Total Market Value}}$[16] 3. Factor Name: Single Quarter SP - **Factor Construction Idea**: Measures valuation by comparing quarterly revenue to market value[16] - **Factor Construction Process**: - Formula: $Single\ Quarter\ SP = \frac{\text{Quarterly Revenue}}{\text{Total Market Value}}$[16] 4. Factor Name: EPTTM - **Factor Construction Idea**: Measures profitability by comparing trailing twelve months (TTM) net profit to market value[16] - **Factor Construction Process**: - Formula: $EPTTM = \frac{\text{TTM Net Profit}}{\text{Total Market Value}}$[16] 5. Factor Name: SPTTM - **Factor Construction Idea**: Measures valuation by comparing TTM revenue to market value[16] - **Factor Construction Process**: - Formula: $SPTTM = \frac{\text{TTM Revenue}}{\text{Total Market Value}}$[16] 6. Factor Name: One-Month Volatility - **Factor Construction Idea**: Measures risk by calculating the average intraday true range over the past 20 trading days[16] - **Factor Construction Process**: - Formula: $One\ Month\ Volatility = \text{Average of Intraday True Range over 20 trading days}$[16] 7. Factor Name: Three-Month Volatility - **Factor Construction Idea**: Measures risk by calculating the average intraday true range over the past 60 trading days[16] - **Factor Construction Process**: - Formula: $Three\ Month\ Volatility = \text{Average of Intraday True Range over 60 trading days}$[16] 8. Factor Name: One-Year Momentum - **Factor Construction Idea**: Measures momentum by calculating the return over the past year, excluding the most recent month[16] - **Factor Construction Process**: - Formula: $One\ Year\ Momentum = \text{Return over the past year excluding the most recent month}$[16] 9. Factor Name: Expected EPTTM - **Factor Construction Idea**: Measures profitability based on rolling expected earnings per share (EPS)[16] - **Factor Construction Process**: - Formula: $Expected\ EPTTM = \text{Rolling Expected EPS}$[16] 10. Factor Name: Expected BP - **Factor Construction Idea**: Measures valuation based on rolling expected book-to-price ratio[16] - **Factor Construction Process**: - Formula: $Expected\ BP = \text{Rolling Expected Book-to-Price Ratio}$[16] 11. Factor Name: Expected PEG - **Factor Construction Idea**: Measures valuation by comparing expected price-to-earnings ratio to growth rate[16] - **Factor Construction Process**: - Formula: $Expected\ PEG = \text{Expected PE Ratio / Growth Rate}$[16] 12. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Measures earnings surprise by comparing actual quarterly net profit to expected net profit, normalized by the standard deviation of expected net profit[16] - **Factor Construction Process**: - Formula: $SUE = \frac{\text{Actual Quarterly Net Profit - Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}}$[16] --- Factor Backtesting Results 1. CSI 300 Index - **Best-performing factors (recent week)**: Expected EPTTM (1.19%), One-Month Volatility (1.17%), BP (1.15%)[18] - **Worst-performing factors (recent week)**: Single Quarter Revenue YoY Growth (-0.61%), Three-Month Institutional Coverage (-0.38%), Three-Month Earnings Revisions (-0.26%)[18] 2. CSI 500 Index - **Best-performing factors (recent week)**: SPTTM (1.69%), Expected BP (1.58%), Single Quarter EP (1.56%)[20] - **Worst-performing factors (recent week)**: One-Year Momentum (-1.01%), Expected PEG (-0.38%), Standardized Unexpected Revenue (-0.29%)[20] 3. CSI 1000 Index - **Best-performing factors (recent week)**: EPTTM (2.36%), SPTTM (2.14%), Expected EPTTM (2.10%)[22] - **Worst-performing factors (recent week)**: Expected Net Profit QoQ (-0.65%), One-Year Momentum (-0.48%), Single Quarter Revenue YoY Growth (-0.39%)[22] 4. CSI A500 Index - **Best-performing factors (recent week)**: Single Quarter SP (1.99%), SPTTM (1.89%), One-Month Volatility (1.69%)[24] - **Worst-performing factors (recent week)**: Single Quarter Revenue YoY Growth (-1.07%), One-Year Momentum (-0.86%), Three-Month Institutional Coverage (-0

Group 1 - The core viewpoint of the article highlights the performance of various index-enhanced portfolios, indicating that the CSI 300 and CSI 500 index-enhanced portfolios achieved positive excess returns, while the CSI 1000 index-enhanced portfolio experienced a slight decline in excess returns this week [1][2][18]. Group 2 - The CSI 300 index-enhanced portfolio recorded an excess return of 0.44% this week and 0.80% year-to-date [1][2]. - The CSI 500 index-enhanced portfolio also achieved an excess return of 0.44% this week and 1.81% year-to-date [1][2]. - The CSI 1000 index-enhanced portfolio saw a decrease of 0.13% in excess return this week, with a year-to-date excess return of 0.50% [1][2]. Group 3 - In the CSI 300 component stocks, factors such as three-month turnover, dividend yield, and one-month turnover performed well [5]. - For the CSI 500 component stocks, factors like executive compensation, expected net profit month-on-month, and dividend yield showed strong performance [6]. - In the CSI 1000 component stocks, factors such as expected PEG, SPTTM, and one-month volatility performed well [10]. Group 4 - The public fund index-enhanced products for the CSI 300 had a maximum excess return of 1.67%, a minimum of -2.70%, and a median of 0.11% this week [18]. - The CSI 500 index-enhanced products had a maximum excess return of 1.55%, a minimum of -0.45%, and a median of 0.38% this week [19]. - The CSI 1000 index-enhanced products recorded a maximum excess return of 1.59%, a minimum of -0.87%, and a median of 0.30% this week [21].