Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure (MFE) Portfolio **Model Construction Idea**: The MFE portfolio aims to maximize the exposure of a single factor while controlling for constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach evaluates the effectiveness of factors under realistic constraints in enhanced index portfolios [56][57][59] **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} \\ & 0\leq w\leq l \\ & 1^{T}w=1 \\ & \Sigma|w-w_{0}|\leq to_{h} \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector - **Constraints**: 1. Style exposure deviation (\( X \)): \( s_{l} \) and \( s_{h} \) are the lower and upper bounds for style factor deviation 2. Industry exposure deviation (\( H \)): \( h_{l} \) and \( h_{h} \) are the lower and upper bounds for industry deviation 3. Stock weight deviation (\( w_{l} \) and \( w_{h} \)): Limits on individual stock weight deviation relative to the benchmark 4. Component weight limits (\( b_{l} \) and \( b_{h} \)): Constraints on the weight of benchmark components 5. No short selling and upper limits on stock weights 6. Full investment constraint: \( 1^{T}w=1 \) 7. Turnover constraint: \( \Sigma|w-w_{0}|\leq to_{h} \), where \( w_{0} \) is the previous period's weight [56][57][59] **Model Evaluation**: The model effectively balances factor exposure and practical constraints, ensuring stable returns and avoiding excessive concentration in specific stocks [60] --- Quantitative Factors and Construction Methods - **Factor Name**: Six-Month UMR **Factor Construction Idea**: The six-month UMR factor measures risk-adjusted momentum over a six-month window, capturing medium-term momentum trends [19][8][44] **Factor Construction Process**: - The UMR (Up-Minus-Down Ratio) is calculated as the ratio of upward movements to downward movements in stock prices over a specified period - The six-month UMR specifically uses a six-month window to compute this ratio, adjusted for risk [19][8][44] **Factor Evaluation**: This factor demonstrates strong performance in various index spaces, particularly in the CSI 500 and CSI All Share indices, indicating its effectiveness in capturing medium-term momentum [8][44] - **Factor Name**: Three-Month UMR **Factor Construction Idea**: Similar to the six-month UMR, this factor focuses on shorter-term momentum trends over a three-month window [19][8][44] **Factor Construction Process**: - The three-month UMR is calculated using the same methodology as the six-month UMR but with a three-month window for data aggregation [19][8][44] **Factor Evaluation**: This factor shows consistent performance across multiple indices, including the CSI 500 and CSI All Share indices, making it a reliable short-term momentum indicator [8][44] - **Factor Name**: Pre-Tax Earnings to Total Market Value (EPTTM) **Factor Construction Idea**: This valuation factor evaluates the earnings yield of a stock, providing insights into its relative valuation [19][8][44] **Factor Construction Process**: - EPTTM is calculated as the ratio of pre-tax earnings to the total market value of a stock, with adjustments for rolling time windows (e.g., one year) [19][8][44] **Factor Evaluation**: EPTTM consistently ranks among the top-performing valuation factors, particularly in the CSI 300 and CSI 800 indices, reflecting its robustness in identifying undervalued stocks [8][44] --- Backtesting Results of Models - **MFE Portfolio**: - The MFE portfolio demonstrates strong performance under various constraints, with backtesting results showing significant alpha generation relative to benchmarks like CSI 300, CSI 500, and CSI 1000 [60][61] --- Backtesting Results of Factors - **Six-Month UMR**: - CSI 500: Weekly return of 0.99%, monthly return of 1.65%, annualized return of -4.07% [26] - CSI All Share: Weekly return of 1.23%, monthly return of 1.59%, annualized return of 7.43% [44] - **Three-Month UMR**: - CSI 500: Weekly return of 0.94%, monthly return of 1.31%, annualized return of 0.68% [26] - CSI All Share: Weekly return of 1.02%, monthly return of 1.63%, annualized return of 5.64% [44] - **EPTTM**: - CSI 300: Weekly return of 0.74%, monthly return of 1.42%, annualized return of 3.89% [22] - CSI 800: Weekly return of 1.00%, monthly return of 1.91%, annualized return of 2.87% [30]

Quantitative Models and Factor Construction Quantitative Models and Construction Methods - **Model Name**: MFE (Maximized Factor Exposure) Portfolio - **Model Construction Idea**: The MFE portfolio is designed to maximize single-factor exposure while controlling for constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate[61][62][65] - **Model Construction Process**: The optimization model is as follows: ``` $\begin{array}{ll} max & f^{T}w \\ s.t. & s_{l}\leq X(w-w_{b})\leq s_{h} \\ & h_{l}\leq H(w-w_{b})\leq h_{h} \\ & w_{l}\leq w-w_{b}\leq w_{h} \\ & b_{l}\leq B_{b}w\leq b_{h} \\ & 0\leq w\leq l \\ & 1^{T}w=1 \\ & \Sigma|w-w_{0}|\leq to_{h} \end{array}$ ``` - **Objective Function**: Maximize single-factor exposure, where $f$ represents factor values, and $w$ is the weight vector of stocks in the portfolio - **Constraints**: 1. Style exposure limits: $X$ is the factor exposure matrix, $w_b$ is the benchmark weight vector, $s_l$ and $s_h$ are the lower and upper bounds for style exposure[64] 2. Industry exposure limits: $H$ is the industry exposure matrix, $h_l$ and $h_h$ are the lower and upper bounds for industry exposure[64] 3. Stock weight deviation limits: $w_l$ and $w_h$ are the lower and upper bounds for stock weight deviation[64] 4. Component stock weight limits: $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark, $b_l$ and $b_h$ are the lower and upper bounds for component stock weights[64] 5. No short selling and individual stock weight limits[64] 6. Full investment constraint: The sum of weights equals 1[64] 7. Turnover rate constraint: $w_0$ is the previous period's weight, and $to_h$ is the turnover rate upper limit[64] - **Model Evaluation**: The MFE portfolio effectively isolates single-factor performance under realistic constraints, making it a robust tool for factor effectiveness testing[61][65] Model Backtesting Results - **MFE Portfolio Backtesting**: - The MFE portfolio is constructed monthly, and its historical returns are calculated after deducting a 0.3% transaction fee. The results are used to evaluate the effectiveness of factors in specific benchmark indices[65] --- 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 profitability trends[14][19] - **Factor Construction Process**: - Formula: $\text{DELTAROE} = \text{ROE}_{\text{current}} - \text{ROE}_{\text{previous}}$ - Where ROE is calculated as net income divided by average equity[19] - **Factor Evaluation**: DELTAROE is a strong indicator of profitability improvement and has shown consistent positive performance across multiple indices[6][45][49] - **Factor Name**: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Captures the deviation of actual earnings from analyst expectations, standardized by the standard deviation of forecast errors[19] - **Factor Construction Process**: - Formula: $\text{SUE} = \frac{\text{Actual Earnings} - \text{Expected Earnings}}{\text{Standard Deviation of Forecast Errors}}$[19] - **Factor Evaluation**: SUE effectively identifies stocks with unexpected earnings surprises, often leading to significant price movements[6][25][45] - **Factor Name**: Trend - **Factor Construction Idea**: Measures price momentum using exponentially weighted moving averages (EWMA) with different half-lives[14] - **Factor Construction Process**: - Formula: $\text{Trend}_{120} = \frac{\text{EWMA (half-life=20)}}{\text{EWMA (half-life=120)}}$ - Formula: $\text{Trend}_{240} = \frac{\text{EWMA (half-life=20)}}{\text{EWMA (half-life=240)}}$[14] - **Factor Evaluation**: Trend factors capture momentum effects and have shown strong performance in recent weeks[9][11] --- Factor Backtesting Results - **DELTAROE**: - **Performance**: - CSI 300: Weekly return 0.41%, monthly return 1.59%[6][22] - CSI 500: Weekly return 0.95%, monthly return 1.19%[6][26] - CSI 800: Weekly return 1.08%, monthly return 1.62%[6][30] - CSI 1000: Weekly return 1.79%, monthly return 1.54%[6][34] - CSI All Share: Weekly return 1.84%, monthly return 2.41%[6][46] - **SUE**: - **Performance**: - CSI 300: Weekly return 0.30%, monthly return 1.53%[6][22] - CSI 500: Weekly return 1.20%, monthly return 1.59%[6][26] - CSI 800: Weekly return 0.34%, monthly return 0.98%[6][30] - CSI 1000: Weekly return 1.46%, monthly return 2.39%[6][34] - CSI All Share: Weekly return 0.96%, monthly return 1.14%[6][46] - **Trend**: - **Performance**: - Weekly return: 1.15%[9][11] - Monthly return: 4.58%[11] - Annualized return (1 year): 19.73%[11] - Annualized return (10 years): 13.98%[11]