Quantitative Models and Construction Methods 1. 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 rate[64][65] - **Model Construction Process**: - The optimization model is formulated as follows: $$ \begin{array}{ll} \max & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & 0 \leq w \leq l \\ & 1^{T}w = 1 \\ & \Sigma|w-w_{0}| \leq to_{h} \end{array} $$ - **Explanation of Parameters**: - \( f^{T}w \): Weighted exposure of the portfolio to the factor - \( w \): Portfolio weight vector - \( w_{b} \): Benchmark weight vector - \( X, H, B_{b} \): Matrices representing factor, industry, and benchmark exposures - \( s_{l}, s_{h}, h_{l}, h_{h}, w_{l}, w_{h}, b_{l}, b_{h}, to_{h} \): Constraints on factor exposure, industry exposure, stock weight deviation, and turnover rate - Constraints include: - Limiting style and industry deviations relative to the benchmark - Controlling stock weight deviations and turnover rates - Ensuring full investment (weights sum to 1) and no short selling[64][65][67] - The portfolio is rebalanced monthly, and historical returns are calculated after deducting transaction costs to evaluate factor effectiveness[68] - **Model Evaluation**: The MFE model effectively isolates the impact of individual factors while adhering to practical constraints, making it a robust tool for factor evaluation[64][65] --- Quantitative Factors and Construction Methods 1. Factor Name: Trend - **Factor Construction Idea**: Captures the momentum of stock price trends over different time horizons[16] - **Factor Construction Process**: - Two variations: - **Trend_120**: \( \text{EWMA}(\text{halflife}=20) / \text{EWMA}(\text{halflife}=120) \) - **Trend_240**: \( \text{EWMA}(\text{halflife}=20) / \text{EWMA}(\text{halflife}=240) \) - \( \text{EWMA} \): Exponentially Weighted Moving Average[16] - **Factor Evaluation**: Demonstrates strong performance in capturing price continuation patterns, particularly in volatile markets[11][13] 2. Factor Name: Volatility - **Factor Construction Idea**: Measures the variability of stock returns over a specified period[16] - **Factor Construction Process**: - Variants include: - **Stdvol**: Standard deviation of daily returns over the past 243 days - **Ivff**: Fama-French 3-factor idiosyncratic volatility over the past 243 days - **Range**: \( \text{High Price}/\text{Low Price} - 1 \) over the past 243 days - **MaxRet_6**: Average of the six highest daily returns over the past 243 days - **MinRet_6**: Average of the six lowest daily returns over the past 243 days[16] - **Factor Evaluation**: Effective in identifying high-risk stocks, though performance may vary across market conditions[11][13] 3. Factor Name: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Represents the valuation of a stock relative to its book value[20] - **Factor Construction Process**: - Formula: \( \text{BP} = \text{Net Assets} / \text{Market Capitalization} \)[20] - **Factor Evaluation**: Consistently performs well in value-oriented strategies, particularly in markets favoring undervalued stocks[42][43] 4. Factor Name: Three-Month Institutional Coverage - **Factor Construction Idea**: Measures the level of analyst coverage over the past three months[20] - **Factor Construction Process**: - Formula: Count of research reports published by institutions over the past three months[20] - **Factor Evaluation**: Strongly correlated with market sentiment and stock visibility, often leading to positive price momentum[8][46] --- Factor Backtesting Results 1. Trend Factor - **Recent Weekly Return**: 2.39% - **Recent Monthly Return**: 5.57% - **Year-to-Date Return**: -0.70% - **Annualized Return (1 Year)**: 24.36% - **Annualized Return (10 Years)**: 14.25%[11][13] 2. Volatility Factor - **Recent Weekly Return**: -1.75% - **Recent Monthly Return**: -3.95% - **Year-to-Date Return**: 4.10% - **Annualized Return (1 Year)**: 24.26% - **Annualized Return (10 Years)**: -13.16%[11][13] 3. BP Factor - **Recent Weekly Return**: 0.68% - **Recent Monthly Return**: 0.06% - **Year-to-Date Return**: -4.33% - **Annualized Return (1 Year)**: -1.51% - **Annualized Return (10 Years)**: -0.61%[42][43] 4. Three-Month Institutional Coverage Factor - **Recent Weekly Return**: 1.70% - **Recent Monthly Return**: 1.29% - **Year-to-Date Return**: 4.96% - **Annualized Return (1 Year)**: 1.66% - **Annualized Return (10 Years)**: 4.39%[46][48]

Quantitative Models and Construction Methods Model Name: MFE (Maximized Factor Exposure) Portfolio - **Model Construction Idea**: The MFE portfolio aims to maximize the exposure to a single factor while controlling for various constraints such as industry exposure, style exposure, and stock weight limits[75][76]. - **Model Construction Process**: - The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & 0 \leq w \leq l \\ & 1^{T}w = 1 \\ & \Sigma|w-w_{0}| \leq to_{h} \end{array} $$ - **Explanation**: - \( f \): Factor values - \( w \): Stock weight vector to be solved - Constraints include style exposure, industry exposure, stock weight deviation, component stock weight limits, and turnover rate[75][76][77]. - The model is solved using linear programming to efficiently determine the optimal weights[76]. - **Model Evaluation**: The MFE portfolio is evaluated based on its historical performance relative to the benchmark index, considering constraints such as industry and style exposures[78][79]. Quantitative Factors and Construction Methods Factor Name: Trend - **Factor Construction Idea**: The Trend factor captures the momentum of stock prices over different time horizons[12][17]. - **Factor Construction Process**: - **Trend_120**: $$ \text{EWMA}(\text{halflife}=20) / \text{EWMA}(\text{halflife}=120) $$ - **Trend_240**: $$ \text{EWMA}(\text{halflife}=20) / \text{EWMA}(\text{halflife}=240) $$ - **Factor Evaluation**: The Trend factor showed a positive return of 2.15% this week, indicating a strong market preference for trend-following strategies[12]. Factor Name: Single Quarter Net Profit YoY Growth - **Factor Construction Idea**: This factor measures the year-over-year growth in net profit for a single quarter[2][8]. - **Factor Construction Process**: - Calculation: $$ \text{Single Quarter Net Profit YoY Growth} = \frac{\text{Current Quarter Net Profit} - \text{Previous Year Same Quarter Net Profit}}{\text{Previous Year Same Quarter Net Profit}} $$ - **Factor Evaluation**: This factor performed the best among the CSI All Share Index components this week[2][8]. Factor Backtesting Results Trend Factor - **Recent Week**: 2.15%[12] - **Recent Month**: 5.62%[14] - **Year-to-Date**: -1.74%[14] - **Last Year**: 26.90%[14] - **Historical Annualized**: 14.22%[14] Single Quarter Net Profit YoY Growth Factor - **Recent Week**: 1.69%[57] - **Recent Month**: 3.19%[57] - **Year-to-Date**: 8.08%[57] - **Last Year**: 3.65%[57] - **Historical Annualized**: 3.20%[57]

Quantitative Models and Factor Construction Factor Names and Construction Details - **Factor Name: Trend** - **Construction Idea**: Captures the market's preference for trend-following strategies, using exponential weighted moving averages (EWMA) with different half-lives to measure price trends[11][16] - **Construction Process**: - **Trend_120**: $ \text{EWMA(halflife=20)}/\text{EWMA(halflife=120)} $ - **Trend_240**: $ \text{EWMA(halflife=20)}/\text{EWMA(halflife=240)} $[16] - **Evaluation**: Demonstrates strong performance in short-term market environments, reflecting increased preference for trend-following strategies[11] - **Factor Name: Certainty** - **Construction Idea**: Measures market confidence in stable and predictable investments, using metrics like institutional holdings and analyst coverage[16] - **Construction Process**: - **Instholder Pct**: Proportion of institutional holdings - **Cov**: Analyst coverage adjusted for market capitalization - **Listdays**: Number of days since listing[16] - **Evaluation**: Improved performance indicates restored market confidence in certainty-driven strategies[11] - **Factor Name: Value** - **Construction Idea**: Focuses on valuation metrics such as book-to-price (BP) and earnings yield (EP)[16] - **Construction Process**: - **BP**: $ \text{Net Assets}/\text{Market Value} $ - **EP**: $ \text{Earnings}/\text{Market Value} $[16] - **Evaluation**: Shows recovery in market preference for value-oriented investments[11] - **Factor Name: Liquidity** - **Construction Idea**: Assesses the impact of liquidity on asset pricing using turnover rates and liquidity betas[16] - **Construction Process**: - **TO**: Average logarithmic turnover over 243 days - **Liquidity Beta**: Regression of individual stock turnover against market turnover[16] - **Evaluation**: Underperformed significantly, reflecting reduced demand for high-liquidity assets[12] - **Factor Name: Volatility** - **Construction Idea**: Measures the impact of price volatility on asset returns using historical and idiosyncratic volatility metrics[16] - **Construction Process**: - **Stdvol**: Standard deviation of returns over 243 days - **Ivff**: Idiosyncratic volatility from Fama-French 3-factor model over 243 days[16] - **Evaluation**: Weak performance indicates declining interest in high-volatility assets[12] - **Factor Name: Momentum** - **Construction Idea**: Captures the continuation of price trends over different time horizons[16] - **Construction Process**: - **UMR_1Y**: Risk-adjusted momentum over 12 months - **UMR_6M**: Risk-adjusted momentum over 6 months[16] - **Evaluation**: Mixed results, with long-term momentum factors underperforming[12] Factor Backtesting Results - **Trend Factor** - Weekly return: 2.26%[11] - Monthly return: 2.98%[13] - YTD return: -3.81%[13] - 1-year return: 24.24%[13] - Historical annualized return: 14.10%[13] - **Certainty Factor** - Weekly return: 1.36%[11] - Monthly return: -2.87%[13] - YTD return: -11.74%[13] - 1-year return: -20.09%[13] - Historical annualized return: 2.63%[13] - **Value Factor** - Weekly return: 0.78%[11] - Monthly return: -2.14%[13] - YTD return: -10.78%[13] - 1-year return: -27.42%[13] - Historical annualized return: 7.14%[13] - **Liquidity Factor** - Weekly return: -3.85%[12] - Monthly return: 0.07%[13] - YTD return: 15.79%[13] - 1-year return: 29.31%[13] - Historical annualized return: -3.52%[13] - **Volatility Factor** - Weekly return: -2.83%[12] - Monthly return: -1.05%[13] - YTD return: 5.31%[13] - 1-year return: 28.00%[13] - Historical annualized return: -13.15%[13] - **Momentum Factor** - Weekly return (1-year UMR): 0.15%[24] - Monthly return (1-year UMR): 0.21%[24] - YTD return (1-year UMR): 1.69%[24] - 1-year return (1-year UMR): 1.22%[24] - Historical annualized return (1-year UMR): 3.87%[24] MFE Portfolio Construction - **Construction Process**: - Objective: Maximize single-factor exposure while controlling for industry, style, and stock-specific constraints - 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} \\ & 0 \leq w \leq l \\ & 1^{T}w=1 \\ & \Sigma|w-w_{0}| \leq to_{h} \end{array} $[61][62] - Constraints: - Style and industry exposure limits - Stock weight deviation limits - Turnover rate limits[64][65] - Backtesting: Monthly rebalancing, transaction cost of 0.3% applied, and performance evaluated against benchmarks[66]

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 Factor Name: DELTAROE - **Construction Idea**: Measures the change in return on equity (ROE) over a specific period, reflecting the company's profitability dynamics[6][19][46] - **Construction Process**: - Calculate the difference in ROE between the current period and the previous period - Formula: $\Delta ROE = ROE_{current} - ROE_{previous}$[19][46] - **Evaluation**: Demonstrates strong performance across multiple indices, indicating its effectiveness in capturing profitability trends[6][46][49] Factor Name: Standardized Unexpected Earnings (SUE) - **Construction Idea**: Quantifies the deviation of actual earnings from expected earnings, standardized by the standard deviation of forecast errors[19] - **Construction Process**: - Formula: $SUE = \frac{E_{actual} - E_{expected}}{\sigma_{forecast\ errors}}$ - Where $E_{actual}$ is the actual earnings, $E_{expected}$ is the consensus forecast, and $\sigma_{forecast\ errors}$ is the standard deviation of forecast errors[19] - **Evaluation**: Effective in identifying earnings surprises, with positive performance in various market conditions[6][19] Factor Name: Trend - **Construction Idea**: Captures momentum by comparing short-term and long-term exponential weighted moving averages (EWMA) of stock prices[14] - **Construction Process**: - Two variations: - $Trend_{120} = \frac{EWMA_{halflife=20}}{EWMA_{halflife=120}}$ - $Trend_{240} = \frac{EWMA_{halflife=20}}{EWMA_{halflife=240}}$ - Where $EWMA$ is the exponentially weighted moving average with specified half-life[14] - **Evaluation**: Exhibits strong positive returns, indicating market preference for momentum strategies[9][11] Factor Name: Liquidity - **Construction Idea**: Measures stock liquidity through turnover rates and regression-based liquidity beta[14] - **Construction Process**: - Average log turnover over 243 days - Regression of individual stock turnover against market turnover to derive liquidity beta[14] - **Evaluation**: Mixed performance, with sensitivity to market conditions[10][14] Factor Name: Volatility - **Construction Idea**: Captures price fluctuations using various measures of historical volatility[14] - **Construction Process**: - Standard deviation of daily returns over 243 days - Range-based volatility: $Range = \frac{High_{243} - Low_{243}}{Low_{243}}$ - Maximum and minimum return averages over six days within 243 days[14] - **Evaluation**: Underperforms in high-volatility environments, indicating reduced investor appetite for risk[10][14] --- 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] - CNI 2000: Weekly return 4.71%, monthly return 7.07%[6][37] - CSI All Share: Weekly return 1.84%, monthly return 2.41%[6][46] Standardized Unexpected Earnings (SUE) - **Performance**: - CSI 500: Weekly return 1.20%, monthly return 1.59%[6][26] - CSI 800: Weekly return 0.34%, monthly return 0.98%[6][30] - CNI 2000: Weekly return 1.65%, monthly return 14.68%[6][37] - CSI All Share: Weekly return 0.96%, monthly return 1.14%[6][46] Trend - **Performance**: - Weekly return 1.15%, monthly return 4.58%, annualized return 19.73%[9][11] Liquidity - **Performance**: - Weekly return -0.43%, monthly return -3.25%, annualized return 37.53%[10][11] Volatility - **Performance**: - Weekly return -0.95%, monthly return -2.71%, annualized return 31.87%[10][11] --- MFE Portfolio Construction - **Optimization Model**: - Objective: Maximize single-factor exposure - Formula: $\begin{array}{ll}max&f^{T}w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &0\leq w\leq l\\ &1^{T}w=1\\ &\Sigma|w-w_{0}|\leq to_{h}\end{array}$[61][62] - Constraints: - Style and industry exposure limits - Stock weight deviation limits - Turnover rate limits[64][65] - **Backtesting**: - Monthly rebalancing - Transaction cost: 0.3% per side - Metrics: Historical returns, risk-adjusted performance[65]

Quantitative Models and Factor Analysis Quantitative Factors and Construction Methods - **Factor Name**: Non-liquidity Shock **Construction Idea**: Measures the impact of illiquidity on stock returns **Construction Process**: Calculated as the average absolute daily return over the past 20 trading days divided by the corresponding daily trading volume[6][16][19] **Evaluation**: Demonstrated strong performance across multiple indices, indicating its effectiveness in capturing illiquidity effects[6][19][21] - **Factor Name**: Six-Month UMR **Construction Idea**: Captures momentum adjusted for risk over a six-month window **Construction Process**: Risk-adjusted momentum is calculated using a six-month rolling window, incorporating volatility adjustments[6][16][19] **Evaluation**: Consistently performed well in recent periods, showing robustness across different market conditions[6][19][21] - **Factor Name**: One-Year UMR **Construction Idea**: Similar to Six-Month UMR but uses a one-year window for risk-adjusted momentum **Construction Process**: Momentum is adjusted for risk using a one-year rolling window, factoring in volatility[6][16][19] **Evaluation**: Effective in capturing long-term momentum trends, though performance varies by index[6][19][21] - **Factor Name**: Three-Month Volatility **Construction Idea**: Measures short-term price fluctuations **Construction Process**: Calculated as the standard deviation of daily returns over the past 60 trading days[6][16][19] **Evaluation**: Demonstrated strong negative correlation with returns, indicating its utility in identifying high-risk assets[6][19][21] - **Factor Name**: One-Month Turnover **Construction Idea**: Reflects trading activity and liquidity over a short period **Construction Process**: Average daily turnover rate over the past 20 trading days[6][16][19] **Evaluation**: Effective in capturing liquidity dynamics, though performance varies across indices[6][19][21] Factor Backtesting Results - **Non-liquidity Shock**: - Recent Week: 0.58% (HS300), 0.91% (CSI500), 0.93% (CSI800), 0.87% (CSI1000), 1.14% (CSI All)[19][23][27][31][42] - Recent Month: 0.31% (HS300), 0.64% (CSI500), 0.77% (CSI800), 2.40% (CSI1000), 1.33% (CSI All)[19][23][27][31][42] - **Six-Month UMR**: - Recent Week: 0.54% (HS300), -0.09% (CSI500), 0.57% (CSI800), 0.73% (CSI1000), 0.73% (CSI All)[19][23][27][31][42] - Recent Month: 1.53% (HS300), 2.09% (CSI500), 2.35% (CSI800), 3.49% (CSI1000), 3.85% (CSI All)[19][23][27][31][42] - **One-Year UMR**: - Recent Week: 0.46% (HS300), 0.06% (CSI500), 0.88% (CSI800), 0.52% (CSI1000), 0.76% (CSI All)[19][23][27][31][42] - Recent Month: 1.15% (HS300), 2.19% (CSI500), 2.50% (CSI800), 2.85% (CSI1000), 3.74% (CSI All)[19][23][27][31][42] - **Three-Month Volatility**: - Recent Week: 0.24% (HS300), 0.78% (CSI500), 0.59% (CSI800), 0.65% (CSI1000), 0.86% (CSI All)[19][23][27][31][42] - Recent Month: 0.84% (HS300), 3.24% (CSI500), 2.17% (CSI800), 3.63% (CSI1000), 3.60% (CSI All)[19][23][27][31][42] - **One-Month Turnover**: - Recent Week: -0.05% (HS300), 0.48% (CSI500), 0.04% (CSI800), 0.57% (CSI1000), 0.50% (CSI All)[19][23][27][31][42] - Recent Month: 0.19% (HS300), 2.47% (CSI500), 0.19% (CSI800), 3.87% (CSI1000), 1.65% (CSI All)[19][23][27][31][42] Quantitative Model Construction - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Construction Idea**: Optimizes portfolio weights to maximize exposure to a single factor while controlling for constraints **Construction Process**: - Objective Function: Maximize $f^T w$, where $f$ is the factor value and $w$ is the weight vector - Constraints: Include style exposure, industry deviation, stock weight limits, turnover, and full investment constraints - Formula: $\begin{array}{ll}max&f^{T}w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &0\leq w\leq l\\ &1^{T}w=1\\ &\Sigma|w-w_{0}|\leq to_{h}\end{array}$[57][58][61] **Evaluation**: Provides a robust framework for testing factor effectiveness under realistic constraints[57][58][61] Model Backtesting Results - **MFE Portfolio**: - Demonstrated strong performance in capturing factor-specific returns while adhering to constraints such as turnover and industry exposure[57][58][61]

金融工程 | 动态跟踪 报告发布日期 2025 年 03 月 16 日 杨怡玲 yangyiling@orientsec.com.cn 执业证书编号：S0860523040002 研究结论 风格表现监控 ——东方因子周报 ⚫ 以沪深 300 指数为选股空间，最近一周，单季 ROE、一个月反转等因子表现较好， 而非流动性冲击、三个月换手等因子表现较差。最近一月，一个月反转、单季 ROA 等因子表现较好，而 BP、非流动性冲击等因子表现较差。 ⚫ 以中证 500 指数为选股空间，最近一周，预期 EPTTM、一个月反转等因子表现较 好，而单季营收同比增速、分析师认可度等因子表现较差。最近一月，高管薪酬、 一年动量等因子表现较好，而六个月 UMR、三个月换手等因子表现较差。 ⚫ 以中证 800 指数为选股空间，最近一周，一个月反转、预期 EPTTM 等因子表现较 好，而非流动性冲击、一个月换手等因子表现较差。最近一月，一个月反转、 PB_ROE_排序差等因子表现较好，而 BP、六个月 UMR 等因子表现较差。 ⚫ 以中证 1000 指数为选股空间，最近一周，一个月反转、预期 EPTTM 等因子表现较 好，而标准化预期 ...