一、本周指数增强组合表现 沪深300指数增强组合本周超额收益-0.87%，本年超额收益11.58%。 中证500指数增强组合本周超额收益-0.22%，本年超额收益11.11%。 中证1000指数增强组合本周超额收益0.02%，本年超额收益14.85%。 中证A500指数增强组合本周超额收益-1.49%，本年超额收益10.27%。 二、本周选股因子表现跟踪 沪深300成分股中标准化预期外收入、一年动量、单季营收同比增速等因子 表现较好。 中证500成分股中EPTTM一年分位点、高管薪酬、DELTAROA等因子表现较 好。 中证1000成分股中标准化预期外收入、三个月反转、单季营收同比增速等因 子表现较好。 中证A500指数成分股中单季营收同比增速、三个月反转、一年动量等因子表 现较好。 公募基金重仓股中一年动量、单季营利同比增速、单季营收同比增速等因子 表现较好。 三、本周公募基金指数增强产品表现跟踪 沪深300指数增强产品本周超额收益最高0.69%，最低-1.53%，中位 数-0.57%。 中证500指数增强产品本周超额收益最高0.78%，最低-1.40%，中位 数-0.31%。 中证1000指数增强产品本周 ...

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.86% this week and 10.78% year-to-date [1][6] - The CSI 500 index enhanced portfolio recorded an excess return of 0.16% this week and 11.24% year-to-date [1][6] - The CSI 1000 index enhanced portfolio experienced an excess return of -0.29% this week but has a year-to-date excess return of 15.73% [1][6] - The CSI A500 index enhanced portfolio had an excess return of 0.29% this week and 11.42% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as DELTAROE, expected PEG, and expected EPTTM performed well [1][7] - In the CSI 500 component stocks, factors like one-year momentum, expected net profit month-on-month, and one-month reversal showed strong performance [1][7] - For the CSI 1000 component stocks, factors such as DELTAROA, single-quarter net profit year-on-year growth rate, and single-quarter surprise magnitude performed well [1][7] - In the CSI A500 index component stocks, factors like expected PEG, DELTAROE, and expected EPTTM showed good performance [1][7] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 0.82%, a minimum of -0.24%, and a median of 0.26% this week [1][18] - The CSI 500 index enhanced products achieved a maximum excess return of 0.95%, a minimum of -0.73%, and a median of 0.14% this week [1][22] - The CSI 1000 index enhanced products recorded a maximum excess return of 0.69%, a minimum of -0.64%, and a median of -0.02% this week [1][25] - The CSI A500 index enhanced products had a maximum excess return of 0.85%, a minimum of -0.33%, and a median of 0.34% this week [1][24]

Quantitative Factors and Models Summary Quantitative Factors and Their Construction - **Factor Name**: Standardized Unexpected Revenue (SUR) - **Construction Idea**: Measures the deviation of actual revenue from analysts' expectations, standardized by the standard deviation of expected revenue[20][27][31] - **Construction Process**: $ SUR = \frac{Actual\ Revenue - Expected\ Revenue}{Standard\ Deviation\ of\ Expected\ Revenue} $ - The numerator represents the difference between actual and expected revenue - The denominator is the standard deviation of expected revenue, ensuring comparability across stocks[20][27][31] - **Evaluation**: Demonstrated strong performance across multiple indices, indicating its effectiveness in capturing unexpected revenue trends[8][27][31] - **Factor Name**: Delta ROA - **Construction Idea**: Tracks the year-over-year change in Return on Assets (ROA) to capture profitability trends[20][31][39] - **Construction Process**: $ \Delta ROA = ROA_{Current\ Quarter} - ROA_{Same\ Quarter\ Last\ Year} $ - ROA is calculated as $ \frac{Net\ Income}{Total\ Assets} $ - The factor highlights improvements or deteriorations in asset efficiency[20][31][39] - **Evaluation**: Consistently strong performance, particularly in small-cap indices like the CSI 1000 and CSI 2000, suggesting its relevance in growth-oriented stocks[8][39][43] - **Factor Name**: Standardized Unexpected Earnings (SUE) - **Construction Idea**: Similar to SUR, measures the deviation of actual earnings from analysts' expectations, standardized by the standard deviation of expected earnings[20][31][39] - **Construction Process**: $ SUE = \frac{Actual\ Earnings - Expected\ Earnings}{Standard\ Deviation\ of\ Expected\ Earnings} $ - The numerator captures the earnings surprise - The denominator ensures standardization for comparability[20][31][39] - **Evaluation**: Strong performance in indices like CSI 500 and CSI 800, indicating its ability to capture earnings surprises effectively[8][27][31] - **Factor Name**: Delta ROE - **Construction Idea**: Measures the year-over-year change in Return on Equity (ROE) to identify shifts in shareholder profitability[20][31][39] - **Construction Process**: $ \Delta ROE = ROE_{Current\ Quarter} - ROE_{Same\ Quarter\ Last\ Year} $ - ROE is calculated as $ \frac{Net\ Income}{Shareholders'\ Equity} $ - Highlights changes in equity efficiency over time[20][31][39] - **Evaluation**: Demonstrated strong performance in growth-oriented indices, particularly the CSI 1000 and Growth Enterprise Market (GEM) indices[8][39][43] Factor Backtesting Results - **Standardized Unexpected Revenue (SUR)** - CSI 500: Weekly return 1.43%, monthly return 1.66%, annualized return 12.83%[27] - CSI 800: Weekly return 1.36%, monthly return 2.61%, annualized return 4.26%[31] - CSI All Share: Weekly return 1.37%, monthly return 1.95%, annualized return 6.91%[47] - **Delta ROA** - CSI 1000: Weekly return 0.56%, monthly return 1.67%, annualized return 11.51%[35] - CSI 2000: Weekly return 1.90%, monthly return 1.90%, annualized return 27.67%[39] - CSI All Share: Weekly return 1.10%, monthly return 2.33%, annualized return 7.78%[47] - **Standardized Unexpected Earnings (SUE)** - CSI 500: Weekly return 1.39%, monthly return 2.75%, annualized return 7.19%[27] - CSI 800: Weekly return 0.52%, monthly return 1.33%, annualized return 3.04%[31] - CSI All Share: Weekly return 1.09%, monthly return 2.46%, annualized return 0.72%[47] - **Delta ROE** - CSI 1000: Weekly return 0.30%, monthly return 1.59%, annualized return 8.89%[35] - CSI 2000: Weekly return 1.24%, monthly return 1.21%, annualized return 90.84%[39] - GEM: Weekly return 1.03%, monthly return 2.76%, annualized return 21.85%[43] Quantitative Model Construction - **Model Name**: Maximized Factor Exposure (MFE) Portfolio - **Construction Idea**: Constructs portfolios that maximize exposure to a single factor while controlling for industry, style, and stock-specific constraints[62][63][66] - **Construction Process**: $ \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} $ - Maximizes factor exposure $ f^{T}w $ - Constraints include style, industry, stock-specific deviations, and turnover limits[62][63][66] - **Evaluation**: Effective in isolating factor performance under realistic portfolio constraints, widely used in index enhancement strategies[62][63][66] Model Backtesting Results - **MFE Portfolio** - CSI 300: Weekly excess return max 1.67%, min -0.65%, median 0.21%[54] - CSI 500: Weekly excess return max 1.13%, min -0.76%, median 0.24%[57] - CSI 1000: Weekly excess return max 1.11%, min -0.52%, median 0.24%[61]

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]

- The report tracks the performance of Guosen JinGong's index enhancement portfolios and public fund index enhancement products, alongside monitoring the performance of common stock selection factors across different stock selection spaces [12][13][16] - Guosen JinGong's index enhancement portfolios are constructed based on three main components: return prediction, risk control, and portfolio optimization. These portfolios are benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500 [13][15] - The MFE (Maximized Factor Exposure) portfolio is used to test the effectiveness of individual factors under real-world constraints. The optimization model maximizes single-factor exposure while controlling for style, industry, stock weight deviations, and other constraints. The formula for the optimization model is: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style exposure (`X`), industry exposure (`H`), stock weight deviation (`w`), and component stock weight limits (`B_b`) [40][41][42] - The factor library includes over 30 factors categorized into valuation, reversal, growth, profitability, liquidity, corporate governance, and analyst dimensions. Examples include BP (Net Asset/Market Cap), single-quarter EP (Net Profit/Market Cap), and EPTTM (TTM Net Profit/Market Cap) [17][18] - Factor performance varies across different stock selection spaces. For CSI 300, factors like single-quarter EP, EPTTM, and expected EPTTM performed well recently, while factors like three-month volatility and expected net profit QoQ performed poorly [19][20] - For CSI 500, factors such as single-quarter ROE, DELTAROE, and single-quarter EP showed strong performance recently, whereas factors like one-year momentum and three-month reversal underperformed [21][22] - In the CSI 1000 space, factors like standardized unexpected earnings, EPTTM, and single-quarter EP performed well, while factors like non-liquidity impact and three-month institutional coverage lagged [23][24] - For CSI A500, factors such as expected EPTTM, single-quarter ROE, and expected PEG showed strong performance, while factors like one-year momentum and expected net profit QoQ underperformed [25][26] - In the public fund heavy index space, factors like expected PEG, expected EPTTM, and single-quarter EP performed well recently, while factors like one-month reversal and one-month volatility performed poorly [27][28] - Public fund index enhancement products are tracked for their excess returns relative to benchmarks. For CSI 300 products, the highest weekly excess return was 1.02%, and the lowest was -0.37%, with a median of 0.08% [29][33] - CSI 500 products showed a weekly excess return range of 1.87% to -0.44%, with a median of 0.38% [34][35] - CSI 1000 products had a weekly excess return range of 1.06% to -0.43%, with a median of 0.38% [36][37] - CSI A500 products showed a weekly excess return range of 0.73% to -0.19%, with a median of 0.17% [38][39]

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]

Quantitative Models and Construction Methods Factor: 3-Month Earnings Revision - **Construction Idea**: Measures the upward or downward revisions in earnings estimates over the past three months, reflecting changes in analysts' expectations[6][23][42] - **Construction Process**: Calculated as the difference between the number of upward and downward revisions in earnings estimates over the last three months, normalized by the total number of estimates[19][42] - **Evaluation**: Demonstrates strong performance in multiple index universes, indicating its effectiveness in capturing short-term earnings momentum[6][23][42] Factor: UMR (Up-Market Ratio) - **Construction Idea**: Captures momentum by analyzing risk-adjusted returns over different time windows (1 month, 3 months, 6 months, 1 year)[6][19][42] - **Construction Process**: - **1-Month UMR**: Risk-adjusted momentum over a 1-month window - **3-Month UMR**: Risk-adjusted momentum over a 3-month window - **6-Month UMR**: Risk-adjusted momentum over a 6-month window - **1-Year UMR**: Risk-adjusted momentum over a 12-month window[19][42] - **Evaluation**: Consistently performs well across multiple index universes, particularly in capturing medium-term momentum trends[6][23][42] Factor: EPTTM (Earnings-to-Price Trailing Twelve Months) - **Construction Idea**: A valuation factor that measures the earnings yield based on trailing twelve months' earnings[19][42] - **Construction Process**: Calculated as the ratio of trailing twelve months' earnings to the current market price[19][42] - **Evaluation**: Shows strong performance in certain index universes, particularly in value-oriented strategies[6][23][42] Factor: DeltaROE - **Construction Idea**: Measures the change in return on equity (ROE) over a specific period, reflecting improvements or deteriorations in profitability[19][42] - **Construction Process**: Calculated as the difference in ROE between the current period and the same period in the previous year[19][42] - **Evaluation**: Effective in identifying companies with improving profitability trends[6][23][42] Factor: Analyst Coverage (3-Month) - **Construction Idea**: Tracks the number of analysts covering a stock over the past three months, reflecting market attention and sentiment[19][42] - **Construction Process**: Count of unique analysts issuing reports on a stock in the last three months[19][42] - **Evaluation**: Performs well in identifying stocks with increasing market interest[6][23][42] --- Factor Backtesting Results 3-Month Earnings Revision - **Recent 1 Week**: 1.94% (China Securities All Index)[43] - **Recent 1 Month**: 0.82% (China Securities All Index)[43] - **Year-to-Date**: 2.50% (China Securities All Index)[43] UMR (Up-Market Ratio) - **1-Month UMR**: - **Recent 1 Week**: 1.30% (China Securities All Index)[43] - **Recent 1 Month**: 2.57% (China Securities All Index)[43] - **Year-to-Date**: 3.85% (China Securities All Index)[43] - **3-Month UMR**: - **Recent 1 Week**: 0.75% (China Securities All Index)[43] - **Recent 1 Month**: 2.14% (China Securities All Index)[43] - **Year-to-Date**: 2.48% (China Securities All Index)[43] - **6-Month UMR**: - **Recent 1 Week**: 0.72% (China Securities All Index)[43] - **Recent 1 Month**: 4.19% (China Securities All Index)[43] - **Year-to-Date**: 1.12% (China Securities All Index)[43] - **1-Year UMR**: - **Recent 1 Week**: 0.74% (China Securities All Index)[43] - **Recent 1 Month**: 3.92% (China Securities All Index)[43] - **Year-to-Date**: 0.80% (China Securities All Index)[43] EPTTM - **Recent 1 Week**: 0.83% (China Securities All Index)[43] - **Recent 1 Month**: 3.70% (China Securities All Index)[43] - **Year-to-Date**: -0.22% (China Securities All Index)[43] DeltaROE - **Recent 1 Week**: 0.19% (China Securities All Index)[43] - **Recent 1 Month**: -0.31% (China Securities All Index)[43] - **Year-to-Date**: 1.66% (China Securities All Index)[43] Analyst Coverage (3-Month) - **Recent 1 Week**: 1.86% (China Securities All Index)[43] - **Recent 1 Month**: 2.24% (China Securities All Index)[43] - **Year-to-Date**: 4.89% (China Securities All Index)[43] --- MFE Portfolio Construction - **Construction Method**: - Maximizes single-factor exposure while controlling for industry, style, and stock-specific deviations relative to the benchmark index[56][57][59] - Constraints include: - Style exposure limits - Industry exposure limits - Stock weight deviation limits - Turnover limits[56][57][59] - **Optimization Model**: $\begin{array}{ll}max&f^{T}w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &0\leq w\leq l\\ &1^{T}w=1\\ &\Sigma|w-w_{0}|\leq to_{h}\end{array}$[56][57] - **Evaluation**: Effective in isolating factor performance under realistic portfolio constraints[56][57][60]

Group 1 - The core viewpoint of the article is to track the performance of index-enhanced portfolios and stock selection factors across different indices, highlighting their excess returns and factor effectiveness [1][2][3]. Group 2 - The performance of the CSI 300 index-enhanced portfolio showed an excess return of 0.15% for the week and 0.96% for the year [1][2]. - The performance of the CSI 500 index-enhanced portfolio indicated an excess return of -0.12% for the week and 1.77% for the year [1][2]. - The performance of the CSI 1000 index-enhanced portfolio reflected an excess return of -0.62% for the week and -0.14% for the year [1][2]. Group 3 - In the CSI 300 component stocks, factors such as dividend yield, DELTAROA, and three-month institutional coverage performed well [1]. - In the CSI 500 component stocks, factors like one-year momentum, DELTAROA, and standardized expected excess income showed strong performance [1]. - In the CSI 1000 component stocks, factors including quarterly net profit year-on-year growth, DELTAROA, and quarterly revenue year-on-year growth were effective [1]. Group 4 - The public fund index-enhanced products for the CSI 300 had a maximum excess return of 1.34% and a minimum of -0.63% for the week, with a median of 0.12% [1][17]. - The public fund index-enhanced products for the CSI 500 had a maximum excess return of 0.97% and a minimum of -0.82% for the week, with a median of 0.02% [1][19]. - The public fund index-enhanced products for the CSI 1000 had a maximum excess return of 1.24% and a minimum of -1.00% for the week, with a median of -0.01% [1][21].