Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 1.05% this week and 6.86% year-to-date [7] - The CSI 500 index enhanced portfolio recorded an excess return of 0.14% this week and 3.75% year-to-date [7] - The CSI 1000 index enhanced portfolio had an excess return of 0.91% this week and 5.64% year-to-date [7] - The CSI A500 index enhanced portfolio saw an excess return of 1.04% this week and 4.74% year-to-date [7] Group 2: Factor Performance Tracking - In the CSI 300 component stocks, factors such as one-month volatility, EPTTM one-year percentile, and quarterly net profit year-on-year growth performed well [8] - In the CSI 500 component stocks, factors like quarterly revenue year-on-year growth, expected net profit quarter-on-quarter, and quarterly surprise performance showed strong results [8] - For the CSI 1000 component stocks, factors including one-year momentum, three-month earnings revisions, and quarterly ROA performed well [8] - In the CSI A500 index component stocks, factors such as quarterly surprise performance, quarterly net profit year-on-year growth, and three-month reversal showed strong performance [8] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.89%, a minimum of -0.53%, and a median of 0.32% this week [23] - The CSI 500 index enhanced products recorded a maximum excess return of 1.78%, a minimum of -2.09%, and a median of 0.24% this week [25] - The CSI 1000 index enhanced products achieved a maximum excess return of 1.83%, a minimum of -0.64%, and a median of 0.18% this week [28] - The CSI A500 index enhanced products had a maximum excess return of 1.73%, a minimum of -0.44%, and a median of 0.30% this week [29]

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Construction Idea**: The MFE portfolio is designed to maximize single-factor exposure while controlling for various real-world constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach ensures the factor's predictive effectiveness under practical constraints [40][41][42] **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, $f^{T}w$ is the weighted exposure of the portfolio to the factor, and $w$ is the stock weight vector to be solved [41] - **Constraints**: - **Style Exposure**: $X$ is 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 [41] - **Industry Exposure**: $H$ is the industry exposure matrix, where $H_{ij}=1$ if stock $i$ belongs to industry $j$, otherwise $H_{ij}=0$. $h_l$, $h_h$ are the lower and upper bounds for industry deviation [41] - **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for stock weight deviation relative to the benchmark [41] - **Component Weight Control**: $B_b$ is a 0-1 vector indicating whether a stock belongs to the benchmark index. $b_l$, $b_h$ are the lower and upper bounds for component weight control [41] - **No Short Selling**: Ensures non-negative weights and limits individual stock weights to $l$ [41] - **Full Investment**: Ensures the portfolio is fully invested with $\mathbf{1}^{T}w=1$ [42] **Evaluation**: This model effectively tests factor validity under real-world constraints, making it more applicable for practical portfolio construction [40][41][42] --- Quantitative Factors and Construction Methods - **Factor Name**: BP **Construction Idea**: Measures valuation by comparing net assets to market capitalization [17] **Construction Process**: Formula: $BP = \frac{\text{Net Assets}}{\text{Market Capitalization}}$ [17] **Evaluation**: BP is a widely used valuation factor, but its effectiveness varies across different sample spaces [17] - **Factor Name**: Single-Season EP **Construction Idea**: Evaluates profitability by comparing quarterly net profit to market capitalization [17] **Construction Process**: Formula: $EP = \frac{\text{Quarterly Net Profit}}{\text{Market Capitalization}}$ [17] **Evaluation**: This factor is useful for identifying undervalued stocks with strong quarterly performance [17] - **Factor Name**: Single-Season SP **Construction Idea**: Measures revenue efficiency by comparing quarterly revenue to market capitalization [17] **Construction Process**: Formula: $SP = \frac{\text{Quarterly Revenue}}{\text{Market Capitalization}}$ [17] **Evaluation**: SP is effective in identifying companies with high revenue relative to their market value [17] - **Factor Name**: EPTTM **Construction Idea**: Tracks profitability using trailing twelve-month net profit relative to market capitalization [17] **Construction Process**: Formula: $EPTTM = \frac{\text{TTM Net Profit}}{\text{Market Capitalization}}$ [17] **Evaluation**: EPTTM is a robust profitability factor, especially for long-term performance analysis [17] - **Factor Name**: SPTTM **Construction Idea**: Measures revenue efficiency using trailing twelve-month revenue relative to market capitalization [17] **Construction Process**: Formula: $SPTTM = \frac{\text{TTM Revenue}}{\text{Market Capitalization}}$ [17] **Evaluation**: SPTTM is useful for identifying companies with consistent revenue generation [17] --- Factor Backtesting Results **沪深 300 Sample Space** - **Best Performing Factors (Recent Week)**: One-Month Volatility (+0.57%), EPTTM Year Percentile (+0.52%), Single-Season Net Profit YoY Growth (+0.51%) [19] - **Worst Performing Factors (Recent Week)**: Expected EPTTM (-0.37%), Three-Month Turnover (-0.32%), Specificity (-0.28%) [19] **中证 500 Sample Space** - **Best Performing Factors (Recent Week)**: Single-Season Revenue YoY Growth (+0.96%), Expected Net Profit QoQ (+0.90%), Single-Season Outperformance (+0.64%) [21] - **Worst Performing Factors (Recent Week)**: Single-Season ROA (-0.79%), Specificity (-0.76%), BP (-0.58%) [21] **中证 1000 Sample Space** - **Best Performing Factors (Recent Week)**: One-Year Momentum (+0.91%), Three-Month Analyst Upgrades (+0.67%), Single-Season ROA (+0.65%) [23] - **Worst Performing Factors (Recent Week)**: Expected Net Profit QoQ (-0.71%), Expected BP (-0.54%), BP (-0.52%) [23] **中证 A500 Sample Space** - **Best Performing Factors (Recent Week)**: Single-Season Outperformance (+0.70%), Single-Season Net Profit YoY Growth (+0.59%), Three-Month Reversal (+0.57%) [25] - **Worst Performing Factors (Recent Week)**: Single-Season SP (-0.66%), Dividend Yield (-0.54%), Expected BP (-0.49%) [25] **公募重仓指数 Sample Space** - **Best Performing Factors (Recent Week)**: One-Year Momentum (+1.09%), Expected Net Profit QoQ (+0.90%), Single-Season Net Profit YoY Growth (+0.76%) [27] - **Worst Performing Factors (Recent Week)**: SPTTM (-0.81%), Single-Season SP (-0.68%), EPTTM (-0.66%) [27] --- Model Backtesting Results **沪深 300 Enhanced Portfolio** - **Recent Week**: Excess return range: +1.89% to -0.53%, median: +0.32% [32] - **Recent Month**: Excess return range: +1.94% to -2.99%, median: +0.03% [32] - **Recent Quarter**: Excess return range: +8.04% to -2.14%, median: +1.26% [32] - **Year-to-Date**: Excess return range: +9.54% to -2.33%, median: +1.22% [32] **中证 500 Enhanced Portfolio** - **Recent Week**: Excess return range: +1.78% to -2.09%, median: +0.24% [35] - **Recent Month**: Excess return range: +4.51% to -1.78%, median: +0.58% [35] - **Recent Quarter**: Excess return range: +3.35% to -5.31%, median: -0.40% [35] - **Year-to-Date**: Excess return range: +4.58% to -4.86%, median: -0.01% [35] **中证 1000 Enhanced Portfolio** - **Recent Week**: Excess return range: +1.83% to -0.64%, median: +0.18% [37] - **Recent Month**: Excess return range: +2.94% to -1.60%, median: +0.35% [37] - **Recent Quarter**: Excess return range: +5.44% to -2.15%, median: +1.96% [37] - **Year-to-Date**: Excess return range: +5.37% to -1.17%, median: +2.03% [37] **中证 A500 Enhanced Portfolio** - **Recent Week**: Excess return range: +1.73% to -0.44%, median: +0.30% [39] - **Recent Month**: Excess return range: +2.07% to -1.91%, median: +0.22% [39] - **Recent Quarter**: Excess return range: +5.91% to -2.02%, median: +0.99% [39] - **Year-to-Date**: Excess return range: +5.91% to -1.72%, median:

Core Viewpoint - The article discusses the rise of quantitative stock selection strategies in the private equity sector in China, driven by the increasing availability of financial data and advancements in AI technology, as well as significant market volatility in recent years [2]. Group 1: Advantages of Quantitative Stock Selection - Quantitative stock selection is based on mathematical models and algorithms that systematically analyze vast amounts of data to select stocks, contrasting with traditional subjective selection methods that rely on analysts' judgments [7]. - The core advantages of quantitative stock selection include efficiency, as computers can process multidimensional data across thousands of stocks in seconds, and diversification, as these strategies typically involve holding hundreds of stocks to mitigate individual stock risk [14]. Group 2: Main Strategies in Quantitative Stock Selection - The three main methods of quantitative stock selection are: 1. Multi-factor models, which use various factors to explain future stock returns, with extensive historical backtesting to identify effective factor combinations [9]. 2. Statistical arbitrage, which captures pricing discrepancies based on mean reversion principles among related assets [11]. 3. Event-driven strategies, which monitor real-time events affecting stock prices and generate trading signals based on quantifiable impacts [12]. Group 3: Performance Comparison - Over the past five years, quantitative stock selection has shown lower drawdowns, higher returns, and better Sharpe ratios compared to subjective stock selection, except in 2024, a transitional year [15][20]. - The median returns of quantitative strategies outperformed subjective strategies in all years except 2024, with average returns consistently favoring quantitative methods [15]. Group 4: Differences Between Quantitative Stock Selection and Index Enhancement - Quantitative stock selection and index enhancement both utilize quantitative models but differ in their investment approach; the former seeks absolute returns without being tethered to a specific index, while the latter aims to enhance returns relative to a benchmark index [27]. Group 5: Considerations for Ordinary Investors - Ordinary investors should evaluate quantitative stock selection strategies based on the stability of excess returns across market cycles, risk control capabilities such as maximum drawdown and Sharpe ratio, and the research team's expertise in factor discovery and model iteration [28].

- The report highlights that large-cap stocks outperformed small-cap stocks last week, and technical factors performed relatively well[1][5] - The report summarizes the performance of various quantitative stock portfolios constructed by Guotai Haitong Securities' financial engineering team for the past week, March, and 2026 year-to-date (YTD)[8] - The report provides detailed performance metrics for multiple factor portfolios, including aggressive, balanced, and enhanced index portfolios, as well as specific combinations like PB-earnings, GARP, and small-cap value and growth portfolios[9][10][11][13][15][26][28][30][33][35][37][39][40] - The report evaluates the performance of single factors, including style factors (market cap, PB, PE_TTM), technical factors (reversal, turnover rate, volatility), and fundamental factors (ROE, SUE, expected net profit adjustment)[44][45][46][50][51][53][54] - The report provides specific performance values for each factor and portfolio, such as weekly, monthly, and YTD returns, excess returns, tracking errors, and maximum relative drawdowns[9][10][11][13][15][26][28][30][33][35][37][39][40][45][46][50][51][53][54]

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 to a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviations, and turnover limits. This approach ensures that the factor's predictive power is tested under realistic portfolio constraints [41][42]. - **Model Construction Process**: - The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array} $$ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, and \( w \) is the stock weight vector [42]. - **Constraints**: - **Style Exposure**: \( X \) is the style factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure [42]. - **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviations [42]. - **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviations relative to the benchmark [42]. - **Constituent Weight Control**: \( B_b \) is a binary vector indicating benchmark constituents, and \( b_l, b_h \) are the lower and upper bounds for constituent weights [42]. - **No Short Selling**: Ensures non-negative weights and limits individual stock weights to \( l \) [42]. - **Full Investment**: Ensures the portfolio is fully invested with \( \mathbf{1}^{T}w = 1 \) [43]. - The MFE portfolio is constructed monthly, and historical returns are backtested with a 0.3% transaction cost applied to both sides of trades [45]. - **Model Evaluation**: The MFE approach is effective in testing factor efficacy under realistic constraints, ensuring that factors deemed "effective" are more likely to contribute to actual portfolio performance [41][42]. --- Factor Construction and Methodology 1. Factor Name: Book-to-Price Ratio (BP) - **Factor Construction Idea**: Measures valuation by comparing a company's book value to its market value [17]. - **Factor Construction Process**: - Formula: \( \text{BP} = \frac{\text{Net Assets}}{\text{Market Value}} \) [17]. 2. Factor Name: Single-Quarter ROE - **Factor Construction Idea**: Evaluates profitability by calculating the return on equity for a single quarter [17]. - **Factor Construction Process**: - Formula: \( \text{ROE} = \frac{\text{Net Profit (Quarterly)} \times 2}{\text{(Beginning Equity + Ending Equity)}} \) [17]. 3. Factor Name: Single-Quarter Revenue Growth (YoY) - **Factor Construction Idea**: Measures growth by comparing quarterly revenue to the same quarter in the previous year [17]. - **Factor Construction Process**: - Formula: \( \text{Revenue Growth (YoY)} = \frac{\text{Revenue (Current Quarter)} - \text{Revenue (Same Quarter Last Year)}}{\text{Revenue (Same Quarter Last Year)}} \) [17]. 4. Factor Name: DELTAROA - **Factor Construction Idea**: Captures changes in return on assets (ROA) compared to the same quarter in the previous year [17]. - **Factor Construction Process**: - Formula: \( \text{DELTAROA} = \text{ROA (Current Quarter)} - \text{ROA (Same Quarter Last Year)} \) [17]. 5. Factor Name: Non-Liquidity Shock - **Factor Construction Idea**: Measures the impact of illiquidity on stock prices over a 20-day period [17]. - **Factor Construction Process**: - Formula: \( \text{Non-Liquidity Shock} = \frac{\text{Absolute Daily Returns}}{\text{Average Trading Volume (20 Days)}} \) [17]. --- Factor Backtesting Results 1. Single-Quarter ROE - **Performance**: - Recent Week: 1.07% - Recent Month: 1.80% - Year-to-Date: 2.56% - Historical Annualized: 5.38% [19]. 2. DELTAROA - **Performance**: - Recent Week: 0.95% - Recent Month: 0.09% - Year-to-Date: 1.15% - Historical Annualized: 4.99% [19]. 3. Single-Quarter Revenue Growth (YoY) - **Performance**: - Recent Week: 0.94% - Recent Month: 0.78% - Year-to-Date: 1.50% - Historical Annualized: 4.65% [19]. 4. Non-Liquidity Shock - **Performance**: - Recent Week: 0.54% - Recent Month: -0.21% - Year-to-Date: -0.49% - Historical Annualized: 0.34% [19]. 5. Book-to-Price Ratio (BP) - **Performance**: - Recent Week: -0.87% - Recent Month: -0.34% - Year-to-Date: -0.49% - Historical Annualized: 2.56% [19]. --- Model Backtesting Results 1. CSI 300 Enhanced Portfolio - Weekly Excess Return: 1.90% - Year-to-Date Excess Return: 5.84% [5][14]. 2. CSI 500 Enhanced Portfolio - Weekly Excess Return: 1.94% - Year-to-Date Excess Return: 3.61% [5][14]. 3. CSI 1000 Enhanced Portfolio - Weekly Excess Return: 1.13% - Year-to-Date Excess Return: 4.68% [5][14]. 4. CSI A500 Enhanced Portfolio - Weekly Excess Return: -0.90% - Year-to-Date Excess Return: 3.71% [5][14].

Group 1 - The core viewpoint of the article is to track the performance of various index enhancement portfolios and stock selection factors, highlighting their excess returns and the effectiveness of different factors in various indices [1][4][22]. Group 2 - The performance of the CSI 300 index enhancement portfolio showed an excess return of 0.55% for the week and 3.93% year-to-date [9]. - The CSI 500 index enhancement portfolio achieved an excess return of 2.40% for the week and 1.53% year-to-date [9]. - The CSI 1000 index enhancement portfolio recorded an excess return of 0.20% for the week and 3.61% year-to-date [9]. - The CSI A500 index enhancement portfolio had an excess return of 1.00% for the week and 4.83% year-to-date [9]. Group 3 - In the CSI 300 component stocks, factors such as expected EPTTM, EPTTM, and EPTTM one-year quantile performed well [10]. - In the CSI 500 component stocks, factors like expected EPTTM, single-quarter EP, and standardized expected non-operating income showed strong performance [12]. - For the CSI 1000 component stocks, factors such as expected PEG, three-month reversal, and expected BP performed well [15]. - In the CSI A500 index component stocks, expected EPTTM, EPTTM, and three-month reversal were among the top-performing factors [18]. - In public fund heavy stocks, expected EPTTM, expected PEG, and three-month reversal also showed good performance [21]. Group 4 - The public fund index enhancement products tracked showed varying excess returns, with the CSI 300 index enhancement product having a maximum of 0.88% and a minimum of -2.18% for the week [28]. - The CSI 500 index enhancement product had a maximum excess return of 2.84% and a minimum of -0.66% for the week [27]. - The CSI 1000 index enhancement product recorded a maximum excess return of 1.23% and a minimum of -0.55% for the week [31]. - The CSI A500 index enhancement product achieved a maximum excess return of 1.12% and a minimum of -1.22% for the week [34].

Quantitative Models and Construction Methods Model Name: Maximized Factor Exposure Portfolio (MFE) - **Model Construction Idea**: The MFE portfolio is designed to test the effectiveness of single factors under real-world constraints, such as industry exposure, style exposure, stock weight limits, and turnover rate. This approach ensures that factors deemed "effective" can genuinely contribute to return prediction in practical portfolio construction [42][43][44]. - **Model Construction Process**: - The optimization model aims to maximize single-factor exposure, with the objective function defined as: $$ \begin{array}{ll} \max & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array} $$ - **Explanation of Parameters**: - \( f \): Factor values - \( w \): Stock weight vector - \( X \): Factor exposure matrix for style factors - \( H \): Industry exposure matrix - \( w_b \): Benchmark index weight vector - \( s_l, s_h \): Lower and upper bounds for style factor exposure - \( h_l, h_h \): Lower and upper bounds for industry exposure - \( w_l, w_h \): Lower and upper bounds for individual stock weight deviation - \( b_l, b_h \): Lower and upper bounds for benchmark component weights - Constraints include: - Style factor exposure limits (\( s_l, s_h \)) [43] - Industry exposure limits (\( h_l, h_h \)) [43] - Individual stock weight deviation limits (\( w_l, w_h \)) [43] - Benchmark component weight limits (\( b_l, b_h \)) [43] - No short selling and full investment (\( \mathbf{1}^{T}w = 1 \)) [44] - The portfolio is rebalanced monthly, and historical returns are calculated after accounting for transaction costs (0.3% on both sides) [46]. - **Model Evaluation**: The MFE portfolio effectively identifies factors that can predict returns under practical constraints, making it a robust tool for factor validation in real-world scenarios [42][43]. --- Quantitative Factors and Construction Methods Factor Name: EPTTM (Earnings-to-Price Trailing Twelve Months) - **Factor Construction Idea**: Measures valuation by comparing trailing twelve-month earnings to market capitalization [17]. - **Factor Construction Process**: - Formula: \( \text{EPTTM} = \frac{\text{Net Income (TTM)}}{\text{Market Capitalization}} \) [17]. - **Factor Evaluation**: Demonstrates strong performance across multiple sample spaces, particularly in valuation-based strategies [19][20][22]. Factor Name: Pre-Expected EPTTM - **Factor Construction Idea**: Uses consensus analyst expectations for earnings to calculate a forward-looking valuation metric [17]. - **Factor Construction Process**: - Formula: \( \text{Pre-Expected EPTTM} = \frac{\text{Consensus Expected Earnings (TTM)}}{\text{Market Capitalization}} \) [17]. - **Factor Evaluation**: Consistently ranks among the top-performing factors in various sample spaces, indicating its predictive power for returns [19][20][22]. Factor Name: BP (Book-to-Price) - **Factor Construction Idea**: Measures valuation by comparing book value to market capitalization [17]. - **Factor Construction Process**: - Formula: \( \text{BP} = \frac{\text{Book Value}}{\text{Market Capitalization}} \) [17]. - **Factor Evaluation**: Shows strong historical performance, particularly in mid-cap and small-cap sample spaces [20][22]. Factor Name: Three-Month Reversal - **Factor Construction Idea**: Captures short-term mean reversion by measuring the price change over the past three months [17]. - **Factor Construction Process**: - Formula: \( \text{Three-Month Reversal} = \text{Cumulative Return over Last 60 Trading Days} \) [17]. - **Factor Evaluation**: Effective in identifying short-term price corrections, though performance varies across sample spaces [19][20][22]. --- Factor Backtesting Results Performance in CSI 300 Sample Space - **Top-Performing Factors (Recent Week)**: Pre-Expected EPTTM (1.64%), EPTTM (1.34%), EPTTM Year Percentile (1.32%) [19]. - **Underperforming Factors (Recent Week)**: Single-Quarter Operating Profit YoY (-0.87%), One-Year Momentum (-0.83%), Pre-Expected Net Profit QoQ (-0.79%) [19]. Performance in CSI 500 Sample Space - **Top-Performing Factors (Recent Week)**: Pre-Expected EPTTM (2.90%), Single-Quarter EP (2.40%), Standardized Unexpected Earnings (2.25%) [20]. - **Underperforming Factors (Recent Week)**: Pre-Expected Net Profit QoQ (0.06%), Standardized Unexpected Revenue (0.13%), Three-Month Institutional Coverage (0.22%) [20]. Performance in CSI 1000 Sample Space - **Top-Performing Factors (Recent Week)**: Pre-Expected PEG (1.87%), Three-Month Reversal (1.33%), Pre-Expected BP (1.19%) [22]. - **Underperforming Factors (Recent Week)**: One-Year Momentum (-1.91%), Single-Quarter ROA (-0.62%), Three-Month Institutional Coverage (-0.58%) [22]. Performance in CSI A500 Sample Space - **Top-Performing Factors (Recent Week)**: Pre-Expected EPTTM (2.51%), EPTTM (1.99%), Three-Month Reversal (1.91%) [25]. - **Underperforming Factors (Recent Week)**: One-Year Momentum (-1.07%), Three-Month Institutional Coverage (-0.41%), Pre-Expected Net Profit QoQ (-0.18%) [25]. Performance in Public Fund Heavyweight Index Sample Space - **Top-Performing Factors (Recent Week)**: Pre-Expected EPTTM, Pre-Expected PEG, Three-Month Reversal [27]. - **Underperforming Factors (Recent Week)**: One-Year Momentum, Three-Month Institutional Coverage, Standardized Unexpected Revenue [27].

Group 1 - The report emphasizes the effectiveness of a composite factor in predicting overnight returns, achieving a Rank IC of -0.1687 [10][7][6] - By incorporating overnight trading strategies into existing index replication, excess returns of 4%-7% can be achieved with a tracking error of approximately 1.2% [10][11][8] - The combination of existing index enhancement models with overnight trading strategies can further increase excess returns by 2%-5% [11][10] Group 2 - The report highlights the potential for U.S. consumer brands to expand overseas, particularly in the home appliance sector, driven by favorable economic conditions such as interest rate cuts [14][17] - It suggests focusing on companies with low inventory levels in the tool industry, such as QuanFeng Holdings and Techtronic Industries, while also considering high-dividend leaders like Gujia Home and Haier Smart Home for long-term investments [14][16] - The report notes that the U.S. market offers stable business models and high valuations, particularly in sectors like pet products and home furnishings, which are expected to benefit from increased overseas sales [15][14] Group 3 - China Energy Construction (601868.SH) is identified as a leader in energy construction, with a target price of 3.96 CNY per share based on an 18x PE ratio for 2026, and is rated as a "buy" [19][3] - The company is expected to see growth in its investment and operation business, with a significant increase in its renewable energy project capacity from 498.7 MW at the end of 2022 to 1602.73 MW by mid-2025 [19][20] - The report indicates that the company is actively developing a hydrogen energy integration model, with over 50 projects in key regions, including the largest green hydrogen project globally [20][19] Group 4 - The mechanical equipment industry is witnessing major oil service companies like Baker Hughes and Halliburton entering the data center market, focusing on power generation and energy management solutions [22][23] - The report highlights that Baker Hughes anticipates a market size of $100 billion for its power systems by 2030, with significant orders expected from data center applications [22][24] - Jerry Holdings is noted for its strategic partnerships and has secured substantial orders in the North American market, leveraging its expertise in energy infrastructure for data centers [25][24] Group 5 - Contemporary Amperex Technology Co., Ltd. (300750.SZ) reported a revenue of 423.7 billion CNY for 2025, reflecting a year-on-year increase of 17.04%, with a net profit of 72.2 billion CNY, up 42.28% [27][28] - The company is expected to benefit from strong demand in the European electric vehicle market, with plans to increase its overseas production capacity significantly [28][29] - The report maintains a "buy" rating for the company, projecting net profits of 95.3 billion CNY in 2026, representing a growth of 32% [29][28]

Performance of Index Enhancement Portfolios - The CSI 300 index enhancement portfolio achieved an excess return of 0.31% this week and 3.36% year-to-date [1][6] - The CSI 500 index enhancement portfolio recorded an excess return of 1.11% this week but a negative return of -1.15% year-to-date [1][6] - The CSI 1000 index enhancement portfolio had an excess return of 1.60% this week and 3.40% year-to-date [1][6] - The CSI A500 index enhancement portfolio saw an excess return of 0.05% this week and 3.77% year-to-date [1][6] Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as EPTTM, expected EPTTM, and single-quarter EP performed well [1][9] - In the CSI 500 component stocks, expected EPTTM, single-quarter EP, and EPTTM were the top-performing factors [1][11] - For the CSI 1000 component stocks, expected EPTTM, BP, and expected BP showed strong performance [1][13] - In the CSI A500 index component stocks, expected EPTTM, EPTTM, and single-quarter EP were the best-performing factors [1][15] - Among publicly offered fund heavy stocks, single-quarter EP, EPTTM, and expected EPTTM also performed well [1][17] Public Fund Index Enhancement Product Performance Tracking - The CSI 300 index enhancement products had a maximum excess return of 1.02% and a minimum of -2.08% this week, with a median of -0.08% [1][20] - The CSI 500 index enhancement products achieved a maximum excess return of 1.65% and a minimum of -0.86% this week, with a median of 0.01% [1][23] - The CSI 1000 index enhancement products recorded a maximum excess return of 1.32% and a minimum of -1.01% this week, with a median of 0.04% [1][24] - The CSI A500 index enhancement products had a maximum excess return of 1.14% and a minimum of -0.88% this week, with a median of 0.03% [1][25]

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. This approach ensures that the factor's predictive power is tested under realistic portfolio constraints, making it more applicable to actual investment scenarios [39][40]. - **Model Construction Process**: - The optimization model is formulated as follows: $$ \begin{array}{ll} \text{max} & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T}w = 1 \end{array} $$ - **Objective Function**: Maximize the single-factor exposure, where \(f\) represents the factor values, \(f^T w\) is the weighted exposure of the portfolio to the factor, and \(w\) is the weight vector of stocks [40]. - **Constraints**: 1. **Style Exposure**: \(X\) is the factor exposure matrix for style factors, and \(s_l\) and \(s_h\) are the lower and upper bounds for style factor deviations [40]. 2. **Industry Exposure**: \(H\) is the industry exposure matrix, and \(h_l\) and \(h_h\) are the lower and upper bounds for industry deviations [40]. 3. **Stock Weight Deviation**: \(w_l\) and \(w_h\) are the lower and upper bounds for individual stock weight deviations relative to the benchmark [40]. 4. **Constituent Stock Weight**: \(B_b\) is a binary vector indicating whether a stock is a benchmark constituent, and \(b_l\) and \(b_h\) are the lower and upper bounds for constituent stock weights [40]. 5. **No Short Selling**: Ensures non-negative weights and limits individual stock weights to a maximum of \(l\) [40]. 6. **Full Investment**: Ensures the portfolio is fully invested, with the sum of weights equal to 1 [41]. - The MFE portfolio is constructed monthly, and historical returns are calculated after accounting for transaction costs (0.3% on both sides) [43]. - **Model Evaluation**: The MFE portfolio approach is effective in testing factor performance under realistic constraints, making it a robust method for evaluating factor predictability in practical investment scenarios [39][40]. --- Quantitative Factors and Construction Methods 1. Factor Name: EPTTM (Earnings-to-Price Trailing Twelve Months) - **Factor Construction Idea**: Measures the profitability of a company relative to its market valuation, using trailing twelve months (TTM) earnings [16]. - **Factor Construction Process**: - Formula: \( \text{EPTTM} = \frac{\text{Net Income (TTM)}}{\text{Market Capitalization}} \) [16]. - **Factor Evaluation**: Demonstrates strong performance across multiple sample spaces, particularly in the short term, indicating its effectiveness as a valuation factor [18][19][21]. 2. Factor Name: Pre-Expected EPTTM - **Factor Construction Idea**: Similar to EPTTM but uses consensus analyst forecasts for earnings instead of historical data [16]. - **Factor Construction Process**: - Formula: \( \text{Pre-Expected EPTTM} = \frac{\text{Consensus Forecasted Net Income (TTM)}}{\text{Market Capitalization}} \) [16]. - **Factor Evaluation**: Consistently ranks among the top-performing factors, highlighting its predictive power in various market conditions [18][19][21]. 3. Factor Name: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Represents the ratio of a company's book value to its market value, often used as a valuation metric [16]. - **Factor Construction Process**: - Formula: \( \text{BP} = \frac{\text{Book Value}}{\text{Market Capitalization}} \) [16]. - **Factor Evaluation**: Exhibits strong performance in mid-cap and small-cap sample spaces, making it a reliable valuation factor [19][21][24]. 4. Factor Name: Standardized Unexpected Earnings (SUE) - **Factor Construction Idea**: Measures the deviation of actual earnings from expected earnings, standardized by the standard deviation of forecast errors [16]. - **Factor Construction Process**: - Formula: \( \text{SUE} = \frac{\text{Actual Quarterly Net Income} - \text{Expected Quarterly Net Income}}{\text{Standard Deviation of Forecast Errors}} \) [16]. - **Factor Evaluation**: Effective in capturing earnings surprises, particularly in growth-oriented sample spaces [16]. --- Factor Backtesting Results 1. EPTTM - **Recent Week**: 1.46% (HS300), 1.66% (Public Fund Index) [18][26] - **Recent Month**: 0.97% (HS300), 2.47% (Public Fund Index) [18][26] - **Year-to-Date**: 1.55% (HS300), 0.62% (Public Fund Index) [18][26] 2. Pre-Expected EPTTM - **Recent Week**: 1.44% (HS300), 1.66% (Public Fund Index) [18][26] - **Recent Month**: 0.66% (HS300), 1.78% (Public Fund Index) [18][26] - **Year-to-Date**: 1.14% (HS300), -0.51% (Public Fund Index) [18][26] 3. BP - **Recent Week**: 0.55% (HS300), 0.85% (Public Fund Index) [18][26] - **Recent Month**: 0.09% (HS300), 1.90% (Public Fund Index) [18][26] - **Year-to-Date**: 0.42% (HS300), 1.79% (Public Fund Index) [18][26] 4. SUE - **Recent Week**: -0.07% (HS300) [18] - **Recent Month**: -0.24% (HS300) [18] - **Year-to-Date**: 0.06% (HS300) [18]