在当下A股市场风格快速轮动的背景下，如何通过指数增强产品争取稳定超额收益，成为投资者关注的 焦点。 中信保诚基金量化基金经理姜鹏认为，当前，市场正处于"哑铃行情"向均衡修复的阶段，中证A500指 数在市值风格与阿尔法空间上具备双重优势，叠加精细化的量化模型与动态风控，有望成为捕捉超额收 益的更优路径。 "变化正在发生。"姜鹏敏锐观察，"随着政策环境改善、风险偏好回升，我们可以看到资金开始从哑铃 的两端向中段回流。基本面扎实、估值合理且尚未被充分挖掘的上市公司，或迎来配置窗口"。 他强调，选择在此时推出中证A500指增产品，核心在于胜率与赔率的平衡，"我们希望去投资更具吸引 力的标的，提升投资的性价比。当前环境下，个人认为中证A500指数契合这一特征。" 具体到中证A500指数的特性，姜鹏表示："从截至7月末的指数成份股来看，中证A500指数成份股中， 约70%与沪深300指数重合，这部分提供了基本面的稳定性；另外约30%则更接近中证500指数的特色， 涵盖较多TMT等新兴产业，具备更高成长弹性。这种稳中有进的结构，使其在安全边际与向上空间之 间形成良好的均衡。" 精细化因子挖掘 与动态风控 在指数底座之上，中 ...

Group 1 - The core viewpoint of the article is to track and analyze the performance of index-enhanced portfolios and common stock selection factors across various indices, including CSI 300, CSI 500, CSI 1000, and CSI A500 [2][3][19] - The CSI 300 index-enhanced portfolio recorded an excess return of -0.87% for the week and 11.58% year-to-date [7][19] - The CSI 500 index-enhanced portfolio had an excess return of -0.22% for the week and 11.11% year-to-date [7][19] - The CSI 1000 index-enhanced portfolio achieved an excess return of 0.02% for the week and 14.85% year-to-date [7][19] - The CSI A500 index-enhanced portfolio experienced an excess return of -1.49% for the week and 10.27% year-to-date [7][19] Group 2 - In the CSI 300 component stocks, factors such as standardized unexpected revenue, one-year momentum, and quarterly revenue year-on-year growth performed well [8][10] - In the CSI 500 component stocks, factors like EPTTM one-year percentile, executive compensation, and DELTAROA showed strong performance [8][10] - For the CSI 1000 component stocks, factors such as standardized unexpected revenue, three-month reversal, and quarterly revenue year-on-year growth were notable [8][10] - In the CSI A500 index component stocks, factors like quarterly revenue year-on-year growth, three-month reversal, and one-year momentum performed well [8][10] Group 3 - The performance of public fund index-enhanced products was tracked, with the CSI 300 index-enhanced product showing a maximum excess return of 0.69% and a minimum of -1.53% for the week [23][25] - The CSI 500 index-enhanced product had a maximum excess return of 0.78% and a minimum of -1.40% for the week [25] - The CSI 1000 index-enhanced product recorded a maximum excess return of 1.65% and a minimum of -0.96% for the week [28] - The CSI A500 index-enhanced product achieved a maximum excess return of 0.65% and a minimum of -1.59% for the week [29]

Quantitative Models and Construction Methods - **Model Name**: Maximized Factor Exposure Portfolio (MFE) **Model Construction Idea**: The model aims to maximize single-factor exposure while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate. This approach tests the effectiveness of factors under real-world constraints, ensuring their predictive power in portfolio construction [39][40][41] **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}\\ &\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 $f^{T}w$ is the weighted exposure of the portfolio to the factor. $w$ is the stock weight vector to be optimized. - **Constraints**: - **Style Exposure**: $X$ is the matrix of stock exposures to style factors, $w_b$ is the benchmark weight vector, and $s_l$, $s_h$ are the lower and upper bounds for style factor exposure deviation. - **Industry Exposure**: $H$ is the industry exposure matrix, $h_l$, $h_h$ are the lower and upper bounds for industry deviation. - **Stock Weight Deviation**: $w_l$, $w_h$ are the lower and upper bounds for individual stock weight deviation. - **Component Weight Control**: $B_b$ is a binary vector indicating whether a stock belongs to the benchmark index, $b_l$, $b_h$ are the lower and upper bounds for component stock weight. - **No Short Selling**: Ensures non-negative weights and limits individual stock weights. - **Full Investment**: Ensures the portfolio is fully invested with weights summing to 1 [39][40][41] **Model Evaluation**: The model effectively tests factor validity under real-world constraints, ensuring factors contribute to portfolio returns in practical scenarios [39][40][41] Factor Construction and Methods - **Factor Name**: Standardized Unexpected Earnings (SUE) **Factor Construction Idea**: Measures the deviation of actual quarterly net profit from expected net profit, standardized by the standard deviation of expected net profit [17] **Factor Construction Process**: $ SUE = \frac{\text{Actual Quarterly Net Profit} - \text{Expected Net Profit}}{\text{Standard Deviation of Expected Net Profit}} $ **Factor Evaluation**: Useful for identifying stocks with earnings surprises, which may lead to price adjustments [17] - **Factor Name**: One-Month Reversal **Factor Construction Idea**: Captures short-term price reversal by measuring the return over the past 20 trading days [17] **Factor Construction Process**: $ \text{One-Month Reversal} = \text{Return over the past 20 trading days} $ **Factor Evaluation**: Effective in detecting short-term mean-reverting behavior in stock prices [17] - **Factor Name**: Delta ROA **Factor Construction Idea**: Measures the change in return on assets (ROA) compared to the same quarter of the previous year [17] **Factor Construction Process**: $ \Delta ROA = \text{Current Quarter ROA} - \text{ROA of the Same Quarter Last Year} $ **Factor Evaluation**: Indicates improvement or deterioration in asset efficiency, which can signal fundamental changes [17] Factor Backtesting Results **Performance in CSI 300 Sample Space** - **Standardized Unexpected Earnings**: Weekly excess return 1.35%, monthly excess return 3.78%, annual excess return 8.35% [19] - **One-Year Momentum**: Weekly excess return 1.27%, monthly excess return 1.98%, annual excess return -1.17% [19] - **Single-Quarter Revenue Growth**: Weekly excess return 1.08%, monthly excess return 3.86%, annual excess return 11.82% [19] **Performance in CSI 500 Sample Space** - **EPTTM Year Percentile**: Weekly excess return 1.69%, monthly excess return 1.74%, annual excess return 3.77% [21] - **Delta ROA**: Weekly excess return 1.00%, monthly excess return 2.43%, annual excess return 9.72% [21] - **Standardized Unexpected Earnings**: Weekly excess return 0.87%, monthly excess return 3.32%, annual excess return 7.87% [21] **Performance in CSI 1000 Sample Space** - **Standardized Unexpected Earnings**: Weekly excess return 0.75%, monthly excess return 3.69%, annual excess return 7.64% [23] - **Three-Month Reversal**: Weekly excess return 1.34%, monthly excess return 0.24%, annual excess return 5.36% [23] - **Single-Quarter Revenue Growth**: Weekly excess return 1.43%, monthly excess return 4.58%, annual excess return 11.12% [23] **Performance in CSI A500 Sample Space** - **Single-Quarter Revenue Growth**: Weekly excess return 1.43%, monthly excess return 4.58%, annual excess return 11.12% [25] - **Delta ROA**: Weekly excess return 0.63%, monthly excess return 4.33%, annual excess return 10.97% [25] - **Three-Month Reversal**: Weekly excess return 1.34%, monthly excess return 0.24%, annual excess return 5.36% [25] **Performance in Public Fund Heavy Index Sample Space** - **One-Year Momentum**: Weekly excess return 1.11%, monthly excess return 3.36%, annual excess return 1.15% [27] - **Delta ROA**: Weekly excess return 0.63%, monthly excess return 4.33%, annual excess return 10.97% [27] - **Standardized Unexpected Earnings**: Weekly excess return 0.75%, monthly excess return 3.69%, annual excess return 7.64% [27]

Quantitative Models and Construction Methods 1. Model Name: Layered Progressive Stock Selection for Profitability Factor - **Model Construction Idea**: The model aims to enhance the profitability factor by progressively filtering stocks based on historical ROE and financial stability, ensuring higher future ROE probabilities [38][35][36] - **Model Construction Process**: - Step 1: Select the top 100 stocks based on historical ROE (ROE_ttm) [38] - Step 2: From the top 100, further filter the top 50 stocks with the highest financial stability scores, which include metrics like ROE stability, revenue growth stability, and leverage stability [27][38] - Step 3: Construct an equal-weighted portfolio with the final 50 stocks [38] - **Model Evaluation**: The layered approach effectively reduces the probability of ROE decline by one interval (5%) and increases the likelihood of maintaining high ROE levels in the future [38][36] 2. Model Name: Dividend Growth Factorization - **Model Construction Idea**: This model predicts future dividend growth by constructing a stock pool based on historical dividend stability and earnings growth expectations [49][51] - **Model Construction Process**: - Step 1: Select stocks with stable dividend payout ratios over the past three years and positive earnings growth expectations [49] - Step 2: Select stocks with dividend amounts growing over the past two years and positive earnings growth expectations [49] - Step 3: Combine the two pools to form a comprehensive stock pool [49] - Step 4: Construct sub-factors such as dividend payout deviation, sell-side forecast count, and recent financial report growth, standardize and sum them, and take the maximum value across perspectives [51] - **Model Evaluation**: The model improves the prediction accuracy of dividend growth, achieving over a 10% improvement in win rates for both the CSI 300 and CSI 500 indices [51][52] 3. Model Name: Growth Factor Improvement via Reverse Exclusion - **Model Construction Idea**: Instead of further refining high-growth stocks, this model excludes stocks unlikely to achieve future net profit growth, enhancing the growth factor's predictive power [70][69] - **Model Construction Process**: - Step 1: Start with 100 high-growth stocks based on historical growth factors [70] - Step 2: Exclude stocks meeting any of the following conditions: - FY1 consensus forecast ≤ 0 - FY1 consensus forecast is null - Consensus forecast downgraded in the past 4, 13, or 26 weeks [70] - Step 3: Construct a portfolio with the remaining stocks [70] - **Model Evaluation**: The exclusion method significantly improves the prediction rate of actual net profit growth and reduces the probability of selecting companies with declining net profits [70][69] 4. Model Name: Composite Three-Factor Portfolio - **Model Construction Idea**: This model integrates the improved profitability, dividend, and growth factors into a unified portfolio to enhance index performance [81][83] - **Model Construction Process**: - Step 1: Combine the stock pools from the three improved factors (profitability, dividend, growth) [81] - Step 2: Select approximately 120 stocks from the combined pool, ensuring industry neutrality and periodic rebalancing [83] - **Model Evaluation**: The composite portfolio demonstrates consistent performance improvement over the equal-weighted three-factor portfolio, with notable gains in the CSI 300 and CSI 500 indices [83][86] 5. Model Name: Three-Factor Portfolio + Volume-Price Factors - **Model Construction Idea**: This model incorporates volume-price factors (low volatility, low liquidity, momentum) into the three-factor portfolio to capture additional returns during strong volume-price factor periods [100][97] - **Model Construction Process**: - Step 1: Start with the three-factor composite portfolio [100] - Step 2: Select the top 75 stocks based on volume-price factor scores (low volatility, low liquidity, momentum) [100] - Step 3: Construct an equal-weighted portfolio with the selected stocks [100] - **Model Evaluation**: The addition of volume-price factors further enhances long-term returns and maintains stable excess returns compared to the equal-weighted six-factor portfolio [100][103] 6. Model Name: 75+25 Composite Portfolio - **Model Construction Idea**: This model combines the three-factor portfolio with a 25-stock pool selected based on volume-price factors across the entire market, aiming to maximize expected returns [109][112] - **Model Construction Process**: - Step 1: Select 75 stocks from the three-factor portfolio [109] - Step 2: Select 25 stocks from the entire market based on volume-price factors (growth, profitability, low volatility, small market cap) [109] - Step 3: Combine the two pools into a 100-stock portfolio [109] - **Model Evaluation**: The 75+25 portfolio achieves significant improvements in annualized returns and Sharpe ratios, benefiting from the strong performance of volume-price factors in recent years [112][125] --- Model Backtest Results 1. Layered Progressive Stock Selection for Profitability Factor - CSI 300: Win rate improved from 78.03% to 86.28% [36] - CSI 500: Win rate improved from 78.72% to 86.55% [36] 2. Dividend Growth Factorization - CSI 300: Win rate improved from 54.90% to 73.24% [51] - CSI 500: Win rate improved from 40.14% to 54.28% [51] 3. Growth Factor Improvement via Reverse Exclusion - CSI 300: Win rate improved from 83.38% to 92.88% [69] - CSI 500: Win rate improved from 80.21% to 90.13% [69] 4. Composite Three-Factor Portfolio - CSI 300: Annualized return improved from 6.36% to 9.34%, Sharpe ratio improved from 0.34 to 0.49 [86] - CSI 500: Annualized return improved from 5.46% to 7.36%, Sharpe ratio improved from 0.26 to 0.34 [86] 5. Three-Factor Portfolio + Volume-Price Factors - CSI 300: Annualized return improved from 7.81% to 11.55%, Sharpe ratio improved from 0.40 to 0.62 [103] - CSI 500: Annualized return improved from 6.75% to 9.15%, Sharpe ratio improved from 0.32 to 0.45 [103] 6. 75+25 Composite Portfolio - CSI 300: Annualized return improved from 7.84% to 14.56%, Sharpe ratio improved from 0.41 to 0.75 [112] - CSI 500: Annualized return improved from 7.35% to 13.18%, Sharpe ratio improved from 0.36 to 0.62 [112]

Core Insights - The median excess return of index-enhanced products has significantly improved compared to last year [1] - The China Securities 1000 index-enhanced products have shown particularly outstanding excess returns, followed by the China Securities 500 index-enhanced products [1] Group 1: Market Dynamics - The strong performance of public index-enhanced products this year is attributed to two main factors: accelerated rotation among market sectors and overall activity in small-cap stocks [1] - There is a noticeable cycle of rotation between large-cap and small-cap stocks in the Chinese market, with the current small-cap advantage cycle starting in 2021 [1] Group 2: Investment Strategy - Future index profitability and growth potential should be considered for index allocation, alongside short-term factors such as valuation and market sentiment [1]

Group 1: Weekly Index Enhanced Portfolio Performance - The CSI 300 index enhanced portfolio achieved an excess return of 0.93% this week and 12.11% year-to-date [1][6] - The CSI 500 index enhanced portfolio recorded an excess return of -0.58% this week and 10.97% year-to-date [1][6] - The CSI 1000 index enhanced portfolio had an excess return of -1.56% this week and 14.33% year-to-date [1][6] - The CSI A500 index enhanced portfolio saw an excess return of -0.15% this week and 11.56% year-to-date [1][6] Group 2: Stock Selection Factor Performance Tracking - In the CSI 300 component stocks, factors such as single-season ROA, standardized expected external income, and standardized expected external profit performed well [1][9] - In the CSI 500 component stocks, factors like one-year momentum, single-season surprise magnitude, and standardized expected external profit showed strong performance [1][9] - For the CSI 1000 component stocks, one-year momentum, EPTTM one-year percentile, and standardized expected external profit were notable [1][9] - In the CSI A500 index component stocks, DELTAROA, standardized expected external income, and DELTAROE performed well [1][9] - Among public fund heavy stocks, one-year momentum, DELTAROA, and single-season revenue year-on-year growth were strong [1][9] Group 3: Public Fund Index Enhanced Product Performance Tracking - The CSI 300 index enhanced products had a maximum excess return of 1.91%, a minimum of -1.41%, and a median of -0.09% this week [1][20] - The CSI 500 index enhanced products recorded a maximum excess return of 0.52%, a minimum of -2.05%, and a median of -0.51% this week [1][21] - The CSI 1000 index enhanced products achieved a maximum excess return of 0.94%, a minimum of -1.70%, and a median of -0.53% this week [1][22] - The CSI A500 index enhanced products had a maximum excess return of 0.71%, a minimum of -1.10%, and a median of -0.25% this week [1][25]

- The report tracks the performance of Guosen JinGong's index enhancement portfolios and public fund index enhancement products, as well as monitors the performance of common stock selection factors across different sample spaces[11][12][15] - 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[12][14] - The report introduces the concept of Maximized Factor Exposure (MFE) portfolios to test the effectiveness of single factors under real-world constraints. The optimization model maximizes single-factor exposure while controlling for style, industry, stock weight deviations, and other constraints[41][42][43] - The optimization model for MFE portfolios is expressed as: $\begin{array}{ll}max&f^{T}\ w\\ s.t.&s_{l}\leq X(w-w_{b})\leq s_{h}\\ &h_{l}\leq H(w-w_{b})\leq h_{h}\\ &w_{l}\leq w-w_{b}\leq w_{h}\\ &b_{l}\leq B_{b}w\leq b_{h}\\ &\mathbf{0}\leq w\leq l\\ &\mathbf{1}^{T}\ w=1\end{array}$ where `f` represents factor values, `w` is the stock weight vector, and constraints include style exposure, industry exposure, stock weight deviations, and component stock weight limits[41][42] - The report tracks the performance of single-factor MFE portfolios across different sample spaces, including CSI 300, CSI 500, CSI 1000, CSI A500, and public fund heavy positions index. Factors are evaluated based on their excess returns relative to benchmarks[15][18][26] - Common stock selection factors are categorized into valuation, reversal, growth, profitability, liquidity, company governance, and analyst dimensions. Examples include BP (Book-to-Price), ROA (Return on Assets), and one-year momentum[16][17] - In the CSI 300 sample space, factors such as single-season ROA, standardized unexpected income, and standardized unexpected earnings performed well recently, while factors like one-month volatility and three-month volatility performed poorly[19] - In the CSI 500 sample space, factors such as one-year momentum and standardized unexpected earnings showed strong performance recently, while factors like one-month turnover and three-month volatility underperformed[21] - In the CSI 1000 sample space, factors such as one-year momentum and standardized unexpected earnings performed well recently, while factors like BP and single-season SP (Sales-to-Price) performed poorly[23] - In the CSI A500 sample space, factors such as DELTAROA (Change in ROA) and standardized unexpected income performed well recently, while factors like three-month volatility and one-month turnover performed poorly[25] - In the public fund heavy positions index sample space, factors such as one-year momentum and DELTAROA performed well recently, while factors like one-month turnover and three-month turnover underperformed[27] - Public fund index enhancement products are tracked for their excess returns relative to benchmarks. For CSI 300 products, recent weekly excess returns ranged from -1.41% to 1.91%, with a median of -0.09%[32] - For CSI 500 products, recent weekly excess returns ranged from -2.05% to 0.52%, with a median of -0.51%[34] - For CSI 1000 products, recent weekly excess returns ranged from -1.70% to 0.94%, with a median of -0.53%[37] - For CSI A500 products, recent weekly excess returns ranged from -1.10% to 0.71%, with a median of -0.25%[40]

Group 1 - The stock market is experiencing a positive trend, leading to a significant increase in the issuance of index funds, with over 180 billion yuan raised in the first half of the year, accounting for over 90% of the total new issuance of equity funds [1] - Among the various types of index funds, enhanced index funds have gained considerable market attention, aiming to achieve "Beta + Alpha" returns by maintaining a controlled deviation from benchmark indices while outperforming them [1] - The average net value growth rate of enhanced index funds over the past year was 34.29%, with nearly 70% of these funds achieving positive excess returns, particularly those tracking the ChiNext Index, which had an average excess return of 2.67% [1] Group 2 - The ChiNext Index, a core broad-based index in A-shares, consists of 100 stocks with high market capitalization and liquidity, with the top five sectors being power equipment, electronics, pharmaceuticals, communications, and non-bank financials [2] - Since its inception on May 31, 2010, the ChiNext Index has seen a cumulative increase of nearly 150%, outperforming other broad-based indices like the CSI 300 and the Shanghai Composite Index [2] - The current price-to-earnings ratio (PE TTM) of the ChiNext Index is 36 times, which is in the 22nd percentile since its inception, indicating that it is undervalued compared to 78% of the time [2] Group 3 - Based on the growth potential and allocation value of the ChiNext Index, GF Fund launched the GF ChiNext Index Enhanced Fund on August 15, aiming to optimize its investment portfolio through quantitative analysis and fundamental research [3] - The fund will construct its basic investment portfolio based on the weight of the constituent stocks in the benchmark index and seeks to enhance returns through active management strategies [3] - GF Fund has developed a series of enhanced index products tracking various indices, including the CSI 300 and the STAR Market 100, with a focus on capturing both Beta and Alpha returns [3]

Group 1 - The core viewpoint is that the private equity market is experiencing a significant recovery, with over 90% of large private equity firms achieving positive returns this year, driven by structural market opportunities and active trading [2][3] - As of the end of July, the average return for large private equity firms was reported at 16.6%, with 54 out of 55 firms showing positive returns, indicating a strong performance in the sector [2] - The number of large private equity firms has increased to 90, reflecting the expansion of the industry amid favorable market conditions [1][2] Group 2 - The issuance market for private equity has notably improved, with a total of 1,298 private equity securities investment funds registered in July, marking an 18% increase from the previous month [3] - Large private equity firms dominated the new fund registrations in July, with significant numbers of new funds being launched, particularly in index-enhanced strategies [3] - Investor sentiment has improved, with institutional investors increasing their participation and shifting their preferences towards long-biased strategies, while individual investors are also showing signs of renewed interest [3] Group 3 - Large private equity firms are maintaining aggressive positions and actively adjusting their portfolios to capitalize on structural opportunities in the market [4][5] - The current investment focus includes sectors such as technology, innovative pharmaceuticals, non-bank financials, and cyclical stocks, with a high portfolio allocation of over 80% [4] - There is an expectation of profit-taking in popular sectors due to recent gains, particularly during the busy earnings reporting period in August, leading to potential adjustments in investment strategies [5]

Quantitative Models and Construction Methods Model Name: DFQ-FactorGCL - **Model Construction Idea**: Based on hypergraph convolutional neural networks and temporal residual contrastive learning for stock return prediction[6] - **Model Construction Process**: The model uses hypergraph convolutional neural networks to capture complex relationships between stocks and temporal residual contrastive learning to enhance prediction accuracy[6] - **Model Evaluation**: The model is effective in capturing stock trends and improving prediction accuracy[6] Model Name: Neural ODE - **Model Construction Idea**: Reconstructing time series dynamic systems for deep learning factor mining[6] - **Model Construction Process**: The model uses ordinary differential equations to model the continuous dynamics of stock prices, allowing for more accurate factor extraction[6] - **Model Evaluation**: The model provides a novel approach to factor mining, improving the robustness and accuracy of predictions[6] Model Name: DFQ-FactorVAE-pro - **Model Construction Idea**: Incorporating feature selection and environmental variable modules into the FactorVAE model[6] - **Model Construction Process**: The model uses variational autoencoders with additional modules for feature selection and environmental variables to enhance stock selection[6] - **Model Evaluation**: The model improves stock selection by considering more comprehensive factors and environmental variables[6] Quantitative Factors and Construction Methods Factor Name: Beta - **Factor Construction Idea**: Bayesian compressed market Beta[16] - **Factor Construction Process**: The factor is constructed by compressing the market Beta using Bayesian methods to capture market sensitivity[16] - **Factor Evaluation**: The factor is effective in identifying stocks with high market sensitivity[12] Factor Name: Volatility - **Factor Construction Idea**: Average logarithmic turnover rate over the past 243 days[16] - **Factor Construction Process**: The factor is calculated using the average logarithmic turnover rate and its regression with the market turnover rate over the past 243 days[16] - **Factor Evaluation**: The factor captures the demand for high volatility assets[12] Factor Name: Liquidity - **Factor Construction Idea**: Average logarithmic turnover rate over the past 243 days[16] - **Factor Construction Process**: The factor is calculated using the average logarithmic turnover rate and its regression with the market turnover rate over the past 243 days[16] - **Factor Evaluation**: The factor indicates the demand for high liquidity assets[12] Factor Name: Value - **Factor Construction Idea**: Book-to-market ratio (BP) and earnings yield (EP)[16] - **Factor Construction Process**: The factor is calculated using the book-to-market ratio and earnings yield[16] - **Factor Evaluation**: The factor shows limited recognition of value investment strategies[12] Factor Name: Growth - **Factor Construction Idea**: State-owned enterprise stock proportion[16] - **Factor Construction Process**: The factor is calculated using the proportion of state-owned enterprise stocks[16] - **Factor Evaluation**: The factor indicates the market's attention to state-owned enterprise stocks[12] Factor Name: Cubic Size - **Factor Construction Idea**: Market capitalization power term[16] - **Factor Construction Process**: The factor is calculated using the market capitalization power term[16] - **Factor Evaluation**: The factor shows the market's reduced attention to micro-cap stocks[12] Factor Name: Trend - **Factor Construction Idea**: EWMA with different half-lives[18] - **Factor Construction Process**: The factor is calculated using EWMA with half-lives of 20, 120, and 240 days, standard volatility, FF3 specific volatility, range, and maximum and minimum returns over the past 243 days[18] - **Factor Evaluation**: The factor indicates the market's reduced preference for trend investment strategies[12] Factor Name: Certainty - **Factor Construction Idea**: Sales growth, institutional holding percentage, net asset growth, analyst coverage, and listing days[18] - **Factor Construction Process**: The factor is calculated using sales growth, institutional holding percentage, net asset growth, analyst coverage, and listing days[18] - **Factor Evaluation**: The factor shows the market's reduced confidence in certainty investment strategies[12] Factor Performance Monitoring Performance in Different Index Spaces - **CSI 300 Index**: Factors like expected PEG, DELTAROE, and single-quarter EP performed well, while three-month reversal and one-month volatility performed poorly[7][24][26] - **CSI 500 Index**: Factors like one-year momentum and expected ROE change performed well, while three-month reversal and three-month institutional coverage performed poorly[7][28][30] - **CSI 800 Index**: Factors like expected ROE change and DELTAROE performed well, while one-month volatility and three-month reversal performed poorly[7][32][34] - **CSI 1000 Index**: Factors like DELTAROA and single-quarter net profit growth performed well, while public holding market value and standardized unexpected revenue performed poorly[7][36][37] - **CNI 2000 Index**: Factors like non-liquidity impact and expected PEG performed well, while public holding market value and one-month volatility performed poorly[7][39][41] - **ChiNext Index**: Factors like three-month earnings adjustment and single-quarter EP performed well, while expected net profit change and expected ROE change performed poorly[7][43][45] - **CSI All Index**: Factors like one-month UMR and one-month reversal performed well, while one-month volatility and three-month volatility performed poorly[7][47][50] Factor Backtesting Results CSI 300 Index - **Expected PEG**: 0.75% (recent week), 2.07% (recent month), 7.23% (year-to-date), 5.96% (annualized)[24] - **DELTAROE**: 0.73% (recent week), 2.19% (recent month), 7.91% (year-to-date), 5.07% (annualized)[24] - **Single-quarter EP**: 0.71% (recent week), 0.96% (recent month), 5.93% (year-to-date), 7.58% (annualized)[24] CSI 500 Index - **One-year momentum**: 0.84% (recent week), 2.33% (recent month), 3.83% (year-to-date), 3.00% (annualized)[28] - **Expected ROE change**: 0.76% (recent week), 0.28% (recent month), 6.15% (year-to-date), 7.67% (annualized)[28] - **Three-month UMR**: 0.74% (recent week), -0.38% (recent month), 0.29% (year-to-date), -1.06% (annualized)[28] CSI 800 Index - **Expected ROE change**: 0.93% (recent week), 1.76% (recent month), 2.27% (year-to-date), -3.20% (annualized)[32] - **Expected PEG**: 0.83% (recent week), 2.60% (recent month), 10.99% (year-to-date), 10.96% (annualized)[32] - **DELTAROE**: 0.79% (recent week), 2.64% (recent month), 11.60% (year-to-date), 8.99% (annualized)[32] CSI 1000 Index - **DELTAROA**: 0.63% (recent week), 1.57% (recent month), 8.06% (year-to-date), 15.10% (annualized)[36] - **Single-quarter net profit growth**: 0.57% (recent week), 1.03% (recent month), 8.04% (year-to-date), 10.77% (annualized)[36] - **One-month UMR**: 0.47% (recent week), -0.92% (recent month), 1.13% (year-to-date), -3.13% (annualized)[36] CNI 2000 Index - **Non-liquidity impact**: 1.26% (recent week), 1.99% (recent month), 12.11% (year-to-date), 21.51% (annualized)[39] - **Expected PEG**: 0.54% (recent week), 0.32% (recent month), 10.32% (year-to-date), 36.23% (annualized)[39] - **Three-month institutional coverage**: 0.54% (recent week), 4.56% (recent month), 5.41% (year-to-date), -1.19% (annualized)[39] ChiNext Index - **Three-month earnings adjustment**: 0.66% (recent week), 0.53% (recent month), -12.72% (year-to-date), -28.10% (annualized)[43] - **Single-quarter EP**: 0.66% (recent week), 0.69% (recent month), 2.90% (year-to-date), 24.70% (annualized)[43] - **PB_ROE rank difference**: 0.61% (recent week), -0.26% (