Group 1: Core Insights - The Chinese private equity industry is transitioning from quantitative expansion to qualitative transformation, supported by the "Golden Yangtze" private equity empowerment plan launched by Changjiang Securities, Industrial Bank, and Securities Times [1] - As of October 2025, the private fund scale reached 22.05 trillion yuan, marking a significant increase of 1.31 trillion yuan from September, surpassing the previous historical peak of 20.81 trillion yuan in September 2023 [3] - The surge in private equity is primarily driven by private securities investment funds, which saw a record high of 7.01 trillion yuan, with a month-on-month increase of 1.04 trillion yuan, reflecting a growth rate of 17.42% [3] Group 2: Market Trends and Opportunities - The A-share market has experienced significant growth this year, with various sectors, including precious metals and industrial metals, performing well, creating a favorable environment for private equity [3] - The "Golden Yangtze" initiative aims to provide comprehensive services to private fund managers, facilitating connections between long-term capital and quality managers [6] - The commodity market is viewed as having strategic allocation opportunities due to factors such as resource scarcity, low supply growth, and a recovering global economy, which are expected to drive demand for commodities [9][10] Group 3: Investment Strategies and Insights - Factor investing is entering a new era, with a noticeable decline in factor momentum and a divergence in factors across different market segments [7][8] - Private fund managers emphasize the importance of focusing on undervalued sectors and long-term value creation, suggesting a shift towards low-valuation areas for better returns [11][12] - The ongoing technological revolution, particularly in AI, is seen as a critical opportunity for China's economic growth, with a focus on innovation and investment in leading companies [13]

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 Combination - **Model Construction Idea**: The PB-ROE-50 combination is designed to capture excess returns by selecting stocks with favorable Price-to-Book (PB) and Return on Equity (ROE) characteristics[25] - **Model Construction Process**: The model selects stocks based on PB and ROE metrics, with adjustments for market capitalization and rebalancing on a periodic basis. The detailed construction process is referenced in a prior report[25][26] - **Model Evaluation**: The model demonstrated significant excess returns across different stock pools during the analyzed period, indicating its effectiveness in identifying profitable investment opportunities[25][26] 2. Model Name: Institutional Research Combination - **Model Construction Idea**: This model leverages the insights from public and private institutional research to identify stocks with potential for excess returns[28] - **Model Construction Process**: The model uses a strategy that tracks stocks selected by public and private institutional research, with adjustments made relative to the CSI 800 index. The methodology is detailed in prior reports[28] - **Model Evaluation**: The model showed strong performance, achieving positive excess returns, which highlights the value of institutional research in stock selection[28][29] 3. Model Name: Block Trade Combination - **Model Construction Idea**: This model identifies stocks with high block trade activity and low volatility, which are likely to outperform[32] - **Model Construction Process**: The model is constructed based on the "high transaction, low volatility" principle, using monthly rebalancing. The detailed methodology is outlined in a prior report[32] - **Model Evaluation**: The model effectively captures excess returns, demonstrating the informational value of block trade activity[32][33] 4. Model Name: Private Placement Combination - **Model Construction Idea**: This model focuses on stocks involved in private placement events, aiming to capture event-driven excess returns[38] - **Model Construction Process**: The model is constructed by considering factors such as market capitalization, rebalancing cycles, and position control, with the private placement announcement date as the key event. The detailed methodology is provided in a prior report[38] - **Model Evaluation**: The model showed a slight negative excess return during the analyzed period, suggesting limited effectiveness in the current market environment[38][39] --- Model Backtesting Results 1. PB-ROE-50 Combination - Weekly excess return: CSI 500: 0.47%, CSI 800: 1.54%, All-market: 1.59%[25][26] - Year-to-date excess return: CSI 500: 2.06%, CSI 800: 15.14%, All-market: 18.32%[26] 2. Institutional Research Combination - Weekly excess return: Public research: 3.63%, Private research: 3.32%[28][29] - Year-to-date excess return: Public research: 16.50%, Private research: 15.79%[29] 3. Block Trade Combination - Weekly excess return: 2.93%[32][33] - Year-to-date excess return: 39.24%[33] 4. Private Placement Combination - Weekly excess return: -0.01%[38][39] - Year-to-date excess return: -3.70%[39] --- Quantitative Factors and Construction Methods 1. Factor Name: Momentum Spring Factor - **Factor Construction Idea**: Captures momentum effects by identifying stocks with strong recent performance trends[12][13] - **Factor Construction Process**: The factor is calculated based on recent price movements, adjusted for market and sector effects. The exact formula is not provided in the report[12][13] - **Factor Evaluation**: Demonstrated strong positive returns across multiple stock pools, indicating its effectiveness in capturing momentum effects[12][13] 2. Factor Name: Early Morning Return Factor - **Factor Construction Idea**: Measures the return performance of stocks during the early trading hours[12][13] - **Factor Construction Process**: The factor is calculated by isolating returns during the early trading hours and adjusting for market and sector influences[12][13] - **Factor Evaluation**: Showed consistent positive returns, highlighting its potential as a predictive factor[12][13] 3. Factor Name: Single Quarter ROA - **Factor Construction Idea**: Focuses on the return on assets (ROA) for a single quarter to identify efficient companies[12][13] - **Factor Construction Process**: The factor is derived from quarterly financial reports, specifically the ratio of net income to total assets, adjusted for market and sector effects[12][13] - **Factor Evaluation**: Demonstrated positive returns, indicating its utility in identifying fundamentally strong companies[12][13] --- Factor Backtesting Results 1. Momentum Spring Factor - Weekly return: CSI 300: 2.26%, CSI 500: 2.56%, Liquidity 1500: 3.25%[12][13][19] - Monthly return: CSI 300: 3.00%, CSI 500: 0.55%, Liquidity 1500: -0.79%[13][15][19] - Year-to-date return: CSI 300: 8.96%, CSI 500: 5.35%, Liquidity 1500: 9.79%[13][15][19] 2. Early Morning Return Factor - Weekly return: CSI 300: 1.88%, CSI 500: 2.66%, Liquidity 1500: 3.21%[12][13][19] - Monthly return: CSI 300: 1.48%, CSI 500: 1.81%, Liquidity 1500: 0.66%[13][15][19] - Year-to-date return: CSI 300: 4.90%, CSI 500: 4.18%, Liquidity 1500: 11.40%[13][15][19] 3. Single Quarter ROA - Weekly return: CSI 300: 1.90%, CSI 500: -0.90%, Liquidity 1500: 0.68%[12][13][19] - Monthly return: CSI 300: 1.19%, CSI 500: 0.47%, Liquidity 1500: -1.20%[13][15][19] - Year-to-date return: CSI 300: 20.46%, CSI 500: -3.19%, Liquidity 1500: 10.47%[13][15][19]

- The report tracks the performance of various style factors, including market capitalization, non-linear market capitalization, profitability, momentum, volatility, and beta factors[2] - The construction process involves creating long-short portfolios at the end of each month, going long on the top 10% of stocks with the highest factor values and shorting the bottom 10% with the lowest factor values, with equal weighting[16] - The recent performance shows strong long positions in market capitalization, non-linear market capitalization, and profitability factors, while momentum, volatility, and beta factors had strong short positions[16] Factor Performance Tracking - The fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[3][4][5] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[3][4][5] - GRU factors had weak long-short performance, with the barra1d model showing some pullback, while other models had insignificant returns[3][4][5] CSI 300 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with growth and surprise growth factors performing negatively, while static financial factors performed positively[4] - Technical factors had negative long-short returns, with momentum factors showing more significant negative returns, favoring low momentum and low volatility stocks[4] - GRU factors had mixed long-short performance, with the barra1d model showing significant pullback, while the barra5d and close1d models performed strongly[4] CSI 500 Component Stocks Factor Performance - Fundamental factors showed mixed long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[5] - Technical factors had negative long-short returns, with short-term factors showing more significant performance, favoring low volatility and low momentum stocks[5] - GRU factors had good long-short performance, with the open1d and barra1d models showing slight pullback, while the close1d and barra5d models performed strongly[5] CSI 1000 Component Stocks Factor Performance - Fundamental factors showed similar long-short returns, with static financial factors performing positively, while growth and surprise growth factors performed negatively[6] - Technical factors had negative long-short returns, favoring low volatility and low momentum stocks[6] - GRU factors had strong long-short performance, with the barra1d model showing some pullback, while the close1d and open1d models performed strongly[6] Long-Only Portfolio Performance - The GRU long-only portfolio showed weak performance, with various models underperforming the CSI 1000 index by 0.54% to 1.12%[7] - The barra5d model performed strongly year-to-date, outperforming the CSI 1000 index by 8.55%[7] - The multi-factor portfolio showed weak performance, underperforming the CSI 1000 index by 0.47%[7] Factor Performance Metrics - Momentum factor: -1.93% (one week), -8.36% (one month), -24.78% (six months), 19.89% (year-to-date), 17.64% (three-year annualized), 17.58% (five-year annualized)[17] - Volatility factor: 1.82% (one week), -2.33% (one month), 16.17% (six months), 6.56% (year-to-date), 7.58% (three-year annualized), -11.09% (five-year annualized)[17] - Beta factor: -1.54% (one week), 5.68% (one month), 0.60% (six months), 19.29% (year-to-date), 7.50% (three-year annualized), 8.99% (five-year annualized)[17] - Liquidity factor: 0.91% (one week), 42.89% (one month), 9.98% (six months), 12.24% (year-to-date), -20.32% (three-year annualized), -24.87% (five-year annualized)[17] - Valuation factor: 0.82% (one week), 0.46% (one month), 0.14% (six months), 3.77% (year-to-date), 14.92% (three-year annualized), 5.46% (five-year annualized)[17] - Growth factor: 0.71% (one week), 2.28% (one month), 2.34% (six months), 3.16% (year-to-date), 49.33% (three-year annualized), -4.78% (five-year annualized)[17] - Leverage factor: 0.35% (one week), 2.37% (one month), 3.68% (six months), 15.17% (year-to-date), 6.40% (three-year annualized), 1.98% (five-year annualized)[17] - Profitability factor: 0.49% (one week), -0.64% (one month), 7.01% (six months), 14.10% (year-to-date), 3.12% (three-year annualized), 0.51% (five-year annualized)[17] - Non-linear market capitalization factor: 4.22% (one week), 0.44% (one month), 3.16% (six months), -32.83% (year-to-date), -38.38% (three-year annualized), -30.29% (five-year annualized)[17] - Market capitalization factor: 5.39% (one week), 0.59% (one month), 2.18% (six months), -37.92% (year-to-date), -40.48% (three-year annualized), -34.25% (five-year annualized)[17]

Quantitative Models and Construction Methods 1. Model Name: PB-ROE-50 - **Model Construction Idea**: This model aims to combine the Price-to-Book (PB) ratio and Return on Equity (ROE) to create a portfolio of 50 stocks[23] - **Model Construction Process**: The model selects stocks based on their PB and ROE values, aiming to balance valuation and profitability. The portfolio is rebalanced periodically to maintain the desired characteristics[23] - **Model Evaluation**: The model's performance is tracked across different stock pools, showing its effectiveness in various market conditions[23] - **Model Test Results**: - **CSI 500**: Weekly excess return -1.30%, YTD excess return 1.58%, weekly absolute return -7.01%, YTD absolute return 20.95%[24] - **CSI 800**: Weekly excess return -2.09%, YTD excess return 13.40%, weekly absolute return -6.31%, YTD absolute return 30.05%[24] - **All Market**: Weekly excess return -1.46%, YTD excess return 16.48%, weekly absolute return -6.44%, YTD absolute return 36.70%[24] 2. Model Name: Institutional Research Portfolio - **Model Construction Idea**: This model tracks the stock selection strategies of public and private institutional research[25] - **Model Construction Process**: The model is constructed based on the stock picks of institutional investors, adjusting the portfolio based on their research and investment decisions[25] - **Model Evaluation**: The model's performance is evaluated by comparing its returns to the CSI 800 index[25] - **Model Test Results**: - **Public Research Stock Selection**: Weekly excess return -1.91%, YTD excess return 12.42%, weekly absolute return -6.14%, YTD absolute return 28.92%[26] - **Private Research Tracking**: Weekly excess return -3.65%, YTD excess return 12.06%, weekly absolute return -7.80%, YTD absolute return 28.51%[26] 3. Model Name: Block Trade Portfolio - **Model Construction Idea**: This model leverages the information from block trades, focusing on stocks with high transaction amounts and low volatility[29] - **Model Construction Process**: The portfolio is constructed based on the "high transaction, low volatility" principle, with monthly rebalancing[29] - **Model Evaluation**: The model's performance is tracked relative to the CSI All Share Index[29] - **Model Test Results**: - **Weekly excess return**: -2.84%[30] - **YTD excess return**: 35.29%[30] - **Weekly absolute return**: -7.75%[30] - **YTD absolute return**: 58.77%[30] 4. Model Name: Private Placement Portfolio - **Model Construction Idea**: This model analyzes the event effects of private placements to identify investment opportunities[35] - **Model Construction Process**: The portfolio is constructed around the announcement dates of private placements, considering factors like market capitalization and rebalancing cycles[35] - **Model Evaluation**: The model's performance is evaluated relative to the CSI All Share Index[35] - **Model Test Results**: - **Weekly excess return**: -1.42%[36] - **YTD excess return**: -3.89%[36] - **Weekly absolute return**: -6.40%[36] - **YTD absolute return**: 12.80%[36] Quantitative Factors and Construction Methods 1. Factor Name: Intraday Volatility and Trading Volume Correlation - **Factor Construction Idea**: This factor measures the correlation between intraday volatility and trading volume[12] - **Factor Construction Process**: The factor is calculated by correlating the intraday price volatility with the trading volume over a specified period[12] - **Factor Evaluation**: The factor shows positive returns in the CSI 300 stock pool[12] - **Factor Test Results**: - **Weekly return**: 1.23%[13] - **Monthly return**: 3.14%[13] - **Annual return**: -2.31%[13] - **10-year return**: 22.87%[13] 2. Factor Name: ROE Stability - **Factor Construction Idea**: This factor measures the stability of a company's Return on Equity over time[12] - **Factor Construction Process**: The factor is calculated by assessing the variance in ROE over a specified period[12] - **Factor Evaluation**: The factor shows positive returns in the CSI 300 stock pool[12] - **Factor Test Results**: - **Weekly return**: 1.14%[13] - **Monthly return**: 1.82%[13] - **Annual return**: 0.95%[13] - **10-year return**: 3.68%[13] 3. Factor Name: Downside Volatility Proportion - **Factor Construction Idea**: This factor measures the proportion of downside volatility in the total volatility of a stock[12] - **Factor Construction Process**: The factor is calculated by dividing the downside volatility by the total volatility over a specified period[12] - **Factor Evaluation**: The factor shows positive returns in the CSI 300 stock pool[12] - **Factor Test Results**: - **Weekly return**: 1.13%[13] - **Monthly return**: 2.09%[13] - **Annual return**: -6.82%[13] - **10-year return**: 30.09%[13] 4. Factor Name: Single Quarter Total Asset Gross Profit Margin - **Factor Construction Idea**: This factor measures the gross profit margin of a company's total assets for a single quarter[14] - **Factor Construction Process**: The factor is calculated by dividing the gross profit by the total assets for a single quarter[14] - **Factor Evaluation**: The factor shows positive returns in the CSI 500 stock pool[14] - **Factor Test Results**: - **Weekly return**: 1.82%[15] - **Monthly return**: -0.84%[15] - **Annual return**: 6.56%[15] - **10-year return**: 82.05%[15] 5. Factor Name: Net Profit Margin TTM - **Factor Construction Idea**: This factor measures the trailing twelve months (TTM) net profit margin of a company[16] - **Factor Construction Process**: The factor is calculated by dividing the net profit by the total revenue for the trailing twelve months[16] - **Factor Evaluation**: The factor shows positive returns in the Liquidity 1500 stock pool[16] - **Factor Test Results**: - **Weekly return**: 1.82%[17] - **Monthly return**: -0.58%[17] - **Annual return**: 1.94%[17] - **10-year return**: -17.46%[17] Factor Backtest Results CSI 300 Stock Pool - **Intraday Volatility and Trading Volume Correlation**: Weekly return 1.23%, monthly return 3.14%, annual return -2.31%, 10-year return 22.87%[13] - **ROE Stability**: Weekly return 1.14%, monthly return 1.82%, annual return 0.95%, 10-year return 3.68%[13] - **Downside Volatility Proportion**: Weekly return 1.13%, monthly return 2.09%, annual return -6.82%, 10-year return 30.09%[13] CSI 500 Stock Pool - **Single Quarter Total Asset Gross Profit Margin**: Weekly return 1.82%, monthly return -0.84%, annual return 6.56%, 10-year return 82.05%[15] Liquidity 1500 Stock Pool - **Net Profit Margin TTM**: Weekly return 1.82%, monthly return -0.58%, annual return 1.94%, 10-year return -17.46%[17]

Quantitative Models and Construction - **Model Name**: FIFAA (Flexible Indeterminate Factor Asset Allocation) **Model Construction Idea**: Combines quantitative academic rigor with subjective forward-looking flexibility, using historical data (ex-post) and subjective views (ex-ante) to derive asset-factor exposure and optimize portfolio allocation[2][15][74] **Model Construction Process**: 1. **Factor Selection**: Select tradable, low-correlation macroeconomic factors with clear economic logic. Factors include global equities (economic growth), U.S. Treasuries (interest rate/defensive), credit, inflation protection, and currency protection[15][16][20] 2. **Asset-Factor Mapping**: Use LASSO regression to calculate historical beta exposure, then adjust using subjective views derived from professional investor interviews. Subjective single-factor beta is converted into multi-factor beta using matrix transformations[16][35][39] - Formula for historical beta regression: $$y\,=\,X W\,=\,w_{1}x_{1}+\cdots+w_{n}x_{n}$$[32] Loss function for Ridge regression: $$L(w)\,=\,\sum_{i=1}^{n}(y_{i}-\sum w_{j}x_{i j})+\lambda\sum w_{j}^{2}$$[33] Subjective beta transformation: $$\beta_{f}^{*}\,=\,(1\quad F_{f})\,{\binom{\beta_{f}}{\beta_{!f}}}$$[35] $$\beta=F^{-1}\beta^{*}$$[39] 3. **Factor Exposure Optimization**: Optimize factor exposure based on subjective risk/reward judgment or quantitative methods[17] 4. **Portfolio Optimization**: Maximize expected returns while minimizing factor exposure differences. Constraints include absolute exposure differences ≤ 10% of the larger exposure value[44] - Optimization formula: $$m a x(w^{T}r)$$ $$s.\,t.\,w^{T}I\;=\;1$$ $$a b s(w^{T}\beta_{i}-w^{T}\beta_{j})<0.1*m a x(a b s(w^{T}\beta_{i}),a b s(w^{T}\beta_{j}))$$[44] 5. **Rebalancing**: Allow slight deviations in factor exposure to reduce transaction costs and frequency[18] **Model Evaluation**: Provides higher returns and risk-adjusted performance compared to equal-weighted portfolios. Simplified implementation demonstrates practical feasibility[2][74] Model Backtesting Results - **Default Parameters**: - Historical beta optimization: Annualized return 13.63%, annualized volatility 11.47%, max drawdown -18.97%[49][50] - Adjusted beta optimization: Annualized return 15.43%, annualized volatility 16.46%, max drawdown -33.86%[49][50] - Equal-weight portfolio: Annualized return 10.32%, annualized volatility 11.91%, max drawdown -25.27%[49][50] - **Different Adjustment Coefficients**: - Coefficient range (0.1-0.5): Annualized return varies between 15.16%-15.43%, annualized volatility between 15.73%-16.46%, max drawdown between -30.51% to -37.50%[57][59] - **Different Expected Returns**: - Neutral expected return scenarios (5%, 10%, 15%): Annualized return ranges from 13.63%-15.90%, annualized volatility from 11.47%-16.45%, max drawdown from -18.97% to -36.67%[69][70][71][72] Quantitative Factors and Construction - **Factor Name**: Macroeconomic Factors (Economic Growth, Interest Rate, Inflation) **Factor Construction Idea**: Represent macroeconomic trends using tradable indices to ensure simplicity and reduce calculation errors[15][20][30] **Factor Construction Process**: - Economic growth: Represented by stock indices (e.g., Wind All A Index, S&P 500)[30] - Interest rate: Represented by bond indices (e.g., China Bond Treasury Wealth Index)[30] - Inflation: Composite of commodity indices (e.g., Nanhua Industrial, Agricultural, Energy, and Black Metal indices)[20][30] **Factor Evaluation**: Tradable and low-correlation factors ensure practical applicability and reduce subjective judgment uncertainty[15][16][20] Factor Backtesting Results - **Macroeconomic Factor Correlation Matrix**: - Wind All A vs. S&P 500: 0.15 - Wind All A vs. China Bond Treasury: -0.12 - Wind All A vs. Commodity Composite: 0.30[28][30] - **Factor Performance**: - Economic growth factor (Wind All A): Annualized return 13.63%-15.43% depending on optimization method[49][50][69] - Inflation factor (Commodity Composite): Adjusted beta optimization shows higher returns during inflationary periods[49][50][69]

Group 1: Core Insights - FTSE Russell anticipates that European sovereign wealth funds and pension funds may increase their allocation to China in the next 6 to 12 months, viewing China as an independent asset class rather than just part of emerging markets [1][6] - Global investors are shifting from defensive cash and short-duration bonds to risk assets, including developed and emerging market equities and bonds, with a notable flow of new funds into Chinese and Greater China assets [2][6] - The upgrade of Vietnam's market from frontier to secondary emerging market status by FTSE Russell is expected to facilitate easier access for global investors, although the impact on other emerging markets is minimal [1][8] Group 2: European Investor Concerns - European institutional investors are facing a complex environment shaped by structural, macroeconomic, and regulatory challenges, including high stock valuations, interest rate uncertainty, and geopolitical tensions [4] - There is a growing interest among European asset managers in diversifying their portfolios away from overexposed positions in the US and Europe, with a focus on China's leadership in technology and artificial intelligence [6][4] Group 3: Investment Trends - The demand for Chinese indices, particularly those focused on technology, artificial intelligence, and electric vehicles, is increasing among global investors, with significant inflows into products like the Invesco China Technology ETF [7] - The transition of Vietnam to a secondary emerging market is expected to attract approximately $1 to $1.5 billion in passive fund inflows, with active management inflows projected to be 4 to 5 times that amount [9][8] Group 4: ESG Investment Evolution - There is a notable shift in investor behavior towards more integrated and thematic approaches to ESG investing, with a focus on understanding how ESG factors impact investment returns [11] - Regulatory frameworks in Europe, such as the Corporate Sustainability Reporting Directive (CSRD), are enhancing corporate disclosure standards, which is crucial for ESG investment transparency [11]

- The report constructs an ETF hotspot trend strategy based on the highest and lowest price trends of ETFs, selecting those with both highest and lowest prices in an upward trend. Further, it constructs a support-resistance factor based on the relative steepness of the regression coefficients of the highest and lowest prices over the past 20 days, and selects the top 10 ETFs with the highest turnover rate in the past 5 days/20 days to construct a risk parity portfolio[27][30] - The report tracks the performance of various style factors, noting that the value factor recorded a positive return of 2.36%, the leverage factor recorded a positive return of 1.08%, and the volatility factor slightly rebounded with a return of 0.19%[41][42] - The report evaluates the performance of different alpha factors, highlighting that the quick ratio factor had the best performance with a weekly excess return of 1.32%, followed by the debt-asset ratio factor with a weekly excess return of 1.21%, and the earnings variability over 5 years factor with a weekly excess return of 1.04%[44][46][47] - The ETF hotspot trend strategy recorded a cumulative excess return over the CSI 300 index since the beginning of the year[28][29] - The value factor achieved a weekly return of 2.36%, the leverage factor achieved a weekly return of 1.08%, and the volatility factor achieved a weekly return of 0.19%[41][42] - The quick ratio factor achieved a weekly excess return of 1.32%, the debt-asset ratio factor achieved a weekly excess return of 1.21%, and the earnings variability over 5 years factor achieved a weekly excess return of 1.04%[44][46][47]

Core Viewpoint - The article provides a comprehensive analysis of market factors and their performance over the week, highlighting the positive and negative returns of various investment factors across different stock pools [4][5][6]. Factor Performance Summary - In the large-cap stock pool (CSI 300), the best-performing factors included large net inflows (1.63%), price-to-earnings ratio (1.50%), and the standard deviation of 5-day trading volume (1.40%). Conversely, the worst-performing factors were quarterly operating profit growth rate (-1.67%), 5-day reversal (-1.83%), and total asset growth rate (-2.26%) [5]. - In the mid-cap stock pool (CSI 500), the top factors were downside volatility ratio (2.64%), large net inflows (2.22%), and price-to-book ratio (2.09%), while the underperformers included total asset growth rate (-0.37%), early morning return factor (-0.78%), and momentum spring factor (-1.00%) [5]. - In the liquidity-focused stock pool (Liquidity 1500), the leading factors were logarithmic market value (1.76%), correlation between intraday volatility and trading volume (1.52%), and downside volatility ratio (1.38%). The lagging factors included ROE stability (-1.76%), total asset growth rate (-1.94%), and ROA stability (-2.08%) [5]. Industry-Specific Factor Performance - The net asset growth rate factor performed well in the steel industry, while it was underwhelming in most other sectors. The net profit growth rate factor excelled in the comprehensive industry [6]. - The 5-day momentum factor showed significant momentum effects in the comprehensive, coal, and electrical equipment industries, while reversal effects were notable in the oil, petrochemical, and beauty care sectors [6][7]. Combination Tracking - The PB-ROE-50 combination experienced excess return drawdowns across stock pools, with excess returns of -0.23% in the CSI 500, -0.98% in the CSI 800, and -1.39% in the overall market stock pool [8]. - The public fund research selection strategy and private fund research tracking strategy achieved positive excess returns, with the public strategy outperforming the CSI 800 by 1.82% and the private strategy by 1.06% [9]. - The block trading combination outperformed the CSI All Index, achieving an excess return of 2.39% [10]. - The targeted issuance combination also outperformed the CSI All Index, with an excess return of 2.16% [11].

Quantitative Models and Construction 1. Model Name: Barra Style Factors - **Model Construction Idea**: The Barra style factors are designed to capture various market characteristics such as valuation, momentum, volatility, and growth, among others, to explain stock returns[14][15] - **Model Construction Process**: - The factors are calculated based on specific financial and market metrics. For example: - **Beta**: Historical beta - **Size**: Natural logarithm of total market capitalization - **Momentum**: Weighted average of historical excess return series - **Volatility**: Weighted average of historical residual return volatility - **Valuation**: Inverse of price-to-book ratio - **Liquidity**: Weighted average of turnover ratios (monthly, quarterly, yearly) - **Profitability**: Weighted average of various profitability metrics such as analyst forecasted earnings-to-price ratio, inverse of price-to-cash flow ratio, and inverse of trailing twelve-month price-to-earnings ratio - **Growth**: Weighted average of earnings growth rate and revenue growth rate - **Leverage**: Weighted average of market leverage, book leverage, and debt-to-asset ratio[15] - **Model Evaluation**: The model is widely used in the industry to capture systematic risk factors and explain stock returns. It is considered robust and comprehensive in its approach to factor construction[14][15] 2. Model Name: GRU (Generalized Risk Utility) Model - **Model Construction Idea**: GRU models are used to capture complex relationships in stock returns by leveraging advanced statistical and machine learning techniques. They are designed to identify patterns in historical data and predict future performance[4][6][8] - **Model Construction Process**: - GRU models are trained on historical data to identify patterns in stock returns - The models are applied to different stock pools (e.g., CSI 300, CSI 500, CSI 1000) to evaluate their performance - Specific GRU models include `barra1d`, `barra5d`, `open1d`, and `close1d`, which differ in their time horizons and data inputs[4][6][8] - **Model Evaluation**: GRU models show mixed performance, with some models like `barra5d` and `close1d` performing strongly, while others like `barra1d` exhibit significant drawdowns in certain periods[4][6][8] --- Model Backtesting Results 1. Barra Style Factors - **Momentum**: Weekly return 3.49%, monthly return -6.50%, YTD return -14.88%[17] - **Beta**: Weekly return 2.21%, monthly return -7.75%, YTD return 28.44%[17] - **Volatility**: Weekly return 1.90%, monthly return -3.76%, YTD return 6.09%[17] - **Liquidity**: Weekly return 1.67%, monthly return 46.39%, YTD return 8.77%[17] - **Size**: Weekly return 0.45%, monthly return -6.89%, YTD return -39.47%[17] - **Non-linear Size**: Weekly return 0.28%, monthly return -6.47%, YTD return -34.37%[17] - **Growth**: Weekly return 0.22%, monthly return 2.03%, YTD return 0.89%[17] - **Profitability**: Weekly return 1.43%, monthly return 3.55%, YTD return 14.39%[17] - **Leverage**: Weekly return 2.13%, monthly return 4.08%, YTD return 16.59%[17] - **Valuation**: Weekly return 3.52%, monthly return 6.78%, YTD return 4.37%[17] 2. GRU Models - **barra1d**: Weekly return -0.34%, monthly return -0.65%, YTD return 4.71%[33][34] - **barra5d**: Weekly return 1.44%, monthly return 5.42%, YTD return 7.34%[33][34] - **open1d**: Weekly return 0.32%, monthly return 1.81%, YTD return 6.02%[33][34] - **close1d**: Weekly return 1.41%, monthly return 4.17%, YTD return 4.33%[33][34] - **Multi-factor Combination**: Weekly return 0.57%, monthly return 2.54%, YTD return 0.89%[33][34] --- Quantitative Factors and Construction 1. Factor Name: Fundamental Factors - **Factor Construction Idea**: Fundamental factors are derived from financial metrics to capture the underlying financial health and performance of companies[4][6][7] - **Factor Construction Process**: - Metrics such as return on assets (ROA), return on equity (ROE), and revenue growth are calculated using trailing twelve-month (TTM) data - Factors are industry-neutralized before testing[19] - **Factor Evaluation**: Fundamental factors show mixed performance, with some factors like "growth" and "profitability" performing well, while others like "static financial factors" exhibit negative returns in certain periods[4][6][7] 2. Factor Name: Technical Factors - **Factor Construction Idea**: Technical factors are based on price and volume data to capture market trends and investor behavior[4][6][7] - **Factor Construction Process**: - Metrics such as momentum, volatility, and turnover are calculated over different time horizons (e.g., 20-day, 60-day, 120-day) - Factors are industry-neutralized before testing[19] - **Factor Evaluation**: Technical factors generally show positive returns for momentum-based factors, while volatility-based factors often exhibit negative returns[4][6][7] --- Factor Backtesting Results 1. Fundamental Factors (CSI 300) - **ROA Growth**: Weekly return 0.38%, monthly return 2.38%, YTD return 26.31%[23] - **Net Profit Surprise Growth**: Weekly return 1.10%, monthly return 2.62%, YTD return 42.59%[23] - **ROC Surprise Growth**: Weekly return 2.23%, monthly return 2.23%, YTD return 35.35%[23] 2. Technical Factors (CSI 500) - **20-day Momentum**: Weekly return 5.99%, monthly return 1.74%, YTD return 3.65%[26] - **120-day Momentum**: Weekly return 1.76%, monthly return 4.01%, YTD return 3.55%[26] - **20-day Volatility**: Weekly return -1.15%, monthly return -4.31%, YTD return 25.86%[26]

Core Viewpoint - The article provides a comprehensive analysis of market performance, highlighting the varying returns of different factors and strategies within the stock market, indicating a mixed sentiment among investors and the potential for selective investment opportunities [4][5][6][7][8][9][10]. Factor Performance - In the overall market, the valuation factor achieved a positive return of 0.40%, while the market capitalization factor and non-linear market capitalization factor recorded negative returns of -0.72% and -0.40% respectively, suggesting a small-cap style market performance [4]. - In the CSI 300 stock pool, the best-performing factors included the inverse TTM price-to-earnings ratio (3.05%), price-to-earnings ratio (2.30%), and price-to-book ratio (2.06%), while the worst performers were TTM gross profit margin (-2.11%), total asset growth rate (-1.80%), and quarterly gross profit margin (-1.58%) [5]. - In the CSI 500 stock pool, the top factors were the inverse TTM price-to-earnings ratio (2.71%), price-to-book ratio (2.07%), and price-to-earnings ratio (1.74%), with the lowest performers being TTM gross profit margin (-2.13%), quarterly gross profit margin (-2.02%), and quarterly ROA year-on-year (-1.50%) [5]. - In the liquidity 1500 stock pool, the leading factors were the inverse TTM price-to-earnings ratio (1.74%), price-to-earnings ratio (1.68%), and price-to-book ratio (1.34%), while the worst were post-opening returns (-3.00%), TTM gross profit margin (-2.64%), and quarterly gross profit margin (-2.50%) [5]. Industry Factor Performance - The fundamental factors showed varied performance across industries, with net asset growth rate, net profit growth rate, earnings per share, and TTM operating profit factors yielding positive returns in the oil and petrochemical industry [6]. - Among valuation factors, the BP factor performed well, achieving positive returns across most industries, while residual volatility and liquidity factors showed significant positive returns in the comprehensive industry [6]. - The market exhibited a notable small-cap style across most industries during the week [6]. Strategy Performance - The PB-ROE-50 combination achieved positive excess returns in the CSI 500 and CSI 800 stock pools, with excess returns of 1.00% and 0.48% respectively, while the overall market stock pool recorded an excess return of -2.00% [7]. - The private equity research tracking strategy yielded negative excess returns, while the public equity research selection strategy achieved an excess return of 0.00% relative to the CSI 800, and the private equity tracking strategy had an excess return of -1.96% [8]. - The block trading combination achieved positive excess returns relative to the CSI All Share Index, with an excess return of 1.08% [9]. - The targeted issuance combination also recorded positive excess returns relative to the CSI All Share Index, with an excess return of 1.93% [10].