Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model is used to capture temporal dependencies in financial data, leveraging its recurrent structure to predict stock movements and generate long-short signals[4][5][6] - **Model Construction Process**: - Input data includes historical stock prices, technical indicators, and fundamental factors - The GRU network processes sequential data to learn patterns over time - Outputs are used to construct long-short portfolios based on predicted returns[4][5][6] - **Model Evaluation**: The GRU model demonstrates strong performance in certain market conditions, though its results vary across different stock pools[4][5][6] 2. Model Name: Barra Models (barra1d, barra5d) - **Model Construction Idea**: Barra models are factor-based models designed to decompose stock returns into systematic and idiosyncratic components, enabling factor-based portfolio construction[4][5][6] - **Model Construction Process**: - Factors such as size, value, momentum, and volatility are calculated for each stock - Stocks are ranked based on factor scores, and portfolios are constructed by going long the top 10% and short the bottom 10% of stocks based on factor rankings - barra1d uses daily data, while barra5d aggregates data over a 5-day window[4][5][6] - **Model Evaluation**: barra1d shows consistent strong performance, while barra5d experiences significant drawdowns in certain periods[4][5][6] --- Backtesting Results of Models GRU Model - **Open1d**: Weekly excess return: -1.80%, Monthly: -1.96%, YTD: 5.24%[33] - **Close1d**: Weekly excess return: -2.40%, Monthly: -3.10%, YTD: 4.04%[33] Barra Models - **Barra1d**: Weekly excess return: -0.63%, Monthly: -0.34%, YTD: 3.13%[33] - **Barra5d**: Weekly excess return: -1.80%, Monthly: -2.08%, YTD: 6.42%[33] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements[15] - **Factor Construction Process**: Calculated as the historical beta of the stock relative to the market index[15] 2. Factor Name: Size - **Factor Construction Idea**: Captures the size effect, where smaller firms tend to outperform larger firms[15] - **Factor Construction Process**: Natural logarithm of total market capitalization[15] 3. Factor Name: Momentum - **Factor Construction Idea**: Stocks with strong past performance tend to continue performing well in the short term[15] - **Factor Construction Process**: - Weighted combination of historical excess return volatility (0.74), cumulative excess return deviation (0.16), and residual return volatility (0.10)[15] 4. Factor Name: Volatility - **Factor Construction Idea**: Measures the risk or variability in stock returns[15] - **Factor Construction Process**: Weighted combination of historical residual return volatility and other metrics[15] 5. Factor Name: Valuation - **Factor Construction Idea**: Identifies undervalued stocks based on fundamental metrics[15] - **Factor Construction Process**: Inverse of price-to-book ratio[15] 6. Factor Name: Liquidity - **Factor Construction Idea**: Measures the ease of trading a stock[15] - **Factor Construction Process**: Weighted combination of monthly turnover (0.35), quarterly turnover (0.35), and annual turnover (0.30)[15] 7. Factor Name: Profitability - **Factor Construction Idea**: Captures the financial health and earnings quality of a firm[15] - **Factor Construction Process**: Weighted combination of analyst-predicted earnings yield, cash flow yield, and other profitability metrics[15] 8. Factor Name: Growth - **Factor Construction Idea**: Identifies firms with strong earnings and revenue growth[15] - **Factor Construction Process**: Weighted combination of earnings growth rate (0.24) and revenue growth rate (0.47)[15] 9. Factor Name: Leverage - **Factor Construction Idea**: Measures the financial risk associated with a firm's debt levels[15] - **Factor Construction Process**: Weighted combination of market leverage (0.38), book leverage (0.35), and debt-to-asset ratio (0.27)[15] --- Backtesting Results of Factors Fundamental Factors - **Growth**: Weekly excess return: 2.41%, Monthly: -2.18%, YTD: 3.20%[28] - **Profitability**: Weekly excess return: 0.22%, Monthly: 40.98%, YTD: 6.12%[28] Technical Factors - **Momentum (20-day)**: Weekly excess return: 1.72%, Monthly: 4.23%, YTD: -5.29%[30] - **Volatility (120-day)**: Weekly excess return: 4.85%, Monthly: 8.64%, YTD: -14.60%[30]

Quantitative Models and Construction Methods - **Model Name**: Index Enhancement Strategy (80% Component Constraint) **Model Construction Idea**: The model aims to replicate the performance of typical index enhancement products by adjusting the distribution of components across different market capitalization domains[4][33][34] **Model Construction Process**: 1. The model constrains the component weight to 80% while adjusting the allocation in micro-cap stocks. 2. Specific constraints include: - Industry exposure: 0.1% - Weight cap for CSI 2000 components: 0.2% - Weight cap for micro-cap stocks: 0.1% - Monthly rebalancing frequency 3. Performance metrics such as excess return, tracking error, IR, and maximum drawdown are calculated for different micro-cap allocations (0%, 5%, 10%)[34][35] **Model Evaluation**: The model demonstrates that higher micro-cap allocations can enhance excess returns, albeit with slightly increased tracking error and drawdown[35] - **Model Name**: Index Enhancement Strategy (Relaxed Component Constraint) **Model Construction Idea**: This model explores the impact of relaxing the component weight constraint to 40% while varying micro-cap allocations and market capitalization exposures[33][39] **Model Construction Process**: 1. The component weight constraint is relaxed to 40%, and micro-cap allocations are adjusted (0%, 10%, 20%). 2. Additional constraints include: - Industry exposure: 0.1% - Weight cap for CSI 2000 components: 0.2% - Weight cap for micro-cap stocks: 0.1% - Monthly rebalancing frequency 3. Performance metrics such as excess return, tracking error, IR, and maximum drawdown are calculated for different scenarios[39][40] **Model Evaluation**: Relaxing the component constraint significantly improves excess returns, especially with higher micro-cap allocations, though it introduces higher tracking error and drawdown risks[40] Model Backtesting Results - **Index Enhancement Strategy (80% Component Constraint)**: - CSI 300 (0% micro-cap): Excess Return: 7.97%, Tracking Error: 3.34%, IR: 5.38, Max Drawdown: -1.16% - CSI 300 (5% micro-cap): Excess Return: 8.52%, Tracking Error: 3.45%, IR: 5.58, Max Drawdown: -1.19% - CSI 300 (10% micro-cap): Excess Return: 8.70%, Tracking Error: 3.57%, IR: 5.51, Max Drawdown: -1.33% - CSI 500 (0% micro-cap): Excess Return: 7.55%, Tracking Error: 3.87%, IR: 4.38, Max Drawdown: -1.52% - CSI 500 (5% micro-cap): Excess Return: 8.23%, Tracking Error: 3.88%, IR: 4.78, Max Drawdown: -1.38% - CSI 500 (10% micro-cap): Excess Return: 9.20%, Tracking Error: 3.98%, IR: 5.24, Max Drawdown: -1.39% - CSI 1000 (0% micro-cap): Excess Return: 10.12%, Tracking Error: 4.28%, IR: 5.40, Max Drawdown: -1.50% - CSI 1000 (5% micro-cap): Excess Return: 9.76%, Tracking Error: 4.31%, IR: 5.16, Max Drawdown: -1.69% - CSI 1000 (10% micro-cap): Excess Return: 9.76%, Tracking Error: 4.31%, IR: 5.16, Max Drawdown: -1.69%[35] - **Index Enhancement Strategy (Relaxed Component Constraint)**: - CSI 300 (0% micro-cap): Excess Return: 10.87%, Tracking Error: 4.35%, IR: 5.73, Max Drawdown: -1.29% - CSI 300 (10% micro-cap): Excess Return: 13.96%, Tracking Error: 7.01%, IR: 4.64, Max Drawdown: -3.02% - CSI 500 (0% micro-cap): Excess Return: 10.25%, Tracking Error: 6.65%, IR: 3.52, Max Drawdown: -2.19% - CSI 500 (20% micro-cap): Excess Return: 17.08%, Tracking Error: 7.98%, IR: 5.07, Max Drawdown: -2.43% - CSI 1000 (0% micro-cap): Excess Return: 10.84%, Tracking Error: 6.24%, IR: 3.98, Max Drawdown: -1.54% - CSI 1000 (20% micro-cap): Excess Return: 16.81%, Tracking Error: 7.38%, IR: 5.38, Max Drawdown: -2.04%[40] Quantitative Factors and Construction Methods - **Factor Name**: Market Capitalization (Size) **Factor Construction Idea**: Market capitalization is used as a linear factor to segment stocks into deciles, with smaller-cap stocks expected to deliver higher excess returns[19][22] **Factor Construction Process**: 1. Divide the market into 10 deciles based on market capitalization. 2. Calculate the excess return for each decile. 3. Analyze the trend of excess returns across deciles[22] **Factor Evaluation**: The smallest decile (G01) delivers the highest excess return (22.4%), while returns decrease progressively with increasing market capitalization[22] - **Factor Name**: Mid-Cap (Nonlinear Size) **Factor Construction Idea**: Mid-cap is modeled as a cubic function to capture the performance of stocks outside the large-cap and small-cap domains[2][25] **Factor Construction Process**: 1. Define mid-cap stocks using a cubic function of market capitalization. 2. Analyze the overlap between mid-cap and market capitalization groups. 3. Evaluate the excess return of mid-cap groups[25][26] **Factor Evaluation**: Mid-cap stocks exhibit significant overlap with small-cap stocks, and the smallest mid-cap group (G01) delivers high excess returns (21.6%)[22][25] Factor Backtesting Results - **Market Capitalization (Size)**: - G01: 22.4%, G02: 15.0%, G03: 22.6%, G04: 20.4%, G05: 13.6%, G06: 13.2%, G07: 10.9%, G08: 6.9%, G09: 3.9%, G10: -5.6%[22] - **Mid-Cap (Nonlinear Size)**: - G01: 21.6%, G02: 13.7%, G03: -0.5%, G04: 0.0%, G05: 1.5%, G06: 0.8%, G07: 0.5%, G08: -2.1%, G09: -0.2%, G10: -2.7%[22]

Report Industry Investment Rating No relevant content provided. Core Viewpoints of the Report - As of May 30, 2025, the average position of active equity funds dropped to a low point this year [2]. - In the short - term, one should be cautious about the sustainability of the excess returns of micro - cap stocks and control the allocation risk of micro - cap style related assets [3]. - It is recommended to focus on high - quality targets with improved profitability and solid fundamentals [3]. - The service innovation of the public fund industry is expected to enhance its comprehensive competitiveness [3]. Summary According to Relevant Catalogs 1. Weekly Market Review - From May 26 to May 30, 2025, the overall trading in the A - share market became dull, with the average daily trading volume falling to a relatively low level near one trillion yuan, and risk appetite declined [3][11]. - The previous strong Beizheng 50 index continued to lead the gains, but one should be cautious about the sustainability of the excess returns of micro - cap stocks [3][11]. - The net outflows of domestic Hong Kong - related ETFs have occurred in recent weeks, and the net buying strength of southbound funds has also weakened [3]. - The public fund industry is accelerating the optimization of products and services, and many fund companies have opened the conversion business between different shares of the same fund [3][12]. - The performance of major broad - based indexes was differentiated. The Beizheng 50 performed relatively strongly, while the Shanghai Composite Index, Shenzhen Component Index, ChiNext Index, and CSI 300 all declined [12]. - The performance of primary industries was also differentiated. The automobile sector had the largest decline, while the top - performing industries included comprehensive finance, national defense and military industry, medicine, agriculture, forestry, animal husbandry, and fishery, and communication [15]. 2. Public Funds 2.1 Public Fund Position Calculation - As of May 30, 2025, the average position of active equity funds was about 85.98%. The average positions of common stock funds, partial - stock hybrid funds, and allocation funds decreased, and the average position of "fixed - income +" funds also decreased [2][20]. 2.2 Style Trends of Active Equity Products - As of May 30, 2025, the exposure of the mid - cap style of active partial - stock funds increased relatively. The positions of large - cap growth and small - cap value increased, while the positions of large - cap value, small - cap growth decreased [3][28]. 2.3 Industry Trends of Active Equity Products - From May 26 to May 30, 2025, the allocation ratios of active equity funds in industries such as food and beverage, machinery, and commercial retail increased, while the ratios in industries such as computer and electronics decreased [3][31]. 2.4 ETF Market Tracking - From May 26 to May 30, 2025, the funds of broad - based ETFs flowed back, exchange - traded credit bond ETFs continued to attract capital, and some medical theme ETFs had obvious profit - taking [33]. - The net inflow of broad - based ETFs was about 6.171 billion yuan, the net outflow of industry ETFs was about 121 million yuan, the net inflow of style and theme ETFs was 1.403 billion yuan, the net outflow of cross - border ETFs was about 2.049 billion yuan, the net inflow of bond ETFs was about 15.106 billion yuan, and the net outflow of commodity ETFs was 106 million yuan [33]. 2.5 Newly Established Funds - From May 26 to May 30, 2025, there were 30 newly established domestic funds, including 4 active equity funds. The total newly issued shares of active equity funds were about 1.735 billion shares, at the 78.4% quantile in the past year [38]. 3. Main/Active Fund Flows - From May 26 to May 30, 2025, the main and active funds had a net inflow into the banking sector and a net outflow from sectors such as electronics, computers, automobiles, and power equipment and new energy [5][51]. - In terms of individual stocks and industries, there were differences in the net inflow and outflow directions of main funds and active funds [5][51].