Quantitative Models and Construction Methods - **Model Name**: CSI 300 Enhanced Portfolio **Model Construction Idea**: The model aims to enhance the performance of the CSI 300 Index by leveraging quantitative strategies to generate excess returns over the benchmark[4][8][14] **Model Construction Process**: The portfolio is constructed by selecting stocks from the CSI 300 Index based on quantitative factors and optimization techniques. The model seeks to maximize excess returns while controlling tracking error relative to the benchmark[8][14] **Model Evaluation**: The model demonstrates strong performance in generating consistent excess returns over the CSI 300 Index, indicating its effectiveness in capturing alpha[14] - **Model Name**: CSI 500 Enhanced Portfolio **Model Construction Idea**: Similar to the CSI 300 Enhanced Portfolio, this model focuses on enhancing the performance of the CSI 500 Index by applying quantitative strategies[8][14] **Model Construction Process**: Stocks are selected from the CSI 500 Index using quantitative factors, and the portfolio is optimized to achieve excess returns while maintaining a controlled tracking error[8][14] **Model Evaluation**: The model shows mixed results, with some periods of underperformance relative to the benchmark, suggesting room for improvement in factor selection or optimization[14] - **Model Name**: CSI 1000 Enhanced Portfolio **Model Construction Idea**: This model targets the CSI 1000 Index, aiming to generate excess returns through quantitative strategies tailored to small-cap stocks[8][14] **Model Construction Process**: The portfolio is constructed by selecting stocks from the CSI 1000 Index based on quantitative factors and optimizing for excess returns while managing tracking error[8][14] **Model Evaluation**: The model performs well, particularly in capturing alpha from small-cap stocks, with positive excess returns over the benchmark[14] - **Model Name**: GARP Portfolio **Model Construction Idea**: The GARP (Growth at a Reasonable Price) portfolio combines growth and valuation factors to identify stocks with strong growth potential at reasonable valuations[32] **Model Construction Process**: Stocks are selected based on a combination of growth metrics (e.g., earnings growth) and valuation metrics (e.g., PE ratio). The portfolio is optimized to balance growth and valuation considerations[32] **Model Evaluation**: The portfolio demonstrates strong performance, with significant excess returns over the CSI 300 Index, indicating the effectiveness of the GARP strategy[32] - **Model Name**: Small-Cap Value Portfolio **Model Construction Idea**: This portfolio focuses on small-cap stocks with attractive valuation metrics, aiming to capture value premiums in the small-cap segment[34][36] **Model Construction Process**: Stocks are selected based on valuation factors such as PB and PE ratios. The portfolio is optimized to maximize exposure to value factors while maintaining diversification[34][36] **Model Evaluation**: The portfolio shows mixed results, with one version underperforming the benchmark and another version generating positive excess returns, highlighting the importance of factor selection and portfolio construction[34][36] - **Model Name**: Small-Cap Growth Portfolio **Model Construction Idea**: This portfolio targets small-cap stocks with strong growth potential, leveraging growth factors to identify high-growth opportunities[38] **Model Construction Process**: Stocks are selected based on growth metrics such as earnings growth and revenue growth. The portfolio is optimized to maximize exposure to growth factors while maintaining diversification[38] **Model Evaluation**: The portfolio underperforms the benchmark, suggesting challenges in capturing growth premiums in the small-cap segment[38] Model Backtesting Results - **CSI 300 Enhanced Portfolio**: Weekly return 0.91%, monthly return 5.64%, annual return 5.64%, excess return over benchmark 3.44%[8][14] - **CSI 500 Enhanced Portfolio**: Weekly return 1.54%, monthly return 7.98%, annual return 7.98%, excess return over benchmark -2.30%[8][14] - **CSI 1000 Enhanced Portfolio**: Weekly return 2.56%, monthly return 8.89%, annual return 8.89%, excess return over benchmark 0.50%[8][14] - **GARP Portfolio**: Weekly return 1.23%, monthly return 4.89%, annual return 4.89%, excess return over benchmark 2.69%[32] - **Small-Cap Value Portfolio 1**: Weekly return 0.64%, monthly return 5.91%, annual return 5.91%, excess return over benchmark -0.60%[34] - **Small-Cap Value Portfolio 2**: Weekly return 2.84%, monthly return 7.92%, annual return 7.92%, excess return over benchmark 1.40%[36] - **Small-Cap Growth Portfolio**: Weekly return 1.20%, monthly return 6.21%, annual return 6.21%, excess return over benchmark -0.31%[38] Quantitative Factors and Construction Methods - **Factor Name**: Size Factor **Factor Construction Idea**: Captures the performance difference between small-cap and large-cap stocks[42] **Factor Construction Process**: Stocks are ranked by market capitalization, and the top 10% (small-cap) and bottom 10% (large-cap) are selected to form long and short portfolios, respectively. The size factor return is calculated as the difference between the long and short portfolio returns[42] **Factor Evaluation**: The size factor shows positive returns in the short term but mixed results over longer periods, indicating variability in its effectiveness[42] - **Factor Name**: PB Factor **Factor Construction Idea**: Measures the valuation premium or discount of stocks based on their price-to-book ratio[42] **Factor Construction Process**: Stocks are ranked by PB ratio, and the top 10% (low PB) and bottom 10% (high PB) are selected to form long and short portfolios, respectively. The PB factor return is calculated as the difference between the long and short portfolio returns[42] **Factor Evaluation**: The PB factor shows negative returns, suggesting challenges in capturing valuation premiums[42] - **Factor Name**: ROE Factor **Factor Construction Idea**: Identifies stocks with high profitability based on return on equity[53] **Factor Construction Process**: Stocks are ranked by ROE, and the top 10% (high ROE) and bottom 10% (low ROE) are selected to form long and short portfolios, respectively. The ROE factor return is calculated as the difference between the long and short portfolio returns[53] **Factor Evaluation**: The ROE factor demonstrates strong positive returns, indicating its effectiveness in identifying profitable stocks[53] Factor Backtesting Results - **Size Factor**: Weekly return 0.91%, annual return 0.16% (all-market), 5.33% (CSI 300), -9.74% (CSI 500), -2.90% (CSI 1000)[42][43] - **PB Factor**: Weekly return -1.83%, annual return -5.94% (all-market), -8.16% (CSI 300), -12.18% (CSI 500), -8.70% (CSI 1000)[42][43] - **ROE Factor**: Weekly return 2.47%, annual return 1.10% (all-market), 0.13% (CSI 300), -2.02% (CSI 500), 1.16% (CSI 1000)[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 of a single factor while controlling for various constraints such as industry exposure, style exposure, stock weight deviation, and turnover rate[64][65] - **Model Construction Process**: - The optimization model is formulated as follows: $$ \begin{array}{ll} \max & f^{T}w \\ \text{s.t.} & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & 0 \leq w \leq l \\ & 1^{T}w = 1 \\ & \Sigma|w-w_{0}| \leq to_{h} \end{array} $$ - **Explanation of Parameters**: - \( f^{T}w \): Weighted exposure of the portfolio to the factor - \( w \): Portfolio weight vector - \( w_{b} \): Benchmark weight vector - \( X, H, B_{b} \): Matrices representing factor, industry, and benchmark exposures - \( s_{l}, s_{h}, h_{l}, h_{h}, w_{l}, w_{h}, b_{l}, b_{h}, to_{h} \): Constraints on factor exposure, industry exposure, stock weight deviation, and turnover rate - Constraints include: - Limiting style and industry deviations relative to the benchmark - Controlling stock weight deviations and turnover rates - Ensuring full investment (weights sum to 1) and no short selling[64][65][67] - The portfolio is rebalanced monthly, and historical returns are calculated after deducting transaction costs to evaluate factor effectiveness[68] - **Model Evaluation**: The MFE model effectively isolates the impact of individual factors while adhering to practical constraints, making it a robust tool for factor evaluation[64][65] --- Quantitative Factors and Construction Methods 1. Factor Name: Trend - **Factor Construction Idea**: Captures the momentum of stock price trends over different time horizons[16] - **Factor Construction Process**: - Two variations: - **Trend_120**: \( \text{EWMA}(\text{halflife}=20) / \text{EWMA}(\text{halflife}=120) \) - **Trend_240**: \( \text{EWMA}(\text{halflife}=20) / \text{EWMA}(\text{halflife}=240) \) - \( \text{EWMA} \): Exponentially Weighted Moving Average[16] - **Factor Evaluation**: Demonstrates strong performance in capturing price continuation patterns, particularly in volatile markets[11][13] 2. Factor Name: Volatility - **Factor Construction Idea**: Measures the variability of stock returns over a specified period[16] - **Factor Construction Process**: - Variants include: - **Stdvol**: Standard deviation of daily returns over the past 243 days - **Ivff**: Fama-French 3-factor idiosyncratic volatility over the past 243 days - **Range**: \( \text{High Price}/\text{Low Price} - 1 \) over the past 243 days - **MaxRet_6**: Average of the six highest daily returns over the past 243 days - **MinRet_6**: Average of the six lowest daily returns over the past 243 days[16] - **Factor Evaluation**: Effective in identifying high-risk stocks, though performance may vary across market conditions[11][13] 3. Factor Name: BP (Book-to-Price Ratio) - **Factor Construction Idea**: Represents the valuation of a stock relative to its book value[20] - **Factor Construction Process**: - Formula: \( \text{BP} = \text{Net Assets} / \text{Market Capitalization} \)[20] - **Factor Evaluation**: Consistently performs well in value-oriented strategies, particularly in markets favoring undervalued stocks[42][43] 4. Factor Name: Three-Month Institutional Coverage - **Factor Construction Idea**: Measures the level of analyst coverage over the past three months[20] - **Factor Construction Process**: - Formula: Count of research reports published by institutions over the past three months[20] - **Factor Evaluation**: Strongly correlated with market sentiment and stock visibility, often leading to positive price momentum[8][46] --- Factor Backtesting Results 1. Trend Factor - **Recent Weekly Return**: 2.39% - **Recent Monthly Return**: 5.57% - **Year-to-Date Return**: -0.70% - **Annualized Return (1 Year)**: 24.36% - **Annualized Return (10 Years)**: 14.25%[11][13] 2. Volatility Factor - **Recent Weekly Return**: -1.75% - **Recent Monthly Return**: -3.95% - **Year-to-Date Return**: 4.10% - **Annualized Return (1 Year)**: 24.26% - **Annualized Return (10 Years)**: -13.16%[11][13] 3. BP Factor - **Recent Weekly Return**: 0.68% - **Recent Monthly Return**: 0.06% - **Year-to-Date Return**: -4.33% - **Annualized Return (1 Year)**: -1.51% - **Annualized Return (10 Years)**: -0.61%[42][43] 4. Three-Month Institutional Coverage Factor - **Recent Weekly Return**: 1.70% - **Recent Monthly Return**: 1.29% - **Year-to-Date Return**: 4.96% - **Annualized Return (1 Year)**: 1.66% - **Annualized Return (10 Years)**: 4.39%[46][48]

- The report uses the Mahalanobis distance of weekly price and trading volume changes to measure industry crowding levels[3][17] - The construction process involves identifying industries in the first quadrant (price and volume both rising) and the third quadrant (price and volume both falling) and marking points outside the ellipse as industries with significant short-term deviations at a 99% confidence level[17] - The building materials industry experienced short-term trading overselling last week[3][18]

Core Viewpoints - The current macroeconomic environment is generally favorable for equity, bond, industrial products, and gold assets, while the technical analysis indicates a downward trend for equity, bond, and industrial products, and an upward trend for gold assets [1][3][21]. Macroeconomic Perspective - The analysis categorizes macroeconomic indicators into upward and downward trends, assessing their impact on asset returns. A significant difference in average returns is noted based on the trend direction of these indicators [3][4]. - The macroeconomic indicators suggest a positive outlook for equity, bond, industrial products, and gold assets [5][6]. Technical Perspective - The trend indicators for various asset classes show that as of March 31, 2025, equity, bond, and industrial products are trending downwards, while gold is trending upwards [10][11]. - The equity asset valuation is currently low, with a historical 5-year ERP percentile of 78.36% [14][15]. Asset Flow Indicators - As of February 2025, the equity asset's net inflow is 462 billion, indicating a state of capital inflow [17][18]. Summary of Views - The combined scores from macro and technical analyses indicate a bullish outlook for equity and gold, a neutral stance for industrial products, and a bullish view for bonds [19][21]. Asset Allocation Performance Tracking - Historical performance data shows that the fixed ratio combined with macro and technical indicators yielded a return of 1.20% in March 2025, with an annualized return of 11.92% since March 2006 [2][27]. - The volatility-controlled and risk-parity combinations yielded returns of 1.72% and 1.18%, respectively, with annualized returns of 9.33% and 9.66% since March 2006 [28].