证券研究报告 | 金融工程 gszqdatemark 2025 10 08 年 月 日 量化周报 科技板块出现分化 科技板块出现分化。节前（9.29-9.30），大盘震荡上行，上证指数全周收 涨 1.43%。在此背景下，科技板块出现分化，电子、计算机表现相对强势， 而通信、传媒则表现相对弱势。市场的本轮上涨自 4 月 7 日以来，日线级 别反弹已经持续了 5 个多月，反弹幅度也基本在 30%左右，各大指数和 板块的上涨基本都轮动了一遍，超 2/3 的行业日线级别上涨处于超涨状 态，几乎所有的规模指数及一半以上的行业更是走出了复杂的 9-15 浪的 上涨结构，而银行、非银也已经率先形成了日线级别下跌，军工、钢铁、 建筑、交运、医药离确认日线级别下跌的日子也不远了。因此我们认为本 轮日线级别上涨大概率已临近尾声。短期，市场的波动进一步加大后，投 资者后续可积极关注市场未来是否出现放量滞涨、放量大跌及缩量反弹迹 象。中期来看，上证指数、上证 50、沪深 300、中证 500、深证成指、创 业板指、科创 50 纷纷确认周线级别上涨，而且在日线上只走出了 3 浪结 构，中期牛市刚刚开始；此外，已有 26 个行业处于周线 ...

Core Viewpoint - The article emphasizes the importance of setting a reasonable and scientific performance benchmark for public funds, particularly in the context of the growing scale of the CSI 300 index. It discusses how active equity funds can consistently outperform benchmarks by managing style and industry deviations effectively [1][17]. Group 1: Benchmark and Performance - The CSI 300 index serves as the primary benchmark, composed of various style factors. Active fund managers primarily focus on quality, prosperity, and momentum factors, while dividend and low valuation factors can lead to underperformance when they are strong [1][17]. - The difficulty of beating benchmarks is a common challenge for asset management institutions globally, with only about 50% of active equity funds in A-shares outperforming their benchmarks over the past 20 years [17][18]. Group 2: Style and Industry Deviation - Controlling style deviation is more critical than controlling industry deviation for fund managers aiming to outperform benchmarks. Excessive deviation can significantly impact performance negatively [3][22]. - Successful fund managers tend to exhibit smaller deviations in style and industry, maintaining a balanced approach regardless of market conditions [5][24]. Group 3: Stock Selection and Market Timing - Stock selection is more impactful on performance than industry selection, with a focus on identifying high-potential stocks rather than frequently rotating industries [26]. - Market timing is debated among fund managers, with evidence suggesting that while many lack timing ability, strategic timing can enhance returns during volatile periods [12][34]. Group 4: Risk Management and Strategy - A U-shaped risk convexity strategy is proposed to enhance the risk-return profile of portfolios, emphasizing the importance of managing volatility in equity assets [27][28]. - The relationship between volatility and returns is highlighted, with low volatility stocks often yielding better returns in the A-share market, contrary to the general belief that higher volatility equates to higher returns [9][29]. Group 5: Future Considerations - The article suggests that in the absence of clear industry trends, public funds must balance their strategies to achieve stable excess returns by leveraging combination management approaches [20][21].

Group 1 - The macro growth factor continues to rise, while inflation indicators show a weakening rebound, with domestic CPI turning negative at -0.4% and PPI's decline narrowing to -2.9%, indicating persistent internal demand issues [4] - The Federal Reserve's interest rate cut expectations are driving upward global liquidity expectations, benefiting Asian equity markets, with the Korean Composite Index rising by 5.94% and the Hang Seng Tech Index by 5.31% [4][9] - The A-share market shows a preference for growth styles, with the Sci-Tech 50 Index increasing by 5.48%, while small-cap indices outperform large-cap blue chips [4] Group 2 - Recommendations for asset allocation include favoring high-grade credit bonds in the bond market, adjusting duration flexibly, and focusing on bank and insurance sector movements [5] - In the overseas equity market, the report suggests monitoring interest rate-sensitive sectors due to limited short-term rebound potential for the dollar and significantly raised interest rate cut expectations [5] - For gold, it is recommended to increase allocations to gold and silver as they are core assets during the interest rate cut cycle, with expectations for Shanghai gold to break previous highs [5] Group 3 - The report indicates that the overall liquidity environment remains supportive for market valuation recovery and structural trends, with a significant decrease in average daily trading volume in the A-share market [56] - The A-share valuation levels have increased, with the price-to-earnings ratio rising to 50.38 times and the price-to-book ratio reaching 5.60 times, suggesting that market expectations for future corporate earnings may be overly optimistic [60] - The report highlights that the earnings expectations for A-shares are weaker than historical averages, with a projected rolling one-year earnings growth rate of 10.3% and revenue growth rate of 5.9% [61]

Quantitative Models and Factor Analysis Quantitative Models and Construction - **Model Name**: GRU-based Models - **Construction Idea**: GRU (Gated Recurrent Unit) models are used to capture sequential patterns in financial data, aiming to predict stock movements based on historical trends and other input features [3][4][5] - **Construction Process**: GRU models are trained on historical data to optimize their predictive capabilities. Specific variations of GRU models include `barra1d`, `barra5d`, `open1d`, and `close1d`, which differ in their input features and time horizons [3][4][5] - **Evaluation**: GRU models show mixed performance, with `barra1d` consistently achieving positive returns, while other variations like `close1d` and `barra5d` experience significant drawdowns [3][4][5] Model Backtesting Results - **GRU Models**: - `barra1d`: Weekly excess return of 0.14%, monthly return of 1.20%, and YTD return of 4.77% [32][33] - `barra5d`: Weekly excess return of -0.59%, monthly return of -2.84%, and YTD return of 5.03% [32][33] - `open1d`: Weekly excess return of 0.22%, monthly return of -1.23%, and YTD return of 5.45% [32][33] - `close1d`: Weekly excess return of -0.20%, monthly return of -2.64%, and YTD return of 2.92% [32][33] --- Quantitative Factors and Construction - **Factor Name**: Style Factors (Barra) - **Construction Idea**: Style factors are designed to capture systematic risks and returns associated with specific stock characteristics, such as size, momentum, and valuation [14][15] - **Construction Process**: - **Beta**: Historical beta of the stock - **Size**: Natural logarithm of total market capitalization - **Momentum**: Mean of historical excess returns - **Volatility**: Weighted combination of historical excess return volatility, cumulative excess return deviation, and residual return volatility - **Valuation**: Inverse of price-to-book ratio - **Liquidity**: Weighted turnover rates over monthly, quarterly, and yearly periods - **Profitability**: Weighted combination of analyst-predicted earnings yield, cash flow yield, and other profitability metrics - **Growth**: Weighted combination of earnings and revenue growth rates - **Leverage**: Weighted combination of market leverage, book leverage, and debt-to-asset ratio [15] - **Evaluation**: Style factors exhibit varying performance, with size, non-linear size, and liquidity factors showing strong long positions, while valuation and growth factors perform better in short positions [16][17] - **Factor Name**: Fundamental Factors - **Construction Idea**: Fundamental factors are derived from financial statements and aim to capture the financial health and growth potential of companies [17][18][20] - **Construction Process**: - **ROA Growth**: Growth in return on assets - **ROC Growth**: Growth in return on capital - **Net Profit Growth**: Growth in net profit - **Sales-to-Price Ratio**: Inverse of price-to-sales ratio - **Operating Profit Growth**: Growth in operating profit [21][25][27] - **Evaluation**: Fundamental factors like ROA and ROC growth show positive returns, while static financial metrics like sales-to-price ratio exhibit mixed results [21][25][27] - **Factor Name**: Technical Factors - **Construction Idea**: Technical factors are based on price and volume data, aiming to capture momentum and volatility patterns [18][20][24] - **Construction Process**: - **Momentum**: Calculated over 20, 60, and 120-day periods - **Volatility**: Measured over similar time horizons - **Median Deviation**: Deviation of stock prices from the median [25][27][30] - **Evaluation**: High-momentum stocks generally outperform, while long-term volatility factors show weaker performance [25][27][30] --- Factor Backtesting Results - **Style Factors**: - Size: Weekly return of 0.22%, monthly return of 1.20%, and YTD return of 4.77% [16][17] - Valuation: Weekly return of -0.20%, monthly return of -2.64%, and YTD return of 2.92% [16][17] - **Fundamental Factors**: - ROA Growth: Weekly return of 1.31%, monthly return of 12.03%, and YTD return of 33.49% [21][25] - ROC Growth: Weekly return of 1.74%, monthly return of 4.75%, and YTD return of 10.89% [21][25] - **Technical Factors**: - 20-day Momentum: Weekly return of 3.25%, monthly return of 12.92%, and YTD return of 2.35% [25][27] - 60-day Volatility: Weekly return of 3.65%, monthly return of 16.15%, and YTD return of 28.43% [25][27]

Quantitative Models and Construction 1. Model Name: CSI 500 Enhanced Portfolio - **Model Construction Idea**: The model aims to generate excess returns relative to the CSI 500 index by leveraging a quantitative strategy based on factor models and portfolio optimization techniques [45] - **Model Construction Process**: - The portfolio is constructed using a strategy model that selects stocks based on specific quantitative factors [45] - The portfolio weights are optimized to maximize the expected return while controlling for risk and tracking error relative to the CSI 500 index [45] - The model's performance is evaluated on a weekly basis, and adjustments are made to the portfolio as needed [45] - **Model Evaluation**: The model has demonstrated significant excess returns over the CSI 500 index since 2020, though it experienced underperformance in the most recent week [45] 2. Model Name: CSI 300 Enhanced Portfolio - **Model Construction Idea**: Similar to the CSI 500 Enhanced Portfolio, this model seeks to outperform the CSI 300 index using quantitative factor-based strategies and portfolio optimization [51] - **Model Construction Process**: - Stocks are selected based on quantitative factors, and portfolio weights are optimized to achieve excess returns while managing risk and tracking error relative to the CSI 300 index [51] - The portfolio is reviewed and adjusted periodically to align with the strategy model's recommendations [51] - **Model Evaluation**: The model has achieved consistent excess returns over the CSI 300 index since 2020, with a slight outperformance in the most recent week [51] --- Model Backtesting Results CSI 500 Enhanced Portfolio - Weekly return: 1.82% - Underperformance relative to the benchmark: -1.56% - Cumulative excess return since 2020: 49.43% - Maximum drawdown: -4.99% [45] CSI 300 Enhanced Portfolio - Weekly return: 1.40% - Outperformance relative to the benchmark: 0.02% - Cumulative excess return since 2020: 39.41% - Maximum drawdown: -5.86% [51] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures the sensitivity of a stock's returns to market movements, capturing the systematic risk of the stock [55] - **Factor Construction Process**: - Beta is calculated using regression analysis of a stock's returns against the market index returns over a specified period [55] - The formula is: $ \beta = \frac{\text{Cov}(R_i, R_m)}{\text{Var}(R_m)} $ where $R_i$ is the stock return, $R_m$ is the market return, Cov is covariance, and Var is variance [55] - **Factor Evaluation**: High Beta stocks have recently outperformed, reflecting a market preference for higher systematic risk [56] 2. Factor Name: Residual Volatility (RESVOL) - **Factor Construction Idea**: Captures the idiosyncratic risk of a stock, representing the volatility of its returns unexplained by market movements [55] - **Factor Construction Process**: - Residual volatility is derived from the standard deviation of the residuals in a regression of stock returns on market returns [55] - The formula is: $ \text{RESVOL} = \sqrt{\frac{\sum (R_i - \alpha - \beta R_m)^2}{n-2}} $ where $R_i$ is the stock return, $R_m$ is the market return, $\alpha$ is the intercept, $\beta$ is the slope, and $n$ is the number of observations [55] - **Factor Evaluation**: Residual volatility has shown a significant negative excess return in the recent period, indicating underperformance of high idiosyncratic risk stocks [56] 3. Factor Name: Nonlinear Size (NLSIZE) - **Factor Construction Idea**: Captures the nonlinear relationship between stock size and returns, complementing the traditional size factor [55] - **Factor Construction Process**: - Nonlinear size is calculated as the square of the logarithm of market capitalization: $ \text{NLSIZE} = (\log(\text{Market Cap}))^2 $ [55] - **Factor Evaluation**: Nonlinear size has underperformed recently, reflecting a lack of market preference for mid-sized stocks [56] --- Factor Backtesting Results Beta Factor - Weekly pure factor return: Positive [56] Residual Volatility Factor - Weekly pure factor return: Negative [56] Nonlinear Size Factor - Weekly pure factor return: Negative [56]

Quantitative Models and Construction 1. Model Name: barra1d - **Model Construction Idea**: This model is part of the GRU factor family and is designed to capture short-term market dynamics through daily data inputs[4][6][8] - **Model Construction Process**: The barra1d model uses daily market data to calculate factor exposures and returns. It applies industry-neutralization and standardization processes to ensure comparability across stocks. The model is rebalanced monthly, selecting the top 10% of stocks with the highest factor scores for long positions and the bottom 10% for short positions, with equal weighting[17][28][29] - **Model Evaluation**: The barra1d model demonstrated strong performance in multiple stock pools, showing resilience in volatile market conditions[4][6][8] 2. Model Name: barra5d - **Model Construction Idea**: This model extends the barra1d framework to a five-day horizon, aiming to capture slightly longer-term market trends[4][6][8] - **Model Construction Process**: Similar to barra1d, the barra5d model uses five-day aggregated data for factor calculation. It follows the same industry-neutralization, standardization, and rebalancing processes as barra1d[17][28][29] - **Model Evaluation**: The barra5d model experienced significant drawdowns in recent periods, indicating sensitivity to market reversals[4][6][8] 3. Model Name: open1d - **Model Construction Idea**: This model focuses on open price data to identify short-term trading opportunities[4][6][8] - **Model Construction Process**: The open1d model calculates factor exposures based on daily opening prices. It applies the same industry-neutralization and rebalancing methodology as other GRU models[17][28][29] - **Model Evaluation**: The open1d model showed moderate performance, with some drawdowns in recent periods[4][6][8] 4. Model Name: close1d - **Model Construction Idea**: This model emphasizes closing price data to capture end-of-day market sentiment[4][6][8] - **Model Construction Process**: The close1d model uses daily closing prices for factor calculation. It follows the same construction and rebalancing methodology as other GRU models[17][28][29] - **Model Evaluation**: The close1d model demonstrated stable performance, with positive returns in certain stock pools[4][6][8] --- Model Backtesting Results 1. barra1d Model - Weekly Excess Return: +0.57%[29][30] - Monthly Excess Return: +0.75%[29][30] - Year-to-Date Excess Return: +4.38%[29][30] 2. barra5d Model - Weekly Excess Return: -2.17%[29][30] - Monthly Excess Return: -3.76%[29][30] - Year-to-Date Excess Return: +4.13%[29][30] 3. open1d Model - Weekly Excess Return: -0.97%[29][30] - Monthly Excess Return: -2.85%[29][30] - Year-to-Date Excess Return: +4.20%[29][30] 4. close1d Model - Weekly Excess Return: -1.68%[29][30] - Monthly Excess Return: -4.50%[29][30] - Year-to-Date Excess Return: +1.90%[29][30] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures historical market sensitivity of a stock[15] - **Factor Construction Process**: Calculated as the regression coefficient of a stock's returns against market returns over a specified period[15] 2. Factor Name: Size - **Factor Construction Idea**: Captures the size effect, where smaller firms tend to outperform larger ones[15] - **Factor Construction Process**: Defined as the natural logarithm of total market capitalization[15] 3. Factor Name: Momentum - **Factor Construction Idea**: Identifies stocks with strong recent performance[15] - **Factor Construction Process**: Combines historical excess return mean, volatility, and cumulative deviation into a weighted formula: $ Momentum = 0.74 * \text{Volatility} + 0.16 * \text{Cumulative Deviation} + 0.10 * \text{Residual Volatility} $[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 volatility and other measures[15] 5. Factor Name: Valuation - **Factor Construction Idea**: Captures the value effect, where undervalued stocks tend to outperform[15] - **Factor Construction Process**: Defined as the inverse of the 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 turnover rates over monthly, quarterly, and yearly horizons: $ Liquidity = 0.35 * \text{Monthly Turnover} + 0.35 * \text{Quarterly Turnover} + 0.30 * \text{Yearly Turnover} $[15] 7. Factor Name: Profitability - **Factor Construction Idea**: Identifies stocks with strong earnings performance[15] - **Factor Construction Process**: Weighted combination of various profitability metrics, including analyst forecasts and financial ratios[15] 8. Factor Name: Growth - **Factor Construction Idea**: Captures the growth potential of a stock[15] - **Factor Construction Process**: Weighted combination of earnings and revenue growth rates[15] --- Factor Backtesting Results 1. Beta Factor - Weekly Return: +0.14%[21] - Monthly Return: +1.65%[21] - Year-to-Date Return: +5.29%[21] 2. Size Factor - Weekly Return: +0.36%[21] - Monthly Return: +1.00%[21] - Year-to-Date Return: +6.37%[21] 3. Momentum Factor - Weekly Return: +2.21%[24] - Monthly Return: +8.80%[24] - Year-to-Date Return: +23.30%[24] 4. Volatility Factor - Weekly Return: +2.82%[24] - Monthly Return: +12.29%[24] - Year-to-Date Return: +25.25%[24] 5. Valuation Factor - Weekly Return: +1.47%[21] - Monthly Return: +2.30%[21] - Year-to-Date Return: -2.26%[21] 6. Liquidity Factor - Weekly Return: +1.80%[21] - Monthly Return: +5.91%[21] - Year-to-Date Return: +19.70%[21] 7. Profitability Factor - Weekly Return: +4.57%[21] - Monthly Return: +7.53%[21] - Year-to-Date Return: +27.56%[21] 8. Growth Factor - Weekly Return: +2.76%[24] - Monthly Return: +6.51%[24] - Year-to-Date Return: +14.51%[24]

Core Insights - Style factors serve as a stable foundation for stock portfolios, with changes in factor correlations expected to create tactical Alpha opportunities [2][19] - Chinese stock factors exhibit significant long-term risk-return characteristics, with style factors showing statistically significant risk-adjusted returns exceeding market factors [2][4] Long-term Performance of Pure Factor Portfolios - Over the past 20 years (2005-2024), the annualized return for the market factor is 4.2% with a volatility of 26.9%, resulting in a risk-adjusted return of only 0.16, which is statistically insignificant [2] - Among 14 style factors analyzed, nine demonstrated statistically significant risk-adjusted returns, with a threshold of 0.44 for significance [2] - Notable annualized returns for specific factors include: - Momentum: 4.9% - Earnings: 2.7% - Valuation: 2.1% - Growth: 1.7% - Beta: 6.6% [2] Recent Performance of Style Factors - The returns of style factors vary based on market mechanisms, particularly the beta factor, which shows a return of 1.5% in the top quintile of market returns and drops to -0.1% in the bottom quintile [6] - Earnings yield, beta, momentum, and profit factors exhibit significant volatility depending on market performance [6] Changes in Factor Correlations - The correlation between long-term value and beta has shifted from near zero to a historical low of -0.65, indicating a growing presence of stocks with both value and defensive characteristics [13] - Momentum's correlation with beta also decreased to -0.70 but has since rebounded to -0.40, while the correlation between value and momentum has returned to near zero from a historical high of 0.78 [13] Tactical Alpha Opportunities - The dispersion of style factor returns is currently above average, with a 12-week moving average dispersion of 0.45%, close to the 75th percentile of the past 20 years [19] - High dispersion indicates inconsistent factor performance, potentially allowing skilled active managers to achieve Alpha by deviating from benchmark indices [19]

Quantitative Models and Construction Methods Model Name: Black Swan Index - **Construction Idea**: Measure the extremity of market transactions based on the deviation of style factor returns[24][25] - **Construction Process**: 1. Calculate the daily return deviation of style factors: $$ \sigma_{s,t}=\frac{\bar{r}_{s,t}-\bar{r}_{s}}{\sigma_{s}} $$ where $\bar{r}_{s,t}$ is the daily return of style factor $s$ on day $t$, $\bar{r}_{s}$ is the average daily return of style factor $s$ over the entire sample period, and $\sigma_{s}$ is the standard deviation of daily returns of style factor $s$ over the entire sample period[25] 2. Calculate the Black Swan Index: $$ BlackSwan_{t}=\frac{1}{N}\times\sum_{s\in S}\left|\sigma_{s,t}\right| $$ where $BlackSwan_{t}$ is the Black Swan Index on day $t$, $S$ is the set of all style factors, and $N$ is the number of style factors[25] - **Evaluation**: The Black Swan Index effectively captures the extremity of market transactions, indicating higher probabilities of extreme tail risks[24][25] Model Name: Heuristic Style Classification for Cognitive Risk Control - **Construction Idea**: Address the discrepancy between individual and collective cognition in style classification to control cognitive risk[80][81] - **Construction Process**: 1. Calculate the value and growth factors for each stock based on predefined metrics[85] 2. Construct value and growth portfolios by selecting the top 10% and bottom 10% stocks based on factor scores[82] 3. Perform time-series regression to classify stocks into value, growth, or balanced styles: $$ r_{t,t}\sim\beta_{\mathit{Value}}\cdot r_{\mathit{Value},t}+\beta_{\mathit{Growth}}\cdot r_{\mathit{Growth},t}+\varepsilon_{t} $$ subject to $0\leq\beta_{\mathit{Value}}\leq1$, $0\leq\beta_{\mathit{Growth}}\leq1$, and $\beta_{\mathit{Value}}+\beta_{\mathit{Growth}}=1$[97] 4. Use weighted least squares (WLS) to estimate regression coefficients based on the most differentiated trading days[98] - **Evaluation**: The heuristic style classification method captures market consensus more accurately than traditional factor scoring methods, reducing cognitive risk[80][81] Model Name: Louvain Community Detection for Hidden Risk Control - **Construction Idea**: Cluster stocks based on excess return correlations to identify hidden risks[116][117] - **Construction Process**: 1. Calculate weighted correlation of excess returns between stocks: $$ Corr_{w}(X,Y)=\frac{Cov_{w}(X,Y)}{\sigma_{w,X}\cdot\sigma_{w,Y}}=\frac{\sum_{i=1}^{n}w_{i}(x_{i}-\overline{X_{w}})(y_{i}-\overline{Y_{w}})}{\sqrt{\sum_{i=1}^{n}w_{i}(x_{i}-\overline{X_{w}})^{2}}\cdot\sqrt{\sum_{i=1}^{n}w_{i}(y_{i}-\overline{Y_{w}})^{2}}} $$ where $w_{i}$ is the weight for day $i$, reflecting market volatility[118] 2. Use Louvain algorithm to cluster stocks based on weighted correlation matrix[117] 3. Ensure clusters have at least 20 stocks and remove clusters with fewer stocks[121] - **Evaluation**: The Louvain community detection method effectively identifies hidden risks by clustering stocks with similar return patterns, which traditional risk models may overlook[116][117] Model Name: Dynamic Style Factor Control - **Construction Idea**: Control style factors dynamically based on their volatility clustering effect[128][129] - **Construction Process**: 1. Identify style factors with high volatility or significant volatility increase: $$ \text{High volatility: Rolling 3-month volatility in top 3} $$ $$ \text{Volatility increase: Rolling 3-month volatility > historical mean + 1 standard deviation} $$ 2. Set the exposure of these style factors to zero in the portfolio[136] - **Evaluation**: Dynamic style factor control captures major market risks without significantly affecting portfolio returns, leveraging the predictability of volatility clustering[128][129] Model Name: Adaptive Stock Deviation Control under Target Tracking Error - **Construction Idea**: Adjust stock deviation based on tracking error to control portfolio risk[146][147] - **Construction Process**: 1. Calculate rolling 3-month tracking error for different stock deviation levels[153] 2. Set the maximum stock deviation that keeps tracking error within the target range[153] - **Evaluation**: Adaptive stock deviation control effectively reduces tracking error during high market volatility, maintaining portfolio stability[146][147] Model Backtest Results Traditional CSI 500 Enhanced Index - **Annualized Excess Return**: 18.77%[5][162] - **Maximum Drawdown**: 9.68%[5][162] - **Information Ratio (IR)**: 3.56[5][162] - **Return-to-Drawdown Ratio**: 1.94[5][162] - **Annualized Tracking Error**: 4.88%[5][162] CSI 500 Enhanced Index with Full-Process Risk Control - **Annualized Excess Return**: 16.51%[5][169] - **Maximum Drawdown**: 4.90%[5][169] - **Information Ratio (IR)**: 3.94[5][169] - **Return-to-Drawdown Ratio**: 3.37[5][169] - **Annualized Tracking Error**: 3.98%[5][169]

Quantitative Models and Construction Methods - **Model Name**: GRU **Model Construction Idea**: GRU is used for industry rotation and stock selection based on historical data and market trends[3][5][7] **Model Construction Process**: GRU utilizes gated recurrent units to process sequential data, capturing temporal dependencies in stock price movements and industry performance. It incorporates multiple factors such as momentum, volatility, and valuation metrics to predict future trends[3][5][7] **Model Evaluation**: GRU demonstrates strong performance in multi-factor combinations and industry rotation strategies, with notable differentiation across different stock pools[3][5][7] - **Model Name**: Barra **Model Construction Idea**: Barra focuses on style factors to explain stock returns and risks[14][15][16] **Model Construction Process**: Barra includes multiple style factors such as Beta, Size, Momentum, Volatility, Non-linear Size, Valuation, Liquidity, Profitability, Growth, and Leverage. Each factor is calculated using specific formulas: - **Beta**: Historical beta - **Size**: Natural logarithm of total market capitalization - **Momentum**: Mean of historical excess return series - **Volatility**: $0.74 \times \text{historical excess return volatility} + 0.16 \times \text{cumulative excess return deviation} + 0.1 \times \text{historical residual return volatility}$ - **Non-linear Size**: Cubic transformation of market capitalization - **Valuation**: Reciprocal of price-to-book ratio - **Liquidity**: $0.35 \times \text{monthly turnover rate} + 0.35 \times \text{quarterly turnover rate} + 0.3 \times \text{annual turnover rate}$ - **Profitability**: Weighted combination of analyst forecast earnings-price ratio, reciprocal of cash flow ratio, reciprocal of trailing twelve-month P/E ratio, and forecasted growth rates - **Growth**: Weighted combination of earnings growth rate and revenue growth rate - **Leverage**: Weighted combination of market leverage, book leverage, and debt-to-asset ratio[15] **Model Evaluation**: Barra style factors provide a comprehensive framework for analyzing stock returns, with strong differentiation in multi-factor strategies[14][15][16] Model Backtesting Results - **GRU Model**: - **open1d**: Weekly excess return 0.61%, monthly 1.56%, yearly 7.78% - **close1d**: Weekly excess return 0.02%, monthly 1.45%, yearly 7.28% - **barra1d**: Weekly excess return -0.24%, monthly -0.07%, yearly 3.61% - **barra5d**: Weekly excess return 0.06%, monthly 1.35%, yearly 8.63% - **Multi-factor combination**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] Quantitative Factors and Construction Methods - **Factor Name**: Beta **Factor Construction Idea**: Measures historical sensitivity to market movements[15] **Factor Construction Process**: Calculated as historical beta using regression analysis of stock returns against market returns[15] - **Factor Name**: Size **Factor Construction Idea**: Captures the impact of market capitalization on stock returns[15] **Factor Construction Process**: Natural logarithm of total market capitalization[15] - **Factor Name**: Momentum **Factor Construction Idea**: Reflects the persistence of stock price trends[15] **Factor Construction Process**: Mean of historical excess return series[15] - **Factor Name**: Volatility **Factor Construction Idea**: Measures risk associated with stock price fluctuations[15] **Factor Construction Process**: $0.74 \times \text{historical excess return volatility} + 0.16 \times \text{cumulative excess return deviation} + 0.1 \times \text{historical residual return volatility}$[15] - **Factor Name**: Non-linear Size **Factor Construction Idea**: Captures non-linear effects of market capitalization on returns[15] **Factor Construction Process**: Cubic transformation of market capitalization[15] - **Factor Name**: Valuation **Factor Construction Idea**: Reflects the relative attractiveness of stock prices[15] **Factor Construction Process**: Reciprocal of price-to-book ratio[15] - **Factor Name**: Liquidity **Factor Construction Idea**: Measures ease of trading stocks[15] **Factor Construction Process**: $0.35 \times \text{monthly turnover rate} + 0.35 \times \text{quarterly turnover rate} + 0.3 \times \text{annual turnover rate}$[15] - **Factor Name**: Profitability **Factor Construction Idea**: Captures earnings quality and growth potential[15] **Factor Construction Process**: Weighted combination of analyst forecast earnings-price ratio, reciprocal of cash flow ratio, reciprocal of trailing twelve-month P/E ratio, and forecasted growth rates[15] - **Factor Name**: Growth **Factor Construction Idea**: Reflects revenue and earnings growth trends[15] **Factor Construction Process**: Weighted combination of earnings growth rate and revenue growth rate[15] - **Factor Name**: Leverage **Factor Construction Idea**: Measures financial risk associated with debt levels[15] **Factor Construction Process**: Weighted combination of market leverage, book leverage, and debt-to-asset ratio[15] Factor Backtesting Results - **Beta**: Weekly excess return -0.24%, monthly -0.07%, yearly 3.61%[31][32][33] - **Size**: Weekly excess return 0.02%, monthly 1.45%, yearly 7.28%[31][32][33] - **Momentum**: Weekly excess return 0.61%, monthly 1.56%, yearly 7.78%[31][32][33] - **Volatility**: Weekly excess return 0.06%, monthly 1.35%, yearly 8.63%[31][32][33] - **Non-linear Size**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Valuation**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Liquidity**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Profitability**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Growth**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33] - **Leverage**: Weekly excess return 0.61%, monthly 0.82%, yearly 3.22%[31][32][33]

Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model integrates fundamental and technical features to predict stock performance, leveraging historical data and recurrent neural network structures for time-series analysis [3][4][5]. - **Model Construction Process**: - Input features include fundamental indicators (e.g., financial ratios) and technical indicators (e.g., momentum, volatility) [3][4]. - The GRU (Gated Recurrent Unit) architecture processes sequential data to capture temporal dependencies [3]. - The model is trained on historical data, with optimization targeting the minimization of prediction errors [3]. - **Model Evaluation**: The GRU model shows mixed performance across different stock pools, with notable underperformance in certain scenarios [3][5][6]. 2. Model Name: Barra1d - **Model Construction Idea**: The Barra1d model is a factor-based model that emphasizes short-term price movements and volatility [3][4][5]. - **Model Construction Process**: - Factors include short-term momentum and volatility metrics [3][4]. - The model applies a linear regression framework to estimate factor exposures and returns [3]. - Portfolio construction involves long positions in stocks with high factor scores and short positions in stocks with low scores [3][4]. - **Model Evaluation**: Barra1d demonstrates strong performance in multiple stock pools, with consistent positive returns in backtests [4][5][6]. 3. Model Name: Barra5d - **Model Construction Idea**: Barra5d extends the Barra1d model by incorporating a longer time horizon for factor evaluation [3][4][5]. - **Model Construction Process**: - Factors include medium-term momentum and volatility metrics [3][4]. - The model uses a similar regression-based approach as Barra1d but adjusts for longer-term trends [3]. - Portfolio construction follows the same long-short strategy as Barra1d [3][4]. - **Model Evaluation**: Barra5d shows strong year-to-date performance, outperforming benchmarks in multiple scenarios [5][6][7]. --- Model Backtest Results GRU Model - **Close1d**: Weekly return -1.59%, YTD return 8.61% [31] - **Barra1d**: Weekly return 0.80%, YTD return 22.50% [31] - **Barra5d**: Weekly return 0.63%, YTD return 28.18% [31] Multi-Factor Portfolio - Weekly excess return: -0.19% - YTD excess return: 2.73% [34] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures historical sensitivity of stock returns to market movements [15]. - **Factor Construction Process**: - Calculated as the slope of the regression of stock returns against market returns over a specified period [15]. - **Factor Evaluation**: Beta factor showed strong long-side performance in recent weeks [16]. 2. Factor Name: Momentum - **Factor Construction Idea**: Captures the persistence of stock price trends [15]. - **Factor Construction Process**: - Calculated as the mean of historical excess returns over a defined period [15]. - **Factor Evaluation**: Long-term momentum factors demonstrated positive returns, while short-term momentum factors underperformed [18][20]. 3. Factor Name: Volatility - **Factor Construction Idea**: Measures the variability of stock returns [15]. - **Factor Construction Process**: - Weighted combination of historical return volatility, cumulative deviation, and residual volatility [15]. - **Factor Evaluation**: Volatility factors showed strong positive returns, particularly in long-term horizons [18][20]. 4. Factor Name: Growth - **Factor Construction Idea**: Reflects the growth potential of companies based on financial metrics [15]. - **Factor Construction Process**: - Weighted combination of earnings growth rate and revenue growth rate [15]. - **Factor Evaluation**: Growth factors exhibited strong positive returns across multiple stock pools [18][20][23]. --- Factor Backtest Results Beta Factor - Weekly return: Positive [16] Momentum Factor - Long-term momentum: Weekly return 2.87% [22] - Short-term momentum: Weekly return -3.48% [22] Volatility Factor - Long-term volatility: Weekly return 4.01% [22] - Short-term volatility: Weekly return 2.87% [22] Growth Factor - Weekly return: Positive [18][20][23]