Group 1 - The article discusses the significant development of the ETF market in China, highlighting its diverse product offerings that cater to various investment needs across different asset classes and markets [2] - The ETF market includes coverage of domestic and international markets, with products spanning stocks, bonds, and commodities, providing a comprehensive toolset for investors [2] - The article emphasizes the evolution of the ETF ecosystem, which supports refined and diversified asset allocation strategies for investors [2] Group 2 - The absolute return strategy pool aims to construct portfolios with low correlation among different asset classes, presenting five specific strategies with varying target volatility and historical annualized returns [3] - The relative return strategy focuses on style rotation, capturing market opportunities through switching among growth, value, large-cap, and small-cap styles, with five strategies showing significant annualized returns [4] - Additionally, the article outlines industry rotation strategies designed to exploit structural market opportunities, detailing two specific strategies with their respective annualized returns [4] Group 3 - The article reports on China's successful test of the Long March 10 rocket and the Dream Chaser spacecraft, marking a significant milestone in the country's manned lunar exploration efforts [7] - It outlines the planned timeline for China's lunar exploration program, aiming for a manned moon landing by 2030, with a series of missions leading up to that goal [9] - The article suggests that the space exploration projects, particularly the manned lunar program, are expected to drive growth in new sectors such as space tourism and commercial space ventures during the 14th Five-Year Plan period [9]

Group 1: Small Cap and Growth Style Rotation - The report suggests an overweight allocation to small-cap style for February, with a balanced allocation to value and growth styles. The long-term view remains positive on small-cap and growth styles for the next year [1][2][9] - As of the end of January, the quantitative model signal was 0.5, indicating a preference for small-cap stocks. Historical data shows that small-cap stocks tend to outperform in February [9][10] - The current valuation spread for the market capitalization factor is 0.88, which is below historical peaks of 1.7 to 2.6, suggesting that small-cap stocks still have significant upside potential [19][23] Group 2: Value and Growth Style Rotation - The latest quantitative model signal for January indicates a neutral stance (0) for value and growth styles, recommending an equal-weight allocation for February. The long-term outlook favors growth style for the upcoming year [26][29] - As of the end of January, the model's return for the value and growth strategy was 4.01%, with no excess return compared to the equal-weight benchmark [26][29] Group 3: Factor Performance Tracking - In January, the value, volatility, and growth factors showed positive returns of 1.37%, 1.17%, and 0.69% respectively, while large-cap, quality, and momentum factors experienced negative returns [34][35] - The report highlights that the performance of the eight major factors indicates a trend where value and volatility factors are currently favored, while large-cap and quality factors are underperforming [34][35]

证券研究报告 | 金融工程 2026 年 2 月 1 日 维持整体看多与大盘成长偏强观点 ——A 股趋势与风格定量观察 20260201 1. 当前市场观察 2. 市场最新观点 王武蕾 S1090519080001 wangwulei@cmschina.com.cn 王禹哲 S1090525080001 wangyuzhe@cmschina.com.cn 风险提示：择时和风格轮动模型结论基于合理假设前提下结合历史数据统计规 律推导而出，市场环境变化下可能导致出现模型失效风险。 定期报告 敬请阅读末页的重要说明 ❑ 本周市场震荡回调，大盘风格回升。具体来看，万得全 A 指数下跌约 1.59%，上证 50、沪深 300 分别上涨约 1.13%、0.09%，中证 1000 下跌约 2.55%。国证成长下跌约 0.59%，国证价值上涨约 1.01%，创业板指下跌约 0.09%，科创 50 下跌约 2.85%。 ❑ 本周维持整体乐观的观点。基本面上，1 月制造业 PMI 录得 49.30，处于历 史中性水平，虽然结构上仍呈现"上游强于下游"的局面，但与前期并无明显 变化，叠加节前备货效应趋于尾声，市场对此类信号或趋于钝 ...

Quantitative Models and Construction Methods - **Model Name**: Monte Carlo Backtesting **Model Construction Idea**: Shift from historical path fitting to future robustness testing by generating multiple random paths to evaluate strategy performance across diverse scenarios [1][10] **Model Construction Process**: 1. Generate thousands of random price paths that follow historical statistical characteristics (e.g., return distribution, volatility, correlation) but differ from the original historical path [10] 2. Perform stress tests on strategies across these simulated paths to observe performance under various market conditions [10] 3. Calculate risk metrics such as Sharpe ratio, maximum drawdown, and value-at-risk (VaR) based on the distribution of strategy returns [10] **Model Evaluation**: Effectively reduces overfitting to specific historical paths and provides a more comprehensive robustness assessment [10][46] - **Model Name**: Non-Parametric Monte Carlo Simulation **Model Construction Idea**: Use historical data directly without assuming any parametric distribution, preserving cross-sectional correlation [2][13] **Model Construction Process**: 1. **Method 1**: Multi-Asset Time-Series Return Joint Rearrangement - Extract daily returns of all assets as a "data block" - Randomly sample and sequentially concatenate these blocks to form simulated paths [18] 2. **Method 2**: Multi-Asset Time-Series Return Block Bootstrap - Divide historical returns into fixed-length overlapping/non-overlapping blocks - Randomly sample blocks and concatenate them to form simulated paths [19] **Model Evaluation**: Preserves cross-sectional correlation but disrupts time-series structures like volatility clustering and autocorrelation [14][20] - **Model Name**: Residual Bootstrap (Factor Model-Based) **Model Construction Idea**: Separate systematic risk and idiosyncratic risk using factor models, then randomize residuals for simulation [2][23] **Model Construction Process**: 1. Construct risk factors (e.g., market, size, value, momentum) and calculate historical daily returns [23] 2. Perform cross-sectional regression to estimate factor exposures (β) and extract residual returns [23] 3. Randomly shuffle residuals while preserving cross-sectional correlation [23] 4. Reconstruct paths using historical factor returns and randomized residuals [23] **Model Evaluation**: Useful for analyzing alpha and risk exposure but limited by the explanatory power of the factor model [24][25] - **Model Name**: Geometric Brownian Motion (GBM) Simulation **Model Construction Idea**: Assume asset returns follow a normal distribution and simulate paths using drift and volatility parameters [2][28] **Model Construction Process**: $$d S_{i}(t)=\mu_{i}S_{i}(t)d t+\sigma_{i}S_{i}(t)d W_{i}(t),i=1,\ldots,n$$ - \( \mu_{i} \): Drift rate (expected return) - \( \sigma_{i} \): Volatility - \( W_{i}(t) \): Standard Brownian motion Discretized path: $$S_{i}^{(j)}(t_{k})=X_{i}(0)\,e x p[(\,k\Delta t+\sum_{l=1}^{k}\sum_{p=1}^{n}L_{i p}Z_{l,p}^{(j)}\,]$$ - \( L \): Cholesky decomposition of covariance matrix - \( Z_{l,p}^{(j)} \): Independent standard normal random variables [28] **Model Evaluation**: Accurately replicates volatility and correlation but fails to capture tail risks and price jumps [28][47] Model Backtesting Results - **Monte Carlo Backtesting**: - Historical price path Sharpe ratio: 0.96 (25-day window) - Simulated path Sharpe ratio: 0.19 (25-day window, GBM method) [45][46] - **Non-Parametric Monte Carlo Simulation**: - Historical price path Sharpe ratio: 0.96 (25-day window) - Simulated path Sharpe ratio: 0.22 (15-day window, joint rearrangement method) [45][46] - **Residual Bootstrap**: - Historical price path Sharpe ratio: 0.96 (25-day window) - Simulated path Sharpe ratio: 0.19 (25-day window) [45][46] - **Geometric Brownian Motion (GBM)**: - Historical price path Sharpe ratio: 0.96 (25-day window) - Simulated path Sharpe ratio: 0.19 (25-day window) [45][46] Quantitative Factors and Construction Methods - **Factor Name**: Momentum and Volatility Dual Factor **Factor Construction Idea**: Combine momentum and volatility factors using Z-score normalization and equal weighting [35] **Factor Construction Process**: $$S c o r e_{i}=0.5*Z S c o r e_{i,m o m}+0.5*Z S c o r e_{i,v o l}$$ - Momentum and volatility calculated over different window lengths (N ∈ [15, 20, 40]) [35] **Factor Evaluation**: Provides a balanced scoring mechanism for style rotation strategies [35][37] Factor Backtesting Results - **Momentum and Volatility Dual Factor**: - Historical price path cumulative return: 535% (25-day window) - Simulated path cumulative return: 62.25% (15-day window, GBM method) [38][42]

Group 1: Small and Large Cap Style Rotation - The report suggests an overweight position in small-cap style for November based on quantitative model signals and calendar effects, as historical data indicates small caps tend to outperform in November [1][8]. - The current market capitalization factor valuation spread is 0.88, indicating that small caps still have room for growth compared to large caps, which are at historical high levels of 1.7 to 2.6 [8][16]. - Year-to-date, the small and large cap rotation quantitative model has achieved a return of 27.85%, with an excess return of 2.86% relative to the benchmark [8][9]. Group 2: Value and Growth Style Rotation - The monthly quantitative model signal for value style is 1, recommending an overweight position in value style for November [23][26]. - Year-to-date, the value-growth style rotation strategy has yielded a return of 19.95%, with an excess return of 1.35% compared to the equal-weighted benchmark [23][26]. - The current model indicates that fundamental, macroeconomic, and valuation dimensions are all pointing towards value [26][27]. Group 3: Factor Performance Tracking - In October, the dividend, momentum, and value factors achieved positive returns of 0.43%, 0.38%, and 0.15% respectively, while large-cap, volatility, growth, quality, and liquidity factors experienced negative returns [29][30]. - Year-to-date, the volatility, momentum, and growth factors have positive returns of 10.17%, 1.54%, and 1.29%, while liquidity, large-cap, dividend, quality, and value factors have negative returns [29][30].

Group 1 - The report introduces a quantitative model solution for addressing the value-growth style switching issue, combining investment expectations based on odds and win rates [1][8] - The overall market growth style portfolio achieved a return of 4.58%, while the value style portfolio returned 2.24% in the last week [1][8] Group 2 - The estimated odds for the growth style is 1.08, while for the value style it is 1.12, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rate for the growth style is 63.24%, compared to 36.76% for the value style, based on seven win rate indicators [3][19] Group 3 - The latest investment expectation for the growth style is calculated to be 0.32, while the value style has an investment expectation of -0.22, leading to a recommendation for the growth style [4][21] - Since 2013, the annualized return of the style rotation model based on investment expectations is 27.99%, with a Sharpe ratio of 1.04 [4][22]

Group 1 - The report introduces a quantitative model solution for addressing the value-growth style switching issue, based on the combination of odds and win rates [1][8] - The recent performance of the growth style portfolio was -4.26%, while the value style portfolio returned -1.17% [1][8] Group 2 - The estimated odds for the growth style is 1.09, and for the value style, it is 1.12, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rate for the growth style is 63.24%, while the value style has a win rate of 36.76%, based on seven indicators [3][16] Group 3 - The latest investment expectation for the growth style is calculated to be 0.32, while the value style has an investment expectation of -0.22, leading to a recommendation for the growth style [4][18] - Since 2013, the annualized return of the style rotation model based on investment expectations is 27.59%, with a Sharpe ratio of 1.03 [4][19]

Group 1 - The core viewpoint of the report is the innovative approach to combining investment expectations based on odds and win rates to address the issue of value and growth style rotation [1][8] - The report indicates that the growth style portfolio had a return of -0.48% last week, while the value style portfolio had a return of -0.82% [1][8] Group 2 - The estimated odds for the growth style is 1.11, while the value style is estimated at 1.13, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rate for the growth style is 63.24%, compared to 36.76% for the value style, based on seven win rate indicators [3][16] Group 3 - The latest investment expectation for the growth style is calculated to be 0.33, while the value style's investment expectation is -0.22, leading to a recommendation for the growth style [4][18] - Since 2013, the annualized return of the style rotation model based on investment expectations is 28.06%, with a Sharpe ratio of 1.04 [4][19]

Group 1 - The report introduces a quantitative model solution for addressing the issue of value and growth style switching, based on the combination of odds and win rates [1][8] - Last week, the overall market growth style portfolio achieved a return of 3.34%, while the value style portfolio returned 1.02% [1][8] Group 2 - The estimated odds for the growth style is 1.11, while the value style is estimated at 1.09, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rate for the growth style is 68.88%, compared to 31.12% for the value style, based on eight win rate indicators [3][16] Group 3 - The latest investment expectation for the growth style is calculated to be 0.45, while the value style has an investment expectation of -0.35, leading to a recommendation for the growth style [4][18] - Since 2013, the annualized return of the style rotation model based on investment expectations is 27.90%, with a Sharpe ratio of 1.03 [4][19]

Group 1: Core Insights - The report introduces a quantitative model solution for addressing the value-growth style switching issue based on odds and win rates [1][8] - The recent performance shows that the growth style portfolio achieved a return of 2.54%, while the value style portfolio returned 2.24% [1][8] Group 2: Odds - The relative valuation levels of market styles are key factors influencing expected odds, which are negatively correlated [2][14] - The current estimated odds for the growth style is 1.11, while for the value style it is 1.09 [2][14] Group 3: Win Rates - Among seven win rate indicators, four point to growth and three to value, resulting in a current win rate of 53.87% for growth and 46.13% for value [3][16] Group 4: Investment Expectations and Strategy Returns - The investment expectation for the growth style is calculated at 0.14, while for the value style it is -0.04, leading to a recommendation for the growth style [4][18] - Since 2013, the annualized return of the style rotation model based on investment expectations is 27.62%, with a Sharpe ratio of 1.02 [4][19]