Group 1 - The report discusses a style rotation strategy based on similarity algorithms, emphasizing the use of historical similar phases to form indicators for style rotation signals [1] - The study validates that similarity signals can effectively guide large-cap and growth-value rotation strategies, improving upon previously proposed frameworks based on odds and win rates [1] Group 2 - Four elasticity measurement algorithms are introduced: DTW, DTW-S, SBD, and MSM, each with distinct advantages and disadvantages in handling time series data [4] - DTW allows for non-linear alignment of time series, addressing issues of time axis shifts, while DTW-S introduces constraints to improve computational efficiency and reduce over-warping risks [4][23] - SBD excels in shape matching but has limitations in local pattern recognition, while MSM is designed for natural transformations in time series data [30][36] Group 3 - The report compares the effectiveness of strategies based on absolute and relative return perspectives, finding that relative return perspectives significantly outperform absolute ones [42] - The analysis indicates that the relative return perspective directly identifies the relative state of styles, avoiding inaccuracies in comparisons [42] Group 4 - The study incorporates similarity signals into an existing framework based on odds and win rates, resulting in improved annualized excess returns for both large-cap and growth-value strategies [4] - The annualized excess return for large-cap strategies increased from 16.76% to 18.13%, while for growth-value strategies, it rose from 13.79% to 15.27% [4]

Core Viewpoint - The report discusses the effectiveness of style rotation strategies in ETF allocations, highlighting the construction of various strategies based on different rebalancing frequencies and investment logic [1]. Group 1: Quarterly Style Rotation Strategy - The quarterly style rotation strategy is based on macroeconomic and micro price-volume dimensions, identifying factors that drive market growth, value, and size rotation. The model achieved an annualized excess return of 20.40% from January 2014 to February 2026, with a monthly win rate of 63.70% when using a value-growth rotation model with dividend and GEM ETFs against the CSI 800 benchmark [2]. - From January 2017 to February 2026, the size rotation model using CSI 300 and CSI 1000 ETFs achieved an annualized excess return of 8.97% with a monthly win rate of 61.82% compared to the CSI 800 benchmark [2]. Group 2: Monthly Style Rotation Strategy - The monthly value-growth rotation strategy, constructed from macro, valuation, and fundamental dimensions, achieved an annualized return of 22.67% from 2014 to February 2026, with an annualized excess return of 16.32% against the CSI 800 benchmark and a monthly win rate of 63.19% [3]. - The monthly size rotation strategy, developed from macro, valuation, fundamental, funding, sentiment, and price-volume dimensions, achieved an annualized return of 26.84%, with an annualized excess return of 20.64% against the CSI 800 benchmark and a monthly win rate of 71.23% [3]. - The monthly dividend growth rotation strategy, which analyzes the impact of dividend stocks versus bond value, U.S. Treasury rates, credit and economic conditions, and industry prosperity, achieved an annualized excess return of 13.29% against the CSI 300 total return benchmark, with a monthly win rate of 61.19% [3].

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]

Quantitative Models and Construction Methods 1. Model Name: Short-term Timing Strategy - **Model Construction Idea**: The model aims to provide short-term timing signals based on macroeconomic, valuation, sentiment, and liquidity indicators[11][12][13]. - **Model Construction Process**: - **Macroeconomic Indicators**: 1) Manufacturing PMI > 50 gives optimistic signals; current value is 49.30, indicating cautious sentiment[15]. 2) Credit pulse growth rate at 79.66% percentile over the past 5 years, indicating strong credit growth and optimistic signals[12][15]. 3) M1 growth rate (HP filtered) at 71.19% percentile over the past 5 years, indicating strong M1 growth and optimistic signals[12][15]. - **Valuation Indicators**: 1) PE median value at 99.17% percentile over the past 5 years, indicating cautious sentiment[12][15]. 2) PB median value at 98.92% percentile over the past 5 years, indicating cautious sentiment[12][15]. - **Sentiment Indicators**: 1) Beta dispersion at 55.93% percentile over the past 5 years, indicating neutral sentiment[13][15]. 2) Volume sentiment score at 89.00% percentile over the past 5 years, indicating optimistic sentiment[13][15]. 3) Volatility at 35.15% percentile over the past 5 years, indicating neutral sentiment[13][15]. - **Liquidity Indicators**: 1) Money market interest rate at 40.68% percentile over the past 5 years, indicating neutral sentiment[13][15]. 2) RMB exchange rate expectation at -0.69%, at 22.03% percentile over the past 5 years, indicating optimistic sentiment[13][15]. 3) 5-day average net financing amount at 0.00% percentile over the past 5 years, indicating optimistic sentiment[13][15]. - **Model Evaluation**: The model demonstrates strong performance in terms of annualized returns and risk control, with significant improvement over the benchmark strategy[14][18]. 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model identifies rotation opportunities between growth and value styles based on macroeconomic, valuation, trend, and crowding indicators[23]. - **Model Construction Process**: - **Macroeconomic Indicators**: Weak signals from credit pulse, M0, M1, and fiscal expenditure; only inflation scissors and US bond yields support growth style, leading to a value-biased macro signal[23]. - **Valuation Indicators**: Growth valuation remains neutral to slightly low compared to value, with room for upward movement[23]. - **Trend Indicators**: Short-term growth style has slightly corrected, but medium-term distribution shows expansion characteristics[23]. - **Crowding Indicators**: Growth style crowding has decreased to reasonable levels[23]. - **Model Evaluation**: The model has achieved an annualized return of 14.64% since 2011, with an annualized excess return of 7.98% over the benchmark strategy[23][24]. 3. Model Name: Small-Cap vs Large-Cap Style Rotation Model - **Model Construction Idea**: The model uses 11 effective rotation indicators to construct a comprehensive signal for small-cap and large-cap style rotation[27]. - **Model Construction Process**: - **Rotation Indicators**: 1) A-share leaderboard buying intensity[27]. 2) R007 interbank rate[27]. 3) Financing balance changes[27]. 4) Thematic investment sentiment[27]. 5) Credit spread[27]. 6) Option volatility risk premium[27]. 7) Beta dispersion[27]. 8) PB differentiation[27]. 9) Block trade premium/discount rate[27]. 10) CSI 1000 MACD (10,20,10)[27]. 11) CSI 1000 trading volume[27]. - **Signal Aggregation**: Signals are aggregated to determine small-cap or large-cap bias. Current signals favor large-cap style[27]. - **Model Evaluation**: The model has consistently generated positive excess returns annually since 2014, with an annualized excess return of 13.20% for the comprehensive smoothed signal[28]. --- Model Backtesting Results 1. Short-term Timing Strategy - **Annualized Return**: 16.61% - **Benchmark Annualized Return**: 5.04% - **Annualized Excess Return**: 11.58% - **Maximum Drawdown**: 15.05% - **Sharpe Ratio**: 0.9788 - **Monthly Win Rate**: 66.46% - **Quarterly Win Rate**: 61.11% - **Annual Win Rate**: 80.00%[14][18][20]. 2. Growth-Value Style Rotation Model - **Annualized Return**: 14.64% - **Benchmark Annualized Return**: 6.66% - **Annualized Excess Return**: 7.98% - **Maximum Drawdown**: 40.08% - **Sharpe Ratio**: 0.65 - **Monthly Excess Win Rate**: 66.49% - **Annualized Tracking Error**: 5.88% - **Information Ratio (IR)**: 1.36[23][24]. 3. Small-Cap vs Large-Cap Style Rotation Model - **Annualized Return**: 20.82% (Comprehensive Smoothed Signal) - **Benchmark Annualized Return**: 7.62% - **Annualized Excess Return**: 13.20% - **Maximum Drawdown**: 40.70% - **Average Turnover Interval**: 20 trading days - **Win Rate (Per Trade)**: 50.32%[27][28].

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]