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]

Summary of Conference Call Notes Industry or Company Involved - The discussion revolves around quantitative investment strategies and market style dynamics, specifically focusing on the performance of different investment styles such as growth, value, and small-cap strategies. Core Points and Arguments 1. **Market Style Influence on Investment Strategies** Different fundamental quantitative investment approaches are significantly influenced by market styles. Growth styles perform better in favorable economic conditions, while value styles excel during value-dominant periods. Adjusting allocations based on market conditions is essential to maximize alpha and beta contributions [1][2][4]. 2. **Quantitative Model Characteristics** The model developed by CICC emphasizes risk considerations rather than momentum. It incorporates temporal information to assess the current risk level and allocate high alpha assets when risks are low, enhancing overall returns [1][5][6]. 3. **Style Risk Attribute Model** The model evaluates style risk using indicators such as valuation differences, capital participation, and intra-portfolio differentiation. Valuation differences are positively correlated with future returns, particularly in growth and value styles, with a correlation of around 0.5 [1][10]. 4. **Active Inflow Rate Indicator** The active inflow rate indicator shows varying correlations across styles. For growth styles, high inflow rates may indicate overcrowding, while for small-cap and value styles, increased inflows can signal positive recognition. Extreme inflow rates across all styles indicate potential risks [11]. 5. **Concentration and Differentiation Effects** In growth and small-cap styles, higher concentration correlates with better future returns, while in value and dividend styles, greater differentiation leads to improved returns. Different strategies should be applied based on the specific style [12]. 6. **Effectiveness of Timing Indicators** The effectiveness of timing indicators, such as valuation differences and capital participation, is statistically validated. These indicators provide unique insights and can be used simultaneously without diminishing their effectiveness [13]. 7. **Dynamic Allocation and Rotation Strategies** Dynamic allocation strategies involve independent monthly assessments of investment styles based on their current risk and value. Rotation strategies focus on selecting the highest probability styles for concentrated holdings [18][19]. 8. **Performance of Style Rotation Model** Historical data shows that the style rotation model performs well at key style nodes, with an average turnover rate of about 45%. The model has maintained consistent performance across various years, with only a few years showing slight losses [21][22]. 9. **Sample Out-of-Sample Data Validation** Out-of-sample data has validated the model's effectiveness, with significant year-to-date returns exceeding 30% as of June [23]. 10. **Future Tracking and Evaluation** Continuous tracking and evaluation will be conducted monthly, providing timely updates on market styles and critical indicators. This proactive approach aims to enhance the robustness of the quantitative investment framework [24]. Other Important but Possibly Overlooked Content - The report emphasizes the importance of risk control in investment strategies, highlighting that while dynamic allocation can reduce maximum drawdowns, it may not always yield higher absolute returns compared to fixed allocation strategies [20].

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] - The recent performance of the growth style portfolio was 2.32%, while the value style portfolio achieved a return of 2.76% [1][8] Group 2 - The estimated odds for the growth style is 1.12, while the value style is estimated at 1.08, indicating a negative correlation between relative valuation levels and expected odds [2][14] - The current win rates indicate that 4 out of 7 indicators favor growth, resulting in a win rate of 53.87% for growth and 46.13% for value [3][16] Group 3 - The latest investment expectation for the growth style is calculated at 0.14, while the value style has an investment expectation of -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 27.19%, with a Sharpe ratio of 1.00 [4][19]

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 recent performance of the growth style portfolio was 5.49%, while the value style portfolio returned 3.33% [1][8] Group 2 - The estimated odds for the growth style is 1.10, and for the value style, it is 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%, while the value style has a win rate of 31.12%, based on seven indicators [3][16] Group 3 - The latest investment expectation for the growth style is calculated to be 0.44, 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 26.96%, with a Sharpe ratio of 0.99 [4][19]