报告导读： 本报告对大小盘轮动月度策略、价值成长轮动月度策略以及风格因子表现进行 跟踪。根据量化模型信号， 1 月建议超配小盘风格，均衡配置价值成长风格。 大小盘风格轮动月度策略。 月度观点： 12 月底量化模型最新信号为 0.17 ，指向小盘。日历效应上，历史 1 月大盘相对占优；建议 1 月超配小盘风格。中 长期观点，当前市值因子估值价差为 0.89 ，近期有所下降，距离历史顶部区域 1.7~2.6 仍有距离，中长期并不拥挤，继续看好小盘。截止 12 月底，模型收 益 27.56% ，相对等权基准（ 26.84% ）的超额收益为 0.71% 。结合主观观点的策略收益 28.97% ，超额收益 2.13% 。策略构建详见报告《量化视角多 维度构建大小盘风格轮动策略 _20241102 》。 价值成长风格轮动月度策略。 月度量化模型信号为 0 ，建议 1 月等权配置成长和价值风格。截止 12 月底，模型收益 22.72% ，相对等权基准（ 20.4% ）的超额收益为 1.93% 。策略构建详见报告《量化视角多维度构建月度和周度价值成长风格轮动策略 _20250305 》。 风格因子表现跟踪。 8 个大类因子中 ...

Core Insights - The report suggests an overweight allocation to large-cap and value styles for December based on quantitative model signals and calendar effects [1][2]. Size and Style Rotation Monthly Strategy - The latest quantitative model signal for the end of November is -0.17, indicating a preference for large-cap stocks. Historically, large-cap stocks have outperformed in December, leading to a recommendation for an overweight allocation in December [1]. - The year-to-date return for the size rotation quantitative model is 24.71%, with an excess return of 1.5% compared to an equal-weight benchmark of 23.21% [1]. - The combined strategy, incorporating subjective views, has yielded a return of 26.1%, with an excess return of 2.89% [1]. Value and Growth Style Rotation Monthly Strategy - The monthly quantitative model signal is -0.33, indicating a preference for value stocks. Historically, value style has slightly outperformed in December, leading to a recommendation for an overweight allocation in December [2]. - The year-to-date return for the value-growth style rotation model is 20.37%, with an excess return of 2.99% compared to an equal-weight benchmark of 16.88% [2]. Style Factor Performance Tracking - Among eight major factors, dividend and quality factors showed high positive returns in November, while large-cap and momentum factors exhibited high negative returns [2]. - For the year, volatility and growth factors had high positive returns, while liquidity and large-cap factors had high negative returns [2]. - In November, residual volatility, short-term reversal, and earnings quality factors had high positive returns, while momentum, profitability, and large-cap factors had high negative returns [2]. Factor Covariance Matrix Update - The report updates the latest factor covariance matrix as of November 28, 2025, which is essential for predicting stock portfolio risks using a multi-factor model [3].

Core Insights - The report suggests an overweight position in small-cap and value styles for November based on quantitative model signals and calendar effects [1][5] Size and Style Rotation Monthly Strategy - As of the end of October, the quantitative model signal was -0.17, indicating a preference for large-cap stocks; however, historical data shows that small-cap stocks tend to outperform in November [1] - The current market capitalization factor valuation spread is 0.88, which is still below the historical peak range of 1.7 to 2.6, indicating that the market is not overcrowded and small-cap stocks remain attractive in the medium to long term [1] - Year-to-date, the size rotation quantitative model has yielded a return of 27.85%, with an excess return of 2.86% relative to an equal-weight benchmark [1] - The combined strategy, incorporating subjective views, has achieved a return of 26.6% with an excess return of 1.61% [1] Value and Growth Style Rotation Monthly Strategy - The monthly quantitative model signal for October was 1, recommending an overweight position in value stocks [1] - Year-to-date, the value-growth style rotation strategy has returned 18.96%, with an excess return of 1.35% compared to an equal-weight benchmark of growth and value indices [1] Style Factor Performance Tracking - Among eight major factors, the dividend and momentum factors showed high positive returns in October, while large-cap and volatility factors exhibited high negative returns [2] - Year-to-date, the volatility and momentum factors have shown strong positive returns, while liquidity and large-cap factors have shown negative returns [2] - In October, the profitability, dividend yield, and momentum factors had high positive returns, while large-cap, profitability, and beta factors had high negative returns [2] - Year-to-date, the beta, profitability volatility, and momentum factors have shown strong positive returns, while mid-cap, liquidity, and large-cap factors have shown negative returns [2] Factor Covariance Matrix Update - The report updates the latest factor covariance matrix as of October 31, 2025, which is crucial for predicting stock portfolio risks [2]

Quantitative Models and Construction Methods 1. Model Name: Size Rotation Model Based on Odds and Win Rates - **Model Construction Idea**: The model integrates the concepts of odds and win rates to capture the rotation between large-cap and small-cap stocks. Odds are derived from valuation differences, while win rates are calculated using multiple indicators[4][30][40] - **Model Construction Process**: - **Odds Calculation**: - Define odds as the ratio of average positive returns to the absolute value of average negative returns - Formula: $ \mathbb{R}_{\mathbb{B}}^{\pm}\,\mathbb{R}=-\frac{\sum_{i=1}^{n}r e t u r n_{i}\,/n}{\sum_{j=1}^{m}r e t u r n_{j}\,/m} $ where $ \mathbb{R}_{\mathbb{B}}^{+} $ represents positive returns and $ \mathbb{R}_{\mathbb{B}}^{-} $ represents negative returns[30][31] - Use historical valuation differences between large-cap (CSI 300) and small-cap (CSI 2000) indices to estimate odds through linear regression[32][36] - **Win Rate Calculation**: - Combine multiple indicators (e.g., Shibor, short-term credit spread, market trend, market volatility, style momentum, style crowding, and calendar effects) to derive a composite win rate signal - Assign scores: 1 for large-cap signals, 0 for small-cap signals, and 0.5 for neutral signals. The average score represents the win rate[40][72] - **Kelly Formula for Allocation**: - Use the Kelly formula to calculate optimal allocation weights for large-cap and small-cap stocks based on odds and win rates - Formula: $ x = \frac{p*b - (1-p)}{b} $ where $ p $ is the win rate, $ b $ is the odds, and $ x $ is the allocation proportion[77] - Adjust weights to ensure they sum to 1 and avoid negative values, forming a complete rotation strategy[77][78] - **Model Evaluation**: The model effectively captures the rotation between large-cap and small-cap stocks, achieving significant excess returns and risk-adjusted performance[78] 2. Model Name: Weighted Size Rotation Strategy - **Model Construction Idea**: Adjust allocation weights between large-cap and small-cap stocks based on the difference in configuration scores derived from odds and win rates[82] - **Model Construction Process**: - Calculate the difference in configuration scores between large-cap and small-cap stocks - Standardize the score difference using a Z-score over the past 250 weeks - Map the standardized score to allocation weights using a predefined mapping table[83] - **Model Evaluation**: This strategy reduces maximum drawdown while maintaining a high level of excess returns and information ratio[84] 3. Model Name: Detailed Style Rotation Model - **Model Construction Idea**: Combine the size rotation model with a growth-value rotation model to form a detailed style rotation strategy, targeting large-cap growth, large-cap value, small-cap growth, and small-cap value[87] - **Model Construction Process**: - Use the size rotation model to determine the size preference (large-cap or small-cap) - Use the growth-value rotation model to determine the style preference (growth or value) - Combine the two signals to allocate to one of the four detailed styles[87] - **Model Evaluation**: The model demonstrates outstanding rotation effects, achieving the highest excess returns and information ratio among all strategies[90][92] --- Model Backtesting Results 1. Size Rotation Model Based on Odds and Win Rates - Total Return: 531.87% - Annualized Return: 23.70% - Annualized Volatility: 23.03% - Maximum Drawdown: 25.25% - Information Ratio (IR): 2.27 - Return-to-Drawdown Ratio: 2.79[79] 2. Weighted Size Rotation Strategy - Total Return: 204.13% - Annualized Return: 13.69% - Annualized Volatility: 22.02% - Maximum Drawdown: 29.17% - Information Ratio (IR): 2.47 - Return-to-Drawdown Ratio: 4.66[84] 3. Detailed Style Rotation Model - Total Return: 1329.51% - Annualized Return: 35.91% - Annualized Volatility: 23.97% - Maximum Drawdown: 23.37% - Information Ratio (IR): 3.11 - Return-to-Drawdown Ratio: 3.87[92] --- Quantitative Factors and Construction Methods 1. Factor Name: Shibor Signal - **Construction Idea**: Reflects the impact of liquidity conditions on small-cap and large-cap stocks[42] - **Construction Process**: - Calculate the historical percentile of the latest Shibor rate over the past year - Signal: If the percentile > 50%, favor large-cap; otherwise, favor small-cap[42] - **Backtesting Results**: - Annualized Excess Return: 11.46% - Information Ratio (IR): 1.23[43] 2. Factor Name: Short-Term Credit Spread - **Construction Idea**: Captures the impact of short-term credit market conditions on size rotation[47] - **Construction Process**: - Calculate the spread between 1-year and 7-day AAA+ short-term bond yields - Signal: If the 20-day average spread > 250-day average, favor large-cap; otherwise, favor small-cap[47] - **Backtesting Results**: - Annualized Excess Return: 7.41% - Information Ratio (IR): 0.79[48] 3. Factor Name: Market Trend - **Construction Idea**: Reflects the impact of market activity on size rotation[51] - **Construction Process**: - Compare the 5-day and 20-day moving averages of the CSI All Share Index - Signal: If the 5-day MA > 20-day MA and market volume is increasing, favor small-cap; otherwise, favor large-cap[51] - **Backtesting Results**: - Annualized Excess Return: 3.52% - Information Ratio (IR): 0.48[52] 4. Factor Name: Market Volatility - **Construction Idea**: Reflects the impact of market stability on size rotation[54] - **Construction Process**: - Compare the 20-day market volatility with its 3-year average - Signal: If volatility < average, favor large-cap; otherwise, favor small-cap[54] - **Backtesting Results**: - Annualized Excess Return: 13.18% - Information Ratio (IR): 1.42[55] 5. Factor Name: Style Momentum - **Construction Idea**: Captures the momentum effect in size rotation[57] - **Construction Process**: - Compare the past 4-week returns of CSI 300 and CSI 2000 indices - Signal: If CSI 300 return > CSI 2000 return, favor large-cap; otherwise, favor small-cap[57] - **Backtesting Results**: - Annualized Excess Return: 8.16% - Information Ratio (IR): 0.87[58] 6. Factor Name: Style Crowding - **Construction Idea**: Reflects the risk of style overcrowding and potential reversals[60] - **Construction Process**: - Calculate the historical percentile of the 20-day trading volume of the largest 20% and smallest 20% stocks - Signal: If large-cap volume > 75th percentile, favor small-cap; if small-cap volume > 75th percentile, favor large-cap[60] - **Backtesting Results**: - Annualized Excess Return: 6.63% - Information Ratio (IR): 0.93[61] 7. Factor Name: Calendar Effect - **Construction Idea**: Reflects the impact of periodic events on size rotation[63] - **Construction Process**: - Calculate the historical win rate of large-cap over small-cap for each calendar month - Signal: If the win rate > 50%, favor large-cap; otherwise, favor small-cap[66] - **Backtesting Results**: - Annualized Excess Return: 4.73% - Information Ratio (IR): 0.50[67] 8. Factor Name: Composite Win Rate Signal - **Construction Idea**: Combines all individual factors into a single composite signal[72] - **Construction Process**: - Average the scores of all individual factors to derive the composite win rate - Signal: If the composite score > 0.5, favor large-cap; otherwise, favor small-cap[72] - **Backtesting Results**: - Annualized Excess Return: 19.72% - Information Ratio (IR): 2.17[73]

Core Viewpoint - The article discusses the recent shift in market style, highlighting that large-cap stocks have outperformed small-cap stocks since the end of August, contrasting with the previous four years where small-cap stocks dominated the market performance [2][14]. Market Style Changes - Since the end of August, large-cap stocks have shown better performance compared to small-cap stocks during a period of market fluctuation [2][14]. - Over the past four years, small-cap stocks had a significant advantage, with the CSI 2000 and CSI 1000 indices rising by 79.3% and 66.4% respectively, while the CSI 300 index only increased by 40.0% [2][14]. Emerging Growth Sector - The proportion of emerging growth sectors in China's capital market has significantly increased, with technology and high-end manufacturing companies making up an average of 60.3% of IPO fundraising from 2020 to 2025 [5][10]. - The number of large-cap companies in the technology and manufacturing sectors has also risen, with 36 out of the top 100 A-share companies belonging to these sectors [5][11]. Impact on Market Style - The changing market capitalization structure in emerging growth sectors is affecting the performance of large-cap and small-cap stocks. The correlation between emerging growth styles and small-cap stocks is decreasing as large-cap emerging growth companies become more prevalent [13][14]. - The article suggests that the current macroeconomic environment supports the emerging growth sector, with policies favoring innovation and technology [14]. Future Outlook - The article anticipates a potential shift in market style, with large-cap growth stocks likely to outperform in the medium term (3-6 months) due to supportive macroeconomic conditions and increasing institutional investment in large-cap emerging growth companies [14]. - Long-term trends indicate that emerging growth sectors will maintain relative advantages, with an expected increase in the number and market capitalization of large-cap growth companies [14].

Core Viewpoint - The A-share market experienced a significant shift on October 14, with technology stocks undergoing adjustments while bank stocks surged, particularly led by Chongqing Bank, which rose over 5% [1]. Bank Sector Performance - Chongqing Bank led the gains with a rise of 5.39%, reaching a price of 9.78 [2] - Other banks such as Xiamen Bank, Yuzhou Rural Commercial Bank, and Jiangsu Bank also saw notable increases, with gains of 4.04%, 4.14%, and 3.88% respectively [2] - Major banks like China Merchants Bank, Industrial and Commercial Bank of China, and Agricultural Bank of China experienced increases of over 2% [1][2] ETF Activity - The Bank ETF (512800) saw a price increase of 2.29%, marking its fourth consecutive day of gains, with a trading volume exceeding 2.1 billion [2][3] - Over the past four days, the Bank ETF has attracted a net inflow of 1.894 billion, indicating strong investor interest [3] Market Sentiment and Strategy - Analysts suggest a potential shift towards high-dividend stocks, particularly in the banking sector, as the market may see a rotation towards large-cap blue-chip stocks to mitigate volatility [5] - The upcoming dividend distribution period for banks is expected to enhance their attractiveness, with a possibility of a rebound in the sector [5] - The overall sentiment indicates that banks may benefit from a balanced market style towards the end of the year, with a focus on stable interest margins and improved asset quality [5]

Group 1: Market Style Rotation Insights - The report suggests an overweight allocation to large-cap stocks for October, with a balanced allocation between value and growth styles based on quantitative model signals [1][8] - The quantitative model signal for the end of September was -0.17, indicating a preference for large-cap stocks, while the mid to long-term view remains optimistic about small-cap stocks due to the current valuation gap of 0.86, which is significantly lower than historical highs [8][16] - Year-to-date, the large-cap rotation model has achieved a return of 27.85%, with an excess return of 3.07% compared to benchmarks like CSI 300 and CSI 2000 [8][16] Group 2: Value and Growth Style Rotation - The latest quantitative model signal for value and growth styles is 0, recommending an equal-weight allocation for October [23][24] - Year-to-date, the value-growth rotation strategy has yielded a return of 18.96%, with an excess return of 1.35% relative to the equal-weight benchmark [23][24] - The current model indicates that the fundamental dimension favors growth, while the macro dimension favors value, with no clear signal from the valuation dimension [24] Group 3: Factor Performance Tracking - In September, volatility, large-cap, growth, and value factors showed positive returns of 2.08%, 1.87%, 1.18%, and 0.01% respectively, while liquidity, quality, momentum, and dividend factors experienced negative returns [28][29] - Year-to-date, the volatility, growth, and momentum factors have positive returns of 11.32%, 1.91%, and 1.16%, while liquidity, dividend, large-cap, value, and quality factors have negative returns [28][29] - Among 20 style factors, short-term reversal, beta, large-cap, earnings variability, and growth factors had the highest positive returns in September, while liquidity, book-to-price, dividend yield, residual volatility, and mid-cap factors had the highest negative returns [32][35]

Group 1: Core Insights - The report suggests an overweight allocation to small-cap stocks for September, based on a quantitative model signal of 0.17 at the end of August, indicating a preference for small-cap style [1] - The long-term view remains optimistic for small-cap stocks, with the current market capitalization factor valuation spread at 1.01, which is still below the historical peak range of 1.7 to 2.6 [1] - Year-to-date, the small-cap rotation strategy has yielded a return of 28.19%, with an excess return of 4.24% compared to benchmarks like CSI 300 and CSI 2000 [1] Group 2: Value and Growth Style Rotation - The monthly quantitative model signal for value and growth style is 0, suggesting an equal-weight allocation for September [1] - The year-to-date return for the value and growth style rotation strategy is 14.33%, with an excess return of 1.35% relative to equal-weight benchmarks [1] Group 3: Factor Performance Tracking - Among eight major factors, volatility and large-cap factors showed positive returns in August, while liquidity and quality factors had negative returns [2] - Year-to-date, volatility and momentum factors have performed positively, whereas liquidity and large-cap factors have shown negative returns [2] - In August, beta, large-cap, and short-term reversal factors had positive returns, while profitability quality, seasonality, and liquidity factors had negative returns [2] Group 4: Factor Covariance Matrix Update - The report updates the stock covariance matrix, which is crucial for predicting portfolio risk, using a multi-factor model to combine factor covariance and stock-specific risk matrices [2]

Group 1: Market Strategy Insights - The report indicates that small-cap stocks are likely to outperform in August, supported by a quantitative model signal of 0.5, suggesting an overweight position in small-cap stocks [1] - Year-to-date, the small-cap strategy has yielded a return of 15.74%, outperforming the equal-weight benchmark return of 11.79% by 3.95% [1] - The value-growth rotation strategy shows a quantitative model signal of -0.33, indicating a shift towards value stocks, with a year-to-date return of 11.11% and an excess return of 7.63% [2] Group 2: Factor Performance Tracking - Among eight major factors, volatility and value factors have shown positive returns this month, while liquidity and momentum factors have shown negative returns [2] - Year-to-date, volatility and quality factors have performed well, whereas liquidity and large-cap factors have underperformed [2] - The report highlights that the beta, investment quality, and momentum factors have positive returns this month, while residual volatility, mid-cap, and long-term reversal factors have negative returns [2] Group 3: Covariance Matrix Update - The report updates the factor covariance matrix as of July 31, 2025, which is crucial for predicting stock portfolio risks [3] - The covariance matrix is constructed using a multi-factor model that combines factor covariance and stock-specific risk matrices for accurate estimation [3]

Historical Review of Size and Style Rotation - From 2008 to 2010, small-cap stocks outperformed due to significant economic stimulus and abundant liquidity, with small-cap stocks being more sensitive to funding[6] - Between 2011 and 2013, large-cap stocks gained favor as economic growth pressures increased, highlighting their defensive attributes[8] - The period from 2013 to 2015 saw a resurgence of small-cap stocks driven by the rise of new industries and increased M&A activity, with leverage funds entering the market[9] - From 2016 to 2021, large-cap stocks dominated as supply-side reforms improved profitability for leading companies, while M&A activity cooled[10] - In the 2021 to 2023 period, small-cap stocks regained strength due to changes in funding structure and the rise of new industries like AI[12] Growth vs. Value Style Rotation - From 2011 to 2014, value stocks outperformed as the economy shifted from stimulus-driven growth to self-sustained growth, with GDP growth declining[15] - In 2015, growth stocks saw a rebound due to the rise of the internet and new industries, despite ongoing economic pressures[19] - The period from July 2016 to October 2018 favored value stocks as traditional industries improved amid tightening liquidity[21] - From November 2018 to July 2021, growth stocks outperformed due to the rise of new industries and favorable liquidity conditions[23] - From August 2021 to August 2024, value stocks are expected to dominate due to tightening global liquidity and geopolitical uncertainties[25] Key Indicators and Future Outlook - The historical analysis indicates that size and style rotations are influenced by fundamental factors, liquidity, valuation, and policy[27] - The correct prediction rate for small-cap outperformance since 2005 is 69%, while for growth vs. value since 2011 is 77%[2] - In the first half of 2025, small-cap stocks outperformed with a 7.54% increase in the CSI 1000 index compared to a 1.37% increase in the CSI 300 index[2] - The outlook for the second half of 2025 suggests a potential shift towards large-cap stocks due to institutional investor preferences and external uncertainties[2]