大小盘风格轮动月度策略。 月度观点： 2 月底量化模型最新信号为 0.83 ，指向小盘。日历效应上，历史 3 月小盘相对占优；建议 3 月继续超配小盘风格。 中长期观点，当前市值因子估值价差为 0.86 ，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 2 月底，模型 本年收益 13.35% ，相对等权基准（ 7.47% ）的超额收益为 5.88% 。结合主观观点的策略收益 13.35% ，超额收益 5.88% 。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。 月度观点： 2 月底量化模型信号为 -0.67 ，建议 3 月超配价值风格。中长期观点，未来一年更看好成长风格。截止 2 月底， 模型收益 5.22% ，相对等权基准（ 5.22% ）的超额收益为 0% 。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，本月流动性、动量因子正向收益较高；大市值、质量因子负向收益较高。本年价值、波动率因子正向收益较高；大市 值、质 ...

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]

Group 1: Macroeconomic Research - The core viewpoint of the report indicates that the December inflation in the US did not show the rebound that the market had feared, with the core CPI growth rate being lower than expected. The year-on-year CPI growth remained at 2.7%, unchanged from November, while the month-on-month growth was 0.3%, also unchanged from September. The core CPI year-on-year growth was 2.6%, slightly below the market expectation of 2.7% [1][2][3] - The structure of inflation shows weak performance in core goods, particularly due to second-hand vehicles, while core services have shown a general recovery. The month-on-month growth for core goods was 0%, and even excluding second-hand vehicles, the growth remained low. In contrast, the housing component rebounded from 0.2% in September to 0.4% in December [3][15] Group 2: Financial Engineering Research - The report suggests an overweight position in small-cap stocks for January based on quantitative model signals, while recommending an equal allocation between value and growth styles. The model signal for small-cap stocks was 0.17 at the end of December, indicating a favorable outlook [4][6] - The performance of style factors indicates that momentum and value factors yielded positive returns, while dividend factors showed negative returns. The report highlights that the model's return was 27.56%, with an excess return of 0.71% compared to the equal-weight benchmark [6][24] Group 3: Company Research - Haidilao - The report discusses Haidilao's recent management changes, with the founder taking over as CEO, which is expected to enhance employee motivation and boost investor confidence. The new board members have extensive experience within the company, contributing to operational and strategic development [8][10][22] - Haidilao's operational performance remains robust, with significant customer traffic reported during the New Year period, indicating strong demand. The company is also advancing its "Red Pomegranate" plan, which includes the launch of new dining concepts [11][23] - The investment recommendation for Haidilao is to maintain an "overweight" rating, with projected net profits for 2025-2027 being 42.36 billion, 47.41 billion, and 52.69 billion yuan respectively. The target price is set at 19.10 HKD, reflecting a valuation slightly above the industry average [9][21]

Group 1 - The report suggests an overweight allocation to small-cap stocks for January, while recommending an equal-weight allocation to value and growth styles based on quantitative model signals [1] - As of the end of December, the quantitative model signal for small-cap stocks was 0.17, indicating a preference for small-cap over large-cap stocks [1] - The long-term view indicates that the current market capitalization factor valuation spread is 0.89, which is still below the historical peak range of 1.7 to 2.6, suggesting continued optimism for small-cap stocks [1] Group 2 - The quantitative model signal for value and growth styles is 0, recommending an equal-weight allocation for January [1] - As of the end of December, the model's return for value and growth styles was 22.72%, with an excess return of 1.93% compared to the equal-weight benchmark of 20.4% [1] - The report provides detailed strategy construction in a separate document focused on monthly and weekly value and growth style rotation strategies [1] Group 3 - Among eight major style factors, momentum and value factors showed high positive returns, while dividend factors exhibited high negative returns [2] - For the year, volatility and growth factors had high positive returns, while liquidity and large-cap factors showed negative returns [2] - The report updates the factor covariance matrix, which is essential for predicting stock portfolio risk, using a multi-factor model [2]

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]