Core Viewpoint - The article discusses the calendar effect observed in the A-share market, where certain market styles exhibit better performance in specific months, suggesting the presence of seasonal factors influencing these patterns [2][4]. Summary by Sections Calendar Effect of Styles - The small-cap style shows significant volatility in the first half of the year, with better performance in the second half. Specifically, April has weak performance for small-cap stocks, while March and May yield higher average returns [4][13]. - The growth style demonstrates a "high early, low late" pattern, with notable excess returns in January and June-July, achieving a win rate of 90.9% [4][13]. - The quality style exhibits strong performance in both January (excess return of 1.4%, win rate of 81.8%) and December (excess return of 0.5%, win rate of 80%) [4][14]. - The dividend style performs well in April and August, with a win rate of 83.3%, but shows lower performance in June and October [4][15]. Mechanisms Behind Calendar Effects - The performance of growth and small-cap styles is significantly influenced by the rhythm of financial report disclosures, with concentrated disclosures in January, April, and July favoring growth stocks [5][20]. - High dividend announcement days and ex-dividend days can impact the performance of dividend stocks, with positive excess returns following high dividend announcements and negative returns post ex-dividend days [5][24]. - Institutional investors exhibit seasonal changes in risk preferences, with a tendency to favor growth stocks mid-year and quality stocks towards year-end, impacting the performance of respective styles [5][35]. Implications for Investment Strategy - Investors are advised to focus on growth opportunities during the earnings announcement periods in January and April while avoiding small-cap stocks during these times [23][35]. - Attention should be given to the concentration of dividend announcements in March and April, which can enhance the performance of dividend styles, while caution is warranted during the ex-dividend periods [30][34].

Group 1 - The core viewpoint of the article highlights a significant rebound in trading sentiment, with the Wind Micro Index breaking through previous levels and achieving a one-month return of 9.31% and a year-to-date increase of 74.49% [2][3] - Small-cap stocks, represented by the CSI 2000 and CSI 1000 indices, have shown relatively strong performance in the past month, contrasting with the sluggish response of large-cap indices like the CSI 300 [2][3] Group 2 - The shift in fund preferences is driven by profit-taking in certain tech growth sectors, leading active funds to seek higher elasticity in small-cap stocks as the large-cap market lacks a clear trend [4][6] - Year-end trading characteristics are evident as some trading-oriented funds return to high-elasticity sectors, further propelling small-cap indices upward [5][6] - Policy measures aimed at expanding domestic demand and promoting innovation are more sensitive to small and medium-sized enterprises, making them more responsive to policy changes [6] Group 3 - Quantitative micro-cap strategies have shown an average return of 3.53% over the past month, significantly lagging behind the micro-cap index's over 9% increase, attributed to the different operational mechanisms of indices and quantitative strategies [7] - The recent rise in the micro-cap index is primarily driven by a few highly liquid and elastic stocks, which are difficult to weight heavily in quantitative models due to high trading costs and volatility [7][8] - Quantitative strategies focus on capturing more sustainable style premiums through a multi-factor system, which may exhibit slight delays in exposure during the initial phase of a style shift [7][8] Group 4 - The appeal of quantitative micro-cap strategies lies in their ability to provide exposure to micro-cap style returns while minimizing extreme volatility associated with indices [8][9] - These strategies have a lower correlation with other asset classes, effectively reducing portfolio volatility [9] - The quantitative framework filters out noise from extremely small stocks, focusing on fundamentals and trading quality to stabilize returns [10] - In a market environment favoring small and micro-caps, quantitative strategies offer a relatively controlled way to participate in high-elasticity stocks [11][12]

受益于流动性宽松以及部分个股的价值回归，近期代表中小盘风格的中证2000、国证2000指数持续在高 位徘徊，多只中小盘乃至小微盘主题的权益基金净值也在持续创下历史新高。 在沪指徘徊在4000点之际，有公募基金预判认为，当前市场大概率进入多风格轮动阶段，资金可能流向 低估微盘股，且微盘策略在牛市中还能起到资产组合的防守作用。但拥挤度也是小微盘不可忽视的风 险，有基金公司表示，这些股票的日常交易活跃度较低，买卖盘深度不足，中小盘股普遍存在交易活跃 度不稳定、资金承接能力弱的问题，一旦市场出现调整，容易出现买卖价差扩大、变现困难的情况。 年内，除了此起彼伏的主题投资外，中小盘股也表现出众。 小盘股强势依旧 在大盘点位冲击4000点之际板块轮动也愈发剧烈，包括创新药、人工智能等在内的热门板块近期均有一 定震荡，但除了行业投资外，"中小盘"和"小微盘"两大主题却持续强势。 据统计，近期代表中小盘风格的中证2000指数依旧在高位徘徊，截至11月12日报收3141点，国证2000指 数也有类似涨幅，两大指数距离此前创下的十年新高仅一步之遥。 虽然有研报指出，三季度内主动权益类公募基金对股票调仓呈"亲大盘、远中小盘" 的特征 ...

Core Viewpoint - The article highlights the strong performance of small and micro-cap stocks in the current market, driven by liquidity easing and value recovery of certain stocks, with indices like the CSI 2000 and National 2000 remaining at high levels [2][3]. Group 1: Market Performance - The CSI 2000 index was reported at 3141 points as of November 12, nearing a ten-year high, indicating robust performance in the small-cap sector [3]. - Several funds focusing on small and micro-cap stocks, such as Nuon Fund and CITIC Prudential, have seen significant net value increases, with quarterly gains of 9.34%, 6.24%, 1.41%, and 8.99% respectively [3]. - The average market capitalization of the top holdings in these funds is around several hundred million, significantly lower than large-cap stocks, which are in the billion range [3]. Group 2: Investment Strategies - Investors are increasingly favoring high-elasticity stocks, with micro-cap stocks showing greater potential for price movement due to their smaller market size and higher free float [4][5]. - The current market environment is characterized by a shift in funding structure, with lower leverage levels compared to early 2024, making the market less prone to large fluctuations [6]. - Micro-cap strategies are seen as a way to capture excess returns due to the inefficiencies in pricing, as these stocks are often less covered by analysts and can be mispriced [6]. Group 3: Liquidity Factors - The article emphasizes that the current liquidity environment is favorable for small and micro-cap stocks, as increased social financing and M2 growth lead to more funds flowing into these stocks for higher returns [5][6]. - The low liquidity characteristic of micro-cap stocks poses challenges, such as difficulties in executing large trades and managing liquidity effectively [8]. - Despite the positive outlook, there are concerns about the stability of trading activity and the ability to execute trades without significant price impact, especially during market corrections [7][8].

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]

- The report provides a snapshot of the performance of broad-based indices in the A-share market, highlighting daily, weekly, monthly, and yearly changes in index returns, with the highest annual return observed for the ChiNext Index at 42.85%[10][11][13] - It compares indices against their moving averages (MA5, MA10, MA20, MA60, MA120, MA250) and their 250-day high and low levels, showing that all indices remain above their 5-day moving averages, except the CSI 2000, which fell below its 10-day moving average[13][14] - The turnover rate and trading volume share are analyzed, with CSI 2000 having the highest turnover rate at 3.56, while the CSI 300 accounts for the largest trading volume share at 26.89%[16][17] - Daily return distributions are examined, revealing that the ChiNext Index has the largest negative skewness and kurtosis deviation, while the CSI 300 has the smallest[23][24] - Risk premium analysis is conducted using the 10-year government bond yield as the risk-free rate, showing that the CSI 1000 and CSI 2000 have higher volatility in risk premiums compared to other indices[26][27][30] - PE-TTM ratios are evaluated as valuation metrics, with CSI 500 and CSI All Index showing the highest 5-year percentile values at 98.18% and 97.44%, respectively, while the ChiNext Index has the lowest at 58.51%[38][41][42] - Dividend yield analysis indicates that the ChiNext Index and CSI 1000 have the highest 5-year historical percentile values at 69.42% and 46.2%, respectively, while CSI 2000 and CSI 500 have the lowest at 20.25% and 16.28%[46][51][52] - The report also tracks the percentage of stocks trading below their net asset value (break-net ratio), with the highest ratio observed for the SSE 50 at 18.0% and the lowest for the ChiNext Index at 1.0%[53]

来源：新浪基金∞工作室 风险提示：市场有风险，投资需谨慎。本文为AI大模型自动发布，任何在本文出现的信息（包括但不 限于个股、评论、预测、图表、指标、理论、任何形式的表述等）均只作为参考，不构成个人投资建 议。 10月23日，截止午间收盘，中证2000ETF华夏（562660）跌0.62%，报1.607元，成交额821.91万元。中 证2000ETF华夏（562660）重仓股方面，大东南截止午盘跌1.38%，万和电气跌1.85%，高新兴跌 0.92%，可立克跌2.40%，天奥电子跌2.75%，万泽股份涨0.57%，锌业股份涨0.26%，海利得涨0.66%， 瑞丰光电跌1.22%，锐明技术跌6.32%。 中证2000ETF华夏（562660）业绩比较基准为中证2000指数收益率，管理人为华夏基金管理有限公司， 基金经理为鲁亚运、陈国峰，成立（2023-09-06）以来回报为61.89%，近一个月回报为0.46%。 ...

Core Viewpoint - The China Securities 2000 ETF managed by Huaxia Fund has experienced a decline of 1.61% as of the midday close on October 13, with a trading volume of 7.232 million yuan [1] Group 1: ETF Performance - The China Securities 2000 ETF (562660) closed at 1.590 yuan, reflecting a drop of 1.61% [1] - Since its inception on September 6, 2023, the fund has achieved a return of 61.68%, with a monthly return of 1.75% [1] Group 2: Major Holdings Performance - Major holdings in the ETF include: - Dazhongnan: down 3.01% - Wanhe Electric: down 3.73% - Gaoxin Xing: down 1.41% - Keli Ke: down 4.18% - Tian'ao Electronics: down 0.06% - Wanze Shares: up 0.71% - Zinc Industry Shares: down 0.51% - Hailide: down 1.75% - Ruifeng Optoelectronics: down 2.60% - Ruiming Technology: down 4.75% [1]

Core Viewpoint - The China Securities 2000 Enhanced ETF (159552) experienced a decline of 1.95% as of the midday close on October 13, with a trading volume of 24.76 million yuan [1] Group 1: ETF Performance - The China Securities 2000 Enhanced ETF (159552) closed at 1.962 yuan, with a year-to-date return of 99.99% since its inception on June 19, 2024 [1] - The ETF's one-month return stands at 2.68% [1] Group 2: Top Holdings Performance - Major holdings in the ETF include: - Ice Glacier Network: down 4.75% - New Asia Electronics: down 4.79% - Huazheng New Materials: down 1.36% - Junya Technology: down 1.23% - Shenchi Electromechanical: down 1.31% - Brother Technology: unchanged at 0.00% - Haitai Technology: down 2.10% - Changrong Co.: down 2.39% - Focus Technology: down 4.05% - Jingquan Hua: up 3.31% [1]

Core Viewpoint - The 中证2000ETF嘉实 (159535) has experienced a decline of 2.36% as of the midday close on September 23, with a trading volume of 1.627 million yuan [1] Group 1: Fund Performance - The fund's performance benchmark is the 中证2000 index return [1] - Since its establishment on September 14, 2023, the fund has achieved a return of 40.10% [1] - The fund's return over the past month is 0.03% [1] Group 2: Major Holdings Performance - Major holdings in the fund include: - 每日互动: down 4.50% [1] - 汉威科技: down 0.38% [1] - 宏创控股: down 3.84% [1] - 东土科技: down 7.11% [1] - 恒宝股份: down 6.64% [1] - 台基股份: up 1.69% [1] - 热景生物: down 2.29% [1] - 仕佳光子: down 5.77% [1] - 华胜天成: down 6.19% [1] - 泰恩康: down 2.43% [1]