相关研究 证券分析师 沈思逸 A0230521070001 shensv@swsresearch.com 邓虎 A0230520070003 denqhu@swsresearch.com 联系人 沈思逸 A0230521070001 shensy@swsresearch.com 请务必仔细阅读正文之后的各项信息披露与声明 2025 年 11 月 24 日 -致性下降, 多指标指 量化择时周报 20251123 量化策略 量家 | 1 . 情绪模型观点： 市场情绪得分周内冲高回落 | | --- | | 1.1 从分项指标出发：价量一致性、主力买入力量指标快速下降 5 | | ２．其他择时模型观点：银行短期得分快速提升，价值风格与 | | 小盘风格占优 …………………………………………………………………………………………………… 10 | | 2.1 银行行业短期得分快速提升，价值风格与小盘风格占优……………… 10 | | 3．风险提示………………………………………………………………………………………………………………………………………………………………………………………………………………………………………… ...

益 量 化 研 究 2025 年 11 月 24 日 价量一致性下降，多指标指向情绪 降温 ——量化择时周报 20251123 相关研究 证券分析师 沈思逸 A0230521070001 shensy@swsresearch.com 邓虎 A0230520070003 denghu@swsresearch.com 联系人 沈思逸 A0230521070001 shensy@swsresearch.com 请务必仔细阅读正文之后的各项信息披露与声明 本研究报告仅通过邮件提供给 中庚基金 使用。1 权 量 化 策 略 证 券 研 究 报 告 - ⚫ 市场情绪得分周内冲高回落：截至 11 月 21 日，市场情绪指标数值为 3.8，较上周五的 3.9 小幅降低，情绪出现下滑，从情绪角度来看观点偏空。从所有分项指标分数之和的变 化来看，本周情绪指数综合得分周内快速下降，市场交易活跃度不断走低。 ⚫ 价量共振走弱，市场短期情绪显著降温：本周价量一致性指标周内快速回落，资金关注度 与标的涨幅相关性明显降低，显示市场价量匹配程度下降，情绪快速走低；科创 50 相对 全 A 成交占比继续下滑并下穿布林带下界，反映风险偏好进一步 ...

报告导读： 从技术面来看，Wind全A指数连续一周处于SAR反转点位之下，且两者距离 并未收敛；均线强弱指数表明市场还有一定下行空间；情绪模型显示市场情绪较弱。结 合以上几点，我们认为，市场下周或将维持震荡。 上周（20251117-20251121，后文同）： 上周（20251117-20251121，后文同），上证50指数下跌2.72%，沪深300指数下跌3.77%，中证500指数 下跌5.78%，创业板指下跌6.15%。当前全市场PE（TTM）为21.3倍，处于2005年以来的70.1%分位点。日历效应上，2005年以来，各宽基指数在11月下 半月均表现不佳。 因子拥挤度观察： 低估值因子拥挤度出现下降。小市值因子拥挤度0.39，低估值因子拥挤度-0.69，高盈利因子拥挤度-0.02，高盈利增长因子拥挤度0.05。 行业拥挤度： 有色金属、通信、综合、电力设备和钢铁的行业拥挤度相对较高，基础化工和银行的行业拥挤度上升幅度相对较大。 风险提示： 市场系统性风险、海外市场波动风险、模型误设风险。 报告来源 以上内容节选自国泰海通证券已发布的证券研究报告。 报告名称： 量化择时和拥挤度预警周报（2025112 ...

证券研究报告 | 金融工程 2025 年 11 月 23 日 维持观望，大盘风格或仍将占优 2. 市场最新观点 风险提示：择时和风格轮动模型结论基于合理假设前提下结合历史数据统计规 律推导而出，市场环境变化下可能导致出现模型失效风险。 定期报告 敬请阅读末页的重要说明 王武蕾 S1090519080001 wangwulei@cmschina.com.cn 王禹哲 S1090525080001 wangyuzhe@cmschina.com.cn ❑ 择时观点上，本周继续维持震荡观望的判断，核心原因有三点，较前期有所 扩充：一是交易维度信号偏弱，目前全市场 Beta 离散度上行、PB 分化度下 行、全 A 交易量能下行，三者均给出偏向谨慎信号。简而言之，即市场缺乏 交易主线，未能形成新的趋势。二是基本面维度有喜有忧，即中上游景气度 回升较为明显，但下游景气度以及信贷数据不及预期。三是全球流动性风险 仍未解除，上周市场回调的主要原因在于美联储 12 月降息预期显著回落导 致全球流动性收缩，虽然周五美联储"三把手"威廉姆斯表示"近期内有进 一步调整利率的空间"，带动美股企稳，但当前美联储内部分歧仍较大，在 12 月 ...

Core Viewpoint - The market is expected to experience fluctuations in the upcoming week, despite the recent decline in major indices, as the strength index did not show significant downward movement, indicating a divergence in trends [1][2]. Market Overview - The liquidity shock index for the CSI 300 was 0.67, higher than the previous week's 0.40, suggesting current market liquidity is 0.67 standard deviations above the average of the past year [2]. - The PUT-CALL ratio for the SSE 50 ETF decreased to 1.04 from 1.22, indicating reduced caution among investors regarding the short-term performance of the SSE 50 ETF [2]. - The five-day average turnover rates for the SSE Composite Index and Wind All A were 1.27% and 1.91%, respectively, reflecting a decline in trading activity, positioned at the 75.55% and 81.44% percentiles since 2005 [2]. Macroeconomic Factors - The onshore and offshore RMB exchange rates increased by 0.31% and 0.35% respectively over the past week [2]. - October's CPI rose by 0.2% year-on-year, surpassing the previous value of -0.3% and the consensus expectation of -0.04%. The PPI decreased by 2.1% year-on-year, better than the previous -2.3% and the expected -2.28% [2]. - New RMB loans in October totaled 220 billion, falling short of the expected 459.98 billion and the previous 1.29 trillion. M2 growth was 8.2% year-on-year, exceeding the expected 8.04% but lower than the previous 8.4% [2]. Calendar Effects - Historical data from 2005 indicates that major indices such as the SSE Composite, CSI 300, and others have shown poor performance in the latter half of November, with average declines of -0.61% to -0.9% [2]. Technical Analysis - The Wind All A index broke above the reversal indicator on October 27, indicating a potential upward trend [2]. - The current market score based on the moving average strength index is 218, placing it at the 79.2% percentile for 2023 [2]. - The sentiment model score is 3 out of 5, with both trend and weighted models signaling a positive outlook [2]. Factor Crowding - The factor crowding levels remain stable, with small-cap factor crowding at 0.37, low valuation factor at -0.25, high profitability factor at -0.18, and high growth factor at 0.08 [3]. Industry Crowding - Industries such as non-ferrous metals, comprehensive, telecommunications, electric equipment, and steel show relatively high crowding levels, while basic chemicals and banking have seen a significant increase in crowding [4].

Group 1: Market Sentiment Model Insights - The market sentiment score has rapidly increased to 3.9 as of November 14, up from 3 the previous week, indicating a further recovery in market sentiment and a bullish outlook [2][8] - The price-volume consistency indicator has stabilized and rebounded, showing a phase of sentiment recovery after a previous decline, with increased trading activity and a positive correlation between price elasticity and attention to stocks [11][12] - The overall trading volume for the entire A-share market increased by 1.56% week-on-week, with an average daily trading volume of 20,438.27 billion yuan, indicating sustained market activity [15] Group 2: Industry Trends and Performance - The short-term trend scores for industries such as beauty care, pharmaceuticals, banking, food and beverage, and textiles have shown upward momentum, with steel, electric equipment, construction decoration, environmental protection, and coal being the strongest short-term performers [40][41] - The industry trend consistency has significantly improved, breaking through the upper Bollinger Band, indicating a stronger consensus on industry outlooks and enhancing the beta effect of sector indices [25][28] - The financing balance ratio continues to rise, reflecting an increase in market leverage sentiment and a more active trading atmosphere in the financing market [29][31] Group 3: Industry Crowding and Investment Opportunities - The correlation coefficient between industry crowding and weekly price changes is 0.60, indicating a significant positive relationship, with high crowding in sectors like basic chemicals, agriculture, and forestry, which have seen high price increases [44][46] - Sectors with high crowding but low price increases, such as electric equipment and environmental protection, may have potential for catch-up gains if fundamental catalysts arise [44] - Low crowding sectors like communication, electronics, and computers, which have seen lower price increases, present opportunities for gradual long-term investment as risk appetite improves [44][46]

Group 1 - Market sentiment score has continued to rise, reaching 3 as of November 7, up from 2.7 the previous week, indicating further recovery in market sentiment and a generally bullish outlook [1][6] - The trading volume of the entire A-share market slightly decreased this week, with an average daily trading volume of 20,123.50 billion yuan, showing a decline in market activity [1][12] - The industry trend scores have shown significant improvement, with utilities, power equipment, coal, environmental protection, and steel being the strongest short-term trends, particularly utilities with a score of 100 [1][33] Group 2 - The short-term trend scores for the steel industry have rapidly increased, maintaining a dominant position for value and large-cap styles [1][33] - The banking sector also saw a quick rise in short-term trend scores, reinforcing the dominance of value and large-cap styles [1][33] - The model indicates that the overall market and value styles are currently favored, with signals suggesting a potential strengthening of these trends in the future [1][44] Group 3 - The inter-industry trading volatility has risen sharply, indicating increased activity and liquidity in sector switching, with the index breaking through the upper Bollinger band [1][16] - The correlation between funding attention and stock price increases has shown a rebound, suggesting a marginal improvement in short-term market sentiment [1][11] - The financing balance ratio continues to rise, reflecting an increase in market leverage and a more active trading atmosphere [1][22] Group 4 - The model's overall indicators suggest that the market is currently experiencing a structural shift, with high trading congestion in sectors like power equipment, transportation, and coal, while sectors like computers and food and beverage show lower congestion levels [1][36][40] - The report highlights that high congestion in sectors with significant price increases may pose volatility risks, while low congestion sectors could present opportunities for excess returns if conditions improve [1][36][40] - The report emphasizes the importance of tracking industry congestion to identify potential structural risks and optimize asset allocation strategies [1][36]

Market Performance - The recent five trading days saw the Sci-Tech 50 Index increase by 0.01%, the ChiNext Index by 0.65%, the large-cap value index by 2.33%, the large-cap growth index by 0.28%, the SSE 50 by 0.89%, and the small-cap index represented by the CSI 2000 by 0.52% [1] - Sectors such as electric equipment and coal performed well, while computer and beauty care sectors lagged behind [1] Valuation Levels - As of November 7, 2025, the static PE of the CSI All Index is at an 82nd percentile, with the SSE 50 and CSI 300 at 77% and 74% respectively, while the ChiNext Index is close to 53% [1] - The valuation of the ChiNext Index is relatively at the historical median level [1] Risk Premium - The risk premium, calculated as the inverse of the static PE of the CSI All Index minus the yield of ten-year government bonds, stands at 2.78% as of November 7, 2025, with a two-standard deviation boundary at 4.74% [1] ETF Fund Flows - In the last five trading days, ETF inflows amounted to 37.2 billion yuan, while margin trading decreased by approximately 700 million yuan [2] Industry Themes - The latest thematic allocation includes banking, energy, and dividends, specifically focusing on indices such as the CSI Bank Index, CSI Energy Index, and CSI Central Enterprises Dividend Index [2][3] Long-term Market Sentiment - The proportion of stocks above the 200-day moving average is being tracked to gauge long-term market sentiment [13] Financing Balance - The financing balance is being monitored to assess market liquidity and investor sentiment [16] Individual Stock Performance - Statistics on individual stock performance year-to-date based on return ranges are being compiled to identify trends [18] Oversold Indices - Observations are being made regarding indices that are considered oversold, indicating potential investment opportunities [20]

Quantitative Models and Construction Simple Return Model - **Model Name**: Simple Return Model - **Construction Idea**: The model uses the simple return of closing prices to track trends and make trading decisions [12][13] - **Construction Process**: 1. Calculate the simple return as: $ \text{Simple Return} = \frac{\text{Closing Price (Day t)}}{\text{Closing Price (Day t-1)}} - 1 $ 2. Compute the 60-day Exponential Moving Average (EMA) of the simple return 3. If the 60-day EMA value is greater than 0, take a long position; otherwise, close the long position [12][13] - **Evaluation**: The model performed poorly in backtesting, with low win rates (below 30%) and failing to outperform the benchmark indices [13] Trend Return Difference Model - **Model Name**: Trend Return Difference Model - **Construction Idea**: The model improves upon the simple return model by introducing the concept of upward and downward return differences to better capture market trends [17][18] - **Construction Process**: 1. Define upward return as: $ \text{Upward Return} = \frac{\text{Highest Price (Day t) - Opening Price (Day t)}}{\text{Closing Price (Day t)}} $ 2. Define downward return as: $ \text{Downward Return} = \frac{\text{Opening Price (Day t) - Lowest Price (Day t)}}{\text{Closing Price (Day t)}} $ 3. Calculate the upward and downward return difference: $ \text{Upward-Downward Return Difference} = \text{Upward Return} - \text{Downward Return} $ 4. Compute the 60-day EMA of the upward-downward return difference 5. If the 60-day EMA value is greater than 0, take a long position; otherwise, close the long position [17][18] - **Evaluation**: The model outperformed the simple return model and the benchmark indices in terms of annualized return, Sharpe ratio, and risk control. It is characterized as a mid-term model with an average long position holding period of approximately 3 weeks [18] Turnover Comprehensive Return Difference Model - **Model Name**: Turnover Comprehensive Return Difference Model - **Construction Idea**: Combines turnover and upward-downward return difference to enhance trend-following capabilities by assigning higher weights to trends during high turnover periods [26][27] - **Construction Process**: 1. Define turnover comprehensive return difference as: $ \text{Turnover Comprehensive Return Difference} = \text{Upward-Downward Return Difference} \times \text{Turnover} $ 2. Compute the 60-day EMA of the turnover comprehensive return difference 3. If the 60-day EMA value is greater than 0, take a long position; otherwise, close the long position [27][28] - **Evaluation**: The model demonstrated superior performance compared to the simple return model and the upward-downward return difference model. It effectively distinguishes market trends and performs better in high turnover scenarios [27][28] Composite Signal Turnover Comprehensive Return Difference Model - **Model Name**: Composite Signal Turnover Comprehensive Return Difference Model - **Construction Idea**: Combines the turnover comprehensive return difference signals from both the Hang Seng Index and the Hang Seng China Enterprises Index to eliminate the randomness caused by differences in index composition [32][33] - **Construction Process**: 1. Define the composite signal: - If either the Hang Seng Index or the Hang Seng China Enterprises Index turnover comprehensive return difference signal indicates a long position, take a long position in the respective index 2. Compute the 60-day EMA of the composite signal 3. If the composite signal's 60-day EMA value is greater than 0, take a long position; otherwise, close the long position [32][33] - **Evaluation**: The model significantly outperformed the benchmark indices and single-signal turnover comprehensive return difference models, showcasing robust trend-following capabilities [35][36] --- Model Backtesting Results Simple Return Model - **Hang Seng Index**: Annualized return 1.26%, maximum drawdown 52.96%, Sharpe ratio -0.044 [15][16] - **Hang Seng China Enterprises Index**: Annualized return 1.91%, maximum drawdown 68.79%, Sharpe ratio 0.034 [15][16] Trend Return Difference Model - **Hang Seng Index**: Annualized return 4.23%, maximum drawdown 22.98%, Sharpe ratio 0.154 [19][20] - **Hang Seng China Enterprises Index**: Annualized return 6.15%, maximum drawdown 37.2%, Sharpe ratio 0.267 [19][20] Turnover Comprehensive Return Difference Model - **Hang Seng Index**: Annualized return 3%, maximum drawdown 28.84%, Sharpe ratio 0.039 [31] - **Hang Seng China Enterprises Index**: Annualized return 9.73%, maximum drawdown 24.56%, Sharpe ratio 0.47 [31] Composite Signal Turnover Comprehensive Return Difference Model - **Hang Seng Index**: Annualized return 7.78%, maximum drawdown 23.81%, Sharpe ratio 0.401 [33][36] - **Hang Seng China Enterprises Index**: Annualized return 10.03%, maximum drawdown 24.63%, Sharpe ratio 0.484 [33][36] Sensitivity Analysis of Composite Signal Turnover Comprehensive Return Difference Model - **Hang Seng Index**: - 40-day EMA: Annualized return 6.1%, maximum drawdown 26.78%, Sharpe ratio 0.281 [39] - 50-day EMA: Annualized return 7.02%, maximum drawdown 27.44%, Sharpe ratio 0.34 [39] - 60-day EMA: Annualized return 7.78%, maximum drawdown 23.81%, Sharpe ratio 0.401 [39] - 70-day EMA: Annualized return 7.31%, maximum drawdown 27.2%, Sharpe ratio 0.375 [39] - 80-day EMA: Annualized return 6.86%, maximum drawdown 24.9%, Sharpe ratio 0.343 [39] - **Hang Seng China Enterprises Index**: - 40-day EMA: Annualized return 8.3%, maximum drawdown 26.72%, Sharpe ratio 0.382 [40] - 50-day EMA: Annualized return 8.97%, maximum drawdown 28.88%, Sharpe ratio 0.416 [40] - 60-day EMA: Annualized return 10.03%, maximum drawdown 24.63%, Sharpe ratio 0.484 [40] - 70-day EMA: Annualized return 9.36%, maximum drawdown 29.04%, Sharpe ratio 0.454 [40] - 80-day EMA: Annualized return 9.04%, maximum drawdown 25.04%, Sharpe ratio 0.438 [40]

Group 1: Market Sentiment Model Insights - The market sentiment score has slightly increased to 2.2 as of October 24, compared to 1.9 the previous week, indicating a partial recovery in market sentiment [6][8] - The overall market sentiment is showing increased differentiation, with a decline in price-volume consistency, suggesting reduced capital activity [8][12] - The total trading volume for the entire A-share market has significantly decreased compared to the previous week, with a peak trading volume of 1,991.617 billion RMB on October 24 [14][16] Group 2: Sector Performance Insights - As of October 24, the banking, oil and petrochemical, transportation, public utilities, and construction decoration sectors have shown an upward trend in short-term scores [33] - The coal sector currently has the highest short-term score of 93.22, indicating strong short-term performance [33][34] - The model indicates that the market is currently favoring large-cap and value styles, with strong signals for both [33][44] Group 3: Industry Crowding Insights - Recent high price increases in the electronics and power equipment sectors are accompanied by high capital crowding, suggesting potential volatility risks due to valuation and sentiment corrections [36][41] - The average crowding levels are highest in the power equipment, environmental protection, non-ferrous metals, textile and apparel, and coal sectors [37][40] - Low crowding sectors such as non-bank financials, beauty care, media, computing, and food and beverage have shown lower price increases, indicating potential for excess returns if fundamentals improve [36][40]