Core Viewpoint - The article discusses the differentiation of quantitative funds and strategies, their performance in various market conditions, and the importance of understanding their underlying logic for effective asset allocation [3][27]. Group 1: Differentiation of Quantitative Funds - Quantitative funds can be categorized based on their sources of returns: those that earn both Beta and Alpha, and those that focus solely on Alpha through market-neutral strategies [6][8]. - A specific strategy called quantitative timing adjusts positions based on model calculations to capture timing Alpha, often combined with stock index CTA for a composite approach [8]. - The choice of benchmark is crucial for index-enhanced strategies, with common benchmarks including CSI 300, CSI 500, and others, each having distinct characteristics [9][10]. Group 2: Performance Analysis - Over the past five years, small and micro-cap indices have generally outperformed larger indices, attributed to their higher turnover and the presence of mispricing opportunities [12]. - Quantitative index-enhanced strategies have shown significant excess returns, especially when the underlying Beta is smaller, leading to better performance in volatile markets [13][14]. - The annualized volatility and maximum drawdown for quantitative strategies are generally lower compared to traditional indices, providing a more favorable investment experience [14][15]. Group 3: Effectiveness and Limitations of Quantitative Strategies - Quantitative strategies thrive in high-volatility environments where numerous trading opportunities exist, allowing for the capture of mispricing [18]. - Conversely, these strategies may fail in low-volatility markets where crowded trades lead to diminished excess returns and increased risk of significant drawdowns [19][21]. - The evolution of quantitative strategies is essential as market conditions change, requiring continuous adaptation to maintain effectiveness [23]. Group 4: Role of Quantitative Strategies in Asset Allocation - Quantitative strategies provide a distinct source of return and risk, complementing subjective strategies in a diversified portfolio [27]. - In aggressive portfolios, quantitative strategies can serve as more traceable and explainable positions, while in balanced allocations, they can enhance overall sharpness [28][29]. - The value of a multi-strategy approach lies in its ability to perform optimally across different market conditions, mitigating the risks associated with relying on a single strategy [31].

Market Overview - The market is expected to maintain a consolidation phase in the short term, as indicated by the technical analysis and sentiment model signals [1][2] - The liquidity shock indicator for the CSI 300 index was 0.03, lower than the previous week (0.50), suggesting current market liquidity is above the one-year average by 0.03 standard deviations [2] - The PUT-CALL ratio for the SSE 50 ETF decreased to 0.83 from 1.02, indicating reduced caution among investors regarding the short-term outlook [2] - The five-day average turnover rates for the SSE Composite Index and Wind All A were 1.01% and 1.62%, respectively, reflecting increased trading activity [2] Macroeconomic Factors - The onshore and offshore RMB exchange rates experienced slight fluctuations, with weekly increases of 0.12% and 0.03%, respectively [2] - The official manufacturing PMI for November was reported at 49.2, slightly above the previous value (49) but below the consensus expectation (49.3) [2] - The S&P Global China Manufacturing PMI was 49.9, down from the previous value (50.6) [2] Historical Performance - Historical data shows that from 2005 onwards, the SSE Composite Index, CSI 300, and other major indices have had a high probability of rising in the first half of December, with average gains of 1.81%, 2.45%, 1.55%, and -0.02% respectively [2] - The A-share market showed a slight upward trend last week, with the SSE 50 Index up by 0.47%, CSI 300 up by 1.64%, CSI 500 up by 3.14%, and the ChiNext Index up by 4.54% [2] Factor Analysis - The crowding degree for small-cap factors has significantly decreased, with a value of 0.16, while the low valuation factor crowding degree is at -0.65 [3] - High profitability factor crowding degree is at -0.09, and high growth profitability factor crowding degree is at 0.05 [3] - Industry crowding degrees are relatively high in telecommunications, non-ferrous metals, comprehensive sectors, power equipment, and electronics, while machinery and defense industries have seen a notable increase in crowding [3]

Group 1: Market Sentiment - The market sentiment score continued to decline, reaching 2.4 as of December 5, down from 3.15 the previous week, indicating a bearish outlook from a sentiment perspective [2][8] - The overall trading activity in the market decreased, with total A-share trading volume dropping by 2.35% week-on-week, averaging 16,961.78 billion yuan, reflecting reduced market activity [15] - The financing balance ratio has been on the rise, reaching a three-year high, suggesting an increase in leveraged funds and a structural recovery in market risk appetite [28] Group 2: Sector Performance - The short-term scores for sectors such as telecommunications, household appliances, national defense, social services, and building materials have shown upward trends, with the petroleum and petrochemical sector having the highest short-term score of 79.66 [40][41] - The industry trading volatility has slightly decreased, indicating a slowdown in the pace of capital switching between sectors, with liquidity marginally weakening [23][26] - The correlation between sector crowding and weekly price changes is negligible, suggesting that high crowding sectors like national defense and telecommunications have experienced significant gains, but caution is advised regarding potential high-level pullbacks [44][46] Group 3: Timing Models - The current model indicates a preference for large-cap and value styles, with signals suggesting a potential strengthening of these trends in the future [40][50] - The communication sector has seen a rapid increase in short-term scores, indicating a favorable outlook for this sector [40] - The model's analysis of the relative strength index (RSI) suggests that while the value style is currently dominant, there may be a weakening of this signal in the near future [50]

Core Insights - The report highlights the "Net Profit Discontinuity Strategy" which has achieved an absolute return of 63.03% this year, significantly outperforming the benchmark index by 39.07% [3][11] - The "Davis Double-Click Strategy" has shown a historical annualized return of 26.45% from 2010 to 2017, with consistent excess returns exceeding 11% each year during that period [3][7] - The "Enhanced CSI 300 Portfolio" has provided a relative excess return of 17.41% this year, indicating strong performance compared to the CSI 300 index [13][17] Group 1: Davis Double-Click Strategy - The Davis Double-Click Strategy involves buying stocks with low price-to-earnings (PE) ratios that have growth potential, aiming to sell once growth is realized and PE increases, thus achieving a "double-click" effect on earnings per share (EPS) and PE [3][6] - Historical backtesting from 2010 to 2017 shows the strategy's annualized excess return of 21.08% against the benchmark [7] - The strategy has generated a cumulative absolute return of 48.89% this year, outperforming the CSI 500 index by 24.92% [8] Group 2: Net Profit Discontinuity Strategy - The Net Profit Discontinuity Strategy focuses on stocks that show significant upward price gaps on the first trading day following earnings announcements, indicating market approval of earnings surprises [10][11] - This strategy has achieved an annualized return of 29.22% since 2010, with a cumulative absolute return of 63.03% this year, outperforming the benchmark by 39.07% [11][12] - The strategy's performance is based on selecting stocks that have exceeded earnings expectations over the past two months [10] Group 3: Enhanced CSI 300 Portfolio - The Enhanced CSI 300 Portfolio is constructed based on investor preferences, including GARP (Growth at a Reasonable Price), growth, and value investing styles [13][17] - The portfolio aims to identify undervalued stocks with strong profitability and growth potential, utilizing factors like PBROE and PEG [13] - This year, the portfolio has achieved a relative excess return of 17.41% compared to the CSI 300 index, demonstrating its effectiveness [17]

- Model Name: Industry Trend Allocation Model; Model Construction Idea: The model aims to identify industry trends and allocate investments accordingly; Model Construction Process: The model uses historical data and technical indicators to identify industry trends. It focuses on industries such as liquor and non-bank financials for mid-term reversal signals, and recommends technology sectors, commercial aerospace, and consumer electronics based on the TWO BETA model. The model also shows that the battery and industrial metals sectors continue to trend upwards[2][5][7]; Model Evaluation: The model is effective in identifying industry trends and making allocation recommendations based on historical data and technical indicators[2][5][7] - Model Name: TWO BETA Model; Model Construction Idea: The model aims to recommend sectors based on their beta values; Model Construction Process: The model uses beta values to identify sectors with high growth potential. It continues to recommend the technology sector, with a focus on commercial aerospace and consumer electronics[2][5][7]; Model Evaluation: The model is effective in identifying high-growth sectors based on beta values[2][5][7] - Model Name: Timing System; Model Construction Idea: The model aims to distinguish the overall market environment using long-term and short-term moving averages; Model Construction Process: The model calculates the distance between the 120-day and 20-day moving averages of the WIND All A index. The latest data shows the 20-day moving average at 6247 and the 120-day moving average at 5930, with a difference of 5.33%. The model also considers the 5-day moving average and the trend line to determine the market's oscillating pattern[2][5][7]; Model Evaluation: The model is effective in identifying market trends and oscillations based on moving averages[2][5][7] - Model Name: Position Management Model; Model Construction Idea: The model aims to manage stock positions based on valuation indicators and short-term trends; Model Construction Process: The model uses the PE and PB ratios of the WIND All A index to determine the stock position. The PE ratio is at the 80th percentile, indicating a moderate level, while the PB ratio is at the 50th percentile, indicating a lower level. Based on these indicators and short-term trends, the model suggests a 70% stock position for absolute return products[8]; Model Evaluation: The model is effective in managing stock positions based on valuation indicators and short-term trends[8] Model Backtesting Results - Industry Trend Allocation Model, Weekly Excess Return: 1.40%[1] - TWO BETA Model, Weekly Excess Return: 1.40%[1] - Timing System, Weekly Excess Return: 1.40%[1] - Position Management Model, Weekly Excess Return: 1.40%[1]

Market Performance - The Sci-Tech 50 Index decreased by 0.08% over the last five trading days, while the ChiNext Index increased by 1.86%. The large-cap value index rose by 0.74%, and the large-cap growth index increased by 1.61%. The Shanghai 50 Index gained 1.09%, and the small-cap index represented by the CSI 2000 rose by 0.19%. The metals and communications sectors performed well, while media and real estate lagged behind [1]. Risk Premium and Valuation Levels - As of December 5, 2025, the risk premium, calculated as the inverse of the static PE of the CSI All Share Index minus the yield of ten-year government bonds, stands at 2.81%. The two-standard deviation boundary is 4.72% [1]. - The valuation level indicates that the CSI All Share Index's PETTM is at the 80th percentile, with the Shanghai 50 and CSI 300 at 75% and 72%, respectively. The ChiNext Index is close to 49%, while the CSI 500 and CSI 1000 are at 61% and 57%, respectively. The ChiNext Index's valuation is relatively at the historical median level [1]. ETF Fund Flow - Over the last five trading days, ETF funds experienced an outflow of 1.4 billion yuan, while margin trading increased by approximately 11.5 billion yuan. The average daily trading volume across both markets was 168.24 billion yuan [2]. Thematic Indexes - The latest thematic allocations include the CSI Communication Equipment Index, the CSI Chengdu-Chongqing Economic Circle Index, the CSI Low Volatility Dividend 100 Index, the ChiNext Momentum Growth Index, and the National Food Index [2][3][11]. Market Sentiment and Risk Appetite - The report includes observations on market sentiment based on the proportion of stocks above the 200-day moving average and tracks the risk appetite between equity and bond assets [12][13]. Financing Balance - The financing balance statistics indicate trends in margin trading and overall market leverage [15]. Individual Stock Performance - The report provides a distribution of individual stock performance based on year-to-date return ranges, highlighting the performance of various stocks in the current market environment [17]. Oversold Indices - An analysis of indices that are currently considered oversold is included, providing insights into potential investment opportunities [19].

证 券 研 究 报 告 【金工周报】（20251201-20251205） 指数择时多空交织，后市或中性震荡 本周回顾 本周市场普遍上涨，上证指数单周上涨 0.37%，创业板指单周上涨 1.86%。 A 股模型： 短期：成交量模型所有宽基指数中性。特征龙虎榜机构模型中性。特征成交量 模型看空。智能算法沪深 300 模型看多，智能算法中证 500 模型看多。 金融工程 中期：涨跌停模型中性。上下行收益差模型所有宽基指数看多。月历效应模型 中性。 长期：长期动量模型看多。 综合：A 股综合兵器 V3 模型看空。A 股综合国证 2000 模型看空。 港股模型： 中期：成交额倒波幅模型看多。恒生指数上下行收益差模型中性。 本周行业指数涨跌互现，涨幅前五的行业为：有色金属、通信、国防军工、非 银行金融、机械，跌幅前五的行业为：传媒、房地产、食品饮料、纺织服装、 农林牧渔。从资金流向角度来说，除煤炭、建材外所有行业主力资金净流出， 其中基础化工、计算机、电子、传媒、医药主力资金净流出居前。 本周股票型基金总仓位为 97.29%，相较于上周增加了 70 个 bps，混合型基金 总仓位 86.86%，相较于上周减少了 80 ...

Quantitative Models and Construction Methods 1. Model Name: Short-term Timing Strategy - **Model Construction Idea**: The model is based on historical data and statistical rules to identify short-term market timing signals, combining macroeconomic, valuation, sentiment, and liquidity indicators to generate a comprehensive timing signal[16][17][19] - **Model Construction Process**: 1. **Macroeconomic Indicators**: - Manufacturing PMI: If PMI > 50, it gives a positive signal; otherwise, a cautious signal. - Credit Pulse: The YoY growth rate of medium- and long-term RMB loans is used, with a higher percentile indicating a positive signal. - M1 YoY Growth Rate: Filtered using HP filter; higher percentiles indicate a positive signal. 2. **Valuation Indicators**: - PE Median Percentile: A higher percentile indicates a cautious signal due to mean reversion. - PB Median Percentile: A higher percentile also indicates a cautious signal due to mean reversion. 3. **Sentiment Indicators**: - Beta Dispersion: Neutral signal if within a certain range. - Volume Sentiment Score: Lower percentiles indicate a cautious signal. - Volatility: Neutral signal if within a certain range. 4. **Liquidity Indicators**: - Money Market Rate: Lower percentiles indicate a positive signal. - Exchange Rate Expectation: A stronger RMB against the USD gives a positive signal. - 5-day average net financing amount: Lower percentiles indicate a positive signal. 5. Combine all signals to generate a comprehensive timing signal[16][17][19] - **Model Evaluation**: The model demonstrates significant improvement over the benchmark strategy, with higher annualized returns, lower maximum drawdown, and better Sharpe ratio[18][21] 2. Model Name: Growth-Value Style Rotation Model - **Model Construction Idea**: The model uses a quantitative economic mid-cycle analysis framework, incorporating profitability, interest rate, and credit cycles to determine the relative attractiveness of growth versus value styles[26][27] - **Model Construction Process**: 1. **Macroeconomic Indicators**: - Profitability Cycle Slope: A steeper slope favors growth. - Interest Rate Cycle Level: Higher levels favor value. - Credit Cycle Strength: A stronger credit cycle favors growth. 2. **Valuation Indicators**: - Growth-Value PE Spread: A higher 5-year percentile indicates a preference for growth. - Growth-Value PB Spread: A higher 5-year percentile also indicates a preference for growth. 3. **Sentiment Indicators**: - Turnover Spread: A higher 5-year percentile indicates a preference for growth. - Volatility Spread: A higher 5-year percentile indicates a balanced preference for both growth and value. 4. Combine all signals to generate a comprehensive style rotation signal[26][27][28] - **Model Evaluation**: The strategy has shown significant improvement over the benchmark, with higher annualized returns, lower maximum drawdown, and better Sharpe ratio. However, in 2025, the strategy underperformed the benchmark slightly[27][29] 3. Model Name: Small-Cap vs. Large-Cap Style Rotation Model - **Model Construction Idea**: The model is based on 11 effective rotation indicators, including liquidity, sentiment, and valuation metrics, to determine the relative attractiveness of small-cap versus large-cap styles[30] - **Model Construction Process**: 1. **Indicators Used**: - Indicators such as R007, financing balance changes, trading volume, and sentiment metrics are analyzed. - For each indicator, a signal is generated to favor either small-cap or large-cap styles. 2. **Comprehensive Signal**: - Combine all individual signals into a comprehensive small-cap or large-cap rotation signal. - The model currently favors large-cap due to weak small-cap indicators such as low trading volume and negative sentiment[30][32] - **Model Evaluation**: The strategy has consistently generated positive annual excess returns since 2014, with a significant improvement over the benchmark in terms of annualized returns and maximum drawdown[31][32] --- Model Backtesting Results 1. Short-term Timing Strategy - Annualized Return: 16.41% - Annualized Volatility: 14.81% - Maximum Drawdown: 14.07% - Sharpe Ratio: 0.9655 - Return-to-Drawdown Ratio: 1.1667 - Monthly Win Rate: 66.24% - Quarterly Win Rate: 60.38% - Annual Win Rate: 78.57%[18][21] 2. Growth-Value Style Rotation Model - Annualized Return: 12.74% - Annualized Volatility: 20.80% - Maximum Drawdown: 43.07% - Sharpe Ratio: 0.5853 - Return-to-Drawdown Ratio: 0.2958 - Monthly Win Rate: 58.33% - Quarterly Win Rate: 59.62%[29] 3. Small-Cap vs. Large-Cap Style Rotation Model - Annualized Return: 19.73% - Annualized Excess Return: 12.67% - Maximum Drawdown: 40.70% - Average Turnover Interval: 20 trading days - Win Rate (per trade): 49.57%[32]

权 益 量 化 研 究 2025 年 11 月 30 日 价量匹配改善，情绪指标维持震荡 ——量化择时周报 20251130 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 | 1．情绪模型观点：市场情绪得分周内继续回落 4 | | --- | | 1.1 从分项指标出发：价量匹配改善、主力资金回流，情绪指标维持震 | | 荡、分化 5 | | 2．其他择时模型观点：美容护理短期得分快速提升，价值风 | | 格与小盘风格占优 10 | | 2.1 美容护理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 14 | 请务必仔细阅读正文之后的各项信息披露与声明 第2页 共15页 简单金融 成就梦想 证 券 研 究 报 告 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 量 化 策 略 - ⚫ 市场情绪得分周内继续回落： ...

报告导读： 从技术面来看， Wind 全 A 指数依旧处于 SAR 反转点位之下；均线强弱指数 在指数绝对点位上升幅度并不大的情况下出现上行，表明市场依旧存在下行可能；情绪模 型继续显示市场情绪较弱。我们认为，市场下周维持震荡可能性较大。 下周（20251201-20251205，后文同）市场观点：市场下周维持震荡可能性较大。 从量化指标上看，基于沪深300指数的流动性冲击指标周五为0.50，高 于前一周（0.15），意味着当前市场的流动性高于过去一年平均水平0.50倍标准差。上证50ETF期权成交量的PUT-CALL比率震荡，周五为1.02，持平于前 一周（1.02），投资者对上证50ETF短期走势相对谨慎。上证综指和Wind全A五日平均换手率分别为0.98%和1.59%，处于2005年以来的65.67%和 73.47%分位点，交易活跃度有所下降。从宏观因子上看，上周人民币汇率震荡，在岸和离岸汇率周涨幅分别为0.43%、0.48%。日历效应上，2005年以 来，上证综指、沪深300、中证500、创业板指在12月上半月上涨概率分别为 70% 、 65% 、 55% 、 60% ，涨幅均值分别为 1.81% 、 ...