证券研究报告 建议择机入场 2025 年 11 月 23 日│中国内地 量化投资周报 本周观点：建议择机入场 上周，受全球流动性压力、美联储降息预期反复以及 AI 叙事松动多重因素 影响，全球风险偏好下降——VIX 指数攀升至近三个月高位，各类风险资产 均承压，其中比特币、微盘股等对流动性和风偏更敏感的资产领跌。我们的 模型认为 A 股经过上周的调整，整体上消化了过高的估值，观点由防御转 为看平。叠加周五美联储释放了略积极的降息信号，Nowcasting 模型预测 11 月 CPI 或将继续上行至 3.7%-3.8%，但核心 CPI 预计保持平稳，或有利 于市场风偏的恢复。建议择机入场，优选低位防御板块，本周行业轮动模型 加大了对低位消费板块的押注，风格上仍看好红利。 A 股大盘择时模型：上周回调消化了高估值压力，可择机入场 我们以万得全 A 指数作为 A 股大盘代理，从估值、情绪、资金、技术四个 维度对 A 股大盘进行整体方向性判断。今年以来，模型多空择时的扣费后 收益 43.84%，同期 A 股大盘涨跌幅为 20.09%，超额收益为 23.76%；上周 模型超额收益为 10.41%。上周，受全球流动性压力 ...

Quantitative Models and Construction Methods - **Model Name**: A-Share Multi-Dimensional Timing Model **Model Construction Idea**: The model evaluates the directional judgment of the A-share market using four dimensions: valuation, sentiment, capital, and technical indicators. Valuation and sentiment dimensions adopt a mean-reversion logic, while capital and technical dimensions use trend-following logic. The model combines these dimensions to provide a comprehensive view of market trends [2][9][15]. **Model Construction Process**: 1. The model uses the Wind All A Index as a proxy for the A-share market. 2. Each dimension generates daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. 3. Valuation indicators include equity risk premium (ERP). 4. Sentiment indicators include option put-call ratio, implied volatility, and futures member position ratio. 5. Capital indicators include financing purchase amount. 6. Technical indicators include Bollinger Bands and the difference in the proportion of individual stock trading volume [11][15]. 7. The final multi-dimensional score is calculated as the sum of the scores from the four dimensions, determining the overall market view [9][15]. **Model Evaluation**: The model effectively captures market trends and provides actionable insights for timing decisions [9]. - **Model Name**: Style Timing Model **Model Construction Idea**: The model evaluates timing for dividend and size styles using trend-based indicators and crowding metrics [3][17][22]. **Model Construction Process**: 1. **Dividend Style Timing**: - The model uses three indicators: relative momentum of the CSI Dividend Index vs. CSI All Index, 10Y-1Y term spread, and interbank pledged repo transaction volume. - Each indicator generates daily signals with values of 0, ±1, representing neutral, bullish, or bearish views. - The final score is the sum of the three indicators, determining the overall view on dividend style [17][21]. 2. **Size Style Timing**: - The model uses the crowding degree of small-cap and large-cap styles, calculated based on momentum difference and trading volume ratio between the Wind Micro-Cap Index and CSI 300 Index. - Crowding degree is determined by averaging the top three results of six different window lengths for small-cap and large-cap styles. - High crowding is triggered when small-cap crowding exceeds 90% or large-cap crowding falls below 10%. - In high crowding zones, a small parameter double moving average model is used to capture short-term reversals. In low crowding zones, a large parameter double moving average model is used to follow medium- to long-term trends [22][24][26]. **Model Evaluation**: The model provides effective timing signals for style rotation, especially in different market conditions [22][24]. - **Model Name**: Industry Rotation Model **Model Construction Idea**: The model uses genetic programming to directly extract factors from industry index data, focusing on price-volume and valuation characteristics. It employs a dual-objective genetic programming approach to enhance factor diversity and reduce overfitting [4][29][32]. **Model Construction Process**: 1. The model uses 32 CITIC industry indices as underlying assets. 2. Factors are updated quarterly, and the model rebalances weekly. 3. The dual-objective genetic programming approach evaluates factors using |IC| and NDCG@5 metrics to assess monotonicity and performance of long positions. 4. Factors are combined using a greedy strategy and variance inflation factor to reduce collinearity. 5. The highest-weight factor is constructed as follows: - Perform cross-sectional regression of standardized monthly trading volume against the rolling 4-year percentile of price-to-book ratio (P/B). Take residuals as variable A. - Sum the smallest 9 values of variable A over the past 15 trading days to obtain variable B. - Standardize variable B using z-score, reverse values greater than 2.5, and sum the standardized values over the past 15 trading days [29][33][37]. **Model Evaluation**: The model effectively identifies industry rotation factors with strong monotonicity and performance, while reducing overfitting risks [29][33]. - **Model Name**: China Domestic All-Weather Enhanced Portfolio **Model Construction Idea**: The model adopts a macro factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively allocates based on macro expectation momentum [5][38][41]. **Model Construction Process**: 1. **Macro Quadrant Division and Asset Selection**: Divide growth and inflation dimensions into four quadrants based on whether they exceed or fall short of expectations. Determine suitable assets for each quadrant using quantitative and qualitative methods. 2. **Quadrant Portfolio Construction and Risk Measurement**: Construct sub-portfolios with equal weights for assets within each quadrant, focusing on downside risk. 3. **Risk Budgeting Model for Quadrant Weights**: Adjust quadrant risk budgets monthly based on "quadrant views" derived from macro expectation momentum indicators, which consider buy-side expectation momentum and sell-side expectation deviation momentum [38][41]. **Model Evaluation**: The model effectively balances macro risks and enhances portfolio performance through active allocation [38][41]. --- Model Backtesting Results - **A-Share Multi-Dimensional Timing Model**: - Annualized Return: 25.23% - Maximum Drawdown: -28.46% - Sharpe Ratio: 1.17 - Calmar Ratio: 0.89 - Year-to-Date (YTD): 40.98% - Last Week's Return: 0.15% [14] - **Style Timing Model**: - **Dividend Style Timing**: - Annualized Return: 16.04% - Maximum Drawdown: -25.52% - Sharpe Ratio: 0.87 - Calmar Ratio: 0.63 - YTD: 21.75% - Last Week's Return: 0.23% [20] - **Size Style Timing**: - Annualized Return: 26.25% - Maximum Drawdown: -30.86% - Sharpe Ratio: 1.09 - Calmar Ratio: 0.85 - YTD: 65.89% - Last Week's Return: 1.07% [27] - **Industry Rotation Model**: - Annualized Return: 32.60% - Annualized Volatility: 17.95% - Sharpe Ratio: 1.82 - Maximum Drawdown: -19.63% - Calmar Ratio: 1.66 - Last Week's Return: 0.27% - YTD: 36.44% [32] - **China Domestic All-Weather Enhanced Portfolio**: - Annualized Return: 11.53% - Annualized Volatility: 6.16% - Sharpe Ratio: 1.87 - Maximum Drawdown: -6.30% - Calmar Ratio: 1.83 - Last Week's Return: 0.66% - YTD: 9.02% [42]