证券研究报告|金融工程研究报告 [Table_Date] 2025 年 8 月 1 日 [Table_Title] 量价行业轮动组合今年以来月胜率为 100% [Table_Title2] ——行业轮动组合月报 [Table_Summary] ► 量价行业轮动组合 2025 年前 7 个月皆跑赢基准 量价行业轮动组合 7 月份上涨 5.54%，相对于行业等权的 超额收益为 0.82%。今年以来，量价行业轮动组合上涨 13.69%，相对于行业等权组合的超额收益为 4.36%，月胜率为 100%。 2025 年 8 月份量价复合因子值排名较高的行业为：纺织 服装、国防军工、轻工制造、商贸零售、家电。 ► "正预期与非拥挤"行业组合 7 月超额 2.02% 我们根据分析师预期变化、市场信心、报告覆盖加速 度、机构覆盖加速度、财务报表超预期、业绩预告超预期构 建分析师预期复合因子；同时，通过 6 个量价因子构建拥挤 度指标，剔除拥挤度指标最低的 15 个行业，重新选择剔除后 分析师预期复合因子值最高的五个行业，构建"正预期与非 拥挤"行业轮动组合。 2010 年至 2025 年 7 月，"正预期与非拥挤"行业组合的 ...

Quantitative Models and Construction Methods 1. Model Name: Volume-Price Industry Rotation Strategy - **Model Construction Idea**: The strategy is based on six dimensions of volume-price factors, including momentum, trading volatility, turnover rate, long-short comparison, volume-price divergence, and volume-amplitude alignment. These factors are tested on a single-factor basis at the monthly frequency for the CSI Level-1 industries, resulting in 11 effective and logically strong industry factors[6] - **Model Construction Process**: 1. Construct 11 volume-price factors based on the six dimensions mentioned above 2. At the end of each month, select the top five industries with the highest composite factor scores from the CSI Level-1 industries (excluding "Comprehensive" and "Comprehensive Finance") 3. Apply equal weighting within factors and equal weighting across industries to form the final strategy[7] - **Model Evaluation**: The model demonstrates strong logical consistency and effectiveness in identifying outperforming industries[6] --- Quantitative Factors and Construction Methods 1. Factor Name: Second-Order Momentum - **Factor Construction Idea**: Measures the exponential weighted moving average (EWMA) of the closing price relative to its historical mean[7] - **Factor Construction Process**: $ \text{Second-Order Momentum} = \text{Close}_t \cdot \text{EWMA}(\text{Close}_{t-\text{window1}:t}) - \text{mean}(\text{Close}_{t-\text{window1}:t}) $ - Parameters: "Close" represents the closing price, "window1" defines the lookback period[7] 2. Factor Name: Momentum Term Spread - **Factor Construction Idea**: Captures the difference in momentum over two different time windows[7] - **Factor Construction Process**: $ \text{Momentum Term Spread} = \frac{\text{Close}_t - \text{Close}_{t-\text{window1}}}{\text{Close}_{t-\text{window1}}} - \frac{\text{Close}_t - \text{Close}_{t-\text{window2}}}{\text{Close}_{t-\text{window2}}} $ - Parameters: "window1" and "window2" represent two different lookback periods[7] 3. Factor Name: Trading Amount Volatility - **Factor Construction Idea**: Measures the standard deviation of trading amounts over a specific window[7] - **Factor Construction Process**: $ \text{Trading Amount Volatility} = -\text{STD}(\text{Amount}) $ - Parameters: "Amount" refers to the trading amount, and "STD" is the standard deviation operator[7] 4. Factor Name: Volume-Price Divergence Covariance - **Factor Construction Idea**: Measures the covariance between ranked closing prices and ranked volumes over a specific window[7] - **Factor Construction Process**: $ \text{Volume-Price Divergence Covariance} = \text{rank}(\text{covariance}[\text{rank}(\text{Close}), \text{rank}(\text{Volume}), \text{window}]) $ - Parameters: "Close" represents the closing price, "Volume" represents the trading volume, and "window" defines the lookback period[7] 5. Factor Name: Volume-Amplitude Alignment - **Factor Construction Idea**: Measures the correlation between ranked volumes and ranked price ranges over a specific window[7] - **Factor Construction Process**: $ \text{Volume-Amplitude Alignment} = \text{correlation}[\text{rank}(\text{Volume}_{i-1}), \text{rank}(\text{High}_i - \text{Low}_i), \text{window}] $ - Parameters: "High" and "Low" represent the highest and lowest prices, respectively, and "window" defines the lookback period[7] --- Backtesting Results of the Model 1. Volume-Price Industry Rotation Strategy - **Cumulative Return (2010-2025)**: 694.50%[9] - **Cumulative Excess Return over Equal-Weighted Industry Portfolio**: 605.20%[9] - **April 2025 Monthly Return**: -1.59%[9] - **April 2025 Excess Return over Equal-Weighted Industry Portfolio**: 0.81%[9]