基于动态宏观事件因子的中证港股通大消费指数择时策略 为了探索中国宏观经济对香港大消费主题上市公司整体状况和走势的影响，我们选取中证港股通大消费主题指数作为 研究对象，尝试从动态宏观事件因子的角度构建择时策略框架。我们用经济、通胀、货币和信用四维度的 20 余个宏 观数据指标，基于数据样本内时间段的收益率胜率指标和开仓波动调整收益率指标数值，筛选出这些宏观数据每期最 优的事件因子和最优的数据处理方式，并且从中挑选出了 5 个对中证港股通大消费主题指数择时效果较好的宏观因 子。 在选定了最终使用的宏观指标之后，我们使用这些宏观数据构建的宏观事件因子来搭建择时策略：当大于 2/3 的因子 发出看多信号，则当期该大类因子的信号标记为 1；当少于 1/3 的因子发出看多信号时，则当期大类因子信号标记为 0；若当因子发出看多信号的比例处于两个区间之后，则大类因子标记为对应具体的比例。将每期大类因子的得分作 为当期的择时仓位信号。 建议关注标的介绍：泰康中证港股通大消费 A（006786.OF） 目前跟踪中证港股通大消费主题指数的基金为泰康中证港股通大消费主题基金，是择时策略可投资的标的，该基金成 立于 2019 年 5 ...

Quantitative Models and Construction Methods - **Model Name**: Dynamic Macro Event Factor-based CSI Hong Kong Stock Connect Consumer Index Timing Strategy **Model Construction Idea**: The model explores the impact of China's macroeconomic factors on the overall performance and trends of Hong Kong-listed consumer companies, using dynamic macro event factors to construct a timing strategy framework [2][3][20] **Model Construction Process**: 1. **Macro Data Selection**: Select 20+ macroeconomic indicators across four dimensions: economy, inflation, currency, and credit, such as PMI, PPI, M1, etc [21][23] 2. **Data Preprocessing**: - Align data frequency to monthly frequency by either taking the last trading day of the month or calculating the monthly average for daily data - Fill missing values using the median of the first-order difference of the past 12 months added to the previous value $ X_{t}=X_{t-1}+Median_{diff12} $ [27] - Apply filtering using one-sided HP filter to avoid future data leakage $ \hat{t}_{t|t,\lambda}=\sum\nolimits_{s=1}^{t}\omega_{t|t,s,\lambda}\cdot y_{s}=W_{t|t,\lambda}(L)\cdot y_{t} $ [28] - Derive factors using transformations such as year-on-year, month-on-month, and moving averages [29] 3. **Macro Event Factor Construction**: - Determine event breakthrough direction by calculating the correlation between data and next-period asset returns - Identify leading or lagging relationships by deriving lagged event factors (0-4 periods) and selecting the most suitable lag period - Generate event factors using three types: data breaking through moving average, data breaking through median, and data moving in the same direction, with different parameters (e.g., moving average length: 2-12, rolling window: 2-12, same direction period: 1-5) [30][32] 4. **Event Factor Evaluation and Screening**: - Use two metrics: win rate of returns and volatility-adjusted returns during opening positions - Initial screening criteria: t-test significance at 95% confidence level, win rate >55%, occurrence frequency > rolling window period/6 [31][32] 5. **Combining Event Factors**: Select the highest win rate event factor as the base factor, then combine it with the second-highest win rate factor with a correlation <0.85. If the combined factor improves the win rate, it is selected; otherwise, the base factor is used [33] 6. **Dynamic Exclusion**: If no event factor passes the screening, the macro indicator is marked as empty for the period and excluded from scoring [33] 7. **Optimal Rolling Window Determination**: Test rolling windows of 48, 60, 72, 84, and 96 months to find the most suitable parameter for each macro indicator based on volatility-adjusted returns during opening positions [33] 8. **Final Macro Indicators**: Five macro factors were selected based on their performance in the sample period: - PMI: Raw Material Prices (96-month rolling window) - US-China 10Y Bond Spread (72-month rolling window) - Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY (48-month rolling window) - M1: YoY (48-month rolling window) - New Social Financing: Rolling 12M Sum: YoY (96-month rolling window) [34][35] 9. **Timing Strategy Construction**: - If >2/3 of factors signal bullishness, the category factor signal is marked as 1 - If <1/3 of factors signal bullishness, the category factor signal is marked as 0 - If the proportion of bullish signals falls between these ranges, the category factor is marked with the specific proportion - The score of each category factor is used as the timing position signal for the period [3][35] **Model Evaluation**: The strategy effectively captures systematic opportunities and avoids systematic risks, demonstrating superior performance compared to the benchmark in terms of annualized returns, maximum drawdown, Sharpe ratio, and return-drawdown ratio [2][3][20] --- Model Backtesting Results - **Dynamic Macro Event Factor-based CSI Hong Kong Stock Connect Consumer Index Timing Strategy** - **Annualized Return**: 10.44% - **Annualized Volatility**: 18.47% - **Maximum Drawdown**: -29.72% - **Sharpe Ratio**: 0.59 - **Return-Drawdown Ratio**: 0.35 [2][11][22] --- Quantitative Factors and Construction Methods - **Factor Name**: PMI: Raw Material Prices **Factor Construction Idea**: Use raw data to capture macroeconomic trends affecting asset returns [35] **Factor Construction Process**: Utilize raw data with a 96-month rolling window [35] - **Factor Name**: US-China 10Y Bond Spread **Factor Construction Idea**: Reflect the impact of interest rate differentials on asset returns [35] **Factor Construction Process**: Utilize raw data with a 72-month rolling window [35] - **Factor Name**: Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY **Factor Construction Idea**: Measure credit expansion and its influence on asset returns [35] **Factor Construction Process**: Utilize raw data with a 48-month rolling window [35] - **Factor Name**: M1: YoY **Factor Construction Idea**: Capture monetary supply changes and their impact on asset returns [35] **Factor Construction Process**: Utilize raw data with a 48-month rolling window [35] - **Factor Name**: New Social Financing: Rolling 12M Sum: YoY **Factor Construction Idea**: Reflect credit growth and its effect on asset returns [35] **Factor Construction Process**: Utilize raw data with a 96-month rolling window [35] **Factor Evaluation**: The selected factors demonstrated strong performance in the sample period, with high win rates and volatility-adjusted returns during opening positions [34][35] --- Factor Backtesting Results - **PMI: Raw Material Prices** - **Rolling Window**: 96 months [35] - **US-China 10Y Bond Spread** - **Rolling Window**: 72 months [35] - **Financial Institutions: Medium-Long Term Loan Balance: Monthly New Additions: Rolling 12M Sum: YoY** - **Rolling Window**: 48 months [35] - **M1: YoY** - **Rolling Window**: 48 months [35] - **New Social Financing: Rolling 12M Sum: YoY** - **Rolling Window**: 96 months [35]