Quantitative Models and Construction Methods 1. Model Name: Timing Strategy Based on Dynamic Macro Event Factors for CSI Southbound Consumer Index - **Model Construction Idea**: The model aims to explore the impact of China's macroeconomic environment on the overall performance and trends of Hong Kong-listed consumer companies. It uses dynamic macro event factors to construct a timing strategy framework[3][4][21] - **Model Construction Process**: 1. **Macro Data Selection**: Over 20 macro indicators across four dimensions (economy, inflation, monetary, and credit) were tested, including PMI, PPI, M1, etc.[22][24] 2. **Data Preprocessing**: - Align data frequency to monthly - Fill missing values using the formula: $$ X_{t} = X_{t-1} + Median_{diff12} $$ - Apply filtering (e.g., one-sided HP filter): $$ \hat{t}_{t|t,\lambda} = \sum_{s=1}^{t} \omega_{t|t,s,\lambda} \cdot y_{s} = W_{t|t,\lambda}(L) \cdot y_{t} $$ - Derive factors using transformations like YoY, MoM, and moving averages[28][29][30] 3. **Event Factor Construction**: - Identify event breakout directions based on the correlation between data and asset returns - Generate event factors using methods like data breaking through moving averages, medians, or directional changes - Construct 28 different event factors per indicator[31][33] 4. **Factor Evaluation and Selection**: - Use metrics like "win rate of returns" and "volatility-adjusted returns" for screening - Select the top-performing factors based on statistical significance, win rate (>55%), and occurrence frequency[32][34] 5. **Final Macro Factor Selection**: - Five macro factors were selected based on their performance in the backtest, including "PMI: Raw Material Prices" and "YoY Growth of Aggregate Financing"[35][36] 6. **Timing Signal Construction**: - If >2/3 of factors signal bullish, the category signal is marked as 1 - If <1/3 signal bullish, the category signal is marked as 0 - Intermediate proportions are marked accordingly - Aggregate category scores determine the timing position signal[4][36][38] - **Model Evaluation**: The strategy effectively captures systematic opportunities and mitigates risks, outperforming benchmarks in most years and controlling drawdowns during market downturns[12][21] --- Model Backtest Results 1. Timing Strategy Based on Dynamic Macro Event Factors - **Annualized Return**: 9.31% (2018/11–2025/7)[11][23] - **Maximum Drawdown**: -29.72%[11][23] - **Sharpe Ratio**: 0.54[11][23] - **Return-to-Drawdown Ratio**: 0.31[11][23] - **Average Position**: 43%[11] - **Monthly Return (2025/7)**: 2.79% (vs. benchmark 2.48%)[11][13] --- Quantitative Factors and Construction Methods 1. Factor Name: PMI: Raw Material Prices - **Factor Construction Idea**: Captures inflationary pressures and their impact on consumer sector performance[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 96 months[36] 2. Factor Name: US-China 10Y Bond Spread - **Factor Construction Idea**: Reflects monetary policy divergence and its influence on capital flows[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 72 months[36] 3. Factor Name: YoY Growth of Aggregate Financing (12M Rolling) - **Factor Construction Idea**: Measures credit expansion and its implications for economic growth[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 96 months[36] 4. Factor Name: M1 YoY Growth - **Factor Construction Idea**: Tracks monetary liquidity and its correlation with asset prices[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 48 months[36] 5. Factor Name: YoY Growth of Medium- to Long-Term Loans (12M Rolling) - **Factor Construction Idea**: Indicates long-term credit trends and their impact on investment[36] - **Factor Construction Process**: - Data Type: Original data - Rolling Window: 48 months[36] --- Factor Backtest Results 1. PMI: Raw Material Prices - **Rolling Window**: 96 months[36] 2. US-China 10Y Bond Spread - **Rolling Window**: 72 months[36] 3. YoY Growth of Aggregate Financing (12M Rolling) - **Rolling Window**: 96 months[36] 4. M1 YoY Growth - **Rolling Window**: 48 months[36] 5. YoY Growth of Medium- to Long-Term Loans (12M Rolling) - **Rolling Window**: 48 months[36]

（转自：国金证券第5小时） 作者：高智威、许坤圣 ■ 投资逻辑 月度择时模型观点及策略表现 根据国金金融工程团队发布的《量化掘基系列之二：量化择时把握港股通大消费板块投资机会》,我们 构建了基于动态宏观事件因子的中证港股通大消费主题指数择时策略。 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。 风险提示 1、以 ...