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]