经济边际下行，持有小盘、成长：高维宏观周期驱动风格、行业月报(2026/3)-20260313

Quantitative Models and Construction Methods 1. Model Name: Broad-based Index Timing Strategy - Model Construction Idea: Utilize macroeconomic variable combinations to predict the future returns of the CSI All Share Index. The strategy involves making long or short decisions based on the predicted values exceeding a threshold[31][34]. - Model Construction Process: 1. Combine liquidity and inventory sub-strategies to predict whether the CSI All Share Index will rise. 2. If any predicted value exceeds the threshold (0.6), go long on the CSI All Share Index; otherwise, go short[31]. - Model Evaluation: The model effectively captures the impact of macroeconomic variables on the index, providing a systematic approach to timing[34]. 2. Model Name: Dividend Index Timing Strategy - Model Construction Idea: Use combinations of inflation and inventory, as well as inventory and credit, to predict the future returns of the Dividend Index. The strategy involves making long or short decisions based on the average predicted values exceeding a threshold[40]. - Model Construction Process: 1. Calculate the average predicted value of inflation + inventory and inventory + credit sub-strategies. 2. If the average exceeds the threshold (0.6), go long on the Dividend Index; otherwise, go short[40]. - Model Evaluation: The model demonstrates strong defensive characteristics of the Dividend Index, particularly under specific macroeconomic conditions[40]. 3. Model Name: Style Rotation Strategy - Model Construction Idea: Leverage macroeconomic factor combinations to predict the future returns of six style indices. Allocate capital to the top two indices with the highest predicted returns[49][54]. - Model Construction Process: 1. Use combinations of inflation + inventory and inflation + credit to predict the future returns of six style indices. 2. Smooth the predicted returns and rank them at the end of each month. 3. Allocate capital equally to the top two indices for the next month[54]. - Model Evaluation: The strategy effectively captures the differentiated impacts of macroeconomic factors on various styles, providing a robust framework for style rotation[49][54]. --- Model Backtesting Results 1. Broad-based Index Timing Strategy - Annualized Return: 15.34% - Annualized Volatility: 22.02% - Sharpe Ratio: 0.74 - Maximum Drawdown: -28.10% - Excess Return: 10.31% - Tracking Error: 34.16% - IR: 0.30 - Relative Maximum Drawdown: -50.30%[36]. 2. Dividend Index Timing Strategy - Annualized Return: 10.32% - Annualized Volatility: 13.74% - Sharpe Ratio: 0.75 - Maximum Drawdown: -19.92% - Excess Return: 7.97% - Tracking Error: 9.23% - IR: 0.86 - Relative Maximum Drawdown: -12.47%[42]. 3. Style Rotation Strategy - Annualized Return: 14.79% - Annualized Volatility: 21.81% - Sharpe Ratio: 0.64 - Maximum Drawdown: -45.93% - Excess Return: 4.61% - Tracking Error: 10.28% - IR: 0.52 - Relative Maximum Drawdown: -81.71%[59]. --- Quantitative Factors and Construction Methods 1. Factor Name: Macroeconomic Factor Variables - Factor Construction Idea: Select significant macroeconomic sub-variables through regression analysis and weight them inversely by their standard deviation over the past year. Use HP filter to adjust for short-term fluctuations and identify long-term trends[2]. - Factor Construction Process: 1. Perform regression of macroeconomic indices against broad-based indices and proxy macroeconomic variables. 2. Select sub-variables with significant t-values. 3. Weight the selected variables inversely by their past-year standard deviation. 4. Apply a one-sided HP filter to remove short-term noise and identify long-term trends[2]. - Factor Evaluation: The factor construction process effectively integrates macroeconomic trends and states, providing a comprehensive framework for understanding asset price drivers[2]. 2. Factor Name: High-dimensional Macroeconomic Variables - Factor Construction Idea: Combine marginal changes and states of macroeconomic variables to address inconsistencies in traditional macroeconomic factor transmission[2][8]. - Factor Construction Process: 1. Identify five dimensions of macroeconomic variables: economic prosperity, inflation, interest rates, inventory, and credit. 2. Combine marginal changes and time-series rankings of these variables to construct high-dimensional macroeconomic factors[9]. - Factor Evaluation: The high-dimensional approach improves the stability and predictive power of macroeconomic factors, addressing the limitations of single-dimensional indicators[8][9]. --- Factor Backtesting Results 1. Macroeconomic Factor Variables - Liquidity (Up): 70.30% probability of index rise - Liquidity (Down): 58.33% probability of index rise - Inventory (Up): 65.84% probability of index rise - Inventory (Down): 63.91% probability of index rise[37]. 2. High-dimensional Macroeconomic Variables - Inflation (Up): 58.91% probability of index rise - Inflation (Down): 67.33% probability of index rise - Inventory (Up): 64.13% probability of index rise - Inventory (Down): 63.91% probability of index rise[47].