- The report references the Fama-MacBeth method to simulate macro risk factors, transforming the logic of configuring macro risks through asset allocation into a logic of configuring macro risks by configuring assets[1][12][18] - Real macro factor data uses forecast values of relevant monthly macro indicators or asset monthly returns (interest rates/credit), performing univariate time series regression with each asset to obtain risk loadings, and applying a half-life weighting to historical loadings to smooth out instability caused by asset volatility[1][18][22] - The macro factor risk is decomposed into underlying asset portfolios to construct a macro factor risk parity portfolio[1][18][22] - The optimization results of risk parity for macro factors show certain economic growth elasticity, with both returns and volatility higher than those based on asset risk parity[2][39] - The report also discusses the factors influencing stock-bond correlation, referencing AQR's approach, which decomposes stock-bond correlation into economic growth volatility, inflation volatility, and the correlation between economic growth and inflation[3][42][43] - The study finds that economic growth volatility negatively contributes to stock-bond correlation, while interest rate volatility positively contributes, and the correlation between economic growth and interest rates is a positive contributing variable in domestic asset research[3][42][48] - Adding the inflation level factor further improves the explanatory power, with domestic data showing that the inflation level is a significant positive variable for stock-bond correlation[3][48][51] - Using a three-year historical window to calculate the coefficients of each variable, the study combines real values and consensus forecast data to calculate the change in stock-bond correlation for the next month, showing that the estimated and predicted values have the same trend and consistent signs with the real values[3][48][54] Quantitative Models and Construction Methods 1. **Model Name**: Macro-Factor Mimicking - **Construction Idea**: Transform the logic of configuring macro risks through asset allocation into configuring macro risks by configuring assets[1][12][18] - **Construction Process**: - Use forecast values of relevant monthly macro indicators or asset monthly returns (interest rates/credit) - Perform univariate time series regression with each asset to obtain risk loadings - Apply a half-life weighting to historical loadings to smooth out instability caused by asset volatility - Decompose macro factor risk into underlying asset portfolios to construct a macro factor risk parity portfolio[1][18][22] - **Formula**: $$ r_{t}=\alpha_{t}+B\cdot f_{t}+\varepsilon_{t} $$ $$ \Sigma=B\cdot F\cdot B^{T}+E $$ $$ \sigma_{P}{}^{2}\ =\ w^{T}\cdot\Sigma\ \cdot w=\ (w^{T}\cdot B)\cdot F\cdot(B^{T}\cdot w)+w^{T}\cdot E\cdot w $$ $$ \%\text{RC}\ =(w^{T}\cdot B)_{i}\cdot\frac{\partial\sigma_{P}}{\partial(w^{T}\cdot B)_{i}}/\sigma_{P}=\frac{(w^{T}\cdot B)_{i}\cdot(F\cdot(B^{T}\cdot w))_{i}}{w^{T}\cdot\Sigma\ \cdot w} $$ where B is the time-series calculated risk loadings, f is the factor returns, Σ is the asset risk matrix, and F is the macro factor return risk matrix[23][24] - **Evaluation**: The optimization results of risk parity for macro factors show certain economic growth elasticity, with both returns and volatility higher than those based on asset risk parity[2][39] Model Backtest Results 1. **Macro-Factor Mimicking Model**: - **Annualized Return**: 9.86% (12-month half-life), 9.46% (no half-life)[29] - **Annualized Volatility**: 9.55% (12-month half-life), 9.44% (no half-life)[29] - **Maximum Drawdown**: -14.30% (12-month half-life), -15.20% (no half-life)[29] - **2016 Return**: 37.24% (12-month half-life), 18.65% (no half-life)[29] - **2017 Return**: 2.17% (12-month half-life), 7.29% (no half-life)[29] - **2018 Return**: -5.02% (12-month half-life), -7.45% (no half-life)[29] - **2019 Return**: 14.61% (12-month half-life), 14.23% (no half-life)[29] - **2020 Return**: 12.20% (12-month half-life), 7.57% (no half-life)[29] - **2021 Return**: 14.63% (12-month half-life), 10.27% (no half-life)[29] - **2022 Return**: 0.36% (12-month half-life), 8.15% (no half-life)[29] - **2023 Return**: 5.41% (12-month half-life), 3.68% (no half-life)[29] - **2024 Return**: 6.83% (12-month half-life), 15.40% (no half-life)[29] - **2025.07.31 Return**: 7.44% (12-month half-life), 11.53% (no half-life)[29]

Core Viewpoint - The article emphasizes the importance of effectively controlling risks and reducing volatility in asset management, advocating for a "macro factor" risk parity strategy that adapts to changing macroeconomic environments, contrasting it with traditional asset risk parity models [1][13][15]. Group 1: Macro Factor Risk Parity Framework - The construction of a macro factor risk parity framework involves four steps: selecting factors, calculating risk exposure, determining target risk exposure, and matching target risk exposure to asset weights [2][16][17]. - The mainstream methods for constructing macro factors include using low-frequency economic data, principal component analysis (PCA), and regression methods to fit higher-frequency macro factors [3][18][19]. Group 2: Factor Construction and High-Frequency Transformation - The article outlines a refined approach to factor construction, summarizing it as "defining dimensions, screening assets, and high-frequency transformation," which combines the advantages of various methods [3][18][19]. - The transformation of low-frequency macro factors into high-frequency factors is achieved through factor mimicking, which involves regression analysis to identify strong correlations with asset prices [5][29][31]. Group 3: Risk Exposure and Asset Sensitivity - A risk exposure matrix is created to show the sensitivity of assets to different macro variables, using robust OLS regression to capture dynamic features [6][33][34]. - The analysis reveals that large-cap stocks are more sensitive to economic growth, while mid-cap stocks are more sensitive to liquidity conditions [6][35][38]. Group 4: Performance of Different Strategies - The "lightweight" strategy, focusing on growth and inflation factors, has shown an annualized return of 7.7% with a volatility of 5.4% since 2016, outperforming traditional asset risk parity strategies [7][40][41]. - The "three-dimensional" strategy, incorporating M1, BCI, and PPI, has yielded an annualized return of 9.0% with a volatility of 7.8%, indicating a more diversified asset allocation [8][9]. - The "broad-spectrum" strategy, which includes multiple macro factors, has achieved an annualized return of 7.5% with a lower volatility of 4.0%, demonstrating a higher Sharpe ratio compared to simpler models [9][10].