Quantitative Models and Construction Methods 1. Model Name: Black-Litterman (BL) Model - **Model Construction Idea**: The BL model is an improvement over the traditional mean-variance optimization (MVO) model. It integrates subjective views with quantitative models using Bayesian theory to optimize asset allocation weights. This approach addresses the sensitivity of MVO to expected returns and provides a more robust asset allocation solution[12][13]. - **Model Construction Process**: - The BL model combines subjective views of investors with market equilibrium returns to derive optimized portfolio weights. - The model uses the following formula to calculate the posterior expected returns: $ \mu = [( \tau \Sigma )^{-1} + P^T \Omega^{-1} P]^{-1} [( \tau \Sigma )^{-1} \Pi + P^T \Omega^{-1} Q] $ - $\mu$: Posterior expected returns - $\tau$: Scalar representing the uncertainty in the prior estimate of returns - $\Sigma$: Covariance matrix of asset returns - $\Pi$: Equilibrium returns derived from market capitalization weights - $P$: Matrix representing the views on assets - $\Omega$: Covariance matrix of the views - $Q$: Vector of expected returns based on the views - The optimized portfolio weights are then derived using the posterior expected returns and the covariance matrix[12][13]. - **Model Evaluation**: The BL model effectively addresses the sensitivity of MVO to expected returns and provides a more robust and efficient asset allocation framework. It also allows for the incorporation of subjective views, making it more flexible and practical for real-world applications[12]. 2. Model Name: Risk Parity Model - **Model Construction Idea**: The risk parity model aims to equalize the risk contribution of each asset in a portfolio. It is an improvement over the traditional mean-variance optimization model and focuses on diversifying risk rather than capital allocation[17][18]. - **Model Construction Process**: - Step 1: Select appropriate underlying assets. - Step 2: Calculate the risk contribution of each asset to the portfolio using the formula: $ RC_i = w_i \cdot \sigma_i \cdot \rho_{i,portfolio} $ - $RC_i$: Risk contribution of asset $i$ - $w_i$: Weight of asset $i$ - $\sigma_i$: Volatility of asset $i$ - $\rho_{i,portfolio}$: Correlation of asset $i$ with the portfolio - Step 3: Solve the optimization problem to minimize the deviation between actual and target risk contributions, subject to the constraint that the sum of weights equals 1[18][19]. - **Model Evaluation**: The risk parity model provides a balanced risk allocation across assets, making it suitable for achieving stable returns across different economic cycles. It is particularly effective in reducing portfolio volatility and drawdowns[18]. 3. Model Name: Macro Factor-Based Asset Allocation Model - **Model Construction Idea**: This model constructs a macro factor system covering six key risks: growth, inflation, interest rates, credit, exchange rates, and liquidity. It bridges macroeconomic research with asset allocation by translating macroeconomic views into actionable portfolio strategies[21][22]. - **Model Construction Process**: - Step 1: Calculate the factor exposure levels of assets at the end of each month. - Step 2: Use a risk parity portfolio as the benchmark and calculate the benchmark factor exposure. - Step 3: Based on macroeconomic forecasts for the next month, assign subjective factor deviation values. For example, if inflation is expected to rise, assign a positive deviation to the inflation factor. - Step 4: Combine the benchmark factor exposure with the subjective factor deviations to derive the target factor exposure for the portfolio. - Step 5: Solve the optimization problem to determine the asset allocation weights for the next month[22][25]. - **Model Evaluation**: This model effectively incorporates macroeconomic views into asset allocation, providing a systematic framework for translating macroeconomic insights into portfolio decisions. It is particularly useful for capturing macroeconomic trends and their impact on asset performance[21]. --- Model Backtesting Results 1. Black-Litterman (BL) Model - **Domestic Asset BL Model 1**: Weekly return: -0.32%, November return: 0.05%, 2025 YTD return: 4.0%, annualized volatility: 2.18%, maximum drawdown: 1.31%[14][16][17] - **Domestic Asset BL Model 2**: Weekly return: -0.15%, November return: 0.08%, 2025 YTD return: 3.77%, annualized volatility: 1.95%, maximum drawdown: 1.06%[14][16][17] - **Global Asset BL Model 1**: Weekly return: -0.17%, November return: -0.26%, 2025 YTD return: 0.78%, annualized volatility: 2.0%, maximum drawdown: 1.64%[14][16][17] - **Global Asset BL Model 2**: Weekly return: 0.01%, November return: 0.08%, 2025 YTD return: 2.7%, annualized volatility: 1.59%, maximum drawdown: 1.28%[14][16][17] 2. Risk Parity Model - **Domestic Asset Risk Parity Model**: Weekly return: -0.27%, November return: -0.09%, 2025 YTD return: 3.6%, annualized volatility: 1.32%, maximum drawdown: 0.76%[20][28] - **Global Asset Risk Parity Model**: Weekly return: -0.2%, November return: -0.07%, 2025 YTD return: 3.04%, annualized volatility: 1.42%, maximum drawdown: 1.2%[20][28] 3. Macro Factor-Based Asset Allocation Model - **Macro Factor-Based Asset Allocation Model**: Weekly return: -0.31%, November return: -0.01%, 2025 YTD return: 4.43%, annualized volatility: 1.55%, maximum drawdown: 0.64%[27][28]

- The BL model is an improvement of the traditional mean-variance optimization (MVO) model, developed by Fisher Black and Robert Litterman in 1990. It integrates Bayesian theory to combine subjective views with quantitative asset allocation models, optimizing asset weights based on investor forecasts of market returns. This model addresses MVO's sensitivity to expected returns and offers higher tolerance compared to purely subjective investment approaches, providing efficient asset allocation solutions[12][13] - The BL model was implemented for both global and domestic assets. For global assets, it utilized indices such as S&P 500, Hang Seng Index, and Nanhua Commodity Index. For domestic assets, it included indices like CSI 300, CSI 1000, and SHFE Gold. Two versions of BL models were developed for each market, focusing on equities, bonds, commodities, and gold[13][14] - The Risk Parity model, introduced by Bridgewater in 2005, aims to equalize risk contributions across asset classes in a portfolio. It calculates initial asset weights based on expected volatility and correlation, then optimizes deviations between actual and expected risk contributions to determine final weights[17][18] - The Risk Parity model was constructed in three steps: selecting appropriate underlying assets, calculating risk contributions of each asset to the portfolio, and solving optimization problems to determine asset weights. It was applied to both global and domestic assets, using indices like CSI 300, CSI 1000, and COMEX Gold for domestic assets, and S&P 500, Hang Seng Index, and Nanhua Commodity Index for global assets[19][21] - The macro factor-based asset allocation model incorporates six macro risks: growth, inflation, interest rates, credit, exchange rates, and liquidity. Using Factor Mimicking Portfolio methodology, high-frequency macro factors were constructed. The strategy involves calculating asset factor exposures, determining benchmark exposures, setting subjective factor deviations based on macro forecasts, and solving for asset weights to reflect macro risk judgments[23][26] - The macro factor-based model was applied to domestic assets, including indices like CSI 300, CSI 1000, and SHFE Gold. For example, in September 2025, subjective factor deviations were set as 0 for growth, inflation, interest rates, and credit, 1 for exchange rates, and 0 for liquidity, reflecting macroeconomic conditions at the time[25][27] - Domestic BL Model 1 achieved weekly returns of 0.1%, monthly returns of 0.38%, and annual returns of 3.97%, with annualized volatility of 2.23% and maximum drawdown of 1.31%[14][17] - Domestic BL Model 2 recorded weekly returns of -0.01%, monthly returns of 0.48%, and annual returns of 3.68%, with annualized volatility of 2.02% and maximum drawdown of 1.06%[14][17] - Global BL Model 1 delivered weekly returns of 0.54%, monthly returns of 0.03%, and annual returns of 1.02%, with annualized volatility of 2.04% and maximum drawdown of 1.64%[14][17] - Global BL Model 2 achieved weekly returns of 0.37%, monthly returns of 0.35%, and annual returns of 2.43%, with annualized volatility of 1.65% and maximum drawdown of 1.28%[14][17] - Domestic Risk Parity Model recorded weekly returns of 0.14%, monthly returns of 0.34%, and annual returns of 3.47%, with annualized volatility of 1.34% and maximum drawdown of 0.76%[21][22] - Global Risk Parity Model achieved weekly returns of 0.22%, monthly returns of 0.39%, and annual returns of 2.99%, with annualized volatility of 1.46% and maximum drawdown of 1.2%[21][22] - Macro Factor-Based Model delivered weekly returns of -0.25%, monthly returns of 0.73%, and annual returns of 4.29%, with annualized volatility of 1.54% and maximum drawdown of 0.64%[27][28]

- The Black-Litterman (BL) model is an improved version of the mean-variance optimization (MVO) model developed by Fisher Black and Robert Litterman in 1990. It combines Bayesian theory with quantitative asset allocation models, allowing investors to incorporate subjective views into asset return forecasts and optimize portfolio weights. This model addresses MVO's sensitivity to expected returns and provides a more robust framework for efficient asset allocation[12][13][14] - The BL model was implemented for both global and domestic assets. For global assets, it utilized indices such as the S&P 500, Hang Seng Index, and COMEX Gold. For domestic assets, it included indices like CSI 300, CSI 1000, and SHFE Gold. Two variations of the BL model were constructed for each asset category[13][14][18] - The Risk Parity model, introduced by Bridgewater in 2005, aims to equalize risk contributions across asset classes in a portfolio. It calculates initial asset weights based on expected volatility and correlation, then optimizes deviations between actual and expected risk contributions to determine final portfolio weights[17][18][20] - The Risk Parity model was applied to both global and domestic assets. Global assets included indices such as CSI 300, S&P 500, and COMEX Gold, while domestic assets incorporated CSI 300, CSI 1000, and SHFE Gold. The model followed a three-step process: selecting assets, calculating risk contributions, and solving optimization problems for portfolio weights[18][20][21] - The Macro Factor-based Asset Allocation model constructs a framework using six macroeconomic risk factors: growth, inflation, interest rates, credit, exchange rates, and liquidity. It employs Factor Mimicking Portfolio methods to calculate high-frequency macro factors and integrates subjective views on macroeconomic conditions into asset allocation decisions[22][24][25] - The Macro Factor-based model involves four steps: calculating factor exposures for assets, determining benchmark factor exposures using a Risk Parity portfolio, incorporating subjective factor deviations based on macroeconomic forecasts, and solving for asset weights that align with target factor exposures[22][24][25] Model Performance Metrics - Domestic BL Model 1: Weekly return -0.11%, September return -0.14%, 2025 YTD return 3.23%, annualized volatility 2.19%, maximum drawdown 1.31%[14][17] - Domestic BL Model 2: Weekly return -0.11%, September return -0.13%, 2025 YTD return 2.84%, annualized volatility 1.99%, maximum drawdown 1.06%[14][17] - Global BL Model 1: Weekly return 0.04%, September return 0.11%, 2025 YTD return 0.84%, annualized volatility 1.99%, maximum drawdown 1.64%[14][17] - Global BL Model 2: Weekly return 0.00%, September return 0.03%, 2025 YTD return 1.84%, annualized volatility 1.63%, maximum drawdown 1.28%[14][17] - Domestic Risk Parity Model: Weekly return -0.06%, September return 0.05%, 2025 YTD return 2.99%, annualized volatility 1.35%, maximum drawdown 0.76%[20][21] - Global Risk Parity Model: Weekly return -0.07%, September return 0.13%, 2025 YTD return 2.50%, annualized volatility 1.48%, maximum drawdown 1.20%[20][21] - Macro Factor-based Model: Weekly return 0.04%, September return 0.26%, 2025 YTD return 3.29%, annualized volatility 1.32%, maximum drawdown 0.64%[26][27]

Group 1 - The report highlights that domestic equity assets performed well in July 2025, with the risk parity strategy achieving a year-to-date return of 2.65% [2][5][20] - The report provides a summary of various asset allocation strategies, indicating that the domestic asset BL strategy 1 and 2 yielded returns of 2.40% and 2.34% respectively, while the risk parity strategy and macro factor-based strategy returned 2.65% and 2.59% respectively [21][41][42] - The report notes that the domestic equity market saw significant gains, with the CSI 1000 index rising by 4.8% and the Hang Seng Index increasing by 2.78% in July [8][9][10] Group 2 - The report discusses the correlation between different asset classes, indicating that the correlation between the CSI 300 and the total wealth index of government bonds was -38.08%, suggesting a potential for diversification [15][16] - The report outlines the performance of various asset allocation models, with the domestic risk parity strategy showing a maximum drawdown of 0.76% and an annualized volatility of 1.46% [41][42] - The macroeconomic outlook suggests downward risks for growth factors, while inflation expectations may stabilize due to recent policy measures [45][47]