- 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 indicates that the overall asset allocation strategy has experienced fluctuations due to domestic equity asset volatility, with various models recording different degrees of decline [1][4][7] - The performance of major asset classes from September 15 to September 19, 2025, shows that the S&P 500, Hang Seng Index, and other indices recorded gains, while convertible bonds and gold experienced declines [7][10] - The domestic asset BL model 1 and model 2 both reported a weekly return of -0.04%, while the global asset BL models had slightly better performance with a return of -0.01% for model 1 and -0.03% for model 2 [15][17] Group 2 - The Black-Litterman (BL) model is highlighted as an improvement over traditional mean-variance models, integrating subjective views with quantitative models to optimize asset allocation [12][13] - The domestic asset risk parity model achieved a return of -0.02% for the week, while the global asset risk parity model recorded a positive return of 0.05% [21][22] - The macro factor-based asset allocation strategy reported a weekly return of -0.1%, with a year-to-date return of 3.25%, indicating its performance amidst changing economic conditions [27][28]