Core Insights - The core of asset allocation is to systematically balance risk and return through the design of cross-asset class combinations, aiming for a resilient portfolio across different macroeconomic cycles and market environments [3][7][48] - The article reviews mainstream asset allocation models and their application effects in both Chinese and global contexts, recommending the Black-Litterman and mean-variance models for enhanced returns, while suggesting risk parity and volatility-targeting models for absolute return risk balance [3][48] Asset Allocation Framework - The asset allocation framework involves setting allocation goals, determining asset centers, clarifying investment constraints, and dynamically adjusting weights [3][43] - The framework's design dimensions include four interrelated aspects: return enhancement, risk diversification, liquidity management, and long-term stability, with weights adjusted based on specific investor needs [3][43] Model Effectiveness Comparison - The article compares the effectiveness of various models in Chinese and global asset allocation from 2015 to 2025, focusing on nine strategies that are relatively less dependent on subjective parameters [4][12] - In the Chinese asset allocation context without asset weight limits, the Black-Litterman model achieved an annualized return of 13.64% with a volatility of 13.13%, outperforming the equal-weight benchmark by 6.28% [4][46] - The mean-variance model also showed strong performance with an annualized return of 13.55% and a volatility of 13.51%, closely matching the characteristics of the Black-Litterman model [4][46] Risk and Return Characteristics - The article notes that under reasonable assumptions, return-driven models significantly outperform benchmarks, while risk-driven models excel in absolute return risk control but struggle to beat benchmarks without leverage [5][46] - When asset weight limits are imposed, the characteristics of return and risk models tend to balance, smoothing the inherent features of the models [5][46] Global Asset Allocation Insights - In the global allocation context without asset weight limits, the performance and ranking of models are similar to those in the Chinese context, with a recommendation for the Black-Litterman and LSTM-Black-Litterman models for enhanced returns [6][48] - The article highlights that risk-driven models, except for the risk budget model, generally underperform benchmarks but maintain good absolute return risk control, with Sharpe ratios above 1 [6][48]