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]

Core Viewpoint - The article emphasizes the importance of asset allocation as a systematic approach to balancing risk and return through diversified asset combinations, highlighting the need for a resilient portfolio that can withstand various macroeconomic cycles and market environments [2][8]. Asset Allocation Theoretical Framework - The framework for asset allocation consists of four interrelated dimensions: return enhancement, risk diversification, liquidity management, and long-term stability, with weights adjusted based on investor needs [3]. - The implementation path includes setting benchmark weights for major asset classes, defining constraints to meet investment preferences, and dynamically adjusting weights based on external conditions and internal valuations [3]. - Three strategies for asset allocation are discussed: strategic asset allocation, tactical asset allocation, and dynamic asset allocation [3]. Classic Models and Their Effectiveness - The article categorizes classic asset allocation models into three main types: return-driven, risk-driven, and macro-driven, comprising a total of 11 models with varying strengths and weaknesses [10]. - A quantitative backtest from 2015 to 2025 evaluates the effectiveness of nine selected strategies, including Mean-Variance Optimization (MVO), Black-Litterman (BLM), and Risk Parity (RP) models, in both Chinese and global asset allocation contexts [4][13]. Backtesting Results for Chinese Asset Allocation - In scenarios without asset weight limits, the BLM and MVO models demonstrated strong return capabilities, while RP and Volatility Targeting models excelled in absolute risk control [5][21]. - The BLM model achieved an annualized return of 13.64% with a volatility of 13.13%, outperforming the equal-weight benchmark by 6.28% [22]. - The article notes that under reasonable assumptions, return-driven models generally outperform benchmarks, while risk-driven models excel in risk control but struggle to beat benchmarks without leverage [6][21]. Backtesting Results for Global Asset Allocation - The performance and ranking of models in global allocation scenarios were similar to those in Chinese contexts, with BLM and LSTM-BLM recommended for enhanced returns, and VT and RP models for absolute risk balance [7][28]. - The BLM model maintained strong performance even with a 50% asset weight limit, indicating its effectiveness in dynamic adjustment without relying on high allocations [28]. Detailed Model Performance Statistics - The article provides detailed performance statistics for various models, including annualized returns, volatility, Sharpe ratios, and maximum drawdowns, highlighting the comparative effectiveness of each model under different constraints [22][31]. - For instance, the BLM model under a 50% weight limit achieved an annualized return of 10.74% with a Sharpe ratio of 1.01, demonstrating its robustness even with constraints [22][31].