量化配置视野：AI配置模型国债和黄金配置比例提升

• The artificial intelligence global asset allocation model applies machine learning to asset allocation problems, using factor investment ideas to score and rank assets, ultimately constructing a monthly quantitative equal-weighted strategy for global asset allocation[38][39][40] - The dynamic macroeconomic event factor-based stock-bond rotation strategy includes three risk preference models (conservative, balanced, and aggressive), utilizing macro timing modules and risk budgeting frameworks to determine stock and bond weights[43][44][45] - The dividend style timing model uses 10 indicators from economic growth and monetary liquidity dimensions, constructing a timing strategy for the dividend index, which shows significant stability improvement compared to the CSI Dividend Total Return Index[51][54][55] Model Backtesting Results - Artificial intelligence global asset allocation model: annualized return 38.76%, Sharpe ratio 1.07, maximum drawdown -6.56%, year-to-date return 6.81%[39][40][42] - Dynamic macroeconomic event factor-based stock-bond rotation strategy: aggressive model annualized return 20.14%, Sharpe ratio 1.30, maximum drawdown -13.72%, year-to-date return 14.42%; balanced model annualized return 10.92%, Sharpe ratio 1.19, maximum drawdown -6.77%, year-to-date return 4.13%; conservative model annualized return 5.94%, Sharpe ratio 1.50, maximum drawdown -3.55%, year-to-date return 0.97%[43][49][50] - Dividend style timing model: annualized return 16.52%, Sharpe ratio 1.07, maximum drawdown -13.77%, year-to-date return 0%[51][54][55]