- The AI-based global asset allocation model suggests a weight of 74.76% for the government bond index, 24.97% for SHFE gold, and 0.27% for the Hang Seng Index for February[5][44] - The model's performance in January showed a monthly return of -0.25%, compared to the benchmark strategy's return of 0.14%[5][44] - Historical performance from January 2021 to January 2026 indicates an annualized return of 7.22%, a Sharpe ratio of 1.07, and a maximum drawdown of 6.66%[46] - The stock-bond allocation model, based on macro timing and risk budgeting, suggests stock weights of 10.19%, 16.91%, and 70.00% for conservative, balanced, and aggressive profiles, respectively, for February[6][50] - The model's performance in January showed monthly returns of 3.65%, 1.22%, and 0.39% for aggressive, balanced, and conservative profiles, respectively[6][50] - Historical performance from January 2005 to January 2026 indicates annualized returns of 20.15%, 10.85%, and 5.87% for aggressive, balanced, and conservative profiles, respectively[51][57] - The dividend timing model recommends a 100% position in the CSI Dividend Index for February[7][58] - The model's performance shows an annualized return of 15.85%, an annualized volatility of 17.26%, a maximum drawdown of -21.22%, and a Sharpe ratio of 0.90[7][59] - The model's recent one-month return is 0.00%, compared to the CSI Dividend Total Return Index's return of 3.76%[7][59]

- The **Artificial Intelligence Global Asset Allocation Model** applies machine learning to asset allocation problems, utilizing factor investment principles to score and rank assets, ultimately constructing a monthly quantitative equal-weighted strategy for global asset allocation[38][39][41] - The model's suggested weights for January include: government bond index (68.27%), SHFE gold (28.55%), Nasdaq (1.02%), ICE Brent oil (1.24%), and CSI 500 (0.92%)[38][41] - Historical performance from January 2021 to December 2025 shows annualized return of 6.78%, Sharpe ratio of 1.04, maximum drawdown of 6.66%, and excess annualized return of -0.38% compared to the benchmark[39][42] - Year-to-date return for the strategy is 7.18%, while the benchmark return is 18.14%[40][42] - The **Dynamic Macro Event Factor-Based Stock-Bond Rotation Strategy** incorporates macro timing modules and risk budgeting frameworks to generate stock-bond allocation weights for three risk profiles: aggressive, balanced, and conservative[43][44][45] - January stock weights are: aggressive (55.00%), balanced (14.60%), and conservative (0.00%)[43][45] - December macro signals include 60% strength for both economic growth and monetary liquidity dimensions[43][45] - Historical performance from January 2005 to December 2025 shows annualized returns of 20.03% (aggressive), 10.84% (balanced), and 5.88% (conservative), outperforming the benchmark's 8.97%[44][49] - Year-to-date returns are 15.77% (aggressive), 4.23% (balanced), and 0.70% (conservative), compared to the benchmark's 15.95%[44][49] - The **Dividend Style Timing Strategy** leverages 10 indicators from economic growth and monetary liquidity dimensions to construct a timing strategy for dividend indices, showing enhanced stability compared to the CSI Dividend Total Return Index[50][51][53] - January recommended allocation for CSI Dividend is 0%, as most signals did not indicate a bullish outlook[50][54] - Historical performance includes annualized return of 16.18%, maximum drawdown of -21.22%, and Sharpe ratio of 0.93, outperforming the CSI Dividend Total Return Index's annualized return of 11.28% and Sharpe ratio of 0.57[50][53]

- 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]