- The report constructs a high-dimensional macro cycle recognition framework based on five dimensions: economic prosperity, inflation, interest rates, inventory, and credit. This approach addresses the instability of single-dimension indicators and improves the identification of macroeconomic cycles[9][8][3] - Macro factor variables are constructed by regressing macro indices against broad-based indices and proxy macro variables, selecting significant variables based on t-values. The inverse of the past year's standard deviation is used for weighting. Additionally, a one-sided HP filter is applied to macroeconomic data to eliminate short-term fluctuations and focus on long-term trends. Macro trends are categorized using factor momentum (upward or downward) and macro states are divided into high, medium, and low positions using time-series percentiles[2][8][9] - A broad-based timing strategy for CSI All Index is constructed using macro variable combinations. The strategy predicts future returns based on liquidity and inventory sub-strategies. If either prediction exceeds a threshold (0.6), the index is bought; otherwise, it is sold. From January 2012 to November 2025, the strategy achieved an annualized return of 14.71%, with an excess return of 10.5% relative to the CSI All Index[29][30][33] - A dividend index timing strategy is constructed using macro variable combinations, specifically inflation+inventory and inventory+credit sub-strategies. The average prediction value determines whether to buy or sell the index. From January 2012 to November 2025, the strategy achieved an annualized return of 10.64%, with an excess return of 8.41% relative to the dividend index[35][39][38] - A style rotation strategy is constructed using effective macro factor combinations, specifically inflation+inventory and inflation+credit. Monthly predictions of future returns for six style indices are ranked, and the top two indices are equally weighted for allocation. From September 2014 to November 2025, the strategy achieved an annualized return of 12.64%, with an excess return of 5.49% relative to equal-weighted style indices[43][47][50]

- 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]

- The report includes a global asset allocation model based on artificial intelligence, which uses machine learning to score and rank various assets for monthly equal-weighted allocation strategy[30][31] - The global asset allocation model suggests weights for May: government bond index (66.09%), Nasdaq index (17.59%), German DAX index (13.83%), and Nikkei 225 (2.49%)[30] - Historical performance of the global asset allocation model from January 2021 to April 2025 shows an annualized return of 13.76%, Sharpe ratio of 0.75, maximum drawdown of 16.53%, and excess annualized return of 9.02%[30][36] - The dynamic macro event factor-based stock-bond rotation strategy includes three different risk preference models: conservative, balanced, and aggressive[37] - The stock-bond allocation models for April show stock weights of 45% for aggressive, 13.82% for balanced, and 0% for conservative[37][39] - Historical performance of the stock-bond allocation models from January 2005 to April 2025 shows annualized returns of 19.93% for aggressive, 11.00% for balanced, and 6.06% for conservative[37][44] - The dividend timing model uses economic growth and monetary liquidity indicators to construct a timing strategy for the dividend index, showing an annualized return of 15.84%, maximum drawdown of -21.70%, and Sharpe ratio of 0.89[45][49] - The dividend timing model's recommended position for April is 0%, with most economic growth indicators showing bearish signals and cautious monetary liquidity signals[45] Model Performance Metrics - Global asset allocation model: annualized return 13.76%, Sharpe ratio 0.75, maximum drawdown 16.53%[30][36] - Stock-bond allocation models: annualized returns 19.93% (aggressive), 11.00% (balanced), 6.06% (conservative)[37][44] - Dividend timing model: annualized return 15.84%, Sharpe ratio 0.89, maximum drawdown -21.70%[45][49]

- The global asset allocation model uses machine learning to score and rank assets based on factor investment principles, constructing a monthly quantitative equal-weight strategy for global asset allocation[39][43][44] - The model's historical performance from January 2021 to March 2025 shows an annualized return of 6.45%, Sharpe ratio of 1.01, maximum drawdown of 6.66%, and excess annualized return of 1.28%, outperforming the benchmark across all dimensions[39][44][45] - The dynamic macro event factor-based stock-bond rotation strategy includes three risk preference models (conservative, balanced, aggressive), with April stock weights of 0%, 13.73%, and 25%, respectively[45][46][47] - The macro timing module and risk budget framework signal strengths for April are 50% for monetary liquidity and 0% for economic growth[45][46][48] - Historical performance of the stock-bond rotation strategy from January 2005 to March 2025 shows annualized returns of 20.02% (aggressive), 11.02% (balanced), and 6.03% (conservative), all outperforming the benchmark[45][51][47] - The dividend timing model recommends a 100% allocation to the CSI Dividend Index for April, with economic growth indicators mostly bearish and monetary liquidity signals positive[53][54][52] - The dividend timing strategy achieves an annualized return of 16.86%, maximum drawdown of -21.22%, and Sharpe ratio of 0.95, significantly improving stability compared to the CSI Dividend Total Return Index[53][54][52]