- **Domestic Asset BL Model** - **Model Name**: Black-Litterman (BL) Model - **Construction Idea**: The BL model integrates subjective views with quantitative asset allocation using Bayesian theory, optimizing asset weights based on market analysis and expected returns. It addresses the sensitivity of mean-variance models to expected returns and provides higher fault tolerance compared to purely subjective investments [26][27][33] - **Construction Process**: 1. Use historical returns of assets over the past five years to estimate market equilibrium returns (Π) 2. Specify a risk aversion coefficient (e.g., λ = 10), which corresponds to a target volatility 3. Alternatively, assign fixed weights (e.g., stock:bond:convertible bond:commodity:gold = 10:80:5:2.5:2.5) and reverse calculate the risk aversion coefficient dynamically for each period [33] - **Evaluation**: The BL model effectively combines subjective views with quantitative methods, providing robust asset allocation solutions [26][27] - **Domestic Asset Risk Parity Model** - **Model Name**: Risk Parity Model - **Construction Idea**: The model aims to equalize the risk contribution of each asset to the overall portfolio, optimizing asset weights based on expected volatility and correlation [32][35] - **Construction Process**: 1. Select appropriate underlying assets 2. Calculate each asset's risk contribution to the portfolio 3. Solve optimization problems to determine final asset weights 4. Use daily returns over the past five years to estimate the covariance matrix for stability [35] - **Evaluation**: The model provides stable returns across economic cycles and is well-suited for domestic investors [32][35] - **Macro Factor-Based Asset Allocation Strategy** - **Model Name**: Macro Factor-Based Strategy - **Construction Idea**: The strategy bridges macroeconomic research with asset allocation by constructing high-frequency macro factors (e.g., growth, inflation, interest rates, credit, exchange rates, liquidity) and aligning asset weights with subjective macroeconomic views [41][46] - **Construction Process**: 1. Calculate factor exposure levels for assets monthly 2. Use risk parity portfolios as benchmarks to compute baseline factor exposures 3. Adjust factor exposure targets based on subjective macroeconomic views (e.g., inflation up = positive deviation) 4. Solve for asset weights using the model [41][46] - **Evaluation**: The strategy effectively incorporates macroeconomic insights into asset allocation, enhancing adaptability to changing economic conditions [41][46] - **Backtest Results for Models** - **Domestic Asset BL Model 1**: - Annualized return: 3.58% - Max drawdown: 1.31% - Annualized volatility: 2.19% [31][33] - **Domestic Asset BL Model 2**: - Annualized return: 3.18% - Max drawdown: 1.06% - Annualized volatility: 1.99% [31][33] - **Domestic Asset Risk Parity Model**: - Annualized return: 3.12% - Max drawdown: 0.76% - Annualized volatility: 1.34% [39][40] - **Macro Factor-Based Strategy**: - Annualized return: 3.42% - Max drawdown: 0.65% - Annualized volatility: 1.32% [46][47]

Group 1 - The report highlights that domestic equity assets performed well in July 2025, with the risk parity strategy achieving a year-to-date return of 2.65% [2][5][20] - The report provides a summary of various asset allocation strategies, indicating that the domestic asset BL strategy 1 and 2 yielded returns of 2.40% and 2.34% respectively, while the risk parity strategy and macro factor-based strategy returned 2.65% and 2.59% respectively [21][41][42] - The report notes that the domestic equity market saw significant gains, with the CSI 1000 index rising by 4.8% and the Hang Seng Index increasing by 2.78% in July [8][9][10] Group 2 - The report discusses the correlation between different asset classes, indicating that the correlation between the CSI 300 and the total wealth index of government bonds was -38.08%, suggesting a potential for diversification [15][16] - The report outlines the performance of various asset allocation models, with the domestic risk parity strategy showing a maximum drawdown of 0.76% and an annualized volatility of 1.46% [41][42] - The macroeconomic outlook suggests downward risks for growth factors, while inflation expectations may stabilize due to recent policy measures [45][47]

Core Viewpoint - The report highlights the performance of various domestic asset allocation strategies in May 2025, indicating a mixed performance across different strategies and asset classes, with a notable focus on the risk parity strategy achieving the highest year-to-date return of 1.73% [1][3]. Group 1: Asset Strategy Performance - Domestic Asset BL Strategy 1 recorded a May return of -0.22% and a year-to-date return of 0.96% [1][3]. - Domestic Asset BL Strategy 2 had a May return of -0.1% and a year-to-date return of 1.05% [1][3]. - The Domestic Asset Risk Parity Strategy achieved a May return of 0.29% and a year-to-date return of 1.73% [1][3]. - The Macro Factor-Based Asset Allocation Strategy reported a May return of 0.27% and a year-to-date return of 1.45% [1][3]. Group 2: Major Asset Trends - In May 2025, domestic equity assets performed well, with the Hang Seng Index, CSI 300, and others showing significant gains, while gold experienced a pullback [2]. - The Hang Seng Index rose by 3.96%, CSI 300 by 1.85%, and the total wealth index of corporate bonds by 0.41% [2]. - The South China Commodity Index and SHFE gold saw declines of 2.4% and 1.39%, respectively [2]. - Correlation analysis indicated a -36.97% correlation between CSI 300 and the total wealth index of government bonds over the past year [2]. Group 3: Macroeconomic Insights - As of the end of May 2025, the manufacturing PMI was at 49.5%, indicating a slight improvement in manufacturing sentiment [4]. - The PPI for April showed a year-on-year decline of -2.7%, with expectations for May at -3.17%, indicating ongoing deflationary pressures [4]. - The central bank conducted a MLF operation of 550 billion yuan, net injecting 400 billion yuan to support special bond issuance [4]. - The social financing scale stood at 424 trillion yuan at the end of April 2025, reflecting the credit environment [4].

Quantitative Models and Construction Methods 1. Model Name: Macro Scoring Model - **Model Construction Idea**: The model evaluates macroeconomic factors to generate asset allocation signals, providing directional views on various asset classes such as equities, bonds, and commodities [13][15] - **Model Construction Process**: - The model aggregates multiple macroeconomic factors, including domestic and global indicators such as inflation, monetary policy, credit conditions, and economic sentiment - Each factor is scored, and the scores are combined to derive an overall macro score for each asset class - The scoring results are used to determine the directional view (e.g., bullish, neutral) for each asset class [13][15] - **Model Evaluation**: The model provides a systematic and data-driven approach to assess macroeconomic conditions and their implications for asset allocation [13] 2. Model Name: US Equity Timing Model - **Model Construction Idea**: This model aims to predict the medium-term performance of US equities by analyzing three dimensions: economic sentiment, capital flows, and financial stress [16] - **Model Construction Process**: - The model assigns equal weights to three sub-indicators: economic sentiment, capital flows, and financial stress - The latest readings of these indicators are aggregated to calculate a composite timing score - For example, the latest composite score is 52.5, reflecting a moderately positive outlook for US equities [16] - **Model Evaluation**: While the model maintains a bullish view, its effectiveness may be reduced due to data lag, particularly in the context of external shocks like tariff uncertainties [16] 3. Model Name: Gold Timing Model - **Model Construction Idea**: This model identifies the timing for gold investments based on macroeconomic risks, such as tariff disputes and rising US debt levels [19] - **Model Construction Process**: - The model uses a timing indicator that oscillates around a zero axis - The indicator reflects the balance of macroeconomic risks and their potential impact on gold prices - Currently, the indicator has fallen near the zero axis due to a temporary reduction in US deficits, but the long-term trend remains upward due to expected fiscal pressures [19] - **Model Evaluation**: The model highlights gold as a strong hedge against macroeconomic uncertainties, particularly in high-risk environments [19] 4. Model Name: Crude Oil Timing Model - **Model Construction Idea**: This model evaluates the outlook for crude oil prices based on global economic conditions and supply-demand dynamics [21] - **Model Construction Process**: - The model constructs an oil sentiment index, which currently stands at 0.3 - The index reflects factors such as stable global economic data and a weakening US dollar, balanced against risks from tariff policies and OPEC's production cycle [21] - **Model Evaluation**: The model suggests that crude oil prices are likely to remain range-bound due to mixed macroeconomic signals [21] --- Model Backtesting Results 1. Macro Scoring Model - **May Return**: 0.1% - **1-Year Return**: 8.0% - **Maximum Drawdown**: 3.3% [23] 2. US Equity Timing Model - **Latest Composite Score**: 52.5 [16] 3. Gold Timing Model - **Latest Indicator Value**: Near 0 axis [19] 4. Crude Oil Timing Model - **Latest Sentiment Index**: 0.3 [21]