Group 1: Core Insights - The report highlights that the stock side employs a small-cap growth portfolio combined with a non-timing stock-bond monthly rebalancing strategy, projecting cumulative returns of 5.93% and 11.15% by 2025 [1][4] - As of August 15, 2025, the total market size of fixed income plus funds reached 1,784.66 billion, with 1,177 products, of which 412 achieved historical net value highs last week [2][9] - The report indicates a divergence in performance among various fund types, with median returns for mixed bond type funds being -0.07% for level one, 0.17% for level two, and 0.33% for mixed bond type funds [2][12] Group 2: Asset Allocation and ETF Rotation - The macro environment forecast for Q3 2025 suggests an inflationary trend, with the CSI 300 index, the total wealth index of government bonds, and AU9999 contracts yielding 3.11%, -0.32%, and 1.03% respectively since August [3][4] - Recommended industry ETFs for August 2025 include those focused on artificial intelligence, semiconductors, non-ferrous metals, banking, and major consumer sectors, with a weekly return of 4.01% and a cumulative return of 5.81% for the month [3][4] Group 3: Absolute Return Strategy Performance - The macro-timing driven stock-bond 20/80 rebalancing strategy yielded a return of 0.47% last week, while the stock-bond risk parity strategy returned -0.02% [4][9] - The small-cap growth style within the stock-bond 20/80 combination showed the most significant performance, with a year-to-date return of 11.15% [4][9] - The report notes that the cumulative return for the small-cap growth portfolio, when adjusted for timing strategies, reached 12.81% [4][9]

- The Black-Litterman model (BL model) is used for asset allocation, combining investor views with market equilibrium[17][20] - The construction process of the BL model involves adjusting the expected returns based on investor views and then optimizing the portfolio using mean-variance optimization[17][20] - The Risk Parity model aims to allocate risk equally across all assets in a portfolio, rather than allocating capital equally[27][30] - The construction process of the Risk Parity model involves calculating the risk contribution of each asset and solving an optimization problem to equalize these contributions[28][29][30] - The Counter-Cyclical Allocation model adjusts asset allocation based on economic cycles, aiming to reduce risk during downturns and increase exposure during upturns[11][43] - The Macro Momentum Timing model uses macroeconomic indicators to time market entries and exits, aiming to capture trends and avoid downturns[11][60] - The Sentiment Timing model uses investor sentiment indicators to time market entries and exits, aiming to capitalize on market overreactions[67] Model Performance Metrics - **Black-Litterman Model**: Annualized return 6.58%, maximum drawdown 3.18%, annualized volatility 2.15%, Sharpe ratio 1.86, Calmar ratio 2.07[22][24] - **Risk Parity Model**: Annualized return 6.07%, maximum drawdown 3.78%, annualized volatility 2.26%, Sharpe ratio 1.58, Calmar ratio 1.61[31] - **Counter-Cyclical Allocation Model**: Annualized return 7.36%, maximum drawdown 8.85%, annualized volatility 6.12%, Sharpe ratio 1.13, Calmar ratio 0.85[43][47] - **Macro Momentum Timing Model**: Annualized return 7.06%, maximum drawdown 6.60%, annualized volatility 6.06%, Sharpe ratio 1.13, Calmar ratio 1.97[60] - **Sentiment Timing Model**: Annualized return 7.74%, maximum drawdown 24.91%, annualized volatility 17.49%, Sharpe ratio 1.01, Calmar ratio 0.62[67][87]

Group 1 - The report indicates that the stock side employs a small-cap value portfolio combined with a non-timing stock-bond monthly rebalancing strategy of 10/90 and 20/80, with cumulative returns of 4.97% and 9.28% respectively by 2025 [1][4] - As of July 25, 2025, the total market size of fixed income + funds reached 1,775.714 billion, with 1,173 products, and 136 of them reached historical net value highs last week [2][9] - The performance of various fund types showed divergence, with median returns for mixed bond type funds being -0.15% for level one, 0.09% for level two, and 0.19% for biased bond mixed funds [2][14] Group 2 - The macro environment forecast for Q3 2025 suggests an inflationary trend, with the CSI 300 index rising by 4.85% since July, while the China Government Bond Index fell by 0.43% [3] - Recommended industry ETFs for July 2025 include those focused on securities companies, semiconductors, non-ferrous metals, and major consumer sectors, with a weekly return of 4.72% and a cumulative return of 6.97% for the month [3][4] - The absolute return strategy performance showed that the macro-timing driven stock-bond 20/80 rebalancing strategy yielded a return of 0.20% last week, while the stock-bond risk parity strategy had a return of -0.20% [4][16] Group 3 - The small-cap value style within the stock-bond 20/80 combination performed notably well, achieving a year-to-date return of 9.28%, while other strategies like PB earnings and high dividend stocks yielded 4.01% and 2.65% respectively [4][16] - The report highlights that 136 fixed income + products reached historical net value highs, with a breakdown of 30 level one mixed bond funds, 41 level two mixed bond funds, and 35 biased bond mixed funds [18][20] - The report also provides insights into the performance of conservative, balanced, and aggressive funds, with median returns of -0.09%, 0.09%, and 0.29% respectively [14][18]

Quantitative Models and Construction Methods 1. Model Name: Macro Timing Model - **Model Construction Idea**: The model predicts future macroeconomic environments using proxy variables and selects optimal assets for absolute return portfolios based on these predictions[25] - **Model Construction Process**: - The model uses proxy variables to forecast macroeconomic conditions such as inflation, economic growth, interest rates, exchange rates, and risk sentiment[25] - Based on these forecasts, the model selects assets that are expected to perform best in the predicted environment[25] - Example formula: $ \text{Expected Return} = \alpha + \beta \times \text{Macro Variable} $ where $\alpha$ is the intercept and $\beta$ is the coefficient representing the sensitivity to the macro variable[25] - **Model Evaluation**: The model is effective in predicting macroeconomic conditions and selecting optimal assets for different environments[25] - **Model Test Results**: - Q1 2025 predictions: Inflation environment - Asset returns: CSI 300: 0.10%, CSI 2000: 6.05%, Nanhua Commodity Index: 3.26%, China Bond Total Wealth Index: 0.51%[25] 2. Model Name: Macro Momentum Model - **Model Construction Idea**: The model uses multiple dimensions such as economic growth, inflation, interest rates, exchange rates, and risk sentiment to time major asset classes like stocks and bonds[25] - **Model Construction Process**: - The model constructs macro momentum indicators based on economic growth, inflation, interest rates, exchange rates, and risk sentiment[25] - These indicators are used to time investments in major asset classes[25] - Example formula: $ \text{Momentum Score} = \sum_{i=1}^{n} w_i \times \text{Indicator}_i $ where $w_i$ is the weight of the $i$-th indicator and $\text{Indicator}_i$ is the value of the $i$-th indicator[25] - **Model Evaluation**: The model is effective in timing investments based on macroeconomic conditions[25] - **Model Test Results**: - February 2025 returns: CSI 300: 3.19%, China Bond Total Wealth Index: 0.08%, Shanghai Gold Exchange AU9999 contract: 6.43%[25] Model Backtest Results 1. Macro Timing Model - **Weekly Return**: -0.12%[32] - **Monthly Return**: 0.36%[32] - **Year-to-Date Return**: -0.31%[32] - **Annualized Volatility**: 2.71%[32] - **Maximum Drawdown**: 0.51%[32] - **Sharpe Ratio**: -0.95[32] 2. Macro Momentum Model - **Weekly Return**: -0.20%[32] - **Monthly Return**: 0.18%[32] - **Year-to-Date Return**: 0.15%[32] - **Annualized Volatility**: 1.50%[32] - **Maximum Drawdown**: 0.47%[32] - **Sharpe Ratio**: 0.87[32] Quantitative Factors and Construction Methods 1. Factor Name: PB Profitability - **Factor Construction Idea**: The factor selects stocks based on their price-to-book (PB) ratio and profitability metrics[38] - **Factor Construction Process**: - Stocks are ranked based on their PB ratio and profitability metrics[38] - The top-ranked stocks are selected for the portfolio[38] - Example formula: $ \text{PB Profitability Score} = \frac{\text{Net Income}}{\text{Book Value}} $ where $\text{Net Income}$ is the company's net income and $\text{Book Value}$ is the company's book value[38] - **Factor Evaluation**: The factor is effective in selecting stocks with high profitability relative to their book value[38] 2. Factor Name: High Dividend Yield - **Factor Construction Idea**: The factor selects stocks based on their dividend yield[38] - **Factor Construction Process**: - Stocks are ranked based on their dividend yield[38] - The top-ranked stocks are selected for the portfolio[38] - Example formula: $ \text{Dividend Yield} = \frac{\text{Annual Dividends}}{\text{Stock Price}} $ where $\text{Annual Dividends}$ is the total dividends paid annually and $\text{Stock Price}$ is the current stock price[38] - **Factor Evaluation**: The factor is effective in selecting stocks with high dividend yields[38] Factor Backtest Results 1. PB Profitability Factor - **Weekly Return**: 0.09%[39] - **Monthly Return**: 0.36%[39] - **Year-to-Date Return**: 0.38%[39] - **Annualized Volatility**: 2.63%[39] - **Maximum Drawdown**: 1.82%[39] - **Sharpe Ratio**: -0.44[39] 2. High Dividend Yield Factor - **Weekly Return**: 0.03%[39] - **Monthly Return**: 0.15%[39] - **Year-to-Date Return**: 0.01%[39] - **Annualized Volatility**: 2.34%[39] - **Maximum Drawdown**: 1.39%[39] - **Sharpe Ratio**: -0.64[39]