Core Insights - The article highlights the unprecedented challenges faced by global investors due to high interest rates maintained by the Federal Reserve, leading to a decline in stock market valuations and an inverted yield curve in U.S. Treasuries, while gold prices reach historical highs driven by safe-haven demand [1] Group 1: Stock Market Dynamics - The stock market exhibits significant structural differentiation, with the technology sector remaining resilient due to AI computing demand, as evidenced by an 18.7% year-to-date increase in the Philadelphia Semiconductor Index, while traditional consumer sectors are pressured by declining household savings rates [1] - Active management funds have achieved an average excess return of 4.2 percentage points, underscoring the value of professional investment in a differentiated market [1] - Smart investment advisory systems utilizing machine learning algorithms have identified multiple small-cap stocks with potential for excess returns [1] Group 2: Fixed Income Market - The fixed income market is undergoing a reconfiguration of pricing mechanisms, with the 10-year U.S. Treasury yield fluctuating around 4.5% and credit spreads widening by 37 basis points compared to historical averages [2] - Institutional investors are employing duration strategies and credit downgrades to capture alpha returns, with investment-grade corporate bonds beginning to show allocation value [2] - The green bond market has surpassed $2.3 trillion in size, achieving a compound annual growth rate of 19%, providing new options for ESG investors [2] Group 3: Gold Market Trends - Gold's monetary attributes are revitalized in the digital currency era, with geopolitical risks and central bank purchases pushing gold prices above $2,500 per ounce [4] - The trading volume of digital gold certificates has increased by 240% year-on-year, merging physical gold with blockchain technology, enhancing liquidity to stock-like levels with an average daily trading volume of $4.7 billion [4] Group 4: Asset Allocation Strategies - Dynamic risk-return balance is essential for cross-asset allocation, with the optimal current portfolio allocation being 45% stocks, 30% bonds, and 25% gold, where gold's volatility contribution has decreased to 14% [4] - The correlation coefficient indicates an improved hedging efficiency of gold against stock assets, rising to 0.38 [4] - The application of smart rebalancing algorithms has effectively controlled the annualized portfolio volatility within 9.2% [4] Group 5: Market Behavior Insights - The capital market is in a constant evolution of efficiency versus risk, as evidenced by a record net outflow of 8.3 billion yuan from northbound funds under the Shanghai-Hong Kong Stock Connect, while gold ETFs have seen 21 consecutive weeks of net subscriptions [4] - Data shows that a three-year systematic investment strategy has achieved an annualized return of 8.7%, significantly outperforming single-asset allocation strategies [4]

- 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]

- Model Name: Domestic Asset BL Model 1; Model Construction Idea: The BL model is an improvement of the traditional mean-variance model, combining subjective views with quantitative models using Bayesian theory; Model Construction Process: The model optimizes asset allocation weights based on investor market analysis and asset return forecasts, effectively addressing the sensitivity of the mean-variance model to expected returns; Model Evaluation: The BL model provides a higher fault tolerance compared to purely subjective investments, offering efficient asset allocation solutions[14][15] - Model Name: Domestic Asset BL Model 2; Model Construction Idea: Similar to Domestic Asset BL Model 1; Model Construction Process: The model is built on the same principles as Domestic Asset BL Model 1 but with different asset selections; Model Evaluation: Similar to Domestic Asset BL Model 1[14][15] - Model Name: Global Asset BL Model 1; Model Construction Idea: Similar to Domestic Asset BL Model 1; Model Construction Process: The model is built on the same principles as Domestic Asset BL Model 1 but targets global assets; Model Evaluation: Similar to Domestic Asset BL Model 1[14][15] - Model Name: Global Asset BL Model 2; Model Construction Idea: Similar to Global Asset BL Model 1; Model Construction Process: The model is built on the same principles as Global Asset BL Model 1 but with different asset selections; Model Evaluation: Similar to Global Asset BL Model 1[14][15] - Model Name: Domestic Asset Risk Parity Model; Model Construction Idea: The risk parity model aims to equalize the risk contribution of each asset in the portfolio; Model Construction Process: The model calculates the risk contribution of each asset and optimizes the deviation between actual and expected risk contributions to determine final asset weights; Model Evaluation: The model provides stable returns across different economic cycles[20][21] - Model Name: Global Asset Risk Parity Model; Model Construction Idea: Similar to Domestic Asset Risk Parity Model; Model Construction Process: The model is built on the same principles as Domestic Asset Risk Parity Model but targets global assets; Model Evaluation: Similar to Domestic Asset Risk Parity Model[20][21] - Model Name: Macro Factor-Based Asset Allocation Model; Model Construction Idea: The model constructs a macro factor system covering growth, inflation, interest rates, credit, exchange rates, and liquidity; Model Construction Process: The model uses the Factor Mimicking Portfolio method to construct high-frequency macro factors and optimizes asset weights based on subjective macro views; Model Evaluation: The model bridges macro research and asset allocation, reflecting subjective macro judgments in asset allocation[23][24][27] - Domestic Asset BL Model 1, Weekly Return: 0.02%, July Return: 0.61%, 2025 YTD Return: 2.46%, Annualized Volatility: 2.16%, Maximum Drawdown: 1.31%[17][19] - Domestic Asset BL Model 2, Weekly Return: -0.06%, July Return: 0.48%, 2025 YTD Return: 2.41%, Annualized Volatility: 1.93%, Maximum Drawdown: 1.06%[17][19] - Global Asset BL Model 1, Weekly Return: -0.09%, July Return: 0.56%, 2025 YTD Return: 0.95%, Annualized Volatility: 1.95%, Maximum Drawdown: 1.64%[17][19] - Global Asset BL Model 2, Weekly Return: -0.07%, July Return: 0.51%, 2025 YTD Return: 1.59%, Annualized Volatility: 1.7%, Maximum Drawdown: 1.28%[17][19] - Domestic Asset Risk Parity Model, Weekly Return: -0.02%, July Return: 0.36%, 2025 YTD Return: 2.7%, Annualized Volatility: 1.46%, Maximum Drawdown: 0.76%[22][23] - Global Asset Risk Parity Model, Weekly Return: -0.03%, July Return: 0.3%, 2025 YTD Return: 2.16%, Annualized Volatility: 1.66%, Maximum Drawdown: 1.2%[22][23] - Macro Factor-Based Asset Allocation Model, Weekly Return: -0.03%, July Return: 0.38%, 2025 YTD Return: 2.76%, Annualized Volatility: 1.36%, Maximum Drawdown: 0.64%[28][29]

Quantitative Models and Construction Methods 1. Model Name: General Risk Parity Model - **Model Construction Idea**: The risk parity model aims to equalize the risk contribution of each asset in the portfolio. When assets are uncorrelated, the risk parity allocation is equivalent to inverse volatility weighting, where higher volatility assets receive lower weights[18]. - **Model Construction Process**: - The risk contribution of each asset is calculated to ensure equal risk allocation. - Formula: $ w_i = \frac{1}{\sigma_i} $, where $ w_i $ is the weight of asset $ i $ and $ \sigma_i $ is the volatility of asset $ i $[18]. - **Model Evaluation**: This model is effective in balancing risk across assets, particularly when asset correlations are low[18]. 2. Model Name: Adjusted Risk Budget Model - **Model Construction Idea**: Adjust the risk budget based on the number of assets and their characteristics. The risk budget multiplier is proportional to the square root of the number of assets[29]. - **Model Construction Process**: - Static risk budgets are assigned to assets, with equity risk budget set at 25 and commodity/gold risk budgets at 36. - Dynamic adjustments are made using the Sharpe ratio over the past six months, with the maximum budget set at 1.5 times the static budget[36]. - **Model Evaluation**: The dynamic adjustment improves the model's responsiveness to market conditions, enhancing performance metrics like Sharpe ratio and reducing drawdowns[36]. 3. Model Name: Macro Risk Parity Model - **Model Construction Idea**: Incorporate macroeconomic factors into the risk parity framework to refine asset allocation based on macro factor correlations[38]. - **Model Construction Process**: - Decompose macro factor returns and calculate their correlations. - Formula: $ w_i = \frac{1}{\sigma_i} \times \text{Macro Factor Adjustment} $, where macro factor adjustment accounts for the correlation between macro factors and asset returns[38]. - **Model Evaluation**: This model enhances returns by aligning asset allocation with macroeconomic conditions, while maintaining risk parity principles[38]. 4. Model Name: Risk Budget Timing Model - **Model Construction Idea**: Adjust risk budgets dynamically based on asset timing signals, such as Sharpe ratios and macroeconomic states[59]. - **Model Construction Process**: - Fixed-income assets maintain a constant risk budget of 1. - Equity assets are adjusted based on a 1-month Sharpe ratio threshold of 0.5, with budgets increased to 64 if exceeded. - Convertible bonds are adjusted similarly with a threshold of 0.6 and a budget of 36. - Macro timing multiplies equity risk budgets by 4 during favorable conditions and reduces them by 4 during unfavorable conditions[59]. - **Model Evaluation**: This model significantly improves returns and reduces drawdowns by incorporating timing signals into risk budget adjustments[60]. --- Model Backtesting Results General Risk Parity Model - Annualized Return: 6.47% - Maximum Drawdown: -2.84% - Volatility: 2.79% - Sharpe Ratio: 2.25 - Monthly Win Rate: 74.76% - Monthly Profit-Loss Ratio: 6.14[55] Adjusted Risk Budget Model - Annualized Return: 7.99% - Maximum Drawdown: -4.01% - Volatility: 3.79% - Sharpe Ratio: 2.03 - Monthly Win Rate: 71.84% - Monthly Profit-Loss Ratio: 4.26[55] Risk Budget Timing Model - Annualized Return: 9.11% - Maximum Drawdown: -3.64% - Volatility: 3.62% - Sharpe Ratio: 2.41 - Monthly Win Rate: 71.84% - Monthly Profit-Loss Ratio: 5.50[61] --- Quantitative Factors and Construction Methods 1. Factor Name: Sharpe Ratio Adjustment - **Factor Construction Idea**: Use the Sharpe ratio as a timing signal to adjust risk budgets dynamically[59]. - **Factor Construction Process**: - Calculate the 1-month Sharpe ratio for each asset. - Compare the Sharpe ratio to predefined thresholds (e.g., 0.5 for equity, 0.6 for convertible bonds). - Adjust risk budgets based on whether the Sharpe ratio exceeds the threshold[59]. 2. Factor Name: Macro Timing Signal - **Factor Construction Idea**: Use macroeconomic states to determine equity allocation adjustments[59]. - **Factor Construction Process**: - Identify macroeconomic states using predefined signals. - Multiply equity risk budgets by 4 during favorable states and divide by 4 during unfavorable states[59]. --- Factor Backtesting Results Sharpe Ratio Adjustment Factor - Equity Risk Budget: Increased to 64 if Sharpe ratio exceeds 0.5[59] - Convertible Bond Risk Budget: Increased to 36 if Sharpe ratio exceeds 0.6[59] Macro Timing Signal Factor - Equity Risk Budget: Multiplied by 4 during favorable macro states, divided by 4 during unfavorable states[59]

Group 1 - The core idea of the article emphasizes the importance of asset allocation as a means for ordinary investors to navigate the uncertainties of the financial market, likening it to Noah's Ark for survival and growth [2][3] - The article discusses the theoretical foundation of asset allocation, highlighting Harry Markowitz's mean-variance model and its significance in reducing risk through the scientific combination of low-correlated assets [4] - It presents empirical evidence showing that 91% of mutual fund performance differences from 1970 to 2020 were due to asset allocation strategies rather than stock selection or market timing [4] Group 2 - The practical value of asset allocation is illustrated through examples of risk diversification, such as the "see-saw effect" between stocks and bonds during market downturns, which can significantly reduce portfolio drawdowns [5] - Behavioral finance insights are shared, indicating that proper asset allocation can mitigate emotional responses during market volatility, reducing the psychological impact of asset fluctuations [5] - The article provides a performance comparison of a diversified asset allocation strategy from 2010 to 2020, showing an annualized return of 7.2% with a maximum drawdown of only 9.8% [5] Group 3 - The article outlines strategic tools for asset allocation, including the "Four Seasons" method that adjusts asset allocation based on economic cycles [6] - It discusses lifecycle-based asset allocation, recommending different asset mixes for various age groups to align risk exposure with life stages [7] - The use of various financial instruments, such as ETFs, convertible bonds, and REITs, is suggested to enhance portfolio diversification and returns [8] Group 4 - Historical lessons are drawn from past financial crises, demonstrating the effectiveness of diversified asset allocation strategies in mitigating losses compared to concentrated positions [9][10] - The article highlights the performance of Bridgewater's All Weather strategy during periods of economic stress, showcasing its ability to generate positive returns while traditional equities suffered losses [10] Group 5 - The future of asset allocation is discussed in the context of technological advancements, including big data, AI optimization, and blockchain, which are transforming the investment landscape [11] - The article concludes with a philosophy of viewing asset allocation as a means to achieve financial security and stability rather than speculative gains, emphasizing disciplined investment practices [12][13] Group 6 - The "Snowball Three-Part Method" is introduced as a risk management framework that balances stocks, bonds, and commodities to create a defensive investment strategy [26][27] - The method emphasizes dynamic rebalancing to maintain target asset allocations and enhance returns through systematic adjustments based on market conditions [28] - The article discusses the potential for generating excess returns through strategic asset allocation, including timing and sector rotation based on market conditions [30] Group 7 - A proposed asset allocation strategy is presented, incorporating global assets, bonds, A-shares, and alternative investments to create a robust defensive structure [34][36] - The strategy aims to mitigate geopolitical risks through diversified global exposure and balance between interest rate and credit risk [37] - The allocation includes a focus on high-dividend assets to provide stability during market downturns, reinforcing the importance of income-generating investments [38] Group 8 - The article emphasizes the importance of dynamic balancing and threshold management in maintaining optimal asset allocations, ensuring that portfolios remain aligned with market conditions [44] - It discusses the need for liquidity management to address unexpected redemption demands, highlighting the role of cash and cash-equivalent assets [53] - The overall philosophy of the proposed asset allocation strategy is to build a "anti-fragile" investment system capable of withstanding market volatility while capturing structural opportunities [54][55]

Report Industry Investment Rating No relevant content provided. Core Viewpoints - In June, all major market indices' valuations were adjusted upwards. In terms of price - to - earnings ratio and price - to - book ratio, the historical percentile increases of CSI 300 and CSI All - Share were among the top, while the ChiNext Index remained at a historical low. Among the 31 Shenwan primary industries, 23 industries rose, with the top 5 gainers being communication, national defense and military industry, non - ferrous metals, electronics, and media; the top 5 losers were food and beverage, beauty care, household appliances, coal, and transportation [1]. - In June, 70 new funds were issued, with a total issuance scale of 62.728 billion yuan. The issuance of active equity funds was booming, while the issuance of passive equity funds decreased slightly. Only commodity - type funds declined, with a 1.66% drop, and the largest gain was in equity - biased funds, up 2.68% [2]. - From the perspective of fund style indices, the growth style outperformed the value style, and the large - cap style underperformed the small - cap style. Overall, the mid - cap growth style performed outstandingly, rising 5.83%, while the large - cap value style had the smallest increase, about 2.52% [2]. - In the ETF market, last month, there was a net inflow of 59.605 billion yuan. Bond - type ETFs had a net inflow of over 90 billion yuan, and stock - type ETFs had a net outflow of 31.54 billion yuan [3]. - In June, the risk - parity model rose 1.59%, and the risk - budget model rose 2.34% [5]. Summary by Directory 1. Last Month's Market Review 1.1 Domestic Market Situation - In June, all major indices in the Shanghai and Shenzhen markets rose. The ChiNext Index rose by over 8%, and the Shenzhen Component Index and CSI 500 rose by over 4%. Among the 31 Shenwan primary industries, 23 industries rose. The top 5 gainers were communication, national defense and military industry, non - ferrous metals, electronics, and media; the top 5 losers were food and beverage, beauty care, household appliances, coal, and transportation. In the bond market, the ChinaBond Composite Full - Price Index rose 0.31%, and the total full - price indices of ChinaBond Treasury bonds, financial bonds, and credit bonds rose between 0.13% and 0.40%. The CSI Convertible Bond Index rose 3.34%. In the commodity market, the Nanhua Commodity Index rose 4.03% [13]. 1.2 European, American, and Asia - Pacific Market Situation - In June, most European, American, and Asia - Pacific markets rose. In the US stock market, the S&P 500 rose 4.89%, the Dow Jones Industrial Average rose 4.21%, and the Nasdaq rose 6.57%. In the European market, the French CAC 40 fell 1.11%, and the German DAX fell 0.37%. In the Asia - Pacific market, the Hang Seng Index rose 3.36%, and the Nikkei 225 rose 6.64% [21]. 1.3 Market Valuation Situation - In June, all major market indices' valuations were adjusted upwards. In terms of price - to - earnings ratio and price - to - book ratio, the historical percentile increases of CSI 300 and CSI All - Share were among the top, while the ChiNext Index remained at a historical low. Among industries, the top five industries with the highest historical percentiles of price - to - earnings ratio in the Shenwan primary index last month were real estate, banking, automotive, chemical, and electronics. The real estate industry's price - to - earnings ratio percentile reached 96.6%. The five industries with lower historical percentiles of price - to - earnings ratio were agriculture, forestry, animal husbandry and fishery, non - bank finance, food and beverage, light manufacturing, and household appliances, all with percentiles less than 25% [24]. 2. Overall Situation of Public Offering Funds 2.1 Fund Issuance Situation - In June, 70 new funds were issued, with a total issuance scale of 62.728 billion yuan. Among them, 33 stock - type funds were issued with a scale of 11.646 billion yuan; 14 hybrid funds were issued with a scale of 6.317 billion yuan; 14 bond - type funds were issued with a scale of 35.293 billion yuan; 4 FOF funds were issued with a scale of 7.5 billion yuan; 3 REITs funds were issued with a scale of 1.3 billion yuan; and 2 QDII funds were issued with a scale of 0.67 billion yuan. A total of 17 active equity funds were issued with a scale of 6.738 billion yuan, and 36 index funds were issued with a scale of 28.472 billion yuan. The issuance of active equity funds increased significantly, while that of passive equity funds decreased slightly [32]. 2.2 Fund Market Return Situation - In June, only commodity - type funds declined, with a 1.66% drop, and the largest gain was in equity - biased funds, up 2.68%, with a positive return ratio of 97.63%. From the perspective of fund style indices, the growth style outperformed the value style, and the large - cap style underperformed the small - cap style. The mid - cap growth style performed outstandingly, rising 5.83%, while the large - cap value style had the smallest increase, about 2.52%. Generally, smaller - scale funds in the equity market performed better. The large - scale funds with a scale of 4 - 10 billion had the largest average increase of 2.80%, with a positive return ratio of 97.52%, while the super - large - scale funds over 10 billion had the smallest increase of 2.16%, with a positive return ratio of 88.46% [2][40][43]. 2.3 Active Equity Fund Position Situation - Using Lasso regression to measure the positions of active equity funds, the position on June 30, 2025, was 75.44%, a decrease of 3.76 percentage points from the previous month [47]. 3. ETF Fund Situation - In the ETF market, last month, there was a net inflow of 59.605 billion yuan. Bond - type ETFs had a net inflow of over 90 billion yuan, and stock - type ETFs had a net outflow of 31.54 billion yuan, with funds flowing from broad - based indices such as CSI 300 to bond funds. In terms of liquidity, the average daily trading volume of the overall ETF market this period reached 265.76 billion yuan, the average daily trading volume reached 126.808 billion shares, and the average daily turnover rate reached 8.59%. At the individual bond level, most broad - based index targets had net outflows except for the CSI A500 Index. Huatai - PineBridge CSI 300 ETF had a net outflow of 5.45 billion yuan, while Huatai - PineBridge CSI A500 ETF had a net inflow of 13.54 billion yuan. Among the most actively traded targets, Financial Technology ETF, Hong Kong Securities ETF, Communication Equipment ETF, ChiNext Artificial Intelligence ETF Huabao, and 5G ETF had the highest monthly gains, rising between 15.7% and 18.8%. Food and Beverage ETF, Consumption 30 ETF, Wine ETF, Leading Home Appliance ETF, and Southeast Asia Technology ETF had the highest monthly losses, falling between 1.6% and 4.4%. In terms of fund flow, the top funds with net inflows also included Hong Kong Stock Connect Innovation Pharmaceutical ETF, Bank ETF, A500ETF Harvest, and Hong Kong Non - Bank ETF; the top funds with net outflows also included CSI 300ETF E Fund, ChiNext ETF, Harvest CSI 300ETF, and CSI A500ETF Fullgoal [3][51][52]. 4. Model Operation Situation - Four types of large - asset allocation models were constructed using stocks, bonds, commodities, and QDII. In June, the risk - parity model rose 1.59%, and the risk - budget model rose 2.34%. Since 2015, the annualized return of the risk - parity model has been 4.64%, with a maximum drawdown of 2.31%; the annualized return of the risk - budget model has been 4.45%, with a maximum drawdown of 9.80%. Next month, the asset allocation weights of the models remain unchanged. For the risk - parity model, the ratio of stocks: bonds: commodities: QDII is 6%: 70%: 12%: 11%; for the risk - budget model, it is 13%: 52%: 9%: 25% [62][63][65].

Group 1 - The pharmaceutical sector is rapidly gaining popularity, with a peak in the reporting of themed funds. Approximately 30 pharmaceutical-themed funds were reported in the second quarter, which is roughly equivalent to the total number of such funds established in 2024. Some industry insiders believe that while the current valuations in the pharmaceutical industry are not low, they have not yet entered a bubble phase. From a capital allocation perspective, as the industry's fundamentals improve, actively managed equity funds are expected to return to standard allocations in the pharmaceutical sector [1][3]. - Equity funds are accelerating their positions in the market. Since June, 47 new equity funds have been established, with several funds that have been active for less than half a month already entering the position-building phase. Industry experts suggest that strong policy support is driving the gradual recovery of market valuations, creating abundant structural investment opportunities in the A-share market, prompting fund managers to seize the opportunity to accelerate their positions [1][3]. - The Loan Prime Rate (LPR) remained unchanged in June. The People's Bank of China announced that the one-year LPR is 3.0% and the five-year LPR is 3.5%, both unchanged from the previous month. Industry insiders believe that the stability of the LPR this month aligns with market expectations after a 10 basis point decrease in both tenors in May [1][3]. Group 2 - The Financial Regulatory Bureau has standardized the dividend insurance market, prohibiting arbitrary increases in dividend levels for competitive purposes. The new regulations require that the necessity, reasonableness, and sustainability of dividend levels be thoroughly justified, aiming to curb "involution-style" competition in the industry, prevent interest margin losses, and promote the long-term stable development of dividend insurance business [2]. Group 3 - Australian funds are reducing their holdings in U.S. Treasury bonds due to concerns over policy risks associated with Donald Trump's administration. Some of Australia's largest investors, managing assets equivalent to $30 billion, have shifted towards reducing their exposure to U.S. sovereign debt, reflecting a cautious stance amid potential policy changes [4].

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].

Group 1 - The report discusses the re-evaluation of the All-Weather Strategy, emphasizing the need for a more balanced asset allocation approach in the context of China's low bond volatility, which leads to higher bond allocations than intended under traditional risk parity models [3][20]. - The concept of "Scenario Parity" is introduced, where asset allocation is based on different macroeconomic scenarios (growth and inflation), allowing for a more tailored asset basket that can enhance returns compared to traditional risk parity [3][21]. - The report highlights the performance of the All-Weather ETF launched by Bridgewater, which has shown resilience and recovery from market volatility, with a maximum drawdown of 8.78% shortly after its launch [8][12]. Group 2 - The report outlines the construction of a "Scenario Parity" portfolio using regression analysis to measure asset exposure to macroeconomic factors, resulting in a more effective asset allocation strategy that improves returns while reducing bond exposure [3][22]. - The performance metrics of various asset allocation strategies are compared, showing that the "Scenario Parity" approach yields higher annualized returns and lower drawdowns compared to traditional risk parity strategies [29][55]. - The report emphasizes the importance of macro sensitivity in constructing portfolios, demonstrating that portfolios based on sensitivity measures outperform those based solely on regression analysis, particularly in volatile market conditions [34][55]. Group 3 - The report explores the application of the All-Weather strategy within equity assets, indicating that a focus on macro exposure can lead to better risk diversification and performance, especially in uncertain market environments [41][43]. - The analysis of industry ETFs reveals significant differences in macro exposure, suggesting that a more nuanced approach to sector allocation can enhance overall portfolio performance [45][48]. - The report concludes that using macro sensitivity to guide asset selection within equity portfolios can lead to improved risk-adjusted returns, highlighting the effectiveness of this strategy in various economic scenarios [55][56].

Group 1 - The strategy for low to medium risk asset allocation includes risk parity and risk budgeting models, where risk parity allocates equal risk weights across different assets, while risk budgeting allows investors to set asset risk weights based on their risk preferences [1] - The correlation between major asset classes such as equities (A-shares, Hong Kong stocks, US stocks), bonds, and commodities (precious metals, energy, chemicals) is relatively low, making it suitable to construct portfolios using corresponding ETFs [1] - The long-term correlation between bonds and equities or commodities ranges from 0 to -30%, indicating a "stock-bond seesaw" effect due to the counter-cyclical nature of interest rates affecting bond yields, while equities and commodities reflect the health or expectations of the real economy [1] Group 2 - A simple construction method for the model involves selecting broad-based indices for equities such as CSI 300 ETF, CSI 500 ETF, ChiNext ETF, and National 2000 ETF, while the bond portion can include government bond ETFs, policy financial bond ETFs, and local government bond ETFs [2] - For the commodity portion, gold ETFs and commodity futures ETFs can be included, with advanced construction methods allowing for a core-satellite approach or sector rotation strategy for equities [2]