Core Viewpoint - The article emphasizes the importance of multi-asset public funds in the current volatile capital market, highlighting the success of the Huaan Yingrui Stable Preferred Fund as a benchmark in multi-asset investment strategies [1][2]. Multi-Asset Strategy - Huaan Yingrui Stable Preferred Fund employs a multi-asset strategy that combines risk parity and diverse asset classes to optimize returns while controlling risk [2][3]. - The fund's approach includes a risk parity model that dynamically adjusts asset weights based on their volatility, ensuring equal risk contribution from stocks, bonds, and commodities [2][3]. Asset Matrix Construction - The fund covers eight asset categories, including A-shares, pure bonds, overseas equities, commodities, and REITs, allowing it to find yield opportunities in various macroeconomic environments [3]. - A monthly rebalancing mechanism is in place to maintain risk balance, adjusting asset allocations based on price movements and market conditions [3]. Research and Team Expertise - Huaan Fund leverages its comprehensive product offerings and internal resources to enhance the efficiency of the FOF's asset allocation [4]. - The FOF team, led by multi-asset allocation expert Lu Jingchang, focuses on strategic and tactical asset allocation to improve risk-return profiles [5]. Performance Metrics - As of June 30, 2023, the Huaan Yingrui Stable Preferred Fund achieved a return of 5.59% since its inception on May 19, 2023, outperforming its benchmark of 5.19% and the peer index of 3.32% [3].

Core Viewpoint - The public FOF (Fund of Funds) industry in China is experiencing a resurgence, driven by a shift in investor demand towards multi-asset strategies that provide stability and risk diversification in volatile markets [2][4][7]. Group 1: Industry Development - The public FOF industry has evolved from its inception in 2017, experiencing a boom in 2021 due to regulatory changes and a shift away from guaranteed bank products, leading to increased popularity of stable FOFs [4][5]. - After a period of underperformance and reduced investor interest, the industry is now revitalizing, with fund companies optimizing portfolios and innovating product designs to meet diverse investor needs [4][5][7]. - The current market environment has prompted a collective push towards FOFs, as investors seek long-term wealth growth and stability across different market conditions [7][8]. Group 2: Product Strategy - The Huaan Yingrui Stable Preferred 6-Month Holding Period FOF has emerged as a leading product, benefiting from a multi-asset allocation strategy that balances risk and enhances returns [2][10]. - The fund employs a risk parity model to maintain balanced risk contributions from various asset classes, ensuring stable performance through dynamic rebalancing based on market conditions [10][12]. - The product has undergone significant upgrades, expanding its asset classes to include REITs, commodities, and international equities, thereby enhancing its ability to capture diverse revenue sources [13][14]. Group 3: Manager Expertise - The fund is managed by experienced professionals, including Lu Jingchang, who has extensive experience in FOF investment and a proven track record in managing large-scale fund portfolios [15][16]. - The management team focuses on achieving sustainable risk-adjusted returns through strategic asset allocation and dynamic risk management, adapting to market changes effectively [15][16]. - The company's investment research platform and diversified team structure support the FOF's comprehensive investment approach, enabling it to navigate various market environments successfully [14][15].

Core Viewpoint - The article emphasizes the advantages of using ETFs as a diversified investment tool in a complex market environment, highlighting their superior performance compared to individual stocks over various time frames [2][4]. Performance Analysis - As of August 13, 2023, 83% of individual stocks have risen, while nearly 95% of ETFs/LOFs have increased in value [2][3]. - The performance of ETFs over different periods shows a consistently higher percentage of rising ETFs compared to individual stocks: - 6 months: 77% for stocks vs. 90% for ETFs - 1 year: 93% for stocks vs. 81% for ETFs - 2 years: 65% for stocks vs. 74% for ETFs - 3 years: 59% for stocks vs. 66% for ETFs [3]. Investment Philosophy - The investment philosophy centers on "risk diversification and long-term stability," suitable for investors seeking asset preservation and moderate risk tolerance [6]. - The approach encourages systematic allocation to mitigate market volatility and emphasizes the importance of using professional quantitative tools [8][9]. Investment Strategy - The investment strategy employs a systematic framework that includes a data engine, risk prediction using neural networks, and risk parity for diversified and smooth returns [12]. - The model quantifies safety margins using "downside volatility," adjusting asset allocation based on historical data rather than traditional valuation methods [13]. Selection Criteria - The selection process for ETFs involves evaluating three key indicators: shrinkage in ETF size, liquidity decline, and tracking error expansion [15]. - The strategy focuses on a limited number of holdings, with no single position exceeding 20% of the portfolio [16]. Market Approach - The investment approach is primarily top-down, analyzing macroeconomic risks to determine asset class allocations before selecting specific ETFs [17]. - The model incorporates dynamic thresholds for risk management, triggering automatic adjustments based on volatility predictions [18]. Client Engagement - The service targets investors who are patient and understand the value of diversified investments, while also emphasizing the importance of risk management [22].

Group 1 - The core viewpoint highlights the unprecedented allocation 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 2 - 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 and mid-cap stocks with potential for excess returns [1] Group 3 - 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 4 - 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-levels with an average daily trading volume of $4.7 billion [4] - A dynamic balance of risk and return is necessary for cross-asset allocation, with the optimal current portfolio ratio being 45% stocks, 30% bonds, and 25% gold, where gold's volatility contribution has decreased to 14% and its correlation coefficient with stocks has improved to 0.38 [4] - The application of smart rebalancing algorithms has effectively controlled the annualized portfolio volatility within 9.2% [4] Group 5 - The capital market is in a continuous 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 indicates that a three-year systematic investment strategy has achieved an annualized return of 8.7%, significantly outperforming single-asset allocation strategies [4]

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]

Group 1 - The core viewpoint of the articles highlights the unprecedented challenges faced by global investors due to high interest rates, structural market differentiation, and the need for diversified investment strategies [1][2][3] Group 2 - The stock market is experiencing significant structural differentiation, with the technology sector driven by AI demand showing resilience, while traditional consumer sectors are under pressure due to declining savings rates [1] - The average excess return of actively managed funds has reached 4.2 percentage points, emphasizing the value of professional investment in a complex market environment [1] - The fixed income market is undergoing a pricing mechanism reconstruction, 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] - The green bond market has surpassed $2.3 trillion in size, with a compound annual growth rate of 19%, providing new options for ESG investors [2] - Gold prices have surpassed $2,500 per ounce due to geopolitical risks and central bank purchases, despite positive real interest rates [2] - The trading volume of digital gold certificates has increased by 240% year-on-year, enhancing the liquidity of gold to stock-levels with an average daily trading volume of $4.7 billion [2] - A dynamic balance of risk and return is necessary for cross-asset allocation, with an optimal portfolio currently consisting of 45% stocks, 30% bonds, and 25% gold [3] - The correlation coefficient indicates that gold's hedging efficiency against stock assets has improved to 0.38 [3] - The application of smart rebalancing algorithms has effectively controlled the annualized volatility of portfolios within 9.2% [3] - The divergence in capital flows, such as the record net outflow of northbound funds from the Shanghai-Hong Kong Stock Connect, signals rational investors' reverse positioning [3] - A three-year systematic investment strategy has achieved an annualized return of 8.7%, significantly outperforming single-asset allocation strategies [3]

- 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]