以下文章来源于风云君的研究笔记 ，作者专注私募研究的 风云君的研究笔记 . 深耕私募行业多年，专注私募基金各个策略以及资产配置，希望能分享给大家更深入、更专业的私募那 些事。 过去几年，桥水全天候策略产品表现非常出色，最差产品线一年收益率也在10%到14%之间，平 均收益率大约在16%左右，真正实现了跨越牛熊周期的长期收益。 当然，其策略本身的赚钱逻辑也被越来越多投资人接受。 风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。 作者： 风云君的研究笔记 来源：雪球 节目预告：本周三晚，风云君会邀请雪球基金投研举办一场"宏观策略"的专场直播，从市场环境到策略 深度解读。点击添加风云君企微提前报名>> 上周，上证指数一度逼近3900点整数关口，A股情绪很是火热。 但比大盘更"火热"的，是桥水旗下的全天候策略产品。 到底"火热"到什么程度？ 据了解，早在8月份，产品上架即售罄。还因为认购过于火爆，某头部券商直接开启白名单制，大 部分只对高净客户开放，另外各种配售机制也大幅抬高了申购门槛。很多投资者直呼"买不起"， 只能望而却步。 桥水全天候被市场"疯抢" 自然 离不开其策略的优秀 ...

Core Viewpoint - The article highlights the strong demand for Bridgewater's All Weather strategy products, which have shown impressive performance and have become increasingly popular among investors [2][4]. Group 1: Market Performance - The Shanghai Composite Index approached the 3900-point mark, indicating a bullish sentiment in the A-share market [1]. - Bridgewater's All Weather strategy products were sold out shortly after their launch in August due to overwhelming demand [3]. Group 2: Strategy Performance - The All Weather strategy has consistently delivered strong returns, with the worst-performing product line achieving annual returns between 10% and 14%, and an average return of approximately 16% [4]. - The strategy's success is attributed to its risk parity model, which diversifies investments across various asset classes to balance risk and return [6]. Group 3: Strategy Components - The strategy consists of a beta component (70%) based on a risk parity model and an alpha component (30%) that captures short-term opportunities through various sub-strategies [6][9]. - The beta portion aims to construct a macro risk-balanced portfolio by adjusting asset allocations based on economic growth and inflation [7]. - The alpha portion utilizes a unique factor library and quantitative models to enhance returns without increasing overall portfolio risk [8]. Group 4: Enhanced Macro Hedging Strategies - An enhanced macro hedging strategy combines quantitative models for asset allocation with subjective analysis to capture excess returns in specific asset classes [12][13]. - This strategy aims to achieve long-term returns while also seizing short-term investment opportunities based on economic cycles [14]. Group 5: Quantitative Macro Hedging - A fully quantitative macro hedging strategy employs various models to capture price signals across different time frames, covering a wide range of asset classes [15][16]. - The strategy maintains a balanced risk profile, with equity and debt each comprising 30% of the portfolio, gold at 15%, and other commodities at 25% [16]. - The overall strategy aims to optimize risk-return profiles while ensuring that volatility remains controlled within 8% [17].

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]