导读：在无风险收益不断下行的时代，传统的银行理财已经难以满足投资者的收益需求。对于追求绝对收益目标的机构投资者来说，通过多元化资 产配置形成的稳健收益，成为了更好的解决方案。 作为双轮驱动的多资产平台型资产管理公司，华夏基金不仅提供了品类丰富的公募基金产品，也为机构投资者提供了满足不同投资目标的专户产 品。对于机构投资者的低波稳健收益需求，华夏基金在机构债券部门内设立了专门的多元稳健收益团队。这个团队由固定收益总监范义领衔，通过 中长期优异的业绩，在机构投资者心中形成了良好的口碑。 那么华夏基金的多元稳健收益团队究竟有何不同，他们又是如何匹配机构投资者需求的？近期，我们和华夏基金固定收益总监范义，机构债券投资 部执行总经理周欣做了一次深度交流，解构了这个团队形成长期稳健收益的几大来源： 1）第一个长期，在充分理解负债端"约束条件"下做好选择题。长期投资的第一步是理解负债端特征。管理机构投资者的专户产品和零售客户的公募 基金有着本质不同。机构投资者对于组合的持仓穿透有更严格要求。在这样的"约束条件"下，团队坚决放弃信用下沉策略，转而向多资产和多策略 要收益。 2）第二个长期，超额收益来源具有很强的"鲁棒性"。用 ...

Quantitative Models and Construction Methods Model: Duration Timing Model - **Construction Idea**: Predict the yield curve and map the expected returns of bonds with different durations[20] - **Construction Process**: - Use the improved Diebold2006 model to predict the instantaneous yield curve - Predict level, slope, and curvature factors - Level factor prediction based on macro variables and policy rate following - Slope and curvature factors prediction based on AR(1) model[20] - **Evaluation**: The model effectively predicts the yield curve and provides actionable insights for bond duration management[20] - **Test Results**: - July return: 6.6bp - Benchmark return: -25.8bp - Strategy excess return: 32.4bp[21] Model: Gold Timing Model - **Construction Idea**: Relate the forward real returns of gold and US TIPS to construct the expected return model for gold[32] - **Construction Process**: - Use the formula: $E[Real\_Return^{gold}]=k\times E[Real\_Return^{Tips}]$ - Estimate parameter k using OLS with an extended window - Use the Fed's long-term inflation target of 2% as a proxy[32] - **Evaluation**: The model provides a robust framework for predicting gold returns based on TIPS yields[32] - **Test Results**: - Expected return for the next year: 22.4% - Past year absolute return: 39.77%[33][35] Model: Active Risk Budget Model - **Construction Idea**: Combine the risk parity model with active signals to construct an active risk budget model for optimal stock and bond allocation[37] - **Construction Process**: - Use the Fed model to define equity risk premium (ERP): $ERP={\frac{1}{PE_{ttm}}}-YTM_{TB}^{10Y}$ - Adjust asset weights dynamically based on ERP, stock valuation percentiles, and market liquidity (M2-M1 spread) - Convert equity asset signal scores into risk budget weights using the softmax function: $softmax(x)={\frac{\exp(\lambda x)}{\exp(\lambda x)+\exp(-\lambda x)}}$[39][47] - **Evaluation**: The model dynamically adjusts asset weights based on multiple dimensions, providing a balanced risk-return profile[37] - **Test Results**: - July stock position: 18.72% - Bond position: 81.28% - July portfolio return: 0.84% - August stock position: 7.44% - Bond position: 92.56%[51] Model Backtest Results 1. **Duration Timing Model** - July return: 6.6bp - Benchmark return: -25.8bp - Strategy excess return: 32.4bp[21] 2. **Gold Timing Model** - Expected return for the next year: 22.4% - Past year absolute return: 39.77%[33][35] 3. **Active Risk Budget Model** - July stock position: 18.72% - Bond position: 81.28% - July portfolio return: 0.84% - August stock position: 7.44% - Bond position: 92.56%[51] Quantitative Factors and Construction Methods Factor: High-Frequency Macroeconomic Factors - **Construction Idea**: Use asset portfolio simulation to construct a high-frequency macro factor system to observe market macro expectations[12] - **Construction Process**: - Combine real macro indicators to form low-frequency macro factors - Select assets leading low-frequency macro factors - Use rolling multiple leading regression to determine asset weights and simulate macro factor trends[12] - **Evaluation**: High-frequency macro factors provide leading indicators for market expectations, offering valuable insights for asset allocation[12] Factor: Convertible Bond Valuation Factors - **Construction Idea**: Compare the relative valuation of convertible bonds and stocks, and between convertible bonds and credit bonds[25] - **Construction Process**: - Construct the "100-yuan conversion premium rate" to compare the valuation of convertible bonds and stocks - Use the "modified YTM - credit bond YTM" median to compare the valuation of debt-biased convertible bonds and credit bonds - Construct style rotation portfolios based on market sentiment indicators like 20-day momentum and volatility deviation[25][27] - **Evaluation**: The factors effectively capture the relative valuation and style characteristics of convertible bonds, aiding in portfolio construction[25][27] - **Test Results**: - "100-yuan conversion premium rate": 33.71% - "Modified YTM - credit bond YTM" median: -2.06% - Style rotation annualized return: 24.54% - Maximum drawdown: 15.89% - IR: 1.47 - Monthly win rate: 65.17% - 2025 return: 35.17%[26][29] Factor Backtest Results 1. **High-Frequency Macroeconomic Factors** - High-frequency economic growth: Upward trend - High-frequency consumer inflation: Downward trend - High-frequency producer inflation: Upward trend[17] 2. **Convertible Bond Valuation Factors** - "100-yuan conversion premium rate": 33.71% - "Modified YTM - credit bond YTM" median: -2.06% - Style rotation annualized return: 24.54% - Maximum drawdown: 15.89% - IR: 1.47 - Monthly win rate: 65.17% - 2025 return: 35.17%[26][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]

Core Viewpoint - The report captures global multi-asset investment opportunities based on market conditions and designs corresponding investment strategies, including equity and bond target allocation, low-volatility fixed income combinations, and global asset allocation strategies [1][2]. Group 1: Investment Strategies - The equity-bond target allocation strategy employs a risk budgeting design to construct a portfolio that achieves the desired allocation level, offering a better long-term risk-return profile compared to fixed allocation strategies [2]. - The low-volatility "fixed income+" strategy combines domestic stocks, bonds, and gold with a target allocation of stocks:gold:bonds = 1:1:4, achieving an annualized return of 6.86% and a volatility of 3.50% over the backtest period from January 1, 2015, to May 30, 2025 [2]. - The global asset allocation strategy I, which includes A-shares, bonds, gold, and US stocks, achieved an annualized return of 11.23% and a volatility of 5.88% over the backtest period from January 2, 2014, to May 30, 2025 [3]. Group 2: Market Outlook and Recommendations - For A-shares, the report suggests maintaining a barbell strategy, focusing on high-quality assets in large caps and trading-type assets in small caps, as risks are gradually released after recent pullbacks [4]. - In the domestic bond market, the report recommends focusing on short-term products while considering medium to long-term interest rate bonds or extending the duration of credit bonds due to ongoing economic pressures [4]. - The report indicates that US stocks may continue to experience wide fluctuations due to uncertainties in economic policies and marginal declines in economic conditions [4]. - Japanese stocks may present short-term investment opportunities due to a positive wage-price spiral and continued foreign capital inflows [4]. - Indian stocks are expected to remain in a volatile pattern due to marginal declines in economic conditions and outflows of foreign capital [4]. - Gold prices are anticipated to experience wide fluctuations due to easing tariff policies and escalating geopolitical conflicts, although the long-term upward trend remains clear [4].

Core Viewpoint - The article highlights the impressive performance of the Zhongtai Tianze Stable 6-Month Holding Mixed Fund (FOF), which has achieved a net value growth rate of 7.40% since its establishment on March 21, 2023, outperforming its benchmark by 3.21% [2] Group 1: Asset Allocation Strategy - The fund manager, Tang Jun, emphasizes the importance of asset allocation over merely selecting outstanding fund managers, focusing on forming allocation views first and then selecting the best funds to implement those views [2] - Tang Jun utilizes a macro analysis framework for risk budgeting, similar to Bridgewater's risk parity model, but with a personalized approach that allows for differentiated risk allocation based on his views [3][5] - The strategic asset allocation is based on a "monetary-credit" analysis framework, which influences long-term configuration, while tactical asset allocation focuses on short-term opportunities based on market sentiment and funding conditions [5][9] Group 2: Return Streams and Risk Assessment - The concept of "return streams" is highlighted, where having 15-20 independent return streams can significantly reduce risk without compromising expected returns [6] - The manager assesses the correlation of asset classes with existing portfolios for risk evaluation, rather than relying solely on the inherent risk of asset classes [6] - The selection of funds involves a rigorous style decomposition process to evaluate the fund's alpha performance after removing style beta influences [7] Group 3: Gold and Market Outlook - Gold is maintained as a strategic core holding due to its recognition as a global currency amidst concerns over the credibility of the US dollar [8] - The article outlines potential strategies based on macroeconomic drivers, such as domestic credit expansion and overseas dollar liquidity, which will influence future asset allocation decisions [9] - The performance of US tech stocks, particularly in relation to AI technology trends, is identified as a key factor for future market movements [9]

Core Viewpoint - The report aims to capture global multi-asset investment opportunities based on market conditions and design corresponding investment strategies, including equity and bond target allocation, low-volatility fixed income combinations, and global asset allocation strategies [1][2]. Group 1: Investment Strategies - The equity-bond target allocation strategy utilizes a risk budget design method to construct a portfolio that achieves the desired allocation level while providing a better long-term risk-return profile compared to fixed allocation portfolios [2]. - The low-volatility "fixed income +" strategy constructs a portfolio with a target allocation of equity: gold: bonds = 1:1:4, achieving an annualized return of 6.91% and a maximum drawdown of -4.92% over the backtest period from January 1, 2015, to April 30, 2025 [2]. - The global asset allocation strategy I, which includes A-shares, bonds, gold, and US stocks, achieved an annualized return of 11.22% with a maximum drawdown of -7.97% over the backtest period from January 2, 2014, to April 30, 2025 [3]. Group 2: Market Outlook and Recommendations - As of May 2025, the report suggests a cautious approach to A-shares due to ongoing tariff impacts, recommending a "barbell strategy" focusing on stable cash flow assets and technology + domestic demand as key themes [5]. - The domestic bond market is expected to benefit from a broad interest rate decline due to the central bank's monetary policy easing, with a focus on short-term securities and potential adjustments in long-term bonds [5]. - For US stocks, the uncertainty surrounding Trump's policies remains, with short-term fluctuations expected as the market reacts to tariff impacts on the US economy [5]. - Japanese stocks may present short-term opportunities due to easing tariffs and improving economic conditions [5]. - Indian stocks are anticipated to experience upward movement due to economic resilience and foreign capital inflows [5][6].

Core Viewpoint - The "fixed income +" funds are experiencing a new peak of development in 2025, becoming an important tool for asset allocation in a complex market environment [1][2]. Summary by Sections Growth of "Fixed Income +" Funds - In the first quarter of this year, several public fund companies achieved significant growth in the scale of "fixed income +" products, with some institutions seeing quarterly increments exceeding 10 billion [2][3]. - The "fixed income +" funds are gaining popularity as they offer potential for elastic returns while providing risk buffering in portfolios, making them a crucial asset allocation tool in volatile markets [2][3]. Market Conditions and Opportunities - The first quarter of 2025 saw a renewed expansion in the scale of "fixed income +" funds, with companies like China Europe Fund and Bank of China Fund leading in growth, with China Europe Fund's products seeing an increase of 17.7 billion [3]. - Global stock markets are experiencing fluctuations due to factors like the so-called "reciprocal tariffs" from the U.S., leading to increased market risk aversion [3]. - Recent financial policies announced by the State Council are expected to provide new development opportunities for "fixed income +" funds, with anticipated monetary policy adjustments aligning with market expectations [3]. Strategy Upgrades and Product Evolution - "Fixed income +" products are evolving from being stable allocation tools to strategy-oriented products, with a focus on low volatility, factor enhancement, and risk budgeting [5][8]. - The first "fixed income +" product launched by the company adopts a "10:90" stock-bond allocation framework, emphasizing quantitative strategies for risk management [5]. - Fund managers are increasingly focusing on strategic investments, such as macro hedging and multi-strategy risk parity, to achieve long-term stable returns [5][6]. Diversification and Refinement - The "fixed income +" products are moving towards diversification and refinement, addressing the balance between returns and volatility while enhancing strategies, tools, and management processes [7][8]. - Future developments in "fixed income +" products will include more refined strategies tailored to different risk-return objectives and the incorporation of various asset classes to achieve stable long-term returns [8]. - Traditional "fixed income +" products heavily rely on fund managers' personal experience, prompting a shift towards industrialized manufacturing processes to ensure performance sustainability [8].