Report Industry Investment Rating No relevant content provided. Core Viewpoints of the Report - The report captures global multi - asset investment opportunities (covering domestic assets such as A - shares, bonds, and gold, as well as overseas equity assets like US stocks, Japanese stocks, and Indian stocks) and designs corresponding allocation schemes according to common investment scenarios. All portfolios can be tracked through corresponding ETF/LOF products [7]. - In January 2026, the allocation suggestions are as follows: A - shares may have short - term momentum but also face callback risks, with a focus on cyclical mid - cap blue - chips led by chemicals, domestic AI, satellites, and semiconductors; the domestic bond market is neutral, and short - term varieties can be focused on; US stocks may maintain a neutral shock pattern; Japanese stocks may have a neutral shock pattern; Indian stocks may have a weak shock pattern; gold may remain strong in the short - term but also face volatility risks, and its medium - to - long - term allocation value is significant [7]. - A two - stage robust multi - asset portfolio design method based on "portfolio insurance + risk budget" is introduced, which is decision - making based on risk characteristics, does not rely on asset return forecasts, and has good robustness while considering both return elasticity and risk control [7]. Summary by Relevant Catalogs 1. Market Review and Allocation Outlook 1.1 Market Review - In 2025, gold performed outstandingly, global equity assets showed differentiation (A - shares, Japanese stocks, and US stocks were strong, while Indian stocks declined slightly), and the bond market was relatively sluggish. The return performance of underlying assets was: gold (58.57%) > CSI 800 (23.91%) > Nikkei 225 (22.26%) > Nasdaq 100 (17.50%) > short - term financing (1.78%) > 7 - 10 - year policy - financial bonds (0.22%) > S&P BSE Sensex ( - 0.40%) [16]. 1.2 Asset Allocation Outlook - **A - shares**: Economic prosperity and mild inflation recovery support the medium - to - long - term stock market trend, but there are short - term callback risks. Industry themes such as cyclical mid - cap blue - chips led by chemicals, domestic AI, satellites, and semiconductors can be focused on [18]. - **Domestic bond market**: Due to the risk preference of rising equities and the expectation of mild inflation recovery, bonds are neutral overall, and short - term varieties can be continuously focused on [20]. - **US stocks**: The US economy still has resilience, but due to the downward revision of interest - rate cut expectations and relatively high valuations, US stocks may maintain a neutral shock pattern in the short - term [22]. - **Japanese stocks**: Japan's economy is in a benign "wage - price spiral" and is moderately recovering, but with a marginal net outflow of foreign capital, Japanese stocks may have a neutral shock pattern in the short - term [31]. - **Indian stocks**: The economic prosperity has declined from its peak, and with a marginal net outflow of foreign capital, Indian stocks may have a weak shock pattern in the short - term [34]. - **Gold**: Geopolitical tensions have pushed gold to new highs. It may remain strong in the short - term but also face volatility risks, and its medium - to - long - term allocation value is significant [38]. 2. Robust Portfolio Design Idea: Two - Stage Method of "Portfolio Insurance + Risk Budget" 2.1 Dilemma of Asset Allocation Models in Domestic Investment Applications - The two classic multi - asset portfolio management methods, mean - variance optimization (MVO) and its derivative models, and risk - budget - based models (such as the risk - parity model), have limitations in domestic investment applications. MVO is highly sensitive to changes in returns and risks, and the risk - parity model may lead to an overly low proportion of equity assets in the portfolio [45]. 2.2 Optimization Idea 1: Using Portfolio Insurance Method to Optimize the Sharpe Ratio of High - Risk Assets - The portfolio insurance strategy can optimize the return - risk ratio of high - volatility assets such as A - shares in the medium - to - long - term. Taking the domestic stock - bond CPPI portfolio as an example, it can achieve better risk performance compared to corresponding portfolios [52]. 2.3 Optimization Idea 2: Integrating Target Allocation Central Risk Budget Strategy - By decomposing the risk budget, the target stock - bond allocation central can be integrated into the risk - budget configuration model, and the allocation weights can be dynamically adjusted according to the changes in asset volatility [59]. 2.4 "Portfolio Insurance + Risk Budget": Balancing Return Elasticity and Risk Control - The two - stage combination design method of "portfolio insurance + risk budget" first uses the CPPI method to optimize the Sharpe ratio of single risk assets and then constructs a risk - budget investment portfolio based on the risk characteristics of each sub - portfolio. It can effectively combine return elasticity and risk control and has good robustness [63]. 3. Stock - Bond Target Allocation Central Risk Budget Portfolio 3.1 Investment Scenarios and Scheme Design - In a low - interest - rate environment, the fixed - income plus strategy can alleviate the problem of declining returns of pure - bond assets. Two strategies are designed: the stock - bond target allocation central risk budget strategy (stock - bond RB) and the "CPPI + RB" two - stage stock - bond target allocation central strategy (stock - bond CPPI_RB), with three types of allocation central combinations of 1:9, 2:8, and 3:7 constructed respectively [67][68][69]. 3.2 Portfolio Performance Analysis - During the back - testing period (January 5, 2015 - December 31, 2025), the performance of the strategy integrating the stock - bond target allocation central risk budget is better than that of the fixed - allocation central stock - bond portfolio, and the two - stage stock - bond CPPI_RB portfolio is better than the stock - bond RB portfolio [70]. 3.3 Allocation Weights and Marginal Changes - The stock - bond allocation of the three types of allocation central portfolios meets the requirements of the target allocation central. At the end of December 2025, the stock - bond RB portfolio moderately increased the weight of A - shares and increased the weight of long - term bonds while reducing the weight of short - term bonds within the bond category [75]. 4. Low - Volatility "Fixed - Income Plus" Portfolio 4.1 Investment Scenarios and Scheme Design - To reduce the volatility risk of the stock - bond portfolio during extreme "stock - bond double - kill" market conditions, an appropriate amount of gold is added. The portfolio is designed using the two - stage method of "portfolio insurance (CPPI) + risk budget (RB)", with a target allocation central of stock:gold:bond = 1:1:4 [80][81]. 4.2 Portfolio Performance Analysis - During the back - testing period (January 1, 2015 - December 31, 2025), the low - volatility "fixed - income plus" strategy has an annualized return of 7.08%, an annualized volatility of 3.47%, a maximum drawdown of - 4.92%, a Sharpe ratio of 1.99, and a Calmar ratio of 1.44 [83]. 4.3 Allocation Weights and Marginal Changes - As of December 31, 2025, the latest weights of the strategy are: CSI 800 (10.78%), gold (5.99%), 7 - 10 - year policy - financial bonds (75.09%), and short - term financing (8.14%). In December 2025, the weight of short - term financing was increased, and the weights of other assets were decreased [90]. 4.4 Strategy Implementation: Tracking Based on ETF Assets - The low - volatility "fixed - income plus" strategy can be well tracked by corresponding ETF assets. As of December 31, 2025, the annualized return of the strategy since 2023 is 9.38%, and the annualized returns of the FOF_of_ETFs portfolio based on ETF net value and on - site price are 9.05% and 9.07% respectively [95]. 5. Global Asset Allocation Portfolio 5.1 Investment Scenarios and Scheme Design - In a volatile global situation, global asset allocation can effectively diversify risks and improve the return - risk ratio of the portfolio. A two - stage FOF portfolio design method of "portfolio insurance (CPPI) + risk parity (RP)" is used [102][104]. 5.2 Global Multi - Asset Allocation Strategy I: A - shares + Bonds + Gold + US Stocks - **Performance**: During the back - testing period (January 1, 2014 - December 31, 2025), the annualized return is 11.85%, the annualized volatility is 5.94%, the maximum drawdown is - 7.97%, the Sharpe ratio is 1.91, and the Calmar ratio is 1.49. In 2025, it recorded 20.94% [106]. - **Allocation Weights and Marginal Changes**: As of December 31, 2025, the model allocation weights are: CSI 800 (18.98%), Nasdaq 100 (17.84%), gold (13.66%), and 7 - 10 - year policy - financial bonds (49.51%). In December 2025, the weight of 7 - 10 - year policy - financial bonds was increased, and the weights of other assets were decreased [111]. - **Strategy Implementation**: The strategy can be well tracked by corresponding ETF/LOF assets. As of December 31, 2025, the annualized return of the strategy since 2023 is 16.92%, and the annualized returns of the FOF_of_ETFs portfolio based on ETF net value and on - site price are 16.53% and 17.04% respectively [119]. 5.3 Global Multi - Asset Allocation Strategy II: A - shares + Bonds + Gold + Cross - Border Equities - **Performance**: During the back - testing period (January 1, 2014 - December 31, 2025), the annualized return is 10.25%, the annualized volatility is 5.09%, the maximum drawdown is - 9.97%, the Sharpe ratio is 1.94, and the Calmar ratio is 1.03. In 2025, it recorded 13.56% [126]. - **Allocation Weights and Marginal Changes**: As of December 31, 2025, the model allocation weights are: CSI 800 (9.63%), Nasdaq 100 (9.65%), Nikkei 225 (6.17%), S&P BSE Sensex (17.87%), gold (7.16%), and 7 - 10 - year policy - financial bonds (49.51%). In December 2025, the weights of S&P BSE Sensex and 7 - 10 - year policy - financial bonds were increased, and the weights of other assets were decreased [133]. - **Strategy Implementation**: The strategy can be well tracked by corresponding ETF/LOF assets. As of December 31, 2025, the annualized return of the strategy since 2023 is 14.06%, and the annualized returns of the FOF_of_ETFs portfolio based on ETF net value and on - site price are 13.60% and 14.06% respectively [145].

Core Insights - The Bloomberg Investment Management Forum in Shanghai highlighted the rapid transformation of the asset management industry through quantitative research strategies, emphasizing Bloomberg's commitment to this field over the past 30 years [1][4]. Group 1: Macro Quantitative Scenario Analysis - Bloomberg has developed a factor-based macro quantitative scenario analysis model that integrates macroeconomic variables with underlying drivers such as credit risk and demand changes, utilizing a large covariance matrix updated daily to detail asset correlations and risk transmission [4][6]. - Users can customize macro variable impacts and driver weight distributions to simulate investment portfolio performance under various economic conditions [6]. Group 2: Risk Budgeting in Equity Allocation - The application of risk budgeting strategies in global and A-share markets can help mitigate losses during market volatility by adjusting allocations based on low correlation and volatility differences among A-share stocks [7][9]. - This approach aims to create a more balanced and resilient investment portfolio by ensuring each asset contributes equally to overall portfolio risk rather than focusing solely on weight [9]. Group 3: Cross-Asset Investment and Strategy Index Development - The discussion on cross-asset investment strategies highlighted the increasing demand for diversified asset allocation and risk premium management among institutions, with a focus on innovation to inspire investors and reduce risks [10][12]. - Bloomberg supports quantitative teams with data integration, risk analysis, and scenario simulation to enhance strategy development and risk management efficiency [12]. Group 4: Risk Management in Investment Decisions - Effective risk management is crucial in investment decision-making, with strategies based on risk perspectives aiding in capturing alpha and facilitating quantitative backtesting [13][15]. - The use of risk parity methods combined with asset correlations can enhance portfolio robustness, addressing the challenges of return forecasting [15]. Group 5: Factor Investment and Alternative Data - The exploration of factor investment frameworks and the use of alternative data and machine learning to tackle the "factor zoo" challenge were discussed, with innovative factors developed from Bloomberg's supply chain data [17][19]. - The application of deep learning models for dynamic beta estimation shows significant performance improvements over traditional methods, enhancing the predictive capabilities for future volatility and variance [19].

Core Viewpoint - The podcast episode titled "When the Big Cycle Fails, Where is the New Macro Coordinate?" hosted by fund manager Tang Jun from Zhongtai Asset Management has gained significant attention, with over 60,000 listeners in a week, indicating a strong interest among investors in learning about macroeconomic frameworks and investment strategies [2][5]. Group 1: Framework Construction - The background and reasons for the effectiveness and ineffectiveness of the Merrill Clock are discussed [5]. - The "Credit-Money" framework is introduced, explaining how to describe the current macroeconomic state based on this framework [5]. - The current macroeconomic state leads to specific asset allocation conclusions [5]. Group 2: Allocation and Portfolio Construction - The distinction between active and passive allocation is made, highlighting the problems that active allocation can solve [9]. - Preparations required for engaging in active allocation are outlined [9]. - The role of FOF (Fund of Funds) in addressing specific issues is examined [9]. - The execution of strategic and tactical layers in investment is discussed [9]. Group 3: Reflections on Human Nature - The importance of understanding human behavior in the context of investment allocation is emphasized, inviting listeners to engage in a professional and rigorous intellectual exchange [6]. Group 4: Risk and Return Concepts - The concept of risk budgeting and how to construct a portfolio within a given risk budget is explained [9]. - The notion of return streams and which assets can represent different return streams is analyzed, drawing lessons from Bridgewater's practices [9]. - The significance of having a framework in investment decision-making is highlighted [9]. - The meaning of logic in investment and the application of probabilistic thinking in market timing are discussed [9].

Core Viewpoint - Asset allocation is a complex and multi-dimensional task, often referred to as "physical labor" due to the extensive research required to achieve effective configurations [1][2][27]. Group 1: Passive vs. Active Allocation - Passive allocation, which relies on diversification to reduce volatility, faces challenges in practice, particularly for domestic investors due to limited asset classes and the poor performance of key assets like A-shares [5][9][27]. - Active allocation aims to enhance returns beyond passive strategies by making informed predictions about expected returns, addressing the shortcomings of passive allocation [2][27]. Group 2: Issues with Passive Allocation - Determining expected returns using historical data can lead to "chasing performance," where investors favor assets that have recently performed well, skewing allocation models [5][9]. - The correlation between assets is not stable; for instance, the historical negative correlation between U.S. stocks and bonds has weakened since the 2008 financial crisis, impacting the effectiveness of diversification [6][9]. - The performance of passive allocation is heavily dependent on the underlying assets' returns and their correlations, which can be problematic in markets with limited asset classes [9][27]. Group 3: The Complexity of Active Allocation - Active allocation involves timing decisions, which many investors find challenging, leading to skepticism about its feasibility [17][19]. - While achieving a high accuracy rate in timing is difficult, even a modest success rate can significantly enhance investment returns when combined with sound risk management [18][19]. - The macroeconomic drivers influencing asset performance can change, necessitating continuous adjustments to research frameworks and strategies [21][27]. Group 4: Multi-Dimensional Decision Making - Effective asset allocation requires multiple low-correlation return streams to improve the probability of successful outcomes, as relying on a single asset is often insufficient [22][23]. - A structured decision-making framework that incorporates both strategic and tactical allocations can enhance the robustness of investment strategies [23][24]. - Strict risk budgeting is essential to ensure that asset allocations align with the overall risk tolerance of the portfolio, preventing forced liquidations during market fluctuations [24][25].

Core Viewpoint - In a low-risk return environment, traditional bank wealth management fails to meet investors' yield demands, leading institutional investors to seek stable returns through diversified asset allocation [1] Group 1: Understanding Institutional Investor Needs - The multi-asset team at Huaxia Fund focuses on understanding the "constraint conditions" of the liability side, which is crucial for making investment choices [4] - The team emphasizes communication with institutional clients to understand their specific needs and constraints, leading to a negative list of what cannot be done [4] - The investment strategy is shaped by the clients' requirements for absolute returns and stable relative rankings, avoiding credit downgrading strategies [4][5] Group 2: Sources of Excess Returns - The team adopts a "quality over faith" approach, focusing on the quality of underlying assets rather than relying on policy beliefs, which can be fragile [2][12] - Discipline is essential in managing human weaknesses, as absolute return products cannot tolerate annual losses, necessitating strict adherence to risk budgets [3][15] - The diverse team composition fosters collective wisdom, allowing each member to leverage their unique strengths and expertise in specific asset areas [2][19] Group 3: Investment Strategy and Execution - The team utilizes a macroeconomic strategy and has developed the MVP analysis model, which enhances their competitive advantage in duration strategies [7] - Huaxia Fund has strategically increased the duration of their portfolios, anticipating shifts in economic growth patterns, which has yielded significant excess returns [7][8] - The team recognizes the potential in convertible bonds, which often have pricing discrepancies, allowing for substantial excess returns [8][9] Group 4: Balancing Discipline and Flexibility - The team implements a main account holder model to unify risk and return characteristics across products, enhancing overall performance [15] - Each fund manager is given the autonomy to make investment decisions within the established risk budget, promoting differentiated product management [15][16] - The risk budget sets clear disciplinary boundaries, allowing fund managers to make informed decisions on where to allocate risk [16] Group 5: Multi-Asset Investment Culture - Huaxia Fund aims to create a "Lego" approach in asset management, fostering a culture that supports diverse asset types and strategies [17][22] - The team comprises professionals with varied backgrounds, enhancing their ability to navigate different economic environments and achieve stable returns [19][20] - The reliance on a strong research platform and team collaboration is essential for adapting to market fluctuations and ensuring consistent decision-making [20]

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].