新年初迎配置窗口，建议超配风险资产 [Table_Authors] 方奕(分析师) | | 021-38031658 | | --- | --- | | | fangyi2@gtht.com | | 登记编号 | S0880520120005 | | | 李健(分析师) | | | 010-83939798 | | | lijian8@gtht.com | | 登记编号 | S0880525070013 | | | 王子翌(分析师) | | | 021-38038293 | | | wangziyi@gtht.com | | 登记编号 | S0880523050004 | [Table_Report] 相关报告 11 月超配 AH 股与工业商品 2025.11.10 10 月超配权益与黄金，标配债券 2025.10.15 稳固结构蓄势能，9 月建议超配权益 2025.09.06 黄金屡创新高，国内资产风险平价策略本年收益 达到 0.95% 2025.04.09 A 股稳中求胜，消费风格攻守兼备 2025.04.06 研 究 报 告 策 略 研 究 请务必阅读正文之后的免责条款部分 资 国泰海通资产配置月度方案 ...

Quantitative Models and Construction Methods 1. Model Name: Mean-Variance Model - **Model Construction Idea**: The model determines the optimal portfolio by balancing expected returns and risks, based on the mean and variance of asset returns[8] - **Model Construction Process**: 1. Define the portfolio return as a random variable 2. Use the expected return ($E[R]$) and variance ($Var[R]$) to measure the portfolio's performance 3. Solve the optimization problem to maximize expected return for a given level of risk or minimize risk for a given level of return - Formula: $ \text{Minimize: } \sigma_p^2 = \sum_{i=1}^n \sum_{j=1}^n w_i w_j \sigma_{ij} $ $ \text{Subject to: } \sum_{i=1}^n w_i = 1 $ Where $w_i$ is the weight of asset $i$, $\sigma_{ij}$ is the covariance between assets $i$ and $j$[8] - **Model Evaluation**: Flexible in adjusting portfolios based on expected returns and risks, but struggles to incorporate new market dynamics and subjective views[8] 2. Model Name: Black-Litterman Model - **Model Construction Idea**: Combines the Bayesian framework with the mean-variance model to incorporate subjective views into the portfolio optimization process[8] - **Model Construction Process**: 1. Start with a prior distribution of expected returns based on market equilibrium 2. Incorporate subjective views as additional constraints 3. Use the Bayesian approach to update the prior distribution with subjective views to form a posterior distribution - Formula: $ \Pi = \tau \Sigma w_{mkt} $ $ E[R] = \left( \tau \Sigma^{-1} + P^T \Omega^{-1} P \right)^{-1} \left( \tau \Sigma^{-1} \Pi + P^T \Omega^{-1} Q \right) $ Where $\Pi$ is the implied equilibrium return, $\tau$ is a scaling factor, $\Sigma$ is the covariance matrix, $w_{mkt}$ is the market portfolio weights, $P$ is the view matrix, $\Omega$ is the uncertainty matrix, and $Q$ is the view vector[8] - **Model Evaluation**: Flexible and allows integration of subjective views, but requires strong assumptions about return distributions and is computationally complex[8] 3. Model Name: Risk Parity Model - **Model Construction Idea**: Focuses on balancing the risk contribution of each asset in the portfolio rather than their weights[7] - **Model Construction Process**: 1. Calculate the risk contribution of each asset: $RC_i = w_i \cdot \sigma_i \cdot \rho_{i,p}$ 2. Adjust weights to equalize the risk contributions across all assets - Formula: $ RC_i = w_i \cdot \sigma_i \cdot \rho_{i,p} $ Where $RC_i$ is the risk contribution of asset $i$, $w_i$ is the weight of asset $i$, $\sigma_i$ is the standard deviation of asset $i$, and $\rho_{i,p}$ is the correlation between asset $i$ and the portfolio[7] - **Model Evaluation**: Enhances risk control and can incorporate multiple risk dimensions, but lacks a mechanism to optimize returns and may struggle with unrecognized risks[7] 4. Model Name: All-Weather Model (Bridgewater) - **Model Construction Idea**: Aims to achieve stable performance across all economic environments by focusing on risk parity under growth and inflation sensitivity[11] - **Model Construction Process**: 1. Classify assets based on their sensitivity to growth and inflation 2. Allocate weights to achieve risk parity across these dimensions - Formula: Not explicitly provided, but the model emphasizes balancing risk rather than returns[11] - **Model Evaluation**: Stable allocation structure with a focus on low-risk assets, but may underperform in specific market conditions due to its heavy reliance on bonds and cash[15] --- Model Backtesting Results 1. Mean-Variance Model - **Maximum Drawdown**: Exceeded 4% in some periods (e.g., 2018-2019), but quickly recovered[57] - **Sharpe Ratio**: Higher than benchmarks in optimistic scenarios, demonstrating strong risk-adjusted returns[57] 2. Black-Litterman Model - **Maximum Drawdown**: Similar to the mean-variance model, with better adaptability to subjective views[57] - **Sharpe Ratio**: Improved compared to the mean-variance model due to the integration of subjective views[57] 3. Risk Parity Model - **Maximum Drawdown**: Generally lower than the mean-variance model, reflecting its focus on risk control[57] - **Sharpe Ratio**: Moderate, as the model does not explicitly optimize returns[57] 4. All-Weather Model - **Maximum Drawdown**: Comparable to fixed-ratio models, with a focus on stability[15] - **Sharpe Ratio**: Similar to benchmarks, reflecting its conservative allocation[15] --- Quantitative Factors and Construction Methods 1. Factor Name: Monthly Frequency Slicing - **Factor Construction Idea**: Use historical slices of monthly data to reflect maximum drawdown and market sentiment[41] - **Factor Construction Process**: 1. Extract rolling 20-day returns for each year 2. Use the bottom 20% quantile to estimate pessimistic scenarios and maximum drawdown - Formula: $ \text{Max Drawdown} = \text{Min} \left( \frac{P_t - P_{peak}}{P_{peak}} \right) $ Where $P_t$ is the price at time $t$, and $P_{peak}$ is the peak price[41] - **Factor Evaluation**: Effective in capturing extreme market conditions, but limited in predicting long-term trends[41] 2. Factor Name: BootStrap State Space - **Factor Construction Idea**: Use BootStrap sampling to create a state space of asset returns under different scenarios[45] - **Factor Construction Process**: 1. Sample historical data with replacement to create new sequences 2. Calculate return distributions for pessimistic, neutral, and optimistic scenarios - Formula: $ F = B - \alpha \cdot C $ Where $F$ is the objective function, $B$ is the expected return under risk constraints, $C$ is the penalty for exceeding risk constraints, and $\alpha$ is the penalty parameter[50] - **Factor Evaluation**: Provides a robust framework for scenario analysis, but computationally intensive[45] --- Factor Backtesting Results 1. Monthly Frequency Slicing - **Maximum Drawdown**: Successfully captured extreme drawdowns in historical data, with 90% coverage for A-shares and Hong Kong stocks[40] - **Sharpe Ratio**: Not explicitly provided, but the factor is more focused on risk control[40] 2. BootStrap State Space - **Maximum Drawdown**: Achieved a 4% maximum drawdown target in most scenarios, with only minor deviations in extreme conditions[57] - **Sharpe Ratio**: Optimized under different scenarios, with higher ratios in optimistic environments[57]

Group 1 - The article presents an "all-weather" asset allocation framework consisting of Strategic Asset Allocation (SAA), Tactical Asset Allocation (TAA), and Major Event Review Adjustments to guide investment decisions [1][8] - The framework aims to diversify macro risks through SAA, set long-term allocation benchmarks for portfolio stability, and use TAA to identify short-term risk-return characteristics for asset adjustments [1][8] - The recommendation for November includes an overweight position in Chinese A/H shares and industrial commodities, with equity allocation at 45%, bonds at 45%, and commodities at 10% [1][2] Group 2 - The outlook for Chinese equities is optimistic, suggesting a 45% allocation with overweight positions in A-shares (8.5%) and Hong Kong stocks (8.5%), while maintaining standard allocations for US (15%), European (5%), and Japanese stocks (5%) [2] - The improvement in Sino-US relations and stable domestic financial conditions are seen as favorable for Chinese assets, with a strong demand for quality assets amid ongoing market reforms [2][12] - The bond allocation is suggested to be neutral at 45%, with standard positions in long-term and short-term government bonds for both China and the US [3] Group 3 - The commodity allocation is viewed as neutral to slightly optimistic, recommending a 10% allocation with standard positions in gold (5%) and industrial commodities (3.75%), while underweighting oil (1.25%) [3] - Industrial metals, particularly copper, are expected to experience price increases due to supply-demand imbalances driven by construction, electric grid modernization, and electric vehicle demand [3][14] Group 4 - The macroeconomic analysis emphasizes the importance of tracking macroeconomic expectations and their impact on asset pricing, highlighting that deviations from expectations can lead to significant asset price fluctuations [10][15] - The article discusses the significance of macroeconomic cycles in guiding long-term investment strategies, with a focus on the cyclical nature of economic indicators [19][15]

Group 1 - The report suggests an increase in allocation to Chinese equity assets and gold, while maintaining a standard allocation to bonds due to rising geopolitical uncertainties and potential market volatility [1][5]. - The recommended equity allocation weight is 41.25%, with specific allocations to A-shares (8.75%), Hong Kong stocks (8.75%), US stocks (15.00%), European stocks (2.75%), Japanese stocks (3.25%), and Indian stocks (2.75%) [5][9]. - The report expresses optimism regarding the performance of Chinese A/H shares, viewing current market adjustments as buying opportunities [5][9]. Group 2 - The bond allocation is suggested to be 45%, with standard allocations to long-term and short-term government bonds in both domestic and US markets [5][9]. - The report indicates a neutral to slightly optimistic view on commodities, recommending a 13.75% allocation, with a focus on gold (10%) and a lower allocation to oil (1.25%) [5][9]. - Gold prices are expected to remain strong, having recently surpassed key resistance levels, supported by factors such as Federal Reserve rate cuts and ongoing geopolitical tensions [5][9].

Core Viewpoint - The company has developed a "three-part" asset allocation framework consisting of Strategic Asset Allocation (SAA), Tactical Asset Allocation (TAA), and Major Event Review Adjustments to guide investment decisions. This framework aims to diversify macro risks, set long-term allocation benchmarks, and adjust based on short-term risk-return characteristics and significant events [1][10]. Group 1: Strategic Asset Allocation (SAA) - The SAA framework aims to mitigate macro risks by establishing a long-term allocation benchmark to ensure portfolio stability [1][10]. - The recommended asset allocation for October includes 41.25% in equities, 45% in bonds, and 13.75% in commodities, with specific allocations for A-shares, H-shares, and gold [1][2]. Group 2: Tactical Asset Allocation (TAA) - The TAA approach utilizes quantitative methods to identify assets with superior short-term risk-return characteristics, allowing for moderate adjustments to portfolio weights to enhance returns [1][10]. - The company remains optimistic about Chinese equities, suggesting an overweight position in A-shares and H-shares, while maintaining a neutral stance on bonds and a slightly optimistic view on commodities, particularly gold [2][3]. Group 3: Major Events Review - The company emphasizes the importance of subjective review of major events in conjunction with quantitative results to refine investment strategies, particularly in response to geopolitical uncertainties and market volatility [1][52]. - Recent events, such as the Chinese government's financial reforms and the U.S. Federal Reserve's interest rate adjustments, are expected to influence market dynamics positively, particularly for A-shares and gold [54]. Group 4: Performance Metrics - The performance of various asset classes has shown significant fluctuations, with notable increases in the Shanghai Composite Index and other Chinese indices over the past year, indicating a robust recovery in the equity market [6]. - The macro factor risk parity model has demonstrated effectiveness in enhancing returns while maintaining a balanced asset allocation, achieving an annualized return of 26.5% in 2025 with a Sharpe ratio of 2.59 [48][50].

Core Insights - The report emphasizes the importance of asset allocation strategies based on both long-term and short-term perspectives, utilizing historical data to optimize investment portfolios [21][22][23]. Asset Performance Overview - Equity assets showed strong performance, with the CSI 300 Index and Nasdaq 100 Index rising by 3.5% and 2.4% respectively over the past month, while the Indian Sensex 30 Index declined by 2.9% [7][6]. - In the bond market, government bond yields increased, leading to a 0.2% decline in the government bond index, while corporate bond indices remained stable due to narrowing credit spreads [12][11]. - Commodity assets experienced a 3.8% increase in the South China Commodity Index in July, although gold prices fluctuated, ending the month nearly flat [17][16]. Asset Allocation Strategies - The report suggests a debt-oriented asset allocation strategy comprising 10% Asia-Pacific emerging market stocks, 80% corporate bonds, and 10% gold [28]. - A mixed asset allocation strategy is recommended, including 23% Nasdaq 100 Index, 7% CSI 300 Index, 40% corporate bonds, and 30% commodities [28]. Strategy Performance Tracking - From April 2015 to July 2025, the mean-variance model strategy achieved an annualized return of 6.81% with a maximum drawdown of 3.6% and a Sharpe ratio of 2.76 [25]. - The strategy's performance from January 2025 to July 2025 yielded a cumulative return of 1.97%, with a notable return of -0.15% in July due to insufficient bond contributions and declines in the Indian market index [25][27]. Model Utilization - The report employs a mean-variance model for long-term asset allocation, which outperforms constant mix strategies, and integrates the Black-Litterman model to enhance return stability by combining historical and recent performance data [22][23][24].