Group 1: Strategic Asset Pool Definition and Long-Term Characteristics - The report defines a global strategic asset allocation framework, covering equity, fixed income, alternative assets, and cash[1] - It analyzes long-term characteristics of equity assets in global, developed, and emerging markets, including sovereign and credit bonds, real estate, commodities, and private equity[1] - The analysis provides a solid data and theoretical foundation for subsequent return forecasts and portfolio construction[1] Group 2: Long-Term Economic Assumptions and Return Forecast Models - The report establishes long-term economic assumptions and return forecast models based on key macroeconomic variables such as economic growth, inflation, and interest rates[2] - It creates corresponding long-term return prediction models for various asset classes and estimates correlations and potential risk scenarios among different assets[2] Group 3: Strategic Portfolio Construction and Optimization - Strategic portfolio construction considers investor constraints and goal settings, including return targets, risk tolerance, liquidity needs, and regulatory/tax constraints[3] - Optimization methods include the classic mean-variance model, Black-Litterman model, Kelly-CVaR model, and risk parity model, with the mean-variance model and Kelly-CVaR showing superior long-term returns compared to single asset strategies[3] - The report emphasizes the importance of establishing a global market-weighted portfolio as a benchmark for strategic asset allocation[3] Group 4: Market Trends and Performance Metrics - The MSCI indices indicate that the U.S. market dominates with a weight of 64% in the MSCI ACWI index, followed by Japan at 4.9% and the UK at 3.3%[15] - The report highlights that the long-term volatility of MSCI EM is significantly higher than that of MSCI World, with both indices showing similar return patterns over time[23] - The Sharpe ratio for MSCI World and MSCI EM is similar, with long-term limits around -0.5 to +0.8, indicating comparable risk-adjusted returns[23]

Core Viewpoint - The interview with Sun Yuemeng highlights her approach to investment management, emphasizing the importance of aligning investment solutions with clients' real-life scenarios rather than relying solely on quantitative models [2][3]. Group 1: Investment Philosophy - Sun Yuemeng's background in mathematics and financial engineering allows her to deconstruct complex systems, but she recognizes the need for a broader perspective in investment beyond academic models [3]. - She has developed a "three-layer adjustment" framework that incorporates tactical adjustments based on quantitative signals while maintaining flexibility in investment style [3][4]. - The ultimate goal of her investment strategies is to enhance clients' real wealth experiences, focusing on stability and reassurance in their investment journeys [4][5]. Group 2: Client-Centric Approach - Sun Yuemeng views her role as a "solution provider" rather than merely a "product seller," emphasizing the importance of accompanying clients through market volatility [5][6]. - She believes that the core challenge for investors is not a lack of knowledge but the need to adapt psychologically to market fluctuations, advocating for a mindset that sees manageable volatility as a pathway to long-term returns [5][6]. - Her investment strategy includes a dual-track framework that balances strategic odds with macro factors to assess tactical probabilities, aiming to minimize unnecessary losses during uncertain market conditions [5][6]. Group 3: Gender Perspective in Investment - As a female fund manager, Sun Yuemeng perceives her gender traits as a characteristic that influences her decision-making style, favoring a balanced approach rather than extreme style shifts [6]. - She seeks to connect with investors who appreciate a steady investment style and are focused on long-term holding experiences, fostering a mutual growth journey [6]. - Sun Yuemeng emphasizes the need for investors to transition from a mindset of guaranteed returns to an understanding of the necessity of tolerating controlled volatility for achieving reasonable long-term returns [6].

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]

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].