Core Viewpoint - The article highlights the resurgence of public FOF funds alongside performance recovery, focusing on the successful strategies of fund managers like Tang Jun from Zhongtai Asset Management, who emphasizes a "configuration first" approach in asset allocation [1][2][3] Group 1: Performance and Strategy - Tang Jun's management of Zhongtai Tianze Stable 6-Month Holding A has achieved impressive rankings, with performance rankings of 2 out of 136 and 2 out of 172 for the past two years and one year respectively [1] - The "configuration first" framework proposed by Tang Jun prioritizes establishing a personal asset allocation framework before selecting suitable fund products, contrasting with traditional methods that focus on identifying top fund managers [2][3] Group 2: Asset Allocation Framework - Tang Jun's strategic asset allocation is based on a "currency-credit" framework, analyzing the modern monetary financial system's impact on asset performance, particularly favoring bonds and commodities in a low-interest-rate environment [3][4] - The tactical allocation involves adjusting positions based on market sentiment and funding conditions, with recent adjustments including a reduction in dividend-heavy assets and an increase in growth-oriented investments [4][6] Group 3: Multi-Asset and Low-Correlation Approach - Tang Jun advocates for a focus on "low-correlation multi-return streams," inspired by Ray Dalio's concept of diversifying across 15 to 20 independent return streams to reduce risk without sacrificing expected returns [7][8] - The investment strategy includes a nuanced approach to asset classification, where assets are evaluated based on their correlation with other assets rather than merely by category, allowing for more effective diversification [7][8] Group 4: Performance Metrics - Since its inception, Zhongtai Tianze Stable 6-Month Holding A has shown varying net asset value growth rates, with figures of -3.70%, 7.22%, and 6.91% for 2023, 2024, and the first half of 2025 respectively, compared to its performance benchmark [9]

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 Viewpoint - The FOF market has experienced a strong issuance wave since 2025, with a cumulative scale of 23 billion yuan as of May 14, representing a growth of over 400% compared to the same period last year. The mixed bond FOF has emerged as the dominant product type, appealing to investors seeking stable returns in volatile markets [1][2]. Group 1: FOF Market Performance - As of May 14, 2025, the newly established FOFs have reached a cumulative scale of 23 billion yuan, significantly surpassing the 4.5 billion yuan from the same period in 2024. The largest single FOF product raised 6 billion yuan, with an average issuance size of 1.1 billion yuan, indicating strong investor interest [2]. - The mixed bond FOF has become the absolute mainstay of the 2025 FOF issuance, with a total of 9.2 billion units issued in Q1 and 8.9 billion units in Q2, accounting for 49% of the total FOF stock [2]. Group 2: Investment Strategies and Market Outlook - FOF fund managers are optimistic about fixed income market opportunities in Q2 2025, anticipating potential policy easing such as reserve requirement ratio cuts and interest rate reductions, which could enhance market liquidity and create favorable conditions for fixed income assets [3]. - The overall performance of FOFs has been stable, with over 80% recording positive returns in 2025. The mixed bond FOFs have shown strong performance, with notable returns from specific funds such as Qianhai Kaiyuan Yuyuan at 8.17% and Zhongtai Tianze at 5.67% [4]. Group 3: Asset Allocation Trends - There has been a significant increase in allocations towards technology-themed active equity funds, gold ETFs, and Hong Kong Stock Connect technology ETFs among FOF products, reflecting a refined asset allocation strategy in response to structural market conditions [5][6]. - Gold is recognized for its benefits during interest rate cuts, its safe-haven properties, and low correlation with traditional assets, while the technology sector is seen as a key driver of portfolio returns due to its growth potential and innovative characteristics [6].