Financial Engineering - The report emphasizes a risk-based asset allocation strategy, contrasting it with traditional methods that focus on expected returns. This approach quantifies the investor's risk tolerance and sets a clear risk budget, aiming for a diversified risk contribution from various assets to achieve better risk-adjusted returns over the long term [2][3]. Risk Parity Model - The risk parity model is highlighted as a key strategy, optimizing asset contributions to total portfolio risk equally, thus avoiding the dominance of equities in traditional stock-bond portfolios. Backtesting results show an annualized return of 6.1% with a maximum drawdown of 3.4% and a Sharpe ratio of 3.62, indicating strong robustness [3][4]. Asset Allocation Insights - The report notes a persistent higher allocation to corporate bonds over government bonds since 2017, attributed to increased interest rate volatility in government bonds post "financial deleveraging" in China. This reflects the model's disciplined dynamic adjustment to real market risk structures [3][4]. Enhanced Strategy for Returns - A target volatility strategy is proposed, which dynamically adjusts portfolio leverage to maintain a preset volatility level. This strategy shows high sensitivity to financing costs of leveraged funds and is practical for investors with flexible capital. It aims for a higher Sharpe ratio by setting a target slightly above the full allocation portfolio volatility [5]. - Additionally, a risk budgeting strategy based on Sharpe squared is introduced, focusing on efficient risk allocation to assets with historically higher Sharpe ratios. While it achieves similar absolute returns to risk parity, it offers lower volatility and the highest Sharpe ratio among strategies, though it is dependent on the continuation of historical patterns [5].

Core Viewpoint - The establishment of the China Bond-Gold Backed Credit Bond Risk Parity Index and the China Bond-Gold Backed CDB Bond Risk Parity Index aims to meet market demand for "fixed income plus" investment strategies, utilizing a risk parity model to dynamically adjust the allocation between gold and bonds [1][2]. Group 1: Index Details - The China Bond-Gold Backed Credit Bond Risk Parity Index includes short-duration credit bonds and gold ETF funds, while the China Bond-Gold Backed CDB Bond Risk Parity Index comprises short-duration CDB bonds and gold ETF funds [1]. - Both indices use a risk parity model and moving average strategy to adjust the allocation of gold and bonds, serving as performance benchmarks for these asset combinations [1]. Group 2: Performance Metrics - As of November 28, 2025, the annualized return for the China Bond-Gold Backed Credit Bond Risk Parity Index over the past five years is 5.14%, with an annualized volatility of 1.27% [2]. - The China Bond-Gold Backed CDB Bond Risk Parity Index has an annualized return of 4.46% and an annualized volatility of 1.23% [2]. - The Sharpe ratio for the China Bond-Gold Backed Credit Bond Risk Parity Index is 2.56, while the Kappa ratio is 3.12, with a maximum drawdown of 1.65% [2]. - For the China Bond-Gold Backed CDB Bond Risk Parity Index, the Sharpe ratio is 2.11, the Kappa ratio is 5.89, and the maximum drawdown is 0.76% [2].

Core Insights - The article emphasizes the importance of diversifying investment portfolios to reduce risk, showcasing a significant reduction in drawdown from 39.03% to 3.17% through a diversified fund strategy [1][4][7] Investment Strategy - The author utilized the "High-Level Analysis" feature on the private equity platform to create a diversified fund portfolio, selecting four different fund styles: stock strategy, index growth strategy, gold ETF, and bond strategy [2] - The optimal allocation determined by the risk parity model was as follows: stock strategy fund 12.06%, index growth strategy fund 14.37%, gold ETF 8.33%, and bond strategy fund 65.24% [2] Performance Comparison - A backtest was conducted comparing a single index growth strategy fund investment of 1 million yuan against the diversified portfolio, revealing that the single fund incurred a loss of 390,300 yuan with a maximum drawdown of 39.03% [4] - In contrast, the diversified portfolio only lost 31,700 yuan, resulting in a maximum drawdown of 3.17%, effectively preserving 35.86% of the principal [7][8] High-Level Analysis Features - The "High-Level Analysis" function aims to assist investors in creating scientifically sound investment portfolios and automatically calculating optimal fund allocations [10] - It offers four intelligent fund models, including risk parity, MV model, lower risk model, and higher return model, catering to different investor profiles and risk appetites [10][12][15][17] User Experience and Functionality - The platform allows for detailed tracking of trades, automatic calculation of holding costs, and the generation of review reports, enhancing the investment management experience [19][22]

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 global economic slowdown and increased market volatility are driving investors to seek professional asset management solutions, leading to a significant rise in the demand for FOF (Fund of Funds) products in the public fund sector [1] - As of the end of Q3 this year, the number of public FOF products in China reached 518, with a total scale exceeding 193.49 billion, marking a 16.8% increase from the end of Q2 [1] - Nearly 99% of FOF products achieved positive returns in the past three quarters, demonstrating strong risk resistance and stable return characteristics in a volatile market [1] Group 2 - The successful operation of FOF products relies on a specialized and multidisciplinary research team that collaborates effectively across various fields such as macro analysis, quantitative analysis, risk identification, and comprehensive process control [2] - Minsheng Jianyin Fund has established a diverse and experienced FOF research team, supported by a scientific risk management system and a global asset allocation framework [2] - The upcoming Minsheng Jianyin Multi-Asset Stable Allocation 3-Month Holding Period Mixed FOF is set to be managed by Liu Xin and fund manager Kong Siwei, implementing a "localized all-weather" strategy to dynamically optimize asset allocation [2]

Core Viewpoint - The article discusses the challenges of timing investments in different asset classes and suggests that a macro strategy, which diversifies across multiple assets, can mitigate the need for precise timing [4][5][6]. Market Overview - The A-share market has been fluctuating between 3800 and 3900 points for the past two months, and after breaking through 4000 points, it faces a new directional choice [5]. - Investors are currently conflicted about whether to invest in stocks, fearing high prices, or in stable bonds, worrying about missing out on potential gains [5]. Asset Performance Analysis - Historical data indicates that no single asset class consistently outperforms; different asset classes have strong and weak years [7][9]. - Over the past decade, A-shares outperformed other assets only in 2019 and 2020, while U.S. stocks also faced significant downturns in 2022 [9]. - Bonds showed stability with good returns last year but faced some pullbacks this year, while commodities had a brief bull run in 2021 and 2022 but performed poorly in other years [10]. Asset Class Characteristics - The core returns of different asset classes are driven by distinct underlying logic: - Stocks benefit from corporate profit growth, performing well in a stable economic environment [12]. - Commodities gain from supply-demand imbalances and inflation, thriving during high inflation or economic overheating [12]. - Bonds rely on fixed interest and price appreciation from falling interest rates, excelling during economic slowdowns or deflationary expectations [12]. Timing and Strategy - Timing investments is crucial for achieving satisfactory returns in single asset investments, with two main objectives: trend following and identifying undervalued assets [13]. - Macro strategies, which are multi-asset in nature, do not require timing as they inherently balance risk across various asset classes [14]. - A well-structured macro strategy can capture both rising and undervalued assets, providing better safety margins and lower costs [15]. Long-term Performance of Strategies - Historical performance of private equity strategies shows that without timing, achieving ideal returns is challenging, often leading to significant volatility [17]. - In contrast, macro strategies tend to yield satisfactory returns regardless of the timing of entry, with relatively lower volatility and better holding experiences [17].

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].

Company Overview - Shanghai Jinhesheng Private Fund Management Co., Ltd. was established on August 26, 2020, and completed its registration relocation and name change in July 2025. The company currently manages assets between 1-2 billion and has 32 existing products [4]. - The company adheres to the core value concept of "Integrity, Trust, Kindness, and Virtue," focusing on integrity and professional service, which has led to market recognition and multiple awards [5]. Core Team - The management team is led by Executive Director Sun Tiangang and General Manager Wang Peng, with a focus on various strategy-specific fund managers. The team includes experienced fund managers and researchers with backgrounds in securities and financial institutions [8][9]. - The company has optimized its organizational structure and capability building through capital expansion and talent strategy [9]. Representative Strategy - All-Weather Enhanced Strategy - The All-Weather Enhanced Strategy follows a diversified asset allocation philosophy, aiming for a high risk-return ratio. It invests in stocks, bonds, commodities, and derivatives in both the US and China [11]. - The strategy consists of 65% Beta, which relies on a global portfolio for long-term asset allocation, and 35% Alpha enhancement, achieved through various models and strategies [15]. Core Advantages - The company has a clear equity structure and a stable core research team, ensuring consistent strategy execution. The core research personnel have an average of over 10 years of relevant work experience [16]. - The All-Weather Strategy has a large capacity, allowing for significant scale under the premise of matching team research and management capabilities [17]. - The strategy offers good liquidity, supporting daily subscriptions and redemptions [18]. - It has a low correlation with mainstream stock long strategies, providing valuable low-correlation allocation [19]. - The strategy aims for absolute returns, achieving sustainable long-term returns independent of macroeconomic changes [21]. Continuous Evolution Capability - The company fosters a positive corporate culture and efficient working atmosphere through competitive compensation and reasonable equity incentives, ensuring talent stability and long-term sustainable development [22]. - The All-Weather Strategy model is continuously optimized, with core factors validated through rigorous historical data backtesting, demonstrating long-term stability and effectiveness [22].

Core Insights - The total scale of China's ETF market has exceeded 5 trillion yuan by the end of September 2025, reflecting a growth of over 35% compared to the end of 2024, with nearly 1,300 ETFs available in the market [1] - The launch of the Fuguo Hengyi 3-Month Holding Mixed Fund of Funds (ETF-FOF) on October 27 aims to provide a flexible investment solution for investors to capture trading opportunities in the ETF market [1][2] ETF-FOF Overview - ETF-FOF combines the advantages of ETFs and FOFs, offering convenience, low fees, and high transparency while allowing for professional asset allocation [2] - The product mandates that at least 80% of its non-cash fund assets be invested in ETFs, making it a cost-effective and efficient option for diversified asset allocation [2] Asset Allocation Strategy - The Fuguo Hengyi 3-Month Holding Mixed ETF-FOF aims for low volatility and absolute returns, with a diversified investment framework covering bonds, stocks, cross-border assets, and gold [3] - The performance benchmark includes a specific allocation: 65% to the China Bond Composite Index, 12% to the CSI 800 Index, 12% to the Hang Seng Index, 6% to gold, and 5% to bank deposits [3] Tactical Operations - The fund employs a duration timing strategy for bond ETFs and various strategies for stocks, cross-border assets, and gold to enhance returns [4] - A 3-month holding period is set for the fund, allowing for flexible redemption while aiming to improve the investment experience [4] Management Expertise - The fund is managed by Zhang Ziyan, who has 14 years of experience in securities and a strong background in multi-asset allocation [5] - Fuguo Fund has over 80 ETF products, providing a rich toolkit for the operation of the ETF-FOF, supported by a well-established investment strategy [5] Market Demand - The introduction of the Fuguo Hengyi 3-Month Holding Mixed ETF-FOF addresses the growing demand for diversified asset allocation tools, offering a "worry-free, efficient, and diverse" investment solution [6]

Core Viewpoint - China has entered a low-interest-rate era since 2019, facing constraints on further policy rate cuts due to various factors, including bank net interest margin pressure and residents' savings demands. Despite these challenges, bond assets can still provide underlying returns, and the "fixed income +" strategy is expected to become a significant development direction for asset management institutions, aligning with investors' core demand for stable value growth [1][5][18]. Group 1: Japan's Low-Interest Rate Era and Bond Market Evolution - Japan's low-interest-rate era began in 1999 after a series of financial crises and asset price collapses, leading to a shift in asset allocation towards low-risk assets [2][5]. - The share of overseas bond investments in Japan increased from 33% to 54% between 1997 and 2003, indicating a trend towards globalization in asset management strategies [2][4]. - The introduction of J-REITs in Japan has provided a stable income source, with annualized returns fluctuating between 4.3% and 8.9% from 2013 to 2022, contributing to the growth of the asset management industry [4]. Group 2: Characteristics of China's Low-Interest Rate Era - Since 2019, China's policy interest rates have been on a downward trend, with the 10-year government bond yield dropping below 2.0% [5][6]. - The banking sector's total assets are projected to reach 276.1% of GDP by 2024, with interest income accounting for 77.6%, indicating a significant reliance on interest income [5]. - By the end of 2024, the number of bond funds in China reached 4,534, with a total scale of 23.07 trillion yuan, reflecting a 15.9% year-on-year growth [6][7]. Group 3: Performance of Bond Products - The total scale of money market funds increased by 20.7% in 2024, while short-term bond funds grew by 13%, indicating a strong preference for low-risk investments [6][7]. - The mid-to-long-term pure bond fund index rose by 4.59% in 2024, marking a historical high in returns [8]. - "Fixed income +" products faced redemption challenges in early 2024 but rebounded in the fourth quarter as the stock market recovered, with a projected growth of 13.77% in the first half of 2025 [6][8]. Group 4: "Fixed Income +" Strategy Pathways - The narrow definition of "fixed income +" focuses on equity assets as the core for enhancement, leveraging the dual return attributes of stocks and the supportive policies from the government [10][11]. - The broad definition of "fixed income +" emphasizes a multi-asset integration approach, incorporating commodities, alternative assets, and global diversification to enhance risk-return efficiency [13][14]. - The asset allocation strategy from 2019 to present has yielded an annualized return of 9.17%, demonstrating the effectiveness of diversified asset strategies compared to single assets [14][17]. Group 5: Future Outlook - The "fixed income +" strategy is expected to benefit from the stability of bond underlying returns and the effects of multi-asset enhancement, indicating a broad development space in the future [18].