Report Industry Investment Rating - No investment rating provided in the report. Core Viewpoints - In 2026, the scale of macro - hedge strategies is expected to increase further as their allocation value is increasingly recognized in the market. Risk - parity strategies will play a stronger role as the base position in the portfolio, and the returns of risk - parity managers will experience a certain degree of mean reversion. [36][37] - The performance of subjective CTA strategies in 2026 will be better than that in 2025. The decrease in Sino - US macro uncertainties and the increase in commodity volatility in a low - interest - rate environment will benefit subjective CTA managers. [58] Summary by Directory 01 Macro - Hedge Strategy Research and Outlook Manager Classification and Characteristics - Macro - hedge managers are classified into three types: risk - parity, asset - rotation, and multi - asset multi - strategy. This report focuses on the first two types. Risk - parity managers use the risk - parity model as the basis and enhance it, with relatively consistent performance; asset - rotation managers are based on asset - rotation frameworks like the Merrill Lynch Clock, emphasizing asset timing allocation and having less consistent performance. [6] Domestic Manager Performance in 2025 - As of November 28, 2025, the net value of the "risk - parity" macro - hedge index was 1.172, and that of the "asset - rotation" index was 1.101. In the 46 weeks from January 3 to November 28, 2025, risk - parity managers had positive weekly returns in 30 weeks and negative returns in 16 weeks, with the largest single - week drawdown occurring after the Tomb - Sweeping Festival on April 11. Asset - rotation managers had positive weekly returns in 25 weeks and negative returns in 20 weeks, with the largest single - week drawdown occurring in the week of November 21. In the context of global supply - chain reshaping, risk - parity managers outperformed asset - rotation managers in 2025. [10] Asset Correlation Analysis - In 2025, the negative correlation between treasury bonds and equity indices weakened compared to the end of last year. The China Securities Commodity Index was positively correlated with stock indices and negatively correlated with treasury bonds and gold. Gold, as a safe - haven asset, had a stronger correlation with treasury bonds. There were significant differences in the performance correlations of risk - parity and asset - rotation managers with equity, treasury bonds, and gold. [13] - In terms of equity assets, the correlation between the risk - parity strategy and the CSI 300 was 0.230, and that with the CSI 1000 was 0.186. The correlations of the asset - rotation strategy with the CSI 300 and CSI 1000 were 0.628 and 0.641 respectively. The asset - rotation strategy's returns were more dependent on stocks, and the large drawdown in the week of November 21 was related to the stock decline. [19] - After a five - fold leverage treatment of 10 - year treasury bonds, the correlation between the risk - parity strategy and 10 - year treasury bond futures was 0.221, while that of the asset - rotation strategy was - 0.068. Many managers believed that the treasury bond market was in a bear market, so asset - rotation managers mostly reduced or shorted treasury bonds, while risk - parity managers still held bond positions. [23] - In 2025, gold was one of the strongest - performing assets, with a cumulative net value of the Gold ETF of 1.588 from January 3 to November 28. The correlation between the risk - parity strategy and the Gold ETF was 0.453, while that of the asset - rotation strategy was 0.110. Gold had a greater impact on risk - parity strategies. [26] Overseas Manager Performance in 2025 - As of October 2025, the net value of the "unidentified" macro - hedge index was 1.088, the "subjective" macro - hedge index was 1.129, and the "quantitative" macro - hedge index was 1.159. Quantitative macro - hedge strategies performed the best, followed by subjective strategies, similar to the domestic situation. The maximum drawdowns of the unidentified and quantitative macro - hedge strategies occurred in April, indicating that domestic risk - parity managers may use similar underlying models to overseas ones. [29] - The unidentified macro - hedge strategy index had a more balanced correlation with various asset classes, with a near - zero correlation with New York gold. The subjective macro - hedge index had a high correlation of 0.792 with the S&P 500 and a negative correlation with New York gold, indicating that its returns were more dependent on the US stock market. The quantitative macro - hedge strategy also had a high correlation of 0.627 with the S&P 500 and a relatively high correlation of 0.300 with the S&P GSCI, but a negative correlation with US treasury bonds and gold. [33] Outlook for 2026 - The scale of macro - hedge strategies will increase as their allocation value is recognized. Some investors may replace part of their stock - neutral strategy allocation with low - volatility macro - hedge strategies. The role of risk - parity strategies as the base position in the portfolio will be enhanced, and their return attribution is relatively clear. [36] - The returns of risk - parity managers will experience mean reversion in 2026. Since the probability of bonds and gold replicating their price increases since 2024 is significantly reduced, the returns of these managers will decline. Historically, the long - term return of the basic risk - parity model is around 6 - 8%. [37] 02 Discretionary CTA Strategy Research and Outlook Performance in 2025 - The net value performance of managers in the observation pool in 2025 was weaker than in the same period of 2024. Uncertainties in Sino - US trade friction reduced the trading certainty of discretionary CTA managers based on industrial supply - demand research, weakening their position - holding confidence and return - generating ability. After June, although market sentiment improved, the lack of improvement in the industrial sector led to significant drawdowns for many managers, lowering the annual return. [40] Sector - Specific Performance - Black - sector managers showed some resilience in returns in 2025. In the first half of the year, the collapse of coal costs led to a downward trend in the black - sector prices, with good persistence and low volatility. The concerns about external demand due to Sino - US trade friction coincided with the seasonal decline in coal prices, providing trading opportunities with industrial and macro resonance. In the second half of the year, differences in the implementation of anti - involution policies led to a negative view among industrial - based managers, resulting in significant drawdowns. [45] - Agricultural - product managers were greatly affected by trade frictions between China and the US, Canada, etc. The unpredictable changes in agricultural - product imports and price fluctuations made it difficult for them to generate returns. [45] Industry Changes - Leading managers are iterating towards multi - asset and multi - strategy models. The limited capital capacity of single - asset futures trading, the need to understand the trading behavior of other market participants, and the benefits of multi - asset diversification are the main reasons. [50] - Start - up private - equity funds have shown strong drawdown - control ability since their establishment. Compared with the past, current start - up discretionary CTA private - equity funds have a clearer understanding of investors' risk preferences and a more explicit performance - oriented approach, enabling them to enter institutional investors' asset - allocation pools more quickly. [52] - In a diversified market structure, single - industry logic is insufficient for trading. Managers need to have comprehensive capabilities in macro - judgment, trading, and risk - control. Research determines the winning rate, trading and risk - control determine the profit - loss ratio, and an excellent trader may not be an excellent asset - management manager. [55] Outlook for 2026 - The performance of discretionary CTA strategies in 2026 will be better than in 2025. The decrease in Sino - US macro uncertainties will make commodity supply - demand the dominant factor in trading, and the increase in commodity volatility in a low - interest - rate environment will be beneficial for managers to generate returns. The increase in the scale of discretionary CTA managers based on industrial research will also contribute to the strength of industrial logic in the market. [58]

晨 会 纪 要 [2025]第 232 号 主 题：对近期重要经济金融新闻、行业事件、公司公告等进行点评 时 间：2025 年 12 月 18 日 8:50-9:30 研究所今日晨会要点如下: 一、金融工程 1、基于风险角度的大类资产配置策略（邢维洁） 从风险角度进行大类资产配置 以风险管理为基石的现代资产配置哲学，与传统以预期收益为核心的方法不同，风险配 置的起点是量化投资者的风险承受能力，并以此设定明确的风险预算。其核心操作并非简单 地分配资金，而是追求各类资产对投资组合的风险贡献符合预设目标，从而实现真正的风险 分散。这种方法的优势在于能显著改善下行保护，提供更平滑的投资体验，并构建一个对各 种经济环境都具有韧性的反脆弱系统，最终帮助投资者在长期内获得更优的风险调整后收益。 基于风险平价模型的配置策略 作为风险配置理念的典范，风险平价模型被置于研究的中心。该模型通过数学优化，使 不同波动特性的资产对组合总风险产生相等的贡献，从而避免了传统股债组合中风险被股票 资产主导的弊端。回测结果显示，风险平价模型在观测期内实现了 6.1% 的年化收益率，并 将最大回撤控制在 3.4%，夏普比率达到 3.62，展现了 ...

| 序号 | 指数中文名称 | 指数英文名称 | 全收益指数代码 | | --- | --- | --- | --- | | | 中债-黄金保值信用债风险平价指数 | CBPC Gold Backed Credit Bond Risk Parity Index | CBM02801 | | | 中债-黄金保值国开行债券风险平价指数 | CBPC Gold Backed CDB Bond Risk Parity Index | CBM02901 | 上述指数基期为2015年12月31日，基点为1000。截至2025年11月28日，中债-黄金保值信用债风险平价指数近5年年化收益率为 5.14%，年化波动率为1.27%， | 序号 | 指数中文名称 | 年化收益率 | 年化波动率 | 夏普比率 | 卡玛比率 | 最大回撤 | | --- | --- | --- | --- | --- | --- | --- | | | 中债-黄金保值信用债风险平价指数 | 5.14% | 1.27% | 2.56 | 3.12 | 1.65% | | | 中债-黄金保值国开行债券风险平价指数 | 4.46% | 1.23% | 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].

本文首发于公众号"私募排排网"。 （点击↑↑ 上图查看详情 ） 编 者按 私募排排网数据显示，截至2025年10月底，管理规模在20亿以下的私募管理人有近7200家，占比超90%，是私募行业数量庞大的中坚力量。私募排 排网推出 「打卡100家小而美私募」 栏目，聚焦管理规模适中、策略特色鲜明的优质私募基金管理人。通过深度解析其投资方法论、风控体系及能力 圈建设，为投资者提供差异化的视角与洞察。本期打卡—— 金和晟基金 。 Part.1 公司概况 上海 金和晟私募基金管理有限公司（登记编号P1071636）于2020年8月26日注册成立，2025年7月完成注册地迁移及管理人名称变更。目前管 理资产规模10-20亿，存续产品32只。（ 点击查看产品收益 ） | 序号 | 产品简称 产品策略 | 基金经理 成立日期 | 近6月收益 今年来收益 | 成立以来收 载 | | --- | --- | --- | --- | --- | | 1 金和全天候1号A类份额 宏观策略 | | 空動 2024/4/15 | 应合规要求 | | | 金和中证1000指数增强1 量化多头。 3 | 를 | 王建兵 2021/2/26 ...

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]