相关机构表示，由于宏观政策保持稳定，债券市场情绪有望得到修复。中期来看，在经济基本面和流动 性未发生改变的情况下，尽管商品和股市的表现可能会出现阶段性波动，但尚不足以动摇债市的基础。 3 、持仓透明：ETF每日公布PCF清单，持仓透明。 4 、可进行质押回购：当市场其他资产有投资机会、恰巧手头资金不够充裕时，投资者就可以通过ETF 质押换取资金，来参与其他类资产的投资，到期时再赎回ETF即可。 风险提示：数据来源基金定期报告、wind，相关业绩经托管行核对，过往表现不代表未来。十年国债 ETF成立于2017年8月4日，2017年-2025年上半年净值增长率/业绩比较基准为：-1.55%/-1.01%； 7.6%/8.47%；2.49%/4.81%；1.92%/2.09%；5.19%/5.78%；2.52%/2.87%；4.37%/4.83%；9.02%/8.09%； 0.67%/-0.24%。基金规模数据仅供参考，不代表投资建议。本基金属于债券基金，其预期收益及风险水 平低于股票基金、混合基金，高于货币市场基金。本基金属于国债指数基金，是债券基金中投资风险较 低的品种。本基金采用优化抽样复制策略，跟踪上证10 ...

Core Viewpoint - The recent inflow into dividend ETFs indicates a shift in investor sentiment towards stable cash flow assets amidst market volatility and economic uncertainties [1][4]. Group 1: Market Trends - Major indices opened lower but recovered, alleviating some panic from previous adjustments, yet concerns about market fluctuations remain [1]. - Significant inflows into representative dividend ETFs, such as the China Securities Dividend ETF (515080), Dividend Quality ETF (159209), and Hong Kong Dividend Low Volatility ETF (520550), totaled 160 million in a single day [1]. - The trend of seeking high dividend assets as a safe haven has been ongoing, with the China Securities Dividend ETF (515080) seeing a net inflow of 140 million over 10 trading days [1]. Group 2: ETF Characteristics - The Hong Kong Dividend Low Volatility ETF (520550) features a monthly dividend mechanism, T+0 trading, and a single stock weight limit of 5%, making it a strong candidate for avoiding "dividend yield traps" [3]. - The China Securities Dividend ETF (515080) has a quarterly dividend assessment and has distributed dividends 13 times since its inception, with annual dividend ratios between 4.14% and 4.78% over the past five years, indicating stable and consistent returns [3]. - The China Securities Dividend Quality ETF (159209) focuses on companies with stable dividends, strong profitability, and financial health, aligning with long-term value investment principles [3]. Group 3: Investment Strategy - The influx into dividend ETFs is driven by factors such as U.S.-China talks, Nvidia's scrutiny, and policy corrections against excessive competition, leading to a consensus on the value of stable cash flow and high dividend assets [4]. - The strategy of combining dividend and growth investments, referred to as the "dumbbell" strategy, has gained traction, emphasizing a balanced and diversified asset allocation for long-term, stable returns [4]. - A suggested allocation strategy includes 40% in the China Securities Dividend ETF (515080), 30% in the Dividend Quality ETF (159209), and 30% in the Hong Kong Dividend Low Volatility ETF (520550) to create a cash flow fortress across A+H markets [4].

最近梳理二季度各类产品的增量，不得不感叹一句，FOF也是好起来了。 在经历连续三年的规模下滑之后，今年以来，FOF迎来"两连增"，规模连续两个季度回升。曾经不被看 好的FOF，正以一种低调的姿态重新回到增长的路径上来。 Wind数据显示，截至 2025 年二季末， 全市场FOF 基金数量合计 518 只，较上季末增加 6 只。基金规 模合计 1657.1 亿元，较上季末上升 9.7%。 为什么今年以来FOF基金开始受到市场关注？ 这要先从FOF基金本身说起。简单来说，FOF是一种专门投资于其他基金的基金，通过持有多个基金， 实现资产的多元化配置，以分散风险并追求稳健的投资回报。 国泰基金FOF投资部投资总监曾辉做过一个有趣的比喻：管理FOF基金有点像"开餐厅"。 "如果将股票比作FOF产品的原材料，那么单只基金相当于餐厅的半成品，FOF要做的，就是将半成品 加工成成品。这非常考验餐厅的搭配能力，也就是资产配置的能力。" 说到底，FOF基金并不简单的是一种产品，而是提供了关于资产配置的一站式解决方案。 其次，FOF基金顺应了当前低利率环境下的财富管理需求。目前国有大行一年期定存利率已经进入0字 头，在低利率环境 ...

Group 1 - The World Gold Council's report indicates that global gold demand reached 1249 tons in Q2 2025, a 3% year-on-year increase, driven primarily by gold ETF investments which saw inflows of 170 tons [1] - Gold jewelry consumption fell by 14% year-on-year to 341 tons, marking the lowest quarterly demand since Q3 2020, although the total value of gold jewelry consumption increased by 21% to $36 billion [1] - In China, gold jewelry demand weakened significantly, dropping 20% year-on-year to 69 tons in Q2 2025, with a substantial 45% quarter-on-quarter decline, leading to a total of 194 tons for the first half of the year, a 28% decrease [1] Group 2 - The decline in gold jewelry consumption is attributed to a combination of economic cycles and changes in consumer behavior, with high gold prices reducing purchasing willingness, particularly among younger consumers [2] - Gold ETFs are favored for their liquidity, low transaction costs, and high transparency, serving as a preferred tool for both institutional and individual investors to hedge risks and diversify portfolios [2] - The increase in ETF holdings reduces the available deliverable gold in the market, indirectly supporting higher gold prices, while investors are advised to adopt a layered allocation strategy in a high gold price environment [2]

数据来源/《中国银行业理财市场半年报告（2025年上）》 制表/李静 2025年6月末 2025年3月末 2025年初 余额（万亿元） 占总投资资产比例（%） 余额（万亿元） 占总投资资产比例（%） 余额（万亿元） 占 总投资资产比例（%） 债券 13.78 41.8 13.68 43.9 13.98 43.5 现金及银行存款 8.18 24.8 7.27 23.3 7.68 23.9 非标准化债权类资产 1.82 5.52 1.75 5.6 1.74 5.4 公募基金 1.38 4.2 0.93 3 0.93 2.9 权益类资产 0.78 2.38 0.81 2.6 0.83 2.58 ...

Core Insights - The A-share market has shown significant upward momentum, with the Shanghai Composite Index rising steadily above 3500 points since July [1] - Investors are divided into different camps regarding market trends, highlighting the need for effective asset allocation strategies to navigate volatility [3][4] Market Performance - The annual returns of major asset classes over the past five years reveal that no asset experiences perpetual growth, as they all undergo varying degrees of volatility influenced by different economic cycles [4] - Specific annual returns for 2020 to 2024 are as follows: - Chinese stocks: 18.61% (2024), -0.56% (2023), -16.19% (2022), -8.79% (2021), 22.63% (2020) - Global stocks: 17.45% (2024), 22.13% (2023), -12.39% (2022), 14.13% (2021), 6.94% (2020) - Domestic bonds: 7.60% (2024), 4.77% (2023), 3.30% (2022), 5.09% (2021), 2.97% (2020) - Global bonds: 4.04% (2024), 9.06% (2023), -4.89% (2022), -4.32% (2021), -0.73% (2020) - Gold: 29.29% (2024), 15.37% (2023), 9.33% (2022), -5.62% (2021), 16.77% (2020) - Oil: -1.42% (2024), -8.76% (2023), 20.77% (2022), 47.02% (2021), -26.71% (2020) - Forex: 3.55% (2024), 4.55% (2023), 10.81% (2022), -0.75% (2021), -3.15% (2020) - Industrial commodities: -5.70% (2024), 2.95% (2023), 21.19% (2022), 25.89% (2021), 8.74% (2020) [5] Asset Correlation and Risk Management - Different asset classes exhibit varying correlations, with the China Bond Index often showing a negative correlation with A-shares and Hong Kong stocks, suggesting that including low or negatively correlated assets can effectively hedge market risks [6] - A proposed "fixed income enhancement" strategy serves as a straightforward asset allocation framework, utilizing bonds as a stable foundation while selectively adding equity assets to capture market upside [7] Fund Performance - The "Guofu Anyi Stable 6-Month Holding Mixed Fund" has demonstrated superior performance in both returns and risk management compared to its peers over the past year, with metrics indicating a maximum drawdown of -1.10% versus -3.61% for the average of similar funds, and an annualized volatility of 2.38% compared to 5.38% [8][9] - The fund's annualized performance for the complete accounting year of 2024 is reported at 7.19% for Class A and 6.83% for Class C, against a benchmark performance of 7.42% [9]

Quantitative Models and Construction Methods Model: Duration Timing Model - **Construction Idea**: Predict the yield curve and map the expected returns of bonds with different durations[20] - **Construction Process**: - Use the improved Diebold2006 model to predict the instantaneous yield curve - Predict level, slope, and curvature factors - Level factor prediction based on macro variables and policy rate following - Slope and curvature factors prediction based on AR(1) model[20] - **Evaluation**: The model effectively predicts the yield curve and provides actionable insights for bond duration management[20] - **Test Results**: - July return: 6.6bp - Benchmark return: -25.8bp - Strategy excess return: 32.4bp[21] Model: Gold Timing Model - **Construction Idea**: Relate the forward real returns of gold and US TIPS to construct the expected return model for gold[32] - **Construction Process**: - Use the formula: $E[Real\_Return^{gold}]=k\times E[Real\_Return^{Tips}]$ - Estimate parameter k using OLS with an extended window - Use the Fed's long-term inflation target of 2% as a proxy[32] - **Evaluation**: The model provides a robust framework for predicting gold returns based on TIPS yields[32] - **Test Results**: - Expected return for the next year: 22.4% - Past year absolute return: 39.77%[33][35] Model: Active Risk Budget Model - **Construction Idea**: Combine the risk parity model with active signals to construct an active risk budget model for optimal stock and bond allocation[37] - **Construction Process**: - Use the Fed model to define equity risk premium (ERP): $ERP={\frac{1}{PE_{ttm}}}-YTM_{TB}^{10Y}$ - Adjust asset weights dynamically based on ERP, stock valuation percentiles, and market liquidity (M2-M1 spread) - Convert equity asset signal scores into risk budget weights using the softmax function: $softmax(x)={\frac{\exp(\lambda x)}{\exp(\lambda x)+\exp(-\lambda x)}}$[39][47] - **Evaluation**: The model dynamically adjusts asset weights based on multiple dimensions, providing a balanced risk-return profile[37] - **Test Results**: - July stock position: 18.72% - Bond position: 81.28% - July portfolio return: 0.84% - August stock position: 7.44% - Bond position: 92.56%[51] Model Backtest Results 1. **Duration Timing Model** - July return: 6.6bp - Benchmark return: -25.8bp - Strategy excess return: 32.4bp[21] 2. **Gold Timing Model** - Expected return for the next year: 22.4% - Past year absolute return: 39.77%[33][35] 3. **Active Risk Budget Model** - July stock position: 18.72% - Bond position: 81.28% - July portfolio return: 0.84% - August stock position: 7.44% - Bond position: 92.56%[51] Quantitative Factors and Construction Methods Factor: High-Frequency Macroeconomic Factors - **Construction Idea**: Use asset portfolio simulation to construct a high-frequency macro factor system to observe market macro expectations[12] - **Construction Process**: - Combine real macro indicators to form low-frequency macro factors - Select assets leading low-frequency macro factors - Use rolling multiple leading regression to determine asset weights and simulate macro factor trends[12] - **Evaluation**: High-frequency macro factors provide leading indicators for market expectations, offering valuable insights for asset allocation[12] Factor: Convertible Bond Valuation Factors - **Construction Idea**: Compare the relative valuation of convertible bonds and stocks, and between convertible bonds and credit bonds[25] - **Construction Process**: - Construct the "100-yuan conversion premium rate" to compare the valuation of convertible bonds and stocks - Use the "modified YTM - credit bond YTM" median to compare the valuation of debt-biased convertible bonds and credit bonds - Construct style rotation portfolios based on market sentiment indicators like 20-day momentum and volatility deviation[25][27] - **Evaluation**: The factors effectively capture the relative valuation and style characteristics of convertible bonds, aiding in portfolio construction[25][27] - **Test Results**: - "100-yuan conversion premium rate": 33.71% - "Modified YTM - credit bond YTM" median: -2.06% - Style rotation annualized return: 24.54% - Maximum drawdown: 15.89% - IR: 1.47 - Monthly win rate: 65.17% - 2025 return: 35.17%[26][29] Factor Backtest Results 1. **High-Frequency Macroeconomic Factors** - High-frequency economic growth: Upward trend - High-frequency consumer inflation: Downward trend - High-frequency producer inflation: Upward trend[17] 2. **Convertible Bond Valuation Factors** - "100-yuan conversion premium rate": 33.71% - "Modified YTM - credit bond YTM" median: -2.06% - Style rotation annualized return: 24.54% - Maximum drawdown: 15.89% - IR: 1.47 - Monthly win rate: 65.17% - 2025 return: 35.17%[26][29]

追涨杀跌是人性，我们对比偏股混合型基金的规模变化与沪深300的走势能很明显的看到这一点。 咱们更容易在高点大胆买入，而在低点小心谨慎，导致最终错失配置良机。 只能说，大家对资产配置还处于初学阶段，依然需要FOF等多元资产配置工具。 上半年，FOF规模较年初合计增长了356亿，（截至：2025/6/30；数据来源：万得）是连续3年规模萎缩 之后的首次回暖。 其中，偏债混合型FOF值得关注。 以万得偏债混合型FOF基金指数为例，从过往表现来看，相比沪深300和FOF基金指数波动较小、回撤 较低、夏普较高, 帮助抵御市场波动。 另一方面以债券基金打底，追求低波动的FOF基金，很适合银行渠道的客户群体。 以招行为例，从托管人视角看，截至2025/6/30招行FOF的保有规模已经达到了460亿。 | 基金托管人视角的 | | | --- | --- | | FOF规模保有量排行 | | | 托管行 | 规模(亿) | | 招商银行 | 460 | | 建设银行 | 174 | | 农业银行 | 159 | | 中国银行 | 148 | | 工商银行 | 117 | | : Choice · IFF LADD | 数据统 ...

Core Insights - The article emphasizes the importance of asset allocation as a strategy to navigate market volatility and uncertainty, suggesting that relying solely on a single asset or market is insufficient for long-term stability [4][10]. Market Performance - A review of major asset classes over the past five years reveals that no asset experiences perpetual growth, with different assets undergoing varying degrees of volatility influenced by economic cycles [5][6]. - The performance of various asset classes in 2024 shows significant differences, with Chinese stocks returning 18.61%, global stocks at 17.45%, and gold leading with a return of 29.29% [6]. Asset Correlation - The article discusses the benefits of including low-correlation assets in a portfolio, highlighting that certain assets, like domestic bonds, often exhibit negative correlation with equities, which can help mitigate market risks [7][9]. Investment Strategy - The "fixed income enhancement" strategy is proposed as a straightforward and effective asset allocation framework, utilizing bonds as a stable foundation while allowing for some equity exposure to capture market upside [12]. - The strategy focuses on dividend and stable growth stocks, incorporating both A-shares and Hong Kong stocks to enhance the portfolio's adaptability to market fluctuations [13]. Performance Comparison - The "fixed income enhancement" strategy has reportedly outperformed its peers in terms of both returns and risk management over the past year, indicating its effectiveness in the current market environment [14][17].

Core Insights - The article emphasizes the importance of asset allocation in navigating market volatility and achieving long-term investment stability [1][5][8] Group 1: Economic Cycle and Asset Performance - Over the past five years, various asset classes have experienced different levels of volatility, influenced by economic cycles, highlighting that no asset has a perpetual upward trend [2] - The annual returns of major asset classes for 2020-2024 show significant fluctuations, with Chinese stocks returning 18.61% in 2024, while global stocks returned 17.45% [4] Group 2: Low Correlation Asset Allocation - Allocating assets with low or negative correlation can effectively hedge market risks and reduce overall portfolio volatility [5][6] - The correlation coefficients among major financial indices indicate that certain assets, like domestic bonds, often exhibit negative correlation with equities, which can be beneficial for risk management [6] Group 3: Simplifying Asset Allocation - The article introduces a "fixed income enhancement" strategy as a straightforward framework for asset allocation, using bonds as a stable foundation while selectively adding equity assets to capture market upside [8][10] - The "Guofu Anyi Stable 6-Month Holding Mixed Fund" under Guohai Franklin Fund exemplifies this strategy, focusing on dividend and stable growth stocks, including both A-shares and Hong Kong stocks to enhance adaptability to market fluctuations [9][10] Group 4: Performance Metrics - The "Guofu Anyi Stable 6-Month Holding Mixed Fund" has outperformed its peers in both return and risk control over the past year, achieving a return of 6.16% compared to the peer average of 5.87% [11][12] - The fund's performance metrics indicate a solid strategy, with a clear focus on core assets and a straightforward approach to complex market conditions [13]