晨星(中国)基金研究中心高级分析师吴粤宁对记者表示，如果市场情绪反转或价格回调，追高买入的投 资者可能面临较大损失。此外，除了价格波动，信用债市场还可能存在市场结构失衡、流动性风险集中 等隐患，尤其在市场波动时，ETF的申赎机制可能放大对债券市场的影响。 除了风险资产表现强势导致"股债跷跷板"效应之外，华泰固收称，高拥挤度也是债市的一个风险因素。 当前，债券基金规模提速与高波动并行。刚刚披露完毕的基金二季报数据显示，债券型基金规模扩容较 快。截至二季度末，公募债券型基金规模达到了10.93万亿元，创下历史新高，相比一季度末的10.07万 亿元增长了8600万元。三季度以来，债券型基金规模继续扩容，如债券ETF获资金净流入近千亿元。 近期，债市出现波动，债券基金普遍迎来调整。 7月23日，债券市场延续调整态势，银行间主要利率债收益率集体上行，国债期货收盘全线下跌，30年 期主力合约跌0.21%，10年期主力合约跌0.11%，5年期主力合约跌0.09%，2年期主力合约跌0.03%。 随着A股市场持续反弹，市场风险偏好回升被认为是债市调整的重要原因之一，尤其是利空长债。例如 场内交易的2只30年国债ETF近5日跌幅 ...

丨证券研究报告丨 战略数据研究丨专题报告 [Table_Title] "成长+"系列领涨，小微盘、高波占优—— W118 市场观察 报告要点 [Table_Summary] 当周基金重仓跑赢北向重仓，基金重仓 50 领涨；市场动速方面，行业、风格轮动速度均维持在 较高位；行业板块方面，医疗、通讯业务板块涨幅居前，金融、地产板块回调；风格方面，小 微盘、高波占优，"成长+"系列领涨；主题方面，川渝区域发展、东数西算领涨。 分析师及联系人 [Table_Author] 陈洁敏 SAC：S0490518120005 SFC：BUT348 请阅读最后评级说明和重要声明 %% %% %% %% 1 丨证券研究报告丨 cjzqdt11111 [Table_Title "成长+"系列领涨，小微盘、高波占优 2] —— W118 市场观察 战略数据研究丨专题报告 [Table_Summary2] 机构赚钱效应：基金重仓跑赢北向重仓，基金重仓 50 领涨 市场动速：行业、风格轮动速度均维持在较高位 行业板块：医疗、通讯业务板块涨幅居前，金融、地产板块回调 风格跟踪：小微盘、高波占优，"成长+"系列领涨 主题热点：川渝区域发展、 ...

日本央行审议委员高田创：鉴于关税可能导致美国经济放缓，需密切关注美国与日本货币政策立场分歧 可能带来的风险，这可能引发市场高波动性，尤其是在外汇市场。 ...

Group 1: Overseas Quantitative Dynamics - The impact of tariff events has led to significant drawdowns for quantitative hedge funds, with Renaissance Institutional Equities Fund experiencing an approximately 8% decline in early April despite a 22.7% increase in 2024 [1][2] - Man Group's trend-following strategy also faced over a 10% drawdown, prompting a return to in-office work for some researchers to enhance strategy intervention [1] - Systematica Investments, founded by Leda Braga, saw a 20% drawdown in early April, highlighting the vulnerability of trend-following strategies during such events [1] Group 2: Adoption of AI in Quantitative Strategies - AQR has begun to embrace AI in investment decisions, acknowledging its potential for higher returns despite challenges in explanation during drawdowns [3] - In contrast, domestic private quantitative firms in China are utilizing AI more extensively, with teams like Baiont Quant employing fully self-developed AI algorithms for minute-to-hour level return predictions [3] Group 3: Market Uncertainty and Quantitative Strategies - BlackRock emphasizes the importance of adjusting models to cope with increasing global uncertainty, identifying three main uncertainties in tariff policies: target, scale, and timeline [6] - The evolution of BlackRock's quantitative investment system has led to a more granular approach to risk exposure, now incorporating over a thousand risk factors [7] - BlackRock's strategy focuses on maintaining neutrality in risk exposure while seeking short-term reversal opportunities in a high uncertainty environment [8] Group 4: Macro Hedge Fund Perspectives - Bridgewater highlights the impact of "modern mercantilism" on investment portfolios, noting the challenges posed by chaotic implementation processes and the unique risks facing U.S. assets [10] - Despite recent market volatility, Bridgewater believes that asset prices have not undergone substantial adjustments, indicating potential future opportunities [10] - The interaction between AI development and modern mercantilism is seen as a new dynamic, with AI potentially offsetting some negative impacts on productivity [11] Group 5: AQR's Investment Focus - AQR suggests that high volatility factors, while challenging to maintain, can yield significant long-term Sharpe ratios, advocating for the acceptance of these factors [12][16] - The firm recommends focusing on small-cap stocks, particularly in emerging markets, due to their lower valuations and potential for higher returns compared to U.S. large-cap stocks [19] Group 6: Performance Tracking of Quantitative Products - Factor rotation products from BlackRock and Invesco have outperformed their respective indices over the past five years, with BlackRock's adaptive factor selection demonstrating resilience [21][24] - The performance of machine learning-based ETFs has varied, with QRFT showing strong results in certain months while AIEQ continues to experience significant drawdowns [39] - Bridgewater's All Weather ETF faced notable drawdowns due to tariff events but has since recovered, indicating resilience in its strategy [40]

2025 年 06 月 17 日 量化如何应对宏观不确定性冲击？ ——海外量化季度观察 2025Q2 AQR 开始"拥抱 AI"：近期 AQR 创始人 Cliff Asness 在访谈中承认其已经向 AI"投降"， 开始在投资决策中使用更多的 AI 算法； 德州教师退休基金量化团队集体加入独立资管机构。 ⚫ 海外量化观点： 量化如何应对宏观不确定性冲击：贝莱德认为当前高不确定性环境下，其量化体系主要通 过更细分的风险因子识别并对多数风险保持中性，以及在市场密集的交易中寻找短期反转 机会来战胜市场； 桥水："现代重商主义"的影响：桥水认为资产价格实际上还没有发生实质性调整，后续仍 将有持续的变化，美元资产仍然存在较大的不确定性，他们关注潜在的资本流动来辅助投 资判断，而黄金目前仍然具备很强的配置价值。 AQR：关注高波动因子、新兴市场小盘投资机会：AQR 使用方差比率来衡量因子的波动 水平，数据显示财务类因子具备明显更高的长期收益波动，但从长期来看这些因子的夏普 率也更高，AQR 建议量化管理人接受这些高波动性因子、在短期回撤时也要勇于坚守，并 通过一定的分散化降低短期波动；此外，面对当下复杂的宏观环境，AQR ...

Core Viewpoint - The South Korean Ministry of Finance announced that the country will implement measures to address high volatility in the market [1] Group 1 - The measures are aimed at stabilizing the financial markets amid increasing fluctuations [1] - The government is closely monitoring the situation and is prepared to take further actions if necessary [1] - The announcement reflects concerns over economic stability and investor confidence in South Korea [1]

Group 1 - The South Korean Ministry of Finance announced that it will implement measures to respond to high market volatility [1]

6月10日，申万宏源2025资本市场夏季策略会在北京举办。申万宏源证券执委会成员、申万宏源研究董 事长周海晨在主论坛致开幕辞，申万宏源研究总经理王胜主持主论坛，公司各事业部、相关业务条线负 责人参会交流。近500家上市公司高管与2200余名投资者齐聚一堂，会议期间将进行700余场线下交流。 申万宏源研究债券首席分析师黄伟平进行演讲。黄伟平表示，与2024年的单边牛市不同，2025年债券已 经进入"低利率+利利差+高波动"的状态，需要告别单边牛市思维。 下半年流动性方面，黄伟平表示可以关注两个重点。关注央行买债何时恢复，买债节奏可能会和供给节 奏相匹配。下半年国债净供给高峰通常高于上半年，同时，同等规模看待，央行买入国债的效力实质上 强于降准，前者是既减少供给，又增加流动性，后者是供给不变，仅增加流动性，以国债买入的恢复用 于下半年货币财政配合，或更加匹配。因此，国债买入的恢复或已渐行渐近，且年内第二波净供给高峰 之时，买债力度可能较为可观。 具体到机会方面，黄伟平称，节奏上6-8月份是不错的做多窗口，实体融资需求回落叠加央行可能恢复 国债买入，但全年要告别单边牛市思维、站在高波动的震荡市角度看市场。中长端已经 ...

Quantitative Models and Construction Methods 1. Model Name: GRU (Generalized Recurrent Unit) - **Model Construction Idea**: GRU models are used to capture temporal dependencies and patterns in financial data, leveraging recurrent neural network structures to predict stock performance or factor returns[3][4][5] - **Model Construction Process**: The GRU model is trained on historical stock data, incorporating features such as price movements, volume, and other technical indicators. Specific GRU-based models mentioned include: - **open1d**: Focuses on daily opening prices - **close1d**: Focuses on daily closing prices - **barra1d**: Integrates Barra-style risk factors for daily predictions - **barra5d**: Extends Barra-style risk factors to a 5-day horizon[5][6][25] - **Model Evaluation**: GRU models show mixed performance, with some models like open1d performing well, while others like barra1d and barra5d experience significant drawdowns in certain market conditions[5][6][25] --- Model Backtesting Results GRU Model Performance - **open1d**: - Weekly excess return: 1.22% - Monthly excess return: 2.58% - Year-to-date excess return: 6.08%[29][30] - **close1d**: - Weekly excess return: 1.89% - Monthly excess return: 2.91% - Year-to-date excess return: 4.14%[29][30] - **barra1d**: - Weekly excess return: 0.85% - Monthly excess return: 1.50% - Year-to-date excess return: 3.48%[29][30] - **barra5d**: - Weekly excess return: 0.84% - Monthly excess return: 2.25% - Year-to-date excess return: 5.59%[29][30] --- Quantitative Factors and Construction Methods 1. Factor Name: Barra Style Factors - **Factor Construction Idea**: Barra factors are designed to capture systematic risk exposures across various dimensions such as size, value, momentum, and volatility[13][14] - **Factor Construction Process**: - **Beta**: Historical beta of the stock - **Size**: Natural logarithm of total market capitalization - **Momentum**: Weighted average of historical excess returns, combining volatility, cumulative deviation, and residual volatility $ Momentum = 0.74 \cdot \text{Volatility} + 0.16 \cdot \text{Cumulative Deviation} + 0.1 \cdot \text{Residual Volatility} $ - **Volatility**: Weighted average of historical residual return volatilities - **Valuation**: Inverse of price-to-book ratio - **Liquidity**: Weighted average of turnover ratios (monthly, quarterly, yearly) - **Profitability**: Weighted average of analyst forecasted earnings yield, cash flow yield, and other profitability metrics - **Growth**: Weighted average of earnings and revenue growth rates - **Leverage**: Weighted average of market leverage, book leverage, and debt-to-asset ratio[14][15] - **Factor Evaluation**: Barra factors demonstrate varying performance across different market conditions, with some factors like volatility and liquidity showing strong returns, while others like size and growth exhibit weaker performance[15][16] 2. Factor Name: Technical Factors - **Factor Construction Idea**: Technical factors aim to capture price and volume-based patterns, focusing on momentum and volatility metrics[17][20][24] - **Factor Construction Process**: - **Momentum**: Calculated over different time horizons (e.g., 20-day, 60-day, 120-day) - **Volatility**: Measured as the standard deviation of returns over specific periods (e.g., 20-day, 60-day, 120-day) - **Median Deviation**: Captures the median absolute deviation of returns[27] - **Factor Evaluation**: High-momentum and high-volatility stocks generally outperform, but certain periods show negative returns for these factors, especially in the 120-day horizon[17][27] 3. Factor Name: Fundamental Factors - **Factor Construction Idea**: Fundamental factors are derived from financial statements, focusing on profitability, growth, and valuation metrics[17][20][24] - **Factor Construction Process**: - **Static Financial Metrics**: Return on equity (ROE), return on assets (ROA), and profit margins - **Growth Metrics**: Earnings growth, revenue growth, and cash flow growth - **Surprise Metrics**: Earnings and revenue surprises relative to analyst expectations[19][21][23] - **Factor Evaluation**: Growth and surprise factors perform well, while static financial metrics like ROA and ROE show weaker performance in certain periods[19][21][23] --- Factor Backtesting Results Barra Factors - **Volatility**: Weekly return: 0.75%, Monthly return: 2.73% - **Liquidity**: Weekly return: 0.68%, Monthly return: 1.37% - **Size**: Weekly return: -1.45%, Monthly return: -3.60%[15][16] Technical Factors - **20-day Momentum**: Weekly return: -1.81%, Monthly return: -6.16% - **60-day Volatility**: Weekly return: -1.79%, Monthly return: -0.74% - **120-day Momentum**: Weekly return: -1.68%, Monthly return: -0.80%[27] Fundamental Factors - **ROA Growth**: Weekly return: 0.23%, Monthly return: 1.31% - **Earnings Surprise**: Weekly return: 0.20%, Monthly return: 1.11% - **Revenue Growth**: Weekly return: 0.17%, Monthly return: 0.77%[19][21][23]

欧洲央行副行长金多斯：高波动性尚未妨碍市场的正常运作。 ...