鹏华睿丰债券A/C成立于2025年10月30日。 中国经济网北京11月10日讯近日，鹏华基金公告，鹏华睿丰债券增聘寇斌权。 寇斌权2018年7月加盟鹏华基金管理有限公司，历任量化及衍生品投资部大数据分析研究助理、量化研 究员、高级量化研究员、资深量化研究员，现担任指数与量化投资部基金经理。 | 基金名称 | 鹏华睿丰债券型证券投资基金 | | --- | --- | | 基金简称 | 鹏华睿丰债券 | | 基金主代码 | 025631 | | 基金管理人名称 | 鹏华基金管理有限公司 | | 公告依据 | 《公开募集证券投资基金信息披露管理办 法》、《基金管理公司投资管理人员管理 | | | 指导意见》等相关法规的规定。 | | 基金经理变更类型 | 增聘基金经理 | | 共同管理本基金的其他基金经理姓名 | 方超 | | 新任基金经理姓名 | 寇斌权 | ...

Group 1 - The core point of the news is the appointment of Du Gu Nan Xun as a new fund manager for the Shan Zheng Asset Management Reform Selected Mixed Fund, indicating a strategic move to enhance the fund's management team [1][2]. - Du Gu Nan Xun has extensive experience in the financial industry, having worked at various institutions such as Taiwan KGI Securities, ICBC Credit Suisse Asset Management, and others, which adds significant expertise to the fund's management [1]. - The Shan Zheng Asset Management Reform Selected Mixed Fund was established on January 12, 2018, and has achieved a year-to-date return of 17.26% and a cumulative return of 26.96% since its inception, with a net asset value of 1.2697 yuan as of November 7, 2025 [1]. Group 2 - The fund is officially named Shan Zheng Asset Management Reform Selected Flexible Allocation Mixed Securities Investment Fund, with the main code 005226 [2]. - The announcement of the new fund manager is based on the "Measures for the Disclosure of Information on Publicly Raised Securities Investment Funds" [2]. - The fund will continue to be co-managed by another fund manager, Yan Yue, alongside the newly appointed Du Gu Nan Xun [2].

Quantitative Models and Construction Methods - **Model Name**: Diffusion Index Model **Construction Idea**: The model monitors the future critical points of the diffusion index to predict market trends[6][38][39] **Construction Process**: 1. The diffusion index is calculated based on the relative price changes of constituent stocks over a specific time window 2. Horizontal axis represents future price changes (e.g., from +10% to -10%), while vertical axis represents the length of the review window (e.g., 20 days to 10 days) 3. Example: If all constituent stocks drop by 5% after 5 days, the diffusion index value is 0.69[38] 4. The model uses methods like left-side threshold, right-side threshold, and dual moving average to generate signals - Left-side threshold method triggered an opening signal on September 23, 2025, with a value of 0.0575[43] - Right-side threshold method triggered an opening signal on September 25, 2025, with a value of 0.1825[47] - Dual moving average method provided a bullish signal on October 13, 2025[48] **Evaluation**: The model is effective in identifying high-risk zones and generating trading signals[6][39][48] - **Model Name**: Small Cap Low Volatility 50 Strategy **Construction Idea**: Select 50 stocks with small market capitalization and low volatility from micro-cap stocks[8][34] **Construction Process**: 1. Stocks are selected based on market capitalization and volatility criteria 2. Portfolio is rebalanced bi-weekly 3. Transaction cost is set at 0.3% for both sides 4. Benchmark index: Wind Micro-Cap Index (8841431.WI)[8][34] **Evaluation**: The strategy has shown strong performance in 2025, with significant YTD returns[8][34] Model Backtesting Results - **Diffusion Index Model**: - Current diffusion index value: 0.82, indicating a medium-high level[38][39] - Weekly increase from 0.78 to 0.82[39] - Future prediction: If the index rises by 2% next week, it will trigger the risk threshold[39] - **Small Cap Low Volatility 50 Strategy**: - 2024 return: 7.07%, excess return: -2.93%[8][34] - 2025 YTD return: 77.82%, weekly excess return: 1.50%[8][34] Quantitative Factors and Construction Methods - **Factor Name**: Free Float Ratio Factor **Construction Idea**: Measure the proportion of free-floating shares in total shares[5][16][32] **Construction Process**: 1. Calculate the ratio of free-floating shares to total shares 2. Rank IC value for the week: 0.108; historical average: -0.012[5][16][32] **Evaluation**: Positive weekly IC indicates strong predictive power[5][16][32] - **Factor Name**: Leverage Factor **Construction Idea**: Assess the financial leverage of companies[5][16][32] **Construction Process**: 1. Calculate the ratio of total debt to equity 2. Rank IC value for the week: 0.104; historical average: -0.006[5][16][32] **Evaluation**: Positive weekly IC suggests effective factor performance[5][16][32] - **Factor Name**: 10-Day Total Market Cap Turnover Rate Factor **Construction Idea**: Measure the turnover rate of total market capitalization over 10 days[5][16][32] **Construction Process**: 1. Calculate turnover rate as trading volume divided by total market capitalization over 10 days 2. Rank IC value for the week: 0.099; historical average: -0.059[5][16][32] **Evaluation**: Positive weekly IC indicates good predictive ability[5][16][32] - **Factor Name**: 10-Day Free Float Market Cap Turnover Rate Factor **Construction Idea**: Measure the turnover rate of free-floating market capitalization over 10 days[5][16][32] **Construction Process**: 1. Calculate turnover rate as trading volume divided by free-floating market capitalization over 10 days 2. Rank IC value for the week: 0.098; historical average: -0.061[5][16][32] **Evaluation**: Positive weekly IC suggests strong factor performance[5][16][32] - **Factor Name**: Dividend Yield Factor **Construction Idea**: Measure the dividend yield of stocks[5][16][32] **Construction Process**: 1. Calculate dividend yield as annual dividend divided by stock price 2. Rank IC value for the week: 0.065; historical average: 0.022[5][16][32] **Evaluation**: Positive weekly IC indicates reliable factor performance[5][16][32] Factor Backtesting Results - **Free Float Ratio Factor**: Weekly IC: 0.108; historical average: -0.012[5][16][32] - **Leverage Factor**: Weekly IC: 0.104; historical average: -0.006[5][16][32] - **10-Day Total Market Cap Turnover Rate Factor**: Weekly IC: 0.099; historical average: -0.059[5][16][32] - **10-Day Free Float Market Cap Turnover Rate Factor**: Weekly IC: 0.098; historical average: -0.061[5][16][32] - **Dividend Yield Factor**: Weekly IC: 0.065; historical average: 0.022[5][16][32]

ETF Market Overview - As of November 7, the performance of ETF products varied, with the CSI 2000 showing the highest increase among major broad-based ETFs, while the new energy theme ETF had the largest increase among industry and thematic products [2][9] - In the past two weeks, major broad-based ETFs such as CSI A500, CSI 2000, and Sci-Tech 50 ETF saw net inflows, while the ChiNext ETF experienced the largest net outflow [2][9] - The recent trend indicates a shift from net inflows to net outflows in cyclical and military industry ETFs, while pharmaceutical ETFs saw accelerated inflows [2][16] ETF Fund Flow Analysis - The cumulative fund flow for broad-based ETFs has shown a trend of outflows turning into inflows and then back to outflows since the beginning of 2025, with A-series ETFs consistently experiencing outflows [10] - Recent net outflows for broad-based ETFs have slowed down, with CSI 1000 and CSI 2000 transitioning from net outflows to net inflows [10][16] - As of November 7, the total number of newly established ETFs in the past two weeks was 16, with a total issuance of 6.53 billion units, of which 13 were stock ETFs and 3 were QDII ETFs [24] Thematic ETF Tracking - In the technology theme ETFs, products tracking the Hang Seng Technology index saw the highest net inflows, while those tracking consumer electronics experienced net outflows [30] - For dividend theme ETFs, products tracking the S&P Hong Kong Stock Connect Low Volatility Dividend Index had the highest net inflows, while those tracking the dividend index saw net outflows [32] Popular Thematic ETFs - AI-themed ETFs, which have a high proportion of AI stocks, experienced an average return of -2.99% with a net inflow of 1.56 billion [2] - New energy-themed ETFs had an average return of 7.67% but saw a net outflow of 5.72 billion [2] - The total holdings of ETFs by Central Huijin, Guoxin, and Chengtong reached 391.34 billion units, with a net outflow of 2.11 billion in the past two weeks [2]

Core Insights - The A-share market is experiencing a recovery, leading to a surge in the number of "doubling funds," with an average return of 28.20% for 8,484 active equity funds as of November 7, outperforming major indices like the Shanghai Composite and CSI 300 [2][3] - A total of 69 funds have achieved returns exceeding 100% this year, with the top two funds, Yongying Technology Smart A and C, achieving returns of 205.35% and 203.72% respectively, despite being newly established [3][4] - The majority of high-performing funds are heavily invested in technology sectors, particularly in artificial intelligence, semiconductors, and cloud computing, indicating a strong focus on growth opportunities in these areas [3][6] Fund Performance - The average tenure of fund managers for the 69 "doubling funds" is approximately 2.58 years, with many being newly appointed or newly launched products [4][7] - Notable funds include Yongying Technology Smart A/C, which has a concentrated portfolio in the global cloud computing industry, with top holdings in key technology companies [6][12] - Other high-performing funds include Yongying Ruiheng A/C and Yongying Rong'an A/C, which also achieved returns exceeding 100% and were launched recently [7][15] Fund Size Growth - The "doubling funds" have seen significant growth in size, with some funds experiencing over tenfold increases in scale compared to the previous quarter [12][14] - Funds like Xinao Performance Driven A and Zhonghang Opportunity Navigator A have seen their sizes increase dramatically, with growth rates of 12.06 times and 11.47 times respectively [12][13] - As of November 7, three funds have surpassed 10 billion yuan in size, indicating a strong market demand for these high-performing products [13][14] Fund Company Distribution - E Fund has the highest number of "doubling funds," totaling nine, showcasing its strong research and investment capabilities [15] - Yongying Fund and Anxin Fund each have six "doubling funds," while Huatai-PB Fund has four, indicating a competitive landscape among fund companies [15] - Yongying Fund has also seen its overall management scale increase from 5,284 billion yuan at the end of last year to 6,246 billion yuan by the end of the third quarter this year [15]

Core Viewpoint - COMEX gold futures prices have strengthened significantly, reaching approximately $4083 per ounce, driven by concerns over the U.S. economy and consumer confidence [1] Market Performance - COMEX gold futures experienced a daily fluctuation of over $70, with related ETF products showing strong performance: - Huaxia Gold ETF (518850) increased by 1.70% - Gold Stock ETF (159562) rose by 2.79% - Non-ferrous Metals ETF (516650) gained 0.70% [1] Economic Indicators - The U.S. consumer confidence index fell to 50.3 in November, the lowest level since June 2022, down from 53.6 in October and below the expected 53.2 [1] - The decline in consumer confidence is attributed to concerns over the prolonged federal government shutdown, which has lasted over a month, affecting personal financial situations and future business outlooks [1] Implications for Gold Prices - The weak U.S. economy has heightened consumer concerns, boosting demand for safe-haven assets like gold, which supports gold prices [1] - The resolution of the government shutdown is expected to significantly impact gold prices, making it a key factor for future price movements [1]

此外近期特朗普签署稳定币GENIUS法案。美国政府合法化稳定币可能对于美元信用产生持续影响，进 而对于金价有一定影响。2025年初白宫发布行政命令，明确将"促进合法且可信的美元稳定币在全球的 发展"作为维护美元地位的政策措施之一；且法案要求稳定币发行方100%储备美元或短期美债，有望短 期缓解美债的流动性风险。逻辑上分析，若稳定币的发展有效支撑美元信用，分流黄金对冲主权货币贬 值风险的需求，对黄金可能有一定利空影响；但若在稳定币发展进程中出现预期之外的信用风险（例如 2022年算法稳定币曾出现崩盘风险），则可能推高全市场风险溢价，对黄金构成利好。可持续关注后续 发展历程及相应影响。 长期看，货币超发及财政赤字货币化背景下，美元信用体系受到挑战；加上全球地缘动荡频发推动资产 储备多元化，黄金作为安全资产的需求持续提升。全球"去美元化"的趋势使得黄金有望成为新一轮定价 锚，使得贵金属有望具备上行动能。美联储本轮降息周期的时间或被就业韧性和通胀扰动拉长，但仍具 备较大政策空间，增加了黄金的做多窗口期。后续可持续关注全球宏观经济走势及全球央行购金情况。 关注美国政府停摆情况与美联储官员发言表态。 （文章来源：每日经济 ...

11月10日，平安中证通用航空主题ETF（基金代码：561660,场内简称：通用航空ETF基金）正式上市。 根据中国民航局数据，到2030年，中国低空经济的市场规模预计达2.5万亿元，2035年有望达3.5万亿 元，其中，通用航空市场规模占比过半。 市场投资机会方面，短期来看，通用航空行业有望成为成长风格扩散重要方向。市场风险偏好持续高 位，成长行情有望在内部进行扩散。通用航空前期涨幅相对有限、长期产业趋势明确且空间较大、短期 催化密集（"十五五"规划提出航天强国、交通强国目标后，具体支持政策有望跟进），有望成为扩散方 向。 长期来看，该产业类似于新能车产业链早期，处在长期上涨趋势起点，产业发展确定性强、投资弹性 大。 指数设计方面，通用航空ETF基金紧跟中证通用航空主题指数，该指数聚焦通航飞行器制造、通航基础 设施及通航应用三大核心领域，合计权重超过70%，有望充分受益产业发展。 通用航空ETF基金跟踪中证通用航通主题指数（指数代码：931885），该指数选取50只业务涉及航空材 料及零部件、通航飞行器制造、通航基础设施、通航运营与保障、通航应用等通用航空相关领域的上市 公司证券作为指数样本，反映通用航空主 ...

在风起云涌的量化投资江湖中，淳厚基金量化与指数投资部总监周俊凭借着扎实的投研功底与均衡的投 资视角，逐渐在行业中崭露头角。 拥有12年量化投研①经验的他，曾亲历私募量化大发展期，2023年9月，他选择加入淳厚基金，开启在 公募领域的新征程。 在投资中，周俊始终强调量化策略的"可解释性"。他从因子挖掘到组合构建的过程中均保持逻辑清晰， 避免过度依赖"端到端"的机器学习。他希望通过"强因子弱模型"的投资架构，在因子层面追求逻辑清 晰、低相关性，在模型层面控制复杂度，确保策略出现波动时可追溯、可修复。这种均衡而稳健的风 格，正是周俊在量化行业中行健致远的关键。 坚持均衡投资 以多周期策略捕捉市场机遇 周俊的职业生涯贯穿了公私募两大资管细分领域。量化投资在中国刚启蒙的时候周俊便进入了行业，从 量化研究员成长为核心团队骨干。在量化行业快速发展期，他加入了一家中小型私募，作为核心基金经 理之一，亲历了公司规模从20亿元增长至400亿元的过程，自己也积累了丰富的投资经验。此后，他选 择"私转公"，于2023年9月加入淳厚基金，投身公募行业。 周俊所构建的量化模型，致力于追求以"多周期预测"为核心、兼顾逻辑透明与风险可控的均 ...

截至2025年11月7日，沪深两市ETF两融余额为1181.09亿元，较上一交易日减少26.70亿元。分项来看， ETF融资余额为1097.25亿元，较上一交易日减少26.26亿元；ETF融券余额为83.84亿元，较上一交易日 减少0.44亿元。 在融资交易中，共有216只ETF获得融资净买入。其中，净买入额居首的是华夏恒生互联网科技业ETF （代码：513330，简称：恒生互联），净买入额约8734.79万元。融资净买入金额居前的其他ETF包 括：鹏华化工ETF（代码：159870，净买入额约7273.72万元）、大成恒生科技ETF（代码：159740，净 买入额约6006.08万元）、易方达中概互联ETF（代码：513050，净买入额约5027.51万元）以及华泰柏 瑞光伏ETF（代码：515790，净买入额约3643.72万元）。 ...