□本报记者 王雪青 2026年1月4日，中欧瑞博公众号更新了新一年的策略展望——这是吴伟志亲笔写下的第140篇"伟志思 考"。 从2014年到2026年，12年来，他几乎从不停笔：最早写"投资像种庄稼""别拔苗助长"，后来用"春夏秋 冬"标注市场的季节更替，再用"树粮菜"拆解不同投资风格的收益与代价。他把一位基金经理最难被量 化的资产——思想——沉淀成一套可追溯、可复盘、可传承的方法论；也把一家机构的成长轨迹，写成 一份长期的运行日志。 2014年7月初，A股在阶段底部徘徊。吴伟志却旗帜鲜明地提出："这里是播种的时节，我们会保持较高 的仓位。"这一年，"伟志思考"多次用"播种"来形容投资策略，核心表达是"底部确认""保持耐心""不要 在犹豫纠结中错过春播好时机"。随后，A股市场开启了一段趋势性上涨行情。 事后来看，吴伟志"从估值、利率与政策态度"综合判断市场"土壤已变"的框架，与桥水强调的"宏观因 子驱动资产价格"有异曲同工之妙。 更难的是在市场高潮中感知风险。2015年，吴伟志在市场快速上涨中提示"下车"。2015年5月，他撰文 称，"珍惜涨潮时光""更重要的是能够在退潮之前或退潮初期及时下车"。 2015 ...

本报记者 吴珊 伴随着公募基金高质量发展、市场结构深刻变革，投资者对精准高效的投资工具需求日益迫切。 除了近年来颇受市场各方认可的ETF（交易型开放式指数基金）外，指数增强型基金（以下简称"指增基金"）也在快速成 长——2025年募集资金突破千亿元，超过此前三年总和；2026年伊始，多只新产品密集亮相，成为资金捕捉A股"春季躁动"行 情的热门选择。 目前，一个清晰的信号已然显现：指增基金正褪去"小众"标签，跻身投资者主流配置清单。 "小而美"配置新选 指增基金能快速走红核心在于其独特定位，既紧密跟随市场走势（贝塔），又通过量化模型、多因子策略等主动管理手段 增加"弹性"，力争获取持续稳健的超额收益（阿尔法），成功搭建起被动投资与主动投资之间的桥梁。 Wind资讯数据显示，截至2025年末，指增基金数量已达476只，合计募集资金1004.54亿元，超过此前三年总和；其中有 186只在2025年成立。进入2026年，指增基金热度不减，截至1月31日，新发指增基金已达10只，超过2025年同期。 中小基金公司参与指增基金热情尤为高涨。目前97家布局这一赛道的公募机构中，既有凭借特色策略崭露头角的博道基 金、永赢基金 ...

Group 1 - In January, private equity firms actively participated in offline subscription for new stocks, with 159 firms involved in 5 stocks, acquiring a total of 15.76 million shares worth approximately 338 million yuan [1] - The semiconductor equipment company Hengyun Chang attracted the most attention from private equity, with a total allocation amount of about 114 million yuan and 1.236 million shares acquired [1] - Other notable companies included Zhenstone Co., a wind power materials manufacturer, with an allocation of approximately 86.16 million yuan and 7.707 million shares, and Beixin Life, a medical device company, with about 66.47 million yuan and 3.794 million shares allocated [1] Group 2 - Among the participating private equity firms, over 80% of the 47 firms with allocations of at least 1 million yuan managed over 10 billion yuan, including 27 quantitative private equity firms [1] - The top participant in terms of allocation was Ningbo Huansheng Quantitative Investment Management, with an allocation of approximately 32.68 million yuan and 1.4026 million shares [2] - Other significant participants included Jiukun Investment with about 32.22 million yuan and 1.3153 million shares, and Shanghai Yanfu Investment Management with approximately 25.64 million yuan [2] Group 3 - The concentration of large quantitative private equity firms in offline subscriptions is attributed to their disciplined and systematic investment strategies, which align well with the requirements of new stock subscriptions [3] - The stable returns from new stock subscriptions provide effective support for fund net values, making them attractive for quantitative strategies [3] - Subjective strategy private equity firms also showed active participation, with firms like Shanghai Yingshui Investment and Shanghai Ningquan Asset Management acquiring amounts ranging from 3.96 million to 5.59 million yuan [3]

Quantitative Models and Factor Analysis Quantitative Models and Construction Methods Model 1: ETF Hot Trend Strategy - **Model Name**: ETF Hot Trend Strategy - **Model Construction Idea**: The strategy is based on selecting ETFs with the highest and lowest prices in an upward trend and further filtering them based on the steepness of the regression coefficients of the highest and lowest prices over the past 20 days[31] - **Model Construction Process**: 1. Select ETFs where both the highest and lowest prices are in an upward trend 2. Construct support and resistance factors based on the steepness of the regression coefficients of the highest and lowest prices over the past 20 days 3. Choose the top 10 ETFs with the highest turnover rate in the past 5 days relative to the past 20 days to construct a risk parity portfolio[31] - **Model Evaluation**: The strategy achieved a return of 61.41% since 2025, with an excess return of 38.22% compared to the CSI 300 Index[31] Model 2: ETF Three-Strategy Fusion - **Model Name**: ETF Three-Strategy Fusion - **Model Construction Idea**: The strategy combines three industry rotation strategies driven by quantitative fundamentals, quality low volatility, and distressed reversal to achieve factor and style complementarity and reduce the risk of a single strategy[34] - **Model Construction Process**: 1. Construct industry rotation strategies based on quantitative fundamentals, quality low volatility, and distressed reversal 2. Combine the three strategies in equal weights to select industries from different dimensions[34] - **Model Evaluation**: The strategy achieved a cumulative return of 12.24% from April 10, 2017, to January 30, 2026, with a Sharpe ratio of 0.74[39] Model 3: All-Weather Strategy - **Model Name**: All-Weather Strategy - **Model Construction Idea**: The strategy aims to achieve stable returns by avoiding the "prediction" dilemma through diversified risk. It follows three basic principles: asset selection, risk adjustment, and structural hedging[53] - **Model Construction Process**: 1. Select assets 2. Adjust risks 3. Perform structural hedging to achieve balanced allocation and smooth out volatility[53] - **Model Evaluation**: The high-volatility version achieved an annualized return of 11.8% with an average maximum drawdown of 3.6% and a Sharpe ratio of 2.3. The low-volatility version achieved an annualized return of 8.8% with an average maximum drawdown of 2.0% and a Sharpe ratio of 3.4[61] Model Backtesting Results ETF Hot Trend Strategy - **Return**: 61.41% since 2025[31] - **Excess Return**: 38.22% compared to CSI 300 Index[31] ETF Three-Strategy Fusion - **Cumulative Return**: 12.24% from April 10, 2017, to January 30, 2026[39] - **Sharpe Ratio**: 0.74[39] All-Weather Strategy - **High-Volatility Version**: - **Annualized Return**: 11.8%[61] - **Average Maximum Drawdown**: 3.6%[61] - **Sharpe Ratio**: 2.3[61] - **Low-Volatility Version**: - **Annualized Return**: 8.8%[61] - **Average Maximum Drawdown**: 2.0%[61] - **Sharpe Ratio**: 3.4[61] Quantitative Factors and Construction Methods Factor 1: Profitability Yield Factor - **Factor Name**: Profitability Yield Factor - **Factor Construction Idea**: Measures the profitability of stocks to identify high-profitability stocks[63] - **Factor Construction Process**: Calculate the profitability yield of stocks and select those with the highest profitability yield[63] - **Factor Evaluation**: Achieved a positive return of 3.24% this week, indicating that high-profitability stocks regained market favor[63] Factor 2: Value Factor - **Factor Name**: Value Factor - **Factor Construction Idea**: Measures the value of stocks to identify high-value stocks[63] - **Factor Construction Process**: Calculate the value of stocks and select those with the highest value[63] - **Factor Evaluation**: Achieved a positive return of 2.67% this week, reflecting that high-value stocks gained market attention[63] Factor 3: Leverage Factor - **Factor Name**: Leverage Factor - **Factor Construction Idea**: Measures the leverage of stocks to identify high-leverage stocks[63] - **Factor Construction Process**: Calculate the leverage of stocks and select those with the highest leverage[63] - **Factor Evaluation**: Achieved a positive return of 1.32% this week, indicating that high-leverage stocks gained market attention[63] Factor Backtesting Results Profitability Yield Factor - **Weekly Return**: 3.24%[63] Value Factor - **Weekly Return**: 2.67%[63] Leverage Factor - **Weekly Return**: 1.32%[63]

[Table_Page] 金融工程|量化投资策略月报 2026 年 2 月 1 日 证券研究报告 [Table_Title] 1 月量化转债组合超额 0.43% 量化转债月度跟踪（2026 年 02 月） [Table_Summary] 报告摘要： 图：量化转债组合业绩表现 | 表：转债指数择时信号 | | | --- | --- | | 模型 | 观点 | | 价量模型 | 1 | | 定价模型 | 1 | | 凸性模型 | 0 | | 仓位 | 67% | 数据来源：Wind, 广发证券发展研究中心 | [分析师： Table_Author]张超 | | | --- | --- | | | SAC 执证号：S0260514070002 | | | SFC CE No. BOB130 | | 020-66335132 | | | | zhangchao@gf.com.cn | | 分析师： | 安宁宁 | | | SAC 执证号：S0260512020003 | | | SFC CE No. BNW179 | | 0755-23948352 | | | | anningning@gf.com.cn | | ...

金融工程| 证券研究报告 —周报 2026 年 2 月 1 日 中银量化大类资产跟踪 权益高点震荡，贵金属外盘现领跌行情 股票市场概览 ◼ 本周 A 股市场整体下跌，港股市场整体上涨，美股市场走势分化，其 他海外权益市场走势分化。 A 股风格与拥挤度 成长 vs 红利：相对拥挤度及超额净值近期处于历史较高位置，需注 意成长风格的配置风险。 小盘vs大盘：相对拥挤度及超额净值均未处于历史高位，小盘风格当 前具有较高的配置性价比。 微盘股 vs 中证 800：相对拥挤度及超额净值持续处于历史高位，需注 意微盘股风格的配置风险。 A 股行情跟踪 A 股估值与股债性价比 A 股资金面 利率市场 ◼ 本周中国国债利率下跌，美国国债利率上涨，中美利差处于历史高位。 证券分析师: 郭策 (8610) 66229081 ce.guo@bocichina.com 证券投资咨询业务证书编号：S1300522080002 汇率市场 ◼ 近一周在岸人民币较美元升值，离岸人民币较美元贬值。 商品市场 ◼ 本周中国商品市场整体上涨，美国商品市场整体下跌。 风险提示 ◼ 量化模型因市场剧烈变动失效。 中银国际证券股份有限公司 具备证券投资 ...

Core Viewpoint - The recent historic drop in COMEX gold and silver futures prices has caused significant turmoil in the precious metals investment market, leading to substantial losses for hedge funds and raising concerns about market stability and future price movements [1][2][3]. Market Reaction - On January 30, COMEX gold futures fell from $5,410 to $4,907 per ounce, a drop of over 12%, marking the largest single-day decline in nearly 40 years. COMEX silver futures dropped from $115.89 to $85.25 per ounce, with a maximum intraday decline of 35.30%, the largest since the 1980s [1]. - The net asset value of a hedge fund managing a $60 million precious metals investment fund fell by over 6% in one night, the largest single-day drop since its inception [2]. Causes of Price Drop - The price drop was attributed to the nomination of Kevin Warsh as the next Federal Reserve Chairman, which raised concerns about a more cautious U.S. monetary policy, causing the dollar index to rise from 96.20 to 97.11 [2][3]. - The market was already experiencing a bubble due to excessive bullish positions and record levels of call options, making it vulnerable to sharp corrections [2][7]. Market Sentiment - The sentiment among hedge fund managers was one of shock and panic, as many were unprepared for such a drastic price drop. The market was characterized by extreme bullishness, with gold and silver being among the most crowded trades globally [8][9]. - The rapid decline in prices triggered a "liquidation cascade," where many funds were forced to sell off their positions to avoid further losses, exacerbating the price drop [8][10]. Future Outlook - Despite the recent volatility, there is a belief that gold and silver prices have long-term upward potential due to ongoing geopolitical risks and the weakening global position of the dollar [3][14]. - However, the current environment is marked by high volatility, and investors are advised to manage their positions and leverage carefully to avoid significant losses in the event of further price corrections [3][14][15]. Margin Requirements - Following the price drop, the CME Group raised margin requirements for COMEX gold and silver futures, increasing the margin ratio for high-risk accounts, which may lead to further exits from the market by leveraged investors [14].

- The momentum factor and profitability factor both achieved positive returns of 0.51% in the overall market stock pool this week, indicating a momentum effect in the market[1][18] - The Beta factor and liquidity factor recorded negative returns of -0.81% and -0.41%, respectively, while other style factors showed average performance[1][18] - In the CSI 300 stock pool, the best-performing factors this week were the P/E ratio factor (1.70%), net profit margin TTM (1.03%), and operating profit margin TTM (1.02%)[1][12] - The worst-performing factors in the CSI 300 stock pool were the post-morning return factor (-3.58%), momentum spring factor (-3.50%), and 5-day reversal factor (-2.98%)[1][12] - In the CSI 500 stock pool, the best-performing factors this week were the inverse P/S ratio TTM (3.25%), inverse P/E ratio TTM (2.67%), and P/E ratio factor (2.45%)[1][14] - The worst-performing factors in the CSI 500 stock pool were the 5-minute return skewness factor (-3.71%), 6-day moving average of transaction amount (-2.69%), and 5-day reversal factor (-2.40%)[1][14] - In the liquidity 1500 stock pool, the best-performing factors this week were the operating cash flow ratio (2.27%), momentum-adjusted small orders (1.65%), and single-quarter ROA (1.62%)[2][16] - The worst-performing factors in the liquidity 1500 stock pool were the 5-minute return skewness factor (-3.03%), morning return factor (-2.65%), and 5-day average turnover rate (-2.21%)[2][16] - The PB-ROE-50 portfolio achieved positive excess returns in the CSI 500 stock pool this week, with an excess return of 0.59%[2][23] - The PB-ROE-50 portfolio recorded an excess return of -0.50% in the CSI 800 stock pool and -2.81% in the overall market stock pool[2][23] - The public fund research stock selection strategy and private fund research tracking strategy both recorded negative excess returns this week, with the public fund strategy achieving -4.21% and the private fund strategy achieving -1.85% relative to the CSI 800[2][25] - The block trade portfolio achieved a positive excess return of 0.06% relative to the CSI All Share Index this week[2][30] - The directed issuance portfolio achieved a positive excess return of 0.13% relative to the CSI All Share Index this week[2][35]

低频选股因子周报（2026.01.23-2026.01.30） [Table_Authors] 郑雅斌(分析师) 风险提示：市场环境变动风险，有效因子变动风险。 | | 021-23219395 | | --- | --- | | | zhengyabin@gtht.com | | 登记编号 | S0880525040105 | | | 罗蕾(分析师) | | | 021-23185653 | | | luolei@gtht.com | | 登记编号 | S0880525040014 | [Table_Report] 相关报告 绝对收益产品及策略周报（260119-260123） 2026.01.29 大额买入与资金流向跟踪（20260119-20260123） 2026.01.27 风格 Smart beta 组合跟踪周报（2026.01.19- 2026.01.23） 2026.01.26 红利风格择时周报（0119-0123） 2026.01.26 高频选股因子周报（20260119-20260123） 2026.01.25 证 券 研 究 报 告 请务必阅读正文之后的免责条款部分 1 月份沪深 300 ...

Core Insights - The article discusses the transformative changes in China's private equity industry by 2025, highlighting the rise of AI-driven quantitative strategies, the expansion of private equity scale to over 22 trillion yuan, and the acceleration of overseas investments, leading to a more diversified industry landscape [1][5]. Group 1: Early Exploration - The journey into quantitative research began with a broad exploration approach, emphasizing the importance of absorbing various knowledge and methods without early limitations [7]. - The initial phase involved replicating brokerage research strategies, which revealed significant differences in results due to data cleaning and parameter selection, underscoring the importance of understanding data nuances [8]. - The researcher discovered alternative data sources, such as management discussions in financial reports and non-structured information from company research memos, which were underutilized in mainstream quantitative circles [8]. Group 2: Practical Advancements - A pivotal shift occurred in the researcher’s role, moving from executing quantitative models to actively participating in the construction of a "super fusion strategy," integrating quantitative and subjective investment approaches [10]. - The initial model of "subjective direction, quantitative execution" faced challenges due to misalignment with existing industry classifications, prompting a need for a tailored investment framework [10][12]. - The new approach involved creating a dynamic "industry and concept cluster" that aligns with the firm's unique investment logic, moving away from passive reliance on market classifications [12]. Group 3: Mathematical Foundations - The experience in mathematical Olympiads contributed to a foundational "thinking code," emphasizing rigor, problem decomposition, and a balance between imagination and logical validation in quantitative research [14][15]. - The rigorous pursuit of logical consistency in model construction helps avoid common pitfalls, while the ability to break down complex problems into manageable components enhances problem-solving efficiency [14][15]. Group 4: Theoretical and Empirical Balance - The article emphasizes the importance of seeking theoretical validation through simulations while being cautious of the assumptions made during these simulations [17]. - Historical data analysis serves to identify the limitations of theories rather than merely confirming their accuracy, highlighting the need for critical evaluation of model applicability [17][18]. Group 5: Future Outlook - The private equity industry is undergoing significant changes with the emergence of new technologies and methodologies, yet the core logic of finding structured models from market uncertainties remains unchanged [19]. - The ability to navigate different paradigms and freely traverse knowledge domains is seen as essential for creating real value in the evolving landscape of quantitative finance [19].