Content: --------- <doc id='1'>金融工程日报 A 股单边下行，AI 应用逆势走强、锂电池产业链全线下挫</doc> <doc id='2'>核心观点 金融工程日报 市场表现：20251121 市场全线下跌，规模指数中上证 50 指数表现较好，板 块指数中上证综指表现较好，风格指数中沪深 300 价值指数表现较好。行业 指数全线下跌，家电、传媒、食品饮料、银行、农林牧渔行业表现较好，综 合、有色金属、基础化工、通信、电子行业表现较差。中船系、水产、小红 书平台、拼多多合作商、短剧游戏等概念表现较好，锂矿、锂电正极、锂电 隔膜、盐湖提锂、锂电电解液等概念表现较差。 市场情绪：20251121 收盘时有 33 只股票涨停，有 107 只股票跌停。昨日涨 停股票今日表现低迷，赚钱效应较弱，高开低走，收盘收益为-0.55%，昨日 跌停股票今日收盘收益为-5.58%。今日封板率 61%，较前日提升 10%，连板 率 24%，较前日下降 0%。 市场资金流向：截至 20251120 两融余额为 24917 亿元，其中融资余额 24744 亿元，融券余额 173 亿元。两融余额占流通市值比重为 2.6%，两融交易占市 场成交额比重为 10.1%。</doc> <doc id='3'>折溢价：20251120 当日 ETF 溢价较多的是创科技 ETF，ETF 折价较多的是芯 片 ETF 景顺。近半年以来大宗交易日均成交金额达到 20 亿元，20251120 当 日大宗交易成交金额为 19 亿元，近半年以来平均折价率 6.36%，当日折价率 为 7.71%。近一年以来上证 50、沪深 300、中证 500、中证 1000 股指期货主 力合约的年化贴水率中位数分别为 0.48%、3.39%、10.86%、13.39%，当日上 证 50 股指期货主力合约年化贴水率为 4.00%，处于近一年来 29%分位点，当 日沪深 300 股指期货主力合约年化贴水率为 7.08%，处于近一年来 25%分位 点；当日中证 500 股指期货主力合约年化贴水率为 8.69%，处于近一年来 69% 分位点；当日中证 1000 股指期货主力合约年化贴水率为 8.76%，处于近一年 来 80%分位点。</doc> <doc id='4'>机构关注与龙虎榜：近一周内调研机构较多的股票是九号公司-WD、蓝思科 技、泰恩康、中航重机、沃尔德、超颖电子、宏昌科技、斯菱股份等，九号 公司-WD 被 179 家机构调研。20251121 披露龙虎榜数据中，机构专用席位净 流入前十的股票是天华新能、易点天下、安妮股份、久其软件、榕基软件、 古麒绒材、江龙船艇、荃银高科、华瓷股份、神农种业等，机构专用席位净 流出前十的股票是西藏城投、视觉中国、浪潮软件、德力佳、禾信仪器、中 水渔业、梦天家居、亚通精工、南侨食品、北方长龙等。陆股通净流入前十 的股票是天华新能、江龙船艇、英利汽车、国风新材、久其软件、久之洋、 荃银高科、西藏城投、南侨食品等，陆股通净流出前十的股票是华胜天成、 视觉中国、方大炭素、诺德股份、浪潮软件、易点天下、禾信仪器等。 风险提示：市场环境变动风险；本报告基于历史客观数据统计，不构成投资 建议。</doc> <doc id='5'> | 金融工程·数量化投资 | | | --- | --- | | 证券分析师：张欣慰 | 联系人：李子靖 | | 021-60933159 | 021-60875177 | 021-60933159 021-60875177 zhangxinwei1@guosen.com.cn lizijing1@guosen.com.cn S0980520060001 相关研究报告 《金融工程日报-A 股高开低走，封板率创近一个月新低》 —— 2025-11-20 《金融工程日报-沪指震荡收红，水产股集体大涨、黄金股午后 拉升》 ——2025-11-19 《金融工程日报-沪指震荡走低，锂电产业链回调、互联网电商 概念逆势走强》 ——2025-11-18 《金融工程日报-沪指震荡下跌，锂矿题材逆势爆发》 —— 2025-11-17 《金融工程日报-沪指冲高回落，算力、半导体产业链领跌》 — —2025-11-14</doc> <doc id='6'>市场表现 宽基与风格指数表现 今日(20251121，下同) 市场全线下跌，规模指数中，上证 50 指数表现较好，上 证 50 指数下跌 1.74%，沪深 300 指数下跌 2.44%，中证 500 指数下跌 3.46%，中 证 1000 指数下跌 3.72%，中证 2000 指数下跌 3.99%。 板块指数中，上证综指表现较好，上证综指下跌 2.45%，深证综指下跌 3.43%，中 小 100 指数下跌 3.25%，创业板指下跌 4.02%，科创 50 指数下跌 3.19%，科创 100 指数下跌 4.04%，北证 50 指数下跌 4.71%。 风格指数中，沪深 300 价值指数表现较好，沪深 300 价值指数下跌 1.70%，沪深 300 成长指数下跌 2.47%，中证 500 价值指数下跌 3.30%，中证 500 成长指数下跌 3.16%。主要宽基、板块及风格指数今日表现如下图所示。</doc> <doc id='7'>图1：今日宽基、板块与风格指数表现 行业指数表现 今日行业指数全线下跌，家电、传媒、食品饮料、银行、农林牧渔行业表现较好， 收益分别为-0.27%、-0.50%、-0.89%、-1.00%、-1.58%，综合、有色金属、基础 化工、通信、电子行业表现较差，收益分别为-5.41%、-5.30%、-5.26%、-4.59%、 -4.41%，中信一级行业指数今日表现如下图所示。</doc> <doc id='8'>资料来源：Wind，国信证券经济研究所整理</doc> <doc id='9'>图2：今日中信一级行业指数表现 概念主题表现 今日中船系、水产、小红书平台、拼多多合作商、短剧游戏等概念表现较好，收 益分别为 3.51%、0.65%、0.47%、0.42%、0.22%，锂矿、锂电正极、锂电隔膜、 盐湖提锂、锂电电解液等概念表现较差，收益分别为-9.67%、-8.66%、-8.18%、 -8.08%、-7.99%，今日收益较高与较低的前十个概念指数表现如下图所示。</doc> <doc id='10'>资料来源：Wind，国信证券经济研究所整理</doc> <doc id='11'>图3：今日收益较高与较低的前十个概念指数 资料来源：Wind，国信证券经济研究所整理</doc> <doc id='12'>市场情绪 日内涨跌停家数 我们统计上市满 3 个月以上的股票在今日盘中的实时涨跌停家数情况。今日盘中 最高有 33 只股票

证券研究报告 | 2025年11月22日 优秀基金业绩增强组合： 主动量化策略周报 小盘成长大幅调整，成长稳健组合年内满仓上涨 48.45% 核心观点 金融工程周报 国信金工主动量化策略表现跟踪： 本周，优秀基金业绩增强组合绝对收益-5.06%，相对偏股混合型基金指数超 额收益-0.07%。本年，优秀基金业绩增强组合绝对收益 18.71%，相对偏股 混合型基金指数超额收益-6.33%。今年以来，优秀基金业绩增强组合在主动 股基中排名 59.18%分位点（2053/3469）。 本周，超预期精选组合绝对收益-5.67%，相对偏股混合型基金指数超额收益 -0.68%。本年，超预期精选组合绝对收益 33.39%，相对偏股混合型基金指 数超额收益 8.35%。今年以来，超预期精选组合在主动股基中排名 26.72% 分位点（927/3469）。 本周，券商金股业绩增强组合绝对收益-4.15%，相对偏股混合型基金指数超 额收益 0.85%。本年，券商金股业绩增强组合绝对收益 27.25%，相对偏股 混合型基金指数超额收益 2.21%。今年以来，券商金股业绩增强组合在主动 股基中排名 38.69%分位点（1342/3469 ...

证券研究报告 | 2025年11月22日 本周，券商金股业绩增强组合绝对收益-4.15%，相对偏股混合型基金指数超 额收益 0.85%。本年，券商金股业绩增强组合绝对收益 27.25%，相对偏股 混合型基金指数超额收益 2.21%。今年以来，券商金股业绩增强组合在主动 股基中排名 38.69%分位点（1342/3469）。 主动量化策略周报 小盘成长大幅调整，成长稳健组合年内满仓上涨 48.45% 核心观点 金融工程周报 国信金工主动量化策略表现跟踪： 本周，优秀基金业绩增强组合绝对收益-5.06%，相对偏股混合型基金指数超 额收益-0.07%。本年，优秀基金业绩增强组合绝对收益 18.71%，相对偏股 混合型基金指数超额收益-6.33%。今年以来，优秀基金业绩增强组合在主动 股基中排名 59.18%分位点（2053/3469）。 本周，超预期精选组合绝对收益-5.67%，相对偏股混合型基金指数超额收益 -0.68%。本年，超预期精选组合绝对收益 33.39%，相对偏股混合型基金指 数超额收益 8.35%。今年以来，超预期精选组合在主动股基中排名 26.72% 分位点（927/3469）。 本周，成长稳健组合 ...

核心观点 金融工程周报 港股精选组合绩效回顾 本周，港股精选组合绝对收益-7.53%，相对恒生指数超额收益-2.44%。 本年，港股精选组合绝对收益 56.87%，相对恒生指数超额收益 31.15%。 港股市场创新高热点板块跟踪 证券研究报告 | 2025年11月22日 港股投资周报 医药科技板块大跌，港股精选组合年内上涨 56.87% 我们根据分析师关注度、股价相对强弱、股价路径平稳性、创新高连续性等 角度在过去 20 个交易日创出过 250 日新高的股票池中筛选出平稳创新高股 票。 近期，中国东方航空股份等股票平稳创出新高。 按照板块来看，创新高股票数量最多的是周期板块，其次为消费、制造、医 药、大金融和科技板块，具体个股信息可参照正文。 港股市场一周回顾 宽基指数方面，本周港股通 50 指数收益最高，累计收益-4.80%；恒生科技 指数收益最低，累计收益-7.18%。 行业指数方面，本周电讯业行业收益最高，累计收益-1.58%；原材料业行业 收益最低，累计收益-8.65%。 概念板块方面，本周安防监控概念板块收益最高，累计收益 1.43%；钢铁概 念板块收益最低，累计收益-14.53%。 南向资金监控 ...

证券研究报告 | 2025年11月22日 金融工程日报 A 股单边下行，AI 应用逆势走强、锂电池产业链全线下挫 核心观点 金融工程日报 市场表现：20251121 市场全线下跌，规模指数中上证 50 指数表现较好，板 块指数中上证综指表现较好，风格指数中沪深 300 价值指数表现较好。行业 指数全线下跌，家电、传媒、食品饮料、银行、农林牧渔行业表现较好，综 合、有色金属、基础化工、通信、电子行业表现较差。中船系、水产、小红 书平台、拼多多合作商、短剧游戏等概念表现较好，锂矿、锂电正极、锂电 隔膜、盐湖提锂、锂电电解液等概念表现较差。 市场情绪：20251121 收盘时有 33 只股票涨停，有 107 只股票跌停。昨日涨 停股票今日表现低迷，赚钱效应较弱，高开低走，收盘收益为-0.55%，昨日 跌停股票今日收盘收益为-5.58%。今日封板率 61%，较前日提升 10%，连板 率 24%，较前日下降 0%。 市场资金流向：截至 20251120 两融余额为 24917 亿元，其中融资余额 24744 亿元，融券余额 173 亿元。两融余额占流通市值比重为 2.6%，两融交易占市 场成交额比重为 10.1%。 折 ...

Quantitative Models and Construction Methods 1. Model Name: Guosen Quantitative Index Enhanced Portfolio - **Model Construction Idea**: The model aims to construct enhanced portfolios benchmarked against indices such as CSI 300, CSI 500, CSI 1000, and CSI A500, with the goal of consistently outperforming their respective benchmarks [10][11]. - **Model Construction Process**: 1. **Revenue Prediction**: Predict stock returns using multiple factors. 2. **Risk Control**: Apply constraints on industry exposure, style exposure, stock weight deviation, and turnover rate. 3. **Portfolio Optimization**: Optimize the portfolio to maximize single-factor exposure while adhering to constraints. The optimization model is as follows: $ \begin{array}{ll} max & f^{T} w \\ s.t. & s_{l} \leq X(w-w_{b}) \leq s_{h} \\ & h_{l} \leq H(w-w_{b}) \leq h_{h} \\ & w_{l} \leq w-w_{b} \leq w_{h} \\ & b_{l} \leq B_{b}w \leq b_{h} \\ & \mathbf{0} \leq w \leq l \\ & \mathbf{1}^{T} w = 1 \end{array} $ - **Objective Function**: Maximize single-factor exposure, where \( f \) represents factor values, \( w \) is the stock weight vector, and \( f^{T}w \) is the weighted exposure to the factor. - **Constraints**: - **Style Exposure**: \( X \) is the factor exposure matrix, \( w_b \) is the benchmark weight vector, and \( s_l, s_h \) are the lower and upper bounds for style exposure. - **Industry Exposure**: \( H \) is the industry exposure matrix, and \( h_l, h_h \) are the lower and upper bounds for industry deviation. - **Stock Weight Deviation**: \( w_l, w_h \) are the lower and upper bounds for stock weight deviation. - **Component Stock Weight**: \( B_b \) is a 0-1 vector indicating whether a stock is a benchmark component, and \( b_l, b_h \) are the lower and upper bounds for component stock weight. - **No Short Selling**: Ensure non-negative weights and limit individual stock weights. - **Full Investment**: Ensure the portfolio is fully invested with weights summing to 1 [40][41][42]. 4. **Backtesting**: Rebalance the portfolio monthly, calculate historical returns, and evaluate performance metrics such as excess returns and risk statistics [44]. 2. Model Name: Public Fund Heavyweight Index - **Model Construction Idea**: Construct an index based on the holdings of public funds to evaluate factor performance under "institutional style" [42][43]. - **Model Construction Process**: 1. **Sample Selection**: Include ordinary equity funds and partial equity hybrid funds with a minimum size of 50 million RMB and at least six months of listing history. Exclude recently transformed funds or those with insufficient data. 2. **Data Collection**: Use fund periodic reports (annual, semi-annual, or quarterly) to gather holding information. 3. **Weight Calculation**: Average the stock weights across eligible funds. 4. **Index Construction**: Sort stocks by weight in descending order and select those accounting for 90% of cumulative weight to form the index [43]. --- Model Backtesting Results 1. Guosen Quantitative Index Enhanced Portfolio - **CSI 300 Enhanced Portfolio**: - Weekly excess return: -0.71% - Year-to-date excess return: 16.74% [13] - **CSI 500 Enhanced Portfolio**: - Weekly excess return: 0.12% - Year-to-date excess return: 6.85% [13] - **CSI 1000 Enhanced Portfolio**: - Weekly excess return: -0.94% - Year-to-date excess return: 14.08% [13] - **CSI A500 Enhanced Portfolio**: - Weekly excess return: -1.37% - Year-to-date excess return: 7.55% [13] 2. Public Fund Heavyweight Index - **CSI 300 Index Enhanced Products**: - Weekly excess return: Max 0.70%, Min -1.26%, Median 0.09% - Year-to-date excess return: Max 9.92%, Min -4.53%, Median 2.58% [31] - **CSI 500 Index Enhanced Products**: - Weekly excess return: Max 1.17%, Min -1.13%, Median 0.11% - Year-to-date excess return: Max 13.14%, Min -9.17%, Median 3.94% [33] - **CSI 1000 Index Enhanced Products**: - Weekly excess return: Max 0.89%, Min -1.38%, Median -0.05% - Year-to-date excess return: Max 19.12%, Min -1.84%, Median 8.24% [36] - **CSI A500 Index Enhanced Products**: - Weekly excess return: Max 0.71%, Min -0.86%, Median -0.04% - Year-to-date excess return: Max 2.67%, Min -4.14%, Median -0.76% [39] --- Quantitative Factors and Construction Methods 1. Factor Name: Maximized Factor Exposure (MFE) - **Factor Construction Idea**: Evaluate factor effectiveness under real-world constraints by maximizing single-factor exposure in a portfolio [40][41]. - **Factor Construction Process**: 1. Define constraints for style exposure, industry exposure, stock weight deviation, and component stock weight. 2. Optimize the portfolio to maximize single-factor exposure while adhering to constraints. 3. Rebalance monthly and calculate historical returns [40][41][44]. 2. Factor Name: Public Fund Heavyweight Factors - **Factor Construction Idea**: Test factor performance in the public fund heavyweight index to reflect institutional preferences [42][43]. - **Factor Construction Process**: 1. Use public fund holdings to construct the index. 2. Evaluate factor performance within this index using metrics such as excess returns and risk-adjusted returns [42][43]. --- Factor Backtesting Results 1. Maximized Factor Exposure (MFE) - **CSI 300 Sample Space**: - Best-performing factors (weekly): One-month volatility (0.83%), one-month turnover (0.68%), three-month volatility (0.65%) - Worst-performing factors (weekly): Single-quarter profit growth (-0.26%), three-month institutional coverage (-0.24%), one-year momentum (-0.24%) [18] - **CSI 500 Sample Space**: - Best-performing factors (weekly): Three-month institutional coverage (1.09%), one-month reversal (1.01%), three-month reversal (0.99%) - Worst-performing factors (weekly): Standardized unexpected earnings (-1.00%), DELTAROA (-0.81%), DELTAROE (-0.81%) [20] - **CSI 1000 Sample Space**: - Best-performing factors (weekly): One-month turnover (1.08%), three-month institutional coverage (1.06%), single-quarter ROA (1.04%) - Worst-performing factors (weekly): Single-quarter SP (-1.29%), expected PEG (-1.25%), SPTTM (-1.22%) [22] - **CSI A500 Sample Space**: - Best-performing factors (weekly): One-month turnover (0.82%), three-month turnover (0.75%), one-month volatility (0.74%) - Worst-performing factors (weekly): Expected net profit QoQ (-0.91%), single-quarter net profit growth (-0.61%), expected PEG (-0.41%) [24] - **Public Fund Heavyweight Index**: - Best-performing factors (weekly): One-month volatility (1.32%), one-month turnover (1.23%), three-month turnover (0.89%) - Worst-performing factors (weekly): Single-quarter revenue growth (-0.89%), single-quarter profit growth (-0.88%), single-quarter ROE (-0.81%) [26]

- The report introduces a quantitative model named "250-day new high distance" to track market trends and identify hot spots. The model is based on momentum and trend-following strategies, emphasizing stocks that consistently hit new highs. The calculation formula is: $ 250\text{-day new high distance} = 1 - \frac{Close_{t}}{ts\_max(Close, 250)} $ where $ Close_{t} $ represents the latest closing price, and $ ts\_max(Close, 250) $ is the maximum closing price over the past 250 trading days. If the latest closing price reaches a new high, the distance equals 0; otherwise, it is a positive value indicating the degree of fallback [11][12][13] - The report evaluates the model positively, citing its ability to capture market leaders and trends effectively. It references studies by George (2004), William O'Neil, and Mark Minervini, which highlight the importance of tracking stocks near their 52-week highs for superior returns [11][18] - The report provides backtesting results for the "250-day new high distance" model across major indices as of November 21, 2025. The distances are: - Shanghai Composite Index: 4.83% - Shenzhen Component Index: 8.65% - CSI 300: 6.20% - CSI 500: 9.69% - CSI 1000: 7.59% - CSI 2000: 7.40% - ChiNext Index: 12.16% - STAR 50 Index: 16.45% [12][13][32] - The report introduces a factor named "Stable New High Stocks" to identify stocks with smooth price paths and sustained momentum. The factor construction involves: - Analyst attention: At least five buy or overweight ratings in the past three months - Relative strength: Top 20% in 250-day price change - Price stability: Ranking top 50% based on metrics like price displacement ratio and smoothness of 250-day new high distance over the past 120 days - Trend continuation: Ranking top 50 stocks based on the average 250-day new high distance over the past five days [24][27][28] - The report evaluates the "Stable New High Stocks" factor positively, citing research by Turan G Bali et al. (2011) and Da et al. (2012), which demonstrate the superior returns of stocks with smooth momentum paths compared to those with jumpy price movements [24][27] - Backtesting results for the "Stable New High Stocks" factor show 15 selected stocks, including Heertai, Sray New Materials, and Zangge Mining. These stocks are distributed across manufacturing and cyclical sectors, with manufacturing focusing on construction and cyclical sectors on non-ferrous metals [28][31][33]

Quantitative Models and Construction Methods 1. Model Name: 250-Day New High Distance Model - **Model Construction Idea**: This model tracks the distance of stock prices or indices from their 250-day high to monitor market trends and identify potential market leaders. It is based on the momentum and trend-following strategy, which has been proven effective in various studies[11][18]. - **Model Construction Process**: The 250-day new high distance is calculated as follows: $ 250 \text{-day new high distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max(Close, 250)}} $ Where: - $\text{Close}_{t}$ represents the latest closing price - $\text{ts\_max(Close, 250)}$ represents the maximum closing price over the past 250 trading days If the latest closing price reaches a new high, the distance is 0. If the price has fallen from the high, the distance is a positive value, indicating the degree of decline[11]. - **Model Evaluation**: The model effectively identifies market trends and highlights stocks or indices that are leading the market, aligning with the principles of momentum and trend-following strategies[11][18]. 2. Model Name: Stable New High Stock Selection Model - **Model Construction Idea**: This model focuses on selecting stocks that exhibit stable price paths and consistent momentum, as smoother price trajectories are associated with stronger momentum effects[24][27]. - **Model Construction Process**: The selection process involves the following criteria: - **Analyst Attention**: At least 5 buy or overweight ratings in the past 3 months - **Relative Strength**: 250-day price change in the top 20% of the market - **Price Stability**: Stocks are ranked based on: - **Price Path Smoothness**: Ratio of price displacement to the total price path - **Sustainability of New Highs**: Average 250-day new high distance over the past 120 days - **Trend Continuity**: Average 250-day new high distance over the past 5 days The top 50 stocks based on these criteria are selected[24][27]. - **Model Evaluation**: The model emphasizes the importance of smooth price paths and consistent momentum, which are less likely to attract excessive attention and thus yield stronger returns[24][27]. --- Model Backtesting Results 1. 250-Day New High Distance Model - **Indices' 250-Day New High Distance**: - Shanghai Composite Index: 4.83% - Shenzhen Component Index: 8.65% - CSI 300: 6.20% - CSI 500: 9.69% - CSI 1000: 7.59% - CSI 2000: 7.40% - ChiNext Index: 12.16% - STAR 50 Index: 16.45%[12][13][32] 2. Stable New High Stock Selection Model - **Selected Stocks**: 15 stocks were identified, including Heertai, Sray New Materials, and Zangge Mining. - **Sector Distribution**: - Manufacturing: 5 stocks (e.g., construction industry) - Cyclical: 5 stocks (e.g., non-ferrous metals industry)[28][33] --- Quantitative Factors and Construction Methods 1. Factor Name: 250-Day New High Distance - **Factor Construction Idea**: This factor measures the relative distance of a stock's price from its 250-day high, serving as an indicator of momentum and trend strength[11]. - **Factor Construction Process**: The formula is: $ 250 \text{-day new high distance} = 1 - \frac{\text{Close}_{t}}{\text{ts\_max(Close, 250)}} $ Where: - $\text{Close}_{t}$ is the latest closing price - $\text{ts\_max(Close, 250)}$ is the maximum closing price over the past 250 trading days[11]. - **Factor Evaluation**: The factor effectively captures momentum and trend-following characteristics, making it a reliable indicator for identifying market leaders[11]. 2. Factor Name: Price Path Smoothness - **Factor Construction Idea**: This factor evaluates the smoothness of a stock's price trajectory, as smoother paths are associated with stronger momentum effects[24]. - **Factor Construction Process**: - Calculate the ratio of price displacement to the total price path over a specified period - Rank stocks based on this ratio and select the top performers[24]. - **Factor Evaluation**: The factor highlights stocks with stable momentum, which are less likely to attract excessive attention and thus yield stronger returns[24]. --- Factor Backtesting Results 1. 250-Day New High Distance Factor - **Indices' 250-Day New High Distance**: - Shanghai Composite Index: 4.83% - Shenzhen Component Index: 8.65% - CSI 300: 6.20% - CSI 500: 9.69% - CSI 1000: 7.59% - CSI 2000: 7.40% - ChiNext Index: 12.16% - STAR 50 Index: 16.45%[12][13][32] 2. Price Path Smoothness Factor - **Selected Stocks**: 15 stocks were identified, including Heertai, Sray New Materials, and Zangge Mining. - **Sector Distribution**: - Manufacturing: 5 stocks (e.g., construction industry) - Cyclical: 5 stocks (e.g., non-ferrous metals industry)[28][33]

Core Insights - BNY Mellon's core strategy for expanding its investment research capabilities is centered on precise acquisitions to achieve capability integration and leapfrogging, rather than relying solely on internal development [3] - The group adopts a management philosophy of independent empowerment and collaborative complementarity, forming a distinctive diversified product matrix [3] - BNY Mellon's model provides key insights for Chinese asset management institutions, emphasizing that the extension of capability circles can be achieved through acquisitions while maintaining a balance between independence and integration [3] Group 1: Company Overview - BNY Mellon, formed from the merger of the first U.S. bank and Mellon Financial Corporation in 2007, is the world's largest custodian bank, with over 70% of its revenue derived from fees and commissions from 2020 to 2024 [5][6] - The company's strategic transformation has involved shedding traditional banking operations and focusing on core strengths, marking a significant shift towards a fee-driven financial services model [5] Group 2: Acquisition Strategy - BNY Mellon's asset management capabilities have been built through a series of strategic acquisitions rather than organic growth, leveraging its vast institutional client base to drive demand for asset management products [12] - Key acquisitions include the purchase of Dreyfus in 1994 for cash management expertise, Insight Investment in 2009 for fixed income, and ARX in 2008 to expand into the South American market [12][14] Group 3: Product Matrix and Strategies - BNY Mellon has established a vast product matrix through its asset management subsidiaries, focusing on niche markets and specialized strategies, such as Insight Investment's leadership in fixed income and Newton's tailored equity investment strategies [19][20] - The company offers a comprehensive range of products from active to passive investments, covering traditional equities and bonds to alternative investments [19] Group 4: Research and Investment Capability Development - BNY Mellon's investment research capability development can be divided into two acquisition waves, with the first focusing on foundational asset management capabilities and the second on global expansion and specialization [38] - The company emphasizes differentiated positioning among its subsidiaries, ensuring collaboration rather than overlap in multi-asset strategies [41][42] Group 5: Management Philosophy and Core Competencies - BNY Mellon respects the independence of its acquired asset management subsidiaries while empowering them with resources and distribution channels, optimizing asset management scale based on each subsidiary's expertise [44] - The BNY Investment Institute serves as a central hub for macroeconomic insights and investment strategy support, enhancing the overall research capabilities of the group [44]

Core Insights - The report highlights strong performance in the textile and apparel industry, particularly for Amer Sports, which reported a 26% year-on-year revenue increase for the first three quarters of 2025, reaching $4.465 billion, and a 153% increase in adjusted net profit to $369 million [5][6] - The report also notes the positive outlook for the media and internet sector, with Marble 3D's world model public beta launch and a focus on AI applications [7][8] - In the pharmaceutical sector, Eli Lilly's revenue surged by 52% in Q3 2025, driven by GLP-1 drugs, with Tirzepatide exceeding $10 billion in quarterly revenue [10][11] Textile and Apparel Industry - Amer Sports' Q3 2025 performance showed a 30% revenue increase, with adjusted net profit rising by 161% to $185 million [5][6] - The management has raised its full-year guidance for revenue growth to 23-24%, with an expected EPS of $0.88-$0.92 [6] - Key growth drivers include the Salomon brand, direct-to-consumer (DTC) channels, and strong performance in the Greater China and Asia-Pacific regions [6] Media and Internet Sector - The media industry experienced a decline of 2.31%, underperforming compared to the broader market indices [7] - Marble 3D's public beta launch is expected to enhance opportunities in the sector, with significant advancements in AI technology [8] - The report emphasizes the potential for growth in gaming and IP trends, recommending companies like Giant Network and Kuaishou [8] Pharmaceutical Industry - Eli Lilly's Q3 2025 revenue growth was significantly driven by its GLP-1 drug portfolio, with a notable increase in market coverage due to pricing agreements with the U.S. government [10][11] - Novo Nordisk faced challenges in the competitive landscape for weight loss drugs, leading to multiple downward revisions of its performance guidance [11][12] - The report indicates that 11 out of 16 multinational pharmaceutical companies raised their revenue and profit forecasts for the year, reflecting better-than-expected sales from new products [12]