科创创业ETF（588360）跟踪的是科创创业50指数（931643），单日涨跌幅达20%，该指数从科创板与 创业板中精选市值大、流动性好的50只新兴产业股票，覆盖半导体、新能源、生物医药等核心领域。指 数样本聚焦硬科技及成熟创新企业，具有较高的行业集中度和龙头效应，能够综合反映中国前沿产业的 技术壁垒与成长性表现。 （文章来源：每日经济新闻） 平安证券指出，本轮牛市由科技板块主导，2025年是"人工智能+"时代，TMT有望获得更多超额收益。 电子行业在全球AI创新热潮和算力需求增长驱动下实现领涨，半导体、消费电子和AI算力方向多点开 花；计算机行业受益于政策、技术与基本面改善共振，AI算力和应用方向保持高景气；传媒行业中游 戏子赛道表现突出，情绪消费主线具备估值性价比。新能源领域固态电池技术引领产业变革，传统板块 走出通缩后有望量利齐升。整体来看，科技产业趋势向好，电子、计算机、传媒等行业涨幅领先且存在 业绩支撑，AI产业链将持续深化发展。 ...

Quantitative Models and Construction Methods 1. Model Name: Industry Relative Strength (RSI) Model - **Model Construction Idea**: This model identifies leading industries by calculating their relative strength (RS) based on historical price performance over different time windows [10] - **Model Construction Process**: 1. Use 29 first-level industry indices as the configuration targets [10] 2. Calculate the price change rates for the past 20, 40, and 60 trading days for each industry index [10] 3. Rank the industries based on their price change rates for each time window and normalize the rankings to obtain RS_20, RS_40, and RS_60 [10] 4. Calculate the average of the three rankings to derive the final RS value: $ RS = \frac{RS_{20} + RS_{40} + RS_{60}}{3} $ [10] 5. Industries with RS > 90% by the end of April are identified as potential leading industries for the year [10] - **Model Evaluation**: The model successfully identified key annual industry trends, such as high dividend, resource products, exports, and AI, which were validated by market performance throughout the year [10][12] 2. Model Name: Industry Sentiment-Trend-Crowding Framework - **Model Construction Idea**: This framework provides two industry rotation strategies based on market conditions: 1. High sentiment + strong trend, avoiding high crowding (aggressive strategy) 2. Strong trend + low crowding, avoiding low sentiment (conservative strategy) [6][14] - **Model Construction Process**: 1. Evaluate industries based on three dimensions: sentiment, trend, and crowding [6][14] 2. Use sentiment as the core metric for the aggressive strategy, with crowding as a risk control factor [14] 3. Use trend as the core metric for the conservative strategy, avoiding low-sentiment industries [14] 4. Allocate weights to industries based on their scores in the three dimensions [6][14] - **Model Evaluation**: The framework is effective in adapting to different market conditions and has shown strong performance in historical backtests [6][14] 3. Model Name: Left-Side Inventory Reversal Model - **Model Construction Idea**: This model identifies industries with potential for recovery by analyzing sectors in distress or those with low inventory pressure and high analyst optimism [24] - **Model Construction Process**: 1. Identify industries currently in distress or recovering from past distress [24] 2. Focus on sectors with low inventory pressure and potential for restocking [24] 3. Incorporate analyst long-term positive outlooks for these industries [24] - **Model Evaluation**: The model effectively captures recovery opportunities in industries undergoing inventory restocking cycles, providing significant absolute and relative returns [24] --- Model Backtesting Results 1. Industry Relative Strength (RSI) Model - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned - **Performance Highlights**: - Industries with RS > 90% by April 2024 included coal, utilities, home appliances, banking, petrochemicals, communication, non-ferrous metals, agriculture, and automotive [10] - These industries showed strong performance, with key themes being high dividends, resource products, exports, and AI [10][12] 2. Industry Sentiment-Trend-Crowding Framework - **Annualized Return**: 22.1% (long-only portfolio) [14] - **Excess Return**: 13.8% (annualized) [14] - **Information Ratio (IR)**: 1.51 [14] - **Maximum Drawdown**: -8.0% [14] - **Monthly Win Rate**: 68% [14] - **Performance Highlights**: - 2023 excess return: 7.3% [14] - 2024 excess return: 5.7% [14] - 2025 YTD excess return: 2.8% [14] 3. Left-Side Inventory Reversal Model - **Annualized Return**: Not explicitly mentioned - **Excess Return**: - 2023: 17.0% (relative to equal-weighted industry benchmark) [24] - 2024: 15.4% (relative to equal-weighted industry benchmark) [24] - 2025 YTD: 7.8% (relative to equal-weighted industry benchmark) [24] - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned - **Performance Highlights**: - Absolute return: - 2023: 13.4% [24] - 2024: 26.5% [24] - 2025 YTD: 26.4% [24] --- Quantitative Factors and Construction Methods 1. Factor Name: Sentiment Factor - **Factor Construction Idea**: Measures the overall sentiment of an industry to identify high-growth opportunities [14] - **Factor Construction Process**: 1. Evaluate the sentiment of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by sentiment scores [14] - **Factor Evaluation**: Sentiment is a core metric in the aggressive strategy of the Industry Sentiment-Trend-Crowding Framework, providing strong signals for high-growth opportunities [14] 2. Factor Name: Trend Factor - **Factor Construction Idea**: Measures the strength of market trends to identify industries with strong momentum [14] - **Factor Construction Process**: 1. Evaluate the trend of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by trend scores [14] - **Factor Evaluation**: Trend is a core metric in the conservative strategy of the Industry Sentiment-Trend-Crowding Framework, offering a simple and replicable approach to industry allocation [14] 3. Factor Name: Crowding Factor - **Factor Construction Idea**: Measures the level of crowding in an industry to identify overbought or underbought sectors [14] - **Factor Construction Process**: 1. Evaluate the crowding level of each industry based on relevant metrics (not explicitly detailed in the report) [14] 2. Rank industries by crowding scores [14] - **Factor Evaluation**: Crowding is used as a risk control factor in both aggressive and conservative strategies of the Industry Sentiment-Trend-Crowding Framework [14] --- Factor Backtesting Results 1. Sentiment Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned 2. Trend Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned 3. Crowding Factor - **Annualized Return**: Not explicitly mentioned - **Excess Return**: Not explicitly mentioned - **Information Ratio (IR)**: Not explicitly mentioned - **Maximum Drawdown**: Not explicitly mentioned - **Monthly Win Rate**: Not explicitly mentioned

消息面，鲍威尔于10月14日发表了关于"经济展望与货币政策"的讲话，暗示央行可能在未来几个月结束缩减资产负债表。他还表示，劳动力市场预期持续恶 化，这一信息支撑了投资者对本月再次降息的预期。 今年以来，港股热点频发，创新药出海大单频现、新能源"反内卷"、互联网"反内卷"、人工智能+，不断催化港股科技行情。港股科技ETF（513020）盘中 涨超2%，年内涨幅超50%。 资金也在持续流入中，截至10月15日，港股科技ETF（513020）连续20日吸金超11亿元。 鲍威尔发言提振降息预期，海外流动性提振港股 消息面，美联储主席鲍威尔于10月14日发表了关于"经济展望与货币政策"的讲话，这是他在9月FOMC会议后的首次公开表态。鲍威尔暗示央行可能在未来 几个月结束缩减资产负债表。 | 证券代码 | 证券名称 | 中证一级行业分类 | 中证二级行业分类 | 权重(%) | | --- | --- | --- | --- | --- | | 9988.HK | 阿里巴巴-W | 可选消费 | 零售业 | 11.93 | | 0700.HK | 腾讯控股 | 通信服务 | 传媒 | 10.79 | | 1810.HK ...

Core Insights - The article highlights the top-performing stocks in the S&P China A-Share Dividend Opportunity Index, showcasing significant price increases and dividend yields for various companies [1]. Group 1: Stock Performance - The top stock, 渝农商行 (601077.SH), experienced a price increase of 5.92% and a year-to-date increase of 24.04%, with a dividend yield of 4.25% [1]. - 泸州老窖 (000568.SZ) saw a 4.20% increase, with an 11.18% year-to-date rise and a dividend yield of 4.49% [1]. - 厦门银行 (601187.SH) recorded a 4.04% increase, a 21.43% year-to-date rise, and a dividend yield of 4.63% [1]. Group 2: Dividend Yields - The article lists several companies with notable dividend yields, including 家非亚 (002572.SZ) at 7.81%, and 究矿能源 (600188.SH) at 6.62% [1]. - 农业银行 (601288.SH) has a year-to-date increase of 39.52% and a dividend yield of 3.39% [1]. - 招商银行 (600036.SH) shows a year-to-date increase of 9.53% with a dividend yield of 4.85% [1].

10月15日，港股早盘高开，恒指、恒科指双双涨超1%，科网龙头大面积飘红，哔哩哔哩-W领涨4%，阿 里巴巴-W、小米集团-W双双涨逾2%，美团-W、腾讯控股跟涨逾1%。港股AI核心工具——港股互联网 ETF（513770）场内价格现涨1.53%。 港股互联网ETF（513770）及其联接基金（A类017125；C类017126）跟踪中证港股通互联网指数，阿 里巴巴-W、腾讯控股、小米集团-W是其前3大权重股，权重占比分别为18.92%、15.60%、11.54%，前 10大持仓汇聚各领域互联网龙头公司，合计占比超73%，龙头优势显著，为港股AI核心标的。 | 十大权重 | | | | 更新日期: 2025-10-03 | | --- | --- | --- | --- | --- | | 证券代码 | 证券名称 | 中证一级行业分类 | 中证二级行业分类 | 权重(%) | | 9988 HK | 阿里巴巴-W | 可选消费 | 零售业 | 18.92 | | 0700.HK | 腾讯控股 | 通信服务 | 传媒 | 15.60 | | 1810.HK | 小米集团-W | 信息技术 | 电子 | 11.54 ...

Core Points - The Shanghai municipal government held a meeting to discuss the feedback on the 8.0 version of the business environment optimization action plan and suggestions for the upcoming 9.0 version [1][2] - The meeting featured various representatives from different sectors who shared their observations and recommendations regarding the business environment in Shanghai [1][2] Group 1 - The business environment is crucial for the survival and development of enterprises and reflects the core competitiveness of the city [2] - The Shanghai government has implemented eight versions of the business environment optimization action plan, with over 1,200 reform measures introduced [2] - A new batch of over 200 city-level business environment experience officers has been appointed, representing various fields including experts, business entities, and industry associations [2] Group 2 - The government aims to enhance the efficiency and precision of policy measures by encouraging experience officers to identify issues early and propose innovative solutions [2] - The experience officer mechanism is expected to be normalized, standardized, and operated efficiently to support the ongoing improvement of the business environment [2]

Core Insights - 27 companies listed on the ChiNext board have released their performance forecasts for the first three quarters, with 23 companies expecting profit increases, representing 85.19% of the total [1] - The overall proportion of companies with positive forecasts is 92.59%, with 2 companies expecting to turn a profit and 2 companies forecasting profit declines [1] Performance Forecasts - Among the companies expecting profit increases, 9 companies anticipate a net profit growth of over 100%, while 6 companies expect a growth between 50% and 100% [1] - The company with the highest expected net profit growth is Morning Light Bio, forecasting a median increase of 372.80% [1] - Other notable companies include Glacier Network and Bai Ao Intelligent, with expected net profit growths of 207.09% and 184.12%, respectively [1] Company Performance Data - The following companies are highlighted for their significant expected profit increases: - Morning Light Bio (Code: 300138) - Expected net profit growth: 372.80%, Latest closing price: 13.99, Year-to-date change: 62.33% [1] - Glacier Network (Code: 300533) - Expected net profit growth: 207.09%, Latest closing price: 38.10, Year-to-date change: 92.81% [1] - Bai Ao Intelligent (Code: 300836) - Expected net profit growth: 184.12%, Latest closing price: 56.53, Year-to-date change: 95.40% [1] - Other companies with notable growth include Chuan Jin Nuo (171.61%), Jin Li Yong (168.00%), and Bo Teng (139.09%) [1]

信用利差方面，9月资金面边际收敛，信用利差整体延续窄幅震荡。与去年同期相比，收窄26.29bp，而 较上月末微降0.19bp。 从行业利差中位数的排序来看，截至9月30日，家用电器、房地产、电力设备、社会服务和计算机行业 利差处于高位，分别为137.73bp、97.18bp、87.89bp、84.69bp和84.44bp；食品饮料、传媒、公用事业、 石油石化和交通运输行业利差处于低位，分别为43.57bp、43.66bp、45.61bp、46.78bp和47.43bp。 环比观察，作为消费类行业的代表，家用电器与社会服务行业景气度与居民收入预期和消费意愿高度相 关。在当前宏观经济环境下，消费复苏的路径尚不稳定，导致这两个行业的风险溢价波动较大。其中， 受益于新一轮的消费刺激政策，家用电器行业利差本月下行幅度最大但整体水平仍处于高位。 城投债方面，整体信用利差环比基本持平。从区域表现来看，除甘肃省外，其余地区的城投债利差环比 小幅走阔或收窄。贵州、云南和辽宁等省份的城投债利差年内收窄幅度居前，均超过100bp，与化债政 策持续推进和市场信心改善有关。然而，甘肃省城投债利差月末大幅走阔，表明投资者对该区域城投债 ...

（文章来源：证券日报） 证券日报网讯 10月14日晚间，粤传媒发布公告称，公司之全资子公司广州日报报业经营有限公司（简 称"广报经营"）委托广州产权交易所有限公司通过广东联合产权交易中心，以公开挂牌的方式，以不低 于评估价值1，040，726元为底价，对外转让报废的报印生产设备及相应附属设备等29项报印固定资产 及一批备件。本次交易采取公开挂牌及网络竞价方式实施，最终以1，375，726元竞拍价格成交，确定 陈**先生为该批设备及备件的受让方。近日，公司与陈**先生签订了资产交易合同。 ...

本报讯 （记者张文湘 见习记者占健宇）近一年来，A股市场结构发生显著变化，科技板块表现尤为突出。近日，华鑫证券 研究所所长谭倩发表观点指出，当前科技引领的A股行情背后存在三重逻辑共振，并对后市保持乐观预期。 谭倩表示，过去一年间，以硬科技和新经济为代表的创业板指、科创50、北证50等三大指数累计涨幅均超100%，通信、 电子、计算机、机械设备、传媒等行业指数涨幅也超过80%。人形机器人、半导体、算力、创新药等科技板块轮番表现，共同 推动市场结构性行情的展开。 她认为，首先，宏观层面顶层战略的有力支持，推动新兴行业快速发展。谭倩指出，本轮A股科技行情，背后有着深刻而 坚实的国家战略层级的宏观支撑。国家强调发挥"新型举国体制"优势，突破关键核心技术，通过顶层制度设计、产业政策部署 和金融资源倾斜，全力推动科技创新，释放新质生产力红利。她举例说，国务院印发《关于深入实施"人工智能+"行动的意 见》，推动人工智能与经济社会各领域深度融合。同时，构建"技术-产业-金融"良性循环机制，助力科技成果从实验室走向市 场，着力破解"卡脖子"难题。 其次，中观层面技术突破与产业变革，推动中国科技资产价值重估。谭倩表示，随着新一轮 ...