Core Viewpoint - On October 15, among the 31 primary industries tracked by Shenwan, 14 industries experienced net financing inflows, indicating a positive trend in market sentiment and investment activity [1] Industry Summary - The non-ferrous metals industry topped the list with a net financing inflow of 945 million yuan on the same day [1] - Other industries that saw net financing inflows include power equipment, public utilities, machinery, media, and oil & petrochemicals [1] Company Summary - A total of 1,689 stocks received net financing inflows, with 83 stocks having inflows exceeding 50 million yuan [1] - Nine stocks recorded net financing inflows over 200 million yuan, with Sunshine Power leading at 918 million yuan [1] - Other notable companies with significant net financing inflows include Shenghong Technology, Antai Technology, Beijing Junzheng, Shandong Gold, Zhongjin Gold, and CATL [1]

3.【用AI提高生产力，高盛通知员工年底前可能要裁员】据媒体看到的一份内部备忘录显示，高盛已 通知员工，公司计划用人工智能(AI)提高生产力，因此在今年年底前可能会裁员并放缓招聘。备忘录 将这一战略称为 "OneGS 3.0"，称其人工智能计划的一些优先事项是销售和客户引导流程，以及贷 款流程、监管报告和供应商管理等其他关键领域。高盛发言人表示，公司仍希望在今年年底实现员工 总数的净增长。（财联社） 4. 【李梓萌遭AI仿冒带货！北京查处首例AI虚假广告案】近日，北京市场监管部门查处了首例利用AI 技术进行虚假广告宣传的案件。今年2月份，北京市海淀区市场监管局接到消费者举报，反映北京某 公司通过视频账号宣传所销售的"深海多烯鱼油"能够治疗多种疾病，涉嫌虚假宣传。通过举报人提供 的视频链接，办案人员打开该企业直播间，在这个拥有88万粉丝的账号直播间展板上，显著标注 着"适合头晕头痛、手麻脚麻、记忆下降人群"等医疗用语；直播中还出现了中央广播电视总台主持人 李梓萌的形象。经过立案调查，办案人员发现，该公司销售的深海多烯鱼油产品，实际执行标准为糖 果，属于普通食品，不具备疾病治疗功能。另一方面，经鉴定，视频中的主持 ...

当日关注点 交易信息汇总资金流向 截至2025年10月15日收盘，天威视讯(002238)报收于8.74元，上涨3.43%，换手率2.28%，成交量18.27万 手，成交额1.57亿元。 公司公告汇总 天威视讯关于获得筹办"国资国企在线监管安全运营（深圳）分中心"的自愿性信息披露公告 证券代码：002238 证券简称：天威视讯 公告编号：2025-046 董事会 2025年10月16日 10月15日主力资金净流入723.79万元；游资资金净流入119.43万元；散户资金净流出843.22万元。 以上内容为证券之星据公开信息整理，由AI算法生成（网信算备310104345710301240019号），不构成 投资建议。 深圳市天威视讯股份有限公司于2025年10月15日获得授权筹建"国资国企在线监管安全运营（深圳）分 中心"，该事项已经相关主管部门批准。 此次筹建分中心有助于公司开展国资国企网络安全在线监管业务。公司将依法履行信息披露义务，确保 信息真实、准确、完整。 特此公告。 深圳市天威视讯股份有限公司 来自交易信息汇总：10月15日主力资金净流入723.79万元，显示主力对个股短期关注度提升。 来自公司公 ...

Core Viewpoint - Tianwei Video announced the establishment of the "State-owned Enterprises Online Supervision and Safe Operation (Shenzhen) Sub-center" authorized for construction by October 15, 2025 [2] Company Summary - Tianwei Video received authorization for the construction of a new operational center aimed at enhancing online supervision of state-owned enterprises [2]

Core Viewpoint - Shenzhen Tianwei Video Technology Co., Ltd. has been authorized to establish the "State-owned Assets and State-owned Enterprises Online Supervision and Security Operation (Shenzhen) Sub-center" [1] Group 1: Authorization and Operations - The authorization was approved by the Party Committee of the State-owned Assets and State-owned Enterprises Online Supervision and Security Operation Center and reported to the General Office of the State-owned Assets Supervision and Administration Commission of the State Council [1] - The establishment of the sub-center will facilitate Tianwei Video's engagement in online supervision of network security for state-owned assets and enterprises [1] Group 2: Compliance and Disclosure - The company will strictly adhere to relevant laws and regulations, fulfilling its information disclosure obligations diligently [1] - Tianwei Video emphasizes the importance of timely information disclosure to investors [1]

Core Viewpoint - Tianwei Video (002238) has been authorized to establish the "State-owned Assets and State-owned Enterprises Online Supervision and Security Operation (Shenzhen) Sub-center," which will enhance the company's capabilities in online supervision of network security for state-owned assets and enterprises [1] Group 1 - The establishment of the Shenzhen sub-center is a significant step for the company in expanding its business in online security supervision [1] - The authorization was granted after a review by the Party Committee of the State-owned Assets and State-owned Enterprises Online Supervision and Security Operation Center and reported to the State-owned Assets Supervision and Administration Commission of the State Council [1] - The new center is expected to facilitate the company's operations in the field of network security supervision for state-owned enterprises [1]

Core Viewpoint - The company has been authorized to establish the "State-owned Enterprises Online Supervision and Security Operation (Shenzhen) Sub-center" by October 15, 2025, which will enhance its capabilities in online supervision of state-owned enterprises' cybersecurity [1] Group 1 - The establishment of the sub-center is expected to facilitate the company's engagement in online regulatory activities related to cybersecurity for state-owned enterprises [1] - The company commits to adhering to relevant laws and regulations, ensuring compliance in its operations [1] - The company will fulfill its information disclosure obligations diligently and timely [1]

Group 1 - The core transaction of ST Huawen on October 15 involved a volume of 317,700 shares and a transaction amount of 997,600 yuan, with a transaction price of 3.14 yuan, representing a premium of 8.65% over the closing price of the day [2][3] - The closing price of ST Huawen on the same day was 2.89 yuan, reflecting a decline of 3.34%, with a turnover rate of 2.51% and a total transaction amount of 14.4 million yuan, indicating a net outflow of 26.14 million yuan in main funds throughout the day [2][3] - Over the past five days, ST Huawen's stock has increased by 1.76%, while the total net outflow of funds during this period amounted to 27.23 million yuan [2][3] Group 2 - The latest margin financing balance for ST Huawen is 18.9 million yuan, with an increase of 817,900 yuan over the past five days, representing a growth rate of 0.44% [3] - Huawen Media Investment Group Co., Ltd. was established on September 12, 1991, with a registered capital of 1,997.245457 billion yuan [3]

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