Group 1 - The core viewpoint of the article is that Langke Technology's computing power scheduling platform currently does not support the online one-click deployment of the clawdbot product, although it is gradually building an API service system for AI large model calls [1] Group 2 - Langke Technology's computing power scheduling platform has capabilities for monitoring, scheduling, leasing operations, and system management of computing resources [1] - The company is in the process of developing an API service system for AI large model calls [1] - As of now, the platform does not support the specific product mentioned by the investor [1]

Investment Rating - The report does not explicitly provide an investment rating for the industry, but it suggests a positive outlook based on structural opportunities and market conditions [3]. Core Insights - The North Exchange A-share market experienced a moderate increase, with a trading volume of 18.656 billion yuan on February 25, 2026, up by 2.277 billion yuan from the previous trading day. The North Exchange 50 Index closed at 1,547.201, rising by 0.77%, while the specialized index increased by 1.22% to 2,597.61 [1][8]. - A total of 294 companies were listed on the North Exchange, with 208 stocks rising, 6 remaining flat, and 80 declining. The top five gainers included Tonghui Information (10.3%), Anda Technology (8.2%), and Tianli Composite (6.3%), while the top five losers were Liancheng CNC (-7.1%) and Keli Co., Ltd. (-5.9%) [1][15][16]. - The report highlights structural characteristics within the North Exchange, aligning with the cyclical stock market trends, particularly in rare earths, phosphorus chemicals, and small metals [3]. Summary by Sections Market Review - On February 25, 2026, the North Exchange A-share trading volume reached 18.656 billion yuan, an increase of 2.277 billion yuan from the previous day. The North Exchange 50 Index rose by 0.77% to close at 1,547.201, with a PE_TTM of 65.09. The specialized index also saw a rise of 1.22% [1][8]. Important News - OpenAI's project faced funding issues, shifting its focus to managing internal data center resources rather than owning physical assets. Additionally, OpenAI is behind in custom chip development, with plans to start in 2025 [2][17]. - Murata Manufacturing, a major MLCC manufacturer, is considering raising prices for its passive components [2][18]. Key Company Announcements - Deere Chemical announced a projected revenue of 726 million yuan for 2025, a decrease of 7.21% year-on-year, with a net profit expected to drop by 33.54% [2][19]. - Hongzhi Technology expects a revenue of 47.228 million yuan for 2025, down 1.61% year-on-year, with a net profit decline of 16.96% [2][20][21].

Core Viewpoint - The stock of Hongchang Electronics experienced an abnormal trading fluctuation, with a cumulative closing price increase of over 20% across three consecutive trading days from February 24 to February 26, 2026 [1] Company Summary - Hongchang Electronics conducted a self-examination and confirmed that there are no undisclosed significant matters or information as of the announcement date [1] - The company's production and operational conditions remain normal, with no significant changes in the internal or external operating environment [1] - There are no major events such as asset restructuring, share issuance, significant transactions, business restructuring, share buybacks, equity incentives, bankruptcy reorganization, major business collaborations, or introduction of strategic investors [1] - During the period of stock price fluctuation, there were no reported media articles, market rumors, or trending concepts that could have influenced the stock price [1] - Company directors, senior management, controlling shareholders, and actual controllers did not engage in buying or selling the company's stock during the price fluctuation period [1] Industry Summary - As of February 25, 2026, the latest rolling price-to-earnings (P/E) ratio for Hongchang Electronics is 340.77, significantly higher than the industry average rolling P/E ratio of 55.71 for the "C39 Computer, Communication, and Other Electronic Equipment Manufacturing" sector [1] - The turnover rate for the most recent trading day was 9.75%, which is higher than previous levels [1]

Core Viewpoint - The strong performance of Nvidia's earnings report alleviates concerns about the overseas AI bubble, leading to increased investment in domestic computing power and AI application sectors, particularly through the Huabao Big Data ETF (516700) [1] Group 1: Market Performance - The Huabao Big Data ETF (516700) saw an intraday increase of 2.27%, currently up by 2%, with nearly 9 million yuan in inflows over the past three days, indicating positive market sentiment towards the big data sector [1] - Key stocks in the ETF include Zhongke Shuguang, iFLYTEK, Unisoc, Inspur Information, and China Software, focusing on data centers, cloud computing, and big data processing [5] Group 2: Stock Performance - Notable stock performances include: - Runze Technology up by 18.56% - Aofei Data up by 11.57% - Yuntian Lihui up by 9.63% - Guanghuan New Network up by 7.16% [2][6] Group 3: Industry Insights - The AI development phase is in a critical acceleration period, with increasing demand for underlying computing power and a growing supply chain for related chips, servers, and switches [3] - Data centers are identified as the core infrastructure for AI, with a high demand in the domestic and international computing power markets driving steady growth in the IDC industry [3] Group 4: ETF Composition - By the end of 2025, the Huabao Big Data ETF's index will have a computing power concept weight of 40.91% and an AI application concept weight of 37.43% [4]

Group 1: Core Insights - The integration of technological innovation and industrial innovation is essential for developing new productive forces, as emphasized by President Xi Jinping [1][3][10] - Significant breakthroughs in basic research have been achieved, including the conversion of thorium-uranium nuclear fuel and the development of the "Zu Chongzhi No. 3" superconducting quantum computing prototype [3][11] - The "China Sky Eye" (FAST) has made critical advancements in understanding fast radio bursts, showcasing the capabilities of China's largest single-dish radio telescope [2][3] Group 2: Industry Developments - The development of high-performance carbon fiber in Shanxi represents a significant leap from following to keeping pace with global advancements, providing essential materials for aerospace and renewable energy sectors [5][7][8] - The launch of the first domestic 3D scientific computer, "Tianqiong," marks a major innovation in computational technology, enhancing capabilities in artificial intelligence and scientific research [9][10] - Traditional industries are being revitalized through technological innovation, leading to a transformation towards high-end, intelligent, and green development [8][10] Group 3: Future Outlook - The emphasis on strengthening the role of enterprises in technological innovation is crucial for the deep integration of technology and industry, fostering a robust innovation ecosystem [11] - Continuous optimization of the innovation environment and enhancement of technological empowerment are expected to accelerate the transformation of scientific achievements into productive forces [11]

精伦电子公告称，公司预计2025年度净利润为负，扣除无关业务收入后的营收低于3亿元，年报披露后 股票将触及规定情形被终止上市。公司股票近期价格波动大，2月12 - 13日、24日连续三个交易日跌 停，25 - 26日涨停。截至公告日，公司主营、生产经营及环境无重大变化，无应披露未披露事项。提醒 投资者注意投资和交易风险。 ...

Quantitative Models and Construction Methods 1. Model Name: GAN_GRU Model - **Model Construction Idea**: The GAN_GRU model combines Generative Adversarial Networks (GAN) for feature generation and Gated Recurrent Unit (GRU) for time-series feature encoding to create a stock selection factor[4][13][14] - **Model Construction Process**: - **GAN Component**: - The GAN consists of a generator (G) and a discriminator (D). The generator learns the real data distribution and generates realistic samples, while the discriminator distinguishes between real and generated data[23][24] - Generator loss function: $$L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))]$$ where \(z\) is random noise, \(G(z)\) is the generated data, and \(D(G(z))\) is the discriminator's output probability for generated data being real[24][25] - Discriminator loss function: $$L_{D} = -\mathbb{E}_{x\sim P_{data}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))]$$ where \(x\) is real data, \(D(x)\) is the discriminator's output probability for real data, and \(D(G(z))\) is the discriminator's output probability for generated data[27][29] - GAN training alternates between updating the generator and discriminator parameters through backpropagation[30] - The generator uses an LSTM model to preserve the sequential nature of input features, while the discriminator employs a CNN model to process the 2D structure of the generated features[33][37] - **GRU Component**: - Two GRU layers (GRU(128, 128)) are used, followed by an MLP (256, 64, 64) to output predicted returns (\(pRet\)) as the stock selection factor[22] - Input features include 18 price-volume characteristics (e.g., closing price, turnover rate) sampled over the past 40 days to predict cumulative returns for the next 20 trading days[14][18] - Data preprocessing includes outlier removal, standardization, and cross-sectional normalization[18] - Training is conducted semi-annually with rolling updates, using Adam optimizer, a learning rate of \(1e-4\), and IC as the loss function[18] - **Model Evaluation**: The GAN_GRU model effectively integrates GAN's feature generation capabilities with GRU's time-series encoding, making it suitable for capturing complex price-volume patterns in stock selection[4][13] --- Model Backtesting Results GAN_GRU Model - **IC Mean**: 0.1096*** (2019.02–2026.02)[41] - **ICIR (Non-Annualized)**: 0.87[42] - **Turnover Rate**: 0.82X[42] - **Recent IC**: -0.0105*** (latest period)[41][42] - **1-Year IC Mean**: 0.0517***[41][42] - **Annualized Return**: 38.13%[42] - **Annualized Volatility**: 23.18%[42] - **IR**: 1.64[42] - **Maximum Drawdown**: 27.29%[42] - **Annualized Excess Return**: 22.32%[41][42]

- The GAN_GRU factor is based on the GAN_GRU model, which utilizes Generative Adversarial Networks (GAN) for processing volume-price time series features and then employs the GRU model for time series feature encoding to derive the stock selection factor[4][13] - The GAN_GRU model includes two GRU layers (GRU(128, 128)) followed by an MLP (256, 64, 64), with the final output being the predicted return (pRet) used as the stock selection factor[22] - The GAN model consists of a generator and a discriminator. The generator aims to generate data that appears real, while the discriminator aims to distinguish between real and generated data. The generator's loss function is $L_{G} = -\mathbb{E}_{z\sim P_{z}(z)}[\log(D(G(z)))]$ and the discriminator's loss function is $L_{D} = -\mathbb{E}_{x\sim P_{d a t a}(x)}[\log D(x)] - \mathbb{E}_{z\sim P_{z}(z)}[\log(1-D(G(z)))]$[23][24][27] - The GAN_GRU model's training process involves alternating between training the generator and the discriminator until convergence[29][30] - The GAN_GRU factor's performance from February 2019 to February 2026 shows an IC mean of 0.1096, an annualized excess return of 22.32%, and an ICIR (non-annualized) of 0.87[41][42] - The latest IC value as of February 25, 2026, is -0.0105, with a one-year IC mean of 0.0517[41][42] - The top five industries for the GAN_GRU factor in February 2026, based on IC, are Electric Utilities, Retail, Real Estate, Construction, and Basic Chemicals, with IC values of 0.1257, 0.1196, 0.1151, 0.1130, and 0.1063, respectively[44] - The top five industries for the GAN_GRU factor over the past year, based on IC mean, are Steel, Computers, Media, Retail, and Food & Beverage, with IC means of 0.1404, 0.1175, 0.1132, 0.1014, and 0.0989, respectively[44] - The top five industries for the GAN_GRU factor's long positions in February 2026, based on excess returns, are Oil & Petrochemicals, Communications, Electronics, Non-ferrous Metals, and Computers, with excess returns of 7.91%, 3.11%, 3.06%, 2.78%, and 2.78%, respectively[45] - The top five industries for the GAN_GRU factor's long positions over the past year, based on average monthly excess returns, are Real Estate, Retail, Automobiles, Construction, and Consumer Services, with excess returns of 3.83%, 2.04%, 1.93%, 1.50%, and 1.49%, respectively[46] **Performance Metrics of GAN_GRU Factor:** - IC: 0.1096[41][42] - ICIR (non-annualized): 0.87[41][42] - Turnover Rate: 0.82X[41][42] - Recent IC: -0.0105[41][42] - One-year IC: 0.0517[41][42] - Annualized Return: 38.13%[41][42] - Annualized Volatility: 23.18%[41][42] - Information Ratio (IR): 1.64[41][42] - Maximum Drawdown: 27.29%[41][42] - Annualized Excess Return: 22.32%[41][42]

据交易所数据显示，截至2月26日午后一小时，主力资金流入前20的股票分别为： 胜宏科技（17.49亿元）、 沪电股份（14.35亿元）、 亨通光电（10.44亿 元）、 润泽科技（9.37亿元）、 东山精密（8.66亿元）、 华胜天成（8.47亿元）、 航天动力（7.82亿元）、 聚飞光电（7.74亿元）、 中天科技（6.34亿 元）、 高澜股份（6.17亿元）、 华银电力（5.90亿元）、 芯原股份（5.53亿元）、 东方电气（5.19亿元）、 永鼎股份（4.81亿元）、 川润股份（4.69亿 元）、 深南电路（4.58亿元）、 赛微电子（4.40亿元）、 工业富联（4.28亿元）、 光迅科技（4.15亿元）、 深科技（3.99亿元）。 | 芯原股份 | 6.09 | 5.53亿元 | 电子 | | --- | --- | --- | --- | | 东方电气 | 10.01 | 5.19亿元 | 电力设备 | | 永鼎股份 | 5.48 | 4.81亿元 | 通信 | | 川润股份 | 10.02 | 4.69亿元 | 机械设备 | | 深南电路 | 10 | 4.58亿元 | 电子 | | 塞微电子 | ...

Core Viewpoint - The A-share market is expected to stabilize and rebound, supported by multiple positive factors including declining risk-free rates, comprehensive domestic demand policies, and improving export expectations [1][4]. Group 1: Market Conditions - The A-share market welcomed a "good start" with all three major indices opening higher on the first trading day after the holiday [1][4]. - Factors supporting the market include a decline in risk-free rates and ongoing capital market reforms, which create a favorable liquidity environment for A-shares [1][4]. - Domestic demand policies are being prioritized, with expectations for traditional domestic demand sectors to gradually improve, supported by both policy and fundamental factors [1][4]. Group 2: Economic Outlook - The outlook for China's economy in 2026 is expected to improve significantly, driven by breakthroughs in domestic new technology industries and accelerated globalization [1][4]. - The focus of economic work is shifting towards domestic demand, with expectations of recovery in consumption, rising prices, and stabilization in the real estate sector [1][4]. Group 3: Investment Strategies - Emerging technology is identified as a key investment theme, with value stocks also expected to see a resurgence [1][4]. - Specific sectors to focus on include consumer services, food and beverage, and building materials within the domestic demand space, as well as internet, media, computing, robotics, electronics, and military industries in the emerging technology sector [2][5]. - The financial sector, particularly brokerage and insurance, is highlighted as a stabilizing force in the market, benefiting from the ongoing growth in wealth management demand [2][6].