Group 1 - The China Securities Digital Economy Theme Index (931582) decreased by 1.52% as of March 31, 2025, with mixed performance among constituent stocks [1] - Leading gainers included Huada Jiutian (301269) up 4.43%, Guanglian Da (002410) up 2.28%, and Sanhuan Group (300408) up 2.16%, while leading decliners were Nasda (002180) down 4.51%, Mingzhi Electric (603728) down 4.40%, and Tonghuashun (300033) down 4.32% [1] - The Digital Economy ETF (560800) fell by 1.65%, with the latest price at 0.77 yuan and a trading volume of 10.7362 million yuan [1] Group 2 - The Digital Economy ETF closely tracks the China Securities Digital Economy Theme Index, which selects listed companies in high digitalization sectors to reflect the overall performance of digital economy theme stocks [2] - As of February 28, 2025, the top ten weighted stocks in the index included Dongfang Caifu (300059), SMIC (688981), and Huichuan Technology (300124), collectively accounting for 50.97% of the index [2] Group 3 - The ongoing technological competition among major countries is intensifying, necessitating the localization of AI computing power, supported by policies aimed at increasing the share of self-controlled computing power [1] - The development of leading models like DeepSeek and AIAgent is expected to significantly increase the demand for inference computing power, marking a shift from training-driven to inference-driven demand [1] - Major tech companies are open-sourcing their models, accelerating the democratization of AI and advancing multimodal technology, which presents new development opportunities for AI applications [1]

独立行情中的不确定性 2025 年 03 月 11 日 邮箱：mouyiling@mszq.com 邮箱：meikai@mszq.com 邮箱：fangzhiyong@mszq.com 研究助理：季宏坤 执业证号：S0100124070013 邮箱：jihongkun@mszq.com A 股策略点评 20250311 [Table_Author] 分析师：牟一凌 分析师：梅锴 分析师：方智勇 执业证号：S0100521120002 执业证号：S0100522070001 执业证号：S0100522040003 ➢ 不一样的美股下跌。昨日(20250310)美股整体大幅调整，其中，纳指跌幅创 下 2022 年 10 月以来的新高。实际上，自 2025 年 2 月 20 日以来，美股整体已 经进入下跌趋势，随之而来的是 VIX 指数的持续攀升(图 1)与纳指加速器指标的 持续回落(图 2)。截至 2025 年 3 月 10 日，美股 VIX 指数已经逐步接近 2024 年 8 月的高点，而纳指加速器指标则远低于负两倍标准差水平。从历史上看，每当 纳指加速器指标回落至负一倍标准差以下时，纳指往往会迎来一轮反弹，但 ...

Investment Rating - The report maintains a "Buy" rating for the utility sector, indicating an expected investment return exceeding the market benchmark by more than 15% over the next 6-12 months [6]. Core Insights - The electric power digitalization sector has significant growth potential, with the integration of physical models and general models creating a digital space that connects the physical world with digital representations [2][13]. - The emergence of DeepSeek promotes "AI equity" in the electric power digitalization industry, lowering resource barriers and enhancing innovation potential [3][21]. - Data is identified as the core element determining the effectiveness of digital applications, with companies leveraging unique data advantages to improve predictive accuracy [28][30]. Summary by Sections Electric Power Digitalization Development - Electric power digitalization encompasses three forms and six major scenarios, driven by advanced technologies such as spatial computing, machine learning, and 5G communication [15][18]. - The six major scenarios include digital green power plants, digital grid inspections, self-healing distribution networks, multi-energy collaboration, cross-domain power scheduling, and enabling green low-carbon initiatives [15][17]. DeepSeek's Role in Electric Power Digitalization - DeepSeek, as a representative of generative AI, significantly impacts the electric power sector by facilitating intelligent monitoring and decision-making across the entire power generation, transmission, and distribution chain [21][25]. - The report highlights the advantages of DeepSeek's open-source model, which reduces costs and breaks the monopoly on computing power, thus expanding its application in electric power digitalization [23][24]. Data as a Core Element - The process of digital application realization involves data import, physical model prediction, and decision-making assistance through generative AI, with predictive accuracy being crucial for success [4][28]. - Companies like Guoneng Rixin and Longxin Group are noted for their advanced predictive models and data-driven decision-making capabilities [29][30]. Investment Recommendations - The report suggests focusing on companies with core products in the electric power digitalization sector, such as Guoneng Rixin, Longxin Group, and State Grid Information Communication [32].

Core Viewpoint - The AI large model sector is experiencing a new wave of price reductions, driven by competition and advancements in algorithms and computing cost control [4][5]. Group 1: Price Reductions and Promotions - DeepSeek has launched a time-limited discount for its API calls, reducing DeepSeek-V3 to 50% of its original price and DeepSeek-R1 to 25% [2]. - Major companies like ByteDance, Alibaba Cloud, Tencent, and iFlytek have announced significant price cuts for their models, with ByteDance's Doubao visual understanding model dropping to 0.003 yuan per thousand tokens, an 85% reduction from the industry average [4]. - Free service strategies are being adopted by several firms, with Baidu's Wenxin Yiyan and OpenAI's ChatGPT offering free access starting April 1 [4]. Group 2: Technological Advancements - NVIDIA has open-sourced a new optimization scheme based on the Blackwell architecture, achieving a 20-fold reduction in single token costs and a 25-fold performance increase compared to the previous H100 model [2][3]. - The new FP4 quantization model reaches 99.8% performance of FP8 models in the MMLU general intelligence benchmark, showcasing significant advancements in model efficiency [3]. Group 3: Market Reactions and Investments - The A-share market has seen a surge in large model concept stocks, with 69 companies experiencing an increase, and a net inflow of 90.02 billion yuan in leveraged funds [5]. - Notable stock increases include Capital Online rising by 13.71% and several others by over 5% [5]. - China Unicom has optimized its "adaptive slow thinking" model for DeepSeek series, saving approximately 30% in inference computation [5]. Group 4: Industry Dynamics - The open-sourcing of DeepSeek's cost-reduction methodology is expected to lower training and inference costs across the industry, stimulating rapid expansion of AI applications [5]. - The competitive landscape is intensifying, with companies focusing on algorithm optimization and iteration as key strategies alongside price reductions [4].