上市公司数字技术风险暴露数 据（2007-2024年） 上市公司数字技术风险暴露数据（2007-2024年） 上市公司数字技术风险暴露数据（2007-2024年） 上市公司数字技术风险暴露数据（2007-2024年） 大语言模型是指用于处理自然语言信息的大型人工智能模型，其中，FinBERT是国内首个在金融领域大 规模语料上训练的开流模型。这类模型利用注意力机制，得出对每个词上下文敏感的表示，能够捕获文 本中的长距离依赖性和复杂关系，从而更细致地理解和生成语言。 选择企业年度报告MD&A部分的文本作为识别数字技术风险暴露程度的信息基础基于以下两方面的考 虑：第一，大量文献证实MD&A部分具有客观有效的信息含量，存在风险揭示功能，并能增强财务报 告的有用性。我们发现在MD&A中，部分企业会以独立段落重点写明企业面临的技术风险。例如，某 企业指出："公司技术革新风险：公司互联网视频业务对互联网的依赖程度较高，运营的安全易受到电 讯故障、黑客攻击、病毒等因素的影响。"第二，基于MD&A部分信息识别企业风险暴露情况的做法在 国内外众多文献中已经得到应用。 一、上市公司数字技术风险暴露数据下载地址 1.先在百度搜索以下 ...

Group 1: Core Conclusions - LLM reshapes the information foundation of asset allocation, enhancing the absorption of unstructured information such as sentiment, policies, and financial reports, which traditional quantitative strategies have struggled with [1][11] - The effective implementation of LLM relies on a collaborative mechanism involving "LLM + real-time data + optimizer," where LLM handles cognition and reasoning, external APIs and RAG provide real-time information support, and numerical optimizers perform weighting calculations [1][12] - LLM has established operational pathways in sentiment signal extraction, financial report analysis, investment reasoning, and agent construction, providing a realistic basis for enhancing traditional asset allocation systems [1][3] Group 2: Information Advantage Reconstruction - LLM enables efficient extraction, quantification, and embedding of soft information such as sentiment, financial reports, and policy texts into allocation models, significantly enhancing market expectation perception and strategy sensitivity [2][11] - The modular design of LLM, APIs, RAG, and numerical optimizers enhances strategy stability and interpretability while being highly scalable for multi-asset allocation [2][12] - A complete chain of capabilities from signal extraction to agent execution has been formed, demonstrating LLM's application in quantitative factor extraction and allocation [2][20] Group 3: Case Studies - The first two case studies focus on how sentiment and financial report signals can be transformed into quantitative factors for asset allocation, improving strategy sensitivity and foresight [20][21] - The third case study constructs a complete investment agent process, emphasizing the collaboration between LLM, real-time data sources, and numerical optimizers, showcasing a full-chain investment application from information to signal to optimization to execution [20][31] Group 4: Future Outlook - The integration of LLM with reinforcement learning, Auto-Agent, multi-agent systems, and personalized research platforms will drive asset allocation from a tool-based approach to a systematic and intelligent evolution, becoming a core technological path for building information advantages and strategic moats for buy-side institutions [3][39]