EAGER: III: CIFRAM: Dynamic Identification and Interpretation of Emerging Systemic Risks Using Textual Analysis

该项目将使用语言工具来确定金融危机（如2008年雷曼兄弟破产后的危机）的形成和规模增长的方式和原因。通过对提交给美国证券交易委员会（Securities and Exchange Commission）的10-K文件中大量口头数据的处理，主要调查人员将使用计算机科学家开发的技术来评估口头主题，并将它们与市场数据联系起来，以评估未来的危机是否正在形成。所采用的技术能够识别实际风险，使监管机构和市场参与者能够在重大事态发展之前作出适当反应。在互联网上免费提供数据和计算机代码将降低未来研究人员研究这些问题的成本。主要研究人员还将在会议上展示研究成果，提交论文发表，并将与研究生合作和培训研究生。这些材料将在课堂上传授给未来的商业领袖，MBA学生和本科生可以在课堂上公开讨论研究结果及其影响。这项工作还将提交给监管机构参加的会议，以分享如何利用它们在潜在危机造成广泛损害之前对其进行管理的见解。主要研究者将使用计算语言学的方法，包括潜在狄利克雷分配（Latent Dirichlet Allocation， LDA）和文档相似性分析，来确定一组在金融公司、与金融行业有联系的非金融公司以及经济中所有公司中常见的口头主题。然后，研究人员将使用集群和网络方法来评估和分类经济中公司之间的业务联系，并检查它们如何随时间演变。由此产生的公司相关性网络将在不同的时间间隔内与市场数据进行比较，以了解股票价格在相邻时期（特别是导致重大危机的时期）的变化方式和原因。口头因素将是可解释的，因此这种技术将提供一个完全自动化的描述，为什么公司在不同的时期以不同的方式发展。这种方法将是可复制的，不受研究者偏见的影响，允许数据告知研究者影响市场的最突出问题，即使研究者事先不熟悉特定系统性风险事件的真正驱动因素。一旦理解了共同运动的文本驱动因素，这些因素就可以用来回溯测试动态主题结构在其他系统事件中的演变情况。如果成功，这项研究可以为潜在的未来危机创建一个预警系统，并通过在危机发生之前解决危机的驱动因素作为风险管理工具，从而降低解决成本。欲了解更多信息，请参阅项目网站：http://cbe.lehigh.edu/kathleen-weiss-hanley

This project will employ linguistic tools to determine how and why financial crises, such as the 2008 crisis following the Lehman Brothers bankruptcy, form and grow in magnitude. By examining textual information gleaned by processing large volumes of verbal data from 10-K filings to the Securities and Exchange Commission, the principal investigators will use techniques developed by computer scientists to assess verbal themes and link them to market data to assess whether future crises are forming. The techniques employed enable the identification of actual risks allowing regulators and market participants the ability to respond appropriately in advance of a major development. The free provision of data and computer code on the internet will lower the cost for future researchers to also examine these issues. The principal investigators will also present the research at conferences, submit the work for publication, and will work with and train graduate students. The material will be taught to future business leaders in the classroom, where MBA students and undergraduate students can openly discuss the results and their implications. The work will also be submitted to conferences attended by regulators to share insights on how they can be used to manage potential crises before they can cause extensive damage.The principal investigators will use methods from computational linguistics, including Latent Dirichlet Allocation (LDA) and document similarity analysis, to identify a set of verbal topics that are common among financial firms, non-financial firms with exposure to the finance industry, and then all firms in the economy. The investigators will then use clustering and network methods to assess and categorize the business links among firms in the economy and to examine how they evolve over time. The resulting firm-relatedness network will then be compared to market data during various time intervals to understand how and why stock prices comove differently in neighboring periods, especially periods leading up to major crises. The verbal factors will be interpretable, and hence this technique will provide a fully automated description of why firms comove in different ways in different time periods. This method will be replicable and not subjected to researcher prejudice, allowing the data to inform researchers regarding the most salient issues affecting markets, even if the researcher is ex-ante unfamiliar with the true drivers of a specific systemic risk event. Once the textual drivers of comovement are understood, these factors can be used to back-test how the dynamic topic structure evolves during other systemic events. If successful, this research could create an early warning system for potential future crises and serve as a risk management tool by addressing the drivers of crisis before they occur, thereby reducing the cost of resolution.For further information see the project web site: http://cbe.lehigh.edu/kathleen-weiss-hanley