从文本到银行的相互关系图
From Text to Bank Interrelation Maps
摘要：
在当前全球金融危机之后，在试图评估系统性风险时，银行之间的相互依赖性成为焦点。迄今为止，这种分析主要是基于数字数据。相比之下，本研究试图通过利用金融讨论来进一步深入了解银行之间的相互联系。将银行名称的共提转化为一个网络，可以对其进行可视化和定量分析，以说明单个银行和网络的整体特征。该方法允许网络的时间动态的研究，突出反映现实世界的事件，特别是当前的金融危机的讨论不断变化的模式。例如，它描述了从陷入困境的银行到其他银行和监管当局的联系如何随着时间的推移而出现和消失，以及网络结构的全球变化如何与严重的危机事件相吻合。文本数据的使用具有额外的优势，可以通过其上下文对观察到的相互关系进行更定性的理解。我们用芬兰银行和金融机构的案例研究来说明我们的方法。该数据集包括2004年至2012年期间来自在线，金融和商业相关讨论的390万个帖子。未来的研究包括从更广阔的视角分析欧洲新闻文章，并侧重于改善关系的语义描述。
英文摘要：
In the wake of the ongoing global financial crisis, interdependencies among banks have come into focus in trying to assess systemic risk. To date, such analysis has largely been based on numerical data. By contrast, this study attempts to gain further insight into bank interconnections by tapping into financial discussion. Co-mentions of bank names are turned into a network, which can be visualized and analyzed quantitatively, in order to illustrate characteristics of individual banks and the network as a whole. The approach allows for the study of temporal dynamics of the network, to highlight changing patterns of discussion that reflect real-world events, the current financial crisis in particular. For instance, it depicts how connections from distressed banks to other banks and supervisory authorities have emerged and faded over time, as well as how global shifts in network structure coincide with severe crisis episodes. The usage of textual data holds an additional advantage in the possibility of gaining a more qualitative understanding of an observed interrelation, through its context. We illustrate our approach using a case study on Finnish banks and financial institutions. The data set comprises 3.9M posts from online, financial and business-related discussion, during the years 2004 to 2012. Future research includes analyzing European news articles with a broader perspective, and a focus on improving semantic description of relations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
