Arleo Alessio, Tsigkanos Christos, Leite Roger A, Dustdar Schahram, Miksch Silvia, Sorger Johannes
TU Wien Vienna Austria.
Centre for Visual Analytics Science and Technology (CVAST) Vienna Austria.
Comput Graph Forum. 2023 Feb;42(1):101-116. doi: 10.1111/cgf.14723. Epub 2022 Nov 28.
Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present , a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation-wide aggregate data. Our solution is coupled with a pipeline for the generation of firm-to-firm financial transaction networks, fusing information about individual firms with sector-to-sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight-based evaluation. The analysis shows how enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.
对不断发展的金融环境的动态进行建模是一项复杂的任务，需要领域知识、专业技能以及获取异构信息类型的能力。此类信息可能源自不同规模的多个来源，这使得形成对金融格局的整体印象变得复杂，尤其是在企业间经济关系方面。因此，将这些分散的信息置于共同背景下，是获取有关经济状况有意义见解过程中的关键一步。在本文中，我们提出了一种可视化分析（VA）方法，用于探索从单个公司到全国汇总数据等不同规模的金融数据。我们的解决方案与一个用于生成公司对公司金融交易网络的管道相结合，将有关单个公司的信息与部门对部门的交易数据以及关于经济宏观方面的领域知识融合在一起。每个网络都可以创建多个实例以比较不同的情景。在我们的VA解决方案开发过程中，我们与金融和经济领域的专家进行了合作，并通过基于定性洞察的评估，与来自行业和学术界的七位领域专家对我们的方法进行了评估。分析展示了该方法如何能够产生见解，以及交易模型的纳入如何帮助用户探索国民经济。
