A Survey on Visual Analysis Approaches for Financial Data

• Published in: Computer graphics forum Vol. 35; no. 3; pp. 599 - 617
• Main Authors: Ko, S., Cho, I., Afzal, S., Yau, C., Chae, J., Malik, A., Beck, K., Jang, Y., Ribarsky, W., Ebert, D. S.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.06.2016
• Subjects: Analysis; Analytics; Categories and Subject Descriptors (according to ACM CCS); Computer graphics; Decision making; Decisions; Document types [General and reference]: Surveys and overviews; Economics; Financial planning; Markets; Research and development; Studies; Subject specialists; Tasks; Visual; Visualization
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.12931

Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.