Abstract:
Taking Chinese public fund data as an instance, we collaborate with domain experts and distill basic tasks for analyzing multidimensional temporal fund data. We propose a visual analytics solution for the problems and implement an interactive system which supports investors to explore the data from an overview to details, and thereby choose the most appropriate fund products from thousands of options. Generally, the user can filter for a pre-liminary set of candidates through a line plot showing the overall performance and a parallel coordinate showing various indices. They may compare specific funds with others under a global clutter view, and further analyze on the scatter plot for important data dimensions. In particular, we design a novel hierarchical interac-tive tree that surfaces the underlying investment principle when the user interacts with the system, where users can analyze the fund data dynamically and build their personalized tree and rank criteria for such multidimen-sional data. Through two case studies based on real-life data, we demonstrate the usability and effectiveness of our method.