iFUNDit: Visual Profiling of Fund Investment Styles

R. Zhang, B. K. Ku, Y. Wang, X. Yue, S. Liu, K. Li, H. Qu

Research output: Contribution to journalArticlepeer-review

Abstract

Mutual funds are becoming increasingly popular with the emergence of Internet finance. Clear profiling of a fund's investment style is crucial for fund managers to evaluate their investment strategies, and for investors to understand their investment. However, it is challenging to profile a fund's investment style as it requires a comprehensive analysis of complex multi-dimensional temporal data. In addition, different fund managers and investors have different focuses when analysing a fund's investment style. To address the issue, we propose iFUNDit, an interactive visual analytic system for fund investment style analysis. The system decomposes a fund's critical features into performance attributes and investment style factors, and visualizes them in a set of coupled views: a fund and manager view, to delineate the distribution of funds' and managers' critical attributes on the market; a cluster view, to show the similarity of investment styles between different funds; and a detail view, to analyse the evolution of fund investment style. The system provides a holistic overview of fund data and facilitates a streamlined analysis of investment style at both the fund and the manager level. The effectiveness and usability of the system are demonstrated through domain expert interviews and case studies by using a real mutual fund dataset.

Original languageEnglish (US)
Article numbere14806
JournalComputer Graphics Forum
Volume42
Issue number6
DOIs
StatePublished - Sep 2023

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design

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