sPortfolio: Stratified Visual Analysis of Stock Portfolios.

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ID: 69827
2020
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Abstract
Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates enormous quantities of factor data, through which even experienced portfolio managers find it difficult to navigate. This has led to portfolio analysis and factor research being limited by a lack of intuitive visual analytics tools. Previous portfolio visualization systems have mainly focused on the relationship between the portfolio return and stock holdings, which is insufficient for making actionable insights or understanding market trends. In this paper, we present s Portfolio, which, to the best of our knowledge, is the first visualization that attempts to explore the factor investment area. In particular, sPortfolio provides a holistic overview of the factor data and aims to facilitate the analysis at three different levels: a Risk-Factor level, for a general market situation analysis; a Multiple-Portfolio level, for understanding the portfolio strategies; and a Single-Portfolio level, for investigating detailed operations. The system's effectiveness and usability are demonstrated through three case studies. The system has passed its pilot study and is soon to be deployed in industry.
Reference Key
yue2020sportfolioieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yue, Xuanwu;Bai, Jiaxin;Liu, Qinhan;Tang, Yiyang;Puri, Abishek;Li, Ke;Qu, Huamin;
Journal ieee transactions on visualization and computer graphics
Year 2020
DOI
10.1109/TVCG.2019.2934660
URL
Keywords
Machine learning imbalanced data bankruptcy financial ratios sme

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