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sPortfolio: Stratified Visual Analysis of Stock Portfolios

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Listed:
  • Xuanwu Yue
  • Jiaxin Bai
  • Qinhan Liu
  • Yiyang Tang
  • Abishek Puri
  • Ke Li
  • Huamin Qu

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 sPortfolio, 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.

Suggested Citation

  • Xuanwu Yue & Jiaxin Bai & Qinhan Liu & Yiyang Tang & Abishek Puri & Ke Li & Huamin Qu, 2019. "sPortfolio: Stratified Visual Analysis of Stock Portfolios," Papers 1910.05536, arXiv.org.
    • Handle: RePEc:arx:papers:1910.05536
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    References listed on IDEAS

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    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    3. Peng, Roger, 2008. "A Method for Visualizing Multivariate Time Series Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(c01).
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    Cited by:

    1. I-Cheng Yeh & Yi-Cheng Liu, 2023. "Exploring the growth value equity valuation model with data visualization," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-37, December.

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