Visualizing the clustering of financial networks and profitability of stocks

C. M. Chen*, Y. F. Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


We propose an approach to visualize the clustering of financial networks and a long-term profitability of stocks using financial time series data, by combining several methods of quantitative analysis. For demonstration purposes, this method is applied to investigate the network of Dow Jones Industrial Average (DJIA). Based on the time series data of stock prices during 31 July 2007 to 18 July 2011, our classification method clusters the DJIA components into five groups according to their profitability and property. By comparing the time correlation in the adjusted close price of stocks within the same group, we show that our clustering method results in a better classification of DJIA components than the methods of industry clustering and hierarchical clustering. With this integrated method, we have constructed a two-dimensional map of the DJIA network for visualization, and have related the first and second coordinates of DJIA components in the map to, respectively, their long-term profitability and property. Our analyses show very strong correlations for the sectors of Energy, Basic Materials, Technology, Capital Goods and Consumer/Non-Cyclical, significant correlations for the sectors of Services and Financial and a poor correlation for the Healthcare sector.

Original languageEnglish
Pages (from-to)303-318
Number of pages16
JournalJournal of Complex Networks
Issue number2
Publication statusPublished - 2015 Jun 1


  • Clustering
  • Financial networks
  • Stock profitability
  • Visualization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Management Science and Operations Research
  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics


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