Effects of common factors on stock correlation networks and portfolio diversification

Cheoljun Eom, Jong Won Park

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


This study empirically investigates the effects of common factors on the connectivity of the network among stocks and on the distribution of the investment weights for stocks. The network is defined as a stock correlation network from the minimal spanning tree (MST), and portfolio is defined as an efficient portfolio from the Markowitz mean-variance (MV) optimization function (MVOF). For these research goals, we devise a method using the comparative correlation matrix (C-CM), which does not have the property of a single common factor included in the sample correlation matrix (S-CM). The results reveal that common factors clearly affect the changes of connectivity among stocks in the networks, and that their influence is much greater on stocks with many links to other stocks in the network. Further, common factors significantly affect the determination of the investment weight's distribution for stocks from the MVOF. In particular, among the common factors, a market factor plays a dominant role in both structuring the network among stocks and in constructing the well-diversified portfolio. In addition, the devised method of the C-CM without the property of the market factor in the S-CM plays a crucial role in constructing a more diversified portfolio with better out-of-sample performance in the future period. These results are robust in both the Korean and the U.S. stocks markets.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalInternational Review of Financial Analysis
StatePublished - 1 Jan 2017


  • Common factors
  • Correlation matrix of stocks
  • Minimal spanning tree
  • Portfolio diversification
  • Portfolio optimization
  • Stock correlation network


Dive into the research topics of 'Effects of common factors on stock correlation networks and portfolio diversification'. Together they form a unique fingerprint.

Cite this