Identification of information networks in stock markets

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Abstract

We introduce a novel method to identify information networks in stock markets, which explicitly accounts for the impact of public information on investor trading decisions. We show that public information has a clear effect on the empirical investor networks’ topology. Most importantly, our method strengthens the identified relationship between investors’ network centrality and returns. Furthermore, when less significant links are re- moved, the association between centrality and returns becomes statistically and economically stronger. Findings suggest that our approach leads to a more precise representation of the information network.
Original languageEnglish
Article number104217
Number of pages15
JournalJOURNAL OF ECONOMIC DYNAMICS AND CONTROL
Volume131
DOIs
Publication statusPublished - 2021
Publication typeA1 Journal article-refereed

Keywords

  • information channels
  • Information transfer
  • Investor network
  • Network inference
  • Private information
  • Public information

Publication forum classification

  • Publication forum level 2

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