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Clusters of investors around Initial Public Offering

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  • Margarita Baltakien.e
  • Kk{e}stutis Baltakys
  • Juho Kanniainen
  • Dino Pedreschi
  • Fabrizio Lillo

Abstract

The complex networks approach has been gaining popularity in analysing investor behaviour and stock markets, but within this approach, initial public offerings (IPO) have barely been explored. We fill this gap in the literature by analysing investor clusters in the first two years after the IPO filing in the Helsinki Stock Exchange by using a statistically validated network method to infer investor links based on the co-occurrences of investors' trade timing for 69 IPO stocks. Our findings show that a rather large part of statistically similar network structures form in different securities and persist in time for mature and IPO companies. We also find evidence of institutional herding.

Suggested Citation

  • Margarita Baltakien.e & Kk{e}stutis Baltakys & Juho Kanniainen & Dino Pedreschi & Fabrizio Lillo, 2019. "Clusters of investors around Initial Public Offering," Papers 1905.13508, arXiv.org, revised Nov 2019.
    • Handle: RePEc:arx:papers:1905.13508
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    Cited by:

    1. Piero Mazzarisi & Adele Ravagnani & Paola Deriu & Fabrizio Lillo & Francesca Medda & Antonio Russo, 2022. "A machine learning approach to support decision in insider trading detection," Papers 2212.05912, arXiv.org.
    2. Baltakienė, Margarita & Kanniainen, Juho & Baltakys, Kęstutis, 2021. "Identification of information networks in stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    3. Paola Deriu & Fabrizio Lillo & Piero Mazzarisi & Francesca Medda & Adele Ravagnani & Antonio Russo, 2022. "How Covid mobility restrictions modified the population of investors in Italian stock markets," Papers 2208.00181, arXiv.org.

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