Market-Wide Analysis of Investor Networks in Finland

Posted: 11 May 2020

See all articles by Viet Hung Le

Viet Hung Le

Faculty of Information Technology and Communication Sciences, Tampere University

Date Written: April 16, 2020

Abstract

We estimate investor stock trading networks based on Granger Causality under vector autoregressive model. The obtained network is directed, and this can capture the flow of information and trading patterns across the portfolio. For most securities, the investor stock trading network forms a bow-tie structure with a giant robust strongly connected component and tiny disconnected components. Trading networks are inferred separately for financial-insurance companies and household investors. In addition, an aggregated network is inferred, whose nodes contain the trading information of different investors based on similarities in their socioeconomic attributes. We observe that only the networks inferred for financial-insurance companies feature scale-free property. This indicates that there are institutional investors that serve as information hubs, sending and receiving information about their entire portfolio, and in addition, there are active institutions who either share information among themselves or not share information and trade independently. By analyzing investor categories, rather than individual investors, households represent the most central investor nodes. We also find that the level of information transfer across all securities of households and financial institutions is different. Institutional investors have lagged synchronization in trade timing or possibly transfer information on a large number of securities while this for households is observed on a small number of securities.

Keywords: Bowtie Structure, Information Transfer, Investor Behavior, Investor Network

JEL Classification: G01, G02

Suggested Citation

Le, Viet Hung, Market-Wide Analysis of Investor Networks in Finland (April 16, 2020). Available at SSRN: https://ssrn.com/abstract=3578039

Viet Hung Le (Contact Author)

Faculty of Information Technology and Communication Sciences, Tampere University ( email )

Finland

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