Investor networks consist of nodes that represent investors and links between them. The strength of the links quantifies the trading similarity between investors.
“When investors buy (sell) the same securities simultaneously, it may indicate that they use a similar trading strategy, follow the same financial experts or public information sources. It may also signal private information exchange between investors,” Baltakiene says.
Baltakiene develops a novel method that considers and eliminates the effect of public information on investor behavior to distinguish investor reactions to public information. This approach results in a more precise proxy of an investor information network.
“I investigate the effect of socioeconomic attributes on trade synchronisation by combining an investor network with a social network inferred from insider co-employment records. The results show that investors located closer to each other or who have worked together longer exhibit stronger trade synchronisation,” she tells.
Social network literature indicates that people's similarities and common interests help establish connections and facilitate information exchange. While there may be other explanations for trade synchronisation, synchronised trading coupled with the similarity of socioeconomic attributes may indicate the existence of private information channels.
The doctoral dissertation of MSc Margarita Baltakiene in the field of financial data analytics titled Diffusion of Private Information in Stock Markets: A Network Approach will be publicly examined in the Faculty of Information Technology and Communication Sciences at Tampere University at 12 o’clock on 30 September 2022 at Hervanta Campus in the auditorium TB109 of the Tietotalo building (Korkeakoulunkatu 1, Tampere). The Opponent will be Professor Damien Challet from Paris-Saclay University. The Custos will be Professor Juho Kanniainen from Tampere University. The thesis is co-supervised by Professor Hannu Kärkkäinen, Tampere University.
The dissertation is available online at the http://urn.fi/URN:ISBN:978-952-03-2544-2.
You can follow the dissertation defence also via remote connection (Panopto).
Photo: Kestutis Baltakys