Abstract
Network science has made its way to financial research after the crisis of 2008-2009. Cascading failure of financial institutions revealed the complexity of the system and the importance of connections and relationships between its agents. Financial markets are characterized by complexity and heterogeneity, and information is one of the most critical and desired elements in financial markets. Individuals seek information to make better investment decisions, relieve their cognitive burden in information processing, or compensate lack of expertise. Intuitively, investors should be motivated to connect with other investors, as more central investors gain higher returns, likely because they have access to more valuable information. In addition, social studies point out that connections arise from common foci, geographical proximity, and similarities in socioeconomic attributes like age, gender, and language.
Investor networks remain relatively weakly explored despite the growing interest in network applications. Moreover, detecting private information channels is challenging because the links in investor networks are not directly observable. This dissertation analyzes information transfer channels, investor trading synchronization patterns, investor connections, and the effects of similarity of socioeconomic attributes on information transfer in investor networks to address these research gaps. Communities of investors with persistent trading strategies in time and across securities are observed analyzing investor networks for 69 IPO stocks. In addition, the findings support evidence of institutional herding in financial markets. Furthermore, shorter geographical distances result in a stronger trading synchronization, suggesting private information transfer. Moreover, removing the effects of public information on investment strategies results in a more precise representation of the information network. Finally, the results show that investor socioeconomic attributes, such as differences in age, geographical distance, or the time spent together in the same company affect information transfer in different scenarios.
Investor networks remain relatively weakly explored despite the growing interest in network applications. Moreover, detecting private information channels is challenging because the links in investor networks are not directly observable. This dissertation analyzes information transfer channels, investor trading synchronization patterns, investor connections, and the effects of similarity of socioeconomic attributes on information transfer in investor networks to address these research gaps. Communities of investors with persistent trading strategies in time and across securities are observed analyzing investor networks for 69 IPO stocks. In addition, the findings support evidence of institutional herding in financial markets. Furthermore, shorter geographical distances result in a stronger trading synchronization, suggesting private information transfer. Moreover, removing the effects of public information on investment strategies results in a more precise representation of the information network. Finally, the results show that investor socioeconomic attributes, such as differences in age, geographical distance, or the time spent together in the same company affect information transfer in different scenarios.
| Original language | English |
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| Place of Publication | Tampere |
| Publisher | Tampere University |
| Number of pages | 214 |
| ISBN (Electronic) | 978-952-03-2544-2 |
| ISBN (Print) | 978-952-03-2543-5 |
| Publication status | Published - 2022 |
| Publication type | G5 Doctoral dissertation (articles) |
Publication series
| Name | Tampere University Dissertations - Tampereen yliopiston väitöskirjat |
|---|---|
| Volume | 660 |
| ISSN (Print) | 2489-9860 |
| ISSN (Electronic) | 2490-0028 |