Investor Networks and Information Transfer in Stock Markets
Research output: Book/Report › Doctoral thesis › Collection of Articles
|Number of pages||84|
|Publication status||Published - 1 Nov 2019|
|Publication type||G5 Doctoral dissertation (article)|
|Name||Tampere University Dissertations|
Since the financial crisis of 2007-2008, interdisciplinarymethods have gained substantial importance in modelling the financial systems and especially in the investigation of systemic risk. More and more network scientists contribute to the analysis of financial systems, and network methods are slowly gaining the position as one of the standard approaches when dealing with the complexity of financial markets.
Traditional methods are not able to capture the full spectrum of individual investor behaviour, much less the impact of investor interconnectedness on their behaviour and effect they have on markets. This dissertation portrays the increasing importance and adoption of the view that economic and financial questions should be investigated as complex systems with heterogeneous agents and significant interconnections. Nevertheless, the study of investor behaviour in the stock markets using the methodologies of complex networks is still relatively sparse. The main reason for the lack of research in this area is due to limited data availability.
The main objective of this thesis is to study investor interconnectedness in terms of their trading behaviour and information transfer in the financial markets. This dissertation consists of an introductory part and five research papers. It is intended as an introduction into the research of investor networks, particularly elaborating on the topics studied with my coauthors. The concept of investor networks is in the intersection between the studies of investor behaviour, information transfer and complex networks. Investor networks aim to capture the synchronization between investor trading behaviours, which might come about because of information transfer channels existing between investors.
With this in mind, the thesis provides a survey of the general complex network concepts and measures that are used in economic and financial studies. The other objectives of the dissertation are to address some of the challenges in investor network studies, pointing out some gaps and provide empirical evidence. In particular, a multilayer aggregation framework based on statistical validation is proposed allowing for constructing networks from information about multiple securities and periods, socioeconomically meaningful investor categorization and transaction bootstrapping
for investor network link validation. Moreover, investor networks and their characteristics are investigated for a set of 69 Initial Public Offering (IPO) securities. Besides our introduced multilayer aggregation procedure, this is the first study observing investor clusters over multiple securities. As a contribution to the investigation of word-of-mouth information transfer between household investors, the association between trade timing similarity and the geographical distance is tested. Alternatively, investors might react to public news announcements, and in this respect, the effect of social media releases on investor trading decisions is tested.
By using the multilayer aggregation framework, household investors in Helsinki were identified as the most central investors in terms of trading synchronization links with other investor categories. At the same time, the biggest clusters in IPO securities are formed by institutional investors. Remarkably, the same investor clusters are observed to persist in time and exist over different IPO securities as well as five mature securities. This leads to the conclusion that investors use market-wide instead of security-specific strategies. As for the information transfer in stock markets, geographical distance is found to have a negative association for household trade timing similarity, which suggests the existence of word-of-mouth based information transfer channels. Even further, company announcements on a social media platform are found to have an effect on inactive household trading decisions.