Investors Are Social Animals: Predicting Investor Behavior using Social Network Features via Supervised Learning Approach

摘要

What makes investors tick? In this paper, we explore the possibility that investors invest in companies based on social relationships be it positive or negative, similar or dissimilar. This is largely counter-intuitive compared to past research work. In our research, we find that investors are more likely to invest in a particular company if they have stronger social relationships in terms of closeness, be it direct or indirect. At the same time, if there are too many common neighbors between investors and companies, an investor are less likely to invest in such companies. We use social network features such as those mentioned to build a predictive model based on link prediction in which we attempt to predict investment behavior.