Embedded representations of social interactions

Social interactions have been the focus of social science research for a century, but their study has recently been revolutionized by novel data sources and by methods from computer science, network science, and complex systems science. The study of social interactions is crucial for understanding complex societal behaviours. Social interactions are naturally represented as networks, which have emerged as a unifying mathematical language to understand structural and dynamical aspects of socio-technical systems. Networks are, however, highly dimensional objects, especially when considering the scales of real-world systems and the need to model the temporal dimension. Hence the study of empirical data from social systems is challenging both from a conceptual and a computational standpoint. A possible approach to tackling such a challenge is to use dimensionality reduction techniques that represent network entities in a low-dimensional feature space, preserving some desired properties of the original data. Low-dimensional vector space representations, also known as network embeddings, have been extensively studied, also as a way to feed network data to machine learning algorithms. Network embeddings were initially developed for static networks and then extended to incorporate temporal network data. We focus on dimensionality reduction techniques for time-resolved social interaction data modelled as temporal networks. We introduce a novel embedding technique that models the temporal and structural similarities of events rather than nodes. Using empirical data on social interactions, we show that this representation captures information relevant for the study of dynamical processes unfolding over the network, such as epidemic spreading. We then turn to another large-scale dataset on social interactions: a popular Web-based crowdfunding platform. We show that tensor-based representations of the data and dimensionality reduction techniques such as tensor factorization allow us to uncover the structural and temporal aspects of the system and to relate them to geographic and temporal activity patterns.

Essays on risk-taking in corporate finance

This thesis consists of three independent essays on risk-taking in corporate finance. The first essay explores how community-level social capital (CSC), framed as a cultural characteristic of individuals born in different provinces of Italy, affects investment behavior in equity crowdfunding. Results show that investors born in high-CSC provinces invest more money in ventures characterized by an enhanced risk profile. Observed risk-taking is theoretically linked to higher generalized trust endowed to people born in high-CSC areas. The second essay focuses on how convexity of Chief Financial Officers’ stock options affects their hedging decisions in the oil and gas industry. Highly convex CFOs hedge less commodity price risk, even if the Chief Executive Officer’s incentives are consistent with a more conservative hedging strategy. Finally, the third essay is a systematic literature review on how different sources of compensation-based risk-taking incentives of Chief Executive Officers affect decision-making in corporate finance.