Stockholder conflicts and dividends

Conflicts of interest between majority and minority stockholders affect a large proportion of firms in any economy, but has received little attention in the empirical literature. We examine the link between the potential for such conflicts and the firm's payout policy on a large sample of Norwegian private firms with controlling stockholders and detailed ownership data. Our evidence shows that the stronger the potential conflict between the stockholders, the higher the proportion of earnings paid out as dividends. This tendency to reduce stockholder conflicts by dividend payout is more pronounced when the minority is diffuse and when a family's majority block is held by a single family member. We also find evidence that a minority-friendly payout policy is associated with higher future minority investment in the firm. These results are consistent with the notion that potential agency costs of ownership are mitigated by dividend policy when the majority stockholder benefits from not exploiting the minority.

Social network extraction and analysis based on multimodal dyadic interaction

Social interactions are a very important component in people"s lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Times" Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The links" weights are a measure of the"influence" a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.