Sabine Baumann, Jade University, Institute for Media Management and Journalism
Oliver Eulenstein, Iowa State University
Media companies face competitive market environments characterised by an enormous variety of highly dynamic network structures. Two challenges have to be overcome for their analysis: data is often incomplete and/or noisy and already smaller network analyses are difficult problems to solve, even with computer assistance. Regarding ownership and finance studies specific to media companies there is a very limited amount of literature. This paper uses a multidisciplinary case study approach to investigate previous and new patterns and the underlying dynamics in media investment and finance. The application of novel network algorithms to finance networks can detect clusters of network participants, e.g. shareholders who have invested in media companies, and assist in predicting their behaviour and potential communication patterns.