| This study addresses the problem of analyzing the temporal dynamics of business organizations. In particular, we concentrate on inferring the related businesses, i.e., are there groups of companies that are highly correlated through some measurement (metric)?;We argue that business relationships derived from general literature (i.e., newspaper articles and news items) may help us create a network of related companies (business networks). On the other hand, relative movement of stock prices can give us an indication of related companies (asset graphs).;Hence, our research addresses the problem of construction and analysis of the networks of business organizations, i.e., asset graphs and business networks, and exploring the relationships between these two types of networks.;We start with the asset graphs. An asset graph is a network model of a stock market constructed based on pair-wise stock price correlations. Adapting the methodology from the literature, we present implementations with some extensions, such as consideration of different correlation metrics, global stock markets etc. A new asset graph model is also presented to capture the short term stock market dynamics and general market trends so that we can observe the effects of business events on the asset graphs.;The second problem this study addresses is about business networks. We define a novel business network model that captures business events. Business networks are constructed based on the occurrences of company names in general literature, such as daily news. Our extended business network model captures not only the business relationships between several companies (co-occurrences two or more company names in the same document) with the links between the corresponding companies, but the business events which is about one particular company (occurrence of the company name in a document) by representing them with self-links or loops in the network.;Finally, we combine our approaches mentioned above, asset graphs and business networks, to explore the relationships between these two kinds of networks, and hence, the relationships between the business events, and financial systems. Our approaches include finding the graph intersections, analyses on the stocks with strong negative correlations and 1- neighborhood similarity analysis. Graph intersections illustrate the companies with significant stock correlations and the business events or relationships, which might be considered as the possible reasons for these strong correlations. Analysis about strong negative correlations assumes that if a company's stock price strongly negatively correlated with many others, then, a business event or relationship might be the reason for that, and hence, it might be captured in corresponding business networks. Finally, the analysis about the 1-neigborhoods shows how similar these neighborhoods are in the asset graphs and corresponding business networks. Our results illustrate the relationships between these two kinds of networks. |
