Network Communities and the Foreign Exchange Market

Many systems studied in the biological, physical, and social sciences are composed of multiple interacting components. Often the number of components and interactions is so large that attaining an understanding of the system necessitates some form of simplification. A common representation that captures the key connection patterns is a network in which the nodes correspond to system components and the edges represent interactions. In this thesis we use network techniques and more traditional clustering methods to coarse-grain systems composed of many interacting components and to identify the most important interactions. This thesis focuses on two main themes: the analysis of financial systems and the study of network communities, an important mesoscopic feature of many networks. In the first part of the thesis, we discuss some of the issues associated with the analysis of financial data and investigate the potential for risk-free profit in the foreign exchange market. We then use principal component analysis (PCA) to identify common features in the correlation structure of different financial markets. In the second part of the thesis, we focus on network communities. We investigate the evolving structure of foreign exchange (FX) market correlations by representing the correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of individual nodes and uncovers major trading changes that occurred in the market. Finally, we consider the community structure of networks from a wide variety of different disciplines. We introduce a framework for comparing network communities and use this technique to identify networks with similar mesoscopic structures. Based on this similarity, we create taxonomies of a large set of networks from different fields and individual families of networks from the same field.