Small World of Canadian Capital Markets: Statistical Mechanics of Investment Bank Syndicate Networks, 1952-1989, The

Canadian Journal of Administrative Sciences, Dec 2004 by Baum, Joel A C, Rowley, Timothy J, Shipilov, Andrew V

Abstract

We investigate the structure of investment bank syndicate networks in Canada. We consider two banks to be connected if they have participated in an underwriting syndicate together, and construct networks of such connections using data drawn from the Record of New Issues (Financial Data Group). We show that these interfirm networks form "small worlds", in which banks are both locally clustered and globally connected by short paths of intermediate banks, and are "scale free", in which the connectivity of the network is highly skewed and with most banks tied to a small set of prominent banks. We examine changes over time in the network's small-world and scale-free properties, and demonstrate their theoretical and practical implications for the structure and operation of Canadian capital markets by linking these properties to the network's cliquey-ness, resilience, and speed of information transmission.

R�sum�

Cette �tude porte sur la structure des r�seaux que forment les syndicats d'�mission des banques d'investissement au Canada. Nous posons que deux banques sont li�es si elles ont particip� ensemble � un syndicat d'�mission, et nous retra�ons les r�seaux de liens en utilisant des donn�es extraites du Record of New Issues (Financial Data Group). Nous montrons que ces r�seaux interorganisationnels (RIO) forment des � petits mondes � dans lesquels les banques sont � la fois localement regroup�es et mondialement reli�es par des courts chemins de banques interm�diaires. Les RIO sont �galement sans �chelle (scale free) : la connectivit� dans le r�seau est fortement in�gale et la plupart des banques sont li�es � un petit nombre de banques dominantes. Nous examinons l'�volution des propri�t�s de petit monde et d'absence d'�chelle du r�seau et mettons en �vidence leurs implications th�oriques et pratiques pour la structure et le fonctionnement du march� canadien des capitaux en reliant ces propri�t�s aux caract�res de clique, de resilience et de vitesse de transmission de l'information du r�seau.

Networks are ubiquitous; they surround us; we engage dozens of them daily. The global economy is a network of national economies, which are networks of markets, which in turn are networks of firms, which in turn are networks of people. Increasingly, the technologies and social institutions on which we depend are explicitly engineered as networks. Our understanding of networks, however, has not kept up with our dependence on them.

What is a network? A network is essentially anything that can be represented by a graph: a set of points (also genetically called nodes or vertices), connected by links (edges, ties) representing some qualitative relationship. In social networks, the nodes are people or groups of people, "actors" in the jargon of the sociology, with some pattern of interactions or "ties" between them. A social network, then, is a set of people or groups of people (e.g., organizations) linked by acquaintance, friendship, political alliance, professional collaboration, or business relationships.

Network analysis currently forms the core of the "new economic sociology," which rests on the argument that networks generate and structure markets, creating pathways to sources of information and resources (Smelser & Swedberg, 1994). Social networks have, however, been the subject of both empirical and theoretical study in the social sciences for over 50 years, partly because of inherent interest in patterns of human interaction, but also because their structure has important implications for the spread of information, ideas, and disease, as well as social influence and inequality (Smelser & Swedberg). It is clear, for example, that variation simply in the average number of acquaintances that individuals have can substantially influence the propagation of a rumour, fad, fashion, joke, or this year's strain of the flu.

Traditionally, sociologists have viewed networks as static objects, "as given contexts for action" (Madhavan, Koka, & Prescott, 1998, p. 439). More recently, following a surge in interest in network structure among mathematicians and physicists, partly as a result of studies of the Internet and the World Wide Web and partly the broader movement toward research on complex systems, a growing stream of research has investigated the statistical properties of networks and methods for modeling networks. In this work, networks are conceived as dynamic systems that self-assemble and evolve in time through the addition and removal of actors and ties. The techniques of statistical mechanics, it turns out, are well suited to the study of networks. Indeed, graph theoretic analyses have permitted comparison of seemingly unrelated networks, leading to the exposure of deep similarities among social, biological, and technological networks.

Two important families of network structures have emerged from these studies (for a review, see Albert & Barab�si, 2002). The first is small-world network structures characterized by the combination of a high degree of clustering, meaning that there is a heightened probability of two actors being acquainted if they have one or more other acquaintances in common, and short characteristic path length, meaning that there exist short paths through a network between most pairs of actors (Watts & Strogatz, 1998). The second is scale-free network structures in which the degree distribution of the network-the distribution of ties among actors-is free of a characteristic scale and highly skewed, with a small number of actors having a disproportionately large number of ties (Barab�si & Albert, 1999). Research suggests the widespread presence of the "small-world" pattern (e.g., Adamic, 1999; Davis, Yoo, & Baker, 2003; Kogut & Walker, 2001; Newman, 2001; Powell, White, Koput, & Owen-Smith, 2004; Uzzi, Spiro, & Delis, 2002; Watts, 1999; Watts & Strogatz, 1998) and scale-free degree distributions (e.g., Barab�si & Albert, 1999; Jeong, Mason, Barab�si, & Oltvai, 2001; Jeong, Tombor, Albert, Oltvai, & Barab�si, 2000; Wagner & Fell, 2000) in social, economic, technological, and biological networks.