A Comparison of Frontier and Emerging Capital Market Networks Margaret Moten Daniel Evans
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1 A Comparison of Frontier and Emerging Capital Market Networks Margaret Moten Daniel Evans Network Science Center
2 Why Capital Market Networks? Developed capital markets are correlated with improved economic performance - Levine and Zevros (1996) Macroeconomic theory needs to incorporate: o The network of interacting individuals o The structure of their interactions o The consequences of network activity - Kirman (2010) Network structure affects individual behavior and social welfare - Goyal (2007)
3 Research Objectives Develop Topologies of Capital Market Networks Quantitatively Determine Differences Between Networks If we wish to influence a capital market s efficiency, are there specific organizations, agents, or functions on which we should focus in order to bring about the desired outcome?
4 Social Network Development
5 Frontier & Emerging Capital Markets Tanzania Ghana Trinidad & Tobago Czech Republic
6 Social Network Development Agent Network Organization Network
7 Social Network Development Financial Services Government Professional Services Résumé Broker/Dealer Parliament Consultant
8 Developing a Functional Network
9 Network Functions Agriculture Association Banking Communications Conglomerate Consumer Education Financial Services Government Healthcare Industrial Parastatal Professional Services Real Estate Tourism Transportation
10 Ghana Functions Network
11 Tanzania Functions Network
12 Trinidad & Tobago Functions Network
13 Czech Republic
14 Network Level Comparison Network Level Measures Ghana Trinidad and Tobago Tanzania Czech Republic Row and column count Link count Density Characteristic path length Network Diameter Degree centralization Betweenness centralization Closeness centralization Eigenvector centralization
15 Network Level Centralization Measures
16 Most Prominent Functions Comparison
17 Betweenness Centrality Comparison
18 Eigenvector Centrality Comparison
19 Results Financial Services prominent in all networks Professional Services and Banking not prominent in Tanzania Tanzania s functions network was distinct Functions more interconnected & exert indirect influence Fewer cliques & more clustered Banking not as important, but Industrial and Association functions quite important Czech Republic s market was most dense and had more cliques and shorter paths
20 Implications - Tanzania Link between well-functioning capital markets and development Tanzania has least developed capital market; Czech Republic & Trinidad are more developed Requirements for investors - information availability, contract rights, liquidity To attract investors, Tanzania could: Expand professional services & banking functions Reduce role of associations Increase information flows (reduce importance of gatekeepers and friendship ties)
21 The Way Ahead Clustering /Sub-Group Analysis Node Centrality Correlation Analysis
22 Tanzania Functions Network
23 Initial Grouping (Tanzania) Association Financial Services Banking Industrial Professional Services Government Education Agriculture Tourism Consumer Parastatal Healthcare Communications Conglomerate
24 Czech Republic
25 Initial Grouping (Czech Republic) Communications Banking Industrial Education Professional Services Parastatal Real Estate Conglomerate Tourism Agriculture Transportation Healthcare Consumer Government Financial Services Association
26 Centrality Measure Correlation (Tanzania) Financial Services Association Degree Industrial Professional Services Government Banking Consumer Parastatal 0.1 Communications Transportation Education Conglomerate Agriculture Tourism Healthcare Eigenvector 0.01
27 Centrality Measure Correlation (Tanzania) Financial Services Super Broker Industrial Government Consumer Parastatal Banking Communications Eigenvector Conglomerate Education Tourism Agriculture Gatekeeper Transportation 0.1 Healthcare Peripheral Player Betweenness Broker 0.01
28 Centrality Measure Correlation (Czech Republic) Degree Banking Financial Services Industrial Professional Services 0.1 Real Estate Association Communications Consumer Government Education Transportation Conglomerate Agriculture Healthcare Tourism Parastatal Eigenvector 0.01
29 Centrality Measure Correlation (Czech Republic) Banking Financial Services Super Broker Industrial Real Estate Professional Services Eigenvector Association Communications Healthcare Conglomerate Agriculture Tourism Gatekeeper Consumer Government Education Transportation Parastatal 0.1 Peripheral Player Betweenness Broker 0.01
30 Thanks to Army Research Office This research was funded in part by U.S. Army Project Number B74F and MIPR Number 9FDATXR048 Dan Evans is a participant in the Scientific Services Program sponsored by the Battelle Memorial Institute
31 A Comparison of Frontier and Emerging Capital Market Networks Margaret Moten Daniel Evans Network Science Center
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