Network analysis has become a primary tool in fields as diverse as systems biology, ecology, epidemiology, sociology, economics and finance. We present the work that demonstrates the existence of an empirical linkage between nominal financial networks and the underlying economic fundamentals, across countries . To study the topology of the return correlation network at mesoscopic level, we constructed correlation matrices from sectoral indices for 27 countries, and applied two commonly used clustering algorithms, viz., minimum spanning tree (MST) and multi-dimensional scaling (MDS), to group sectors based on their co-movements. The influence of the sectors in the mesoscopic network was found using the eigenvector centrality (EVC). We proposed a method to find a binary characterization of the ‘core-periphery’ structure by using a modification of the EVC. We showed that those sectors identified as core by the centrality measure, also constitute the backbone of the MST and cluster very closely in the MDS maps, thereby confirming the robustness of our method. To establish the connection between the financial network and the underlying production process, we regressed the EVC on macro-variables: market capitalization, revenue and employment, all aggregated at the sectoral level. The results were reasonably robust with varying degrees of prosperity and across periods of market turbulence (2008–09) as well as periods of relative calmness (2012–13 and 2015–16). We also present the extensions of these analyses at the micro level (stock wise)  and macro level (index wise).