Financial fluctuations anchored to economic fundamentals: A mesoscopic network approach

Abstract

We demonstrate the existence of an empirical linkage between nominal financial networks and the underlying economic fundamentals, across countries. We construct the nominal return correlation networks from daily data to encapsulate sector-level dynamics and infer the relative importance of the sectors in the nominal network through measures of centrality and clustering algorithms. Eigenvector centrality robustly identifies the backbone of the minimum spanning tree defined on the return networks as well as the primary cluster in the multidimensional scaling map. We show that the sectors that are relatively large in size, defined with three metrics, viz., market capitalization, revenue and number of employees, constitute the core of the return networks, whereas the periphery is mostly populated by relatively smaller sectors. Therefore, sector-level nominal return dynamics are anchored to the real size effect, which ultimately shapes the optimal portfolios for risk management. Our results are reasonably robust across 27 countries of varying degrees of prosperity and across periods of market turbulence (2008–09) as well as periods of relative calmness (2012–13 and 2015–16).

Growth dynamics and complexity of national economies in the global trade network

“…Entropic complexity and GDPpc provide an obvious benchmark plane to assess levels of development and stability of countries, even if, of course, one could consider also other quantities, like the eigenvector centrality determined recently in the network study of refs 18 , 19 . Figure 4(a) reports positions on this plane of 223 countries in 2015.…”

“…Indication that such dependence on time could be a realistic feature of a more sophisticated model is provided by the often large variances displayed by the yearly records whose average yields our matrix . In spite of this, we take these averages as time-independent matrix elements, in a spirit which is not far from that of a recent study of the multi-layered network structure underlying financial and macroeconomic dynamics 18 , 19 . When dealing with the data of certain countries we find that the calibration of these parameters reveals the presence of a noise that cannot be fully reproduced by the dynamics of Eq.…”

A Perspective on Correlation-Based Financial Networks and Entropy Measures

“…For this purpose, one starts with computing the crosscorrelations among stock price returns and then constructs any of the correlation-based networks-Minimum Spanning Tree (MST) [14,15], Threshold Network [16], Planar Maximally Filtered Graph (PMFG) [17], etc. Using these networks, one can identify stocks (or sectors) that are in the "core" or "periphery" [18], as well as study their hierarchy/importance of the different stocks driving the market fluctuations. The correlations among stocks change with time, and the underlying dynamics of the market produces very intriguing and correlation structures.…”

Data Driven Approach to the Dynamics of Import and Export of G7 Countries

“…So, the structure of our network is such that the most traded products are also the most central nodes (see Figure 2 ). Interestingly, Sharma et al have recently shown that a very similar structure arises in the financial network at sectoral level by using a methodology based on multi-layered networks [ 25 , 26 ]: In fact, their results show that there exists a one-to-one mapping between the economic size of the sectors and their centrality in the corresponding financial network.…”