Impact of macroeconomic variables on the topological structure of the Brazilian stock market: A complex network approach

Complex networks is an interdisciplinary field of study, effective in modeling various phenomena of strategic and/or market interest. Complex correlation networks between financial assets are mathematical abstractions that represent the relations between the financial returns of certain assets in a given period. The present work analyzed the Brazilian stock market, as well as the macroeconomic variables and indicators correlated to it in the context of complex networks. Based on the concept moving networks, 43 monthly complex networks were developed with relationships based on Pearson correlations between the logarithms of individual asset returns. To evaluate the impact of the oscillations of macroeconomic indicators on the topological structure of the network of assets, autoregressive vector models were used, as well as variance decomposition and Granger causality. The results of the Granger causality tests suggest that Gross Domestic Product, Risk Brazil, Ibovespa points and Interest rate influence the metrics density and number of components. The macroeconomic variables Gross Domestic Product, Risk Brazil and Ibovespa points presented, in general, higher explanatory power in relation to the variances of the density, transitivity and components number metrics. Among the positive and practical aspects related to this work, it is possible to highlight the use of global metrics of complex networks of assets as a support tool for investors and financial analysts in the detection of risk and volatility through oscillations in macroeconomic variables and policies.(c) 2022 Published by Elsevier B.V.