This study analyzed the putative partial correlation structure and clustering patterns of the Brazilian stock market, represented by the stocks composing the Ibovespa index, through Dynamic Bayesian Networks (DBNs) over the period from January 2, 2007, to December 31, 2024. This network was used to calculate distance measures between stocks and to investigate the network topology through mixed and global complex network metrics. The results show that the Brazilian stock market predominantly exhibits positive partial correlations, suggesting joint movements in the long term, although this dynamic may vary in the short term. The network core is relatively dense and composed of several stocks, including those with the largest capitalization and liquidity in the index.
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