  This paper highlights the significance of mesoscale structures, particularly
the core-periphery structure, in financial networks for portfolio optimization.
We build portfolios of stocks belonging to the periphery part of the Planar
maximally filtered subgraphs of the underlying network of stocks created from
Pearson correlations between pairs of stocks and compare its performance with
some well-known strategies of Pozzi et. al. hinging around the local indices of
centrality in terms of the Sharpe ratio, returns and standard deviation. Our
findings reveal that these portfolios consistently outperform traditional
strategies and further the core-periphery profile obtained is statistically
significant across time periods. These empirical findings substantiate the
efficacy of using the core-periphery profile of the stock market network for
both inter-day and intraday trading and provide valuable insights for investors
seeking better returns.
