  Modern portfolio optimization is centered around creating a low-risk
portfolio with extensive asset diversification. Following the seminal work of
Markowitz, optimal asset allocation can be computed using a constrained
optimization model based on empirical covariance. However, covariance is
typically estimated from historical lookback observations, and it is prone to
noise and may inadequately represent future market behavior. As a remedy,
information filtering networks from network science can be used to mitigate the
noise in empirical covariance estimation, and therefore, can bring added value
to the portfolio construction process. In this paper, we propose the use of the
Statistically Robust Information Filtering Network (SR-IFN) which leverages the
bootstrapping techniques to eliminate unnecessary edges during the network
formation and enhances the network's noise reduction capability further. We
apply SR-IFN to index component stock pools in the US, UK, and China to assess
its effectiveness. The SR-IFN network is partially disconnected with isolated
nodes representing lesser-correlated assets, facilitating the selection of
peripheral, diversified and higher-performing portfolios. Further optimization
of performance can be achieved by inversely proportioning asset weights to
their centrality based on the resultant network.
