We investigate the interaction among stocks in the US market over various
time horizons from a network perspective. Unlike the high-frequency data-driven
multiscale correlation networks used in previous works, we propose method-driven
multiscale correlation networks that are constructed by wavelet analysis and topological
methods of minimum spanning tree (MST) and planar maximally filtered graph
(PMFG). Using these techniques, we construct MST and PMFG networks of the US
stock market at different time scales. The key empirical results show that (1) the topological
structures and properties of networks vary across time horizons, (2) there is
a sectoral clustering effect in the networks at small time scales, and (3) only a part
of connections in the networks survives from one time scale to the next. Our results
in terms of MSTs and PMFGs for different time scales supply a new perspective for
participants in financial markets, especially for investors or hedgers who have different
investment or hedging horizons.

• Multiscale correlation networks analysis of the US stock market: a wavelet analysis