  In complex financial networks, systemically important nodes usually play
vital roles. We consider networks consisting of major global assets and explore
how node centrality affects price forecasting by applying a hybrid random
forest algorithm. We find two counterintuitive phenomena: (i) factors with low
centrality have better forecasting ability and (ii) nodes with low centrality
can be predicted more accurately in direction. Using the notion of entropy,
which measures the quantity of information, we show that factors with low
centrality have more useful information and less noise for the forecast asset
price than those with high centrality do. In addition, while predicting a
systemically unimportant node, we demonstrate that the other nodes within the
network have a higher information rate. Finally, we verify the robustness of
our results using an alternative deep learning method.
