  We demonstrate that future market correlation structure can be predicted with
high out-of-sample accuracy using a multiplex network approach that combines
information from social media and financial data. Market structure is measured
by quantifying the co-movement of asset prices returns, while social structure
is measured as the co-movement of social media opinion on those same assets.
Predictions are obtained with a simple model that uses link persistence and
link formation by triadic closure across both financial and social media
layers. Results demonstrate that the proposed model can predict future market
structure with up to a 40\% out-of-sample performance improvement compared to a
benchmark model that assumes a time-invariant financial correlation structure.
Social media information leads to improved models for all settings tested,
particularly in the long-term prediction of financial market structure.
Surprisingly, financial market structure exhibited higher predictability than
social opinion structure.

Statistical Finance