# Predicting future stock market structure by combining social and financial network information

## TL;DR

This paper presents a multiplex network approach combining social media and financial data to predict future stock market correlation structures with significantly improved accuracy, especially over long-term horizons.

## Contribution

It introduces a novel model that integrates social and financial network information using link persistence and triadic closure, enhancing prediction of market structure.

## Key findings

- Up to 40% out-of-sample performance improvement over benchmark models.
- Social media data improves long-term market structure predictions.
- Financial market structure is more predictable than social opinion structure.

## Abstract

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.