Dynamical analysis of financial stocks network: improving forecasting using network properties

TL;DR

This paper uses network analysis of stock return correlations to identify properties that improve the accuracy of stock return forecasts, especially over long time scales, by capturing complex market dynamics.

Contribution

It introduces a method that leverages network properties of stock interactions to enhance return prediction models, achieving significant improvements over baseline methods.

Findings

50% improvement in R2 score for long-term stock return forecasts

3% improvement in short-term return predictions

Network properties partially capture market dynamics

Abstract

Applying a network analysis to stock return correlations, we study the dynamical properties of the network and how they correlate with the market return, finding meaningful variables that partially capture the complex dynamical processes of stock interactions and the market structure. We then use the individual properties of stocks within the network along with the global ones, to find correlations with the future returns of individual S&P 500 stocks. Applying these properties as input variables for forecasting, we find a 50% improvement on the R2score in the prediction of stock returns on long time scales (per year), and 3% on short time scales (2 days), relative to baseline models without network variables.