Forecasting Financial Market Structure from Network Features using Machine Learning

TL;DR

This paper introduces a machine learning model that predicts the evolving structure of financial markets using network features, significantly outperforming traditional correlation-based methods and aiding in portfolio and risk management.

Contribution

The study presents a novel approach combining network analysis and machine learning to forecast market structures with high accuracy, using multiple network filtering methods.

Findings

Model achieves up to 40% improvement over benchmarks.

Non-pairwise features are more important than traditional correlation measures.

Effective across multiple major global market indices.

Abstract

We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST) and Dynamic Threshold Networks (DTN). Experimental results show that the proposed model can forecast market structure with high predictive performance with up to $40\%$ improvement over a time-invariant correlation-based benchmark. Non-pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk.