Topology of correlation based minimal spanning trees in real and model markets

TL;DR

This paper analyzes the topological structure of correlation-based minimal spanning trees in real and simulated markets, revealing complex network features in empirical data that are not captured by simple market models.

Contribution

It provides a topological characterization of stock correlation networks and compares real market data with simple models, highlighting their limitations.

Findings

Empirical minimal spanning trees exhibit complex network features.

Simple market models fail to reproduce the topology of real market correlation networks.

Real market data shows unique topological features not captured by basic models.

Abstract

We present here a topological characterization of the minimal spanning tree that can be obtained by considering the price return correlations of stocks traded in a financial market. We compare the minimal spanning tree obtained from a large group of stocks traded at the New York Stock Exchange during a 12-year trading period with the one obtained from surrogated data simulated by using simple market models. We find that the empirical tree has features of a complex network that cannot be reproduced, even as a first approximation, by a random market model and by the one-factor model.