Inference of forex and stock-index financial networks based on the normalised mutual information rate

TL;DR

This paper introduces a novel information-theoretic method called normalized Mutual Information Rate to infer and analyze the network structures of foreign exchange rates and stock indices across multiple countries, revealing small-world properties and economic insights.

Contribution

It presents a new mathematical approach for network inference in financial data using normalized Mutual Information Rate, with applications to real-world currency and stock market data.

Findings

Both networks exhibit small-world characteristics.

The networks show similar structural properties with differences in assortativity.

Economic relationships are consistent with the inferred network structures.

Abstract

In this paper we study data from financial markets using an information-theory tool that we call the normalised Mutual Information Rate and show how to use it to infer the underlying network structure of interrelations in foreign currency exchange rates and stock indices of 14 countries world-wide and the European Union. We first present the mathematical method and discuss about its computational aspects, and then apply it to artificial data from chaotic dynamics and to correlated random variates. Next, we apply the method to infer the network structure of the financial data. Particularly, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks for which we also perform an analysis to identify their structural properties. Our results show that both are small-world networks sharing similar properties but also having distinct differences in terms of assortativity. Finally, the consistent relationships depicted among the 15 economies are further supported by a discussion from the economics view point.