Causal Non-Linear Financial Networks

TL;DR

This paper advances the study of lead--lag effects in financial markets by using transfer entropy to construct causal networks of stocks, capturing non-linear dependencies and short-term causality.

Contribution

It introduces a causality-based approach using transfer entropy to analyze non-linear interdependencies in financial networks, extending previous correlation-based methods.

Findings

Identifies causal relationships among NYSE 100 stocks

Reveals short-term causality patterns in financial markets

Constructs Bonferroni-corrected causal networks

Abstract

In our previous study we have presented an approach to studying lead--lag effect in financial markets using information and network theories. Methodology presented there, as well as previous studies using Pearson's correlation for the same purpose, approached the concept of lead--lag effect in a naive way. In this paper we further investigate the lead--lag effect in financial markets, this time treating them as causal effects. To incorporate causality in a manner consistent with our previous study, that is including non-linear interdependencies, we base this study on a generalisation of Granger causality in the form of transfer entropy, or equivalently a special case of conditional (partial) mutual information. This way we are able to produce networks of stocks, where directed links represent causal relationships for a specific time lag. We apply this procedure to stocks belonging to the NYSE 100 index for various time lags, to investigate the short-term causality on this market, and to comment on the resulting Bonferroni networks.