Irreversibility of financial time series: a graph-theoretical approach

TL;DR

This paper applies graph-theoretical methods to analyze the irreversibility of financial time series, revealing its usefulness in classifying financial stress periods and ranking companies, and relating it to efficiency and predictability.

Contribution

It introduces a novel graph-theoretical approach using visibility algorithms to quantify time irreversibility in financial data, linking it to financial stress and efficiency.

Findings

Irreversibility metric classifies stress periods effectively.

Principal component analysis clusters financial stress periods.

Irreversibility relates to efficiency and predictability.

Abstract

The relation between time series irreversibility and entropy production has been recently investigated in thermodynamic systems operating away from equilibrium. In this work we explore this concept in the context of financial time series. We make use of visibility algorithms to quantify in graph-theoretical terms time irreversibility of 35 financial indices evolving over the period 1998-2012. We show that this metric is complementary to standard measures based on volatility and exploit it to both classify periods of financial stress and to rank companies accordingly. We then validate this approach by finding that a projection in principal components space of financial years based on time irreversibility features clusters together periods of financial stress from stable periods. Relations between irreversibility, efficiency and predictability are briefly discussed.