Algorithmic Complexity in Real Financial Markets

TL;DR

This paper introduces a novel method using thermodynamics and statistical physics to analyze the complexity of financial market indices, revealing different behaviors before crashes and during stable periods.

Contribution

It applies physical complexity measures rooted in Kolmogorov-Chaitin theory to real financial data, linking complexity variations to market turbulence.

Findings

Physical complexity varies significantly before market crashes.

Complexity measures differ between turbulent and stable market periods.

Results suggest potential for predicting market turbulence.

Abstract

A new approach to the understanding of complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in Kolmogorov-Chaitin theory is applied to binary sequences built up from real time series of financial markets indexes. The study is based on NASDAQ and Mexican IPC data. Different behaviors of this magnitude are shown when applied to the intervals of series placed before crashes and to intervals when no financial turbulence is observed. The connection between our results and The Efficient Market Hypothesis is discussed.