Estimating the Algorithmic Complexity of Stock Markets

TL;DR

This paper introduces a novel non-probabilistic approach using algorithmic information theory to estimate the complexity of stock market data, revealing hidden structures beyond traditional statistical methods.

Contribution

It develops a new method based on Kolmogorov complexity and lossless compression to detect structural regularities in financial time series, including the Dow-Jones Index.

Findings

Detection of structural regularities invisible to classical tests

Identification of hidden structures in financial data

Potential for improved understanding of market complexity

Abstract

Randomness and regularities in Finance are usually treated in probabilistic terms. In this paper, we develop a completely different approach in using a non-probabilistic framework based on the algorithmic information theory initially developed by Kolmogorov (1965). We present some elements of this theory and show why it is particularly relevant to Finance, and potentially to other sub-fields of Economics as well. We develop a generic method to estimate the Kolmogorov complexity of numeric series. This approach is based on an iterative "regularity erasing procedure" implemented to use lossless compression algorithms on financial data. Examples are provided with both simulated and real-world financial time series. The contributions of this article are twofold. The first one is methodological : we show that some structural regularities, invisible with classical statistical tests, can be detected by this algorithmic method. The second one consists in illustrations on the daily Dow-Jones Index suggesting that beyond several well-known regularities, hidden structure may in this index remain to be identified.