Symbol Dynamics, Information theory and Complexity of Economic time series

TL;DR

This paper analyzes the complexity and predictability of S&P 500 economic time series using symbolic dynamics and information theory, specifically entropy, to assess system complexity across different scales without bias.

Contribution

It introduces an entropy-based approach to measure economic time series complexity, emphasizing a coarse-grained symbolic dynamics method over traditional universality tests.

Findings

Entropy measurement reveals the complexity of S&P 500 dynamics.

The approach captures information across multiple time scales.

Economic data exhibits significant complexity and unpredictability.

Abstract

We propose to examine the predictability and the complexity characteristics of the Standard&Poor500 dynamics behaviors in a coarse-grained way using the symbolic dynamics method and under the prism of the Information theory through the concept of entropy and uncertainty. We believe that experimental measurement of entropy as a way of examining the complexity of a system is more relevant than more common tests of universality in the transition to chaos because it does not make any prior prejudices on the underlying causes associated with the system dynamics, whether deterministic or stochastic. We regard the studied economic time series as being complex and propose to express it in terms of the amount of information this last is producing on different time scales and according to various scaling parameters.