An algorithmic information-theoretic approach to the behaviour of financial markets

TL;DR

This paper explores how algorithmic information theory can explain deviations in financial market behaviors from log-normal distributions by analyzing daily closing prices and comparing real data with algorithmic models.

Contribution

It introduces an algorithmic information-theoretic framework to analyze financial market data, linking market behavior to rule-based, algorithmic components.

Findings

Bias away from equiprobable sequences in market data

Partial explanation of market deviations from log-normal distributions

Potential for new tools in market analysis using algorithmic probability

Abstract

Using frequency distributions of daily closing price time series of several financial market indexes, we investigate whether the bias away from an equiprobable sequence distribution found in the data, predicted by algorithmic information theory, may account for some of the deviation of financial markets from log-normal, and if so for how much of said deviation and over what sequence lengths. We do so by comparing the distributions of binary sequences from actual time series of financial markets and series built up from purely algorithmic means. Our discussion is a starting point for a further investigation of the market as a rule-based system with an 'algorithmic' component, despite its apparent randomness, and the use of the theory of algorithmic probability with new tools that can be applied to the study of the market price phenomenon. The main discussion is cast in terms of assumptions common to areas of economics in agreement with an algorithmic view of the market.