Maximum Entropy Production Principle for Stock Returns

TL;DR

This paper explores the application of the Maximum Entropy Production Principle to stock returns, aiming to improve prediction accuracy by leveraging the structural complexity of financial time series.

Contribution

It introduces the use of the Maximum Entropy Production Principle for stock return prediction, connecting structural complexity measures with market predictability.

Findings

Structural complexity correlates with predictability but does not significantly enhance trading profitability.

Maximum Entropy Production Principle can be applied to stock returns to potentially improve forecasting.

Empirical tests on NY and Warsaw markets show promising results for the proposed approach.

Abstract

In our previous studies we have investigated the structural complexity of time series describing stock returns on New York's and Warsaw's stock exchanges, by employing two estimators of Shannon's entropy rate based on Lempel-Ziv and Context Tree Weighting algorithms, which were originally used for data compression. Such structural complexity of the time series describing logarithmic stock returns can be used as a measure of the inherent (model-free) predictability of the underlying price formation processes, testing the Efficient-Market Hypothesis in practice. We have also correlated the estimated predictability with the profitability of standard trading algorithms, and found that these do not use the structure inherent in the stock returns to any significant degree. To find a way to use the structural complexity of the stock returns for the purpose of predictions we propose the Maximum Entropy Production Principle as applied to stock returns, and test it on the two mentioned markets, inquiring into whether it is possible to enhance prediction of stock returns based on the structural complexity of these and the mentioned principle.