The string prediction models as an invariants of time series in forex market

TL;DR

This paper introduces string theory-based prediction models for forex market prices, demonstrating that the PMBCS model outperforms the correlation-based approach and supports profitable trading strategies.

Contribution

It applies string invariants to financial time series prediction, presenting a novel approach that improves prediction accuracy and profitability over traditional methods.

Findings

PMBCS model predicts forex prices more effectively than correlation-based models.

PMBCS achieves relevant annual profit in forex trading.

String invariants provide a new perspective for financial market analysis.

Abstract

In this paper we apply a new approach of the string theory to the real financial market. It is direct extension and application of the work [1] into prediction of prices. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support vector machines (SVM) and artificial neural networks (ANN) on an artificial and a financial time series. Brief overview of the results and analysis is given. The first model is based on the correlation function as invariant and the second one is an application based on the deviations from the closed string/pattern form (PMBCS). We found the difference between these two approaches. The first model cannot predict the behavior of the forex market with good efficiency in comparison with the second one which is, in addition, able to make relevant profit per year.