A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets

TL;DR

This paper introduces a quantum reservoir computing framework using small quantum systems to forecast stock market trends, achieving high accuracy and demonstrating robustness across physical implementations.

Contribution

The study presents a novel small-scale quantum reservoir computing model for financial forecasting, applicable to various quantum hardware platforms.

Findings

Stock trend classification accuracy exceeds 86%.

Model effective on real-world financial data.

Platform-agnostic implementation demonstrated.

Abstract

We present a quantum reservoir computing (QRC) framework based on a small-scale quantum system comprising at most six interacting qubits, designed for nonlinear financial time-series forecasting. We apply the model to predict future daily closing trading volumes of 20 quantum-sector publicly traded companies over the period from April 11, 2020, to April 11, 2025, as well as minute-by-minute trading volumes during out-of-market hours on July 7, 2025. Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding $86 \%$. Importantly, the QRC model is platform-agnostic and can be realized across diverse physical implementations of qubits, including superconducting circuits and trapped ions. These results demonstrate the expressive power and robustness of small-scale quantum reservoirs for modeling complex temporal correlations in financial data, highlighting their potential applicability to real-world forecasting tasks on near-term quantum hardware.