HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction

Financial time-series forecasting remains a challenging task due to complex temporal dependencies and market fluctuations. This study explores the potential of hybrid quantum-classical approaches to assist in financial trend prediction by leveraging quantum resources for improved feature representation and learning. Specifically, we introduce a custom-designed Quantum Neural Network (QNN) regressor based on a variational quantum circuit tailored to financial time-series regression under NISQ constraints. To balance expressivity and trainability, the circuit incorporates mixed entangling operations, feature-aware entanglement, and data re-uploading. We further investigate two hybrid optimization strategies: (1) a sequential approach (HybridQNN1) in which classical temporal feature extraction is followed by quantum regression, and (2) an integrated approach (HybridQNN2) that jointly optimizes classical and quantum parameters end-to-end under a shared loss. Systematic evaluation using TimeSeriesSplit, k-fold cross-validation, and predictive error analysis highlights the ability of these hybrid models to integrate quantum computing into financial forecasting workflows. The findings demonstrate how quantum-assisted learning can contribute to financial modeling, offering insights into the practical role of quantum resources in time-series analysis.