HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction
Hybrid quantum-classical neural network models enhance financial stock prediction by integrating advanced classical time-series methods with custom quantum circuits.
What it examines
The paper introduces a hybrid classical-quantum framework for financial stock market forecasting. It combines deep learning models like RNN and LSTM with quantum neural networks using parameterized quantum circuits. The approach aims to overcome challenges in capturing complex temporal dependencies and market fluctuations for more accurate predictions.
What it concludes
The study shows that hybrid quantum-classical models can improve predictive accuracy over standalone quantum methods, with potential applications in financial forecasting and decision-making. However, current quantum hardware limitations call for further research on scalability, noise reduction, and real-time applications in financial modeling.
Evidence objects
A novel study demonstrates that integrating quantum circuits with recurrent deep learning techniques significantly enhances stock market predictions, capturing complex patterns and reducing error rates compared to purely quantum approaches.
key_findings bullet 1 · key_findings · validation V0
The innovative hybrid quantum-classical neural network employs customized quantum circuits using angle encoding, parameterized methods, and classical preprocessing, encompassing TimeSeriesSplit, k-fold cross-validation, and RMSE metrics to evaluate historical stock performance.
key_findings bullet 2 · key_findings · validation V0
Researchers report the HybridQNN2 model improves pattern recognition under volatile market conditions while integrating technical indicators, though increased qubits lead to higher computational costs and slower responses during market shifts.
key_findings bullet 3 · key_findings · validation V0
Presented is a novel hybrid quantum-classical architecture for stock market prediction that integrates quantum neural networks with classical methods. Its innovative ansatz and dual optimization strategies tackle financial time-series forecasting challenges, offering fresh perspectives, striking originality, and significant impact, compelling readers to explore promising quantum techniques for advancing financial analysis.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
Abstract: Financial time-series forecasting remains a challenging task due to complex temporal dependencies and market fluctuations. This study explores the potential of hybrid… ▽ More 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. A custom Quantum Neural Network (QNN) regressor is introduced, designed with a novel ansatz tailored for financial applications. Two hybrid optimization strategies are proposed: (1) a sequential approach where classical recurrent models (RNN/LSTM) extract temporal dependencies before quantum processing, and (2) a joint learning framework that optimizes classical and quantum parameters simultaneously. 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. △ Less
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