Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study

TL;DR

This paper introduces a Quantum Temporal Convolutional Neural Network (QTCNN) that combines classical and quantum techniques to improve stock return prediction, demonstrating significant performance gains over classical models in a benchmark study.

Contribution

The paper presents a novel hybrid quantum-classical neural network architecture for financial prediction, leveraging quantum circuits to enhance feature representation and reduce overfitting.

Findings

QTCNN achieves a Sharpe ratio of 0.538, outperforming classical models by 72%.

Quantum-enhanced models improve robustness in noisy, dynamic financial environments.

Benchmark results demonstrate the practical potential of quantum machine learning in finance.

Abstract

Quantum machine learning offers a promising pathway for enhancing stock market prediction, particularly under complex, noisy, and highly dynamic financial environments. However, many classical forecasting models struggle with noisy input, regime shifts, and limited generalization capacity. To address these challenges, we propose a Quantum Temporal Convolutional Neural Network (QTCNN) that combines a classical temporal encoder with parameter-efficient quantum convolution circuits for cross-sectional equity return prediction. The temporal encoder extracts multi-scale patterns from sequential technical indicators, while the quantum processing leverages superposition and entanglement to enhance feature representation and suppress overfitting. We conduct a comprehensive benchmarking study on the JPX Tokyo Stock Exchange dataset and evaluate predictions through long-short portfolio construction using out-of-sample Sharpe ratio as the primary performance metric. QTCNN achieves a Sharpe ratio of 0.538, outperforming the best classical baseline by approximately 72\%. These results highlight the practical potential of quantum-enhanced forecasting model, QTCNN, for robust decision-making in quantitative finance.