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

2025-12-06

AI Summary

Financial time series exhibit high noise levels, frequent regime shifts, and pose challenges for classical models due to limited generalization. To address these, this paper proposes the Quantum Temporal Convolutional Neural Network (QTCNN), which integrates a classical multi-scale temporal encoder with a parameter-efficient, trainable quantum convolutional circuit. Leveraging quantum superposition and entanglement, QTCNN enhances feature representation while mitigating overfitting. We evaluate the model on cross-sectional stock return prediction using JPX Tokyo Stock Exchange data and construct long–short portfolios for empirical validation. Out-of-sample testing yields a Sharpe ratio of 0.538—72% higher than the best classical baseline—demonstrating substantial improvements in prediction stability and generalization. This work pioneers the application of lightweight, differentiable quantum convolution to high-frequency quantitative forecasting, establishing a novel paradigm and providing empirical validation for practical quantum–classical hybrid modeling in real-world financial settings.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Enhance stock market prediction with quantum machine learning
• Address noisy input and limited generalization in forecasting models
• Improve cross-sectional equity return prediction using quantum circuits

Innovation

• Quantum Temporal Convolutional Neural Network (QTCNN) for equity prediction
• Combines classical temporal encoder with quantum convolution circuits
• Leverages quantum superposition and entanglement to enhance features