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[2512.06630] Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study
Summary
This study introduces a Quantum Temporal Convolutional Neural Network (QTCNN) for predicting equity returns, demonstrating its superiority over classical models by achieving a Sharpe ratio of 0.538 on the JPX Tokyo Stock Exchange dataset.
Why It Matters
As financial markets become increasingly complex, traditional forecasting methods often fall short. This research highlights the potential of quantum machine learning to enhance prediction accuracy, offering a new avenue for robust decision-making in quantitative finance.
Key Takeaways
- QTCNN combines classical temporal encoders with quantum convolution circuits for improved equity return predictions.
- The model achieved a Sharpe ratio of 0.538, outperforming classical benchmarks by approximately 72%.
- Quantum enhancements help in managing noisy data and improving feature representation.
- The study emphasizes the practical applications of quantum machine learning in finance.
- Benchmarking on real-world data underscores the model's effectiveness in dynamic market conditions.
Computer Science > Machine Learning arXiv:2512.06630 (cs) [Submitted on 7 Dec 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study Authors:Chi-Sheng Chen, Xinyu Zhang, En-Jui Kuo, Rong Fu, Qiuzhe Xie, Fan Zhang View a PDF of the paper titled Quantum Temporal Convolutional Neural Networks for Cross-Sectional Equity Return Prediction: A Comparative Benchmark Study, by Chi-Sheng Chen and Xinyu Zhang and En-Jui Kuo and Rong Fu and Qiuzhe Xie and Fan Zhang View PDF HTML (experimental) 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 lon...
