Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions

Zhengmeng Xu, Yujie Wang, Xiaotong Feng, Yilin Wang, Yanli Li and Hai Lin

Finance Research Letters, 2024, vol. 67, issue PA

Abstract: This paper presents the Quantum Gramian Angular Field (QGAF) method, which integrates quantum computing with deep learning to enhance forecasting in time series analysis. This method effectively converts stock return time series data into a format compatible with Convolutional Neural Network (CNN) training. Our empirical tests, conducted on stock market data from China, Hong Kong, and the United States, demonstrate that QGAF significantly outperforms the traditional GAF approach in accuracy. These findings underscore the potential of combining quantum computing and deep learning in financial time series forecasting.

Keywords: Time series forecasting; Quantum gramian angular field; Convolutional neural network; Stock return predictions (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:67:y:2024:i:pa:s1544612324008705

DOI: 10.1016/j.frl.2024.105840

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