The cross-sectional stock return predictions via quantum neural network and tensor network
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916139634786304 |
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| author | Kobayashi, Nozomu Suimon, Yoshiyuki Miyamoto, Koichi Mitarai, Kosuke |
| author_facet | Kobayashi, Nozomu Suimon, Yoshiyuki Miyamoto, Koichi Mitarai, Kosuke |
| contents | In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical models such as linear regression and neural networks. To evaluate their abilities, we construct portfolios based on their predictions and measure investment performances. The empirical study on the Japanese stock market shows the tensor network model achieves superior performance compared to classical benchmark models, including linear and neural network models. Though the quantum neural network model attains a lowered risk-adjusted excess return than the classical neural network models over the whole period, both the quantum neural network and tensor network models have superior performances in the latest market environment, which suggests the capability of the model's capturing non-linearity between input features. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2304_12501 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The cross-sectional stock return predictions via quantum neural network and tensor network Kobayashi, Nozomu Suimon, Yoshiyuki Miyamoto, Koichi Mitarai, Kosuke Machine Learning Computational Finance Quantum Physics In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical models such as linear regression and neural networks. To evaluate their abilities, we construct portfolios based on their predictions and measure investment performances. The empirical study on the Japanese stock market shows the tensor network model achieves superior performance compared to classical benchmark models, including linear and neural network models. Though the quantum neural network model attains a lowered risk-adjusted excess return than the classical neural network models over the whole period, both the quantum neural network and tensor network models have superior performances in the latest market environment, which suggests the capability of the model's capturing non-linearity between input features. |
| title | The cross-sectional stock return predictions via quantum neural network and tensor network |
| topic | Machine Learning Computational Finance Quantum Physics |
| url | https://arxiv.org/abs/2304.12501 |