The cross-sectional stock return predictions via quantum neural network and tensor network
This work addresses stock prediction for investors by demonstrating potential gains from quantum-inspired methods, though it is incremental as it builds on existing quantum and classical techniques.
The paper tackled stock return prediction by applying quantum and quantum-inspired machine learning algorithms, specifically quantum neural networks and tensor networks, against classical models like linear regression and neural networks. The result showed that the tensor network model achieved superior performance in the Japanese stock market, with both quantum-inspired models performing better in recent market conditions, suggesting improved capture of non-linearities.
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.