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In this paper we investigate the application of quantum and quantum-inspiredmachine learning algorithms to stock return predictions. Specifically, weevaluate performance of quantum neural network, an algorithm suited for noisyintermediate-scale quantum computers, and tensor network, a quantum-inspiredmachine learning algorithm, against classical models such as linear regressionand neural networks. To evaluate their abilities, we construct portfolios basedon their predictions and measure investment performances. The empirical studyon the Japanese stock market shows the tensor network model achieves superiorperformance compared to classical benchmark models, including linear and neuralnetwork models. Though the quantum neural network model attains the loweredrisk-adjusted excess return than the classical neural network models over thewhole period, both the quantum neural network and tensor network models havesuperior performances in the latest market environment, which suggestscapability of model's capturing non-linearity between input features.

The cross-sectional stock return predictions via quantum neural network and tensor network