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

arXiv:2304.12501v2 Announce Type: replace
Abstract: 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 …

abstract algorithm algorithms application arxiv computers cs.lg intermediate machine machine learning machine learning algorithms network neural network paper performance predictions q-fin.cp quant-ph quantum quantum computers quantum neural network scale stock tensor type via