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

arXiv: https://arxiv.org/abs/2304.12501 · HTML: https://mlsanity.com/paper/2304.12501/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 11 of 11)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Recurrent Quantum Neural Networks](https://mlsanity.com/paper/2006.14619/) (2020)
- [Towards Quantum Machine Learning with Tensor Networks](https://mlsanity.com/paper/1803.11537/) (2018)
- [Learning Relevant Features of Data with Multi-scale Tensor Networks](https://mlsanity.com/paper/1801.00315/) (2017)
- [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://mlsanity.com/paper/1904.04912/) (2019)
- [TensorNetwork for Machine Learning](https://mlsanity.com/paper/1906.06329/) (2019)
- [TensorNetwork: A Library for Physics and Machine Learning](https://mlsanity.com/paper/1905.01330/) (2019)
- [Deep Learning for Forecasting Stock Returns in the Cross-Section](https://mlsanity.com/paper/1801.01777/) (2018)
- [A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy](https://mlsanity.com/paper/1910.01491/) (2019)
- [Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention](https://mlsanity.com/paper/2105.10019/) (2021)
- [Deep Fundamental Factor Models](https://mlsanity.com/paper/1903.07677/) (2019)

## Built on by (most cited first, 1 of 1)

- [Quantum vs. Classical Machine Learning: A Benchmark Study for Financial Prediction](https://mlsanity.com/paper/2601.03802/) (2026)

## Abstract (arXiv)

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.