# Deep Learning for Forecasting Stock Returns in the Cross-Section

arXiv: https://arxiv.org/abs/1801.01777 · HTML: https://mlsanity.com/paper/1801.01777/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2018.

## Built on by (most cited first, 6 of 6)

- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Using Twitter Attribute Information to Predict Stock Prices](https://mlsanity.com/paper/2105.01402/) (2021)
- [MAC: A Meta-Learning Approach for Feature Learning and Recombination](https://mlsanity.com/paper/2209.09613/) (2022)
- [The cross-sectional stock return predictions via quantum neural network and tensor network](https://mlsanity.com/paper/2304.12501/) (2023)
- [FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models](https://mlsanity.com/paper/2503.06928/) (2025)
- [Forex Trading Volatility Prediction using Neural Network Models](https://mlsanity.com/paper/2112.01166/) (2021)

## Abstract (arXiv)

Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper implements deep learning to predict one-month-ahead stock returns in the cross-section in the Japanese stock market and investigates the performance of the method. Our results show that deep neural networks generally outperform shallow neural networks, and the best networks also outperform representative machine learning models. These results indicate that deep learning shows promise as a skillful machine learning method to predict stock returns in the cross-section.