Deep Learning for Forecasting Stock Returns in the Cross-Section

Masaya Abe and Hideki Nakayama

Papers from arXiv.org

Abstract: 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.

Date: 2018-01, Revised 2018-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-for
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Citations: View citations in EconPapers (16)

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