Name
Abstract | ||
|---|---|---|
Many studies have been undertaken with machine learning techniques to predict stock returns in terms of time-series prediction. However, from the viewpoint of the cross-sectional prediction with machine learning techniques, there are no examples that verify its profitability in regional and global stock markets. This paper implements deep learning for multi-factor models to predict stock returns in the cross-section in these stock markets and investigates the performance of the method. Our results show that deep neural networks generally outperform representative machine learning models all over the world. These results indicate that deep learning shows promise as a skillful machine learning method to predict stock returns in the cross-section. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-58790-1_6 | JSAI-isAI Workshops |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masaya Abe | 1 | 0 | 0.34 |
| Kei Nakagawa | 2 | 0 | 0.34 |