| Deep learning for forecasting stock returns in the cross-section M Abe, H Nakayama Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia …, 2018 | 139 | 2018 |
| Deep recurrent factor model: interpretable non-linear and time-varying multi-factor model K Nakagawa, T Ito, M Abe, K Izumi In AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019 | 32 | 2019 |
| Cross-sectional stock price prediction using deep learning for actual investment management M Abe, K Nakagawa Proceedings of the 2020 Asia Service Sciences and Software Engineering …, 2020 | 23 | 2020 |
| Ric-nn: A robust transferable deep learning framework for cross-sectional investment strategy K Nakagawa, M Abe, J Komiyama 2020 IEEE 7th International Conference on Data Science and Advanced …, 2020 | 21 | 2020 |
| RM-CVaR: Regularized Multiple -CVaR Portfolio K Nakagawa, S Noma, M Abe Proceedings of the 29th IJCAI Special Track on AI in FinTech., 2020 | 18 | 2020 |
| Deep learning for multi-factor models in regional and global stock markets M Abe, K Nakagawa New Frontiers in Artificial Intelligence: JSAI-isAI International Workshops …, 2020 | 6 | 2020 |
| How do we predict stock returns in the cross-section with machine learning? M Abe, K Nakagawa Proceedings of the 2020 3rd Artificial Intelligence and Cloud Computing …, 2020 | 3 | 2020 |
| A New Initial Distribution for Quantum Generative Adversarial Networks to Load Probability Distributions Y Sano, R Koga, M Abe, K Nakagawa arXiv preprint arXiv:2306.12303, 2023 | 1 | 2023 |
| Enhanced quantile portfolio for multifactor model with deep learning M Abe, K Nakagawa 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI …, 2022 | 1 | 2022 |
| Doubly Robust Mean-CVaR Portfolio K Nakagawa, M Abe, S Kuroki arXiv preprint arXiv:2309.11693, 2023 | | 2023 |
| Controlling False Discovery Rates under Cross-Sectional Correlations J Komiyama, M Abe, K Nakagawa, K McAlinn arXiv preprint arXiv:2102.07826, 2021 | | 2021 |
| Deep Learning for Multi-factor Models in Global Stock Markets MAK Nakagawa International Workshop: Artificial Intelligence of and for Business (AI …, 2019 | | 2019 |
| グローバル株式市場における深層学習を用いたマルチファクター運用の実証分析 阿部真也中川慧 人工知能学会全国大会第33回全国大会(2019), 2019 | | 2019 |
| A sampling technique of the D-Wave to implement Restricted Boltzmann Machine for forecasting stock relative attractiveness (Challenging Collaborations with T-QARD) Masaya Abe, Masayuki Ohzeki, Masamichi Miyama Qubits Europe 2019, https://www.dwavesys.com/media/20jlrutg/24_qubits2019327 …, 2019 | | 2019 |
| 深層学習を用いたマルチファクター運用の実証分析 阿部真也中川慧 第21回人工知 能学会 金融情報学研究会(SIG-FIN)予稿集, https://sigfin.org/021-03/, 2018 | | 2018 |
| A new initial distribution for qGAN to load probability distributions Y Sano, R Koga, M Abe, K Nakagawa IEICE Technical Report; IEICE Tech. Rep., 0 | | |
