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