| Stock price prediction using k‐medoids clustering with indexing dynamic time warping K Nakagawa, M Imamura, K Yoshida Electronics and Communications in Japan 102 (2), 3-8, 2019 | 51* | 2019 |
| Deep Factor Model: Explaining Deep Learning Decisions for Forecasting Stock Returns with Layer-Wise Relevance Propagation K Nakagawa, T Uchida, T Aoshima ECML-PKDD Workshop on Mining Data for Financial Applications, 37-50, 2018 | 36 | 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 | 34 | 2019 |
| Deep portfolio optimization via distributional prediction of residual factors K Imajo, K Minami, K Ito, K Nakagawa Proceedings of the AAAI conference on artificial intelligence 35 (1), 213-222, 2021 | 30 | 2021 |
| 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 |
| The value of reputation capital during the COVID-19 crisis: Evidence from Japan T Manabe, K Nakagawa Finance Research Letters 46, 102370, 2022 | 22 | 2022 |
| 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 | 22 | 2020 |
| Risk-based portfolios with large dynamic covariance matrices K Nakagawa, M Imamura, K Yoshida International Journal of Financial Studies 6 (2), 52, 2018 | 22 | 2018 |
| Trader-company method: a metaheuristic for interpretable stock price prediction K Ito, K Minami, K Imajo, K Nakagawa Proceedings of the 20th International Conference on Autonomous Agents and …, 2021 | 19 | 2021 |
| RM-CVaR: Regularized Multiple -CVaR Portfolio K Nakagawa, S Noma, M Abe Proceedings of the Twenty-Ninth International Conference on International …, 2021 | 19 | 2021 |
| Stock Price Prediction with Fluctuation Patterns Using Indexing Dynamic Time Warping and -Nearest Neighbors K Nakagawa, M Imamura, K Yoshida JSAI International Symposium on Artificial Intelligence, 97-111, 2017 | 19* | 2017 |
| Cryptocurrency network factors and gold K Nakagawa, R Sakemoto Finance Research Letters 46, 102375, 2022 | 18 | 2022 |
| What do good integrated reports tell us?: An empirical study of japanese companies using text-mining K Nakagawa, S Sashida, R Kitajima, H Sakai 2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI …, 2020 | 16* | 2020 |
| Market uncertainty and correlation between Bitcoin and Ether K Nakagawa, R Sakemoto Finance Research Letters 50, 103216, 2022 | 12 | 2022 |
| Economic causal chain and predictable stock returns N Kei, S Shingo, S Hiroki, I Kiyoshi 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI …, 2019 | 12 | 2019 |
| Complex valued risk diversification Y Uchiyama, T Kadoya, K Nakagawa Entropy 21 (2), 119, 2019 | 12 | 2019 |
| No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging S Imaki, K Imajo, K Ito, K Minami, K Nakagawa The Journal of Financial Data Science, 2023 | 11 | 2023 |
| TPLVM: Portfolio Construction by Student’s t-Process Latent Variable Model Y Uchiyama, K Nakagawa Mathematics 8 (3), 449, 2020 | 11 | 2020 |
| Identification of b2b brand components and their performance’s relevance using a business card exchange network T Manabe, K Nakagawa, K Hidawa Pacific Rim Knowledge Acquisition Workshop, 152-167, 2021 | 10 | 2021 |
| Fractional SDE-net: generation of time series data with long-term memory K Hayashi, K Nakagawa 2022 IEEE 9th international conference on data science and advanced …, 2022 | 8* | 2022 |
Masaya AbeNomura Asset Management Co., Ltd.在 nomura-am.co.jp 的电子邮件经过验证
