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
48 * 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
35 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 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
26 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 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 Risk-based portfolios with large dynamic covariance matrices K Nakagawa, M Imamura, K Yoshida
International Journal of Financial Studies 6 (2), 52, 2018
21 2018 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
20 * 2017 The value of reputation capital during the COVID-19 crisis: Evidence from Japan T Manabe, K Nakagawa
Finance Research Letters 46, 102370, 2022
19 2022 Cryptocurrency network factors and gold K Nakagawa, R Sakemoto
Finance Research Letters 46, 102375, 2022
18 2022 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
18 2021 RM-CVaR: Regularized Multiple -CVaR Portfolio K Nakagawa, S Noma, M Abe
Proceedings of the Twenty-Ninth International Conference on International …, 2021
18 2021 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
13 * 2020 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 TPLVM: Portfolio Construction by Student’s t -Process Latent Variable Model Y Uchiyama, K Nakagawa
Mathematics 8 (3), 449, 2020
11 2020 Complex valued risk diversification Y Uchiyama, T Kadoya, K Nakagawa
Entropy 21 (2), 119, 2019
11 2019 Market uncertainty and correlation between Bitcoin and Ether K Nakagawa, R Sakemoto
Finance Research Letters 50, 103216, 2022
10 2022 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 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
9 2023 Taming Tail Risk: Regularized Multiple β Worst-Case CVaR Portfolio K Nakagawa, K Ito
Symmetry 13 (6), 922, 2021
7 2021