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Kei Nakagawa
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2020 – today
- 2024
- [c34]Yuntian Wang, Xiuyuan Wu, Yifan Fang, Keisuke Osawa, Kei Nakagawa, Eiichiro Tanaka:
Development of the Control Method of a 3-DOF KinematicBiological Matched Hip Joint Structure for Lower Limb Exoskeleton. ICARM 2024: 13-18 - [c33]Chang-Wen Wang, Yuntian Wang, Keisuke Osawa, Kei Nakagawa, Eiichiro Tanaka:
Adaptive Gait Model with Hybrid Control for Mass Production Oriented Exoskeleton. ICARM 2024: 146-151 - [c32]Dai Yamawaki, Kaito Takano, Kei Nakagawa:
Does Executive Compensation with ESG Target Improve Firm's ESG Performance? - Evidence from Japan. IIAI-AAI 2024: 267-272 - [c31]Yutaka Kuroki, Kei Nakagawa, Kiyoshi Yakabi:
Relationship Between Qualitative Expressions in MD&A and Managements' Forecast Accuracy. IIAI-AAI 2024: 280-285 - [c30]Tatsuki Masuda, Kei Nakagawa, Takahiro Hoshino:
Dynamic Dual Sparse Topic Model: Integrating Temporal Dynamics and Sparsity with Spike and Slab Priors into Topic Model. IIAI-AAI 2024: 299-304 - [c29]Moeko Asano, Yoshihiko Ichikawa, Kei Nakagawa, Kaito Takano:
Analysis of Investment Behavior of Individual Investors in the FX Market: Influence of FOMC and Beige Book Information. IIAI-AAI 2024: 373-378 - [c28]Tatsuyoshi Ogawa, Kei Nakagawa, Kokolo Ikeda:
Optimal Execution Strategy Using Deep Q-Network with Heuristics Policy. IIAI-AAI 2024: 456-461 - [c27]Kei Nakagawa, Kohei Hayashi:
Lf-Net:Generating Fractional Time-Series with Latent Fractional-Net. IJCNN 2024: 1-8 - [c26]Kei Nakagawa, Masanori Hirano, Kentaro Minami, Takanobu Mizuta:
A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets-A New Microfoundations of GARCH Model. PRIMA 2024: 97-113 - [i10]Kei Nakagawa
, Kohei Hayashi, Yugo Fujimoto:
CFTM: Continuous time fractional topic model. CoRR abs/2402.01734 (2024) - [i9]Kei Nakagawa, Masanori Hirano, Kentaro Minami, Takanobu Mizuta:
A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets - A New Microfoundations of GARCH model. CoRR abs/2409.12516 (2024) - 2023
- [j10]Mingyang Xu, Yifan Hua, Yun Fan Li
Development of an Ankle Assistive Robot with Instantly Gait-Adaptive Method. J. Robotics Mechatronics 35(3): 669-683 (2023) - [c25]Kaito Takano, Tomoki Okada, Yusuke Shimizu, Kei Nakagawa
Text Mining of Future Dividend Policy Sentences from Annual Securities Reports. IIAI-AAI 2023: 281-286 - [c24]Masaki Fujiwara, Yoshiyuki Suimon, Kei Nakagawa
Treasury yield spread prediction with sentiments of Beige Book and macroeconomic data. IIAI-AAI 2023: 337-342 - [c23]Yutaka Kuroki, Tomonori Manabe, Kei Nakagawa
Fact or Opinion? - Essential Value for Financial Results Briefing. IIAI-AAI 2023: 375-380 - [c22]Shingo Sashida, Kei Nakagawa
Multifactor Model with Deep Learning for Currency Investments. IIAI-AAI 2023: 412-417 - [c21]Tatsuki Masuda, Kei Nakagawa
Predicting Financial Asset Returns with Factor and Lead-Lag Relationships: Ridge Regression with Lagged Penalty. IIAI-AAI 2023: 534-539 - 2022
- [j9]Kei Nakagawa, Keita Higashi, Akari Ikeda, Naoto Kadono, Eiichiro Tanaka, Louis Yuge:
Robotic ankle control can provide appropriate assistance throughout the gait cycle in healthy adults. Frontiers Neurorobotics 16 (2022) - [j8]Kei Nakagawa
Time-series gradient boosting tree for stock price prediction. Int. J. Data Min. Model. Manag. 14(2): 110-125 (2022) - [j7]Yun Fan Li
Development of Automatic Controlled Walking Assistive Device Based on Fatigue and Emotion Detection. J. Robotics Mechatronics 34(6): 1383-1397 (2022) - [c20]Yugo Fujimoto, Kei Nakagawa
Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty. IEEE Big Data 2022: 1238-1245 - [c19]Kohei Hayashi
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory. DSAA 2022: 1-10 - [c18]Kei Nakagawa
Investment Strategy via Lead Lag Effect using Economic Causal Chain and SSESTM Model. IIAI-AAI 2022: 287-292 - [c17]Masaya Abe, Kei Nakagawa
Enhanced Quantile Portfolio for Multifactor Model with Deep Learning. IIAI-AAI 2022: 293-296 - [c16]Yuya Kimura, Kei Nakagawa
Industry Momentum Strategy Based on Text Mining in the Japanese Stock Market. IIAI-AAI 2022: 420-423 - [c15]Keigo Fujishima, Kei Nakagawa
Multiple Portfolio Blending Strategy with Thompson Sampling. IIAI-AAI 2022: 449-454 - [i8]Kohei Hayashi, Kei Nakagawa:
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory. CoRR abs/2201.05974 (2022) - [i7]Yugo Fujimoto, Kei Nakagawa
Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty. CoRR abs/2210.17030 (2022) - 2021
- [j6]Ayumu Nono
Entropy Based Student's t-Process Dynamical Model. Entropy 23(5): 560 (2021) - [j5]Kei Nakagawa
Taming Tail Risk: Regularized Multiple β Worst-Case CVaR Portfolio. Symmetry 13(6): 922 (2021) - [c14]Kentaro Imajo, Kentaro Minami, Katsuya Ito, Kei Nakagawa:
Deep Portfolio Optimization via Distributional Prediction of Residual Factors. AAAI 2021: 213-222 - [c13]Katsuya Ito, Kentaro Minami, Kentaro Imajo, Kei Nakagawa:
Trader-Company Method: A Metaheuristics for Interpretable Stock Price Prediction. AAMAS 2021: 656-664 - [i6]Junpei Komiyama, Masaya Abe, Kei Nakagawa, Kenichiro McAlinn:
Controlling False Discovery Rates Using Null Bootstrapping. CoRR abs/2102.07826 (2021) - [i5]Ruixing Cao, Akifumi Okuno, Kei Nakagawa, Hidetoshi Shimodaira:
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction. CoRR abs/2112.13951 (2021) - 2020
- [j4]Naoki Kobayakawa, Mitsuyoshi Imamura, Kei Nakagawa
Impact of Cryptocurrency Market Capitalization on Open Source Software Participation. J. Inf. Process. 28: 650-657 (2020) - [c12]Masaya Abe, Kei Nakagawa
How Do We Predict Stock Returns in the Cross-Section with Machine Learning? AICCC 2020: 63-69 - [c11]Masaya Abe, Kei Nakagawa
Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management. ASSE 2020: 9-15 - [c10]Kei Nakagawa
RIC-NN: A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy. DSAA 2020: 370-379 - [c9]Kei Nakagawa
What Do Good Integrated Reports Tell Us?: An Empirical Study of Japanese Companies Using Text-Mining. IIAI-AAI 2020: 516-521 - [c8]Kei Nakagawa
RM-CVaR: Regularized Multiple β-CVaR Portfolio. IJCAI 2020: 4562-4568 - [c7]Tomonori Manabe
Identification of B2B Brand Components and Their Performance's Relevance Using a Business Card Exchange Network. PKAW 2020: 152-167 - [i4]Yusuke Uchiyama, Kei Nakagawa:
TPLVM: Portfolio Construction by Student's t-process Latent Variable Model. CoRR abs/2002.06243 (2020) - [i3]Masahiro Kato, Kei Nakagawa
Policy Gradient with Expected Quadratic Utility Maximization: A New Mean-Variance Approach in Reinforcement Learning. CoRR abs/2010.01404 (2020)
2010 – 2019
- 2019
- [j3]Yusuke Uchiyama
Complex Valued Risk Diversification. Entropy 21(2): 119 (2019) - [c6]Kei Nakagawa
Economic Causal Chain and Predictable Stock Returns. IIAI-AAI 2019: 655-660 - [c5]Yusuke Uchiyama, Takanori Kadoya, Kei Nakagawa
Verification of Lead-Lag Effect in Financial Markets by the Adaptive Elastic Net Regression. IIAI-AAI 2019: 693-696 - [c4]Masaya Abe, Kei Nakagawa
Deep Learning for Multi-factor Models in Regional and Global Stock Markets. JSAI-isAI Workshops 2019: 87-102 - [i2]Kei Nakagawa, Tomoki Ito, Masaya Abe, Kiyoshi Izumi:
Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model. CoRR abs/1901.11493 (2019) - [i1]Kei Nakagawa
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy. CoRR abs/1910.01491 (2019) - 2018
- [c3]Kei Nakagawa
Deep Factor Model - Explaining Deep Learning Decisions for Forecasting Stock Returns with Layer-Wise Relevance Propagation. MIDAS/PAP@PKDD/ECML 2018: 37-50 - 2017
- [c2]Kei Nakagawa
Stock Price Prediction with Fluctuation Patterns Using Indexing Dynamic Time Warping and k^* -Nearest Neighbors. JSAI-isAI Workshops 2017: 97-111 - 2014
- [j2]Kei Nakagawa, Naofumi Otsuru, Koji Inui
Change-related auditory P50: A MEG study. NeuroImage 86: 131-137 (2014) - [j1]Kei Nakagawa, Koji Inui
Inhibition of somatosensory-evoked cortical responses by a weak leading stimulus. NeuroImage 101: 416-424 (2014) - 2012
- [c1]Kei Nakagawa, Kento Mori, Kenjiro Takemura
Impingement type micro fluidic device using electro-conjugate fluid. MHS 2012: 41-45
Coauthor Index
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