Publication
Referred Journal
[1] Kei Nakagawa,Mitsuyoshi Imamura,Kenichi Yoshida,
"Stock Price Prediction with Fluctuation Patterns using Indexing Dynamic Time Warping and k∗-Nearest Neighbors", 2018,
New Frontiers in Artificial Intelligence, Springer Lecture Note in Computer Science
[2] Kei Nakagawa,Mitsuyoshi Imamura,Kenichi Yoshida,
"Risk-Based Portfolio with Large Dynamic Covariance Matrices", 2018,
International Journal of Financial Studies (Asset Pricing and Portfolio Choice)
[3] Yusuke Uchiyama,Takanori Kadoya,Kei Nakagawa
"Complex Valued Risk Diversification", 2019,
[4] Kei Nakagawa,Mitsuyoshi Imamura,Kenichi Yoshida,
"Stock price prediction using k‐medoids clustering with indexing dynamic time warping", 2019,
Electronics and Communications in Japan, 102(2), 3-8.
[5] Kei Nakagawa,Takumi Uchida,Tomohisa Aoshima,
"Deep Factor Model-Explaining Deep Learning Decisions for Forecasting Stock Returns with Layer-Wise Relevance Propagation-", 2019,
ECML PKDD 2018 Workshops (pp. 37-50). Springer
[6] Yusuke Uchiyama, Kei Nakagawa
"TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model", 2020
[7] Takahiro Imai, Kei Nakagawa,
"Statistical Arbitrage Strategy in Multi-Asset Market using Time Series Analysis ", 2020
Journal of Mathematical Finance
[8] Naoki Kobayakawa ,Mitsuyoshi Imamura, Kei Nakagawa, Kenichi Yoshida
"Impact of Cryptocurrency Market Capitalization on Open Source Software Participation", 2020
[9] Masaya Abe, Kei Nakagawa,
"Deep Learning for Multi-factor Models in Regional and Global Stock Markets, 2020
New Frontiers in Artificial Intelligence, Springer Lecture Note in Computer Science
[10] Kei Nakagawa, Takanobu Kawahara and Akio Ito
"Asset Allocation Strategy with Non-Hierarchical Clustering Risk Parity Portfolio ", 2020
Journal of Mathematical Finance
[11] Kei Nakagawa, Yusuke Uchiyama
"GO-GJRSK Model with Application to Higher Order Risk-based Portfolio", 2020
[12] Ayumu Nono, Yusuke Uchiyama, Kei Nakagawa
"Entropy Based Student’s t-Process Dynamical Model", 2021
[13] Kei Nakagawa, Katsuya Ito
"Taming Tail Risk: Regularized Multiple β Worst-case CVaR Portfolio ", 2021
[14] Tomonori Manabe, Kei Nakagawa
"The Value of Reputation Capital during the COVID-19 Crisis: Evidence from Japan", 2021
[15] Kei Nakagawa, Ryuta Sakemoto
"Cryptocurrency network factors and gold", 2021
[16] Kei Nakagawa, Kenichi Yoshida
"Time-Series Gradient Boosting Tree for Stock Price Prediction", 2022
International Journal of Data Mining, Modelling and Management (IJDMMM)
[17] Kei Nakagawa, Yoshiyuki Suimon
"Inflation rate tracking portfolio optimization method: Evidence from Japan",2022
[18] Kei Nakagawa, Ryuta Sakemoto
"Dynamic Allocations for Currency Investment Strategies",2022
[19] Kei Nakagawa, Ryuta Sakemoto
"Market uncertainty and correlation between Bitcoin and Ether", 2022
[20]Kenji Kubo, Kei Nakagawa, Dipesh Acharya, Daiki Mizukami
"Optimal Liquidation Strategy for Cryptocurrency Marketplaces Using Stochastic Control",2023,
[21] Kei Nakagawa, Ryuta Sakemoto
"Macro factors in the returns on cryptocurrencies", 2023
[22] Shota Imaki, Kentaro Imajo, Katsuya Ito, Kentaro Minami, Kei Nakagawa
"No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging", 2023,
The Journal of Financial Data Science
[23]Kei Nakagawa,Ryuta Sakemoto
"Do commodity factors work as inflation hedges and safe havens?",2023,
[24] Hiroaki Horikawa, Kei Nakagawa
"Relationship between deep hedging and delta hedging: leveraging a statistical arbitrage strategy",2024,
Referred Conference Paper
[1] Kei Nakagawa, Mitsuyoshi Imamura, Kenichi Yoshida,
"Stock Price Prediction using k*-Nearest Neighbors and Indexing Dynamic Time Warping", 2017,
International Workshop: Artificial Intelligence of and for Business (AI-Biz2017)
[2] Kei Nakagawa, Takumi Uchida, Tomohisa Aoshima,
"Deep Factor Model", 2018,
MIDAS @ECML-PKDD 2018 - 3rd Workshop on MIning DAta for financial applicationS
[3] Kei Nakagawa, Tomoki Ito,Masaya Abe, Kiyoshi Izumi
"Deep Recurrent Factor Model", 2019,
AAAI-19 Network Interpretability for Deep Learning
[4] Kei Nakagawa, Shingo Sashida, Hiroki Sakaji, Kiyoshi Izumi
"Economic Causal Chain and Predictable Stock Returns", 2019,
8th International Congress on Advanced Applied Informatics
[5] Yusuke Uchiyama, Takanori Kadoya, Kei Nakagawa
"Verification of lead-lag effect in financial markets by the adaptive elastic net regression", 2019,
8th International Congress on Advanced Applied Informatics(SCAI2019)
[6] Masaya Abe, Kei Nakagawa
"Deep Learning for Multi-factor Models in Global Stock Markets", 2019,
International Workshop: Artificial Intelligence of and for Business (AI-Biz2019)
[7] Tomonori Manabe, Shohei Usui, Kei Nakagawa
"Relationship between corporate brand and market value, profitability, characteristics of business network in Japanese B2B markets", 2020,
International School and Conference on Network Science 2020(NetSci X)
[8] Kei Nakagawa, Masaya Abe, Junpei Komiyama
"A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy", 2020,
AAAI-20 Knowledge Discovery from Unstructured Data in Financial Services (KDF20)
[9] Masaya Abe, Kei Nakagawa
"Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management", 2020,
2020 International Artificial Intelligence and Blockchain Conference (AIBC 2020)
[10] Kei Nakagawa, Shingo Sashida, Ryozo Kitajima, Hiroyuki Sakai
"What Do Good Integrated Reports Tell Us?: An Empirical Study of Japanese Companies Using Text-Mining ", 2020,
8th International Congress on Advanced Applied Informatics(SCAI2020)
[11] Kei Nakagawa, Shuhei Noma, Masaya Abe
"RM-CVaR: Regularized Multiple beta-CVaR Portfolio", 2020,
the 29th International Joint Conference on Artificial Intelligence (IJCAI2020)
[12] Tomonori Manabe, Kei Nakagawa, Keigo Hidawa
"Identification of B2B Brand Components and their Performance's Relevance Using a Business Card Exchange Network",2020,
Principle and Practice of Data and Knowledge Acquisition Workshop@IJCAI2020 (PKAW2020)
[13] Kei Nakagawa, Masaya Abe, Junpei Komiyama
"RIC-NN: A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy",2020,
IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2020)
[14] Masaya Abe, Kei Nakagawa
"How Do We Predict Stock Returns in the Cross-Section with Machine Learning? ",2020,
3rd Artificial Intelligence and Cloud Computing Conference (AICCC2020 )
[15] Kentaro Imajo, Kentaro Minami, Katsuya Ito, Kei Nakagawa
"Deep Portfolio Optimization via Distributional Prediction of Residual Factors ", 2021,
The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21)
[16] Katsuya Ito, Kentaro Minami, Kentaro Imajo, Kei Nakagawa
"Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction", 2021,
20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)
[17] Kei Nakagawa, Akio Ito
"Carry Trading Strategy with RM-CVaR Portfolio", 2021,
9th International Congress on Advanced Applied Informatics (SCAI2021)
[18] Shingo Sashida, Kei Nakagawa
"Stock Return Prediction with SSESTM Model using Quarterly Japanese Company Handbook", 2021,
9th International Congress on Advanced Applied Informatics (SCAI2021)
[19] Yusuke Uchiyama, Kei Nakagawa
"Improving Momentum Strategies using Adaptive Elastic Dynamic Mode Decomposition", 2021,
9th International Congress on Advanced Applied Informatics (SCAI2021)
[20] Masahiro Kato, Kei Nakagawa, Kenshi Abe, Tetsuro Morimura
" Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization", 2021,
NeurIPS 2021 Workshop Deep Reinforcement Learning
[21] Kei Nakagawa, Shingo Sashida, Hiroki Sakaji
"Investment Strategy via Lead Lag Effect using Economic Causal Chain and SSESTM Model", 2022,
10th International Congress on Advanced Applied Informatics (SCAI2022),Outstanding Paper Award
[22] Keigo Fujishima, Kei Nakagawa
"Multiple Portfolio Blending Strategy with Thompson Sampling ", 2022,
10th International Congress on Advanced Applied Informatics (SCAI2022),Honorable Paper Award
[23] Masaya Abe, Kei Nakagawa
"Enhanced Quantile Portfolio for Multifactor Model with Deep Learning ", 2022,
10th International Congress on Advanced Applied Informatics (SCAI2022)
[24] Yuya Kimura, Kei Nakagawa
"Industry Momentum Strategy Based on Text Mining in the Japanese Stock Market", 2022,
10th International Congress on Advanced Applied Informatics (SCAI2022)
[25] Iwatsubo Kentaro, Kei Nakagawa
"Exchange Rate Forecasting with Fundamentals: The Trader-Company Method",2022
Twenty Seventh International Conference Forecasting Financial Markets
[26] Kohei Hayashi, Kei Nakagawa
"Fractional SDE-Net: Generation of Time Series Data with Long-term Memory",2022,
IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2022)
[27] Yugo Fujimoto, Kei Nakagawa, Kentaro Imajo, and Kentaro Minami
"Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty",2022,
2022 IEEE International Conference on Big Data (IEEE BIGDATA 2022)
[28] Kaito Takano, Tomoki Okada, Yusuke Shimizu and Kei Nakagawa
"Text Mining of Future Dividend Policy Sentences from Annual Securities Reports",2023,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2023
[29] Masaki Fujiwara, Yoshiyuki Suimon and Kei Nakagawa
"Treasury yield spread prediction with sentiments of Beige Book and macroeconomic data",2023,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2023
[30] Yutaka Kuroki, Tomonori Manabe and Kei Nakagawa
"Fact or Opinion? – Essential Value for Financial Results Briefing",2023,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2023
[31] Shingo Sashida and Kei Nakagawa
"Multifactor Model with Deep Learning for Currency Investments",2023,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2023
[32] Tatsuki Masuda and Kei Nakagawa
"Predicting Financial Asset Returns with Factor and Lead-Lag Relationships: Ridge Regression with Lagged Penalty",2023,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2023,Outstanding Paper Award
[33] Kei Nakagawa, Kohei Hayashi
"Lf-Net:Generating Fractional Time-Series with Latent Fractional-Net",2024,
The International Joint Conference on Neural Networks (IJCNN2024)
[34] Dai Yamawaki, Kaito Takano, Kei Nakagawa
"Does executive compensation with ESG target improve firm's ESG performance?--Evidence from Japan",2024,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2024
[35]Tatsuki Masuda, Kei Nakagawa, Takahiro Hoshino
"Dynamic Dual Sparse Topic Model: Integrating Temporal Dynamics and Sparsity with Spike and Slab Priors into Topic model",2024,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2024
[36]Yutaka Kuroki, Kei Nakagawa, Kiyoshi Yakabi
"Relationship between qualitative expressions in MD&A and managements' forecast accuracy",2024,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2024
[37]Tatsuyoshi Ogawa, Kei Nakagawa, Kokolo Ikeda
"Optimal execution strategy using Deep Q-Network with heuristics policy",2024,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2024,Competitive Paper Award
[38]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",2024,
Applied Informatics in Finance and Economics (AIFE) in IIAI AAI 2024
[39]Masahiro Kato, Kei Nakagawa, Kenshi Abe, Tetsuro Morimura, Kentaro Baba
"Mean-Variance Efficient Reinforcement Learning" ,2024
IEEE Symposium on Computational Intelligence for Financial Engineering and Economics(CIFEr)2024,Accepted
Working Papers
[1] Katsuya Ito, Kei Nakagawa
"NAPLES;Mining the lead-lag Relationship from Non-synchronous and High-frequency Data" ArXiv
[2] Masahiro Kato, Kei Nakagawa, Kenshi Abe, Tetsuro Morimura
"Mean-Variance Efficient Reinforcement Learning by Expected Quadratic Utility Maximization",2020, ArXiv
[3] Junpei Komiyama, Masaya Abe, Kei Nakagawa, Kenichiro McAlinn
"Controlling False Discovery Rates under Cross-Sectional Correlations",2021, ArXiv
[4] Ruixing Cao, Akifumi Okuno, Kei Nakagawa, Hidetoshi Shimodaira
"Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction",2021, ArXiv
[5] Yusuke Uchiyama, Kei Nakagawa
"Schrödinger Risk Diversification Portfolio",2022, ArXiv
[6] Yuichi Sano, Ryosuke Koga, Masaya Abe, Kei Nakagawa
"A New Initial Distribution for Quantum Generative Adversarial Networks to Load Probability Distributions",2023, ArXiv
[7] Kei Nakagawa, Masaya Abe, Seiichi Kuroki
"Doubly Robust Mean-CVaR Portfolio",2023,ArXiv
[8] Kei Nakagawa, Ryuta Sakemoto
"Commodity Sectors and Factor Investment Strategies",2023, SSRN
[9] Kei Nakagawa,Keisuke Morita, Ryuta Sakemoto
"Stochastic ESG Score and Capital Asset Pricing Model",2023, SSRN