中川慧(なかがわけい)

業績一覧

査読付き論文

査読付き国際会議/ワークショップ

ワーキングペーパー

査読付き論文

[1] 中川慧

"非線形共和分関係に基づくペアトレード戦略", 2016,

テクニカルアナリストジャーナル 2016 vol3 ,優秀賞受賞

[2] 中川慧, 今村光良

"深層学習を用いたカオス時系列解析によるテクニカル分析", 2017,

テクニカルアナリストジャーナル 2017 vol4 ,佳作受賞

[3] 中川慧

"リスクベース・ポートフォリオの高次モーメントへの拡張", 2017,

リスク管理・保険とヘッジ(JAFEE Journal) 朝倉書店

[4] 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

[5] 中川慧, 今村光良, 吉田健一

"価格変動パターンを用いた市場予測 k-Medoids Clustering with Indexing Dynamic Time Warpingの株式市場への適用", 2018,

電気学会論文誌C Vol. 138 No. 8

[6] 今村光良, 中川慧, 吉田健一

"価格変動パターンによる証券/為替/仮想通貨市場の分析", 2018,

電気学会論文誌C Vol. 138 No. 8

[7] Kei Nakagawa, Mitsuyoshi Imamura, Kenichi Yoshida

"Risk-Based Portfolio with Large Dynamic Covariance Matrices", 2018,

International Journal of Financial Studies, 6(2), 52. (Asset Pricing and Portfolio Choice)

[8] 伊藤彰朗, 中川慧

"マクロ・ファクターの定量化とリスク分析への応用", 2018,

証券アナリストジャーナル,56(8), 80-90.

[9] Yusuke Uchiyama, Takanori Kadoya, Kei Nakagawa

"Complex Valued Risk Diversification", 2019,

Entropy, 21(2), 119.

[10] 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.

[11] 中川慧

"ブラック・リッターマン法を用いたリスクベース・ポートフォリオの拡張", 2019,

ファイナンシャルプランニング研究,優秀賞受賞

[12] 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

[13] 今村光良, 中川慧

"大規模動的相関モデルを用いた金融資産間における動的ネットワーク構造の分析", 2019,

WebDB Forum 2019論文集 2019, 69-72

[14] Yusuke Uchiyama, Kei Nakagawa

"TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model", 2020

Mathematics 2020 8, (3)

[15] Takahiro Imai, Kei Nakagawa

"Statistical Arbitrage Strategy in Multi-Asset Market using Time Series Analysis ", 2020

Journal of Mathematical Finance,10 (2), 334-344

[16] 真鍋友則, 中川慧

"B2B 市場における企業ブランドと ROA の関連性", 2020

証券アナリストジャーナル58 (6), 73-83

[17] Naoki Kobayakawa ,Mitsuyoshi Imamura, Kei Nakagawa, Kenichi Yoshida

"Impact of Cryptocurrency Market Capitalization on Open Source Software Participation", 2020

Journal of Information Processing 28, 650-657

[18] 真鍋友則, 中川慧

"B2B企業ブランド評価と株価の価値関連性の実証研究 ", 2020

経営情報学会誌, 29 (2), 87-104

[19] 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

[20] Kei Nakagawa, Takanobu Kawahara, Akio Ito

"Asset Allocation Strategy with Non-Hierarchical Clustering Risk Parity Portfolio", 2020

Journal of Mathematical Finance ,10 (4), 513-524

[21] Kei Nakagawa, Yusuke Uchiyama

"GO-GJRSK Model with Application to Higher Order Risk-based Portfolio", 2020

Mathematics 2020, 8(11), 1990

[22] 高野海斗, 酒井浩之, 中川慧

"学習データの自動生成による深層学習を用いた株主招集通知の重要ページ抽出", 2021

人工知能学会論文誌36 巻 (2021) 1 号

[23] 森田啓介, 中川慧

"株式市場におけるクラウディングの定量化", 2021

証券アナリストジャーナル,59 (6), 90-101

[24] Ayumu Nono, Yusuke Uchiyama, Kei Nakagawa

"Entropy Based Student’s t-Process Dynamical Model", 2021

Entropy, 23 (5), 560

[25] Kei Nakagawa, Katsuya Ito

"Taming Tail Risk: Regularized Multiple β Worst-case CVaR Portfolio ", 2021

Symmetry 2021, 13(6), 922

[26] Tomonori Manabe, Kei Nakagawa

"The Value of Reputation Capital during the COVID-19 Crisis: Evidence from Japan", 2021

Finance Research Letters, 46, 102370

[27] Kei Nakagawa, Ryuta Sakemoto

"Cryptocurrency network factors and gold", 2021

Finance Research Letters,46, 102375

[28] Kei Nakagawa, Kenichi Yoshida

"Time-Series Gradient Boosting Tree for Stock Price Prediction", 2022

International Journal of Data Mining, Modelling and Management (IJDMMM) ,14 (2), 110-125

[29] Kei Nakagawa, Yoshiyuki Suimon

"Inflation rate tracking portfolio optimization method: Evidence from Japan",2022

Finance Research Letters,49, 103130

[30] Kei Nakagawa, Ryuta Sakemoto

"Dynamic Allocations for Currency Investment Strategies",2022

European Journal of Finance,29 (10), 1207-1228

[31] Kei Nakagawa, Ryuta Sakemoto

"Market uncertainty and correlation between Bitcoin and Ether", 2022

Finance Research Letters, 50, 103216

[32]Kenji Kubo, Kei Nakagawa, Dipesh Acharya, Daiki Mizukami

"Optimal Liquidation Strategy for Cryptocurrency Marketplaces Using Stochastic Control",2023,

Finance Research Letters, 53, 103639

[33] Kei Nakagawa, Ryuta Sakemoto

"Macro factors in the returns on cryptocurrencies", 2023

Applied Finance Letters, 11, 146-158.

[34] 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,5(2), 84-99.

[35] Kei Nakagawa,Ryuta Sakemoto

"Do commodity factors work as inflation hedges and safe havens?",2023,

Finance Research Letters, 58, 104585

[36] Hiroaki Horikawa, Kei Nakagawa

"Relationship between deep hedging and delta hedging: leveraging a statistical arbitrage strategy",2024,

Finance Research Letters, 105101

[37] 黒木裕鷹, 真鍋友則, 中川慧

"テキスト分析を用いた本邦決算説明会の情報価値 -企業と投資家の双方向の観点からの定量的検証",2024,

人工知能学会論文誌,Accepted

[38] 赤松朋哉, 中川慧, 山田大貴

"最適輸送理論による離散曲率を用いたグラフアルゴリズムと金融市場への応用",2024,

人工知能学会論文誌,Accepted

[39] 藤原真幸, 中込智樹, 加古海星, 堀川弘晃, 中川慧

"Deep SmoothingとDeep Hedgingを用いたBTC市場における複数オプションのヘッジ戦略分析",2024,

人工知能学会論文誌,Accepted

[40] 高野海斗, 中川慧, 藤本悠吾

"大規模言語モデルによる投信ディスクロージャー資料の市況および見通しコメントの自動生成",2024,

人工知能学会論文誌,Accepted

[41] 河村康平, 酒井浩之, 永並健吾, 高野海斗, 中川慧

"解釈性を考慮した統合報告書の自動評価",2024,

人工知能学会論文誌,Accepted

査読付き国際会議/ワークショップ

[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] Akifumi Okuno, Ruixing Cao, Kei Nakagawa, Hidetoshi Shimodaira

"Optimal nonparametric classification via radial distance",2023,

CMStatistics2023

[34] Kei Nakagawa, Kohei Hayashi

"Lf-Net:Generating Fractional Time-Series with Latent Fractional-Net",2024,

The International Joint Conference on Neural Networks (IJCNN2024) ,Accepted

[35] Dai Yamawaki, Kaito Takano, Kei Nakagawa

"Does executive compensation with ESG target improve firm's ESG performance?--Evidence from Japan",2024,