平野 正徳

1. Honorable Mention Award in 16th IIAI International Congress on Advanced Applied Informatics (2024/07)
2. 2023年度 東京大学大学院 工学系研究科長賞 (2024/03)
   東京大学大学院工学系研究科紹介記事
3. 電子情報通信学会 言語理解とコミュニケーション研究会 (NLC) 2023年度 優秀研究賞 (2024/02)公式HP
4. 人工知能学会 金融情報学研究会(SigFin) 2022年度 学生優秀論文賞 (2023/10)公式HP
5. IEEE Computer Society Japan Chapter JAWS Young Researcher Award (09/2023) 公式 HP
6. JAWS (Joint Agent Workshop & Symposium) JAWS優秀賞 (09/2023) 公式 HP
7. 若手奨励賞, 情報処理学会自然言語処理(NL)研究会 (2023/09) 公式 HP
8. 奨励賞, NLP若手の会(Yans) 第18回シンポジウム (2023/08) 公式HP
9. ELYZA賞(スポンサー賞), NLP若手の会(Yans) 第18回シンポジウム (2023/08) 公式HP
10. Competitive Paper Award in 14th IIAI International Congress on Advanced Applied Informatics (2023/07)
11. Competitive Paper Award in 13th IIAI International Congress on Advanced Applied Informatics (2022/12)
12. Best paper award in the 6th IEEE International Conference on Agents (IEEE ICA 2022) (2022/11)
13. Student best paper award in the 9th International Conference on Behavioral and Social Computing (BESC 2022) (2022/10) 公式HP
14. Symposium on Multi Agent Systems for Harmonization 第6回シンポジウム (SMASH22 Summer Symposium) 準優秀賞 (2022/09) 公式HP
15. Symposium on Multi Agent Systems for Harmonization 第6回シンポジウム (SMASH22 Summer Symposium) 奨励賞 (2022/09) 公式HP
16. Best Paper Award in 12th IIAI International Congress on Advanced Applied Informatics (07/2022)
    東京大学大学院工学系研究科紹介記事 / システム創成学専攻紹介記事
17. 人工知能学会 研究会優秀賞 (2021年度) 賞概要
18. 人工知能学会 全国大会優秀賞 (2021)
    賞概要 / 東京大学大学院工学系研究科紹介記事 / システム創成学専攻紹介記事
19. 令和2年度 東京大学大学院 工学系研究科長賞 (2021/03)
    東京大学大学院工学系研究科紹介記事 / システム創成学専攻紹介記事
20. Nominated for student paper award in the 23rd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2020) (2019/11/19) Official HP
21. Nominated for best paper award in the 22nd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2019) (2019/10/30) Official HP
22. Winner (1st place) of Collusion Track in Supply Chain Management League (SCML) of the Tenth International Automated Negotiating Agents Competition (ANAC 2019) (2019/08/15) Official HP / 東京大学大学院工学系研究科紹介記事/ University Press / システム創成学専攻紹介記事
23. Winner (1st place) of Standard Track in Supply Chain Management League (SCML) of the Tenth International Automated Negotiating Agents Competition (ANAC 2019) (2019/08/15) Official HP / 東京大学大学院工学系研究科紹介記事/ University Press / システム創成学専攻紹介記事
24. 平成30年度 東京大学 工学部長賞 (2019/03)
25. 人工知能学会 全国大会学生奨励賞 (2018)
    賞概要 / 東京大学工学系研究科紹介記事 / システム創成学専攻紹介記事

1. Long Cheng, Kiyoshi Izumi, Masanori Hirano ,
   “Improvement and Analysis of Peak Shift Demand Response Scenarios of Industrial Consumers Using an Electricity Market Model,"
   New Generation Computing, 2024.
   doi.org/10.1007/s00354-024-00282-1, detail
2. Masanori HIRANO, Hiroki SAKAJI, Kiyoshi IZUMI,
   “PGSGAN: Policy Gradient Stock GAN,"
   International Journal of Smart Computing and Artificial Intelligence, vol.8, no.2, p.IJSCAI832, 2024.
   doi.org/10.52731/ijscai.v8.i1.832, detail
3. 鈴木 雅弘, 坂地 泰紀, 平野 正徳, 和泉 潔,
   “FinDeBERTaV2: 単語分割フリーな金融事前学習言語モデル,"
   人工知能学会論文誌, vol.39, no.4, p.FIN23-G_1-14, 2024.
   doi.org/10.1527/tjsai.39-4_FIN23-G, detail
4. 程 竜, 平野 正徳, 和泉 潔,
   “電力市場シミュレーションを用いた大口需要家によるカーボンニュートラル実現のための電力調達戦略分析,"
   情報処理学会論文誌, vol.65, no.1, pp.46-60, 2024.
   doi.org/10.20729/00231730, detail
5. Masanori HIRANO, Ryo WAKASUGI, Kiyoshi IZUMI,
   “Scenario Analysis of Demand Response Using Artificial Electric Power Market Simulations,"
   International Journal of Service and Knowledge Management, vol.7, no.2, p.IJSKM770, 2023.
   doi.org/10.52731/ijskm.v7.i2.770, detail
6. Masanori HIRANO, Kiyoshi IZUMI,
   “Neural-network-based Parameter Tuning for Multi-agent Simulation using Deep Reinforcement Learning,"
   World Wide Web, vol.26, no.5, 2023.
   doi.org/10.1007/s11280-023-01197-5, detail
   Impact Factor: 3.7 (2022). Q1 Journal as of 2023.
7. Masahiro SUZUKI, Hiroki SAKAJI, Masanori HIRANO, Kiyoshi IZUMI,
   “Constructing and Analyzing Domain-Specific Language Model for Financial Text Mining,"
   Information Processing & Management, vol.60, no.2, e103194, 2023.
   doi.org/10.1016/j.ipm.2022.103194, detail
   Impact Factor: 7.466, Q1 Journal as of 2022
8. Masanori HIRANO, Kiyoshi IZUMI, Hiroki SAKAJI,
   “STBM+: Advanced Stochastic Trading Behavior Model for Financial Markets using Residual Blocks or Transformers,"
   New Generation Computing Optional, Springer, vol.40, no.1, pp.7-24, 2022.
   doi.org/10.1007/s00354-021-00145-z, detail
9. Masanori HIRANO, Kiyoshi IZUMI, Hiroyasu MATSUSHIMA, Hiroki SAKAJI,
   “Comparing Actual and Simulated HFT Traders' Behavior for Agent Design,"
   Journal of Artificial Societies and Social Simulation, University of Surry, vol.23, no.6, e6, 2020.
   doi.org/10.18564/jasss.4304, detail
   (Q1 Journal as of 2020)
10. Masanori HIRANO, Kiyoshi IZUMI, Takashi SHIMADA, Hiroyasu MATSUSHIMA, Hiroki SAKAJI,
    “Impact Analysis of Financial Regulation on Multi Assets Markets using Artificial Market Simulations,"
    Journal of Risk and Financial Management, MDPI, vol.13, no.4, e75, 2020.
    doi.org/10.3390/jrfm13040075, detail
11. Masanori HIRANO, Hiroki SAKAJI, Shoko KIMURA, Kiyoshi IZUMI, Hiroyasu MATSUSHIMA, Shintaro NAGAO, Atsuo KATO,
    “Related Stocks Selection with Data Collaboration Using Text Mining,"
    Information (ISSN 2078-2489), MDPI, vol.10, no.3, e102, 2019.
    doi.org/10.3390/info10030102, detail

1. 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,”
   The 25th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2024), Kyoto, Japan, Nov. 18th, 2024.
   detail
   arXiv:2409.12516 (doi.org/10.48550/arXiv.2409.12516)
2. Masanori Hirano,
   “Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Assets Simulators,”
   IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr 2024), Hoboken, NJ, USA, Oct. 22nd, 2024.
   detail
3. Rawin Assabumrungrat, Kentaro Minami, Masanori Hirano,
   “Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study,”
   15th International Conference on Smart Computing and Artificial Intelligence (SCAI 2024) in 16th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2024), pp.329-336, July 7th, 2024.
   doi.org/10.1109/IIAI-AAI63651.2024.00068, detail
   arXiv:2311.07231 (doi.org/10.48550/arXiv.2311.07231), ssrn.com/abstract=4630864 (doi.org/10.2139/ssrn.4630864)
   Accept rate (Full paper): 87 / 293 = 29.7%
4. Masanori Hirano, Kentaro Imajo,
   “Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training,”
   15th International Conference on Smart Computing and Artificial Intelligence (SCAI 2024) in 16th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2024), pp.273-279, July 6th, 2024.
   doi.org/10.1109/IIAI-AAI63651.2024.00059, detail
   arXiv:2404.10555 (doi.org/10.48550/arXiv.2404.10555), ssrn.com/abstract=4796245 (doi.org/10.2139/ssrn.4796245)
   Accept rate (Full paper): 87 / 293 = 29.7%, Honorable mention award!
5. Masanori Hirano,
   “Construction of a Japanese Financial Benchmark for Large Language Models,”
   Joint Workshop of the 7th Financial Technology and Natural Language Processing (FinNLP), the 5th Knowledge Discovery from Unstructured Data in Financial Services (KDF), and The 4th Workshop on Economics and Natural Language Processing (ECONLP) in conjunction with LREC-COLING-2024, pp.1-9, Torino, Italy, May 20th, 2024.
   aclanthology.org/2024.finnlp-1.1, detail
   arXiv:2403.15062 (doi.org/10.48550/arXiv.2403.15062), ssrn.com/abstract=4769124 (doi.org/10.2139/ssrn.4769124)
6. Masahiro SUZUKI, Masanori HIRANO, Hiroki SAKAJI,
   “From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models,”
   2023 IEEE International Conference on Big Data (IEEE BigData 2023), pp.5684-5693, Sorrento, Italy, Dec. 15th, 2023.
   doi.org/10.1109/BigData59044.2023.10386605, detail
   arXiv:2309.03412 (doi.org/10.48550/arXiv.2309.03412), ssrn.com/abstract=4564308 (doi.org/10.2139/ssrn.4564308)
7. Masanori HIRANO, Kentaro MINAMI, Kentaro IMAJO,
   “Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling,”
   4th ACM International Conference on AI in Finance (ICAIF '23), pp.19-26, Brooklyn, NY, USA, Nov. 27th, 2023.
   doi.org/10.1145/3604237.3626846, detail
   arXiv:2307.13217 (doi.org/10.48550/arXiv.2307.13217), ssrn.com/abstract=4520273 (doi.org/10.2139/ssrn.4520273)
8. Masanori HIRANO, Masahiro SUZUKI, Hiroki SAKAJI,
   “llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology,”
   The 12th International Workshop on Web Services and Social Media (WSSM-2023) in The 26th International Conference on Network-Based Information Systems (NBiS-2023), pp.442-454, Chiang Mai, Thailand, Sep. 6th, 2023.
   doi.org/10.1007/978-3-031-40978-3_47, detail
   arXiv:2305.12720 (doi.org/10.48550/arXiv.2305.12720), ssrn.com/abstract=4454626 (doi.org/10.2139/ssrn.4454626)
9. Masanori HIRANO, Kiyoshi IZUMI,
   “Quantitative Evaluation of Multi-agent Simulation using Generative Adversarial Network,”
   9th International Conference on Computational Social Science (IC2S2), Copenhagen, Denmark, July 20th, 2023.
   detail
10. Masanori HIRANO, Hiroki SAKAJI, Kiyoshi IZUMI,
    “Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets,”
    13th International Conference on Smart Computing and Artificial Intelligence (SCAI 2023) in 14th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2023), pp.361-368, Koriyama, Japan, July 9th, 2023.
    doi.org/10.1109/IIAI-AAI59060.2023.00077, detail
    arXiv:2204.13338 (doi.org/10.48550/arXiv.2204.13338), ssrn.com/abstract=4454626 (doi.org/10.2139/ssrn.4454626)
    Accept rate (Full paper): 86 / 251 = 34%, Competitive paper award!
11. Masanori HIRANO, Kentaro IMAJO, Kentaro Minami, Takuya SHIMADA,
    “Efficient Learning of Nested Deep Hedging using Multiple Options,”
    13th International Conference on Smart Computing and Artificial Intelligence (SCAI 2023) in 14th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2023), pp.514-521, Koriyama, Japan, July 9th, 2023.
    doi.org/10.1109/IIAI-AAI59060.2023.00104, detail
    arXiv:2305.12264 (doi.org/10.48550/arXiv.2305.12264), ssrn.com/abstract=4454377 (doi.org/10.2139/ssrn.4454377)
    Accept rate (Full paper): 86 / 251 = 34%
12. Masanori HIRANO, Kiyoshi IZUMI,
    “Efficient Parameter Tuning for Multi-agent Simulation Using Deep Reinforcement Learning,”
    12th International Conference on Smart Computing and Artificial Intelligence (SCAI 2022-Winter) in 13th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2022 winter), pp.130-137, Phuket, Thailand, Dec. 12th, 2022.
    doi.org/10.1109/IIAI-AAI-Winter58034.2022.00035, detail
    Competitive Paper Award
13. Masanori HIRANO, Kiyoshi IZUMI,
    “Quantitative Tuning of Artificial Market Simulation using Generative Adversarial Network,”
    The 6th IEEE International Conference on Agents (ICA 2022), pp.12-17, Online, Nov. 28th, 2022.
    doi.org/10.1109/ICA55837.2022.00009, detail
    Accept rate (Oral): 6 / 17 = 35%, Best paper award
14. Masanori HIRANO, Kiyoshi IZUMI,
    “Does Order Simultaneity Affect the Data Mining Task in Financial Markets? -- Effect Analysis of Order Simultaneity using Artificial Market,”
    The 24th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2022), pp.297-313, Valencia, Spain, Nov. 17th, 2022.
    doi.org/10.1007/978-3-031-21203-1_18, detail
    Accept rate (Long paper): 31 / 100 = 31%
15. Masanori HIRANO, Ryo WAKASUGI, Kiyoshi IZUMI,
    “Analysis of Carbon Neutrality Scenarios of Industrial Consumers Using Electric Power Market Simulations,”
    The 24th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2022), pp.90-105, Valencia, Spain, Nov. 17th, 2022.
    doi.org/10.1007/978-3-031-21203-1_6, detail
    Accept rate (Long paper): 31 / 100 = 31%
16. Masanori HIRANO, Kiyoshi IZUMI,
    “Parameter Tuning Method for Multi-agent Simulation using Reinforcement Learning,”
    The 9th International Conference on Behavioral and Social Computing (BESC 2022), pp.1-7, Matsuyama, Ehime, Japan, Oct. 29th, 2022.
    doi.org/10.1109/BESC57393.2022.9995509, detail
    Accept rate (Long paper): 22 / 83 = 27%; Student best paper.
17. Masahiro SUZUMI, Hiroki SAKAJI, Masanori HIRANO, Kiyoshi IZUMI,
    “Constructing and Analyzing Domain-Specific Language Model for Financial Text Mining,”
    Information Processing and Management Conference, Online, Oct. 21st, 2022.
    detail
18. Masanori HIRANO, Ryo WAKASUGI, Kiyoshi IZUMI,
    “Analysis of Demand Response Scenarios by Industrial Consumers Using Artificial Electric Power Market Simulations,”
    7th International Conference on Business Management of Technology (BMOT 2022) in 12th IIAI International Congress on Advanced Applied Informatics (IIAI AAI 2022), pp.547-554, Kanazawa, Japan, July 7th, 2022.
    doi.org/10.1109/IIAIAAI55812.2022.00111, detail
    Accept rate (Long paper): 80 / 239 = 33%, Best paper award!
19. Masanori HIRANO, Kiyoshi IZUMI, Hiroki SAKAJI,
    “Implementation of Actual Data for Artificial Market Simulation,”
    The 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS2022), pp.1624-1626, Online, May 13th, 2022.
    doi.org/10.5555/3535850.3536056, detail
    www.ifaamas.org/Proceedings/aamas2022/pdfs/p1624.pdf
20. Masanori HIRANO, Hiroki SAKAJI, Kiyoshi IZUMI,
    “Concept and Practice of Artificial Market Data Mining Platform,”
    2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr), pp.1-10, Online, May 5th, 2022.
    doi.org/10.1109/CIFEr52523.2022.9776095, detail
21. Rei TAGUCHI, Hikaru WATANABE, Masanori HIRANO, Masahiro SUZUKI, Hiroki SAKAJI, Kiyoshi IZUMI, Kenji HIRAMATSU,
    “Market Trend Analysis Using Polarity Index Generated from Analyst Reports,”
    The 4th International Workshop on Cross-disciplinary Data Exchange and Collaboration (CDEC2021) in conjunction with 2021 IEEE International Conference on Big Data (Big Data), pp.3486-3494, Online, Dec. 15th, 2021.
    doi.org/10.1109/BigData52589.2021.9671702, detail
22. Masanori HIRANO, Hiroyasu MATSUSHIMA, Kiyoshi IZUMI, Hiroki SAKAJI,
    “STBM: Stochastic Trading Behavior Model for Financial Markets,”
    Selected Papers from the Annual Conference of Japanese Society of Artificial Intelligence (JSAI 2020), Advances in Artificial Intelligence, vol.1357, pp.157-165, 2021.
    doi.org/10.1007/978-3-030-73113-7_14, detail
23. Masanori HIRANO, Hiroyasu MATSUSHIMA, Kiyoshi IZUMI, Hiroki SAKAJI,
    “Implementation of Real Data for Financial Market Simulation using Clustering, Deep Learning, and Artificial Financial Market,”
    The 23rd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2020), pp.3-18, Online, Nov. 19th, 2020.
    doi.org/10.1007/978-3-030-69322-0_1, detail
    Accept rate (Full paper): 19 / 50 = 38%, Nominated for studnet paper award!
24. Masanori HIRANO, Hiroyasu MATSUSHIMA, Kiyoshi IZUMI, Taisei MUKAI,
    “Simulation of Unintentional Collusion Caused by Auto Pricing in Supply Chain Markets,”
    The 23rd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2020), pp.352-359, Online, Nov. 19th, 2020.
    doi.org/10.1007/978-3-030-69322-0_24, detail
25. Masanori HIRANO, Kiyoshi IZUMI, Hiroyasu MATSUSHIMA, Hiroki SAKAJI,
    “Comparison of Behaviors of Actual and Simulated HFT Traders for Agent Design,”
    The 22nd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2019), Torino, Italy, Oct. 31st, 2019.
    detail
    Accept rate (Long paper): 29 / 112 = 26%, Nominated for best paper award!: 3 / 112
    Published as a Journal article
26. Masanori HIRANO, Hiroki SAKAJI, Kiyoshi IZUMI, Hiroyasu MATSUSHIMA,
    “mhirano at the FinSBD Task: Pointwise Prediction Based on Multi-layer Perceptron for Sentence Boundary Detection,”
    The First Workshop on Financial Technology and Natural Language Processing (FinNLP2019) in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI) 2019, pp.102-107, Macao, China, Aug. 12th, 2019.
    aclanthology.org/W19-5518/, detail
27. Masanori HIRANO, Hiroki SAKAJI, Shoko KIMURA, Kiyoshi IZUMI, Hiroyasu MATSUSHIMA, Shintaro NAGAO, Atsuo KATO,
    “Selection of Related Stocks using Financial Text Mining,”
    The 1st International Workshop on Cross-disciplinary Data Exchange and Collaboration (CDEC) in 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp.191-198, Singapore, Singapore, Nov. 17th, 2018.
    doi.org/10.1109/ICDMW.2018.00036, detail
28. Masanori HIRANO, Kiyoshi IZUMI, Hiroki SAKAJI, Takashi SHIMADA, Hiroyasu MATSUSHIMA,
    “Impact Assessments of the CAR Regulation using Artificial Markets,”
    International Workshop on Artificial Market 2018 (IWAM2018) in conjunction with The 21st International Conference on Principles and Practice of Multi-Agent Systems (PRIMA2018), pp.43-58, Tokyo, Japan, Oct. 30th, 2018.
    www.u-mart.org/IWAM2018/CameraReady/IWAM2018_paper_2.pdf, detail

1. 平野 正徳, 今城 健太郎, 齋藤 俊太, 岡田 真太郎, 的矢 知樹, 谷口 徹, 太田 佳敬,
   “金融特化大規模言語モデルの構築と検証,”
   人工知能学会第33回金融情報学研究会 (SIG-FIN), pp.142-149, 東京, 2024/10/20.
   doi.org/10.11517/jsaisigtwo.2024.FIN-033_142, detail
2. ハンダル ルシケシュ, 的矢 知樹, 王 允卓, 平野 正徳,
   “KANOP: コルモゴロフ–アーノルドネットワークを用いたオプション価格モデル,”
   人工知能学会第33回金融情報学研究会 (SIG-FIN), pp.84-91, 東京, 2024/10/19.
   doi.org/10.11517/jsaisigtwo.2024.FIN-033_84, detail
3. 中川 慧, 平野 正徳, 藤本 悠吾,
   “大規模言語モデルを活用した金融センチメント分析における企業固有バイアスの評価,”
   第21回テキストアナリティクス・シンポジウム, vol.124, no.173, NLC2024-15, pp.81-86, 札幌, 2024/09/03.
   ken.ieice.org/ken/paper/20240903bc4F/, detail
4. 的矢 知樹, 王 允卓, 平野 正徳, 今城 健太郎,
   “価値関数学習に基づいた効率的DeepHedging機構,”
   2024年度人工知能学会全国大会(第38回) (JSAI2024), p.JSAI2024_4M3GS1002, 浜松, 2024/05/31.
   doi.org/10.11517/pjsai.JSAI2024.0_4M3GS1002, detail
5. 今城 健太郎, 中川 慧, 的矢 知樹, 平野 正徳, 青木 雅奈, 今長谷 拓,
   “主成分等価法による残差リターン抽出,”
   2024年度人工知能学会全国大会(第38回) (JSAI2024), p.JSAI2024_3D5GS205, 浜松, 2024/05/30.
   doi.org/10.11517/pjsai.JSAI2024.0_3D5GS205, detail
6. 中川 慧, 平野 正徳, 南 賢太郎, 水田 孝信,
   “AIトレーダーが市場へ与える影響--GARCH型モデルのミクロ的基礎づけによる検討,”
   2024年度人工知能学会全国大会(第38回) (JSAI2024), p.JSAI2024_3Xin201, 浜松, 2024/05/30.
   doi.org/10.11517/pjsai.JSAI2024.0_3Xin201, detail
7. 平野 正徳,
   “人工市場シミュレーションを用いたDeep Hedgingの実験的検証,”
   2024年度人工知能学会全国大会(第38回) (JSAI2024), 浜松, 2024/05/29.
   doi.org/10.11517/pjsai.JSAI2024.0_2F1GS503, detail
8. 平野 正徳,
   “金融分野における言語モデル性能評価のための日本語金融ベンチマーク構築,”
   言語処理学会第30回年次大会 (NLP2024), pp.1570-1574, 神戸, 2024/03/18.
   www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/C6-4.pdf, detail
9. 平野 正徳,
   “言語モデル性能評価のための日本語金融ベンチマーク構築と各モデルのパフォーマンス動向,”
   人工知能学会第32回金融情報学研究会 (SIG-FIN), pp.28-35, 渋谷, 2024/03/02.
   doi.org/110.11517/jsaisigtwo.2023.FIN-032_28, detail
10. 平野 正徳, 高田 亮介, 和泉 潔,
    “PAMS: Platform for Artificial Market Simulations ～Pythonベースの人工市場シミュレーションプラットフォームと深層学習との融合～,”
    合同エージェントワークショップ＆シンポジウム2023 (JAWS2023), 登別, 北海道, 2023/09/13.
    detail
11. 鈴木 雅弘, 平野 正徳, 坂地 泰紀,
    “日本語インストラクションデータを用いた対話可能な日本語大規模言語モデルのLoRAチューニング,”
    第20回テキストアナリティクス・シンポジウム, 堺, 大阪, 2023/09/06.
    ken.ieice.org/ken/paper/20230906NCwg/, detail
12. 平野 正徳, 鈴木 雅弘, 坂地 泰紀,
    “llm-japanese-dataset v0: 大規模言語モデルのための日本語チャットデータセット構築,”
    情報処理学会 第257回自然言語処理研究発表会 (NL研), 四谷, 東京, 2023/09/01.
    id.nii.ac.jp/1001/00227482/, detail
13. Long CHENG, Kiyoshi IZUMI, Masanori HIRANO,
    “Analysis of Carbon Neutrality Scenarios of Industrial Consumers Combining Power Generation, Storage, and DR Using an Electricity Market Model,”
    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023), Kumamoto, Japan, 2023/06/08.
    doi.org/10.11517/pjsai.JSAI2023.0_3U1IS305, detail
14. Masanori HIRANO, Kiyoshi IZUMI,
    “Quantitative Evaluation of Multi-agent Simulation using Generative Adversarial Network -- An Alternative of Qualitative Evaluation for Artificial Market Simulation,”
    The 37th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2023), p.3U1IS301, Kumamoto, Japan, 2023/06/08.
    doi.org/10.11517/pjsai.JSAI2023.0_3U1IS301, detail
15. 程 竜, 和泉 潔, 平野 正徳,
    “電力市場シミュレーションを用いた発電・蓄電・DRを組み合わせた大口需要家による電力調達戦略分析,”
    人工知能学会第30回金融情報学研究会 (SIG-FIN), pp.24-31, 文京区, 2023/03/04.
    doi.org/10.11517/jsaisigtwo.2023.FIN-030_24, detail
16. 平野 正徳, 南 賢太郎, 今城 健太郎,
    “原資産価格過程不要な敵対的Deep Hedging,”
    人工知能学会第30回金融情報学研究会 (SIG-FIN), pp.51-57, 文京区, 2023/03/04.
    doi.org/10.11517/jsaisigtwo.2023.FIN-030_51, detail
17. Masanori HIRANO, Kiyoshi IZUMI, Hiroki SAKAJI,
    “Data-driven Agent Design for Artificial Market Simulation,”
    The 36th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2022), p.2S4IS2b01, Kyoto, Japan, 2022/06/15.
    doi.org/10.11517/pjsai.JSAI2022.0_2S4IS2b01, detail
18. 平野 正徳, 今城 健太郎, 南 賢太郎, 島田 拓弥,
    “オプションによるオプションのヘッジを可能にする二重 Deep Hedging 機構,”
    人工知能学会第28回金融情報学研究会 (SIG-FIN), pp.27-34, 文京区, 2022/03/12.
    doi.org/10.11517/jsaisigtwo.2022.FIN-028_27, detail
19. 鈴木 雅弘, 坂地 泰紀, 平野 正徳, 和泉 潔,
    “事前学習と追加事前学習による金融言語モデルの構築と検証,”
    人工知能学会第28回金融情報学研究会 (SIG-FIN), pp.132-137, 文京区, 2022/03/12.
    doi.org/10.11517/jsaisigtwo.2022.FIN-028_132, detail
20. 若杉 亮, 和泉 潔, 平野 正徳,
    “電力市場モデルを用いた大口需要家によるDRシナリオの分析,”
    人工知能学会第28回金融情報学研究会 (SIG-FIN), pp.19-24, 文京区, 2022/03/12.
    doi.org/10.11517/jsaisigtwo.2022.FIN-028_19, detail
21. 若杉 亮, 和泉 潔, 平野 正徳,
    “大口需要家によるカーボンニュートラル実現のための電力調達シナリオの分析,”
    人工知能学会第21回データ指向構成マイニングとシミュレーション研究会 (DOCMAS), 留寿都, 2022/03/9.
    collabodesign.org/docmas/2022/03/09/160/, detail
    研究会優秀賞受賞!
22. 鈴木 雅弘, 坂地 泰紀, 平野 正徳, 和泉 潔,
    “金融文書を用いた事前学習言語モデルの構築と検証,”
    人工知能学会第27回金融情報学研究会 (SIG-FIN), pp.5-10, Online, 2021/10/09.
    sigfin.org/?027-02, detail
23. 田口 怜, 渡邊 光, 平野 正徳, 鈴木 雅弘, 坂地 泰紀, 和泉 潔, 平松 賢士,
    “金融テキストから生成した極性指標を用いた市場の因果性分析,”
    人工知能学会第27回金融情報学研究会 (SIG-FIN), pp.36-43, Online, 2021/10/09.
    sigfin.org/?027-07, detail
24. 鈴木 雅弘, 坂地 泰紀, 平野 正徳, 和泉 潔,
    “金融ドメインにおける事前学習BERTモデルの性能検証,”
    第18回テキストアナリティクス・シンポジウム, 電子情報通信学会技術研究報告, vol.12, no.178, NLC2021-12, pp.26-29, Online, 2021/09/16.
    www.ieice.org/ken/paper/20210916yCfG/, detail
25. Masanori HIRANO, Kiyoshi IZUMI, Hiroki SAKAJI,
    “STBM+: Advanced Stochastic Trading Behavior Model for Financial Markets based on Residual Blocks or Transformers,”
    The 35th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2021), p.2N1-IS-2a-03, Online, 2021/06/09.
    doi.org/10.11517/pjsai.JSAI2021.0_2N1IS2a03, detail
    優秀賞受賞!
26. 若杉 亮, 和泉 潔, 平野 正徳,
    “電力市場マルチエージェントシミュレーションと工場電力消費モデルによるDR効果分析,”
    2021年度人工知能学会全国大会(第35回), p.2I3-GS-5b-04, Online, 2021/06/09.
    doi.org/10.11517/pjsai.JSAI2021.0_2I3GS5b04, detail
27. Masanori HIRANO, Hiroyasu MATSUSHIMA, Kiyoshi IZUMI, Hiroki SAKAJI,
    “STBM: Stochastic Trading Behavior Model for Financial Markets Based on Long Short-Term Memory,”
    The 34th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2020), p.1K4-ES-2-04, Online, 2020/06/09.
    doi.org/10.11517/pjsai.JSAI2020.0_1K4ES204, detail
28. 平野 正徳, 坂地 泰紀, 松島 裕康, 和泉 潔,
    “金融文書のための別タスク学習による教師なし重要文判定,”
    言語処理学会第26回年次大会 (NLP2020), pp.569-572, Online, 2020/03/18.
    www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/B3-4.pdf, detail
29. 平野 正徳, 坂地 泰紀, 木村 笙子, 和泉 潔, 松島 裕康, 長尾 慎太郎, 加藤 惇雄,
    “テキストマイニングを利用したテーマに関連する上場企業検索ツールの開発,”
    人工知能学会第24回金融情報学研究会 (SIG-FIN), pp.1-8, 吉祥寺, 2020/03/15.
    sigfin.org/?024-41, detail
30. 平野 正徳, 和泉 潔, 松島 裕康, 坂地 泰紀, 島田 尚,
    “東京証券取引所における高頻度マーケットメイク戦略の注文行動分析,”
    2019年度人工知能学会全国大会(第33回) (JSAI2019), p.2O1-J-13-04, 新潟, 2019/06/05.
    doi.org/10.11517/pjsai.JSAI2019.0_2O1J1304, detail
31. 平野 正徳, 坂地 泰紀, 木村 笙子, 和泉 潔, 松島 裕康, 長尾 慎太郎, 加藤 惇雄,
    “文書内における単語の共起を利用した上位下位概念の推定,”
    言語処理学会第25回年次大会 (NLP2019), pp.597-600, 名古屋, 2019/03/14.
    www.anlp.jp/proceedings/annual_meeting/2019/pdf_dir/P3-22.pdf, detail
32. 平野 正徳, 米納 弘渡, 和泉 潔,
    “人工市場シミュレーションを用いた自己資本規制の金融市場に与える影響分析,”
    2018年度人工知能学会全国大会(第32回), p.2P205, 鹿児島, 2018/06/06.
    doi.org/10.11517/pjsai.JSAI2018.0_2P205, detail
    [予稿訂正] / 学生奨励賞受賞!

1. Masahiro Hirano,
   “Construction of a Japanese Financial Benchmark for Large Language Models,”
   arXiv:2403.15062 (doi.org/10.48550/arXiv.2403.15062), ssrn.com/abstract=4769124 (doi.org/10.2139/ssrn.4769124)
   detail
2. 平野 正徳,
   “金融分野における言語モデル性能評価のための日本語金融ベンチマーク構築,”
   doi.org/10.51094/jxiv.564, detail
3. Rawin Assabumrungrat, Kentaro Minami, Masanori Hirano,
   “Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study,”
   arXiv:2311.07231 (doi.org/10.48550/arXiv.2311.07231), ssrn.com/abstract=4630864 (doi.org/10.2139/ssrn.4630864)
   detail
4. Masahiro SUZUKI, Masanori HIRANO, Hiroki SAKAJI,
   “From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models,”
   arXiv:2309.03412 (doi.org/10.48550/arXiv.2309.03412), ssrn.com/abstract=4564308 (doi.org/10.2139/ssrn.4564308)
   doi.org/10.48550/arXiv.2309.03412, detail
5. 平野 正徳, 高田 亮介, 和泉 潔,
   “PAMS: Platform for Artificial Market Simulations ～Pythonベースの人工市場シミュレーションプラットフォームと深層学習との融合～,”
   doi.org/10.51094/jxiv.461, detail
6. Masanori HIRANO, Kentaro MINAMI, Kentaro IMAJO,
   “Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling,”
   arXiv:2307.13217 (doi.org/10.48550/arXiv.2307.13217), ssrn.com/abstract=4520273 (doi.org/10.2139/ssrn.4520273)
   detail
7. 鈴木 雅弘, 平野 正徳, 坂地 泰紀,
   “日本語インストラクションデータを用いた対話可能な日本語大規模言語モデルのLoRAチューニング,”
   doi.org/10.51094/jxiv.422, detail
8. 平野 正徳, 鈴木 雅弘, 坂地 泰紀,
   “llm-japanese-dataset v0: 大規模言語モデルのための日本語チャットデータセット構築,”
   doi.org/10.51094/jxiv.383, detail
9. Masanori HIRANO, Masahiro SUZUKI, Hiroki SAKAJI,
   “llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology,”
   arXiv:2305.12720 (doi.org/10.48550/arXiv.2305.12720), ssrn.com/abstract=4454626 (doi.org/10.2139/ssrn.4454626)
   detail
10. Masanori HIRANO, Kentaro IMAJO, Kentaro MINAMI, Takuya SHIMADA,
    “Efficient Learning of Nested Deep Hedging using Multiple Options,”
    arXiv:2305.12264 (doi.org/10.48550/arXiv.2305.12264), ssrn.com/abstract=4454377 (doi.org/10.2139/ssrn.4454377)
    detail
11. Masanori HIRANO, Hiroki SAKAJI, Kiyoshi IZUMI,
    “Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets,”
    arXiv:2204.13338 (doi.org/10.48550/arXiv.2204.13338), ssrn.com/abstract=4095304 (doi.org/10.2139/ssrn.4095304)
    detail

1. 今城 健太郎, 平野 正徳, 鈴木 脩司, 三上 裕明,
   "日本語事前学習向けベンチマーク: 日本語圏特有の常識に関する回答性能の評価"
   NLP若手の会(Yans) 第19回シンポジウム, 大阪, 2023/09/06
2. 鈴木雅弘, 平野正徳, 坂地泰紀
   "日本語インストラクションデータセットの構築とその適用による大規模言語モデルのチューニング"
   NLP若手の会(Yans) 第18回シンポジウム, 東京, 2023/08/31
3. 平野正徳,
   "Deep Hedgingの発展と応用"
   MPTフォーラム, 東京, 2023/6/1
   https://mptforum.org/kiroku/season=2023
4. 平野正徳，和泉潔
   "GANを用いた人工市場シミュレーションの定量的評価に向けて"
   Symposium on Multi Agent Systems for Harmonization (SMASH) 2022 Summer Symposium, 浜松, 2022/9/16
   
5. 鈴木雅弘, 坂地泰紀, 平野正徳, 和泉潔, 松島裕康
   "金融ドメインの事前学習BERTモデルの構築に向けて"
   NLP若手の会(Yans) 第16回シンポジウム, Online, 2021/08/31
   
6. 平野正徳, 坂地泰紀, 和泉潔, 松島裕康
   "テーマに関連する上場企業検索システムの紹介"
   NLP若手の会(Yans) 第14回シンポジウム, 札幌, 2019/08/26
   
7. Masanori HIRANO,
   "SCM League: UTokyo Izumi Lab." in ANAC Session
   Twelfth International Workshop on Agent-based Complex Automated Negotiations (ACAN2019) in conjunction with IJCAI 2019, Macau, China, Aug. 11th, 2019
   

1. Masanori HIRANO, Taisei MUKAI, Hiroyasu MATSUSHIMA, and Kiyoshi IZUMI
   "Insurance Fraud Factory Manager Agent at ANAC2019 Supply Chain Management League,"
   The Tenth International Automated Negotiating Agents Competition (ANAC2019) of IJCAI 2019, Macau, China, Aug. 15th, 2019. (To appear)
   2 tracks 制覇!: 1st place in Standard Track & 1st place in Collusion Track

1. 2021/04 - 2024/04: 日本学術振興会(学振) 特別研究員 (DC1), 9,700,000 円

現在

1. 2024/04 - : Organizin Committee of IEEE Computational Intelligence in Financial Engineering and Economics (CIFEr 2026)
2. 2024/08 - : Program Committee of ACM International Conference on AI in Finance (ICAIF)
3. 2024/04 - : Committee of Special Interest Group on Financial Informatics of Japanese Society for Artificial Intelligence (SIG-FIN)
4. 2023/01 - : Organizer of Applied Informatics in Finance and Economics (AIFE)
5. 2024/04 - : Program Committee of 15th International Conference on Smart Computing and Artificial Intelligence (SCAI 2024) in IIAI AAI 2024
6. 2024/01 -: Program Committee of Joint Workshop of the 7th Financial Technology and Natural Language Processing (FinNLP), the 5th Knowledge Discovery from Unstructured Data in Financial Services (KDF), and The 4th Workshop on Economics and Natural Language Processing (ECONLP)

過去

1. 2021/04 - 2023/04: NLP若手の会(Yans) 運営委員

1. 特許6596565: 抽出システムおよびプログラム

1. japanese-lm-fin-harness (2023 release)
2. izumi-lab/llm-japanese-dataset (2023 release)
3. PAMS (2022 release)
4. pfhedge (2021 release)
5. PlhamJ (2019/04 公開)
6. Plham v0.3 (2019/03 公開)

1. 平野 正徳,
   "会議報告：AAMAS 2022（The 21st International Conference on Autonomous Agents and Multiagent Systems）", 人工知能, Vol.37, No.5, pp. 679-680, 2022. doi.org/10.11517/jjsai.37.5_679
2. 平野 正徳,
   教育用計算機システム(ECCS)相談員の声, "Digital Life", 東京大学情報基盤センター, pp.30-31, Vol.30 (2018.3), 2018. ISSN: 1345-3017