Selection of Related Stocks using Financial Text Mining. - Masanori Hirano 001; Hiroki Sakaji; Shoko Kimura; Kiyoshi Izumi; Hiroyasu Matsushima; Shintaro Nagao; Atsuo Kato (ICDM Workshops, 2018)

Related Stocks Selection with Data Collaboration Using Text Mining. - Masanori Hirano 001; Hiroki Sakaji; Shoko Kimura; Kiyoshi Izumi; Hiroyasu Matsushima; Shintaro Nagao; Atsuo Kato (Inf., 2019)

STBM: Stochastic Trading Behavior Model for Financial Markets. - Masanori Hirano 001; Hiroyasu Matsushima; Kiyoshi Izumi; Hiroki Sakaji (JSAI, 2020)

Simulation of Unintentional Collusion Caused by Auto Pricing in Supply Chain Markets. - Masanori Hirano 001; Hiroyasu Matsushima; Kiyoshi Izumi; Taisei Mukai (PRIMA, 2020)

Implementation of Real Data for Financial Market Simulation Using Clustering, Deep Learning, and Artificial Financial Market. - Masanori Hirano 001; Hiroyasu Matsushima; Kiyoshi Izumi; Hiroki Sakaji (PRIMA, 2020)

Comparing Actual and Simulated HFT Traders' Behavior for Agent Design. - Masanori Hirano 001; Kiyoshi Izumi; Hiroyasu Matsushima; Hiroki Sakaji (J. Artif. Soc. Soc. Simul., 2020)

Market Trend Analysis Using Polarity Index Generated from Analyst Reports. - Rei Taguchi; Hikaru Watanabe; Masanori Hirano 001; Masahiro Suzuki 004; Hiroki Sakaji; Kiyoshi Izumi; Kenji Hiramatsu (IEEE BigData, 2021)

Implementation of Actual Data for Artificial Market Simulation. - Masanori Hirano 001; Kiyoshi Izumi; Hiroki Sakaji (AAMAS, 2022)

Parameter Tuning Method for Multi-agent Simulation using Reinforcement Learning. - Masanori Hirano 001; Kiyoshi Izumi (BESC, 2022)

Concept and Practice of Artificial Market Data Mining Platform. - Masanori Hirano 001; Hiroki Sakaji; Kiyoshi Izumi (CIFEr, 2022)

Quantitative Tuning of Artificial Market Simulation using Generative Adversarial Network. - Masanori Hirano 001; Kiyoshi Izumi (ICA, 2022)

Efficient Parameter Tuning for Multi-agent Simulation Using Deep Reinforcement Learning. - Masanori Hirano 001; Kiyoshi Izumi (IIAI-AAI-Winter, 2022)

Analysis of Demand Response Scenarios by Industrial Consumers Using Artificial Electric Power Market Simulations. - Masanori Hirano 001; Ryo Wakasugi; Kiyoshi Izumi (IIAI-AAI, 2022)

Does Order Simultaneity Affect the Data Mining Task in Financial Markets? - Effect Analysis of Order Simultaneity Using Artificial Market. - Masanori Hirano 001; Kiyoshi Izumi (PRIMA, 2022)

Analysis of Carbon Neutrality Scenarios of Industrial Consumers Using Electric Power Market Simulations. - Masanori Hirano 001; Ryo Wakasugi; Kiyoshi Izumi (PRIMA, 2022)

STBM+: Advanced Stochastic Trading Behavior Model for Financial Markets using Residual Blocks or Transformers. - Masanori Hirano 001; Kiyoshi Izumi; Hiroki Sakaji (New Gener. Comput., 2022)

From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models. - Masahiro Suzuki 004; Masanori Hirano 001; Hiroki Sakaji (IEEE Big Data, 2023)

Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling. - Masanori Hirano 001; Kentaro Minami; Kentaro Imajo (ICAIF, 2023)

Efficient Learning of Nested Deep Hedging using Multiple Options. - Masanori Hirano 001; Kentaro Imajo; Kentaro Minami; Takuya Shimada (IIAI-AAI, 2023)

Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets. - Masanori Hirano 001; Hiroki Sakaji; Kiyoshi Izumi (IIAI-AAI, 2023)

llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and Its Methodology. - Masanori Hirano 001; Masahiro Suzuki 004; Hiroki Sakaji (NBiS, 2023)

PAMS: Platform for Artificial Market Simulations. - Masanori Hirano 001; Ryosuke Takata; Kiyoshi Izumi (CoRR, 2023)

Constructing and analyzing domain-specific language model for financial text mining. - Masahiro Suzuki 004; Hiroki Sakaji; Masanori Hirano 001; Kiyoshi Izumi (Inf. Process. Manag., 2023)

Neural-network-based parameter tuning for multi-agent simulation using deep reinforcement learning. - Masanori Hirano 001; Kiyoshi Izumi (World Wide Web (WWW), 2023)

Evaluating Company-specific Biases in Financial Sentiment Analysis using Large Language Models. - Kei Nakagawa; Masanori Hirano 001; Yugo Fujimoto (IEEE Big Data, 2024)

Enhancing Financial Domain Adaptation of Language Models via Model Augmentation. - Kota Tanabe; Masanori Hirano 001; Kazuki Matoya; Kentaro Imajo; Hiroki Sakaji; Itsuki Noda (IEEE Big Data, 2024)

Construction of Domain-Specified Japanese Large Language Model for Finance Through Continual Pre-Training. - Masanori Hirano 001; Kentaro Imajo (IIAI-AAI, 2024)

Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study. - Rawin Assabumrungrat; Kentaro Minami; Masanori Hirano 001 (IIAI-AAI, 2024)

A Multi-agent Market Model Can Explain the Impact of AI Traders in Financial Markets-A New Microfoundations of GARCH Model. - Kei Nakagawa; Masanori Hirano 001; Kentaro Minami; Takanobu Mizuta (PRIMA, 2024)

Construction of a Japanese Financial Benchmark for Large Language Models. - Masanori Hirano 001 (CoRR, 2024)

Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators. - Masanori Hirano 001 (CoRR, 2024)

The Construction of Instruction-tuned LLMs for Finance without Instruction Data Using Continual Pretraining and Model Merging. - Masanori Hirano 001; Kentaro Imajo (CoRR, 2024)

Improvement and Analysis of Peak Shift Demand Response Scenarios of Industrial Consumers Using an Electricity Market Model. - Long Cheng; Kiyoshi Izumi; Masanori Hirano 001 (New Gener. Comput., 2024)

Financial Fine-Tuning a Large Time Series Model. - Xinghong Fu; Masanori Hirano 001; Kentaro Imajo (CIFEr, 2025)

KANOP: A Data-Efficient Option Pricing Model Using Kolmogorov-Arnold Networks. - Rushikesh Handal; Kazuki Matoya; Yunzhuo Wang; Masanori Hirano 001 (CIFEr, 2025)

A Judge-free LLM Open-ended Generation Benchmark Based on the Distributional Hypothesis. - Kentaro Imajo; Masanori Hirano 001; Shuji Suzuki; Hiroaki Mikami (CoRR, 2025)