3 citations · 9 across the 6 of their papers we have counts for
6 papers
PAMS: Platform for Artificial Market Simulations
Masanori Hirano, Ryosuke Takata, Kiyoshi Izumi
This paper presents a new artificial market simulation platform, PAMS: Platform for Artificial Market Simulations. PAMS is developed as a Python-based simulator that is easily inte…
From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models
Masahiro Suzuki, Masanori Hirano, Hiroki Sakaji
Instruction tuning is essential for large language models (LLMs) to become interactive. While many instruction tuning datasets exist in English, there is a noticeable lack in other…
Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling
Masanori Hirano, Kentaro Minami, Kentaro Imajo
Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market c…
llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology
Masanori Hirano, Masahiro Suzuki, Hiroki Sakaji
This study constructed a Japanese chat dataset for tuning large language models (LLMs), which consist of about 8.4 million records. Recently, LLMs have been developed and gaining p…
Efficient Learning of Nested Deep Hedging using Multiple Options
Masanori Hirano, Kentaro Imajo, Kentaro Minami +1
Deep hedging is a framework for hedging derivatives in the presence of market frictions. In this study, we focus on the problem of hedging a given target option by using multiple o…
Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets
Masanori Hirano, Hiroki Sakaji, Kiyoshi Izumi
This study proposes a new generative adversarial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fa…