Search Results for author:
Found 11 papers, 4 papers with code
Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training
After continual pre-training using the datasets and the base model, the tuned model performed better than the original model on the Japanese financial benchmarks.
Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators
This study proposes a new approach using artificial market simulations for underlying asset simulations in deep hedging.
Construction of a Japanese Financial Benchmark for Large Language Models
With the recent development of large language models (LLMs), models that focus on certain domains and languages have been discussed for their necessity.
Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study
Through comparative experiments, we assessed the empirical performance of these solvers in high-dimensional contexts.
PAMS: Platform for Artificial Market Simulations
This paper presents a new artificial market simulation platform, PAMS: Platform for Artificial Market Simulations.
From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models
We performed Low-Rank Adaptation (LoRA) tuning on both Japanese and English existing models using our instruction dataset.
Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling
In this framework, a hedger and a generator, which respectively model the underlying asset process and the underlying asset process, are trained in an adversarial manner.
llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology
There are two ways to support languages other than English by those LLMs: constructing LLMs from scratch or tuning existing models.
Efficient Learning of Nested Deep Hedging using Multiple Options
That is, we develop a fully-deep approach of deep hedging in which the hedging instruments are also priced by deep neural networks that are aware of frictions.
Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets
In some previous works, GANs for financial markets generated fake orders in continuous spaces because of GAN architectures' learning limitations.
