The 21st International Conference on Autonomous Agents and Multiagent Systems, pp. 1624-1626, May 13, 2022
The 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS2022)
This study proposes a new scheme for implementing actual data into artificial market simulations at the level of trader agents. Because humans can introduce bias or overlook the important features of actual traders, we implemented the actual data and automated the strategy learning (imitating) of agents using machine learning (ML). We then ran artificial market simulations in the treader model, which imitates the actual trading behaviors in an ML architecture. Through this study, we demonstrate the potentials and limitations of the proposed scheme.
Artificial Market; Social Simulation; Data Mining; Financial Market;
@inproceedings{Hirano2022-aamas,
title={{Implementation of Actual Data for Artificial Market Simulation}},
author={Masanori HIRANO and Kiyoshi IZUMI and Hiroki SAKAJI},
booktitle={The 21st International Conference on Autonomous Agents and Multiagent Systems},
isbn={9781450392136},
pages={1624-1626},
doi={10.5555/3535850.3536056},
url={https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1624.pdf},
year={2022}
}