Impact of self-learning based high-frequency traders on the stock market
File(s)
Accepted version
Author(s)
Mansurov, Kirill
Semenov, Alexander
Grigoriev, Dmitry
Radionov, Andrei
Ibragimov, Rustam
Type
Journal Article
Abstract
In this paper we investigate the role of self-learning agents in multi-agent models of financial markets. We develop an agent-based simulation model of a financial market and, in addition to the agents with fixed strategies used in previous research, we introduce an agent with a self-learning strategy. To model the behavior of such an agent, we use deep reinforcement learning algorithms, namely deep deterministic policy gradient (DDPG). Next, we conduct a comparative analysis of the results of the constructed model with outcomes of previously proposed models, as well as with the characteristics of real market. To conduct comparative analysis, we use stylized facts of asset returns that allow us to evaluate and compare the characteristics of the markets. Our results show that a model with a self-learning agent gives a better approximation of the real market than a model with classic agents. In particular, unlike the model with classical agents, the model with a self-learning agent turns out to be not so heavy-tailed. Thus, we demonstrate that for a complete understanding of market processes simulation models should take into account self-learning agents that have a significant presence at modern financial markets.
Finally, we present the python package1, which was developed by us as part of the research implementation. This package allows to simulate the financial market, as well as create your own agents and evaluate their impact on the market.
Finally, we present the python package1, which was developed by us as part of the research implementation. This package allows to simulate the financial market, as well as create your own agents and evaluate their impact on the market.
Date Issued
2023-12
Date Acceptance
2023-05-27
Citation
Expert Systems with Applications, 2023, 232, pp.1-17
ISSN
0957-4174
Publisher
Elsevier
Start Page
1
End Page
17
Journal / Book Title
Expert Systems with Applications
Volume
232
Copyright Statement
Copyright © 2023 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
http://dx.doi.org/10.1016/j.eswa.2023.120567
Publication Status
Published
Article Number
120567
Date Publish Online
2023-06-12