Impacts of social networks in an agent-based artificial stock market

Abstract

We propose an agent-based artificial stock market to investigate the influences of social networks on financial markets. It contains four types of traders whose information sets and trading strategies are different. The application of artificial intelligence is employed in informed and uninformed traders’behaviour and heterogeneity. When information is exogenous, social networks result in higher volatility and trading volume and lower price distortion and bid-ask spread. When information is endogenous, the influences are reversed. The reason is that social networks harm information production after traders tend to rely on information from communication, instead of spending a cost on it.

Publication DOI: https://doi.org/10.1080/17517575.2021.2008514
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Funding Information: The authors would like to thank Professor Victor Chang for the research support from VC Research (VCR 0000146).
Additional Information: This is an Accepted Manuscript version of the following article, accepted for publication in Enterprise Information Systems. Xiaoting Dai, Jie Zhang & Victor Chang (2021) Impacts of social networks in an agent-based artificial stock market, Enterprise Information Systems, DOI: 10.1080/17517575.2021.2008514 . It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Funding Information: The authors would like to thank Professor Victor Chang for the research support from VC Research (VCR 0000146).
Uncontrolled Keywords: artificial intelligence,bid-ask spread,genetic programming,price distortion,Social networks,trading volume,volatility,Computer Science Applications,Information Systems and Management
Publication ISSN: 1751-7583
Last Modified: 08 Jul 2024 08:14
Date Deposited: 13 Jun 2022 14:36
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-12-01
Published Online Date: 2021-12-01
Accepted Date: 2021-11-08
Authors: Dai, Xiaoting
Zhang, Jie
Chang, Victor (ORCID Profile 0000-0002-8012-5852)

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