Authors:
Sadek Benhammada
1
;
Frédéric Amblard
2
and
Salim Chikhi
1
Affiliations:
1
University of Constantine 2, Algeria
;
2
Université Toulouse 1 Capitole, France
Keyword(s):
Agent-based Models, Computational Economics, Artificial Stock Markets, Social Networks, Mimetism.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bioinformatics
;
Biomedical Engineering
;
Computational Intelligence
;
Distributed and Mobile Software Systems
;
Economic Agent Models
;
Enterprise Information Systems
;
Evolutionary Computing
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Technologies
;
Multi-Agent Systems
;
Operational Research
;
Simulation
;
Soft Computing
;
Software Engineering
;
Symbolic Systems
Abstract:
Agent-based artificial stock markets attracted much attention over the last years, and many models have
been proposed. However, among them, few models take into account the social interactions and mimicking
behaviour of traders, while the economic literature describes investors on financial markets as influenced by
decisions of their peers and explains that this mimicking behaviour has a decisive impact on price dynamics
and market stability. In this paper we propose a continuous double auction model of financial market,
populated by heterogeneous traders who interact through a social network of influence. Traders use different
investment strategies, namely: fundamentalists who make a decisions based on the fundamental value of
assets; hybrids who are initially fundamentalists, but switch to a speculative strategy when they detect an
uptrend in prices; noise traders who don’t have sufficient information to take rational decisions, and finally
mimetic traders who imitate the decisions
of their mentors on the interactions network. An experimental
design is performed to show the feasibility and utility of the proposed model.
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