The highly intelligent virtual agents for modeling financial markets
Authored by G Yang, J P Huang, Y Chen
Date Published: 2016
DOI: 10.1016/j.physa.2015.09.071
Sponsors:
Chinese National Natural Science Foundation
Shanghai Key Laboratory of Financial Information Technology
Fok Ying Tung Education Foundation
Program for New Century Excellent Talents in University
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Abstract
Researchers have borrowed many theories from statistical physics, like
ensemble, Ising model, etc., to study complex adaptive systems through
agent-based modeling. However, one fundamental difference between
entities (such as spins) in physics and micro-units in complex adaptive
systems is that the latter are usually with high intelligence, such as
investors in financial markets. Although highly intelligent virtual
agents are essential for agent-based modeling to play a full role in the
study of complex adaptive systems, how to create such agents is still an
open question. Hence, we propose three principles for designing high
artificial intelligence in financial markets and then build a specific
class of agents called iAgents based on these three principles. Finally, we evaluate the intelligence of iAgents through virtual index trading in
two different stock markets. For comparison, we also include three other
types of agents in this contest, namely, random traders, agents from the
wealth game (modified on the famous minority game), and agents from an
upgraded wealth game. As a result, iAgents perform the best, which gives
a well support for the three principles. This work offers a general
framework for the further development of agent-based modeling for
various kinds of complex adaptive systems. (C) 2015 Elsevier B.V. All
rights reserved.
Tags
herd behavior
bubbles
complex adaptive system
investors
Stock-market
Phase-transitions
Stylized
facts