Building an artificial stock market populated by reinforcement‐learning agents
DOI: https://doi.org/10.3846/1611-1699.2009.10.329-341Abstract
In this paper we propose an artificial stock market model based on interaction of heterogeneous agents whose forward‐looking behaviour is driven by the reinforcement‐learning algorithm combined with some evolutionary selection mechanism. We use the model for the analysis of market self‐regulation abilities, market efficiency and determinants of emergent properties of the financial market. Distinctive and novel features of the model include strong emphasis on the economic content of individual decision‐making, application of the Q‐learning algorithm for driving individual behaviour, and rich market setup. Along with that a parallel version of the model is presented, which is mainly based on research of current changes in the market, as well as on search of newly emerged consistent patterns, and which has been repeatedly used for optimal decisions’ search experiments in various capital markets.
First Publish Online: 14 Oct 2010
Keywords:
artifi cial stock market model, market price, agent heterogeneity, stock valueHow to Cite
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Copyright (c) 2009 The Author(s). Published by Vilnius Gediminas Technical University.
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Copyright (c) 2009 The Author(s). Published by Vilnius Gediminas Technical University.
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This work is licensed under a Creative Commons Attribution 4.0 International License.