Building an artificial stock market populated by reinforcement‐learning agents


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

Keyword : artifi cial stock market model, market price, agent heterogeneity, stock value

How to Cite
Rutkauskas, A. V., & Ramanauskas, T. (2009). Building an artificial stock market populated by reinforcement‐learning agents. Journal of Business Economics and Management, 10(4), 329-341.
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Dec 31, 2009
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