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Abstract
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.
Highlights
In this paper we develop an artificial stock market (ASM) model, which could be used to examine some emergent features of a complex system comprised of a large number of heterogeneous learning agents that interact in a detail-rich and realistically designed environment
In this paper we developed an artificial stock market model based on the interaction of heterogeneous agents whose forward-looking behaviour is driven by the reinforcement-learning algorithm combined with some evolutionary selection mechanism and economic reasoning
Quite a strong emphasis on the model’s economic content distinguishes this model from some other ASM models, which are most often based on evolutionary selection procedures and are sometimes criticised for lack of economic fundament
Summary
In this paper we develop an artificial stock market (ASM) model, which could be used to examine some emergent features of a complex system comprised of a large number of heterogeneous learning agents that interact in a detail-rich and realistically designed environment. This version of the model is not calibrated to empirical data, so at this stage the main aim of this research is to offer, implement and test some new ideas that could lay ground for a robust framework for analysis of financial market processes and their determinants.
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