Building an Artificial Stock Market Populated by Reinforcement-Learning Agents
AbstractIn 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.
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Bibliographic InfoPaper provided by Bank of Lithuania in its series Bank of Lithuania Working Paper Series with number 6.
Length: 35 pages
Date of creation: 04 Sep 2009
Date of revision:
agent-based financial modelling; artificial stock market; complex dynamical system; emergent properties; market efficiency; agent heterogeneity; reinforcement learning;
Find related papers by JEL classification:
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
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