Agent-Based Financial Modelling: A Promising Alternative to the Standard Representative-Agent Approach
Tomas Ramanauskas ()
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Tomas Ramanauskas: Bank of Lithuania
No 3, Bank of Lithuania Working Paper Series from Bank of Lithuania
In this paper we provide a brief introduction to the literature on agent-based financial modelling and, more specifically, artificial stock market modelling. In the selective literature review two broad categories of artificial stock market models are discussed: models based on hard-wired rules and models with learning and systemic adaptation. The paper discusses pros and cons of agent-based financial modelling as opposed to the standard representative-agent approach. We advocate the need for the proper account of market complexity, agent heterogeneity, bounded rationality and adaptive (though not simplistic) expectations in financial modelling. We also argue that intelligent adaptation in highly uncertain environment is key to understanding actual financial market behaviour and we resort to a specific area of artificial intelligence theory, namely reinforcement learning, as one plausible and economically appealing algorithm of adaptation and learning.
Keywords: Agent-based financial modelling; artificial stock market; complex dynamical system; market efficiency agent heterogeneity; reinforcement learning (search for similar items in EconPapers)
JEL-codes: G10 G11 G14 Y20 (search for similar items in EconPapers)
Pages: 25 pages
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