 Evolution with Individual and Social Learning in an Agent-Based Stock MarketRyuichi Yamamoto ()
No 228, Computing in Economics and Finance 2005 from Society for Computational Economics Abstract: Recent research has shown a variety of computational techniques to describe evolution in an artificial stock market. One can distinguish the techniques based on at which level the learning of agents is modeled. The previous literature describes learning at either individual or social level. The level of learning is exogenously given, and agents involve only a particular level of learning when they update their rules. But such a setting doesn’t say anything about why agents choose a particular level of learning to update their trading rules. This paper introduces a learning mechanism which allows agents to choose one rule at each period among a set of ideas updated through both individual and social learning. A trading strategy performed well in the past is more likely to be selected by agents regardless it is created at individual or social level. This framework allows agents to choose a decision rule endogenously among a wider set of ideas. With such evolution, the following two questions are examined. First, since agents who have a wider set of ideas to choose are more intelligent, a question would arise if the time series from an economy with intelligent agents would converge to a rational expectation equilibrium (REE). Previous literature like LeBaron (2000) and Arthur et al. (1996) investigates the convergence property to the REE by looking at different time-horizons. It finds that the more information from the market agents get before updating their rules, the market is more likely to converge to the REE. But this paper investigates the convergence property by looking at different degrees of intelligence given a time horizon. The second investigates which level of learning is likely to dominate in the market. This is analyzed by investigating who chooses which level of learning and what proportion of the agents often uses individual or social learning. We analyze a hypothesis that wealthy agents often choose an idea from a set of her private ideas (from individual learning) while some with less wealth frequently imitate ideas from others (from social learning). The result eventually indicates that the agent-based stock market in this paper would possibly explain the mechanism of herding behavior which is often observed in financial markets Keywords: Individual learning; Social learning; Evolution; Asset pricing; Financial time series (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf5:228 Access Statistics for this paper More papers in Computing in Economics and Finance 2005 from Society for Computational Economics Contact information at EDIRC. |
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