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Lookup NU author(s): Viktor Manahov, Professor Robert Hudson
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Many scholars express concerns that herding behaviour causes excess volatility, destabilises financial markets, and increases the likelihood of systemic risk. We use a special form of the Strongly Typed Genetic Programming (STGP) technique to evolve a stock market divided into two groups-a small subset of artificial agents called 'Best Agents' and a main cohort of agents named 'All Agents'. The 'Best Agents' perform best in term of the trailing return of a wealth moving average. We then investigate whether herding behaviour can arise when agents trade Dow Jones, General Electric, and IBM financial instruments in four different artificial stock markets. This paper uses real historical quotes of the three financial instruments to analyse the behavioural foundations of stylised facts such as leptokurtosis, non-IIDness, and volatility clustering. We found evidence of more herding in a group of stocks than in individual stocks, but the magnitude of herding does not contribute to the mispricing of assets in the long run. Our findings suggest that the price formation process caused by the collective behaviour of the entire market exhibit less herding and is more efficient than the segmented market populated by a small subset of agents. Hence, greater genetic diversity leads to greater consistency with fundamental values and market efficiency. (C) 2013 Elsevier B.V. All rights reserved.
Author(s): Manahov V, Hudson R
Publication type: Article
Publication status: Published
Journal: Physica A: Statistical Mechanics and its Applications
Year: 2013
Volume: 392
Issue: 19
Pages: 4351-4372
Print publication date: 25/05/2013
ISSN (print): 0378-4371
ISSN (electronic): 1873-2119
Publisher: Elsevier
URL: http://dx.doi.org/10.1016/j.physa.2013.05.029
DOI: 10.1016/j.physa.2013.05.029
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