Toggle Main Menu Toggle Search

Open Access padlockePrints

Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns

Lookup NU author(s): Viktor Manahov, Professor Robert Hudson


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


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.

Publication metadata

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


DOI: 10.1016/j.physa.2013.05.029


Altmetrics provided by Altmetric