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A Study of Neo-Austrian Economics using an Artificial Stock Market

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Author Info

  • Harald A. Benink

    (Rotterdam School of Management, Erasmus University)

  • Jose Luis Gordillo

    (Universidad Nacional Autónoma de México UNAM)

  • Juan Pablo Pardo

    (Universidad Nacional Autónoma de México UNAM)

  • Christopher R. Stephens

    (Universidad Nacional Autónoma de México UNAM)

Abstract

An agent-based artificial financial market (AFM) is used to study market efficiency and learning in the context of the Neo-Austrian economic paradigm. Efficiency is defined in terms of the 'excess' profits associated with different trading strategies, where excess for an active trading strategy is defined relative to a dynamic buy and hold benchmark. We define an Inefficiency matrix that takes into account the difference in excess profits of one trading strategy versus another ('signal') relative to the standard error of those profits ('noise') and use this statistical measure to gauge the degree of market efficiency. A one-parameter family of trading strategies is considered, the value of the parameter measuring the relative 'informational' advantage of one strategy versus another. Efficiency is then investigated in terms of the composition of the market defined in terms of the relative proportions of traders using a particular strategy and the parameter values associated with the strategies. We show that markets are more efficient when informational advantages are small (small signal) and when there are many coexisting signals. Learning is introduced by considering 'copycat' traders that learn the relative values of the different strategies in the market and copy the most successful one. We show how such learning leads to a more informationally efficient market but can also lead to a less efficient market as measured in terms of excess profits. It is also shown how the presence of exogeneous information shocks that change trader expectations increases efficiency and complicates the inference problem of copycats.

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File URL: http://128.118.178.162/eps/fin/papers/0411/0411038.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Finance with number 0411038.

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Length: 41 pages
Date of creation: 17 Nov 2004
Date of revision:
Handle: RePEc:wpa:wuwpfi:0411038

Note: Type of Document - pdf; pages: 41. Presented at the EFA 2004 Maastricht Meeting
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Web page: http://128.118.178.162

Related research

Keywords: Neoaustrian economics; Market efficiency; Artificial financial market; Learning; Adaptation;

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  2. Blake LeBaron, 1999. "Evolution and Time Horizons in an Agent-Based Stock Market," Computing in Economics and Finance 1999 1342, Society for Computational Economics.
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  9. J. Doyne Farmer & Andrew W. Lo, 1999. "Frontiers of Finance: Evolution and Efficient Markets," Working Papers 99-06-039, Santa Fe Institute.
  10. W. Brian Arthur & John H. Holland & Blake LeBaron & Richard Palmer & Paul Taylor, 1996. "Asset Pricing Under Endogenous Expectation in an Artificial Stock Market," Working Papers 96-12-093, Santa Fe Institute.
  11. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
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  13. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2002. "On the emergent properties of artificial stock markets: the efficient market hypothesis and the rational expectations hypothesis," Journal of Economic Behavior & Organization, Elsevier, vol. 49(2), pages 217-239, October.
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