Market Efficiency and Learning in an Artificial Stock Market: A Perspective from Neo-Austrian Economics

32 Pages Posted: 2 Sep 2007

See all articles by H.A. Benink

H.A. Benink

Tilburg University

Jose Luis Gordillo

Universidad Nacional Autónoma de México (UNAM)

Juan Pablo Pardo-Guerra

London School of Economics and Political Science

Christopher R. Stephens

Universidad Nacional Autonoma de Mexico

Date Written: August 20, 2007

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 is defined relative to a dynamic buy and hold benchmark in order to make a clean separation between trading gains and market gains. 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.

Keywords: market efficiency, learning, Neo-Austrian, artificial agent

JEL Classification: D82, D83, G14

Suggested Citation

Benink, Harald and Gordillo, Jose Luis and Pardo-Guerra, Juan Pablo and Stephens, Christopher R., Market Efficiency and Learning in an Artificial Stock Market: A Perspective from Neo-Austrian Economics (August 20, 2007). Available at SSRN: https://ssrn.com/abstract=1008602 or http://dx.doi.org/10.2139/ssrn.1008602

Harald Benink

Tilburg University ( email )

P.O. Box 90153
Tilburg, DC Noord-Brabant 5000 LE
Netherlands

Jose Luis Gordillo

Universidad Nacional Autónoma de México (UNAM)

Circuito Mario de la Cueva s/n
Lomas de las Palmas, 52760
Mexico

Juan Pablo Pardo-Guerra

London School of Economics and Political Science ( email )

Houghton Street
WC2A 2AE London, England
United Kingdom

Christopher R. Stephens (Contact Author)

Universidad Nacional Autonoma de Mexico ( email )

Circuito Exterior
A. Postal 70-543
Mexico, 04510
Mexico
(52)-555-622-4692 (Phone)
(52)-555-622-4693 (Fax)

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