Abstract:
Time Series Properties of an Artificial Stock Market
This paper presents results from an experimental computer simulated
stock market. In this market artificial intelligence algorithms take
on the role of traders. They make predictions about the future, and
buy and sell stock as indicated by their expectations of future risk
and return. Prices are set endogenously to clear the market. Time
series from this market are analyzed from the standpoint of some well
known empirical features in real markets. The simulated market is
able to replicate several of these phenomenon, including fundamental
and technical predictability, volatility persistence, and
leptokurtosis. Moreover, agent behavior is shown to be consistent
with these features in that they condition on the variables that are
found to be significant in the time series tests. Inside this
experimental model there exists a well-defined linear homogeneous
rational expectations equilibrium. This is used as a benchmark in the
experiments to assess the overall ability of the agents in learning.
It is found that for certain parameters the results in the market are
consistent with this benchmark.
To appear: Journal of Economic Dynamics and Control
23, 1487-1516 (1999)
Last Updated: 15-Nov-99