Evolution and Time Horizons in an Agent Based Stock Market
46 Pages Posted: 19 Apr 2000
Date Written: May 1999
Abstract
Recent research has shown the importance of time horizons in models of learning in finance. The dynamics of how agents adjust to believe that the world around them is stationary may be just as crucial in the convergence to a rational expectations equilibrium as getting parameters and model specifications correct in the learning process. This paper explores the process of this evolution in learning and time horizons in a simple agent based financial market. The results indicate that while the simple model structure used here replicates usual rational expectations results with long horizon agents, the route to evolving a population of both long and short horizon agents to long horizons alone may be difficult. Furthermore, populations with both short and long horizon agents increase return variability, and leave patterns in volatility and trading volume similar to actual financial markets.
JEL Classification: D83, G12
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Paper statistics
Recommended Papers
-
Dealing with the Complexity of Economic Calculations
By John P. Rust
-
A Study of Neo-Austrian Economics Using an Artificial Stock Market
By H.a. Benink, Jose Luis Gordillo, ...
-
A New Measure of Market Inefficiency
By Christopher R. Stephens, H.a. Benink, ...
-
By H.a. Benink, Jose Luis Gordillo, ...
-
Using Multi-Agent Simulation to Understand Trading Dynamics of a Derivatives Market
By Alan King, Olga Streltchenko, ...
-
Post Keynesian Perspectives and Complex Ecologiceconomic Dynamics
-
Bounded Rationality and the Emergence of Simplicity Amidst Complexity
By Cassey Lee