Fuzzy Inductive Reasoning and Nonlinear Dependence in Security Returns: Results from an Artificial Stock Market Environment

Abstract

We present an examination of security returns generated in an artificial stock market. The market is populated by traders who reason inductively while compressing information into a few fuzzy notions which they can in turn process and analyze with fuzzy logic. We demonstrate that nonlinear return dependence is present even after we have controlled for ARCH effects in the simulated data and that these results are similar to those found in an examination of the actual returns of two actively traded stocks used as case studies. We conclude that the model we present provides an explanation for nonlinearities observed in US stock returns. The appeal of the model is its close ties to evidence on how individuals actually reason.

Keywords: Nonlinear Return Behavior, Learning, Fuzzy Logic, Induction, Stock Price Dynamics

JEL Classification: G12, G14, D83, D84