Stock market simulation and inference technique

We present an agent-based stock market simulation in which traders utilise a hybrid mixture of common information criteria based inference procedures, including minimum message length (MML) inference. Traders in our model compete with each other using a range of different inference techniques to infer the parameters and appropriate order of simple auto regressive (AR) models of stock price evolution. We show that such traders are initially profitable while a significant population of random traders exist, and that MML inference traders outperform other inference traders in the presence of a noisy AR signal.