Removing systematic patterns in returns in a financial market model by artificially intelligent traders - Björn-Christopher Witte (Bamberg University, Bamberg Economic Research Group on Government and Growth (BERG), 2011)
The unpredictability of returns counts as a stylized fact of financial markets. To reproduce this fact, modelers usually implement noise terms - a method with several downsides. Above all, systematic patterns are not eliminated but merely blurred. The present article introduces a model in which systematic patterns are removed endogenously. This is achieved in a reality-oriented way: Intelligent traders are able to identify patterns and exploit them. To identify and predict patterns, a very simple artificial neural network is used. As neural network mimic the cognitive processes of the human brain, this method might be regarded as a quite accurate way of how traders identify patterns and forecast prices in reality. The simulation experiments show that the artificial traders exploit patterns effectively and thereby remove them, which ultimately leads to the unpredictability of prices. Further results relate to the influence of pattern exploiters on market efficiency.
Keywords: C45; G14; G17; 330; financial markets; autocorrelations; artificial intelligence; agent-based modeling