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Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data.

Artificial intelligence is a consortium of data-driven methodologies which includes artificial neural networks, genetic algorithms, fuzzy logic, probabilistic belief networks and machine learning as its components. We have witnessed a phenomenal impact of this data-driven consortium of methodologies in many areas of studies, the economic and financial fields being of no exception. In particular, this volume of collected works will give examples of its impact on the field of economics and finance. This volume is the result of the selection of high-quality papers presented at a special session entitled “Applications of Artificial Intelligence in Economics and Finance” at the “2003 International Conference on Artificial Intelligence” (IC-AI ’03) held at the Monte Carlo Resort, Las Vegas, NV, USA, June 23–26 2003. The special session, organised by Jane Binner, Graham Kendall and Shu-Heng Chen, was presented in order to draw attention to the tremendous diversity and richness of the applications of artificial intelligence to problems in Economics and Finance. This volume should appeal to economists interested in adopting an interdisciplinary approach to the study of economic problems, computer scientists who are looking for potential applications of artificial intelligence and practitioners who are looking for new perspectives on how to build models for everyday operations.