<p>Applications of Artificial Intelligence in Finance and Economics</p><p><span class="verdana11Blue"><b><a name="editorialboard"></a>Edited By</b></span>
<br/><b>J.M. Binner</b>, Aston University, Birmingham, UK<br/><b>G. Kendall</b>, University of Nottingham, Nottingham, UK<br/><b>S.H. Chen</b>, National Chengchi University, Taipei, Taiwan<br/>pdf, 2m</p><p>
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</p><p>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, Nevada, 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. <br/><br/>There are still many important Artificial Intelligence disciplines yet to be covered. Among them are the methodologies of independent component analysis, reinforcement learning, inductive logical programming, classifier systems and Bayesian networks, not to mention many ongoing and highly fascinating hybrid systems. A way to make up for their omission is to visit this subject again later. We certainly hope that we can do so in the near future with another volume of 'Applications of Artificial Intelligence in Economics and Finance'.<br/><br/><span class="verdana11Blue"><b><a name="audience"></a>Audience </b></span><br/>Economists<br/><br/><a name="toc"></a><span class="verdana11Blue"><b>Contents</b></span>
<br/>1. Statistical analysis of genetic algorithms in discovering technical trading strategies (S.H. Chen, C.Y. Tsao).<br/>2. A genetic programming approach to model international short-term capital flow (T. Yu, S.H. Chen, T.W. Kuo).<br/>3. Tools for non-linear time series forecasting in economics: An empirical comparison of regime switching vector autoregressive models and recurrent neural networks (J.M. Binner, T. Elger, B. Nilsson, J.A. Tepper).<br/>4. Using non-parametric search algorithms to forecast daily excess stock returns (N.L. Joseph, D.S. Bree, E. Kalyvas).<br/>5. Co-evolving neural networks with evolutionary strategies: A new application to Divisia Money (J. Binner, G. Kendall, A. Gazely).<br/>6. Forecasting the EMU inflation rate: Linear econometric versus non-linear computational models using genetic neural fuzzy systems (S. Kooths, T. Mitze, E. Ringhut).<br/>7. Finding or not finding rules in time series (J. Lin, E. Keogh).<br/>8. A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil (S. Mirmirani, H.C. Li).<br/>9. Searching for Divisia/Inflation Relationships with the aggregate feed forward neural network (V.A. Schmidt, J.M. Binner).<br/>10. Predicting housing value: Genetic algorithm attribute selection and dependence modelling utilising the gamma test (I.D. Wilson, A. J. Jones, D.H. Jenkins, J.A. Ware).<br/></p>
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