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A new generation of computing techniques, commonly known as "intelligent systems", are now being applied to a variety of financial and business modelling tasks. These techniques include genetic algorithms, neural networks, dynamical systems models (chaos theory), expert systems, rule induction, fuzzy systems and hybrids of these techniques. They are also being applied to stock market prediction, portfolio management, credit evaluation, fraud detection, insurance risk assessment, macro-economic modelling, and in many cases are outperforming traditional statistical techniques. This study analyzes the relative merits of the different techniques for financial and economic modelling, and examines current applications.
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A new generation of computing techniques, commonly known as "intelligent systems", are now being applied to a variety of financial and business modelling tasks. These techniques include genetic algorithms, neural networks, dynamical systems models (chaos theory), expert systems, rule induction, fuzzy systems and hybrids of these techniques. They are also being applied to stock market prediction, portfolio management, credit evaluation, fraud detection, insurance risk assessment, macro-economic modelling, and in many cases are outperforming traditional statistical techniques. This study analyzes the relative merits of the different techniques for financial and economic modelling, and examines current applications.
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