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Predicting IPO underpricing with genetic algorithms

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2012-03
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CESER Publications
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Abstract
This paper introduces a rule system to predict first-day returns of initial public offerings based on the structure of the offerings. The solution is based on a genetic algorithm using a Michigan approach. The performance of the system is assessed comparing it to a set of widely used machine learning algorithms. The results suggest that this approach offers significant advantages on two fronts: predictive performance and robustness to outlier patterns. The importance of the latter should be emphasized as the results in this domain are very sensitive to their presence.
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Genetic algorithm, IPO, Underpricing
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International Journal of Artificial Intelligence. 2012. 8(S12), pp. 133-146