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Predicting IPO Underpricing with Genetic Algorithms

Cristóbal Luque, David Quintana, Pedro Isasi

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.

Keywords


Genetic algorithm, IPO, underpricing.

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