IPO underpricing algorithm: Difference between revisions

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===Two-layered evolutionary forecasting===

Luque<ref>{{cite journal|last=Luque|first=Cristóbal|author2=David Quintana |author3=J. M. Valls |author4=Pedro Isasi |title=Two-layered evolutionary forecasting for IPO underpricing|journal=In&nbsp;Proceedings of the Eleventh conference on Congress on Evolutionary Computation&nbsp;(CEC'09)|year=2009|pages=2384–23782374–2378|publisher=IEEE Press|___location=Piscatawy, NJ, USA|doi=10.1109/cec.2009.4983237}}</ref> approaches the problem with outliers by performing linear regressions over the set of data points (input, output). The algorithm deals with the data by allocating regions for noisy data. The scheme has the advantage of isolating noisy patterns which reduces the effect outliers have on the rule-generation system. The algorithm can come back later to understand if the isolated data sets influence the general data. Finally, the worst results from the algorithm outperformed all other algorithms' predictive abilities.

==Agent-based modelling==