Evolutionary rule-based system for IPO underpricing prediction

Genetic And Evolutionary Computation Conference. Washington DC, USA, 25-29 June 2005Academic literature has documented for a long time the existence of important price gains in the first trading day of initial public offerings (IPOs).Most of the empirical analysis that has been carried out to date to explain underpricing through the offering structure is based on multiple linear regression. The alternative that we suggest is a rule-based system defined by a genetic algorithm using a Michigan approach. The system offers significant advantages in two areas, 1) a higher predictive performance, and 2) robustness to outlier patterns. The importance of the latter should be emphasized since the non-trivial task of selecting the patterns to be excluded from the training sample severely affects the results.We compare the predictions provided by the algorithm to those obtained from linear models frequently used in the IPO literature. The predictions are based on seven classic variables. The results suggest that there is a clear correlation between the selected variables and the initial return, therefore making possible to predict, to a certain extent, the closing price.This article has been financed by the Spanish founded research MCyT project TRACER, Ref: TIC2002-04498-C05-04M

A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction

This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization

Discovering hierarchical decision rules with evolutive algorithms in supervised learning

This paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning rules in continuous and discrete domains based on evolutive algorithms. The algorithm produces a hierarchical set of rules, that is, the rules must be applied in a speciÞc order. With this policy, the number of rules may be reduced because the rules could be one inside of another. The evolutive algorithm uses both real and binary codiÞcation for the individuals of the population and introduces several new genetic operators. In addition, this paper discusses the capability of learning systems based on an evolutive algorithm to reduce both the number of rules and the number of attributes involved in the rule set. We have tested our system on real data from the UCI repository. The results of a 10-fold cross validation are compared to C4.5 s and they show an important improvement.Comisión Interministerial de Ciencia y Tecnología TIC99-035

Supervised learning by means of accuracy-aware evolutionary algorithms

This paper describes a new approach, HIerarchical DEcision Rules (HIDER), for learning generalizable rules in continuous and discrete domains based on evolutionary algorithms. The main contributions of our approach are the integration of both binary and real evolutionary coding; the use of specific operators; the relaxing coefficient to construct more flexible classifiers by indicating how general, with respect to the errors, decision rules must be; the coverage factor in the fitness function, which makes possible a quick expansion of the rule size; and the implicit hierarchy when rules are being obtained. HIDER is accuracy-aware since it can control the maximum allowed error for each decision rule. We have tested our system on real data from the UCI Repository. The results of a 10-fold cross-validation are compared to C4.5’s and they show a significant improvement with respect to the number of rules and the error rate.CICYT TIC2001-1143-C03-0