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Jaqueson K. Galimberti and Sergio da Silva
 
''An empirical case against the use of genetic-based learning classifier systems as forecasting devices''
( 2012, Vol. 32 No.1 )
 
 
We adapt a genetic-based learning classifier system to a forecast evaluation exercise by making its key parameters endogenous and taking into account the need of convergence of the learning algorithm, an issue usually neglected in the literature. Doing so, we find it hard for the algorithm to beat simpler ones based on recursive regressions and on the random walk in forecasting stock returns. We then argue that our results cast doubts on the plausibility of using learning classifier systems to represent agents process of expectations formation, an approach commonly found into the agent-based computational finance literature.
 
 
Keywords: genetic-based learning classifier systems, genetic algorithms, stock returns forecasting
JEL: D8 - Information, Knowledge, and Uncertainty: General
 
Manuscript Received : Aug 30 2011 Manuscript Accepted : Jan 25 2012

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