EconPapers    
Economics at your fingertips  
 

An empirical case against the use of genetic-based learning classifier systems as forecasting devices

Jaqueson Galimberti () and Sergio Da Silva ()

Economics Bulletin, 2012, vol. 32, issue 1, 354-369

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: D8 G1 (search for similar items in EconPapers)
Date: 2012-01-25
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.accessecon.com/Pubs/EB/2012/Volume32/EB-12-V32-I1-P32.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ebl:ecbull:eb-11-00608

Access Statistics for this article

More articles in Economics Bulletin from AccessEcon
Bibliographic data for series maintained by John P. Conley ().

 
Page updated 2021-04-06
Handle: RePEc:ebl:ecbull:eb-11-00608