NDLI: CARTMAP: a neural network method for automated feature selection in financial time series forecasting

Content Provider	SpringerLink
Author	Wong, Charles Versace, Massimilia
Copyright Year	2012
Abstract	In the past two decades, there has been much interest in applying neural networks to financial time series forecasting. Yet, there has been relatively little attention paid to selecting the input features for training these networks. This paper presents a novel CARTMAP neural network based on Adaptive Resonance Theory that incorporates automatic, intuitive, transparent, and parsimonious feature selection with fast learning. On average, over three separate 4-year simulations spanning 2004–2009 of Dow Jones Industrial Average stocks, CARTMAP outperformed related and classical alternatives. The alternatives were an industry standard random walk, a regression model, a general purpose ARTMAP, and ARTMAP with stepwise feature selection. This paper also discusses why the novel feature selection scheme outperforms the alternatives and how it can represent a step toward more transparency in financial modeling.
Starting Page	969
Ending Page	977
Page Count	9
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	5
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-01-15
Publisher Place	London
Access Restriction	Subscribed
Subject Keyword	Feature selection Neural networks Financial time series ARTMAP Clustering Adaptive Resonance Theory Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Software Artificial Intelligence

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CARTMAP: a neural network method for automated feature selection in financial time series forecasting