Using non-parametric search algorithms to forecast daily excess stock returns

Nathan Lael Joseph, David S. Brée, Efstathios Kalyvas

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Are the learning procedures of genetic algorithms (GAs) able to generate optimal architectures for artificial neural networks (ANNs) in high frequency data? In this experimental study, GAs are used to identify the best architecture for ANNs. Additional learning is undertaken by the ANNs to forecast daily excess stock returns. No ANN architectures were able to outperform a random walk, despite the finding of non-linearity in the excess returns. This failure is attributed to the absence of suitable ANN structures and further implies that researchers need to be cautious when making inferences from ANN results that use high frequency data. © 2004 Elsevier Ltd. All rights reserved.
Original languageEnglish
Title of host publicationAdvances in Econometrics
PublisherJAI Press
Pages93-125
Number of pages33
Volume19
ISBN (Electronic)978-1-84950-303-7
ISBN (Print)0762311509, 978-0-76231-150-7
DOIs
Publication statusPublished - 1 Jan 2004

Publication series

NameAdvances in Econometrics
Volume19

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  • Cite this

    Joseph, N. L., Brée, D. S., & Kalyvas, E. (2004). Using non-parametric search algorithms to forecast daily excess stock returns. In Advances in Econometrics (Vol. 19, pp. 93-125). (Advances in Econometrics; Vol. 19). JAI Press. https://doi.org/10.1016/S0731-9053(04)19004-X