Back to Basics: The Power of the Multilayer Perceptron in Financial Time Series Forecasting

Ana Lazcano (), Miguel A. Jaramillo-Morán and Julio E. Sandubete
Additional contact information
Ana Lazcano: Faculty of Law, Business and Government, Universidad Francisco de Vitoria, 28223 Madrid, Spain
Miguel A. Jaramillo-Morán: Department of Electrical Engineering, Electronics and Automation, School of Industrial Engineering, University of Extremadura, 06006 Badajoz, Spain
Julio E. Sandubete: Faculty of Law, Business and Government, Universidad Francisco de Vitoria, 28223 Madrid, Spain

Mathematics, 2024, vol. 12, issue 12, 1-18

Abstract: The economic time series prediction literature has seen an increase in research leveraging artificial neural networks (ANNs), particularly the multilayer perceptron (MLP) and, more recently, transformer networks. These ANN models have shown superior accuracy compared to traditional techniques such as autoregressive integrated moving average (ARIMA) models. The most recent models in the prediction of this type of neural network, such as recurrent or Transformers models, are composed of complex architectures that require sufficient processing capacity to address the problems, while MLP is based on densely connected layers and supervised learning. A deep understanding of the limitations is necessary to appropriately choose the ideal model for each of the prediction tasks. In this article, we show how a simple architecture such as the MLP allows a better adjustment than other models, including a shorter prediction time. This research is based on the premise that the use of the most recent models will not always allow better results.

Keywords: time series forecasting; financial forecasting; recurrent neural network; MLP; transformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/12/1920/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/12/1920/ (text/html)

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:gam:jmathe:v:12:y:2024:i:12:p:1920-:d:1419222

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().