Economics at your fingertips  

Neural networks in financial trading

Georgios Sermpinis, Andreas Karathanasopoulos (), Rafael Rosillo () and David Fuente ()
Additional contact information
Andreas Karathanasopoulos: University of Dubai
Rafael Rosillo: University of Oviedo
David Fuente: University of Oviedo

Annals of Operations Research, 2021, vol. 297, issue 1, No 13, 293-308

Abstract: Abstract In this study, we generate 50 Multi-layer Perceptons, 50 Radial Basis Functions, 50 Higher Order Neural Networks and 50 Recurrent Neural Network and we explore their utility in forecasting and trading the DJIA, NASDAQ 100 and the NIKKEI 225 stock indices. The statistical significance of the forecasts is examined through the False Discovery Ratio of Bajgrowicz and Scaillet (J Financ Econ 106(3):473–491, 2012). Two financial everages, based on the levels of financial stress and the financial volatility respectively, are also applied. In terms of the results, we note that RNN have the higher percentage of significant models and present the stronger profitability compared to their Neural Network counterparts. The financial leverages doubles the trading performance of our models.

Keywords: Neural networks; Forecasting; Trading; Multiple hypothesis testing (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:

Ordering information: This journal article can be ordered from

DOI: 10.1007/s10479-019-03144-y

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

Page updated 2023-03-26
Handle: RePEc:spr:annopr:v:297:y:2021:i:1:d:10.1007_s10479-019-03144-y