CAN DEEP MACHINE LEARNING OUTSMART THE MARKET? A COMPARISON BETWEEN ECONOMETRIC MODELLING AND LONG- SHORT TERM MEMORY

Eva Dezsi () and Ioan Alin Nistor ()
Additional contact information
Ioan Alin Nistor: Babes-Bolyai University - Faculty of Business of Cluj

Romanian Economic Business Review, 2016, vol. 11, issue 4.1, 54-73

Abstract: Using long-short term memory (LSTM) recurrent neural network (RNN) architecture, we analyse data from the Romanian stock markets in the attempt to forecast its future trend. Then we try to compare the results using the classical statistical modelling tools, further employing back testing to prove our findings. We believe that the LSTM should be the next tool in balancing portfolios and reducing market risk.

Keywords: LSTM; RNN; Neural Networks; Deep learning; Stock prices prediction (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.rebe.rau.ro/RePEc/rau/journl/WI16S/REBE-WI16S-A7.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:rau:journl:v:11:y:2016:i:4.1:p:54-73

Access Statistics for this article

More articles in Romanian Economic Business Review from Romanian-American University Contact information at EDIRC.
Bibliographic data for series maintained by Alex Tabusca ().