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Export Reference (APA) 
Ferreira, N. B. (2020). Comparative multivariate forecast performance for the G7 stock markets: VECM models vs deep learning LSTM neural networks. In Universidade Politécnica de Valencia (Ed.),  International Conference on Advanced Research Methods and Analytics. (pp. 163-171). Valencia
Export Reference (IEEE) 
N. R. Ferreira,  "Comparative multivariate forecast performance for the G7 stock markets: VECM models vs deep learning LSTM neural networks", in  Int. Conf. on Advanced Research Methods and Analytics, Universidade Politécnica de Valencia, Ed., Valencia, 2020, vol. CARMA20, pp. 163-171
Export BibTeX 
@inproceedings{ferreira2020_1765610936244,
	author = "Ferreira, N. B.",
	title = "Comparative multivariate forecast performance for the G7 stock markets: VECM models vs deep learning LSTM neural networks",
	booktitle = " International Conference on Advanced Research Methods and Analytics",
	year = "2020",
	editor = "Universidade Politécnica de Valencia",
	volume = "CARMA20",
	number = "",
	series = "",
	doi = "10.4995/CARMA2020.2020.11616",
	pages = "163-171",
	publisher = "",
	address = "Valencia",
	organization = "DevStat",
	url = "http://carmaconf.org/"
}
Export RIS 
TY  - CPAPER
TI  - Comparative multivariate forecast performance for the G7 stock markets: VECM models vs deep learning LSTM neural networks
T2  -  International Conference on Advanced Research Methods and Analytics
VL  - CARMA20
AU  - Ferreira, N. B.
PY  - 2020
SP  - 163-171
DO  - 10.4995/CARMA2020.2020.11616
CY  - Valencia
UR  - http://carmaconf.org/
AB  - The prediction of stock prices dynamics is a challenging task since these kind
of financial datasets are characterized by irregular fluctuations, nonlinear
patterns and high uncertainty dynamic changes.
The deep neural network models, and in particular the LSTM algorithm, have
been increasingly used by researchers for analysis, trading and prediction of
stock market time series, appointing an important role in today’s economy.
The main purpose of this paper focus on the analysis and forecast of the
Standard & Poor’s index by employing multivariate modelling on several
correlated stock market indexes and interest rates with the support of VECM
trends corrected by a LSTM recurrent neural network.
ER  -