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Mendes, D. A., Mendes, V., Lopes, T. & Ferreira, N. B. (2021). Multivariate forecast for the G7 stock markets: a hybrid VECM-LSTM deep learning model. CCS2021-SATELLITE ON ECONOPHYSICS 2021.

D. E. Mendes et al.,  "Multivariate forecast for the G7 stock markets: a hybrid VECM-LSTM deep learning model", in CCS2021-SATELLITE ON ECONOPHYSICS 2021, Lyon, 2021

@misc{mendes2021_1734880803342,
	author = "Mendes, D. A. and Mendes, V. and Lopes, T. and Ferreira, N. B.",
	title = "Multivariate forecast for the G7 stock markets: a hybrid VECM-LSTM deep learning model",
	year = "2021",
	howpublished = "Digital",
	url = "https://econophysics.ihu.gr/ec2021/"
}

TY  - CPAPER
TI  - Multivariate forecast for the G7 stock markets: a hybrid VECM-LSTM deep learning model
T2  - CCS2021-SATELLITE ON ECONOPHYSICS 2021
AU  - Mendes, D. A.
AU  - Mendes, V.
AU  - Lopes, T.
AU  - Ferreira, N. B.
PY  - 2021
CY  - Lyon
UR  - https://econophysics.ihu.gr/ec2021/
AB  - The forecasting of stock prices dynamics is a challenging task since these kinds of financial
datasets are characterized by irregular fluctuations, nonlinear patterns, and high uncertainty
changes. Deep neural network models, particularly the LSTM (Long Short Term Memory)
algorithm, have been increasingly used by researchers and practitioners to analyze, trade, and
predict financial time series, defining a new essential tool in several sectors' decision-making
processes.
The primary purpose of this paper focuses on a multivariate forecast of the U.S. stock index
S&P500, using Nasdaq, Dow Jones, and U.S. treasury bills for three months yields of the
secondary market series, with daily and weekly data, between January 2018 and July 2021.
With the support of a hybrid windowed VECM (Vector Error Correction Model) trend
corrected by an LSTM recurrent neural network, we consistently obtain low MAPE forecast
errors (around 4%), even including the COVID-19 crises.
In addition, nonlinear Granger causality, based on transfer entropy, was tested between the
periods with strong intervention by the Federal Bank, concluding that yields variation Granger
causes the stock indices returns. In contrast, this causal relationship outside these periods was
inverted, with the indices' returns causing yields variation.
ER  -