Polytechnic University of Valencia Congress, CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics

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Comparative multivariate forecast performance for the G7 Stock Markets: VECM Models vs deep learning LSTM neural networks
Diana Aldea Mendes, Nuno Rafael Barbosa Ferreira, Vivaldo Manuel Pereira Mendes

Last modified: 30-06-2020


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.

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