Comparative multivariate forecast performance for the G7 stock markets: VECM models vs deep learning LSTM neural networks
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...
| Autor principal: | |
|---|---|
| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2021
|
| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10071/21846 |
| País: | Portugal |
| Oai: | oai:repositorio.iscte-iul.pt:10071/21846 |
| Resumo: | 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. |
|---|