| Autor: |
Thomas Amorgianiotis, Christos Alexakos, Marios Mourelatos, Spiridon D. Likothanassis |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
INISTA |
| Popis: |
Prediction and modelling of the financial indices is a very challenging and demanding problem because its dynamic, noisy and multivariate nature. Modern approaches have also to challenge the fact that they are dependencies between different global financial indices. All this complexity in combination with the large volume of historic financial data raised the need for advanced machine learning solutions to the problem. This article proposes a Deep Learning approach utilizing Long Short-Term Memory (LSTM) Networks for the modelling and trading of financial indices. The technique is evaluated in the use case of the Athens SE FTSE/ASE Large Cap Index in comparison with a hybrid approach combining Genetic Algorithms and Support Vector Machines with promising results. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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