Financial Indices Modelling and Trading utilizing Deep Learning Techniques: The ATHENS SE FTSE/ASE Large Cap Use Case

By Marios Mourelatos, Christos Alexakos, T. Amorgianiotis, S. Likothanassis. 2018

11 citations recorded by the source corpus.

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.

Subfields: Economics & Operations Research, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics & Operations Research

1. A. Ozbayoglu
2. M. Ugur Gudelek
3. Omer Berat Sezer

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [151] used GASVR, LSTM for the forecast.”