Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks

By Georgios Sermpinis, J. Laws, Andreas S. Karathanasopoulos, Christian L. Dunis. 2012

59 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, 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 Artificial Intelligence

1. Omer Berat Sezer
2. M. Ugur Gudelek
3. Yuzhou Chen
4. A. Ozbayoglu
5. Hui Bu
6. Junjie Wu

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Yuzhou Chen
6. Hui Bu
7. Junjie Wu
8. Longbing Cao

In Economics & Operations Research

1. A. Ozbayoglu
2. M. Ugur Gudelek
3. Yuzhou Chen
4. Omer Berat Sezer
5. Hui Bu
6. Junjie Wu

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Hui Bu
6. Junjie Wu
7. Longbing Cao
8. Ze Hu

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [173] used MLP, RNN, GP and other ML techniques along with traditional regression methods for also predicting EUR/USD time series.”
• Stock Market Embedding and Prediction: A Deep Learning Method (2018, background): “With the rapidly growing volume of data and computation resources, machine learning algorithms like neural networks [12,13], fuzzy logic [14], Support Vector regression [15,16], rule-based genetic network programming [17] also provide fantastic performance.”
• Artificial neural networks in business: Two decades of research (2016, unknown): “Hybrid networks, integrating additional methods into neural network environment, were therefore repeatedly tested with promising results [109, 297, 335].”