Forecasting and trading the EUR/USD exchange rate with stochastic Neural Network combination and time-varying leverage

By Georgios Sermpinis, Christian L. Dunis, J. Laws, Charalampos Stasinakis. 2012

99 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research, Optimization & Training, 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 Finance

1. M. Ugur Gudelek
2. A. Ozbayoglu
3. Omer Berat Sezer
4. Georgios Sermpinis
5. Charalampos Stasinakis
6. Christian L. Dunis
7. Weiwei Jiang
8. Longbing Cao

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Georgios Sermpinis
5. Charalampos Stasinakis
6. Christian L. Dunis
7. Weiwei Jiang
8. Longbing Cao

In Optimization & Training

1. Georgios Sermpinis
2. Charalampos Stasinakis
3. Christian L. Dunis
4. A. Ozbayoglu
5. Omer Berat Sezer
6. M. Ugur Gudelek

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): “[174] EUR/USD 2002-2010 Price data 13d 1d RNN, MLP, PSN MAE, MAPE, RMSE, THEIL-U -”
• Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects (2014, methodology): “For more information on MLP, RNN and PSN architectures see Zhang et al. (1998), Ghazali et al. (2006) and Sermpinis et al. (2012).”
• Analyzing time–frequency relationship between oil price and exchange rate in Pakistan through wavelets (2015, unknown): “[46] and, Chen and Zhang [24] used artificial neural network to predict real effective exchange rate.”