Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation
By Cain Evans, Konstantinos Pappas, Fatos Xhafa. 2013
104 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Teh Ying Wah
- Arman Khadjeh Nassirtoussi
- David Chek Ling Ngo
- Saeed Reza Aghabozorgi
- Anthony Grace
- Laith Mohammad Abualigah
- Ahamad Tajudin Abdul Khader
In Finance
In Management Science and Operations Research
- Teh Ying Wah
- Arman Khadjeh Nassirtoussi
- David Chek Ling Ngo
- Saeed Reza Aghabozorgi
- Anthony Grace
- Vadlamani Ravi
- Stefan Feuerriegel
- B. Shravan Kumar
Citations in the corpus
- Text mining for market prediction: A systematic review (2014, background): “Frequency trading has been more popular in the stock market and Evans, Pappas, and Xhafa (2013) are among the first who are utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation.”
- Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies (2017, methodology): “Fernandez and Gomez (2007) utilise metrics falling within the second category discussed by Evans et al. (2013) in their portfolio optimisation study, that is, metrics relating to the performance of the model objectives.”
- 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].”