Foreign Currency Exchange Rates Prediction Using CGP and Recurrent Neural Network

By Mehreen Rehman, Gul Muhammad Khan, Sahibzada Ali Mahmud. 2014

51 citations recorded by the source corpus.

Abstract Feedback in Neuro-Evolution is explored and evaluated for its application in devising prediction models for foreign currency exchange rates. A novel approach to foreign currency exchange rates forecasting based on Recurrent Neuro-Evolution is introduced. Cartesian Genetic Programming (CGP) is the algorithm deployed for the forecasting model. Recurrent Cartesian Genetic Programming evolved Artificial Neural Network (RCGPANN) is demonstrated to produce computationally efficient and accurate model for forex prediction with an accuracy of as high as 98.872% for a period of 1000 days. The approach utilizes the trends that are being followed in historical data to predict five currency rates against Australian dollar. The model is evaluated using statistical metrics and compared. The computational method outperforms the other methods particularly due to its capability to select the best possible feature in real time and the flexibility that the system provides in feature selection, connectivity pattern and network.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, 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 Artificial Intelligence

1. Juho Kanniainen
2. Moncef Gabbouj
3. Dat Thanh Tran
4. Alexandros Iosifidis
5. Martin Magris

In Computer Vision and Pattern Recognition

1. Juho Kanniainen
2. Moncef Gabbouj
3. Dat Thanh Tran
4. Alexandros Iosifidis
5. Martin Magris

In Management Science and Operations Research

1. Adamantios Ntakaris
2. Dat Thanh Tran
3. Juho Kanniainen
4. Alexandros Iosifidis
5. Martin Magris
6. Moncef Gabbouj
7. M. Ugur Gudelek
8. Omer Berat Sezer

Citations in the corpus

• Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “Popular machine learning methods in HFT include regression analysis [1], [2], [3], [4], [5], multilayer feed forward network [6], [7], [8], convolutional neural network [9] , recurrent neural network [10], [11], [12].”
• Benchmark Dataset for Mid-Price Prediction of Limit Order Book data (2017, methodology): “[43] apply Recurrent Cartesian Genetic Programming Evolved Artificial Neural Network (RCGPANN) for predicting five currency rates against the Australian dollar.”