Neural networks performance in exchange rate prediction

By Svitlana Galeshchuk. 2016

163 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, 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 Economics and Econometrics

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

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, background): “Galeshchuk [20] suggests that a Multi-Layer Perceptron (MLP) architecture having three hidden layers is suitable for exchange rate prediction.”