Deep neural networks in foreign exchange market: A predictive classification framework for real-time price movement”

Laxman Doddipatla *

Payments Engine, PNC Bank, Pittsburgh USA.
 
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2023, 10(02), 326-338.
Article DOI: 10.30574/wjaets.2023.10.2.0140
Publication history: 
Received on 10 May 2023; revised on 18 December 2023; accepted on 21 December 2023
 
Abstract: 
The application of deep neural networks (DNNs) in foreign exchange (FX) markets introduces a novel methodology for predicting short-term price movements with greater accuracy. This study explores a classification-based approach, where market price direction is forecasted using a comprehensive model trained on high-frequency historical FX data. By employing advanced deep learning techniques and leveraging co-movement patterns between various currency pairs, the model classi- fies price changes into positive, negative, or neutral outcomes. Through a backtested strategy on multiple FX futures over 43 instruments, this method demonstrates improved decision-making accuracy and offers sig- nificant potential for enhancing algorithmic trading strategies. Our find- ings reveal that incorporating multiple hidden layers in neural networks significantly boosts predictive performance, positioning DNNs as a pow- erful tool for traders and financial analysts.
 
Keywords: 
Deep Neural Networks (DNNs); Foreign Exchange (FX); Markets; Price Prediction; Short-term Price Movements; Currency Pairs
 
Full text article in PDF: 

Click here

 
Copyright information: 
Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0