Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. (1st July 2020)

• Record Type: Journal Article
• Authors: Alonso-Monsalve, Saúl; Suárez-Cetrulo, Andrés L.; Cervantes, Alejandro; Quintana, David
• Is Part Of: Expert systems with applications. Volume 149(2020)
• Publication Date: 2020-07-01
• Subjects: Cryptocurrencies -- Neural network -- Finance -- Technical analysis -- Deep learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113250
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract : Highlights: Short term price trends of some cryptocurrencies can be predicted using deep learning on technical indicators on technical indicators. Bitcoin, Ethereum and Litecoin are statistically easier to predict than Dash and Ripple. Convolution improves prediction, and LSTM combined with convolutional layers provides the best results. Abstract: This study explores the suitability of neural networks with a convolutional component as an alternative to traditional multilayer perceptrons in the domain of trend classification of cryptocurrency exchange rates using technical analysis in high frequencies. The experimental work compares the performance of four different network architectures -convolutional neural network, hybrid CNN-LSTM network, multilayer perceptron and radial basis function neural network- to predict whether six popular cryptocurrencies -Bitcoin, Dash, Ether, Litecoin, Monero and Ripple- will increase their value vs. USD in the next minute. The results, based on 18 technical indicators derived from the exchange rates at a one-minute resolution over one year, suggest that all series were predictable to a certain extent using the technical indicators. Convolutional LSTM neural networks outperformed all the rest significantly, while CNN neural networks were also able to provide good results specially in the Bitcoin, Ether and Litecoin cryptocurrencies.