Alonso Monsalve, SaúlSuárez Cetrulo, Andrés L.Cervantes Rovira, AlejandroQuintana, David2022-04-012022-04-012020-07-01Alonso-Monsalve, S., Suárez-Cetrulo, A.L., Cervantes, A., Quintana, D. (2020). Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications, 149, 113250.0957-4174https://hdl.handle.net/10016/34505This 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.eng© 2020 Elsevier LtdAtribución-NoComercial-SinDerivadas 3.0 Españaneural networksfinancetechnical analysisdeep learningcryptocurrenciesresearch articleInformáticahttps://doi.org/10.1016/j.eswa.2020.113250open access115EXPERT SYSTEMS WITH APPLICATIONS149AR/0000025965