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Performance evaluation of deep learning and boosted trees for cryptocurrency closing price prediction

Oyedele, Azeez A.; Ajayi, Anuoluwapo; Oyedele, Azeez; Bello, Sururah; Oyedele, Lukumon

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Authors

Azeez A. Oyedele

Anuoluwapo Ajayi Anuoluwapo.Ajayi@uwe.ac.uk
Associate Professor - Big Data Application

Azeez Oyedele

Sururah Bello Sururah.Bello@uwe.ac.uk
Senior Research Fellow - Redistributed Manufacturing in Deployed Operations

Lukumon Oyedele L.Oyedele@uwe.ac.uk
Professor in Enterprise & Project Management



Abstract

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial heterogeneous datasets characterized by complex interactions between predictors, which may be difficult for conventional techniques to achieve optimal results. In addition, volatility significantly impacts investment decisions; thus, investors are confronted with how to determine the price and assess their financial investment risks reasonably. This study investigates the performance evaluation of a genetic algorithm tuned Deep Learning (DL) and boosted tree-based techniques to predict several cryptocurrencies' closing prices. The DL models include Convolutional Neural Networks (CNN), Deep Forward Neural Networks, and Gated Recurrent Units. The study assesses the performance of the DL models with boosted tree-based models on six cryptocurrency datasets from multiple data sources using relevant performance metrics. The results reveal that the CNN model has the least mean average percentage error of 0.08 and produces a consistent and highest explained variance score of 0.96 (on average) compared to other models. Hence, CNN is more reliable with limited training data and easily generalizable for predicting several cryptocurrencies' daily closing prices. Also, the results will help practitioners obtain a better understanding of crypto market challenges and offer practical strategies to lower risks.

Journal Article Type Article
Acceptance Date Nov 4, 2022
Online Publication Date Nov 9, 2022
Publication Date Mar 1, 2023
Deposit Date Nov 14, 2022
Publicly Available Date Nov 14, 2022
Journal Expert Systems with Applications
Print ISSN 0957-4174
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 213
Issue Part C
Article Number 119233
DOI https://doi.org/10.1016/j.eswa.2022.119233
Keywords Artificial intelligence, Deep learning, Boosted trees, Optimization, Forecasting, Cryptocurrencies
Public URL https://uwe-repository.worktribe.com/output/10141000
Publisher URL https://www.sciencedirect.com/science/article/pii/S0957417422022515

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