Return Rate Prediction in Blockchain Financial Products Using Deep Learning

Noura Metawa, Mohamemd I. Alghamdi, Ibrahim M. El-Hasnony and Mohamed Elhoseny
Additional contact information
Noura Metawa: College of Business Administration, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Mohamemd I. Alghamdi: Department of Computer Science, Al-Baha University, Al-Bahah 1988, Saudi Arabia
Ibrahim M. El-Hasnony: Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
Mohamed Elhoseny: Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt

Sustainability, 2021, vol. 13, issue 21, 1-16

Abstract: Recently, bitcoin-based blockchain technologies have received significant interest among investors. They have concentrated on the prediction of return and risk rates of the financial product. So, an automated tool to predict the return rate of bitcoin is needed for financial products. The recently designed machine learning and deep learning models pave the way for the return rate prediction process. In this aspect, this study develops an intelligent return rate predictive approach using deep learning for blockchain financial products (RRP-DLBFP). The proposed RRP-DLBFP technique involves designing a long short-term memory (LSTM) model for the predictive analysis of return rate. In addition, Adam optimizer is applied to optimally adjust the LSTM model’s hyperparameters, consequently increasing the predictive performance. The learning rate of the LSTM model is adjusted using the oppositional glowworm swarm optimization (OGSO) algorithm. The design of the OGSO algorithm to optimize the LSTM hyperparameters for bitcoin return rate prediction shows the novelty of the work. To ensure the supreme performance of the RRP-DLBFP technique, the Ethereum (ETH) return rate is chosen as the target, and the simulation results are investigated in different measures. The simulation outcomes highlighted the supremacy of the RRP-DLBFP technique over the current state of art techniques in terms of diverse evaluation parameters. For the MSE, the proposed RRP-DLBFP has 0.0435 and 0.0655 compared to an average of 0.6139 and 0.723 for compared methods in training and testing, respectively.

Keywords: blockchain; financial products; predictive model; deep learning; Adam optimizer; LSTM model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/21/11901/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/21/11901/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:21:p:11901-:d:666496

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().