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Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is...

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Autores principales: Erfanian, Sahar, Zhou, Yewang, Razzaq, Amar, Abbas, Azhar, Safeer, Asif Ali, Li, Teng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601835/
https://www.ncbi.nlm.nih.gov/pubmed/37420506
http://dx.doi.org/10.3390/e24101487
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author Erfanian, Sahar
Zhou, Yewang
Razzaq, Amar
Abbas, Azhar
Safeer, Asif Ali
Li, Teng
author_facet Erfanian, Sahar
Zhou, Yewang
Razzaq, Amar
Abbas, Azhar
Safeer, Asif Ali
Li, Teng
author_sort Erfanian, Sahar
collection PubMed
description Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.
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spelling pubmed-96018352022-10-27 Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach Erfanian, Sahar Zhou, Yewang Razzaq, Amar Abbas, Azhar Safeer, Asif Ali Li, Teng Entropy (Basel) Article Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark. MDPI 2022-10-18 /pmc/articles/PMC9601835/ /pubmed/37420506 http://dx.doi.org/10.3390/e24101487 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Erfanian, Sahar
Zhou, Yewang
Razzaq, Amar
Abbas, Azhar
Safeer, Asif Ali
Li, Teng
Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach
title Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach
title_full Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach
title_fullStr Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach
title_full_unstemmed Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach
title_short Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach
title_sort predicting bitcoin (btc) price in the context of economic theories: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601835/
https://www.ncbi.nlm.nih.gov/pubmed/37420506
http://dx.doi.org/10.3390/e24101487
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