Domicián Máté

Department of Engineering Management and Enterprise, Faculty of Engineering, University of Debrecen, Debrecen,

Hungary

mate.domician@eng.unideb.hu

ORCID 0000-0002-4995-7650

Next step for bitcoin: Confluence of technical indicators and machine learning

Hassan Raza

Department of Management Sciences, Shaheed Zulfikar Ali Bhutto Institute of Science & Technology University, Islamabad,

Pakistan

hassanrazaa@live.com

ORCID 0000-0002-9394-4961

Ishtiaq Ahmad

Department of Management Sciences, National University of Modern Languages University, Islamabad,

Pakistan

iahmed@numl.edu.pk 

ORCID 0000-0001-6038-4470

Sándor Kovács

Coordination and Research Centre for Social Sciences, Faculty of Economics and Business, University of Debrecen, Debrecen,

Hungary

kovacs.sandor@econ.unideb.hu 

ORCID 0000-0002-1216-346X

Abstract. Cryptocurrencies are quickly becoming a key tool in investment decisions. The volatile nature of bitcoin prices has spurred the demand for robust predictive models. The primary objective of this study is to evaluate and compare the effectiveness of different machine learning models with the combination of technical indicators in predicting bitcoin prices. The study used 27 critical technical indicators to evaluate four machine learning techniques, namely Artificial Neural Network (ANN), a Hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), Support Vector Machine (SVM), and Random Forest. The results showed that ANN and SVM achieve a significant prediction accuracy of 81% and 82%, respectively, which is higher than the results of traditional models such as standard ARIMA. In practical applications, these methods often improve prediction accuracy by 20-30% over traditional models. The novelty of the analysis lies in the use of temporal and spatial trends via momentum, ROC, and %K features, making for a holistic approach to cryptocurrency market forecasting. This study underscores the critical importance of specific technical indicators and the imperative role of data mining in revolutionizing cryptocurrency market navigation. The research results highlight opportunities to improve investment strategies and risk management policies in the bitcoin market using machine learning models, making the latter valuable to investors and financial experts.

Received: September, 2023

1st Revision: June, 2024

Accepted: September, 2024

DOI: 10.14254/2071-8330.2024/17-3/4

JEL Classification: C53, C81, G17

Keywords: bitcoin forecasting, comparative analysis, cryptocurrency market trends, machine learning algorithms, predictive model evaluation