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Decoding Market Prices of Sustainable Cryptocurrencies: Fresh Insights from Ensemble Machine Learning and Explainable AI

Indranil Ghosh Orcid Logo, Rabin K. Jana Orcid Logo, Mohammad Abedin Orcid Logo, Pavan Kumar Balivada Orcid Logo

Global Business Review

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1177/09721509261427956

Abstract

This research develops an integrated framework that combines ensemble machine learning and explainable artificial intelligence to predict the performance of sustainable cryptocurrencies, including Avalanche, Build and Build (BNB) Chain, Polkadot and Solana, and to reveal the dependencies between the...

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Published in: Global Business Review
ISSN: 0972-1509 0973-0664
Published: SAGE Publications 2026
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URI: https://cronfa.swan.ac.uk/Record/cronfa71699
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last_indexed 2026-04-03T03:30:53Z
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spelling 2026-04-01T11:23:07.6517189 v2 71699 2026-04-01 Decoding Market Prices of Sustainable Cryptocurrencies: Fresh Insights from Ensemble Machine Learning and Explainable AI 4ed8c020eae0c9bec4f5d9495d86d415 0000-0002-4688-0619 Mohammad Abedin Mohammad Abedin true false 2026-04-01 CBAE This research develops an integrated framework that combines ensemble machine learning and explainable artificial intelligence to predict the performance of sustainable cryptocurrencies, including Avalanche, Build and Build (BNB) Chain, Polkadot and Solana, and to reveal the dependencies between these assets and key explanatory features. The framework employs a comprehensive predictive structure that integrates supervised and unsupervised feature processing with a metaheuristic-tuned ensemble learning model. BorutaShap identifies significant explanatory variables, while isometric mapping obtains an optimized feature representation. Predictions are generated using the Extreme Gradient Boosting algorithm, with hyperparameters optimized through particle swarm optimization. To ensure interpretability, the predictive methodology undergoes rigorous analysis using multiple explainable artificial intelligence techniques that decode dependency patterns at both global and local levels, facilitating a comprehensive understanding of market dynamics for the selected assets. Results reveal that market sentiment, technological outlook and US options market fear are the primary determinants of sustainable crypto asset performance. Journal Article Global Business Review 0 SAGE Publications 0972-1509 0973-0664 Feature processing; Extreme Gradient Boosting; cryptocurrency; sustainability; explainable artificial intelligence 19 3 2026 2026-03-19 10.1177/09721509261427956 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2026-04-01T11:23:07.6517189 2026-04-01T11:14:20.2237872 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Indranil Ghosh 0000-0001-7064-4774 1 Rabin K. Jana 0000-0001-8564-112x 2 Mohammad Abedin 0000-0002-4688-0619 3 Pavan Kumar Balivada 0000-0003-4041-6521 4
title Decoding Market Prices of Sustainable Cryptocurrencies: Fresh Insights from Ensemble Machine Learning and Explainable AI
spellingShingle Decoding Market Prices of Sustainable Cryptocurrencies: Fresh Insights from Ensemble Machine Learning and Explainable AI
Mohammad Abedin
title_short Decoding Market Prices of Sustainable Cryptocurrencies: Fresh Insights from Ensemble Machine Learning and Explainable AI
title_full Decoding Market Prices of Sustainable Cryptocurrencies: Fresh Insights from Ensemble Machine Learning and Explainable AI
title_fullStr Decoding Market Prices of Sustainable Cryptocurrencies: Fresh Insights from Ensemble Machine Learning and Explainable AI
title_full_unstemmed Decoding Market Prices of Sustainable Cryptocurrencies: Fresh Insights from Ensemble Machine Learning and Explainable AI
title_sort Decoding Market Prices of Sustainable Cryptocurrencies: Fresh Insights from Ensemble Machine Learning and Explainable AI
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Mohammad Abedin
author Mohammad Abedin
author2 Indranil Ghosh
Rabin K. Jana
Mohammad Abedin
Pavan Kumar Balivada
format Journal article
container_title Global Business Review
container_volume 0
publishDate 2026
institution Swansea University
issn 0972-1509
0973-0664
doi_str_mv 10.1177/09721509261427956
publisher SAGE Publications
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
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description This research develops an integrated framework that combines ensemble machine learning and explainable artificial intelligence to predict the performance of sustainable cryptocurrencies, including Avalanche, Build and Build (BNB) Chain, Polkadot and Solana, and to reveal the dependencies between these assets and key explanatory features. The framework employs a comprehensive predictive structure that integrates supervised and unsupervised feature processing with a metaheuristic-tuned ensemble learning model. BorutaShap identifies significant explanatory variables, while isometric mapping obtains an optimized feature representation. Predictions are generated using the Extreme Gradient Boosting algorithm, with hyperparameters optimized through particle swarm optimization. To ensure interpretability, the predictive methodology undergoes rigorous analysis using multiple explainable artificial intelligence techniques that decode dependency patterns at both global and local levels, facilitating a comprehensive understanding of market dynamics for the selected assets. Results reveal that market sentiment, technological outlook and US options market fear are the primary determinants of sustainable crypto asset performance.
published_date 2026-03-19T07:01:28Z
_version_ 1861428013927759872
score 11.100739

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