Evaluating interpretable machine learning predictions for cryptocurrencies

El Majzoub, A; Rabhi, FA; Hussain, W

Evaluating interpretable machine learning predictions for cryptocurrencies

El Majzoub, A Rabhi, FA Hussain, W

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Publisher:: JOHN WILEY & SONS LTD
Publication Type:: Journal Article
Citation:: Intelligent Systems in Accounting, Finance and Management, 2023, 30, (3), pp. 137-149
Issue Date:: 2023-07-01

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Field	Value	Language
dc.contributor.author	El Majzoub, A
dc.contributor.author	Rabhi, FA
dc.contributor.author	Hussain, W https://orcid.org/0000-0003-0610-4006
dc.date.accessioned	2024-03-07T04:01:50Z
dc.date.available	2024-03-07T04:01:50Z
dc.date.issued	2023-07-01
dc.identifier.citation	Intelligent Systems in Accounting, Finance and Management, 2023, 30, (3), pp. 137-149
dc.identifier.issn	1550-1949
dc.identifier.issn	2160-0074
dc.identifier.uri	http://hdl.handle.net/10453/176256
dc.description.abstract	This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.
dc.language	English
dc.publisher	JOHN WILEY & SONS LTD
dc.relation.ispartof	Intelligent Systems in Accounting, Finance and Management
dc.relation.isbasedon	10.1002/isaf.1538
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	4601 Applied computing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4609 Information systems
dc.title	Evaluating interpretable machine learning predictions for cryptocurrencies
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-07T04:01:48Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	3

Abstract:

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.

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http://hdl.handle.net/10453/176256