Miss Noora Alsalmi
Accounting for Digital Currencies
Alsalmi, Miss Noora; Ullah, Subhan; Rafique, Muhammad
Authors
SUBHAN ULLAH SUBHAN.ULLAH@NOTTINGHAM.AC.UK
Associate Professor in Accounting
MUHAMMAD RAFIQUE Muhammad.Rafique@nottingham.ac.uk
Assistant Professor
Abstract
The purpose of this paper is twofold: (i) to investigate some of the main issues surrounding the classification of digital currencies, and (ii) to identify the accounting practices and standards tied to digital currencies. This paper discusses two different types of digital currencies, including: central bank digital currencies (CBDCs) and privately issued cryptocurrencies such as Bitcoin. The findings of this study suggest that current accounting standards do not precisely cover the accounting treatment of digital currencies, even though the estimated value of market capitalisation of cryptocurrency in 2022 was USD 200 billion. This conceptual paper identifies the imminent need for an accounting standard to provide guidance on the identification, classification, measurement, and presentation of digital currencies. In the interim, existing accounting standards can be amended to incorporate digital currencies to avoid inconsistent global accounting approaches.
Citation
Alsalmi, M. N., Ullah, S., & Rafique, M. (2023). Accounting for Digital Currencies. Research in International Business and Finance, 64, Article 101897. https://doi.org/10.1016/j.ribaf.2023.101897
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 30, 2023 |
Online Publication Date | Feb 1, 2023 |
Publication Date | 2023-01 |
Deposit Date | Feb 1, 2023 |
Publicly Available Date | Aug 1, 2024 |
Journal | Research in International Business and Finance |
Print ISSN | 0275-5319 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 64 |
Article Number | 101897 |
DOI | https://doi.org/10.1016/j.ribaf.2023.101897 |
Keywords | accounting; blockchain; digital currency; central bank digital currency; cryptocurrencies; decentralised technology; triple-entry accounting M41; G10; H20; F30 : classification JEL |
Public URL | https://nottingham-repository.worktribe.com/output/16795230 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S0275531923000235?via%3Dihub |
Files
1-s2.0-S0275531923000235-main
(1.7 Mb)
PDF
Licence
https://creativecommons.org/licenses/by/2.0/
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
Big Data-Savvy Teams’ Skills, Big Data-Driven Actions and Business Performance
(2019)
Journal Article
Hide-and-seek in corporate disclosure: evidence from negative corporate incidents
(2019)
Journal Article
Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data
(2018)
Journal Article