Determining the Appropriate Accounting Treatment of Cryptocurrencies Based on Accounting Theory
Bibliographic record
Abstract
The International Financial Reporting Standards (IFRS) do not make explicit provisions, in terms of a specifically dedicated standard, for the accounting treatment of cryptocurrencies. This creates uncertainty, and guidance is therefore required in terms of accounting for such investments. Accounting theory has the potential to provide the foundation for this guidance. This study aimed to determine the most appropriate accounting treatment for cryptocurrencies based on the International Accounting Standards Board’s (IASB) Conceptual Framework for Financial Reporting (as a form of accounting theory) that results in decision-useful information. The research further investigated the proposed accounting treatment in terms of IFRS and sought to determine whether this treatment was aligned with the IASB’s conceptual framework. This qualitative study conducted a non-empirical interpretative analysis of the literature (focusing specifically on accounting theory) to address the research aim. The conceptual framework indicated that the most appropriate way to account for cryptocurrencies was to recognise an asset at fair value. This accounting treatment aligns with accounting for assets under International Accounting Standard (IAS) 2 commodities held by broker-traders and the IAS 38 revaluation model. Addressing the problem of accounting for cryptocurrencies with reference to accounting theory makes this study novel. The guidance provided could reduce uncertainty among entities holding investments in cryptocurrencies and could increase the decision-usefulness of financial information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".