Not So New Kid on the Block: Accounting and Valuation Aspects of Non-Fungible Tokens (NFTs)
Bibliographic record
Abstract
Aggregated trading volume in February 2023 across the leading six NFT marketplaces totalled USD 1.89 billion. This reflects a continuing positive trajectory, marked by a 91.9% month-on-month (MoM) growth from January 2023, where NFT trading volume amounted to USD 987.9 million. This study conducts a systematic review and textual analysis of industry and academic articles on NFTs primarily related to Accounting, Finance, and Information Systems where the NFT is treated as a tradable digital asset. The sample period spans 2012 to 30 June 2023, using an initial set of 5549 and a final set of 146 articles. In addition, the authors develop an NFT valuation framework, using Scopus bibliometrics data and public domain materials, that can aid in the fair valuation of NFTs and understanding their accounting implications. We further examine the accounting implications of NFTs in terms of international accounting standards, fair value recognition, taxation, auditing, and the metaverse. NFTs have the potential to become a cross-technology and cross-field topic, attracting interest from auditors, accountants, financial institutions, accounting professional bodies, regulators, governments, and investors.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".