The Relationship between Fair Value, Market Value, and Efficient Markets*
Bibliographic record
Abstract
ABSTRACT This paper proposes that an assumption of reasonable market efficiency is at the essence of the relevance of fair value for financial reporting purposes. The paper's examination of this proposal begins with a review of recent academic literature on market efficiency, and on evidence of inefficiencies and their implications for the ability of the efficient market hypothesis to explain what market prices represent. It concludes that there is wide acceptance in this literature that a reasonable level of efficiency can generally be presumed to exist in active, well‐regulated capital markets. The paper examines the essential attributes of a reasonably efficient market for fair value measurement purposes, and some basic implications for its reliable estimation. This is done in comparison with the provisions of the fair value measurement standard of the Financial Accounting Standards Board (FASB) (Statement of Financial Accounting Standards [SFAS] No. 157). It is concluded that the concept of reasonable market efficiency could provide a sound conceptual framework for defining fair value that is founded in real, observable market prices. It is demonstrated that, in contrast, SFAS No. 157 does not provide a clear, unequivocal concept of fair value, and that it permits estimates of fair value that have no demonstrable basis in real, observable market prices. Nevertheless, it appears that arguments typically put forward by the International Accounting Standards Board and the FASB for the relevance of fair value for financial reporting purposes do imply a presumption of reasonably efficient markets.
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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".