The Worth of Fair Value Accounting: Dissonance between Users and Standard Setters
Bibliographic record
Abstract
Abstract Investors and analysts are designated as the primary users of financial reports by standard setters, yet we know very little about their use of accounting information and about their relationship with standard setters. This paper explores how investors and analysts evaluate the usefulness of fair values to their work. Standard setters typically presume that investors and analysts view accounting as a practice of valuation and, therefore, favor the greater use of fair value measurement. However, using interview evidence, it is shown here that investors and analysts expect accounting to provide them with insights into the performance of a business, and are quite cautious about the limits of using fair values in financial reports. Overall, the paper contributes to a better understanding of the relationship between accounting and its users. It adds specifically to research which has analyzed the disconnect between users and standard setters in terms of standard setters ignoring user needs (Young ), and in terms of users being indifferent about, or uncritical of, outcomes of standard‐setting processes (Durocher, Fortin, and Cote ; Durocher and Gendron ). The paper suggests a re‐theorization of the disconnect between the two groups that involves thinking away from tension, or blame. Drawing on the work of David Stark ( ), the situation observed is conceptualized as one of “dissonance,” where the different ways of evaluating fair values coexist without being involved in a fierce contest. That is, even though the principles of valuation and performance differ, this difference does not lead to open disagreement and political lobbying from investors and analysts. Consequences of this dissonance to our understandings of the (absence of) worth of fair values in capital markets are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".