Re‐exploring Fair Value Accounting and Value Relevance: An Examination of Underlying Securities
Bibliographic record
Abstract
Focusing on closed‐end investment funds, this paper examines whether the value relevance attached to fair value estimates is influenced by the type of investment being held. Our premise is that the fair values of different investment types rely on different valuation models and imply different underlying risks. Using hand‐collected US closed‐end funds data from 2009 to 2011, our results show that the value relevance attached to fair value hierarchy levels’ assets (i.e., Level 1, Level 2, or Level 3) reflects both the source of market information for fair value estimates (i.e., market prices, market inputs, and model‐based) and also the underlying type of investment being valued (e.g., government bonds, equities, corporate bonds, etc.). Within the same fair value category, we show that different types of investments have their own distinct value relevance that is significantly different from that of other types of investments within the same category. Moreover, within the same type of investment, we also show that the value relevance varies across the three fair value category levels. Our results further show that auditing in general only significantly impacts the value relevance of equities in Level 1, Level 2, and Level 3, and corporate bonds in Level 3.
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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 | 0.015 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".