Unfair “Fair Value” in Illiquid Markets: Information Spillover Effects in Times of Crisis
Bibliographic record
Abstract
We investigate the effects of write-downs on market prices and volumes under fair value accounting. We also examine the prominent role that illiquidity plays in exacerbating the direct and spillover effects of exit valuation on equity and credit default swap (CDS) markets. Using hand-collected data on write-down announcements made during and after the 2007–2009 financial crisis, we find that firms that wrote down assets in accordance with fair value rules experience significant abnormal negative stock returns and spikes in the CDS premiums written on their obligations; similar firms without write-downs exhibit sympathetic and significant negative abnormal returns and positive premiums. We find that both the direct effect of the write-downs and the indirect spillover effects resulting from crisis-related illiquidity in the markets for financial assets (affecting the magnitude of write-downs) and in the securities markets (affecting the reaction to the write-downs) during the financial crisis go beyond normal direct and information transfer effects and may have contributed to the adverse consequences of the crisis. This paper was accepted by Shiva Rajgopal, accounting.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".