The Deferred Tax Asset Valuation Allowance and Firm Creditworthiness
Bibliographic record
Abstract
ABSTRACT In this study, I provide evidence that the valuation allowance for deferred tax assets helps predict the future creditworthiness of a firm. Under the provisions of SFAS No. 109, a firm records a deferred tax asset provided it expects to generate sufficient taxable income to realize the asset in the form of tax savings in the future. If a firm does not expect to generate sufficient taxable income to realize the asset, then a valuation allowance is created to reduce the balance. As a result, the valuation allowance indicates management's expectation of future taxable income, which could be informative in predicting the ability of the firm to make future interest and principal payments on debt. Alternatively, the valuation allowance may not be informative regarding creditworthiness if it is a result of overly conservative accounting practices or if it is used as an earnings management tool. I document a negative association between material increases in the valuation allowance and contemporaneous and future changes in credit ratings, evidence that is consistent with the valuation allowance providing a summary measure of a decline in firms' creditworthiness. JEL Classifications: G29; H25; M41. Data Availability: Data are available from sources identified in the paper.
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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".