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Record W4412196335 · doi:10.3390/jrfm18070384

The Information Content of the Deferred Tax Valuation Allowance: Evidence from Venture-Capital-Backed IPO Firms

2025· article· en· W4412196335 on OpenAlexvenueno aff
Eric J. Allen

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
FundersCalifornia State University, FullertonSouthern Methodist UniversityCalifornia State UniversityRice UniversityUniversity of Southern California
KeywordsInitial public offeringValuation (finance)Allowance (engineering)BusinessVenture capitalAccountingDeferred taxBusiness valuationMonetary economicsFinanceEconomicsOperations managementState income taxPublic economicsTax reform

Abstract

fetched live from OpenAlex

This study examines the deferred tax valuation allowance disclosures of a sample of venture-capital-backed IPO firms that incurred a net operating loss (NOL) in the period prior to their public offering (IPO). I find that 82 percent of these firms record an allowance that reduces the associated deferred tax asset to zero, that the choice to record the allowance is largely driven by a firm’s history of losses, and that the allowance is associated with lower future book income. I further propose a new explanation for the presence of the allowance: the Section 382 ownership change limitation, which can cause firms to record an allowance independent of their past profitability or expectations about future earnings. I find that firms consider this limitation when recording the allowance, and that controlling for it can enhance the signal regarding future income.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.019
GPT teacher head0.213
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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