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The Valuation of Loss Carryforwards

2003· article· en· W2155909533 on OpenAlexaffvenue
Tao Zeng

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsValuation (finance)Market valueEconomicsPaymentFinancial economicsAccountingFinance

Abstract

fetched live from OpenAlex

Abstract This paper explores the value‐relevant information of one deferred tax component, that is, loss carryforwards. A tax‐adjusted market valuation model, based on the Feltham‐Ohlson (1995) market valuation model, shows that firm market value depends on the expected future tax payments. Under the assumption that loss carryforwards reduce a firm's future tax payments, the cross‐sectional regression results show that loss carryforwards enhance firm market value, suggesting that the market expects losses will be used to reduce a firm's tax payments in future years. In addition, loss carryforwards are classified into several categories based on the restrictions on the losses (i.e., source, jurisdiction, and timing restrictions). I show that it is the loss carryforwards category with fewer restrictions that significantly enhances firm market value. Résumé Cette présentation étudie les renseignements pertinents à la valeur d'un élément d'impôts différés, c.‐à‐d. des reports de pertes à un exercice ultérieur. Un modèle d'évaluation du marché avec réajustement d'impôt, basé sur sur le modèle d'évaluation du marché de Feltham‐Ohlson (1995), démontre que la valeur marchande d'une firme dépend des paiements prévus d'impôts futurs. Selon l'hypothèse que les reports de pertes à un exercice ultérieur réduisent les paiements d'impôts futurs d'une firme, l'analyse de la régression transversale révèle que les reports de pertes à un exercice ultérieur augmentent la valeur marchande de la firme, ce qui suggère que le marché s'attend à ce que les pertes seront utilisées pour réduire les paiements d'impôts de la firme au cours des années à venir. De plus, les reports de pertes à un exercice ultérieur sont classés dans plusieurs catégories en fonction des restrictions sur les pertes (c.‐à‐d. restrictions couvrant les sources, la juridiction et le calendrier d'application). Je démontre qu'il s'agit de la catégorie de reports de pertes à un exercice ultérieur comportant le moins de restrictions, qui augmente considérablement la valeur marchande d'une firme.

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)0.004
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.070
GPT teacher head0.290
Teacher spread0.220 · 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 designNot applicable
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

Citations12
Published2003
Admission routes2
Has abstractyes

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