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Record W2047559531 · doi:10.1108/eb027006

Accounting for Future Tax Assets and Liabilities under CICA Handbook Section 3465

2003· article· en· W2047559531 on OpenAlexaffabout
Tao Zeng

Bibliographic record

VenueReview of Accounting and Finance · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsTaxable incomeDeferred taxEconomicsLiabilityBook valueMonetary economicsValuation (finance)BusinessAccountingFinanceState income taxTax reformPublic economicsGross incomeEarnings

Abstract

fetched live from OpenAlex

This paper explores the value relevant information of future income taxes under The Canadian Institute of Chartered Accountants (CICA) handbook section 3465. CICA handbook section 3465 requires Canadian companies to use the asset and liability method to account for income taxes. Consistent with prior studies, this paper shows that future tax assets are positively associated with share prices, suggesting that they are valued as assets. Future tax liabilities are negatively associated with share prices, suggesting that they are valued as liabilities. Future tax value allowance, which is created for future tax assets, is negatively associated with share prices. This study also explores the value relevant information of future tax asset and liability categories. In addition, this paper explores what determines the valuation of future tax assets and liabilities. It is argued that future tax assets are more (less) valuable if (no) sufficient future income will be generated in the near future to utilize these tax assets; future tax liabilities will reduce share prices more (less), if there is a higher (lower) likelihood of reversal in the short run. The results support this argument. It is shown that (1) future tax assets are less valuable if the firm's value allowance is higher (i.e., the management does not expect the firm will generate sufficient taxable income in future years to utilize these tax assets), or the firm's leverage is higher (another proxy for no sufficient future taxable income), and (2) future tax liabilities reduce share prices less if the firm's investment in capital properties is increased.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.898
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.299
Teacher spread0.271 · 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 teacher head, 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

Citations1
Published2003
Admission routes2
Has abstractyes

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