Is tax-related information value relevant? Empirical study in the Canadian setting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study aims at examining the value relevance of tax-related information in Canada. Tax-related information in this study includes taxable income, tax aggressiveness, and tax risk (i.e., unsustainable tax planning). Design/methodology/approach This study analyzes the Canadian listed firms covering the period of 2012–2021 using the Feltham–Ohlson valuation model. Findings The findings are: (1) taxable income provides incremental value relevance information; (2) tax risk reduces the value relevance of both taxable income and accounting income and (3) tax aggressiveness reduces the value relevance of accounting income but not of taxable income. Further tests show that the COVID-19 pandemic increases the value relevance of taxable income but decreases the value relevance of accounting income. An analysis of the association between stock price volatility and tax-related information documents that taxable income and accounting income are both informative. Tax risk reduces the informativeness of taxable income, but tax aggressiveness and the pandemic do not. Research limitations/implications The sample in this study covers the period up to 2021. Future research could use more recent data. Additionally, this study examines the Canadian setting. The results may not be generalized to other countries that have different accounting and tax rules. Originality/value This study sheds light on whether tax aggressiveness and tax risk affect the value relevance of taxable income and accounting income separately. In addition, to our knowledge, this is the first study that examines whether tax-related information is informative about stock price volatility.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it