The Red Flags of Tax Reporting on the Market Value
Bibliographic record
Abstract
The aim of this paper is to provide empirical evidence of the red flags in the level of tax reporting among theShariah Compliance companies in Bursa Malaysia. The convenience sampling method was employed among123 Shariah compliance companies of Bursa Malaysia. Meanwhile, the investigation period in this study hadcovered twelve years of continuous data, starting from the year 2001 until the year 2012. This study adopted theCurrent Based Model to calculate the level of Effective Tax Rate (ETR) as an independent variable while thefirm values as dependent variable. It was revealed that due to the Creative Accounting strategies, there is apossibility of tax fraud occurring during the calculation of taxation level. These activities, which were applied inimplementing tax planning mechanisms is however allowed by the GAAP under MFRS. As is commonly known,the purpose of tax reporting is to safeguard the interest of potential shareholders; however, these practices ofaggressive tax planning strategies will result in differing perceptions from tax payers’ and potential shareholders’perspectives. Therefore, aggressive tax planning strategies could be a red flag to financial fraud activities. Thus,this study would disclose some evidence on how financial fraud could be revealed from tax reporting strategies.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".