Establishing the Relationship between Trademark Valuation and Firm Performance: Evidence from Iran
Bibliographic record
Abstract
Valuing intangible assets is a critical issue in modern economics; one of the most important ones is trademarks. In a competitive business environment trademarks can protect and create an advantage for firms. In today’s complex and ever faster growing market, a suitable trademark affects firm performance and it is considered as a fundamental economic asset for organizations. Valuing intangible assets and determining its relation with performance indicators has two main benefits, first it can be useful for various stakeholders such as stockholders, creditors and employees in assessing firm performance and secondly it can draw standard setter’s attention to importance of recognizing and measuring trademarks and other intangible assets in financial statements. The first step in conducting such research is to identify developed and acquired trademarks of listed companies in Tehran Stock Exchange and computing their related value by financial oriented models, then the relationship between trademarks value and accounting performance indicators including net profit (earnings), Return on assets (ROA), Return on Equity (ROE) and Return on sales (ROS) is examined. The results extracted from 2001 to 2011 indicate a significant and direct relationship between mentioned performance indicators and trademarks value. P progr_`oap????oduction targets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".