Evaluating the Financial Impact of Branding Using Trademarks: A Framework and Empirical Evidence
Bibliographic record
Abstract
Firms spend considerable efforts to build brand awareness and associations among consumers. Yet there is a limited understanding of the financial returns of such investments. In this article, the authors present a framework that uses trademarks as measures of firms' branding efforts. They classify trademarks into two categories—brand-identification trademarks and brand-association trademarks—and propose that they are indicators of firm efforts to build brand awareness and associations among consumers, respectively. The authors then evaluate the chain of effects linking such assets with metrics of firms' financial value. A longitudinal analysis of data collected from secondary sources reveals that the stock (i.e., total number) of brand-association trademarks available to firms in time period t increases their cash flow, Tobin's q, return on assets, and stock returns and reduces their cash-flow variability in period t + 1. Furthermore, the authors observe that the stock of brand-identification trademarks owned by firms in period t − 1 influences the effects of brand-association trademarks on cash flow, Tobin's q, and stock returns. Together, these findings provide useful insights into the financial value of branding.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".