Voluntary disclosure of intangibles and analysts’ earnings forecasts and recommendations
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the relationship between voluntary disclosure of intangibles and financial analysts’ earnings forecasts properties. Design/methodology/approach Disclosures about intangible assets were hand-collected through content analysis of annual reports of a sample of US non-financial firms, while analysts’ earnings forecasts properties were collected from Bloomberg Professional database. The authors relied on correlation and multivariate regression analyses to test the research hypotheses. Findings The results show that increased intangible disclosures affect analysts’ earnings forecasts accuracy, dispersion, and favourable consensus recommendations. However, this effect varies according to the nature of intangible assets. Practical implications The results may be of interest to different market participants such as corporate managers, financial analysts, and standards setting bodies that recently published guidelines on voluntary disclosure of intangibles. Originality/value This study develops a new comprehensive index to measure the content of narrative disclosures about a large number of intangibles, such as human, structural, and relational assets. The findings contribute to the current debate on the value-relevance of narrative disclosures on intangibles to investors and financial analysts.
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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 | 0.010 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".