The Effect of Measurement Subjectivity Classifications on Analysts' Use of Persistence Classifications When Forecasting Earnings Items
Bibliographic record
Abstract
Abstract Earnings items are typically classified in financial reports based on their persistence and measurement subjectivity. Archival research examines investors' use of persistence and measurement subjectivity classifications for forecasting and valuation. However, this research typically examines only one of these classifications at a time and ignores the potential interactive implications of an earnings item's persistence and measurement subjectivity classifications. We recruited experienced financial analysts to participate in two experiments that examined the effect of measurement subjectivity classifications on analysts' use of persistence classifications when forecasting earnings items. We find that analysts rely less on an earnings item's persistence classification when measurement subjectivity is high relative to when measurement subjectivity is low. We also find that presentation format affects analysts' use of these two classifications. Specifically, we find that the matrix format (i.e., rows display persistence classifications and columns display measurement subjectivity classifications) facilitates analysts' combined use of persistence and measurement subjectivity classifications relative to the sequential format (i.e., the classifications are displayed separately). These findings suggest that archival research could improve its examination of market participants' use of earnings classifications for forecasting and valuation by recognizing that the implications of an earnings item's persistence classification can vary according to the item's measurement subjectivity classification. By also demonstrating how presentation format affects analysts' use of earnings classifications, our study provides further insights into this fundamental issue in accounting research and standard setting.
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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 | 0.035 | 0.275 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".