Earnings Transparency and Financial Analysts’ Target Price Forecasts
Bibliographic record
Abstract
This paper examines the effect of earnings transparency on analysts’ target price forecast properties. The issuance of target price forecasts by financial analysts is a very recent event and target price forecasts are regarded as the most summarized and explicit estimate of the postulated future value of the firm.The sample consists of financial analysts’ forecasts of annual target price issued for firms listed on U.S. stock exchanges from 2001 to 2017. We measure each firm’s earnings transparency as the contemporaneous co-movement between firm’s earnings and change in earnings and stock returns, consisting in industry-specific and -neutral components in earnings-returns relation.Our results show that target price forecasts for more transparent earnings are less biased and more tend to attain the actual stock prices. These results demonstrate that earnings transparency is positively related with analysts’ target price forecasts. Our empirical results corroborate that more transparent accounting information help the market participants in forming more accurate and attainable forecasts. Our study extends the body of research studying the relation between analysts’ forecast properties and the usefulness of accounting information by investigation target price forecasts.
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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 | 0.006 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".