Attracting Attention in a Limited Attention World: Exploring the Causes and Consequences of Extreme Positive Earnings Surprises
Bibliographic record
Abstract
We investigate why extreme positive earnings surprises occur and the consequences of these events. We posit that managers know before analysts when extremely good earnings news is developing, but can have incentives to allow the earnings news to surprise the market at the earnings announcement. In particular, managers can use an extreme positive earnings surprise to attract investor attention when they believe their stock is neglected and future performance is expected to be strong. Analysts, who must allocate scarce resources across many firms, can also be inattentive and miss signals that suggest good performance is going to be announced. Using various proxies for extreme positive earnings surprises, management expectations for future performance and desire for attention, and analyst neglect, we find evidence that an extreme positive earnings surprise is a predictable event. These findings are incremental to controlling for a firm’s information environment, earnings volatility, and operating leverage. Finally, we show that extreme positive earnings surprises are a successful method for attracting attention, with significant increases in the number of institutional owners, the number of analysts, and trading volume during the subsequent three years. This paper was accepted by Mary Barth, accounting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".