March Market Madness: The Impact of Value‐Irrelevant Events on the Market Pricing of Earnings News
Bibliographic record
Abstract
Abstract Each year, the NCAA basketball tournament (March Madness) is a daytime distraction for millions of people, providing a largely exogenous shock to investor attention. We investigate whether March Madness influences the market response to earnings by diverting investor attention away from earnings news. We find that the price reaction to earnings news released during March Madness is muted. This result generally holds across several samples and additional analyses. We also find that the result is more muted for low institutional ownership firms, consistent with the effect being driven by less‐sophisticated investors. Furthermore, we find that it takes the market 30 to 60 days to correct for the distraction effect. Overall, we provide a unique test of the theory of limited attention by documenting that extraneous events can have a significant impact on the pricing of earnings.
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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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".