Did Tom Brady Save the US stock market? Market Anomaly or Market Efficiency?
Bibliographic record
Abstract
This study reexamines how the National Football conference (NFC) Super Bowl predictor influences investors’ sentiment to affect U.S. stock market returns from 1967 to 2018. The overall empirical evidence significantly supports the NFC predictor-stock market relationship. Even continuous Super Bowl wins by Tom Brady’s New England Patriots could not overturn the Super Bowl stock market predictor (SBSMP). The false positive evidence by the NFC Super Bowl continues to provide investors with extraordinary returns. While the evidence is positive, there is a need for more theoretical research on the SBSMP. The Super Bowl euphoria can create a multiplier effect on the winning team’s local economy and overall stock market (Edmans et al., 2007) and (Palomino et al., 2009). This anomaly has demonstrated that an investor would have outperformed the market by using the Super bowl indicator. However, its predictive power is statistically declining but empirically significant (Thompson & Sen, 2017).
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".