Do Past Extreme Returns Explain the Future Performance? MAX Effect Evidence from the Nordic Countries
Bibliographic record
Abstract
I examine MAX effect, i.e. negative relation between high maximum daily returns in the past month and returns in the next month, in the Nordics. Bali et. al. (2011) first find the MAX effect in U.S.A. I examine the MAX effect in the Nordics because of lack of evidence in the Nordics and the Aboulamer et. al (2016) recent contradictory finding in Canada. I confirm the previous results about MAX effect. I find negative cross-sectional relation between high maximum returns in past month and returns in the next month after controlling for variables: beta, size, book-to-market ratio, momentum, short term reversal and illiquidity. My results are also robust for idiosyncratic volatility puzzle i.e. negative relation between high idiosyncratic volatility and returns introduced by Ang et. al (2009), in fact the idiosyncratic volatility puzzle seems to overturn to positive effect after controlling MAX. The effect is consistent with investors preference for lottery-like stocks which lead to over-demand, higher prices and lower expected returns for high MAX stocks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".