Bibliographic record
Abstract
In their working paper, Kumar, Ruenzi, and Ungeheuer (KRU) document that stocks ranked as daily winners or losers in the previous month underperform unranked stocks during the month after the ranking. KRU explain that the ranked stocks experience a large increase in investor attention, which leads to temporary overpricing and subsequent underperformance. Following KRU, we investigate whether the same effect exists in the Korean stock market and find a robust daily winners and losers effect. First, stocks that were both daily winners and losers in a given month underperform those that were neither daily winners nor losers during the following months. Second, stocks that were never a daily winner or loser during the previous month do not exhibit the idiosyncratic volatility puzzle or the MAX effect. Moreover, the underperformance of ranked stocks is robust after controlling for the idiosyncratic volatility and the MAX effect. We suggest that the overpricing caused by excessive attention to daily winners and losers may be the main driver of the idiosyncratic volatility puzzle and the MAX effect. Lastly, we find that retail investors buy daily winners and losers, while both institutional investors and foreign investors decrease trades in the ranked stocks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".