Value investing or investing in illiquidity? The profitability of contrarian investment strategies, revisited
Bibliographic record
Abstract
We investigate whether the success of contrarian investment strategies can be attributed to differences in the relative illiquidity of stocks categorized as value investments versus those categorized as glamour portfolios. Following Lakonishok et al. (J Financ 49:1541–1578, 1994), we assess the illiquidity characteristics of portfolios that underlie contrarian investment strategies that are based on the level of stock’s book to market. We find strong evidence that those portfolios characterized as value investments are associated with dramatically greater levels of illiquidity than glamour portfolios. We further demonstrate that strategies based on the illiquidity in the year prior to portfolio formation result in return characteristic of ostensibly contrarian strategies. These results suggest that the higher returns associated with contrarian investment strategies are the result of the higher illiquidity associated with value portfolios and represent compensation that the investor receives for accepting illiquidity. They also suggest that researchers should be cautious before attributing apparent anomalies to behavior-driven expectational errors rather than to other attributes unrelated to behavior, such as illiquidity.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".