Market Liquidity and Momentum Profits : Evidence from the Korean Stock Market
Bibliographic record
Abstract
Previous literature in the Korean stock market has shown that the momentum effect is not observed during pre-2000 period while it is observed during post-2000 period. Given that market illiquidity has substantially decreased during post-2000 period, we examine whether the level of market illiquidity affect the momentum profits. The central findings are summarized as follows. First, our full-sample analysis shows that market liquidity is positively associated with momentum profits, meaning that the observed momentum effect during post-2000 period is related to the decrease in market illiquidity. Second, during pre-2000 period, when the market illiquidity is very high, the illiquidity of past losers is extremely high compared to that of past winners. However, there is no significant difference in illiquidity between winners and losers during post-2000 period. Third, based on this result, we conjecture that the momentum effect is related to the different compensation for liquidity risk between past losers and winners, and test whether this is indeed the case. We find significant momentum profits over the whole period when we consider the compensation for the liquidity risk of past losers and winners. In addition, during pre-2000 period, the return on momentum strategy that controls the liquidity risk is substantially higher than the actually observed momentum profits. In sum, our study suggests that the difference in compensation for liquidity risk between past losers and winners is very important in understanding the momentum effect in the Korean stock market.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".