Stock market manipulation: A comparative analysis of East Asian emerging and developed financial markets
Bibliographic record
Abstract
The study investigates the firm's specific characteristics of manipulated firms in East Asian emerging and developed markets using hand-collected 244 manipulated cases between 2001 and 2017. The empirical analysis is conducted using panel logistic regression to identify which stocks are more likely to be manipulated. Result shows that large and highly liquid firms were more likely to be manipulated in both emerging and developed markets. Additionally, marginal effect shows that firms with high free float and market capitalization had a higher probability of being manipulated in these markets. On the contrary, profitable firms were less likely to be manipulated in both developed and emerging markets. Limited studies have been conducted to empirically identify the characteristics of the manipulated stocks across the developed and emerging markets. The regulator can use these results to identify possible and expected manipulation and to design enforcement rules, accordingly. Further, investors can take into consideration these characteristics of manipulated stocks while designing their portfolio in order to reduce the portfolio risk.
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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 | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".