Testing Stock Market Efficiency from Spillover Effect of Panama Leaks
Bibliographic record
Abstract
On 3 April 2016, Mossack Fonseca provided the historically most significant leak of its shareholder’s data for owning offshore companies. Shareholders include many political and influential figures around the globe, which causes a moral hazard. The study analyses the effects of Panama leak events on five stock exchanges to ensure the market efficiency and investor perception related to the Panama leaks. Event study methodology is used on five occasions associated with Panama papers, i.e., the resignation of the Prime Minister of Iceland on 5 April 2016, Jurgen Mossack’s resignation on 7 April 2016, the resignation of the Spanish Minister of Industry on 15 April 2016, the 450 personalities of Pakistan that were nominated in Panama papers on 15 April 2016, and the formation of an inquiry commission to inquire into the matter. The market efficiency of five stock exchanges was checked, i.e., the KSE 100 of Pakistan, the OMXIPI exchange of Iceland, the IBEX 35 of Spain, the New York stock exchange (NYSE), and S&P 500. The market remains efficient for most events and investor behaviour changes for one or two days around the event day (this event has concise term significant abnormal returns in all stock exchanges or concise term significant abnormal macroeconomic effects are observed in all stock exchanges).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".