Bibliographic record
Abstract
This thesis is presented in order to obtain the Bachelor’s degree HES. The aim of this current thesis is to answer the following question: What are the consequences of the short selling ban enacted from the 12th of August 2011 until the 12th of February 2012 on the CAC40? In the first part, we define in what consists short selling and describe when it is used. We learn that it is used as a speculative tool when a company is overpriced, it is also used as a hedging technique and it is especially a useful tool to reduce the volatility in a portfolio. Later we learn about the political context as well as the stock market situation that led the French regulator AMF to the introduction of the short selling ban. The three key moments that triggered the introduction of the short selling ban are the downgrade of America’s AAA credit rating to an AA, the doubt about France’s AAA credit rating and mainly the risk of contagion of the European debt crisis to the countries Spain and Italy. In the second part we make a quantitative analysis with the use of multiple regression analysis to determine the impact on the short selling ban on the stock returns, the volatility, the skewness and the kurtosis. First we learn that the ban failed to support prices and increased volatility on the restricted stocks. Finally, it deteriorated the price discovery, as the skewness was less negative during the short selling ban. No conclusion can be drawn from our results concerning the occurrence of extreme outcomes because the analysis on the kurtosis were not significant.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".