Impact of Trading Activity on Price Volatility: Case of Tunisian Stock Market
Bibliographic record
Abstract
This paper aims at examining how trading activity impacts price volatility. We propose first to estimate the return volatility following Jones, Kaul and Lipson (1994) and Chan and Fong (2000). Second, we will attempt to detect the best measure of the trading activity that better explains the price volatility. For this reason, we use a sample of 48 listed firms in the Tunisian Stock Exchange “the BVMT” during the period from 02/01/2015 to 29/05/2015. Results show the significance of the size of trades beyond that of the number of trades and traded capitals. We rank the stocks into three trade size categories based on their market capitalization in order to examine whether daily price volatility increases more with the number of shares traded in a particular size category than with other size categories. We found a negative relation between trading activity and price volatility for large and medium size firms and a positive relation for the smallest stocks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".