Assessing the Impact of S&P SL20 Index Construction on Listed Companies in Colombo Stock Exchange (CSE)
Bibliographic record
Abstract
A stock market index is designed to measure the performance of value of a set of stocks. The set of stock can be entire market of a particular country or a sector. Indices can be used not only to see how the stock market, for instance, has changed over time, but it allows easy comparison between stocks that represent different sectors or even different stocks. An index construction or rebalancing of existing index is a major market event that investor might know before the event take place. The index inclusion reflects a positive situation about the quality, risks and possible future return of the stock. This study examine whether any price and trading volume effects arise from S&P SL 20 index construction. S&P SL 20 index was launched in 26, June 2012, based on 20 blue chip companies in Sri Lanka. The current study employs the standard event study methodology to identify the abnormal returns associated with the launching of the S&P SL 20 index. Three normal return benchmarks, namely the market-adjusted model, mean-adjusted model and the market model have been used for the purpose of finding abnormal returns. Price series and volumes of stocks in S&P SL 20 list (after and before) were considered and those are retrieved from Colombo stock exchange. The study finds that the abnormal returns following the launch of the S&P SL 20 index is statistically insignificant.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".