Are Karachi Stock Exchange Firms Investment Promoting? - Evidence of Efficient Market Hypothesis Using Panel Cointegration
Bibliographic record
Abstract
This study investigates the evidence of efficient market hypothesis for the firms listed in Karachi Stock Exchange (KSE), which signifies the nature of the stock market. According to this hypothesis, the empirical information available is not enough to predict the present movement of share prices. After using the Panel cointegration approach between current share price and past share price in the light unknown structural break on the daily data of 75 selected firms over the period June 2004 to March 2014, comprising 2370 observations per firm. The study found that the firms listed in Karachi Stock Exchange are inefficient firms, therefore, for the case of KSE ? 100 offer information which can be used to make economic profits. Hence Karachi Stock Exchange provide enough predictable information using past trends so that investor can gain economic profit from it, so it will take time for the market to become mature and the creation of competition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".