Corporate Derivatives and Ownership Concentration: Empirical Evidence of Non-Financial Firms Listed on Pakistan Stock Exchange
Bibliographic record
Abstract
Risk management has been gaining tremendous fame for the last couple of years. Firms in developed and developing countries are facing a variety of risks, i.e., foreign exchange risk, interest rate risk, commodity price risk, and equity risk. It calls for such hedging techniques that mitigate this risk level, thus, allowing corporations to enjoy a solid return. This paper draws attention to a new determinant of hedging, i.e., the role of ownership concentration in risk management using derivative instruments. For this purpose, a sample data of 101 non-financial firms listed on the Pakistan Stock Exchange (PSX) for six years, ranging from 2010–2016, is used. The Mann-Whitney test for difference in users and no-users is applied along with logistic regression to check the effect of ownership concentration on derivative usage. The finding of this study reveals that concentrated owners are less likely to use derivatives for hedging purposes due to concentrated owners’ interests (top five shareholders & largest shareholder, family owners). Whereas, executives are more likely to engage in the use of derivatives to increase the value of their stocks. However, associated companies are significantly less involved in hedging activities. These results are extremely advantageous for policymakers in corporations to create a more stable corporate environment.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".