The effect of hedging exchange rate risk, interest rate risk and commodity price risk with derivative instruments on firm value
Bibliographic record
Abstract
The purpose of this paper is to analyze the effects of firm value on hedging for exchange rates, interest rates and commodity price risks using derivative instruments as well as examining different types of derivative instruments, including forward contract, future contract, option contract, and swap contract, used as hedging instruments to assess their various effects on firm value. The proxy used for the firm value variable is Tobin’s Q, and the ordinary least squares regression is employed for the research method. The study used 348 records from non-financial companies listed on the Indonesia Stock Exchange over the period 2015–2018. There are different results. First of all, the use of hedging for exchange rate risk with derivative instruments has a positive and significant effect on firm value. Secondly, the use of hedging for interest rate risk with derivative instruments has a negative but not significant effect on firm value. In addition, the use of hedging for commodity price risk with derivative instruments has a positive but not significant effect on firm value. Moreover, the effects from hedging using derivative contracts in general on firm value does not give results that are different from the use of hedging risk for exchange rates, interest rates and commodity prices with derivative instruments.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".