Real Option Analysis versus DCF Valuation - An Application to a Tunisian Oilfield
Bibliographic record
Abstract
The most widely used methods of choosing investments are undoubtedly the NPV. This method is often criticized because it does not allow to take into account certain main characteristics of the investment decision, notably the irreversibility, the uncertainty and the possibility of delaying the investment. On the other hand, the real options approach (ROA) is proposed to capture the flexibility associated with an investment project. This article examines whether the value of an undeveloped oil field varies according to whether the ROA or NPV assessment is used. In addition, to value the option to defer, we developed a continuous time model derived from previous work by Brenan and Schwartz (1985), McDonald and Siegel (1986) and Paddock, Siegel, and Smith (1988). The originality of the proposed model gives rise to a simple and uncomplicated method for determining the value of the option. Findings indicate that the two evaluation methods lead to the same decision, the project is economically profitable. In this oil investment project studied, despite the positive value of the option, the importance of projected cash-flows and optimistic forecasts of the price of oil, led us not to exercise the option and to undertake the project immediately.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".