REAL OPTIONS VALUATION AND ITS RELATIONSHIP TO BAYESIAN DECISION-MAKING METHODS
Bibliographic record
Abstract
It is well accepted that conventional NPV criterion fails to capture investment flexibility, and the market approach using riskless-arbitrage-pricing is ideally suited to price real options. However, when valuing complex real options, it is difficult to satisfy the restrictive assumptions required for risk-free arbitrage pricing. Using two-action linear payoff analysis, we show that when it is possible to delay and obtain additional information, an irreversible capital investment decision should be valued as an option taking into considering the value of flexibility. This option value is not based on risk-less arbitrage, but on a more fundamental concept in decision theory - the opportunity loss criterion. Our approach relates to the Quasi-Option concept of Arrow and Fisher (1974) and Henry (1974) that considers the value of gaining more information before making irreversible environment decisions. Lund (1991) provides an excellent analysis of the relationship between the Arrow and Fisher's [1] Quasi-Option Value and Black and Schole's Market based Model [4], and suggests using both ideas for valuing real options. Conrad (1980), Fisher and Hanemann (1987) and Hanemann (1989) discuss the relationship between Quasi-Option Value and Expected Value of Perfect Information (EVPI) with respect to environmental decisions. We extend the opportunity loss concept to value real options and analyze its relationship to EVPL We show that the value of a quasi-real option is equal to value of information. In the special case where a lognormal terminal distribution is assumed, we show that the EVPI is equivalent to the Black and Scholes model. We demonstrate how EVPI can be used to make investment decisions under uncertainty within an options framework. The suggested approach allows revision of option values sequentially using Bayesian methods at each decision point within the well-known decision theory framework. Financial economists have not considered the intersection between Bayesian decision framework and value of investment flexibility, that allow for a less restrictive set of assumptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".