Bibliographic record
Abstract
In the mid‐1980s, financial economists began building option‐based models to value corporate investments in real assets, laying the foundation for an extensive academic literature in this area. The 1990s saw several books, numerous conferences, and many articles aimed at corporate practitioners, who began to experiment with these techniques. Now, as we approach the end of 2001, the real options approach to valuing real investments has established a solid, albeit limited, foothold in the corporate world. Based on their recent interviews with 39 individuals from 34 companies in seven different industries, the authors of this article attempt to answer the question, “How is real options being practiced, and what impact is it having in the corporate setting?” The article identifies three main corporate uses of real options—as a strategic way of thinking, an analytical valuation tool, and an organization‐wide process for evaluating, monitoring, and managing capital investments. For example, in some companies, real options is used as an input into an M&A process in which rigorous numerical analysis plays only a small role. In such cases, real options contributes as a qualitative way of thinking, with little formality either in terms of analytical rigor or organizational procedure. In other firms, real options is used in a commodity trading environment where options are clearly specified in contracts and simply need to be valued. In this case, real options functions as an analytical tool, though generally only in specialized areas of the firm and not on an organization‐wide basis. In still other companies, real options is used in a technology or R&D context where the firm's success is driven by identifying and managing potential sources of flexibility. In such cases, real options functions as an organization‐wide process with both a broad conceptual and analytical core. The companies that have shown the greatest interest in real options generally operate in industries where large investments with uncertain returns are commonplace, such as oil and gas, and life sciences. Major applications include the evaluation of exploration and production investments in oil and gas firms, generation plant investments in power firms, R&D portfolios in pharmaceutical and biotech firms, and technology investment portfolios in high‐tech firms. While the approaches to implementation are quite varied, there appears to be a common path to the successful adoption of real options. The key steps of the adoption process are: (1) conducting pilot projects; (2) getting buy‐in from senior‐level and rank‐and‐file managers; (3) codifying real options through expert working groups, specialist training, and customization; and (4) institutionalizing and integrating real options firm‐wide. After citing best practices for each of these four steps, the authors close by predicting that a “network” effect and acceptance by Wall Street will serve as catalysts for more widespread corporate use of real options.
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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.029 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".