Valuing Real Capital Investments Using The Least-Squares Monte Carlo Method
Why this work is in the frame
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Bibliographic record
Abstract
The recently developed least-squares Monte Carlo (LSM) method provides a simple and efficient technique for valuing American-type options. The proposed method is applicable to the cases of compound real options, like the other numerical techniques such as finite difference and lattice methods, with the additional advantage to handle easily the cases of multiple uncertain state variables with different and complex stochastic processes. With this advantage, the LSM method is not only efficient for valuing multi-factor American options, but it can also be extended for valuing complex real investments having many embedded real options and involving multiple uncertain state variables. This article examines the applicability of the LSM method in valuing real capital investments. Two valuation examples have been provided to test the efficiency of the proposed method in both the valuation and the decision-making processes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it