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Record W3124036432 · doi:10.1108/17439130610705535

An analytical theory of project investment: a comparison with real option theory

2006· article· en· W3124036432 on OpenAlexaff
Jing Chen

Bibliographic record

VenueInternational Journal of Managerial Finance · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsVariable (mathematics)Fixed costEconomicsVariable costInvestment (military)Relation (database)Return on investmentDuration (music)EconometricsMicroeconomicsMathematical economicsMathematicsComputer scienceProduction (economics)Physics

Abstract

fetched live from OpenAlex

Purpose The paper seeks to develop an analytical theory of project investment. Design/methodology/approach The authors derive a partial differential equation that the variable cost of a project should satisfy, determine a proper initial condition through a thought experiment, and solve the equation. Findings A formula of variable cost as an analytical function of fixed cost, uncertainty of the environment and the duration of a project is obtained. Practical implications The analytical formula enables systematic comparison of returns of different investment under different market conditions to be made. This refines the insights from real option theory in many ways. Since all production systems need fixed investment to lower variable costs, by providing an analytical theory about the relation among fixed costs, variable costs and uncertainty, this theory contributes a new foundation to investment theory and other different fields. Originality/value An analytical theory of project investment about the relation among fixed costs, variable costs, uncertainty of the environment and the duration of a project, which is the core concern in most business decisions, does not exist in the current literature.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.269
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2006
Admission routes1
Has abstractyes

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