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Investment Timing for Dynamic Business Expansion

2009· article· en· W3122627438 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFinancial Management · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsInvestment (military)EarningsEconomicsRate of returnReturns to scaleReturn on investmentDeferralCapital (architecture)Return of capitalMonetary economicsMicroeconomicsEconometricsInvestment performanceFinanceProduction (economics)

Abstract

fetched live from OpenAlex

We investigate the timing of business expansion. With an indefinite sequence of growth opportunities that have constant returns to scale, current investment neither displaces nor impairs future returns. In a dynamic setting with expansion restricted to a fraction of firm size, the endogenously determined cost of capital uniformly exceeds the value maximizing return threshold for expansion. Taking this into account, a manager accelerates investment to facilitate larger and more valuable future investments when earnings stochastically improve. This result is the opposite of deferral that the investment literature recommends due to irreversibility. This means that the managerial application of the cost of capital as an expansion hurdle rate is improperly conservative.

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.

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)0.000
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 score0.382
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.225
Teacher spread0.201 · 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