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Record W2765417906 · doi:10.5430/afr.v7n1p1

Calculating a Consistent Terminal Value in Multistage Valuation Models

2017· article· en· W2765417906 on OpenAlexvenueno aff
Larry C. Holland

Bibliographic record

VenueAccounting and Finance Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsnot available
Fundersnot available
KeywordsTerminal valueValuation (finance)DividendEconomicsCash flowEconometricsStock (firearms)Forecast periodFinancial economicsMicroeconomicsOperating cash flowFinance

Abstract

fetched live from OpenAlex

Valuation analysis based on the present value of future cash flows often requires a multistage valuation model which includes a terminal value. An accurate calculation of the terminal value is very important, particularly if it represents a significant portion of the stock price. A typical analysis would include a finite forecast of cash flows for a five to ten-year period followed by a terminal value that represents all the cash flows thereafter. A common assumption is that the valuation cash flows beyond the finite horizon simply continue to grow at a lower long-term growth rate. The analysis in this paper demonstrates that such an assumption is rarely appropriate except under very restrictive assumptions, if consistent accounting relationships are maintained. Using dividends as the valuation cash flows in an example calculation, the dividend at the point that the growth rate declines is shown to increase by a step function rather than simply growing at a lower, mature growth rate. The size of the step function increase is then shown to change when the values of various key value drivers in the analysis are also allowed to change. Such value drivers include the EBIT margin, the asset intensity, and the relative level of debt. The step function increase in dividends can have a significant effect on the size of the terminal value and highlights the importance of maintaining consistent accounting relationships when forecasting future cash flows in a multistage valuation model.

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)0.010
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
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 head0.225
GPT teacher head0.412
Teacher spread0.187 · 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.

Study designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

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