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Record W4385607399 · doi:10.5539/ijef.v15n9p53

Real Options in the Brazilian Power Generation Sector: Are Domestic Equity Research Analysts Blind-Sighted or Is It Just a Temporary Glitch?

2023· article· en· W4385607399 on OpenAlexvenueno aff
Marcio Santiago Gonçalves, Jéfferson Augusto Colombo

Bibliographic record

VenueInternational Journal of Economics and Finance · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCapital Investment and Risk Analysis
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoAgence Nationale de la Recherche
KeywordsEquity (law)Representativeness heuristicEconomicsCash flowDiscounted cash flowActuarial scienceBusinessFinancial economicsFinanceStatisticsMathematicsPolitical science

Abstract

fetched live from OpenAlex

The Real Options theory (“ROT”) states firms should be approached as a combination of real assets and real options. Domestic equity research analysts do not seem to evaluate companies applying ROT. After reviewing 344 (from a total estimated 368) equity research reports or analyses on Brazilian listed power generation companies produced between December 31, 2020, and April 30, 2021, we find only discounted cash flow (“DCF”) techniques are applied. No single mention to ROT is made. To estimate the magnitude of potential misvaluations, we use the Black-Scholes method to price the growth plans made publicly available by each of those 15 companies in that period and compare the outcome with the analysts’ forecasted equity value upside per company. Our results suggest local analysts have ignored a sizeable intrinsic value to those companies by failing to apply ROT. Potential explanations range from behavioral biases to low power sector representativeness at IBOVESPA.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.255
GPT teacher head0.394
Teacher spread0.139 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2023
Admission routes1
Has abstractyes

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