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Record W2119346415 · doi:10.19030/iber.v10i5.4226

Value Investing: International Comparison

2011· article· en· W2119346415 on OpenAlexaff
Anna Beukes

Bibliographic record

VenueInternational Business & Economics Research Journal (IBER) · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsNorthern Alberta Institute of Technology
Fundersnot available
KeywordsValue premiumValue (mathematics)Equity (law)EconomicsGrowth stockBook valueMarket valueFinancial economicsStock marketEquity valueCapital asset pricing modelAccountingFinanceGeographyRestricted stockPolitical scienceMathematics

Abstract

fetched live from OpenAlex

Based on accumulated empirical evidence, the academic community has generally come to agree that value investment strategies, on average, outperform growth investment strategies (Chan and Lakonishok, 2004:71). An influential article by Fama and French (1992) tested the notion that United States stock prices might be related to the ratio of a firms book value of common equity (BV) to its market value of common equity (MV). It found that companies with high book value relative to market value of equity (BV/MV) outperform the market. This finding led to extensive testing for the value premium in developed countries around the world. Fama and French (1998a) tested it with data from twelve major European countries, as well as from Australia and the Far East. They found that between 1975 and 1995 in almost every country, value stocks delivered a higher return than growth stocks. The value premium has not been tested with the same vigor in third world or developing countries, which raises the question whether the value premium is only a first world phenomena and, if not, how third world value premiums compare to those found in developed countries. This paper compares the size of the value premium in the USA, UK, and some continental European countries with South African data.

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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.301
GPT teacher head0.398
Teacher spread0.097 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2011
Admission routes1
Has abstractyes

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