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Record W2081195272 · doi:10.5539/ijef.v4n9p164

Studying Liquidity Premium Pricing, Size, Value and Risk of Market in Tehran Stock Exchange

2012· article· en· W2081195272 on OpenAlexvenueno aff
Hassan Ghalibaf Asl, Mehdi Karimi, Elham Eghbali

Bibliographic record

VenueInternational Journal of Economics and Finance · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsCapital asset pricing modelStock exchangeSecurity market lineMarket liquidityMarket makerFinancial economicsEconomicsSystematic riskRisk premiumLiquidity riskBusinessStock marketMarket riskEconometricsMonetary economicsFinance

Abstract

fetched live from OpenAlex

Capital asset pricing model (CAPM) considers systematic risk as the only risk priced by the market. In addition to systematic risk, Fama and French three-factor model indicates that the risk of firm size and book to market equity are also priced by the market. In addition to three risk factors addressed by Fama and French, liquidity risk and its pricing by investors in Tehran Stock Exchange is studied by a multivariable regression between 2004 through 2008. Research findings indicate that stock return in Tehran Stock Exchange can be clarified by four factors including market excess return, firm size, BE/ME ratio and stock transaction turnover in a relative plausible level (%40 in average). In the meantime, a significant relationship is observed between market excess share, firm share and stock return. No significant relationship is seen between BE/ME ratio and stock transaction turnover and stock return. In other word, only market risk and firm size (ME) are priced by market.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.232
Teacher spread0.203 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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