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Record W2143095339 · doi:10.5430/afr.v2n3p103

Maximising the Worth of the Young Accountant in Ghana, Treasury Bills or Shares?

2013· article· en· W2143095339 on OpenAlexvenueno aff
Ernest Bruce-Twum

Bibliographic record

VenueAccounting and Finance Research · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsTreasuryInflation (cosmology)FinanceBusinessEconomicsRate of returnActuarial science

Abstract

fetched live from OpenAlex

Treasury bills have gained a high appeal among the Ghanaian population as a security with a high return and virtually no default risk. Stocks are of higher risk, and therefore according to finance theory should offer higher return than treasury bills. It is therefore expected that the young accountant, should be investing in stocks rather than treasury bills, to grow his /her worth faster. The paper looks at the average annual returns on investments in treasury bills and shares in Ghana within a period of fifteen years, i.e. 1991-2005; To determine whether investors who buy shares are given premiums for taking risk; Again to ascertain whether investors are adequately compensated in real terms, that is after considering inflation, and finally to ascertain which of the two investments will maximise the worth of the young accountant. The researcher analyse nominal and real returns of both treasury bills and Shares, over the period and using statistical measure of standard deviation and co-efficient of variation for two investors, arrived at the conclusion that it is worth investing in shares as a young accountant, and better investing in treasury bills when you are 56 years and above and nearing your retirement. It was also confirmed that investors are rewarded for bearing risk. The paper also found out that both investments earn returns above inflation over the period.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.281
Teacher spread0.198 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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