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Record W4386508577 · doi:10.33897/fujbe.v1i1.112

Equities as an Effective Hedge against Inflation: Empirical Evidence from Pakistan

2016· article· en· W4386508577 on OpenAlexaff
Afsar Ali Khan, Muhammad Awais, Haussaun A. Syed, Muhammad Fayaz

Bibliographic record

VenueFoundation University Journal of Business & Economics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsEconomicsMonetary economicsEmerging marketsEquity (law)Inflation (cosmology)Financial marketFinance

Abstract

fetched live from OpenAlex

The inflation stance of world economy has changed significantly and many of the investors now believe that there may be further spike in inflation in medium term. Capital markets serve as a medium to help mobiles the funds from one hand to another and aid in production of more goods and services. Due to political instability, spiking inflation and uncertain climate due to war on terrorism and other security issues, financial markets are not being able to get investors' trust resulting in lack of investment. Inflation which is measured by consumer price index (CPI) shows the overall upward movement in prices of goods and services. The rising prices in response to general inflation can protect investors by increasing the value of stocks in the equity market without affecting their real return. Pakistani economy largely remained impervious to the global financial crisis due to lower exposures to international finance faced multifaceted challenges on external and internal fronts mainly campaign against terrorism, unstable law and order situation, lingering energy shortages and non-materialization of external inflows. This work is limited to developed economies and less work has been done in the developing economies. In this way; this study will contribute valuable insight regarding this relationship in the Pakistani context.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.067
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.049
GPT teacher head0.260
Teacher spread0.211 · 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.

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
Published2016
Admission routes1
Has abstractyes

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