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Record W1999775901 · doi:10.12735/jfe.v2i4p01

The Effect of Policy Rate Changes on Bank Stock Returns in Pakistan

2014· article· en· W1999775901 on OpenAlexvenueno aff
Habib Ur Rahman, Hasan Mohsin, Patrick A. Rivers

Bibliographic record

VenueJournal of Finance & Economics · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStock (firearms)EconomicsFinancial systemBusinessMonetary economicsGeography

Abstract

fetched live from OpenAlex

Objective of this study is to analyze the impact of policy rate changes on bank stock returns in Pakistan by using daily stock returns from 1998 to 2011. We used event study approach by constructing the estimation window of 250 days and an event window of 31 days (15 pre-event days, event day and 15 post event days. The daily stock returns from 1998 to 2011 have been used to analyze the impact of policy rate changes by State Bank of Pakistan (SBP) on banking stock returns. The study used ARIMA model to estimate the normal returns by using estimation window of 250 days. Since Monetary Policy committee decides changes in policy rate, we have used date of MP Committee meeting as an event. Reportedly, 35 meetings were conducted during study period from Jan 1998 to Dec 2011. Abnormal returns are calculated by taking the difference of actual daily stock returns and estimated daily stock returns. Abnormal daily stock returns are aggregated as cumulative abnormal returns (CAR). The CAR at 0.6340 showed a significant impact of policy rate changes on banks stock returns. The study finds 31 out of all 35 events have significant impact on banks stock returns and returns were normal at 4th day of MP announcement. Further, we analyzed the impact with respect to expansionary and contractionary monetary policy and observed that the highest positive impact on banks stock returns was due to expansionary monetary policy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.351

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.239
Teacher spread0.232 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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