The Effect of Policy Rate Changes on Bank Stock Returns in Pakistan
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".