Equities as an Effective Hedge against Inflation: Empirical Evidence from Pakistan
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".