Firm, Industry and Macroeconomics Dynamics of Stock Returns: A Case of Pakistan Non-Financial Sector
Bibliographic record
Abstract
The available research literature on stock performance has primarily stressed the importance of asset price theories, macroeconomic and microeconomic, and institutional differences. However, there is still an open question: Are there any other factors those influence stock performance? This research aims to answer this question by providing new insights into industry factors along with country-level and firm-specific factors in conjunction with the stock performance of the non-financial sector firms listed at the Pakistan Stock Exchange. The study provides new insights into the prevailing research literature by considering an emerging economy, Pakistan. We find that non-financial sector firms are heterogeneous, suggesting applying a fixed effect approach for reliable estimation. To investigate the issue, data from 80 companies spanning 17 years (2004–2020) were analyzed with a fixed-effect model. Our study results revealed that firm tangibility, munificence, gross domestic product, inflation and money supply have negative, while size, growth, dynamism, Herfindahl–Hirschman index, exchange rate and oil prices have a positive relationship with financial performance. The results are robust under alternative estimation approaches and offer useful policy implications.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".