The Concurrent Effects of IFRS Mandate and Formal Institutional Quality on the Aftermarket Performance of IPO Firms in Emerging Countries
Bibliographic record
Abstract
This paper provides the first empirical investigation seeking to find whether International Financial Reporting Standards (IFRS) mandate, changes in the quality of formal institutions, or, the concurrent effect of these two elements can explain the ongoing phenomenon of the aftermarket performance difference of Initial Public Offerings (IPO) firms. We perceive little awareness of the concurrent effect of IFRS mandate and the quality of formal institutions in emerging countries, although these nations account for more than half of the IFRS mandating countries. We employ numerous Difference-in-Differences (DiD) models utilizing reliable IPO and formal institutional data for Saudi Arabia from 2005 to 2017. Our empirical results show that the absence of IFRS influence in the aftermarket performance of IPO firms led us to posit that the quality of formal institutions is the key player in influencing long-term performance of IPO firms in Saudi Arabia. We uncover evidence showing that an improvement in formal institutional quality increases the long-term performance of IPO firms. We find no evidence of a concurrent effect of changes in formal institutional quality and IFRS mandate on the aftermarket performance of IPO firms. Our results show that what does really matter in relation to the aftermarket performance of IPO firms in Saudi Arabia, are the enhancements in the level of formal institutional quality. Our results provide some important implications for IFRS-IPO research.
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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".