Using Statistical Analysis to Investigate the Relevance of Accounting Information in Emerging Financial Markets: An Empirical Study
Bibliographic record
Abstract
Despite extensive literature and numerous published work on the area of value relevance of accounting information, a major part of the studies have been conducted on large and developed capital markets. While there are a number of published articles in the area of value relevance of accounting information in developing markets, there is still a need to investigate more developing markets to see if there are similarities or different attributes of each market that could shed more light on the importance and usefulness of accounting relevance in developing markets. To study further the gap of accounting relevance in developing markets, this study investigates the relationship between the accounting information – represented in the financial ratios, F-Score, M-Score, in addition to market-related measures – and stock price represented in the ratio of Price to Book Value (PBV) per share and Price-Earnings (PE) Ratio. In order to shed light on the significant variables that affect the stock price in emerging markets, this study examines the cement sector in Saudi Arabia. The results of the study indicate that F-score, inventory turnover, current ratio, debt-to-equity ratio, return on assets, and average trading are significant determinants of PBV. Combined, they explain 75.1% of the variations in PBV. The study also shows that F-score and inventory turnover are significant determinants of PE. Combined, they explain 23.1% of the variations in PE.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".