Does Audit Quality Enhance the Value Relevance of Earnings and Book Value on the Market Price of Common Shares? Evidence from Thailand
Bibliographic record
Abstract
This study examines whether audit quality enhances the value relevance of earnings and book value of equity in explaining market prices of common shares in Thailand’s emerging market. Using data from 401 non-financial firms listed on the Stock Exchange of Thailand between 2021 and 2023, we analyze 1203 firm-year observations collected from Bloomberg and company annual reports. Multiple regression results show that earnings per share (EPS), book value per share (BVPS), and audit quality measures are significantly associated with share prices. Audit quality is proxied by audit firm size, audit fees, and financial statement irregularities (Beneish M-score). Big 4 auditors increase the relevance of book value, while higher audit fees strengthen the earnings–price relationship. Conversely, firms with higher M-scores, signaling potential earnings manipulation, display weakened associations between accounting metrics and share value. These findings highlight audit quality’s role in reducing information asymmetry, reinforcing investor trust, and supporting market efficiency in a post-crisis environment. By integrating audit quality into the Ohlson valuation framework, this study contributes to the literature on audit assurance and capital market behavior in emerging economies, offering insights for investors, regulators, and managers regarding the credibility of financial reporting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".