Influence of financial indicators on earnings management behavior: evidence from Vietnamese commercial banks
Bibliographic record
Abstract
The quality of financial disclosures is of great importance than ever, as Vietnam’s international economic integration has been accelerating recently. This issue is currently particularly worrying for the banking sector in Vietnam, as banks play a vital role in economic development. However, there is a growing concern that managers tend to manipulate financial information using earnings management techniques to meet analyst expectations and to enhance the firm value in the short term. Such behavior can lead to inappropriateness in the decision-making process of financial statement users, as well as impair firm value in the long term. Therefore, this study examines the impact of factors related to financial indicators on earnings management of Vietnamese commercial banks to give more insight into the issue. The data of this study was collected from a sample of 30 Vietnamese commercial banks during a 5-year period from 2015 to 2019. By using the Ordinary Least Square (OLS) regression method through Eviews 10.0, the findings revealed that financial leverage and loan loss provision have a positive and significant impact on earnings management. Also, bank size and profitability were negatively associated with earnings management. Based on these findings, in the context of Vietnam, the study proposed policy suggestions to improve the quality of accounting information and to assist users of financial statements in recognizing and restricting earnings management in commercial banks.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".