Concealing Financial Distress With Earnings Management: A Perspective on Malaysian Public Listed Companies
Bibliographic record
Abstract
Earnings Management is prevalent among corporations, resulting to misleading information disclosed on financial statements. By adopting agency theory argument, in line with fulfilling shareholders expectations on financial performance while securing their position and interest within the company, management may be influenced to engage earnings management when the actual financial outcome is in financial distress. Hence, this study attempts to identify whether companies listed on Industrial Product sector of Malaysian Bourse experienced financial distress condition and embark on earnings management. It also examines potential relationship between financial distress conditions and earnings management within the context of companies listed within Industrial Product sector of Malaysian Bourse in 2016 and 2017. Industrial Product sector was chosen as focus of this study due to its critical position in nation’s transformation to become developed country and as the main contributor to the nation’s GDP with largest market capitalization value in local bourse. Financial distress was proxied by Altman Z score and earnings management by discretionary accruals as per Kothari (2005). The study was conducted using the quantitative statistical method by running multiple regressions in SPSS version 23. The study also included three control variables, such as firm size, financial leverage and free cash flow from operation. The sample of this study comprised of 454 firms of Industrial Products Sector, listed on Malaysian Bourse from 2016 to 2017. The result revealed significant negative relationship between financial distress and earnings management by companies within Industrial Product sector. Financial leverage and free cash flow from operation have inverse relationship with earnings management, while firm size has a positive relationship with earnings management. This also means management of Industrial Product Sector companies engage earnings management when their financial condition is not in distress, hence they are not using earnings management to conceal financial distress condition. Instead they may use earnings management to leverage on their non-distress financial condition to attain better share price performance and financing arrangement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 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".