Government-Linked Investment Companies and Real Earnings Management: Malaysian Evidence
Bibliographic record
Abstract
This study examines the association between government-linked investment companies’ (GLICs’) shareholdings and real earnings management activities in Malaysia. Consistent with prior research, this study uses three proxies to measure real earnings management; abnormal cash flow from operations (RCFO), abnormal production costs (RPC), and abnormal discretionary expenses (RDE). This study segregates GLICs’ shareholdings into two categories; Federal Government Pension Investment Funds (FGPIF) and other GLICs (OFGLIC). Using a sample of 213 firm-year observations of Malaysian government-linked companies from 2010 to 2015, this study finds that FGPIF is a more effective monitoring mechanism than OFGLIC in limiting real earnings management. The findings also show that there is a significant and negative relationship between Employee Provident Fund (EPF), Khazanah Nasional Berhad (Khazanah), Permodalan Nasional Berhad (PNB) and RCFO and RPC. The evidence suggests that these three are the most effective government institutional investors in promoting corporate governance, which in turn limit real earning management activities in Malaysia. In general, the findings support the incentive alignment hypothesis, which argues that companies with government intervention are normally better governed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".