COVID-19 IN INDONESIA: ANALYSIS OF DIFFERENCES EARNINGS MANAGEMENT IN THE FIRST QUARTER
Bibliographic record
Abstract
The COVID-19 pandemic, which began in the first quarter (Q1) of 2020 in Indonesia, has certainly had a major impact on the company’s financial performance. The first-quarter financial report should have been able to show the actual condition of the financial company because it can be a projection for investors and analysts regarding the company’s performance in the next period. Unfortunately, many gaps in financial reporting that can provide space for management to commit earnings management. This study aims to prove the difference in earnings management in the Q1 of 2020, namely the period after the COVID-19 pandemic with the Q1 of 2019, namely the period before the COVID-19 pandemic. The data type of the research is secondary data using the financial statements of companies listed on the Indonesian Stock exchange in the Q1 of 2018, the Q1 of 2019, and the Q1 of 2020. The Q1 of 2018 is needed in this research related to the search for the Q1 of the year of 2019 data. Hypothesis testing was conducted using the Wilcoxon test with SPSS 25 software. This research has proven that there is a difference in earnings management in the Q1 of 2019, namely before the COVID-19 pandemic, and the Q1 of 2020, named after the COVID-19 pandemic. The level of earnings management during the COVID-19 pandemic represented in the Q1 of 2020 was lower than the earnings management in the period before the COVID-19 pandemic, namely in the Q1 of 2019.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".