The effect of COVID-19 in European union on the performance of Indonesian publicly listed palm oil companies
Bibliographic record
Abstract
One of the leading industries that affect economic growth in Indonesia is the palm oil industry. The role of this industry depends on the level of exports from European Union countries. Based on the COVID-19 pandemic situation, international trade activities are hampered and could affect industry performance from a stock perspective. Therefore, this study aims to explore the impact of the COVID-19 cases that occurred in the European Union and related macroeconomic variables on the stock performance of the oil palm industry in Indonesia. This research also examines the impact of COVID-19 on certified sustainable companies and companies that are not certified. Panel regression was applied in this study with Eviews 11 Software.This research's observations are 13 palm oil companies in Indonesia which are listed on the Indonesia Stock Exchange (IDX) from March 2, 2020, to August 31, 2020. This study's results reveal that the world CPO prices and market capitalization affect the activities shares of palm oil companies in Indonesia.Meanwhile, from the grouping of certifications within companies, the impact of COVID-19 in the European Union was more substantial on companies that were certified as sustainable. Based on these results, The COVID-19 case in the European Union must be a concern for palm oil companies in Indonesia.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".