The Modeling of Jakarta Composite Index Data Before and During COVID-19 Pandemic and its Alignment into Government Policy in Energy Sector
Bibliographic record
Abstract
The COVID-19 pandemic brings significant effects to the global stock market, including Indonesia. This study investigates the behavior and fluctuation of Jakarta Composite Index (JKSE) before the COVID-19 pandemic arises (2018–2019) and 2 years during the COVID-19 pandemic (2020–2021) and its alignment with the government policy in the energy sector. This study will use the JKSE data before and during the Covid-19 pandemic. The study showed that before COVID-19 pandemic, the JKSE was in normal conditions and showed an increasing trend. However, the study found anomalies in the JKSE volatility when COVID-19 pandemic was officially announced in Indonesia during 1st quarter 2020. This study is able to find the forecasted next 30 days best models that can describe the pattern of JKSE data are AR (2)–GARCH (1,1) models for the closing price of JKSE data before the COVID-19 pandemic and AR (5)–GARCH (1,1) models for the closing price of JKSE data during the COVID-19 pandemic. With the government economic recovery program related to the energy sector, this study was able to forecast the next 30 days for the closing price of JKSE during COVID-19, which showed the improvement of JKSE into the small increasing trend. These findings are expected to increase public investor trust, especially foreign investors investing their money in the JKSE. The positive trend in JKSE will ensure the government continues its economic policy recovery plan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".