Covid-19 Pandemic and the Market Performance Analysis: Evidence From Indonesia
Bibliographic record
Abstract
This paper empirically identified the impact of the Covid-19 pandemic on market performance in Indonesia. We use a cointegrating regression model of FM-OLS and D-OLS along with Cubic Spline missing data interpolation, summary unit root test, unit root test with Dickey-Fuller breakpoint selection, and Johansen cointegration test. Daily time series data of Covid-19 cumulative case, exchange rate, IDX composite, and gold commodity price were analyzed around 119 days after the first announced case in Indonesia. The finding from FM-OLS and D-OLS analysis showed that the Covid-19 pandemic has a positive and significant impact on exchange rate and gold commodity price. The Covid-19 pandemic impact is appeared to be negative and significant in explaining IDX composite price. It seems that during the Covid-19 pandemic, people prefer to do transactions in the commodity market than neither capital nor money market. The diagnostic of cointegrating regression showed that all variables are integrated of order one I (1), and the long-run cointegration among variables in each equation exists. From the unit root test with break point selection, this study revealed that Covid-19 pandemic has an extreme impact on Indonesian market performance only in the first month after the case was announced. The government responses to mitigate the spread of the Covid-19 pandemic and to minimize the possibly profound impact of market performance are appeared to be successful. Besides, it seems that people's fear is diminished, quite high in the first month, and began to shrink in the following month.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".