Sectoral Performance and the Government Interventions during COVID-19 Pandemic: Australian Evidence
Bibliographic record
Abstract
This study explores the contrasting impacts of the COVID-19 pandemic on various industries in Australia. Considering all daily announced information, we analyzed the diverse impacts of COVID-19 on the sectoral stock returns from 26 January to 20 July 2020. Sixteen out of twenty examined stock indices negatively react to the daily rise in COVID-19 confirmed cases. Several actions from the Australian government to control the pandemic are relatively ineffective in boosting the overall financial market; however, some positive interactions are captured in five sectors of industrials, health care, metals and mining, materials, and resources. The result shows that all industries that benefited from government financial assistance are either shielded or less severely affected by the pandemic. While sectors that did not directly receive financial remedies relatively showed no enhancement in their overall performance. Having achieved short-term success in helping the economy, the government recorded an all-time high deficit since 2004 that might eventually lead to adverse effects on the overall economy. The Australian equity market is found to be rationally distinct to the crude oil price risk, while positive correlations between AUD/USD rate and real estate-related sectors are reported.
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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 | 0.001 |
| 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".