Impact of COVID-19 on the Stock Market by Industrial Sector in Chile: An Adverse Overreaction
Bibliographic record
Abstract
This paper studies the reaction of share prices in the Chilean securities market at the sectoral level to the arrival of COVID-19 in the country. The following question is answered: Did the Chilean market act efficiently before the arrival of COVID-19? To answer this question, an event study using a 10-day investment return window was applied to the industrial sectors that make up the IPSA (Selective Stock Price Index). To obtain the abnormal returns (AR) and cumulative abnormal returns (CAR) for the event window, three models were used: (1) adjusted average return, (2) adjusted market return, and (3) the market model. The results of the study show an overreaction to market losses, except in the utilities industry, causing greater losses after the event, which shows that information is slow to be incorporated in the previous stage and suggests that the prices of the assets do not reflect all the information available in the market. A significant finding is that the Chilean stock market responded inefficiently in the face of the arrival of the pandemic. This information is useful for investors in the formation of portfolios and/or investment strategies with a view to the long term.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".