Efficient Markets Hypothesis in the time of COVID-19.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines how the largest stock market of the world, the U.S., and particularly the S&P500 index, reacted during the COVID-19 outbreak (02.01.2020-30.04.2020). Using simple financial and corporate analysis (adopting Constant Growth Model) procedures for our theoretical framework, we juxtapose the released news with the respective market performance in order to examine if the stock market always incorporated the available information in time. We show that the market in some sub-periods was not moving as it was expected, and the runs-test statistically confirmed our assumptions that the US stock market was not efficient during the COVID-19 outbreak. We find that in some cases the market does not incorporate the news instantly, is irrational, and non-sensible. All these make the market’s behavior unpredictable for a rational asset pricing model because as this paper shows even the simplest financial theories could explain rational behavior, but the market presented a different performance.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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 it