Stock Market Performance of the US Hospitality And Tourism During the Covid-19 Pandemic
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
In this study, the stock market performance of the travel and leisure industry during the COVID-19 pandemic is investigated by use of the three-regime Markov switching model. The analysis employs daily data for six subsectors (airlines, gambling, hotels, leisure services, restaurants and bars, as well as travel and tourism) for the US from January 2018 to November 2021. Estimation results provide strong evidence of regime switching behavior with wide differences across subsectors during the course of the COVID-19 pandemic. A longer duration of high volatility characterizes the airline and leisure services indices. These sectors exhibit the most pronounced downturn that was not fully recovered in November 2021. In contrast, the period of high volatility in the restaurant, gaming, and hotel industries is relatively short, and stock market performance recovers almost to the general trend. Of all subsectors, restaurants and bars experience the shortest duration of high volatility, limited to the second quarter of 2020. The stock market indices for the travel and tourism industry (mainly car rentals) are also highly volatile, but this pattern was observed already before the pandemic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it