Yield and Volatility of Service Industry in Long-term Uncertain Market: a Post-pandemic Era Perspective
Bibliographic record
Abstract
The service industry, where hotels, restaurants, and tourism have experienced a cliff-like decline due to the Covid-19 pandemic. This paper evaluates the impact in both China and oversea situations on service industries. We build a VAR model to analyze the changes in returns and the ARMA-GARCH model which analyzes the changes in volatility in these service industries. This paper focuses on previous stock returns to predict the future influence of volatility in hotel, tourism, and catering industries. It finds out that pandemic outbreaks have a negative influence on service industry’s rate of return in both the short term and long term as the epidemic gradually normalizes. Therefore, if service industry wants to have a sustainable return, it must come up with new business models to attract customers both in person and online. In addition, market advertising and improving consumer experiences also play important roles since now a lot of young consumers care much about their living experiences.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".