Impact of COVID-19 Travel Subsidies on Stock Market Returns: Evidence from Japanese Tourism Companies
Bibliographic record
Abstract
This study examines stock market response (SMR) to the Japanese tourism industry (TI) after the government’s announcement of travel subsidies (TRSs) during the COVID-19 pandemic in 2020, using a sample comprising 80 listed Japanese firms in the TI and an event study method (ESM) to determine the impact of government policy responses (GPRs) to the pandemic. This study found that investors in the TI reacted positively to the announcement of subsidies; this positive effect persisted for 50 trading days after the announcement but was weaker for transportation firms. The results suggest that TRSs are important for the TI, with a stronger link to travel-related firms, such as airlines and travel agencies, hotels, and amusement services. However, investors in the TI reacted negatively to policies that directly addressed the pandemic, such as social distance policies (SDPs). These results are robustly confirmed when we measure abnormal returns by using a three-factor model. The results offer useful insights for policymakers and practitioners aiming to mitigate economic loss from disasters such as the COVID-19 pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".