Game Industry in Long-term Uncertain Situation: A Normalized Covid-19 Pandemic Perspective
Bibliographic record
Abstract
An unprecedented pandemic of Covid-19 hit the world and impacted the stock price of the game industry in China and overseas. How largely the game industry stock price is impacted, and the prediction of the future trends is being studied in this paper. To have a better understanding of data and to make sure predictions are accurate, this article employs VAR and ARMA-GARCH models. It is found that the relationship between the game industry and Covid-19 in the beginning stage is highly and positively related in China and overseas markets, but then the relationship turns to be negative. In the long run, the impact of Covid-19 on the game industry turned negligible, and the relationship became stable. It is also found that investors do not seem to learn from their previous experiences, and the earning yield of the game industry is repeated over and over in history, but the time for the recurrence of history has been shortened.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 0.001 |
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".