Analysis and Verification of Fluctuations in Financial Markets by the New Coronavirus Pandemics : Experimental Simulation of Stock Index by Artificial Intelligence
Bibliographic record
Abstract
In 2020, human infection with the new coronavirus（ COVID-19） was confirmed in Wuhan, China. Soon the infection spread in Wuhan, and the city was temporarily closed. The infection spread from China to the world, and in Europe, North America, etc., the number of infected people exceeded that of China, which had a great impact on socioeconomic. In particular, the number of infected people has increased rapidly in the United States and European countries, and cities had been temporarily locked down in the world's financial centers such as New York and London. The number of infected people in Asian countries is increasing, and the wave of infection spread is continuing in Japan. Due to the global pandemic of the new coronavirus, the Tokyo Olympics, which was scheduled to be held in July 2020, has been postponed for one year. As of December 2020, drug company Pfizer has successfully developed a vaccine against the new coronavirus and started vaccination in the United Kingdom and the United States, but it is still time to see if the global infection situation will improve. Under these circumstances, economic activity continues to be exhausted. In Japan as well, a state of emergency was declared from March to May in 2020 and also January in 2021, not only economic activities but also sociocultural activities were severely restricted. As a result, GDP in the second quarter of 2020 was the largest decrease after the war. It also had a significant impact on employment, such as the ratio of job offers to applicants and the unemployment rate. On the other hand, in March, when the spread of the new coronavirus began, the global financial markets were temporarily confused as they were concerned about the future of the global economy. Major stock indexes such as the Dow Jones 30 on the New York Stock Exchange, the Nikkei 225 and TOPIX on the Tokyo Stock Exchange, fell sharply, reminiscent of the Great Depression of 1929. However, in April, the global stock market returned to a solid and steady pace. In November, the Nikkei225 exceeded 26,000 yen and finally reached the highest price since the burst of the bubble economy. In this paper, we consider the situation of the Japanese stock market, which has been high under the spread of the new coronavirus infection, focusing on its correlation with the monetary base. In addition, we will experimentally simulate the stock price index by using artificial intelligence and verify the mechanism of the stock market in the process.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".