COVID-19 Forecasts via Stock Market Indicators
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866908630203236352 |
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| author | Liang, Yi Unwin, James |
| author_facet | Liang, Yi Unwin, James |
| contents | Reliable short term forecasting can provide potentially lifesaving insights into logistical planning, and in particular, into the optimal allocation of resources such as hospital staff and equipment. By reinterpreting COVID-19 daily cases in terms of candlesticks, we are able to apply some of the most popular stock market technical indicators to obtain predictive power over the course of the pandemics. By providing a quantitative assessment of MACD, RSI, and candlestick analyses, we show their statistical significance in making predictions for both stock market data and WHO COVID-19 data. In particular, we show the utility of this novel approach by considering the identification of the beginnings of subsequent waves of the pandemic. Finally, our new methods are used to assess whether current health policies are impacting the growth in new COVID-19 cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_06393 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | COVID-19 Forecasts via Stock Market Indicators Liang, Yi Unwin, James Populations and Evolution Physics and Society Trading and Market Microstructure Applications Reliable short term forecasting can provide potentially lifesaving insights into logistical planning, and in particular, into the optimal allocation of resources such as hospital staff and equipment. By reinterpreting COVID-19 daily cases in terms of candlesticks, we are able to apply some of the most popular stock market technical indicators to obtain predictive power over the course of the pandemics. By providing a quantitative assessment of MACD, RSI, and candlestick analyses, we show their statistical significance in making predictions for both stock market data and WHO COVID-19 data. In particular, we show the utility of this novel approach by considering the identification of the beginnings of subsequent waves of the pandemic. Finally, our new methods are used to assess whether current health policies are impacting the growth in new COVID-19 cases. |
| title | COVID-19 Forecasts via Stock Market Indicators |
| topic | Populations and Evolution Physics and Society Trading and Market Microstructure Applications |
| url | https://arxiv.org/abs/2112.06393 |