COVID-19 forecasts via stock market indicators

Abstract

We propose that technical analysis tools developed to give buy/sell signals in asset trading can be applied to analyze time series datasets in the natural sciences, and we show this explicitly for a study of WHO COVID-19 data. Notably, 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.

Housing market forecasts via stock market indicators

“…The current research is inspired by the work in [7] where stock market indicators were used to forecast COVID-19 cases, and thus we shall dedicate this section to review some of the main results which shall prove useful for our research and for comparison with our own findings. The indicators considered in [7] were candlestick analysis, RSI and MACD indicators.…”

“…The current research is inspired by the work in [7] where stock market indicators were used to forecast COVID-19 cases, and thus we shall dedicate this section to review some of the main results which shall prove useful for our research and for comparison with our own findings. The indicators considered in [7] were candlestick analysis, RSI and MACD indicators. After giving a mathematical definition of those indicators for a time series of datapoints, the authors measured their statistical significance by first considering the null hypothesis, the probability that an external circumstance was the cause of a bearish or bullish trend, not an indicator (recall that an indicator is described as bullish if that indicator predicts a future decrease while an indicator is described as bearish if it describes a future increase in prices).…”

“…In what follows we will give a brief overview of the work in [7] on candlestick analysis, RSI and MACD indicators introduced for the timed datasets of COVID-19 cases, and we will then extend to the timed housing data of [8] on average median housing sales per week in a region.…”

“…2 within the dataset to identify the frequencies and predictive capabilities of each of these patterns occurred.

Figure 2 Candlestick patterns studied in [7] . (a) Hammer pattern; (b) Bullish Engulfing pattern; (c) Dark Cloud Cover pattern; (d) Bearish Engulfing pattern; (e) Hanging man pattern.

…”

“…In a recent Nature Scientific Reports article, Liang and Unwin proposed to use stock market indicators to understand trends in COVID-19 cases in the US [7] . Inspired by this work, we shall consider Zillow's data set of the USA's housing market and study it through examining the usability of statistical indicators usually seen in the stock market such as MACD Bearish, MACD Bullish, RSI Bearish, RSI Bullish, Hanging Man, and Hammer.…”