Abstract
COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.
Highlights
Infectious diseases have always been a threat to humanity, especially those about which little or nothing is known. e World Health Organization (WHO) describes pandemic as “the worldwide spread of a new disease” and in such times the greatest concern is how to save human lives, the first following objective is how to save the economy and preserve the well-being [1]
Authors investigate the impact of COVID-19 on the global economy, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. e proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long shortterm memory (BDLSTM) networks
Bai et al (2016) demonstrate the successful use of the SWT and backpropagation neural network (BPNN) to forecast daily air pollutant concentrations, and the results show that the SWT-BPNN model has better forecasting performance for the three air pollutants than BPNN model without SWT [18]
Summary
Infectious diseases have always been a threat to humanity, especially those about which little or nothing is known. e World Health Organization (WHO) describes pandemic as “the worldwide spread of a new disease” and in such times the greatest concern is how to save human lives, the first following objective is how to save the economy and preserve the well-being [1]. E algorithm that may be efficient in commodity and stock market forecasting is a bidirectional long short-term memory (BDLSTM) [12]. Datasets for each stock market index (Dow Jones Industrial Average, S&P 500, and NASDAQ Composite) along with Crude Oil price were obtained from the Yahoo Finance website [21] while the data of COVID-19 confirmed cases were obtained from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) [22]. E aim of this research is to integrate SWT with BDLSTM in order to predict the movement of the aforementioned commodity and stock market indexes during the COVID-19 outbreak. In order to create the dataset used in this research, historical data of West Texas Intermediate (WTI) Crude Oil price and three stock indexes along with
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