Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Autor: Daniel Štifanić, Jelena Musulin, Adrijana Miočević, Sandi Baressi Šegota, Roman Šubić, Zlatan Car
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Complexity, Vol 2020 (2020)
Druh dokumentu: article
ISSN: 1076-2787
1099-0526
DOI: 10.1155/2020/1846926
Popis: 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.
Databáze: Directory of Open Access Journals
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