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

TL;DR

This paper investigates how COVID-19 affected stock and commodity prices and proposes a predictive model combining Stationary Wavelet Transform and Bidirectional LSTM to improve five-day crude oil price forecasts.

Contribution

It introduces a novel integration of SWT and BDLSTM for stock and commodity price prediction during COVID-19, enhancing forecasting accuracy.

Findings

BDLSTM+WT-ADA outperforms traditional models in five-day crude oil price prediction.

The model effectively incorporates COVID-19 case data into financial forecasting.

Wavelet decomposition improves the model's ability to handle non-stationary financial data.

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 financial movement of Crude Oil price and three U.S. 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.