Characteristics of 2020 stock market crash: The COVID-19 induced extreme event

Abstract

A sudden fall of stock prices happens during a pandemic due to the panic sell-off by the investors. Such a sell-off may continue for more than a day, leading to a significant crash in the stock price or, more specifically, an extreme event (EE). In this paper, Hilbert–Huang transformation and a structural break analysis (SBA) have been applied to identify and characterize an EE in the stock market due to the COVID-19 pandemic. The Hilbert spectrum shows a maximum energy concentration at the time of an EE, and hence, it is useful to identify such an event. The EE’s significant energy concentration is more than four times the standard deviation above the mean energy of the normal fluctuation of stock prices. A statistical significance test for the intrinsic mode functions is applied, and the test found that the signal is not noisy. The degree of nonstationarity test shows that the indices and stock prices are nonstationary. We identify the time of influence of the EE on the stock price by using SBA. Furthermore, we have identified the time scale (τ) of the shock and recovery of the stock price during the EE using the intrinsic mode function obtained from the empirical mode decomposition technique. The quality stocks with V-shape recovery during the COVID-19 pandemic have definite τ of shock and recovery, whereas the stressed stocks with L-shape recovery have no definite τ. The identification of τ of shock and recovery during an EE will help investors to differentiate between quality and stressed stocks. These studies will help investors to make appropriate investment decisions.

Effects of the COVID-19 pandemic on stock price performance of blockchain-based companies

“…The COVID-19 pandemic is an extreme event that has brought uncertainty to the financial markets, led to a sudden fall in stock prices, and has given rise to financial volatility (Mahata et al, 2021). The term 'extreme events' in this article refers to rare natural and human-made disasters, including tsunamis, earthquakes, floods, macroeconomic shocks and crises, major political and global financial crises, wars, and disasters such as epidemics and pandemics (Br€ uck et al, 2011;Gries & Naud e, 2020).…”

Integrating Economic Theory, Domain Knowledge, and Social Knowledge into Hybrid Sentiment Models for Predicting Crude Oil Markets

“…These results are particularly interesting when considering the vast macroeconomic differences between the observed time intervals, which even included the start of the 2020 pandemic. The COVID pandemic led to rare market conditions such as correlations between multiple markets [11], and even to previously unseen conditions such as negative oil prices. The good performance of CrudeBERT variants in these settings is somewhat surprising and deserves further examination.…”

“…Mahata et al [11] suspect that due to the disruptions caused by the early 2020 coronavirus-induced market crash, some models described in the literature may no longer perform well in today's markets. Of particular concern is the lack of representative data for the pandemic period, as data source variability can be a serious source of confusion for machine learning (ML) algorithms [12].…”

Daily fluctuations in COVID-19 infection rates under Tokyo’s epidemic prevention measures – new evidence from adaptive Fourier decomposition

“…Nonetheless, the daily COVID-19 case count encapsulates intricate non-linear data, bearing frequency-domain properties. Many previous studies have used EMD to conduct analysis related to the spread of COVID-19, among which there is a non-linear data analysis study on the number of daily infections with the implementation of public health measures (14)(15)(16)(17)(18)(19)(20). EMD is a class due to the consideration of frequency domain information, but the EMD-based model can only extract the time-frequency information of the time series, and there are still shortcomings such as scale aliasing (21).…”