Statistical properties of the aftershocks of stock market crashes revisited: Analysis based on the 1987 crash, financial-crisis-2008 and COVID-19 pandemic

During any unique crisis, panic sell-off leads to a massive stock market crash that may continue for more than a day, termed as mainshock. The effect of a mainshock in the form of aftershocks can be felt throughout the recovery phase of stock price. As the market remains in stress during recovery, any small perturbation leads to a relatively smaller aftershock. The duration of the recovery phase has been estimated using structural break analysis. We have carried out statistical analyses of 1987 stock market crash, 2008 financial crisis and 2020 COVID-19 pandemic considering the actual crash times of the mainshock and aftershocks. Earlier, such analyses were done considering absolute one-day return, which cannot capture a crash properly. The results show that the mainshock and aftershock in the stock market follow the Gutenberg–Richter (GR) power law. Further, we obtained higher β value for the COVID-19 crash compared to the financial-crisis-2008 from the GR law. This implies that the recovery of stock price during COVID-19 may be faster than the financial-crisis-2008. The result is consistent with the present recovery of the market from the COVID-19 pandemic. The analysis shows that the high-magnitude aftershocks are rare, and low-magnitude aftershocks are frequent during the recovery phase. The analysis also shows that the inter-occurrence times of the aftershocks follow the generalized Pareto distribution, i.e. P(τi)∝1[1+λ(q−1)τi]1(q−1), where λ and q are constants and τi is the inter-occurrence time. This analysis may help investors to restructure their portfolio during a market crash.

A sentiment-based modeling and analysis of stock price during the COVID-19: U- and Swoosh-shaped recovery.

In the aftermath of stock market crash due to COVID-19, not all sectors recovered in the same way. Recently, a stock price model is proposed by Mahata et al. (2021) that describes V- and L-shaped recovery of the stocks and indices, but fails to simulate the U- and Swoosh-shaped recovery that arises due to sharp fall, continuation at the low price and followed by quick recovery, slow recovery for longer period, respectively. We propose a modified model by introducing a new parameter θ = + 1 , 0 , - 1 to quantify investors' positive, neutral and negative sentiments, respectively. The model explains movement of sectoral indices with positive financial anti-fragility ( ϕ ) showing U- and Swoosh-shaped recovery. Simulation using synthetic fund-flow with different shock lengths, ϕ , negative sentiment period and portion of fund-flow during recovery period show U- and Swoosh-shaped recovery. It shows that recovery of indices with positive ϕ becomes very weak with extended shock and negative sentiment period. Stocks with higher ϕ and fund-flow show quick recovery. Simulation of Nifty Bank, Nifty Financial and Nifty Realty show U-shaped recovery and Nifty IT shows Swoosh-shaped recovery. Simulation results are consistent with stock price movement. The estimated time-scale of shock and recovery of these indices are also consistent with the time duration of change of negative sentiment from the onset of COVID-19. We conclude that investors need to evaluate sentiment along with ϕ before investing in stock markets because negative sentiment can dampen the recovery even in financially anti-fragile stocks.

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

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.

Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery.

The emergence of the COVID-19 pandemic, a new and novel risk factor, leads to the stock price crash due to the investors' rapid and synchronous sell-off. However, within a short period, the quality sectors start recovering from the bottom. A stock price model has been developed to capture the price dynamics during shock and recovery phases of such crisis. The main variable and parameter of the model are the net fund flow ( Ψ t ) due to institutional investors, and financial antifragility ( ϕ ) of a company, respectively. We assume that during the crash, the stock price fall is independent of the ϕ . We study the effects of shock length ( T S ) and ϕ on the stock price during the crisis period using the Ψ t obtained from both the synthetic fund flow data and real fund flow data. We observed that the possibility of recovery of stock with ϕ > 0 , termed as quality stock, decreases with an increase in T S beyond a specific period. A quality stock with higher ϕ shows V-shape recovery and outperform others. The T S and recovery period of quality stock are almost equal in the Indian market. Financially stressed stocks, i.e., the stocks with ϕ < 0 , show L-shape recovery during the pandemic. The stock data and model analysis show that the investors, in the uncertainty like COVID-19, invest in the quality stocks to restructure their portfolio to reduce the risk. The study may help the investors to make the right investment decision during a crisis.