Variability of echo state network prediction horizon for partially observed dynamical systems.

Study of dynamical systems using partial state observation is an important problem due to its applicability to many real-world systems. We address the problem by studying an echo state network (ESN) framework with partial state input with partial or full state output. Application to the Lorenz system and Chua's oscillator (both numerically simulated and experimental systems) demonstrate the effectiveness of our method.

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.

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.

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.