Identifying extreme events in the stock market: A topological data analysis.

This paper employs Topological Data Analysis (TDA) to detect extreme events (EEs) in the stock market at a continental level. Previous approaches, which analyzed stock indices separately, could not detect EEs for multiple time series in one go. TDA provides a robust framework for such analysis and identifies the EEs during the crashes for different indices.

High-frequency stock market order transitions during the US-China trade war 2018: A discrete-time Markov chain analysis.

Statistical analysis of high-frequency stock market order transaction data is conducted to understand order transition dynamics. We employ a first-order time-homogeneous discrete-time Markov chain model to the sequence of orders of stocks belonging to six different sectors during the US-China trade war of 2018. The Markov property of the order sequence is validated by the Chi-square test.

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.

Synthesis and characterization of iron oxide superparticles with various polymers.

Iron oxide superparticles referring to a cluster of smaller nanoparticles have recently attracted much attention because of their enhanced magnetic moments but maintaining superparamagnetic behavior. In this study, iron oxide superparticles have been synthesized using a solvothermal method in the presence of six different polymers (., sodium polyacrylate, pectin sodium alginate, chitosan oligosaccharides, polyethylene glycol, and polyvinylpyrrolidine).

Conductivitylike Gilbert Damping due to Intraband Scattering in Epitaxial Iron.

Confirming the origin of Gilbert damping by experiment has remained a challenge for many decades, even for simple ferromagnetic metals. Here, we experimentally identify Gilbert damping that increases with decreasing electronic scattering in epitaxial thin films of pure Fe. This observation of conductivitylike damping, which cannot be accounted for by classical eddy-current loss, is in excellent quantitative agreement with theoretical predictions of Gilbert damping due to intraband scattering.