The COVID-19 pandemic and the degree of persistence of US stock prices and bond yields.

This paper analyses the possible effects of the Covid-19 pandemic on the degree of persistence of US monthly stock prices and bond yields using fractional integration techniques. The model is estimated first over the period January 1966-December 2020 and then a recursive approach is taken to examine whether or not persistence has changed during the following pandemic period (up to February 2021). We find that the unit root hypothesis cannot be rejected for stock prices while for bond yields the results differ depending on the maturity date and the specification of the error term. In general, bond yields appear to be more persistent, although there is evidence of mean reversion in case of 1-year yields under the assumption of autocorrelated errors. The recursive analysis shows no impact of the Covid-19 pandemic on the persistence of stock prices, whilst there is an increase in the case of both 10- and 1- year bond yields but not of their spread.

COVID-19 Spread Prediction and Its Impact on the Stock market price

Predicting the Covid-19 spread and its impact on the stock market is an important research challenge these days. In order to obtain the best forecasting model, we have exploited neuro-evolutionary technique Cartesian genetic programming evolved artificial neural network (CGPANN) based solution to predict the future cases of COVID-19 up to 6-days in advance. This helps authorities and paramedical staff to take precautionary measures on time which helps in counteracting the spreading of the virus. The rising number of COVID cases has caused a significant impact on the stock market. CGPANN being the best performer for the time series prediction model seems ideal for the case under consideration. The proposed model achieved an accuracy as high as 98% predicting COVID-19 cases for the next six days. When compared with other contemporary models CGPANN seems to perform well ahead in terms of accuracy. © 2022 IEEE.

A Robust Deep Learning Model for Predicting the Trend of Stock Market Prices During Market Crash Periods

The stock market is one of the most important investment opportunities for small and large investors. Stock market fluctuations provide opportunities and risks for investors. However, some fluctuations are considered as enormous threats for most investors;significantly when the stock market has fallen sharply due to external factors and does not reach its previous point in a long time. For example, at the beginning of 2020, financial market indices, especially the stock market, fell sharply due to the COVID-19 pandemic, and for a long time, the indices did not grow significantly. Many investors suffered huge losses during this period. Although much research has been done in stock market forecasting and very efficient models have been proposed so far, no special effort has been made to build a model resistant to the collapse of financial markets. We propose a Convolutional Neural Network (CNN)-based ensemble model that is highly resilient to the stock market crash, especially at the beginning of the COVID-19 period. The proposed model not only avoids losing money in financial crises but can bring significant returns to investors. Experimental results show that the ensemble CNN models using Gramian Angular Fields (GAF) has greatly improved the resistance of the model in critical market conditions. © 2022 IEEE.

Investigating the Impact of COVID-19 on Important Economic Indicators

COVID-19 has impacted the world unlike any other world event in our recent memory. Entire humanity has been afflicted by this pandemic. As a consequence of the pandemic, the governments around the world have decided to impose lockdowns restricting economic interactions and relationships in a scale and form which has not been witnessed by the modern man ever in his memory. The general assumption here is that growing COVID-19 patient and mortality counts give rise to a greater sense of uncertainty, and this greatly impacts the prices. It is imperative thus for both the researcher community to observe and investigate the influence of COVID-19 patient and mortality counts on geopolitical and economic index indicators as well as the influence of these COVID-19 indicators upon important economic indicators such as the gold price as well as stock market prices. For this specific purpose, this work investigates the influence of COVID-19 patient and monthly death counts on the economic indicators of gold and stock market prices. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.