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This study aims to use gray models to predict abnormal stock returns.

Data are collected from listed companies in the Tehran Stock Exchange during 2005-2015. The analyses portray three models, namely, the gray model, the nonlinear gray Bernoulli model and the Nash nonlinear gray Bernoulli model.

Results show that the Nash nonlinear gray Bernoulli model can predict abnormal stock returns that are defined by conditions other than gray models which predict increases, and then after checking regression models, the Bernoulli regression model is defined, which gives higher accuracy and fewer errors than the other two models.

The stock market is one of the most important markets, which is influenced by several factors. Thus, accurate and reliable techniques are necessary to help investors and consumers find detailed and exact ways to predict the stock market.

Considering the relationship between the central bank balance sheet and unconventional monetary policy after the 2008 financial crisis, it is crucial to see how the unconventional monetary policy, given near-zero interest rates, affects future stock market performance. This paper analyzes the impact of the Fed's balance sheet size on stock market performance.

To analyze the Fed's balance sheet size's long-term stock market implications, this paper uses the asset pricing framework of market return predictability such as Ordinary least squares (OLS) and Generalized method of moments (GMM) analysis.

Findings in this paper suggest that the Fed's balance sheet size, deflated by asset market wealth, presents evidence of return predictability during 1926–2015 that is robust against standard controls. These results can be explained through the redistribution of risk and the wealth channels of monetary policy transmission. The changing balance sheet size of a central bank (1) affects systemic risk, yields and expectations and (2) signals the future direction of monetary policy and thus economic outlook.

The main implication of these findings is that policymakers should avoid a severe imbalance between a central bank's balance sheet size and assets market wealth.

The empirical evidence in this paper documents a century-old relation between the Fed's balance sheet size and US stock market return using the Fed's balance sheet data for the last 100 years and stock market returns from the Center for research in security prices (CRSP) database.

The purpose of this paper is to study the factors affecting COVID-19 mortality.

An empirical model is developed in which the mortality rate per million is the dependent variable, and life expectancy at birth, physician density, education, obesity, proportion of population over the age of 65, urbanization (population density) and per capita income are explanatory variables. Crosscountry data from 184 countries are used to estimate the quantile regression that is employed.

The estimated results suggest that obesity, the proportion of the population over the age of 65 and urbanization have a positive and statistically significant effect on COVID-19 mortality. Not surprisingly, per capita income has a negative and statistically significant effect on COVID-19 death rate.

The study is based on the COVID-19 mortality data from June 2020, which have constantly being changed. What data reveal today may be different after two or three months. Despite this limitation, it is expected that this study will serve as the basis for future research in this area.

Since the findings suggest that obesity, population over the age of 65 and density are the primary factors affecting COVID-19 death, the policy-makers should pay particular attention to these factors.

To the authors’ knowledge, this is first attempt to estimate the factors affecting the COVID-19 mortality rate. Its novelty also lies in the use of quantile regressions, which is more efficient in estimating empirical models with heterogeneous data.

This research evaluated the impact of credit risk, liquidity risk, profitability, economic growth and good governance on the vulnerability of the Islamic banking system in the Association of Southeast Asian Nations (ASEAN).

The panel regression analysis was used to obtain data from five ASEAN countries that had operated Islamic banks from 2010 to 2019.

The results obtained from the vulnerability model indicated that bank liquidity risk, profitability and good governance have significant impacts on vulnerability. Conversely, credit risk and economic growth showed an insignificant effect on susceptibility. Good governance helps increase investment attractiveness for economic growth and development in Islamic banks in ASEAN.

Some of the limitations of this research include its focus on the vulnerability of Islamic banks in ASEAN countries. The average value of six indices is used as a single index per country with good governance. Therefore, further research needs to consider using all six indices of good governance as factors affecting the vulnerability of Islamic banks, such as control of corruption, government effectiveness, political stability, absence of violence, regulatory quality, the rule of law voice and accountability.

This research describes banking financial circumstances and their internal activities. Furthermore, it helps managers or banking practitioners in the proper management of finance, specifically at the vulnerability level, to aid in the early detection of crisis to enable early aversion or minimal impact.

This research is expected to assist governments in ASEAN countries to establish public policies and build good governance to increase investment interest in the Islamic banking industry.

This research is the author's first attempt at discussing the issues of bank vulnerability related to good governance faced by the Islamic banking system in ASEAN.