This research attempts to explore the total of 21 potential internal and external shocks to the European market during the Covid-19 Crisis. Using the time series of 1 Jan 2020 to 26 June 2020, I employ a machine learning technique, i.e. Least Absolute Shrinkage and Selection Operator (LASSO) to examine the research question for its benefits over the traditional regression methods. This further allows me to cater to the issue of limited data during the crisis and at the same time, allows both variable selection and regularization in the analysis. Additionally, LASSO is not susceptible to and sensitive to outliers and multi-collinearity. The European market is mostly affected by indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index. There is a significant difference in the predictors before and after the pandemic announcement by WHO. Before the Pandemic period announcement by WHO, Europe was hit by the gold market, EUR/USD exchange rate, Dow Jones index, Switzerland, Spain, France, Italy, Germany, and Turkey and after the announcement by WHO, only France and Germany were selected by the lasso approach. It is found that Germany and France are the most predictors in the European market.•A LASSO approach is used to predict the European stock market index during COVID-19•European market is mostly affected by the indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index.•There is a significant difference in the predictors before and after the pandemic announcement by WHO.

Purpose: The study of credit risk has been of the utmost importance when it comes to measuring the soundness and stability of the banking system. Due to the growing importance of Islamic banking system, a fierce competition between Islamic and conventional banks have started to emerge which in turn is impacting credit riskiness of both banking system.Design/methodology/approach: Using the system GMM technique on 283 conventional banks and 60 Islamic banks for the period of 2006–2017, this paper explores the important impact of size and competition on the credit risk in 15 dual banking economies.Findings: The authors found that as bank competition increases credit risk seems to be reduced. On the size effect, the authors found that big Islamic banks are less risky than big conventional banks whereas small Islamic banks are riskier than small conventional banks. The results are robust for different panel data estimation models and sub-samples of different size groups. The findings of this paper provide important insights into the competition-credit risk nexus in the dual banking system.Originality/value: The paper is specifically focused on credit risk in dual banking environment and tries to fill the gap in the literature by studying (1) do the Islamic and conventional banks exhibit a different level of credit risk; (2) does competition in the banking system impact the credit risk of Islamic and conventional banks and finally (3) do the big and small banks exhibit similar levels of credit risk.