Purpose The COVID-19 pandemic virus has affected the largest economies around the world, especially Group 8 and Group 20. The increasing numbers of confirmed and deceased cases of the COVID-19 pandemic worldwide are causing instability in stock indices every day. These changes resulted in the G8 suffering major losses due to the spread of the pandemic. This paper aims to study the impact of COVID-19 events using country lockdown announcement on the most important stock indices in G8 by using seven lockdown variables. To find the impact of the COVID-19 virus on G8, a correlation analysis and an artificial neural network model are adopted. Design/methodology/approach In this study, a Pearson correlation is used to study the strength of lockdown variables on international indices, where neural network is used to build a prediction model that can estimate the movement of stock markets independently. The neural network used two performance metrics including R2 and mean square error (MSE). Findings The results of stock indices prediction showed that R2 values of all G8 are between 0.979 and 0.990, where MSE values are between 54 and 604. The results showed that the COVID-19 events had a strong negative impact on stock movement, with the lowest point on the March of all G8 indices. Besides, the US lockdown and interest rate changes are the most affected by the G8 stock trading, followed by Germany, France and the UK. Originality/value The study has used artificial intelligent neural network to study the impact of US lockdown, decrease the interest rate in the USA and the announce of lockdown in different G8 countries.

Last decade witnessed successive corporate scandals for various firms that points to a failure of corporate control. Expertize and interested parties all over the world proposed to focus on monitoring the management decisions to reduce such failure in firms. Therefore, the structure of ownership became more and more as an important issue to increase both efficiency and effectiveness of management decisions. This study seeks to investigate whether institutional ownership affects the firm's performance for one of the emerging markets; Jordan. Firm's performance is measured through applying two accounting measures Return on Assets (ROA) and Return on Equity (ROE), with 6 explanatory variables. Our sample is unique and contains 82 non-financial Jordanian firms listed at Amman Stock Exchange (ASE) for the period of 2005-2013, by applying panel data regression analysis. It depends on building three OLS models: Pooled, Fixed Effects Model and Random Effects Model. In addition, a test for Breusch and Pagan Lagrangian multiplier (LM), and Hausamn test to choose among the three models which model is most suitable for our data. A main finding of the panel data analysis is that; fixed effect regression is the most convenient model. As a result, there is no strong evidence that there is a relationship between both institutional ownership and firm performance for Jordanian listed firms. This conclusion can be due to the fact that institutional ownership has its own pros and cons, therefore, their existence and influence could affect materially the types and risk level of investment decisions taken by the management which in return will affect the firm's performance as a whole.

Purpose:\ud The purpose of this paper is to investigate the effect of external financing needs on both firm value and corporate governance mechanisms within the UK SME context. This framework is of importance because of the limited external financial resources SMEs might face.\ud \ud Design/methodology/approach:\ud The authors consider the endogeneity problem between corporate governance mechanisms and firm value, and hence, the three stages least squares and the instrumental variables based on two stages least squares estimation methods are employed.\ud \ud Findings:\ud The authors find a positive relationship between external financing needs and firm value. In addition, the authors detect that size and profitability are positively associated with firm value in the sample. Concerning the corporate governance index (CGI), the authors detect that big SMEs and those with low-debt levels have better corporate governance structures.\ud \ud Originality/value:\ud The authors employ a CGI for the sample which is constructed using ten corporate governance variables. The authors also examine different factors that affect SMEs 2019 governance by applying different models including logistic analysis

<p>Financials have been concerned constantly with factors that have impact on both taking and assessing various financial decisions in firms. Hence modelling volatility in financial markets is one of the factors that have direct role and effect on pricing, risk and portfolio management. Therefore, this study aims to examine the volatility characteristics on Jordan’s capital market that include; clustering volatility, leptokurtosis, and leverage effect. This objective can be accomplished by selecting symmetric and asymmetric models from GARCH family models. This study applies; ARCH, GARCH, and EGARCH to investigate the behavior of stock return volatility for Amman Stock Exchange (ASE) covering the period from Jan. 1 2005 through Dec.31 2014. The main findings suggest that the symmetric ARCH /GARCH models can capture characteristics of ASE, and provide more evidence for both volatility clustering and leptokurtic, whereas EGARCH output reveals no support for the existence of leverage effect in the stock returns at Amman Stock Exchange.</p>

The credit card customer churn rate is the percentage of a bank’s customers that stop using that bank’s services. Hence, developing a prediction model to predict the expected status for the customers will generate an early alert for banks to change the service for that customer or to offer them new services. This paper aims to develop credit card customer churn prediction by using a feature-selection method and five machine learning models. To select the independent variables, three models were used, including selection of all independent variables, two-step clustering and k-nearest neighbor, and feature selection. In addition, five machine learning prediction models were selected, including the Bayesian network, the C5 tree, the chi-square automatic interaction detection (CHAID) tree, the classification and regression (CR) tree, and a neural network. The analysis showed that all the machine learning models could predict the credit card customer churn model. In addition, the results showed that the C5 tree machine learning model performed the best in comparison with the three developed models. The results indicated that the top three variables needed in the development of the C5 tree customer churn prediction model were the total transaction count, the total revolving balance on the credit card, and the change in the transaction count. Finally, the results revealed that merging the multi-categorical variables into one variable improved the performance of the prediction models.