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.

Purpose: 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.Design/methodology/approach: 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.Findings: 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.Originality/value: 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.

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.

The past decade has witnessed significant turmoil and political conflicts in several Middle Eastern countries, such as Egypt, Syria, and Libya, called the Arab Spring. These revolutions did not only affect the countries mentioned previously; their neighboring countries were also directly affected. This study explores the impact of the Syrian refugee influx on the stock exchange market of one of its neighboring countries, namely Jordan. The Syrian civil war represents a recent catastrophic event that has resulted in over three million refugees migrating to various countries worldwide. The main objective of this paper is to examine the effect of the Syrian war on Jordan’s stock exchange market. The study utilizes the stock exchange indices as indicators of the performance of the exchange market, including Financials, Services, Industries, and General indices as dependent variables, and seven dummy variables are defined as representatives of the main events occurring in the Syrian civil war during the period 2011–2018 as independent variables. Multiple statistical analysis techniques, including correlation coefficients, error functions, and stepwise regression, are employed to analyze the selected variables. The findings reveal an inverse influence of the Syrian war on Jordan’s stock market. These findings can potentially enhance the development of prediction models for stock indices in Jordan and other countries by incorporating relevant variables.

Stock markets are an essential backbone for the economy worldwide; their indices provide all interested parties with indicators regarding the performance of firms listed in the financial market due to tracking the daily transactions. This study aims to investigate factors that affect the stock exchange directly so that it simplifies building a prediction model for the exchange index in Jordan’s financial market. The study hypothesis assumes that some sub-sectors are most influential in creating the stock market prediction model. Therefore, this study applies four feature selection methods on 23 sub-sectors and Amman Stock Exchange Index (ASEI100) for the period 2008–2018. The top 10 attributes from each selection method are combined, and the frequency table is used to find the highly trusted attributes. Moreover, linear regression with ordinary least square regression is used to test the validity of the top factors that frequently occurred in the four methods and their effect on ASEI. The results found that there are six main sub-sectors directly affecting the general index in Jordan: Health Care Services, Mining and Extraction Industries, Textiles, Leather and Clothing, Real Estate, Financial Services and Transportation. These sectors can be utilised to predict the movements of the Amman Stock Exchange Index in Jordan. Also, the linear regression model output showed a statistically significant relationship between the six sub-sectors (independent variables) and ASEI (dependent variable). Investors can use this paper’s findings to signal the most important sectors in Jordan. Thus, it helps in taking investment decisions.