Time-varying synchronization of European stock markets

We study intraday comovements among three developed (France, Germany, and the United Kingdom) and three emerging (the Czech Republic, Hungary, and Poland) European stock markets. When applying a Dynamic Conditional Correlation GARCH model to 5- min tick intraday stock price data (2003–2006), we find a strong correlation between the German and French markets and also between these two markets and the UK stock market. However, very little systematic positive correlation during a trading day can be detected between the developed and emerging stock markets, or within the emerging group itself. Hungary exhibits higher correlation with the developing markets and the emerging markets and its dynamics show an increasing trend; Poland and the Czech Republic produce less clear-cut results.     

Poverty Dynamics in Turkey

This paper examines poverty dynamics in Turkey using a nationally representative panel data covering the 2005–08 period. The aim is to understand mobility in and out of poverty and its correlates. We find that almost a quarter of the poor are persistently poor. The conditional and unconditional exit rates that we estimate are within the range of values reported for developed countries in the literature. That the income events—but not transfers—dominate both poverty entries and exits is indicative of the major role labor markets play in the lives of the poor. In particular, we argue that given the characteristics of the poor and modest levels of social assistance, the reason for mobility rates close to European averages must be sought in the informal economy.     

Predicting base station return on investment in the telecommunications industry: Machine-learning approaches

Investment in the right location ensures sustainable competition. In the telecommunication sector, the number of base stations (BSs) is one of the most significant investment parameters. When a potential BS is subject to be selected, practitioners will first consider investing in a BS where the return on investment (ROI) is highest. Therefore, the quantifiable objectives are distinctly defined, as it makes sense to choose maximizing features that raise per unit investment. This study provides a solution to evaluate the best BS installation alternative with machine-learning approaches as well as to estimate ROI value by changing the properties that affect the ROI value. For this purpose, the estimation performance of logistic regression, random forest, and XGBoost methods are compared and further strengthened by random forest hyperparameter optimization to provide the best performance. The model, with a success rate of 98.7% according to the $F$-score, showed that it was a robust algorithm. The three most essential features for the ROI value are determined to be voice traffic, data traffic, and frequency cost. These parameters enable a review of the prediction results of telecommunications managers and planning specialists responsible for BS investment.