It is well known that call centers suffer from high levels of employee turnover; however, call centers are services that have excellent operational records of telemarketing activities performed by each employee. With this information, we propose to use the Random Forest and the naïve Bayes algorithms to build classifiers and predict turnover of the sales agents. The results of 2407 sales agents’ operational performance records showed that, although the naïve Bayes is much simpler than Random Forest, both classifiers performed similarly, achieving interesting accuracy rates in turnover prediction. Moreover, evidence was found that incorporating performance differences over time increases significantly the accuracy of the predictive models up to 85%, with the naïve Bayes being quite competitive with the Random Forest classifier when the amount of information is increased. The results obtained in this study could be useful for management decision-making to monitor and identify potential turnover due to poor performance, and therefore, to take a preventive action.

We explore the use of implied volatility indices as a tool for estimate changes in the synchronization of stock markets. Specifically, we assess the implied stock market’s volatility indices’ predictive power on synchronizing global equity indices returns. We built the correlation network of 26 stock indices and implemented in-sample and out-of-sample tests to evaluate the predictive power of VIX, VSTOXX, and VXJ implied volatility indices. To measure markets’ synchronization, we use the Minimum Spanning Tree length and the length of the Planar Maximally Filtered Graph. Our results indicate a high predictive power of all the volatility indices, both individually and together, though the VIX predominates over the evaluated options. We find that an increase in the markets’ volatility expectations, captured by the implied volatility indices, is a good Granger predictor of an increase in the synchronization of returns in the following month. Estimating, monitoring, and predicting returns’ synchronization is essential for investment decision-making, especially for diversification strategies and regulating financial systems.