The fast-growing literature on news analytics provides evidence that financial markets are partially driven by sentiments. In contrast with previous studies that have almost exclusively focused on the direct effects of the news related to single companies or sectors, we investigate the time-varying dynamics of news’ cross-industry influences for a set of US and European stocks over a period of 10 years. The graphical Granger causality of the news sentiments-excess return networks is estimated by applying the adaptive lasso. We find significant spillover effects and show the importance of sentiments related to certain sectors for the whole cross-section of stocks.

The fast-growing literature on news analytics provides evidence that financial markets are partially driven by sentiments. In contrast with previous studies that have almost exclusively focused on the direct effects of the news related to single companies or sectors, we investigate the time-varying dynamics of news’ cross-industry influences for a set of US and European stocks over a period of 10 years. The graphical Granger causality of the news sentiments-excess return networks is estimated by applying the adaptive lasso. We find significant spillover effects and show the importance of sentiments related to certain sectors for the whole cross-section of stocks.

We propose a new approach based on a generalization of the logit model to improve prediction accuracy in U.S. bank failures. Mixed-data sampling (MIDAS) is introduced in the context of a logistic regression. We also mitigate the class-imbalance problem in data and adjust the classification accuracy evaluation. In applying the suggested model to the period from 2004 to 2016, we show that it correctly classifies significantly more bank failure cases than the classic logit model, in particular for long-term forecasting horizons. Some of the largest recent bank failures in the United States that had been previously misclassified are now correctly predicted.