We propose a simple cross-sectional technique to extract daily factors from economic news released at different times and frequencies. Our approach can effectively handle the large number of different announcements that are relevant for tracking current economic conditions. We apply the...

With the progress in natural language processing and machine learning, using deep learning techniques to extract valuable information from financial texts has gained popularity among researchers. However, as deep learning models require large amounts of labelled data, current models for...

We propose a simple cross-sectional technique to extract daily factors from economic news released at different times and frequencies. Our approach can effectively handle the large number of different announcements that are relevant for tracking current economic conditions. We apply the...

This thesis presents a new strategy that unites qualitative and quantitative mass data in form of text news and tick-by-tick asset prices to forecast the risk of upcoming volatility shocks. Holger Kömm embeds the proposed strategy in a monitoring system, using first, a sequence of competing...

The research objectives is to analyze factors impacting on accounting conservatism such as growth opportunity, return on equity, sales growth, return on assets, capital intensity, investment opportunity, financial distress, financial leverage, global diversification and uncertainty environment....