Prof. Dr. Maik Dierkes, Oliver Budras and Dr. Sebastian Schrön (at the IBF until August 2022) conduct research at the interface between finance and accounting using artificial intelligence methods.
The subject of the award-worthy publication is the investigation of the relationship between text-based uncertainty extracted from annual reports of US companies (so-called 10-Ks) and stock returns. The goal is to investigate whether text-based uncertainty has a significant risk premium in the cross-section of stock returns, how quickly it is priced in, and the implications of uncertainty for future corporate profitability.
"We use special natural language processing (NLP) techniques to measure uncertainty, using them to generate a dictionary, said Oliver Budras, research associate at the Institute of Banking and Finance. "This allows us to quantify the uncertainty expressed by management in an annual report. For example, we find that investors demand a positive risk premium for the uncertainty expressed in the annual report and that this information is priced in quite quickly."
The research team also determined that companies with high levels of uncertainty in an annual report have lower future profitability. The totality of these findings allows conclusions to be drawn about investors' uncertainty-averse behavior.