1.3k citations · 3.2k across the 145 of their papers we have counts for
9 papers · 1 filter
Reinforcement Learning in R
Nicolas Pröllochs, Stefan Feuerriegel
Reinforcement learning refers to a group of methods from artificial intelligence where an agent performs learning through trial and error. It differs from supervised learning, sinc…
Adaptive Document Retrieval for Deep Question Answering
Bernhard Kratzwald, Stefan Feuerriegel
State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural netwo…
News-based trading strategies
Stefan Feuerriegel, Helmut Prendinger
The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are par…
Long-term stock index forecasting based on text mining of regulatory disclosures
Stefan Feuerriegel, Julius Gordon
Share valuations are known to adjust to new information entering the market, such as regulatory disclosures. We study whether the language of such news items can improve short-term…
Deep learning in business analytics and operations research: Models, applications and managerial implications
Mathias Kraus, Stefan Feuerriegel, Asil Oztekin
Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift du…
Investor Reaction to Financial Disclosures Across Topics: An Application of Latent Dirichlet Allocation
Stefan Feuerriegel, Nicolas Pröllochs
This paper provides a holistic study of how stock prices vary in their response to financial disclosures across different topics. Thereby, we specifically shed light into the exten…
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization
Bernhard Kratzwald, Stefan Feuerriegel
Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate t…
Deep learning for affective computing: text-based emotion recognition in decision support
Bernhard Kratzwald, Suzana Ilic, Mathias Kraus +2
Emotions widely affect human decision-making. This fact is taken into account by affective computing with the goal of tailoring decision support to the emotional states of individu…
News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions
Stefan Feuerriegel, Julius Gordon
The macroeconomic climate influences operations with regard to, e.g., raw material prices, financing, supply chain utilization and demand quotas. In order to adapt to the economic…