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Machine Learning in Business Analytics

The research group "Machine Learning in Business Analytics" performs multi-disciplinary research at the intersection of computer science, economics, and data science. The activity of our group is directed towards a broad selection of topics including prediction and optimization problems from financial markets and operations research.


Leadership

Bernhard Lutz

Bernhard Lutz, Dr.
Coordinator

Members of the Research Group

Janin Kortum

Janin Kortum

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Sergej Levich

Antal Ratku

Antal Ratku

Tano Müller

Tano Müller

 

Jannik Schäfer

Jannik Schäfer

Torsten Mörstedt

Torsten Mörstedt

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Hans Christian Schmitz

Jörg Ebner

Jörg Ebner

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Katharina Baur

 

 




Research Focus
: Financial Text Mining

The availability of information forms the basis for financial decision-making. We use modern algorithms like word embeddings and multi-instance learning to get a deeper understanding of how corporate disclosures are perceived by investors. In addition, we develop trading strategies based on signals extracted from financial documents.


Research Focus
: NeuroIS (Information Systems and Neuroscience)

The subfield of NeuroIS within the IS discipline addresses research questions about human behavior using measurements from neuroscience like fMRI, EEG, ECG, and eye-tracking. We study phenomena in the context of fake news, product reviews, and recommender to explain how  humans respond to different stimuli. For this purpose, we established a NeuroIS lab with a wide range of measurement devices.
 

Research Focus: Decision Support Systems based on Reinforcement Learning

We consider optimization problems from the field of operations research. Among others, we focus on scheduling problems in the automotive industry and investment decisions based on noisy predictions about future returns and risks. For this purpose, we develop novel methods based on reinforcement learning to learn optimization via "trial-and-error". In addition, we address current challenges of training a reinforcement policy directly in the real-world.