Christian Fieberg

Christian Fieberg serves as a Professor of Data Science at Hochschule Bremen (City University of Applied Sciences) in Bremen, Germany, and holds an Affiliate Professor position at Concordia University in Montreal, Canada. He is actively affiliated with the DTX research cluster within the Faculty of Business and Economics at Hochschule Bremen, where his work bridges theoretical modeling with practical financial applications.

Professor Fieberg's research spans financial economics with particular expertise in risk and portfolio management, sustainable investments, and data-intensive financial analysis. His methodological approach integrates advanced statistical techniques, machine learning algorithms, and econometric modeling to address complex financial problems. He specializes in working with large datasets and developing practical software tools that translate research findings into actionable insights for financial practitioners.

His publication record reveals a strong focus on market predictability, factor modeling, and cross-sectional analysis across various asset classes. Recent work demonstrates increasing attention to cryptocurrency markets and the application of machine learning techniques to international financial markets. His research consistently appears in top-tier finance journals including the Journal of Finance, Journal of Financial and Quantitative Analysis, and Review of Finance, reflecting both methodological rigor and practical relevance.

• Top 50 most research-intensive economists under 40 years of age in German-speaking countries (Wirtschaftswoche)

Professor Fieberg maintains active collaborations with researchers across international institutions, as evidenced by his co-authored publications with scholars from Concordia University, Montpellier Business School, and the University of Bremen. His work with the DTX research cluster emphasizes innovative research approaches and strong academic-industry networking. His current projects include Bond Factor Prediction (2025-2026) and participation in international workshops such as the STARS EU workshop in Sweden.

His laboratory work focuses on developing computational tools for financial analysis using multiple programming environments including Matlab/Octave, R, Stata, Python, and Excel/VBA. The research group maintains active data repositories on Harvard Dataverse for replication purposes, demonstrating commitment to research transparency and reproducibility.