My current work mainly focuses on the application of machine learning in quantitative marketing. In particular, I am working on new approaches for emotion detection and investigating new business models (e.g., music streaming). Over the years, I visited the NYU Stern School of Business (New York, USA), the University of New South Wales (Sydney, Australia), and Bocconi (Milan, Italy) as a guest researcher.

Working papers

  • Hotz-Behofsits, Christian, Nils Wlömert, & Nadia Abou Nabout. NADE: Natural Affect Detection.
  • Hotz-Behofsits, Christian, Nils Wlömert, & Eitan Muller. The Decline of Superstars: Information Spillover Effects in the Streaming Age.

Publications & proceedings

  • Hotz-Behofsits, Christian, Winkler, Daniel, & Wlömert, Nils (2022, January). Music Genres Recon-sidered: Challenging Established Genres with a Data-driven Approach. 55th Annual Hawaii International Conference on System Sciences, 2022. Proceedings of the (pp. 9-pp). IEEE.
  • Hotz‐Behofsits, Christian., Huber, Florian, & Zörner, Thomas O. (2018). Predicting crypto‐currencies using sparse non‐Gaussian state space models. Journal of Forecasting, 37(6), 627-640.