| Deep reinforcement learning for the optimal placement of cryptocurrency limit orders M Schnaubelt European Journal of Operational Research 296 (3), 993-1006, 2022 | 45 | 2022 |
| A comparison of machine learning model validation schemes for non-stationary time series data M Schnaubelt FAU Discussion Papers in Economics, 2019 | 43 | 2019 |
| Separating the signal from the noise–financial machine learning for Twitter M Schnaubelt, TG Fischer, C Krauss Journal of Economic Dynamics and Control 114, 103895, 2020 | 28 | 2020 |
| Testing stylized facts of bitcoin limit order books M Schnaubelt, J Rende, C Krauss Journal of Risk and Financial Management 12 (1), 25, 2019 | 21 | 2019 |
| Machine Learning in Futures Markets F Waldow, M Schnaubelt, C Krauss, TG Fischer Journal of Risk and Financial Management 14 (3), 119, 2021 | 5 | 2021 |
| A topic modeling perspective on investor uncertainty D Perico Ortiz, M Schnaubelt, O Seifert FAU Discussion Papers in Economics, 2023 | 4 | 2023 |
| Valuation ratios, surprises, uncertainty or sentiment: How does financial machine learning predict returns from earnings announcements? M Schnaubelt, O Seifert FAU Discussion Papers in Economics, 2020 | 2 | 2020 |
| Multispectral X-ray imaging to distinguish among dental materials AC Peter, M Schnaubelt, M Gente Imaging Science in Dentistry 47 (4), 247, 2017 | 1 | 2017 |
| Data control: a feedback control design for clustering of networked data V Willert, M Schnaubelt AT-AUTOMATISIERUNGSTECHNIK 64 (8), 618-632, 2016 | 1 | 2016 |
| A Topic Modeling Perspective on Investor Uncertainty DP Ortiz, M Schnaubelt, O Seifert Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, 2023 | | 2023 |
| Die Regelung von Daten: Eine Idee zur Clusteranalyse von vernetzten Datenbeständen V Willert, M Schnaubelt at-Automatisierungstechnik 64 (8), 618-632, 2016 | | 2016 |
