| Publikationstyp: | Konferenz: Sonstiges |
| Art der Begutachtung: | Peer review (Abstract) |
| Titel: | Predicting investor behaviour in European bond markets : a machine-learning approach |
| Autor/-in: | Hillebrand, Martin Schwendner, Peter Winant, Bastien Mravlak, Marko |
| et. al: | No |
| Angaben zur Konferenz: | 4th European Conference on Artificial Intelligence in Finance and Industry, Winterthur, Switzerland, 5 September 2019 |
| Erscheinungsdatum: | 5-Sep-2019 |
| Sprache: | Englisch |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 332.6: Investition |
| Zusammenfassung: | The European Rescue Fund ESM has, in its role as financial backstop of the Euro area, a specific interest in a comprehensive understanding of investor behaviour in order to ensure a stable and broad market access. With numerous transaction data as well as market and macro variables, a learning machine has been trained that forecasts investor demand in syndicated transactions. Out-of-sample tests show already a decent predictive power which is intended to be further improved by intelligent methods of data enhance-ment. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/20832 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Management and Law |
| Enthalten in den Sammlungen: | Publikationen School of Management and Law |
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