Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Systematic exposure assessment using NLP and textual reporting data |
| Organization Unit | |
| Authors |
|
| Supervisors |
|
| Language |
|
| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 60 |
| Date | 2023 |
| Abstract Text | Financial research recently started using the latest breakthroughs in natural language processing. This thesis develops an innovative framework for systematic exposure assessment: it measures the notion of exposure by leveraging language models and gathering a priori expert expressions to quantify the use of corpus- and domain-specific vocabulary. Focusing on risks and emerging technologies in earnings calls, we apply our method to reveal insightful market behaviors. A case study then shows that exposures from earnings conferences reflect the content from corresponding 10-Ks. Since our approach solves major issues of other NLP techniques, we strongly encourage other researchers to explore further applications. |
| PDF File |
Download
|
| Export |
BibTeX
|