Towards Predicting Business Activity Classes from European Digital Corporate Reports
Year: 2024
Type: conference-paper
Abstract: Digital financial reporting enables automated analyses on vast datasets. This study illustrates the benefits of integrating XBRL and machine learning. XBRL, an open-source financial reporting language, was used to create a unified database of over 5600 IFRS-tagged reports. The IFRS taxonomy tags containing textual data on company activities were analyzed using the Zero-Shot Learning algorithm to identify specific activities. This study highlights how digital reporting and machine learning can extract and analyze textual data, offering insights into company activities and demonstrating the potential of these technologies in financial reporting.
Authors: Péter Molnár, Alex Suta, Bence Lukács, Árpád Tóth
Institution: Széchenyi István University
Language: English
Topic: Financial Reporting and XBRL
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4404112248