---
res:
  bibo_abstract:
  - This paper introduces a predictive model for German mergers and acquisitions (M&
    A) activity leveraging deep feedforward neural networks (DFNN) incorporating well-established
    traditional variables (also known as features), along with a ChatGPT-based M&
    A sentiment score (MASS) and unconventional predictors such as beer sales and
    weather data. We demonstrate that the inclusion of sentiment and non-traditional
    variables enhances predictive performance. Our findings provide an important empirical
    foundation for understanding near-term fluctuations in German M& A activity and
    offer a forecasting tool relevant to both practitioners and researchers.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Sönke
      foaf_name: Sievers, Sönke
      foaf_surname: Sievers
      foaf_workInfoHomepage: http://www.librecat.org/personId=46447
  - foaf_Person:
      foaf_givenName: Reeyarn
      foaf_name: Li, Reeyarn
      foaf_surname: Li
      foaf_workInfoHomepage: http://www.librecat.org/personId=102450
  - foaf_Person:
      foaf_givenName: Dominik
      foaf_name: Degen, Dominik
      foaf_surname: Degen
  - foaf_Person:
      foaf_givenName: Jens
      foaf_name: Kengelbach, Jens
      foaf_surname: Kengelbach
  - foaf_Person:
      foaf_givenName: Francesca
      foaf_name: Pietrogrande, Francesca
      foaf_surname: Pietrogrande
  bibo_doi: CFCF1480783
  bibo_volume: Heft 11-12/2025
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1437-8981
  dct_language: eng
  dct_title: 'Beer, Cars & Fundamentals: Predicting German M& A activity@'
...