@techreport{65383,
  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.}},
  author       = {{Sievers, Sönke and Li, Reeyarn and Degen, Dominik and Kengelbach, Jens and Pietrogrande, Francesca}},
  issn         = {{1437-8981}},
  pages        = {{302--308}},
  title        = {{{Beer, Cars & Fundamentals: Predicting German M& A activity}}},
  doi          = {{CFCF1480783}},
  volume       = {{Heft 11-12/2025}},
  year         = {{2025}},
}