Beer, Cars & Fundamentals: Predicting German M& A activity Sievers, Sönke Li, Reeyarn Degen, Dominik Kengelbach, Jens Pietrogrande, Francesca 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. 2025 info:eu-repo/semantics/workingPaper doc-type:workingPaper text http://purl.org/coar/resource_type/c_8042 https://ris.uni-paderborn.de/record/65383 Sievers S, Li R, Degen D, Kengelbach J, Pietrogrande F. <i>Beer, Cars &#38; Fundamentals: Predicting German M&#38; A Activity</i>. Vol Heft 11-12/2025.; 2025:302-308. doi:<a href="https://doi.org/CFCF1480783">CFCF1480783</a> eng info:eu-repo/semantics/altIdentifier/doi/CFCF1480783 info:eu-repo/semantics/altIdentifier/issn/1437-8981 info:eu-repo/semantics/closedAccess