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Dictionaries for post-bankruptcy success prediction: A machine learning approach
Wolfgang Breuer, Andreas Knetsch, Katharina Mersmann
RWTH Aachen University, Germany
Discussant: Christoph Schneider (University of Münster)
This is the first study to analyze bankrupt firms’ reorganization plans. Using machine learning, we generate a dictionary for predicting post-bankruptcy success from these documents. Word counts based on our dictionary predict post-bankruptcy survival even after considering variables utilized in previous studies. Our text-based metrics are the strongest predictors of firm survival in our analysis and are also informative about the operating performance of surviving firms. Our results highlight the potential of reorganization plans for predicting post-bankruptcy success. We demonstrate that established dictionaries mostly evaluate reorganization plans incorrectly, which emphasizes the need for context-specific dictionaries
10:00am - 10:30am
Tech openness: Corporate culture in times of AI-transformation
Sabrina Yufang Sun, Sven Vahlpahl
University of Mannheim, Germany
Discussant: Andreas Knetsch (RWTH Aachen University)
Motivated by recent technological advances and the importance of corporate culture for organizational transformation, we investigate the role of tech open corporate culture in a firm's capital market performance during technological disruptions. We develop and validate a measure of corporate tech openness culture based on Glassdoor employee reviews. Using the launch of a major large language model as our setting, we show that tech openness is positively related to firms' stock market performance following disruptive technological advances. Consistent with corporate culture driving the effect, our baseline results are stronger when investors are more aware of firms' culture. These findings suggest that capital markets recognize tech openness as a valuable intangible asset, highlighting the importance of human factors in technological transformation.
10:30am - 11:00am
The outsized role of tax havens in mergers and acquisitions
Jean-Marie Meier1, Jake Smith2, Christoph Schneider3
1University of Texas at Dallas; 2U.S. Securities and Exchange Commission; 3University of Münster, Germany
Discussant: Sabrina Yufang Sun (University of Mannheim)
Tax havens are used for tax minimization. Whether tax havens affect corporate control in the form of cross-border mergers and acquisitions (M&A) or are merely used as conduits between host and destination countries of (greenfield) foreign direct investment and portfolio investment is an open question. We provide new stylized facts through the first comprehensive analysis of cross-border, tax-haven mergers and acquisitions (M&A). Using novel tax residence data, we investigate 18,193 such transactions from 1990 to 2017, totaling $7.1 trillion in deal value, or 31% of cross border M&A volume. $4.1 of the $7.1 trillion exceeds our prediction based on a gravity model with economic fundamentals. Small havens such as Bermuda alone make up $2.3 trillion or 10% of cross-border M&A volume.
