• Working Paper

    • Identifying M&A Targets from Textual Disclosures: A Transformer Neural Network Approach

      Joint work with Lennart Stitz

      Abstract: Can textual information from firm disclosures help to identify M&A targets? We employ the state-of-the-art transformer neural network RoBERTa based on 113,000 annual financial reports of publicly listed US firms to estimate takeover likelihoods. We show that incorporating publicly available, highly standardized textual information can improve the predictability of corporate takeovers significantly in out-of-sample tests and that this information is not fully incorporated in stock prices. We use explainable artificial intelligence methods to examine the reasons for the improved predictions. Our analyses indicate that the machine learning algorithm is able to identify product offerings and firm-specific capabilities sought by acquirers.

      Available at SSRN

    • The 52-Week High and M&A Deals: International Evidence

      Joint work with Pascal Büsing, Paul F. Hark, and Hannes Mohrschladt

      Abstract: We study the effect of targets' 52-week highs on offer prices in a global sample of mergers and acquisitions across 33 countries. Our results confirm that targets' past stock price peaks serve as an anchor in merger negotiations, albeit at varying magnitudes across different countries. We explain these cross-country differences through a simple framework in which target shareholders incentivize managers to consider an economically immaterial anchor. As predicted by our theoretical framework, the effect of a target's 52-week high on its offer price is most pronounced in countries characterized by stringent corporate governance, stronger legal protections for shareholders, and cultures prone to anchoring. Our results illustrate how corporate governance can exacerbate inefficiencies in the market for corporate control when shareholder beliefs are biased.

      Available at SSRN