• Working Paper

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

      Joint work with Lennart Stitz and Paul Drecker

      Abstract: Can textual information from firm disclosures help to identify M&A targets? We employ two state-of-the-art transformer neural networks, RoBERTa and Longformer, based on 102,987 annual reports of publicly listed U.S. firms to estimate takeover likelihoods. We show that incorporating publicly available, 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 algorithms are able to identify product offerings and firm-specific capabilities sought by acquirers.

      Available at SSRN

    • Strategic Communication With A Myopically Loss Averse Investor

      Joint work with Thomas Langer and Hannes Mohrschladt

      Abstract: We develop a multi-period communication model in which a manager knows the firm's fundamental value before the firm's myopically loss averse investor. The manager may disclose truthfully or provide strategically biased information to influence the investor's evaluation of firm performance. The optimal managerial communication strategy is to report firm values as close to prior market values as possible. This strategy reduces disproportionately painful downward price movements, alleviates stock price volatility, and generates stock price momentum. We empirically support the model's predictions with respect to the interplay between biased managerial communication and stock returns.

      Available at SSRN

    • The 52-Week High and Corporate Announcement Returns

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

      Abstract: We show that the directional effect of the 52-week high on short-term stock market reactions to M&As, SEOs, and earnings announcements is mechanical rather than behavioral. Stocks trading far below their 52-week high react more strongly to news, i.e., their announcement returns have higher magnitudes. Hence, for stocks far below their 52-week high, favorable (unfavorable) corporate announcements mechanically exhibit even more positive (negative) returns. Overall, our results provide a cautionary tale for interpreting supposedly behaviorally driven proxy variables.

      Available at SSRN

    • Offer Price Anchoring in Global Mergers and Acquisitions

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

      Abstract: We show that the effect of targets’ 52-week highs on M&A offer prices varies systematically across countries. We explain these cross-country differences through a simple framework in which target shareholders incentivize corporate managers to consider an economically immaterial anchor. As predicted by our theoretical framework, the effect of the target’s 52-week high on its M&A offer price is more pronounced in countries characterized by stringent corporate governance, stronger legal protections for shareholders, and national cultures prone to heuristic decision- making. Our results demonstrate how strict corporate governance can exacerbate inefficiencies in the market for corporate control when shareholder beliefs are biased.

      Available at SSRN