• Working Paper

    • Bidder Opportunism, Familiarity, and the M&A Payment Choice

      Joint work with Christoph Schneider

      Abstract: A familiarity bias of target shareholders allows bidders to opportunistically choose the M&A payment method. We employ the Stambaugh, Yu and Yuan (2015) mispricing score to identify overvalued bidders, reconfirming that overvaluation is a central driver of the payment choice. Using an instrumental variable based on exogenous price pressure, we provide causal evidence for bidder opportunism. Further analyses show that target shareholders more familiar with the bidder are more likely to accept equity despite particularly adverse market reactions. Our results suggest that behavioral biases of shareholders contribute to the transmission of stock market inefficiencies to the market for corporate control.

      Available at SSRN

    • A Catering Theory of Earnings Guidance: Empirical Evidence and Stock Market Implications

      Joint work with Hannes Mohrschladt

      Abstract: We propose and test a catering theory of earnings guidance. As predicted by our model, managers cater to reference point dependent investor preferences by issuing excessively optimistic earnings forecasts if their investors have experienced poor stock returns. Moreover, earnings guidance is most biased when managers strongly discount future outcomes, when the stock's payoff uncertainty is high, and when managers face low costs for issuing inaccurate forecasts. Catering via earnings guidance succeeds in moving stock market prices and induces mispricing which is corrected around the corresponding final earnings announcement.

      Available at SSRN

    • 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