Target’s Learning in M&A Negotiations

Chong Huang, Qiguang Wang

Research output: Working paper


Empirical studies have documented large pre-bid target stock price runups, suggesting significant effects of target stock market on mergers and acquisitions (M&A) outcomes. To understand such effects, we develop an informational feedback model in which a target learns from its stock price about M&A synergy. In equilibrium, acquisition premium is increasing and nonlinear in runup. Conditional on a runup that indicates a large positive synergy, the premium-runup relation increases from zero to one as target stock market noise shrinks. This implies that our model subsumes existing hypotheses as special cases with different levels of market noise. Both deal success probability and fraction of synergy taken by the target are increasing in runup for a fixed market noise level and are increasing in the market noise for a fixed high runup, providing new testable predictions. Finally, when stock market is sufficiently noisy, publicizing ongoing M&A negotiations may jeopardize value-creating M&A deals.
Original languageEnglish
Publication statusIn preparation - Oct 2022


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