This paper provides one of the first large sample studies documenting a positive causal effect of shareholder approval in corporate decision-making. Using a hand-collected sample of U.S. mergers and acquisitions (M&As) that involve stock payment over the period 1995-2015, we examine whether and how the requirement of shareholder approval affects deal outcome. The challenge faced by many corporate finance studies is the endogeneity of a firm’s governance structure. Our identification strategy relies on listing rules of the NYSE, AMEX, and NASDAQ that shareholder approval is required when an acquirer intends to issue more than 20% new shares to finance a deal. We examine acquirer price reaction to deals in which acquirers intend to issue either above or below the 20% threshold by a small margin. This regression discontinuity design provides a clean causal estimate of the effect of shareholder approval on M&As. We find a large and significant 5.6% jump in acquirer announcement returns at the 20% threshold. We further show that this positive value effect is larger for acquirers with better corporate governance practices as measured by high institutional ownership, particularly high quasi-indexer ownership, and for acquirers buying targets with more severe information asymmetry as measured by listing status (public vs. private targets) and by analyst coverage (high- vs. low-coverage targets). We then provide suggestive evidence on the underlying mechanisms behind this positive value effect: Shareholder approval is associated with acquirers making deals with larger synergies and with acquirers getting a bigger share of those synergies. Finally, we show that shareholder approval leads to better post-merger operating performance in well-governed acquirers. We conclude that the requirement of shareholder approval is effective in addressing agency problems. Joint with Tingting Liu (Creighton University) and Juan (Julie) Wu (University of Georgia)

 Keywords: shareholder approval; mergers and acquisitions; wealth effects; listing rules; regression discontinuity designs

 JEL Classification: G32; G34; G38

Joint with Tingting Liu (Creighton University, USA) & Juan (Julie) Wu (University of Georgia, USA)