Incentive-compatible contracts in merger negotiations: The role of acquirer idiosyncratic stock return volatility

Dimitris Alexakis, Leonidas Barbopoulos

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We show that the acquiring firm's idiosyncratic stock return volatility (sigma) is an important determinant of the selection and perceived valuation effects of earnouts in Mergers and Acquisitions (M&As). Earnout‐based M&As are more often announced by high‐sigma acquirers (nearly 40% of all earnout‐based M&As), yet the documented higher risk‐adjusted returns accrued to acquirers in earnout‐based M&As, relative to M&As settled in cash, stock or mixed payments (the earnout effect), appear in deals announced by low‐sigma acquirers (nearly 20% of all earnout‐based M&As). High‐sigma acquirers employing earnouts appear to break even, or even experience losses, relative to their counterparts employing single up‐front payments. These results are confirmed based on a quasi‐experimental design through which the earnout effect is measured in isolation. We argue that in M&As announced by high‐sigma acquirers, the earnout effect is potentially elusive due to the presence of an acquirer‐specific information revelation effect, resulting from the heightened extent of information asymmetry between (small) acquirers’ managers and outside investors. On the contrary, the use of earnouts in M&As announced by low‐sigma (large) acquirers, whereby the acquirer‐specific information revelation effect is likely negligible, sends a strong signal for value creation that also prevents investors from inducing a size‐related discount.
Original languageEnglish
Pages (from-to)1-38
Number of pages38
JournalFinancial Markets, Institutions and Instruments
DOIs
Publication statusPublished - 29 Sept 2019

Keywords / Materials (for Non-textual outputs)

  • acquirer idiosyncratic stock return volatility (sigma)
  • earnouts
  • information asymmetry
  • propensity score matching (PSM)
  • risk-adjusted returns

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