Monte Carlo Simulations in M&A Valuation: The Acquisition of Tiffany & Co. by LVMH

Abstract

The value of a firm is a quantity contingent on its beholder, which is showcased explicitly in the setting of mergers and acquisitions. For the buying party, inherent uncertainties surrounding valuation inputs and outputs raise the complexity of pricing both the target and expected synergies. This thesis investigates how extending the classical DCF model by Monte Carlo simulations can provide additional insights to acquirers in assessing and determining deal prices in light of uncertainty.
To illustrate the issue at hand, we built on the acquisition of Tiffany & Co. by LVMH in 2021. Framed by the circumstances of the Covid-19 crisis, this deal accentuates the challenge of target pricing even for LVMH as a serial acquirer. In this context, we determined the firm value of Tiffany & Co. from the viewpoint of October 28, 2020, using two approaches. First, we conducted a deterministic DCF valuation with a resulting standalone share price of 113.0 USD and 146.4 USD for the share value including anticipated synergies. Second, we approached the valuation of Tiffany & Co. using Monte Carlo simulations to account for the uncertainty embedded in the parameters of the DCF model. The obtained distribution of the comprehensive share value displayed a mean of 146.7 USD, as well as positive skewness and high kurtosis.
Our findings from the static DCF model suggest that at a final transaction price per share of 131.5 USD, LVMH paid a considerable premium relative to the standalone market price of 114.0 USD and our estimated standalone price of 113.0 USD. Nevertheless, the deal is considered underpriced relative to the expected fair value of 146.4 USD when including synergies. However, these deterministic results obscure relevant information on the uncertainty of outcomes. The simulation outcomes demonstrate that due to the skewness of the distribution, the mean of 146.7 USD should not be considered the ceiling price that LVMH is willing to pay at most. This proposition is due to a lower median of 136.4 USD, inferring a higher chance of Tiffany’s value falling below the mean than above. In effect, we find a probability of 45.8% that the deal will prove overpriced at 131.5 USD. To conclude, we deem Monte Carlo simulations a compelling tool for acquirers to account for variability in input parameters and accordingly base their pricing decision on a probabilistic range of firm values instead of a point estimate.