Estimating the Expected Pay-out of Earnout Contracts in Private Acquisitions

Sammanfattning: The growth of private equity, as well as consolidation trends across other industries, have produced a strong and vibrant mergers and acquisitions market. A challenge during these acquisitions is information asymmetry, which makes agreeing on the transaction price a challenge. An increasingly popular instrument to get around this problem is to use earnout contracts, which puts the difference between what the buyer is willing to pay and what the seller is willing to accept as contingent on future performance of the company. This thesis focuses on testing four different models for estimating the expected pay-out of earnout contracts. The investigated models were geometric Brownian motion, autoregressive integrated moving average, artificial neural network and a hybrid model to forecast the underlying metrics which were used with Monte Carlo methods to compute the expected pay-out of the earnout contract. Furthermore, a bankruptcy adjusted and a model using implied market volatility were evaluated. The results were that the hybrid model showed the most promising predictions when estimating the expected pay-out. The bankruptcy adjustment was not successful since the model failed to reach sufficient accuracy. Using implied market volatility showed inconclusive results.