We often come across earn-outs when building M&A models as it is an effective way to bridge valuation gaps, especially when valuation is difficult to pin-point for high-growth businesses. That said, we thought it would be a good topic to focus on in this article.

What is an Earn-out?

An earn-out is a form of contingent payment of used in M&A transactions. It frequently comes into play when there is a large discrepancy between the valuation that the buyer assigns on the target and what the target assigns on itself. These discrepancies are usually a result of differences between expectations in future growth and performance. A typical earn-out takes place between 3 – 5 years after closing and most earn-outs range from 20%-50% of total purchase price.

Valuing Earn-outs

Before diving deeper, it is important to note how some practitioners like to value earn-outs and how we differ. Some practitioners deem that the appropriate way to value earn-outs is to forecast a single “most likely” stream of cash flows and then discount them to the present using the traditional discounted cash flow (DCF) method.

This method is not suitable in our opinion because it treats the streams of cash flows as certain and therefore ignores the uncertainty of the underlying business, as well as the optionality of the earn-out itself. A more appropriate approach to valuing earn-outs is to recognize that they are a form of contingent payment, and so a simulation should be incorporated into the analysis to reflect the element of uncertainty inherent in the deal.

Now, let’s look at how earn-outs work. Consider a transaction where a conglomerate is buying out a smaller but rapidly growing competitor. The conglomerate has determined that the target is worth $13M. The target, however, values itself at $15M, as it believes its new product line is going to receive significant traction in the market, a factor not currently being valued by the buyer.

Rather than scrap this deal, the conglomerate proposed that it would match the target’s valuation through an earn-out structure, whereby it will pay $10M upon closing now and the rest will be paid via earn-outs if the target indeed achieves said traction and exceeds a given EBIT threshold.

We have produced part of the earn-out clause that appeared on the term sheet below.

Earn-out Payments. As additional consideration for the Merger Consideration [of C$10,000,000], Buyer shall pay to Seller with respect to each Calculation Period within the Earn-out Period an amount, if any, equal to the product of (i) an amount equal to (A) the Earnings Before Interest and Taxes (EBIT) for such Calculation Period [of 5 years from the Closing Date], minus (B) the EBIT Threshold for such Calculation Period; multiplied by (ii) the Earn-out Multiple of 1.5; provided, that in no event shall Buyer be obligated to pay Seller more than C$2,000,000 in any Calculation Period. If the EBIT for a particular Calculation Period does not exceed the applicable EBIT Threshold, no Earn-out Payment shall be due for such Calculation Period.

What this means in plain language is the following:

  • The buyer will pay the seller an earn-out equal to the seller’s EBIT less some agreed-upon EBIT threshold times 1.5, if the subtraction results in a positive number.
  • The maximum earn-out that the seller will pay per year during 5 year period is $2.0M per year.
  • If the subtraction results in a negative number (i.e. the target does not meet the EBIT threshold), then the buyer will not have to pay any earn-out in that period.

Building the Financial Model

As mentioned, the main reason why the seller and buyer differ on the valuation is because of differences in expected forecasts. Therefore, we will need 2 different models to determine whether the earn-out structured mentioned above will be acceptable to both the parties. We begin by laying out the key terms of the proposed earn-out.

Next, we build 2 models using 2 different sets of assumptions from the buyer and seller. To make the model more dynamic and to reflect the optionality of the earn-out, we will also include a Monte Carlo simulation. The simulation will assume normal distribution for sales growth and EBIT margins with standard deviation of 2% and 3% respectively. A higher/lower standard deviation can be reflected depending on the level of conviction the buyer and seller have on these forecasts.

Earnout Model - Alphabridge

Here we see that the buyer can expect to pay roughly $1.0M in present value terms over 5 years in addition to the $10M it will need to pay now. The seller, on the other hand, can expect to receive $5.9M in addition to the $10M if it actually achieves the forecast numbers.

We can see that the two models provide material differences in terms of valuation. However, this is only one scenario of the deal. Relying on this forecast alone would not be wise as one would be assuming that there is no variability to these cash flows. As such, we can conduct multiple iterations on the model using the Monte Carlo simulation to see what the average payoff of this earn-out structure will be. Here is the result based on 100 different trials.

An acceptable earn-out structure will satisfy both the buyer and seller. To the buyer, this means the acceptable earn-out and fixed payment will be equal to or less than the value of the target to the buyer. To the seller, the acceptable earn-out and fixed payment will be equal to or greater than the value it assigns to itself.

We can observe from the scatterplot of the simulation that from the Buyer’s perspective, the most likely amount the Buyer needs to pay the seller is $12M – $13M. On the other hand, this value is $15M – $16M from the Seller’s perspective, given its more optimistic outlook.

This particular earn-out structure works for both parties because it yields an acquisition price that is simultaneously below the Buyer’s valuation and above the Seller’s valuation. This is why earn-outs are a valuation business tool for M&A transactions and why it can enable a win-win situation for both the buyer and the seller.

Alpha Insights

Author Alpha Insights

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