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Earnouts in mergers and acquisitions: A game-theoretic option pricing approach

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Abstract

This paper presents a valuation approach for merger and acquisition (M&A) deals employing contingent earnouts. It is argued that these transactions have option-like features, and the paper uses a game-theoretic option approach to model the value of such claims. More specifically, the paper examines the impact of uncertainty on the optimal timing of M&A using earnouts, and it also investigates the impact of uncertainty on the terms of the earnout. Optimal earnout and initial payment combinations are endogenously derived from the model, and testable hypotheses are developed. The theoretical contribution of this paper is a dynamic decision-making model of the invest-to-learn option generated upon investment in an acquisition. The paper also offers practical implications for the design of acquisitions employing earnouts.

Highlights

► The paper presents a dynamic decision-making model for merger and acquisition (M&A) deals employing contingent earnouts. ► In particular, we use a sequential real option bargaining game to model such claims. ► Optimal contract combinations are endogenously derived and testable hypotheses are developed. ► Uncertainty has an ambiguous effect on timing and the value of managerial flexibility. ► The paper offers practical implications for the design of acquisitions employing earnouts.

Introduction

For almost a century, merger and acquisition (M&A) deals and the factors that give rise to them have been the subject of intense interest in the business economics research domain. One of the key challenges parties face is coming to terms on a price that serves both buyer and seller. For example, parties to prospective M&A deals often diverge in their expectations about future revenue gains or cost savings that a target might realize once a deal is consummated. Earnouts specify deferred payments from bidder to target that are tied to certain performance targets, so they represent a useful tool to address uncertainty in the valuation process by enabling parties to share risk. There appears to be an increasing usage of such contingent contracts in acquisition transactions, with more than 7000 deals since 2000 using earnouts, or roughly 1.5% of deals reported worldwide.1

Despite the potential value of earnouts in facilitating acquisitions, little is known about this type of contingent contract in the M&A research domain. Some empirical studies exist that examine firms’ motives to employ earnouts, yet there is a lack of analytical modeling of the valuation and timing of acquisition transactions employing earnouts. In particular, the few traditional approaches used to value earnouts have the limitation of underestimating their value because they neglect their option-like features. A separate literature exists on the timing of M&A bids and value of flexibility, yet the implications of contingent future payments such as those introduced by earnouts have been neglected in this stream of analytical work. We therefore wish to join and extend these research streams in the M&A literature by examining in more detail how contingent earnouts are structured and by presenting an option valuation model for acquisitions involving earnouts.

The remainder of the paper is organized as follows: Section 2 provides a brief literature review that summarizes current theory and findings on earnouts. Section 3 presents the game-theoretic option valuation model. We first introduce the basic structure of the model, value earnout payments (Section 3.1), determine optimal cooperation levels by targets (Section 3.2), and finally analyze initial timing and deal structuring decisions (Section 3.3). Section 4 offers a summary of the comparative static results and derives testable hypotheses. Section 5 concludes by laying out several directions for future research on the design and implications of earnouts in acquisitions.

Section snippets

Literature review

In what follows we will define an earnout as “an arrangement under which a portion of the purchase price in an acquisition is contingent on achievement of financial or other performance targets after the deal closes” (Bruner and Stiegler, 2001, p. 1). Hence, for acquisition deals involving earnouts, only a fraction of the total consideration is paid up front, and the remaining payments are deferred and contingent upon meeting certain performance targets.

In contrast to other areas of the M&A

The model

We will focus on an M&A transaction between a buyer and a target. Both are assumed to be risk neutral and discount with the riskless interest rate r > 0. The target company generates a net cash flow (or earnings) stream of x(t) per time period, and we assume that these cash flows are uncertain and that their time-varying pattern can be formally expressed by an arithmetic Brownian motion (ABM) process:dx(t)=αdt+σdW,x(0)=x0,with α,σR+ and dW as a standard Brownian motion. Eq. (1) states that

Comparative static results

Unless noted otherwise we will assume the following values: r = 0.05, α = 0.03, σ = 0.2, ψ = 2, T = 2, x0  = 1, θ1 = 0.2, θ2 = 0.25, I¯T=0.25 and IT=0.1. As we have seen, the parties will initiate the M&A deal once the observed asset value x(t) hits an optimal threshold x. As illustrated by Fig. 2, x increases the higher the uncertainty of the target’s performance and the higher the transaction costs IT. This is due to two effects. First, under uncertainty it pays to wait for new information because making

Conclusion

Numerous contracting practices have evolved to reduce some of the exchange hazards and uncertainties inherent in M&A transactions. Contingent earnouts in particular have recently attracted attention in the M&A domain. Recent empirical literature has revealed some of the conditions that make earnouts attractive, yet analytical modeling using options theory offers the potential to shed light on the structuring of earnout deals and the drivers of these deal structures. In particular, critical

Acknowledgements

The authors are particularly grateful to the Editor Robert Graham Dyson, Grzegorz Pawlina, and the anonymous referees for their insightful comments and valuable suggestions.

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