Contingent Considerations Striving for Consensus November 2013

Amanda A. Miller, PhD Executive Director – Complex Securities Group, Valuation and Business Modeling Redwood Shores, California Tel: +1 650-802-4522 Fax: +1 866-215-8262 Email: [email protected]

Amanda Miller has over fifteen years of experience in helping clients to understand and manage risk, assess probabilities of uncertain events, and develop quantitative models to measure value in an uncertain world. She specializes in assisting clients with the valuation of illiquid investments, equity & fixed income derivatives, contingent assets and liabilities and other complex securities. Specific areas of focus include: ►

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Privately-Held Company Securities. Amanda assists clients in the use of option pricing models and probabilistic analysis to value options, warrants, preferred and common stock in privately-held companies with complex capital structures, considering both the economic rights associated with these instruments as well as discounts related to the marketability of these securities in the available exit markets. Clients use these analyses for financial reporting purposes, tax planning and investment decision-making. In addition, she assists clients with the valuation of management incentive plans, including those with performance-based and market conditions, and has led several projects considering the valuation ramifications of various incentive plan structures for global clients. Amanda was a member of the AICPA Task Force that recently released the updated guide, Valuation of Privately-Held Company Securities Issued as Compensation. Private Equity & Venture Capital Investments: Amanda assists private equity, venture capital and other investment companies with valuation issues related to financial reporting related to their portfolio investments under ASC 820. Amanda is a member of the AICPA Task Force that is developing a new guide, Determining Fair Value of Portfolio Company Investments of Venture Capital and Private Equity Firms and other Investment Companies. Illiquid Credit Instruments: Amanda assists clients in understanding the value of their illiquid fixed income investments, including auction rate securities and structured products. Contingent Considerations and Other Probabilistic Instruments. Amanda designs probability-weighted models to capture the full range of possible outcomes and estimate values under uncertainty. These engagements help clients gain confidence in the projections used in financial planning and financial reporting for transactions, technology investments, “but for analyses” for litigation, and strategic decision making. In addition, these analyses are used in the valuation of contingent considerations under ASC 350, for analyzing warranties and other guarantees under ASC 460-10, and to help companies determine whether they need to consolidate certain variable interest entities under ASC 810-10 and for Value at Risk forecasting and model validation for the banking industry.

Amanda earned her Ph.D. at Stanford University in Electrical Engineering in 1994, where her thesis developed a new statistical approach for Positron Emission Tomography (“PET”) imaging. She also holds dual M.S. degrees from Stanford in Statistics and Electrical Engineering, and dual B.S. degrees from the California Institute of Technology in Mathematics and Engineering & Applied Science. She completed the Berkeley Finance Series programs in Derivatives and Bonds, ABS & Risk Management in 2007.

Biljana (Biki) Marijanovic Senior Manager Complex Securities, Valuation & Business Modeling Ernst & Young, LLP [email protected] +1 415-894-8279

Biljana Marijanovic has over six years of experience in assisting clients with the valuation of equity securities and derivatives, debt securities, hybrid securities, contingent assets and liabilities, and other complex contracts. Specific areas of focus are: Valuation of equity derivatives ► Warrants, employee stock options, convertible notes and convertible preferred ► Embedded derivatives present in convertible bonds, convertible preferred or other hybrid instruments. Examples include conversion and early redemption features, make whole provisions, change-in-control and other company specific contingencies of convertible debt ► Preferred and common stock in privately-held companies with complex capital structures Valuation of debt instruments ► Corporate debt, intercompany debt, mezzanine and early stage debt, as well as debt modifications taking into account current trends in a company’s cost of borrowing and performing synthetic credit rating analyses ► illiquid credit Instruments, including convertible notes and related derivatives Contingent considerations and other probabilistic instruments ► Design of probability-weighted models to capture the full range of possible outcomes and estimate values under uncertainty. In particular, valuation of contingent considerations under ASC 805 and mark-to-market accounting. Biljana earned her M.S. at University College, London, UK in Mathematical Modeling. She holds dual B.S. degrees in Applied Mathematics and Electrical Engineering from Southern Methodist University, Dallas, TX.

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Agenda ► ► ► ► ► ►

Introduction Why is it so complicated? Appraisal Foundation’s Working Group Overconfidence Current Valuation Approaches – the Evolution Closing Observations

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Introduction

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Buyer’s vs. Seller’s Perception

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Bridging the Gap

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Why is it so complicated?

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Contingent Consideration Risk Taxonomy

Debt

Digital Option

Equity

Capped Call

Call Option

Increasing Increasinglevel levelof ofrisk risk ►

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The greater the risk, the greater the required rate of return ►

Debt like payments have an associated price of risk equivalent to the cost of debt

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Equity type payout structures have an associated price of risk equivalent to the cost of equity

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Call options have an associated price of risk that depends on the risk of the underlying metric, "moneyness", volatility, and time to expiration; this risk can far exceed the cost of equity 1

Non Linear Payoffs versus Linear Payoffs

Forecast: $10m Pay: 10x EBITDA Price:

Forecast: $10m Pay: 10x EBITDA iff EBITDA ≥ $10m

$100m Price:

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????

Payoff 200 100 50

50

100

200

Payoff

Non Linear Payoffs versus Linear Payoffs What’s the difference?

5

10

20

Linear Page 11

EBITDA

5

10

20

Non-Linear

EBITDA

Non Linear Payoffs versus Linear Payoffs Doing the math – Linear 10 x ($5m x 50% + $15m x 50%) = 10 x $10m = $100m payoff associated with the probability weighted avg EBITDA

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=

50% x (10 x $5m) + 50% x (10 x $15m) = $25m + $75m = $100m probability weighted average payoff associated with each EBITDA

Non Linear Payoffs versus Linear Payoffs Doing the math – Non-Linear 10 x ($5m x 50% + $15m x 50%) = 10 x $10m = $100m payoff associated with the probability weighted avg EBITDA

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=

50% x (0 x $5m) + 50% x (10 x $15m) = $0m+ $75m = $75m probability weighted average payoff associated with each EBITDA

Contingent Consideration Risk Taxonomy Risk of the underlying outcome Risk of the functional form Risk of the acquiring company Page 14

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Business level metrics (before capital structure considerations) carry a firm level of risk.

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Equity level metrics (after capital structure considerations) carry an equity level of risk

► Varies

with the participation of the contingent consideration in the underlying once a hurdle is met

► Payouts

typically resemble a combination of other financial instruments such as debt, equity, digital call options, call spreads, and standard call options

► Generally

represented by the subordinated credit risk of the acquiring company

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In specific cases, the risk of the acquiring company may be highly correlated with the success of the contingent consideration

1

Appraisal Foundation

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Appraisal Foundation’s Working Group – Contingent Considerations ►

Goals ► ►

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Build consensus among the firms regarding appropriate approaches Reduce diversity in practice

Topics to be covered ► ►

Introduction, Purpose and Scope Valuation Methodologies ► ►

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Key questions ► ► ► ►

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Complex vs. simple models used as approximations Approaches for addressing uncertainty Estimating cash flows (calibration to the forecast, volatility) Risk of the underlying metric – revenues vs. earnings Counterparty risk Technical milestones

Overconfidence Assessment

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Overconfidence! ►

Overconfidence has been called the most “pervasive and potentially catastrophic” of all the cognitive biases to which human beings fall victim. It has been blamed for lawsuits, strikes, wars, and stock market bubbles and crashes.

* Moore, D. A., & Healy, P. J. (2008), “The trouble with overconfidence”, Psychological Review, 115(2), 502-517. Page 18

Evolution of Valuation Methodology

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Working with Uncertainty C B

A

outcome 1

D

outcomes

outcome 2

We don’t use stochastic models because we like them. We use them because we need to.

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Current Valuation Approaches – the Evolution ► ►

We have come a long way… 2 Main approaches have emerged ► ►

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PWERM (Probability Weighted Expected Return Method) Options pricing method

PWERM – Pros and Cons Discounts Probability Weighted Pay-off Pros • Management control scenarios and probabilities • Understandable • Flexible

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Cons • Management control scenarios and probabilities • Lots of subjective assumptions • Management tend to be overoptimistic • Difficult to estimate the discount rate • Difficult to apply to path-dependent pay-offs • More difficult to ensure consistency between assumptions

Options Pricing Approach – Pros and Cons Pros • Widely used to value securities with non-linear payoffs • Less assumptions than PWERM • More objective than PWERM • No need to contemplate the impact of the non-linear pay-off on the discount rate • Can handle path-dependent payoffs • Coherent framework makes it easier to ensure consistency of assumptions

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Cons • Perceived to be complex • Not widely understood • More rigid than a PWERM

Main Issues ►

Consistency in assumptions for the underlying metric ►

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PWERM: Probability distribution of underlying (forecast and risk) vs. risk-adjusted discount rate of underlying/IRR Options pricing: Volatility vs. discount rate of underlying

Discount rates for PWERM ► ►

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Valuation specialist’s do not understand risk-neutral valuation Valuation industry is not technical enough to convert probabilities to risk-neutral Wang transform, stochastic deflators, … there are others (nice to know, but not covered today)

Closing Observations

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Thank You Questions?