Valuing Contingent or disputed Assets and Liabilities in Solvency Opinions

Part II of II

This is a two-part article. In this second part, the authors discuss valuation and the approaches used to value contingent assets and liabilities. Read Part I here.

[su_pullquote align=”right”]Resources:

Valuation of Business, Securities, and Intangible Assets for Bankruptcy Purposes

Litigation for Bankruptcy and Insolvency

Determining a Distressed Debtor Company’s Discount Rate

Bankruptcy, Insolvency, and Restructuring

[/su_pullquote]

Traditional Valuation Methodology

An earnout—a classic contingent asset—is a common feature of a merger and acquisitions (M&A) transaction when the seller has a higher perception of the value of the business being sold, or the likelihood of future projections coming to pass, than the buyer does.  It is not uncommon for buyers to say, “I am not willing to pay you today based on those forecasts, but if you can actually deliver, I will pay you in the future.”  An earnout provision is a means to close the pricing gap between the buyer and the seller.

If the earnout is based on future earnings before interest, taxes, depreciation, and amortization (EBITDA)—typically with certain hurdles that must be exceeded—the expert’s task is very similar to the valuation of any business enterprise based on future earnings.  Future earnings are always uncertain.  Uncertainty can be assessed, in part, by using a scenario-based analysis, in which the expert assigns a probability to the uncertain variables using the base case, upside case, and downside case of management’s (or the expert’s) projections of future earnings.  Although this approach still requires the exercise of judgment about the future EBITDA, historical performance, anticipated industry growth, expected margins, and other observable inputs can guide the analysis.  The anticipated future cash flows from the earnout can be discounted to a present value.  For this contingent asset, the simple probability discount rule of Xonics is incomplete.  Rather, the accumulated learning used in valuing a business, or a business line, is needed, with such tailoring as is needed to match the contractual requirements of the earnout.

Cost of Insurance

One of the more novel ideas surrounding the valuation of contingent liabilities is the concept of determining the value of the item based on the cost of the insurance required to insure against the event occurring.  This situation appears very applicable to environmental clean-up costs or potential product liability claims.  There is a vast network of specialized insurers who will insure against the risk of almost anything.  The present value of the cost of insuring against the event over a reasonable time is one way to glean the value of the contingency.  Although the use of indicative coverage prices as a proxy for the value of a contingent liability has not yet been litigated, the authors are aware that some valuation professionals have relied, at least in part, on this approach.

Monte Carlo Simulation

Monte Carlo analysis generates not just a range of possible outcomes, but also a formalized mechanism for estimating the likelihoods of different outcomes.[1]  It is probabilistic in nature and involves statistical random sampling techniques that simulate the various sources of uncertainty and calculate an average or expected value over a range of thousands of resultant outcomes.

In the case of In re Tronox,[2] the plaintiff’s expert used Monte Carlo analysis to determine that approximately 68,000 future claims would be filed and that the total costs of these future claims would be approximately $308 million.[3]  The expert also applied Monte Carlo simulation to estimate the cash flow required for these liabilities from January 2006 through December 2012, in 2005 dollars (the transaction date), to be $123.1 million.[4]  The Monte Carlo simulation generated 40,000 estimated seven-year monthly tort liability cash flow series.[5]

The use of Monte Carlo simulation has survived a Daubert[6] challenge.  In Lyondell Chemical Co. v. Occidental Chemical Corp.,[7] potentially responsible parties that each had waste hauled to a disposal site (Turtle Bayou) by a hazardous waste disposal company and that had entered into settlements with the government to remediate particular areas of site, brought cause of action against other customers of the company for apportionment or contribution.  To assist in allocating cleanup costs among the liable parties, the district court appointed an expert in environmental engineering, who used a Monte Carlo statistical methodology to calculate the volume of Occidental’s waste dumped at Turtle Bayou.[8]

The court described Monte Carlo analysis as follows:

Monte Carlo measures the probability of various outcomes, within the bounds of input variables; to calculate Occidental’s waste volume, for example, [the expert] used the district court’s three volume estimates as inputs.  Instead of simply averaging the input values, Monte Carlo analysis uses randomly-generated data points to increase accuracy, and then looks to the results that those data points generate.  The methodology is particularly useful when reaching an exact numerical result is impossible or infeasible and the data provide a known range—a minimum and a maximum, for example—but leave the exact answer uncertain.  Seventy years after its discovery by physicists involved with nuclear weapons research, Monte Carlo analysis is now at home not only in the physical sciences but in a wide variety of fields including, for instance, the world of high finance.[9]

On appeal, the defendant challenged the plaintiff’s expert’s use of Monte Carlo analysis under Daubert.  Occidental’s Daubert challenge relied on five arguments that the Monte Carlo method used by the expert: 1) has not been peer-reviewed as applied to CERCLA allocations; 2) is not generally accepted for use in CERCLA allocations; 3) was developed specifically for use in this litigation; 4) has not been tested as applied to CERCLA allocations and has a rate of error that cannot be evaluated; and 5) is not relevant because it is “equivocal.”[10]  In affirming district court’s use of Monte Carlo simulation, the appellate court held that “just because a Monte Carlo simulation produces a range of outcomes, rather than one single numerical value, does not mean it is speculative.  If anything, Monte Carlo analysis provides greater certainty than the basic alternatives: using one of the three data points or using the arithmetic average of all three.”[11]

Conclusion

The valuation of contingent or disputed assets or liabilities presents a unique challenge in the assessment of a company’s solvency.  The fact patterns where the issue can be presented can vary markedly, and the variations in fact patterns can, and should, influence the approach the expert takes when estimating values.  The key is that “[p]rofessional judgment must be used to select the approach(es) and the method(s) that best indicate the value of the business interest.”[12]  Courts recognize that valuation “is generally decided through consideration of the approaches [and] methods that are conceptually most appropriate, and those for which the most reliable data is available.”[13]  “No single valuation method is universally applicable to all appraisal purposes.  The context in which the appraisal is to be used is a critical factor.”[14]  Thus, in valuing contingent or disputed assets and liabilities, the expert should evaluate the possible appropriate methods, and then choose and apply the method (or methods) carefully.

Previously published in Banking & Financial Services, Vol 30, No. 5, May 2014.

[1] See generally Michael O. Finkelstein and Bruce Levin, Statistics for Lawyers (2d ed.) 88-89 (Springer-Verlag 2001) (describing Monte Carlo methods and the bootstrap).

[2] Plaintiff’s Post-Trial Proposed Findings of Fact, In re Tronox, No. 09-01198-alg (Bankr. S.D.N.Y. Nov. 20, 2012) ECF No. 591.

[3] In developing the estimates, the expert first identified which of Tronox’s 31 wood treatment sites that had not been the subject of litigation likely would receive future claims by evaluating characteristics shared by the five sites where creosote claims already had been filed.  The expert determined that 26 of the 31 sites would be subject to future litigation.  Second, the expert estimated the number of people potentially exposed to creosote at each of the 26 sites, concluding that claimants would live within two miles of the site (using past settlement agreements and court pleadings).  The expert then estimated the population within the two miles radius using census data.  Third, the expert determined the claiming rate or propensity to sue by comparing the number of actual historical claims within the two mile radius.  The expert determined the claiming rate was 12.5%.  Fourth, the expert estimated the cost of future claims by using the historic cost of $5,110 per resolved claim and adding 37% for defense costs, also based on historical averages.  Finally, the expert allocated the future costs into specific years by determining the “targeting” rate (that is, the rate at which claims were first filed at new sites historically) and used Monte Carlo simulation to model the timing, number, and identity of future sites that would be targeted.  Based on this methodology, the expert determined that approximately 68,000 future claims would be filed at 12 of the 26 sites, costing $308 million.  This estimate was consistent with the claims history, where approximately 25,000 claims had been filed at just five sites in the six years before the IPO, and approximately 15,000 of those claims had been resolved for $98 million.

[4] Ibid.  The expert estimated the seven-year cash flows for the period January 2006 through December 2012 by performing a Monte Carlo simulation that generated 40,000 estimated seven-year monthly creosote cash flow series.  For each month within the seven-year period, she then averaged the 40,000 simulated cash flows series to calculate expected cash flow.  The expert used a 2.5% discount rate.  The seven-year cash flow analysis was important to the Court’s determination under the equitable insolvency test—that is, whether the debtor could pay its debts as they came due.

[5] Ibid.

[6] Daubert v. Merrell Dow Pharms., 509 U.S. 579 (1993).  Daubert essentially holds that the trial judge must perform a “gatekeeper” function of assessing the reasoning and methodology underlying expert’s opinion, and of determining whether it is valid and applicable to a particular set of facts, before admitting expert testimony.  See also Kumho Tire Co., Ltd. v. Carmichael, 526 U.S. 137 (1999); Fed. R. Evid. 702.

[7] 608 F.3d 284 (5th Cir. 2010).

[8] The court-appointed expert was tasked with running a statistical analysis to determine disposal volumes for each liable company.  The disposal volumes, together with chemical analyses of those volumes, would then enable to the court to allocate remediation costs.  Although the expert ran the actual calculations, the court determined the inputs he would use.  To calculate the amount of Occidental’s waste dumped at Turtle Bayou, for example, the court instructed the expert to use three input values: a minimum, an intermediate, and a maximum.

[9] Lyondell, 608 F.3d at 294.

[10] Ibid.

[11] Ibid. at 295.

[12] National Association of Certified Valuators and Analysts Professional Standards, Rule 3.7.

[13] In re Commercial Fin. Servs., Inc., 350 B.R. 520, 532 (2005) [quoting Business Appraisal Standards promulgated by the Institute of Business Appraisers (Publication P–311c) (2001)].

[14] Ibid.

Ian Ratner is Principal of GlassRatnerAdvisory & Capital Group LLC, which is a multi-office specialty financial advisory services firm providing solutions to complex business problems and board level agenda items. The firm applies a unique mix of skill sets and experience to address matters of the utmost importance to the enterprise such as planning and executing a major acquisition or divestiture, pursuing a fraud investigation or corporate litigation, managing through a business crisis or bankruptcy and other top level, non-typical business challenges.

Mr. Ratner can be reached at (404) 835-8840 or by e-mail to iratner@glassratner.com.

Jonathan T. Edwards is Partner at Alston & Bird LLP, a full-service international law firm with offices in Atlanta, Charlotte, Dallas, Los Angeles, New York, San Francisco, Silicon Valley, Washington D.C., Beijing, and Brussels. Mr. Edwards is a partner on Alston & Bird’s Bankruptcy and Financial Restructuring Team. He represents a variety of clients in complex bankruptcy cases, workouts, debt restructurings, distressed acquisitions and dispositions, and complex commercial litigation.

Mr. Edwards can be reached at (404) 881-4985 or by e-mail to jonathan.edwards@alston.com.

Kit Weitnauer is chair of Alston & Bird’s Bankruptcy and Financial Restructuring Group. His recent experience includes representing clients with significant roles in the Lehman Brothers, Residential Capital, Enron, Taylor Bean & Whitaker, Spectrum Brands and IndyMac Bancorp bankruptcies. He served as plaintiffs’ trial counsel (along with local co-counsel) in a five-week jury trial in Oregon that resulted in a verdict that found over $965 million in transfers were made with the actual intent to hinder, delay or defraud his client and that awarded $350 million in punitive damages to his client.

Mr. Weitnauer can be reached at (404) 881-4985or by e-mail to kit.weitnauer@alston.com.

Jeremy L. Wallison is an attorney practicing at Wallison & Wallison, a New York City-based boutique law firm. He focuses on trial court, appellate and arbitral litigation of complex, make-or-break business disputes. For his work in this area, Mr. Wallison, every year since 2014, has been named to the New York Super Lawyers list, a Thomson Reuters publication honoring the top five percent of lawyers in New York in particular practice areas based on a variety of factors, including peer review and professional achievement.

Mr. Wallison can be reached at (212) 292-1011 or by e-mail to jw@wallisonllp.com.