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The European Arbitration Review 2018

Why Experts Disagree

Joel Franks

FTI Consulting

18 October 2017

Quantum or damages experts typically have an overriding duty of independence. Their opinions are required to be impartial, objective, unbiased and uninfluenced by the pressures of the dispute resolution process, and their duty in giving evidence in an arbitration is to assist the tribunal.1 In my experience, most quantum experts take these requirements seriously. However, party appointed experts often arrive at very different assessments of loss.

The lack of agreement between party appointed experts, and in particular the tendency of the claimant’s appointed expert to produce a higher estimate of loss, and the respondent’s appointed expert to produce a lower estimate, has led some commentators to question the usefulness of party appointed experts. In this article, I explain some of the factors that can lead to divergent expert opinions, and why this should not be assumed to be the result of bias. I also explain that differences between experts’ views can be beneficial to the dispute resolution process.

The role of the quantum expert

Quantum experts are often instructed to provide evidence in claims for lost profits (for example, following an alleged breach of contract) and claims for the lost value of an asset (for example, following an expropriation).

In both cases, the claimant’s loss in monetary terms can be calculated by comparing the financial position the claimant would have been in, but for the alleged wrongful acts, with the claimant’s actual financial position. These two financial positions are often referred to as the ‘but for position’ and the ‘actual position’ respectively.

Calculating the financial position of the claimant is usually not straightforward, particularly in the but for position which, by definition, represents a hypothetical situation. It often requires the expert to make projections, which rely on a number of assumptions that often extend well into the future. An expert’s conclusion will be directly influenced by these assumptions, and consequently, different assumptions lead to different conclusions on value and loss. In many cases, there can be a range of reasonable views relating to those assumptions.

In the remainder of this article, I consider some of the factors that may lead quantum experts to arrive at different assessments of loss that are both independent of, and influenced by, the dispute process. I then go on to consider some of the aspects of the dispute process that may lead to the assessment of loss being skewed in the direction of the expert’s instructing party.

Plausible ranges of projections

The first factor that I discuss is the range of plausible outcomes that can exist when projecting financial performance.

Assessments of lost profits or loss of value often require calculations of a stream of cash flows that an entity or asset would have been expected to generate, and/or is actually expected to generate, into the future.

There are a number of factors that need to be considered when forecasting future performance. These can broadly be split into entity-specific factors, such as the expected performance of new products or the expected growth of the market that the entity operates in; and broader macroeconomic factors, such as economic growth and price inflation.

Because forecasting is inherently uncertain, there is a range of plausible forecasts for just about all of these factors. It is not unusual for a range of expectations to exist for even some of the more commonly considered macroeconomic factors, even when considered over a relatively short period of time.

For example, at the beginning of 2017, in the FT’s annual economists’ survey,2 a number of academics and economists from different institutions were asked, ‘How much, if at all, do you expect UK economic growth to slow in 2017’. Of the 122 economists that responded, 45 per cent expected there to be a marked slowdown in economic growth to between 1.1 per cent and 1.5 per cent. However, expectations of economic growth in 2017 ranged from less than 0.5 per cent (3 per cent of responses) to greater than 2.1 per cent (3 per cent of responses).

In other words, even forecasting something such as the economic growth of a developed country, over a short period of time, can lead to a relatively broad range of expectations. Forecasting the growth of an emerging economy, or forecasting over a longer period, would be likely to produce a much broader range of expectations.

Forecasts of market-specific factors are also subject to uncertainty, even in situations where there is an apparent abundance of information. This can be illustrated by the range of forecasts of global oil consumption, a key determinant of the price of oil.

For example, in 2017 Exxon Mobil forecast that by 2040, global oil consumption would increase to 106 million barrels per day, up from approximately 97 million barrels per day in the first quarter of 2017.3 Over the same period, OPEC expected daily consumption to increase even further to 109 million barrels,4 and the US Energy Information Administration5 expected daily consumption to reach between 121 million and 123 million barrels6 (up to 15 per cent above Exxon’s forecast). Statoil, a Norwegian multinational oil and gas company, considered three different scenarios for its forecasts, which ranged from oil consumption falling to just below 80 million barrels per day, to consumption increasing to approximately 115 million barrels per day by 2040 (in other words a range from -18 per cent to +18 per cent).7

In summary, there are likely to be ranges of plausible views on various economic or operational parameters that underpin forecasts of financial performance of a claimant, or views of how the but for position would have evolved. The combination of these views may well result in two experts reaching quite different conclusions as to the projected financial performance of the claimant.

That difference of opinion does not mean that one expert or the other is wrong. Rather, it reflects the nature of the question being put to the expert, which requires projections of fundamentally uncertain parameters. The range of plausible forecasts provides scope for the experts to disagree.

The understanding and application of relevant theory

The second factor that can influence differences between experts is their respective understanding and application of relevant finance theory to the question they are answering.

Valuation, or the quantification of the net present value of a stream of cash flows, is underpinned by finance theory. However, there is no consensus among academics and practitioners on various aspects of finance theory.

Different experts, therefore, may have different views on the state of finance theory, and in particular whether certain theories or propositions are valid. They may also have different views on the appropriate way to apply the theory to address certain aspects of the loss assessment.

For example, in a discounted cash flow valuation exercise, the conclusion is often sensitive to the discount rate or cost of capital applied to forecast cash flows in order to calculate their present value. The discount rate is made up of several factors, and experts might reasonably take different views on a number of them, potentially resulting in different discount rates.

A good illustration is the equity market risk premium, one of the parameters used to estimate the cost of equity. This can be estimated using forward looking methods, such as deducing the premium implicit in share prices, conducting surveys of market participants or by analysing historical data on equity returns. Further, estimates based on historical data can be derived in a variety of ways, using different calculation methods, comparing equity returns to different ‘risk free’ benchmarks, and considering data from different periods, with different approaches leading to different estimates.

For instance, comparing the geometric average of equity returns on US stocks to the interest rates on long term US government bonds over the period 1928 to 2015 implies an equity market risk premium of 4.5 per cent. Alternatively, comparing the arithmetic average of equity returns on US stocks to the interest rates on US treasury bills over the period 2006 to 2015 implies an equity market risk premium of 7.9 per cent.8

Surveys can also help to demonstrate the range of equity market risk premium used by different market participants. Pablo Fernández, a professor of finance at IESE Business School, conducts an annual survey of the equity market risk premium and risk-free rates used by finance and economics professors, analysts and managers of companies. In his 2017 survey, based on a total of 1,613 responses, the average equity market risk premium used when calculating the discount rate for US firms was 5.7 per cent. However, the responses ranged from 1.5 per cent to 12 per cent.

Experts may also disagree on whether it is appropriate to adjust the discount rate to reflect additional risks relevant to the subject company, such as risks associated with the size of the company, or with the country or region in which some or all of its operations or trading partners are located.

Differing views on the methods used to calculate the discount rate will lead to differences in the discount rate used in a set of calculations, and ultimately will affect conclusions on value or lost profits.

Again, contrasting views between the experts does not mean that one expert is necessarily wrong (although one or both may be applying theory inappropriately). If there is no consensus among academics or in academic literature on the assessment of discount rates, it is not surprising that experts could apply theories in different ways. One expert could legitimately refer to academic support for the use of a particular equity market risk premium, while another expert could cite papers in support of his or her own approach.

Divergent views on value in other contexts

I have explained that the challenges in projecting financial performance and the lack of consensus in many aspects of finance theory can potentially lead to a range of reasonable conclusions by experts. The greater the uncertainty over the prospects of the subject company, the greater the range of plausible financial performance and the greater the range of plausible valuations.

That same divergence of views can be observed in contexts other than disputes.

This can be illustrated by looking at valuations of companies performed by academics and valuation practitioners. At the end of February 2017, Apple, one of the largest companies in the world by market capitalisation, had a share price of US$137, which implied a market capitalisation of US$729 billion for the company.9 At around that time, analysts from UBS valued the shares at US$151, analysts from Barclays valued the shares at US$123, and Aswath Damodaran, a Professor of Finance at the Stern School of Business at New York University valued the shares at US$129.10 The difference in the market capitalisations implied by these share price valuations is US$150 billion11 which is just over 20 per cent of the market capitalisation implied by the actual share price. Therefore, despite having the same information available to them, as well as a range of valuation methods at their disposal, even for one of the world’s largest companies, there is still a relatively wide range of views on value.

Conclusions on value can diverge even more where there is greater uncertainty. At the end of August 2017, Tesla (a company in its early stages of operation) had a share price of US$358, which implied a market capitalisation of US$59 billion. At around that time, analysts from Guggenheim Securities valued the shares at US$430, analysts from UBS valued the shares at US$185, and Professor Damodaran valued the shares at US$ 192.12 The difference in the market capitalisations implied by these share price valuations is around US$40 billion13 which is just under 68 per cent of the market capitalisation implied by the actual share price.

If this range of difference can be observed in these contexts, it is not surprising that similarly large ranges of values are put forward by expert valuers in disputes.

The dispute process

So far, I have explained the potential for experts to arrive at very different conclusions. Those differences do not imply one, or both, experts have made an error, or have been subject to bias.

Nonetheless, it is clear that while there is reasonable scope for disagreement between experts, the direction of difference tends to point the same way. The expert instructed by the claimant almost always puts forward a figure higher than the expert instructed by the respondent.

What leads to this result? As I explain below, there are aspects of the dispute process that are likely to affect both the variance between the experts’ conclusions, and the direction of that variance.

Instructions

First, a quantum expert’s conclusions on value can often be a function of his or her instructions. As a result, the assessments of loss put forward by experts may be as much a function of their respective clients’ positions on key matters in dispute as they are of the experts’ views of matters within their expertise.

This can conceivably lead to quantum experts instructed by different parties calculating answers to different questions: one, based on a set of assumptions adopted by the claimant; and the other that is based on a set of assumptions adopted by the respondent.

If the key facts of a matter are in dispute, what one assumes about those facts may very well affect forecasts of financial performance. In some cases, experts are instructed to assume certain facts, and consequently their conclusions are a function of their instructions. In other words, the experts are ‘downstream’ of their respective instructions regarding the facts to adopt.

Further, an assessment of loss may depend on the evidence of a technical or industry expert because that expert provides an opinion on the future market prices (for example), or whether a particular product or technology is technically viable (to give another example). Again, the experts may be instructed to adopt the conclusions of one technical expert over another.

For example, a dispute that requires a quantum expert to value a mining business is likely to require the input of other experts, such as geologists (to estimate the value of the reserves of the mineral that is being mined) or mining engineers (to calculate the cost of, or the potential rate of, extracting the mineral). The figures, forecasts, or other estimates calculated by these technical experts will flow directly into the quantum expert’s calculations, and will influence his or her conclusions.

Finally, an expert may be instructed to assume certain positions in respect of legal issues. These might include the date of valuation, or whether certain heads of loss are, or are not, claimable.

As a consequence, an expert’s conclusions can be a function not only of his or her opinion on valuation or financial matters, but also of the facts he or she is instructed to rely on, the industry experts relied on, and on the instructing counsel’s opinion on certain legal issues.

Selection of experts

Second, it is sometimes the case that experts are appointed because they hold particular views on matters that are relevant to the case. That does not mean the expert is biased, but it does mean that the instructing party has been alive to the views held when making its appointment.

For example, an expert might hold a view that country risk can be diversified by investors and that therefore a ‘country risk premium’ should be not be added to the discount rate. This is likely to increase the assessment of loss relative to an expert who considers that the inclusion of a country risk premium is appropriate. The former expert might be attractive to a claimant when the subject of the loss may attract a high country risk premium.

This is equally relevant to the selection of industry or technical experts. If their views on their subject matter are likely to favour one or other party’s interests in respect of key issues in the dispute, including quantum issues, this can contribute further to the quantum expert’s conclusions being aligned with his or her instructing party.

Conclusion

Given the nature of the question being answered and the inherent uncertainties involved, it is unsurprising that quantum experts reach different conclusions, and that those differences can be significant. The tendency of the expert instructed by the claimant to reach a higher loss figure than the expert instructed by the respondent is also not surprising. One cannot simply conclude, therefore, that divergent views amongst experts is evidence of bias.

Indeed, the complexities and uncertainties that can lead to variances in experts’ conclusions are, in my view, reasons in support of, and not against, the use of party instructed experts in international arbitration.

The dispute resolution process benefits from a tribunal being presented with a range of plausible conclusions based on different views, providing those views are reasonable. By being required to consider two alternative points of view (instead of a single option from a single joint expert), the tribunal must be engaged on issues of quantum, and must consider carefully where in the range an award should be made.

In contrast, a single conclusion from one (tribunal appointed) expert may convey an artificial level of accuracy. Or, put another way, may fail to convey the full scope of the plausible range of conclusions. Such an approach might obscure both alternative projections of key assumptions, and the lack of consensus in relevant finance theory.

There are also benefits from having party appointed experts examining each other’s work. Principally, this scrutiny can help to identify any errors in the experts’ work. More generally, it should also help to maintain high standards of expert evidence.

Finally, and for the avoidance of doubt, none of the analysis above should be construed as justifying an expert consciously taking views that favour his or her client. It is important for quantum experts to identify clearly the key assumptions that affect their conclusions and on what basis they have made those assumptions, and to guard against the influences of bias wherever possible.

The views expressed in this article are those of the author and not necessarily the views of FTI Consulting Inc, its subsidiaries, its affiliates or its other professionals.

Notes

  1. Protocol for the Use of Party-Appointed Expert Witnesses (Chartered Institute of Arbitrators), article 4.
  2. https://www.ft.com/content/a0c3fce4-d0e2-11e6-b06b-680c49b4b4c0.
  3. ExxonMobil, Outlook for Energy (2017) and the US Energy Information Administration.
  4. OPEC, World Oil Outlook (2016).
  5. A provider of official energy statistics funded by the US government.
  6. US Energy Information Administration, International Energy Outlook 2016, Reference case and Low Oil Price case.
  7. Statoil, Energy Perspectives 2016, Long-term macro and market outlook.
  8. Professor Damodaran, Customized Geometric risk premium estimator.
  9. ‘How Augmented Reality Could be the Next Big Thing for Apple’, UBS analyst report, 28 February 2017.
  10. ‘How Augmented Reality Could be the Next Big Thing for Apple’, UBS analyst report, 28 February 2017; ‘IDC Smartphone Data – Apple Implications’, Barclays analyst report, 15 February 2017; and ‘Apple: The Greatest Cash Machine in History?’, Damodaran, 9 February 2017.
  11. A market capitalisation of US$805 billion implied by the UBS valuation, less US$655 billion implied by the Barclays valuation.
  12. ‘Updating Estimates for Debt Raise’, Guggenheim analyst report, 15 August 2017; ‘Headline Q2 EPS Beat Driven by ZEV Credits’, UBS analyst report, 2 August 2017; and ‘A Tesla 2017 Update: A Disruptive Force and a Debt Puzzle’, Damodaran, 11 August 2017.
  13. A market capitalisation of US$70 billion implied by the Guggenheim Securities valuation, less US$30 billion implied by the UBS valuation.